{"status":"ok","message-type":"work-list","message-version":"1.0.0","message":{"facets":{},"total-results":7862972,"items":[{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:20:31Z","timestamp":1753881631431,"version":"3.41.2"},"reference-count":61,"publisher":"Science Publishing Group","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2025,6,3]]},"abstract":"<jats:p xml:lang=\"en\">This paper introduces a novel Concave Matrix Generative Adversarial Network (CM-GAN) designed for advanced cybersecurity threat detection and contextual awareness modeling in digital infrastructures. The proposed framework integrates a concave matrix regularization mechanism to embed non-linear structural dependencies between independent (attack, defense, response) and intervening (user behavior, network load, system vulnerability) variables within the GAN learning process. Unlike traditional GAN-based models, CM-GAN enhances interpretability, training stability, and detection precision. The model is evaluated using the CSE-CIC-IDS2018 dataset and benchmarked against two customized baselines: the Matrix GAN with Awareness (MGAN) and the Matrix-Based GAN (MB-GAN). CM-GAN demonstrates superior performance across binary and multiclass classification tasks, achieving accuracy, recall, and F1-scores exceeding 99%, and demonstrating higher anomaly realism with robust detection fidelity. These results confirm the efficacy of CM-GAN as a structure-aware, context-sensitive solution for real-time cyber-threat intelligence, particularly in resource-constrained environments such as academic networks.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20251001.17","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T02:29:31Z","timestamp":1751423371000},"page":"69-90","source":"Crossref","is-referenced-by-count":0,"title":["A Concave Matrix Generative Adversarial Network Model for Detecting and Enhancing Cyber-Security Threats and Awareness"],"prefix":"10.11648","volume":"10","author":[{"given":"Edward","family":"Fondo","sequence":"first","affiliation":[{"name":"Computer Science and Information Technology, Institute of Computing and Informatics, Technical University of Mombasa, Mombasa, Kenya"}]},{"given":"Fullgence","family":"Mwakondo","sequence":"additional","affiliation":[{"name":"Computer Science and Information Technology, Institute of Computing and Informatics, Technical University of Mombasa, Mombasa, Kenya"}]},{"given":"Kevin","family":"Tole","sequence":"additional","affiliation":[{"name":"Computer Science and Information Technology, Institute of Computing and Informatics, Technical University of Mombasa, Mombasa, Kenya"}]}],"member":"4911","published-online":{"date-parts":[[2025,6,30]]},"reference":[{"key":"ref1","unstructured":"Cybersecurity Ventures. 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European Union Agency for Cybersecurity."},{"key":"ref17","unstructured":"Deloitte Africa. (2020). Africa cyber threat landscape. Deloitte & Touche."},{"key":"ref18","unstructured":"Serianu. (2021). Africa cybersecurity report. Serianu Limited."},{"key":"ref19","unstructured":"UNESCO. (2020). Cybersecurity in higher education. United Nations Educational, Scientific and Cultural Organization."},{"key":"ref20","unstructured":"Juma, V., and Mburu, P. (2021). Cyber threats in African universities: A review. Journal of African Cyber Studies, 4(2): 45-59."},{"key":"ref21","unstructured":"International Journal of Cyber Security and Digital Forensics. (2020). University network vulnerabilities. Int. J. Cyber Sec. Dig. For., 9(1): 33-50."},{"key":"ref22","unstructured":"Beuran, R., Pham, C., Chinen, K., Tan, Y., and Shinoda, Y. (2020). Cybersecurity challenges in academia. IEEE Access, 8: 211025-211037."},{"key":"ref23","unstructured":"Eken, S., and Yildirim, S. (2021). 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Kitsune: An ensemble of autoencoders for online network intrusion detection. Network and Distributed Systems Security Symposium (NDSS).","DOI":"10.14722\/ndss.2018.23204"},{"key":"ref36","unstructured":"Kim, H., and Park, Y. (2021). AI-based threat detection with real-time analytics. Journal of Network Intelligence, 6(4): 800-812."},{"key":"ref37","unstructured":"Lin, Y., and Chen, C. (2022). GAN-based cyber threat simulation and detection. Security and Communication Networks, 2022: Article ID 5678293."},{"key":"ref38","unstructured":"Zhang, W., Zhang, Y., and Wang, S. (2021). Data-driven cybersecurity with GANs. IEEE Transactions on Information Forensics and Security, 16: 5120-5132."},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, X., and Yang, H. (2020). Adversarial learning for intrusion detection. Computers & Security, 92: 101740.","DOI":"10.1016\/j.cose.2020.101740"},{"key":"ref40","unstructured":"Sun, X., and Meng, Y. (2022). 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Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784."},{"key":"ref50","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P. (2016). InfoGAN: Interpretable representation learning by information maximizing GANs. NeurIPS."},{"key":"ref51","doi-asserted-by":"crossref","unstructured":"Moustafa, N., and Slay, J. (2015). UNSW-NB15: A comprehensive data set for network intrusion detection systems. Computers and Security.","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"ref52","unstructured":"Ring, M., Wunderlich, S., Scheuring, D., Landes, D., and Hotho, A. (2019). A survey of network- based intrusion detection data sets. Computers and Security. Mathematical Structures in Computer Science, 30(5): 621-640."},{"key":"ref53","doi-asserted-by":"crossref","unstructured":"Kim, G., Lee, S., and Kim, S. (2014). A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. 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Concave matrix analysis in machine learning. Neural Computing and Applications, 33: 12345-12360."},{"key":"ref59","unstructured":"Hassan, R., and Abdi, M. (2023). Optimizing threat response in constrained systems. Cyber Defense Analytics Journal, 8(1): 34-49."},{"key":"ref60","unstructured":"Owino, P., and Ng\u2019ang\u2019a, S. (2024). Adaptive security models in university networks. Journal of Academic ICT Security, 9(2): 100-117."},{"key":"ref61","doi-asserted-by":"crossref","unstructured":"Wekesa, C., and Musyoka, D. (2023). Challenges in academic cybersecurity. African Educational Cybersecurity Review, 12(1): 23-38.","DOI":"10.3390\/electronics12234822"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20251001.17","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T02:29:58Z","timestamp":1751423398000},"score":18.225115,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20251001.17"}},"issued":{"date-parts":[[2025,6,30]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3,31]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20251001.17","ISSN":["2637-5680","2637-5672"],"issn-type":[{"type":"electronic","value":"2637-5680"},{"type":"print","value":"2637-5672"}],"published":{"date-parts":[[2025,6,30]]},"article-number":"6041123"},{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T12:52:34Z","timestamp":1774270354285,"version":"3.50.1"},"reference-count":64,"publisher":"Science Publishing Group","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2026,3,11]]},"abstract":"<jats:p xml:lang=\"en\">The escalating sophistication and volume of cyberattacks have driven an urgent demand for intelligent Intrusion Detection Systems (IDS) that leverage Data Science (DS) and Machine Learning (ML). Despite rapid advances, existing reviews often focus narrowly on specific aspects without integrating the full data science and machine learning lifecycle. This paper presents a systematic review of DS and ML applications in cyber intrusion detection, covering 153 studies published from 2009 to 2025. The review systematically surveys benchmark datasets, data preprocessing and feature engineering techniques, classical ML and Deep Learning (DL) models, ensemble and hybrid strategies, class imbalance handling, and evaluation methodologies. A unified four-axis taxonomy is proposed to classify the literature, including learning strategy, imbalance handling, explainability level, and deployment context. A quantitative meta-analysis reveals that UNSW-NB15 and CIC-IDS2017 dominate at 71% combined dataset usage, deep learning represents 40% of algorithmic approaches, and only 34% of studies report per-class recall for minority attack types. Nine technically grounded research gaps are identified, spanning preprocessing standardization, cross-dataset evaluation, minority-class recall optimization, adversarial robustness, online and edge deployment, explainability for Security Operations Center (SOC) operations, federated learning, transformer and Large Language Model (LLMs) application, and zero-shot adaptation. The review further identifies eight emerging trends including attention-based and transformer architectures, LLMs, Graph Neural Networks (GNNs), federated and privacy-preserving learning, adversarial robustness, Explainable AI (XAI), zero-shot and few-shot detection, and Internet of Things (IoT) edge-based IDS. A seven-stage actionable architecture is proposed that integrates adaptive preprocessing, contrastive feature learning, recall-aware ensemble detection, XAI decision support, continual learning, and federated aggregation. This review provides researchers and practitioners with a structured roadmap for advancing the next generation of intelligent cyber intrusion detection systems.<\/jats:p>","DOI":"10.11648\/j.mlr.20261101.12","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T11:56:03Z","timestamp":1774266963000},"page":"8-21","source":"Crossref","is-referenced-by-count":0,"title":["Data Science and Machine Learning for Cyber Intrusion Detection: A Systematic Review"],"prefix":"10.11648","volume":"11","author":[{"given":"Yali","family":"Ren","sequence":"first","affiliation":[{"name":"School of Computer Science, Georgia Institute of Technology, Atlanta, the United States"}]}],"member":"4911","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"S. Corbet and J. W. Goodell,\u201cThe reputational contagion effects of ransomware attacks,\u201d Finance Research Letters, vol. 47, pp. 102715, 2022.https:\/\/doi.org\/10.1016\/j.frl.2022.102715","DOI":"10.1016\/j.frl.2022.102715"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, \u201cA survey of machine learning and deep learning in cybersecurity,\u201d IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1646\u20131685, 2020. https:\/\/doi.org\/10.1109\/COMST.2020.2994934","DOI":"10.1109\/COMST.2020.2988293"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"I. H. Sarker, A. S. M. Kayes, S. Badsha, H. Alqahtani, P. Watters, and A. 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Maglaras, S. Moschoyiannis, andH. Janicke, \u201cDeep learning for cyber security intrusion detection: Approaches, datasets, and comparative study,\u201d Journal of Information Security and Applications, vol. 50, 102419, 2020.https:\/\/doi.org\/10.1016\/j.jisa.2019.102419","DOI":"10.1016\/j.jisa.2019.102419"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"I. H. Sarker, \u201cMachine learning: Algorithms, real-world applications and research directions,\u201d SN Computer Science, vol. 2, no. 3, p. 160, 2021.https:\/\/doi.org\/10.1007\/s42979-021-00592-x","DOI":"10.1007\/s42979-021-00592-x"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"A. Thakkar and R. Lohiya, \u201cA survey on intrusion detection system: Feature selection, model, performance measures, application perspective and challenges,\u201d Artificial Intelligence Review, vol. 55, pp. 453\u2013563, 2022. https:\/\/doi.org\/10.1007\/s10462-021-10037-9","DOI":"10.1007\/s10462-021-10037-9"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"N. Moustafa and J. Slay, \u201cUNSW-NB15: A comprehensive data set for network intrusion detection systems,\u201d in Proc. MilCIS, pp. 1\u20136, 2015. [Dataset updated 2023.]https:\/\/doi.org\/10.1109\/MilCIS.2015.7348945","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, \u201cToward generating a new intrusion detection dataset and intrusion traffic characterization,\u201d in Proc. ICISSP, pp. 108\u2013116, 2018. 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IEEE CISDA, pp. 1\u20136, 2009.https:\/\/doi.org\/10.1109\/CISDA.2009.5356528","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"I. Sharafaldin, A. H. Lashkari, S. Hakak, and A. A. Ghorbani, \u201cDeveloping realistic distributed denial of service (DDoS) attack dataset and taxonomy,\u201d in Proc. IEEE ICCST, pp. 1\u20138, 2019.https:\/\/doi.org\/10.1109\/ICCST.2019.8888419","DOI":"10.1109\/CCST.2019.8888419"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, \u201cTowards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset,\u201d Future Generation Computer Systems, vol. 100, pp. 779\u2013796, 2019.https:\/\/doi.org\/10.1016\/j.future.2019.04.026","DOI":"10.1016\/j.future.2019.05.041"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"N. 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Plant"],"prefix":"10.11648","volume":"5","author":[{"given":"Nurudeen","family":"Simbiat Adesola","sequence":"first","affiliation":[]},{"given":"Lasisi","family":"Kayode Hassan","sequence":"additional","affiliation":[]},{"given":"Babatola","family":"Josiah Oladele","sequence":"additional","affiliation":[]},{"given":"Lafe","family":"Olurinde","sequence":"additional","affiliation":[]}],"member":"4911","container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"http:\/\/article.sciencepublishinggroup.com\/pdf\/10.11648.j.mlr.20200501.11.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T07:28:21Z","timestamp":1602919701000},"score":18.22308,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20200501.11"}},"issued":{"date-parts":[[2020]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020]]}},"alternative-id":["1081169"],"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20200501.11","ISSN":["2637-5672"],"issn-type":[{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2020]]}},{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:25:21Z","timestamp":1755221121110,"version":"3.43.0"},"reference-count":48,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2025,5,19]]},"abstract":"<jats:p xml:lang=\"en\">Covid-19 is new virus that is spreading rapidly in all over the world. It is a communicable disease. World Health Organization announced social distancing to control the spread of that virus. All institutions are closed in Pakistan. Education was also effecting with this shutdown. In the age of computing, social computing has emerged as a means of sharing knowledge, conveying ideas, and forming academic discussion groups, to name a few. Social websites or apps are also used for online study due to some critical situation as if nowadays we are facing many problems due to COVID-19. Due to the COVID-19 educational system is disturbed for that purpose we are introducing a different online platform for delivering knowledge and continue the educational system many data mining techniques are applied to social network data for online analysis due to a large number of users and widespread use. This paper describes a method for extracting and analyzing master\u2019s student comments from the online survey that which platform is better for online study and also giving the opinion about most used platform. The proposed technique is implemented using different models or algorithms. By providing various proformas and analyzing vary- iOS student opinions, the said system may assist the administration in improving the learning environment.<\/jats:p>","DOI":"10.11648\/j.mlr.20251002.11","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T08:26:47Z","timestamp":1754641607000},"page":"91-109","source":"Crossref","is-referenced-by-count":0,"title":["Opinion Mining of Student Regarding Educational System Using Online Platform"],"prefix":"10.11648","volume":"10","author":[{"given":"Muhammad","family":"Irfan","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Lahore Sargodha Campus, Sargodha, Pakistan"}]},{"given":"Khadija","family":"Bibi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Comsat University of Islamabad Wah Campus, Wah Cantonment, Pakistan"}]},{"given":"Adeeba","family":"Aslam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Lahore Sargodha Campus, Sargodha, Pakistan"}]},{"given":"Saima","family":"Bibi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Lahore Sargodha Campus, Sargodha, Pakistan"}]},{"given":"Anwar","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Lahore Sargodha Campus, Sargodha, Pakistan"}]}],"member":"4911","published-online":{"date-parts":[[2025,8,4]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"K. 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This paper designs an union method combining the stitching of normal images and unsupervised semantic segmentation of the stitched image. The normal images are stitched by using the image stitching method designed by Ribeiro D.. The semantic segmentation method for the stitched image uses the method opened in the github. The stitched image contains image distortion. The distortion of the stitched image will make the feature extraction unreasonable. The distortion form of the stitched image is different from the distortion form of the panoramic image combined by line images. Therefore, the DCM proposed by Xing Hu is useless to extract features of the stitched image reasonable. This paper improves the DCM as the improved distortion convolution module (IDCM) by using the deformable convolution, the clamp module, the type transformation module, and the gather module. The IDCM is added before the unsupervised semantic segmentation method opened in the github to extract features reasonable. The IDCM-NUSSM method and the ISM-IDCM-NUSSM method are proposed. The experimental results show the better performance of the designed methods.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20240902.16","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T02:23:21Z","timestamp":1733365401000},"page":"75-79","source":"Crossref","is-referenced-by-count":0,"title":["An Union Method Combining the Stitching of Normal Images and the Unsupervised Semantic Segmentation of Stitched Image"],"prefix":"10.11648","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3410-8322","authenticated-orcid":true,"given":"Xing","family":"Hu","sequence":"first","affiliation":[{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian, China;College of Information Technology, Nanchang Vocational University, Nanchang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8047-3327","authenticated-orcid":true,"given":"Xinjian","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Technology, Nanchang Vocational University, Nanchang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1194-0922","authenticated-orcid":true,"given":"Zhengguang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Nanchang Vocational University, Nanchang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3351-5812","authenticated-orcid":true,"given":"Jie","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Information Technology, Nanchang Vocational University, Nanchang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2667-122X","authenticated-orcid":true,"given":"Yi","family":"An","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian, China;School of Electrical Engineering, Xinjiang University, Urumqi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7845-0613","authenticated-orcid":true,"given":"Cheng","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3259-8233","authenticated-orcid":true,"given":"Hongsheng","family":"Tian","sequence":"additional","affiliation":[{"name":"State Grid Tieling Electric Power Supply Company, Liaoning, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6373-4938","authenticated-orcid":true,"given":"Qingru","family":"Guo","sequence":"additional","affiliation":[{"name":"Product Development Department, Googosoft, Jinan, China"}]}],"member":"4911","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Oztekin F., Katar O., Sadak F., Aydogan M., Yildirim T. 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Meas., 2022, 71: 1-12, https:\/\/doi.org\/10.1109\/TIM.2021.3139710","DOI":"10.1109\/TIM.2021.3139710"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20240902.16","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T02:23:39Z","timestamp":1733365419000},"score":18.211609,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20240902.16"}},"issued":{"date-parts":[[2024,11,29]]},"references-count":18,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,7,15]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20240902.16","ISSN":["2637-5680","2637-5672"],"issn-type":[{"type":"electronic","value":"2637-5680"},{"type":"print","value":"2637-5672"}],"published":{"date-parts":[[2024,11,29]]},"article-number":"1101559"},{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:20:31Z","timestamp":1753881631183,"version":"3.41.2"},"reference-count":30,"publisher":"Science Publishing Group","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p xml:lang=\"en\">In the rapid growth of textual data in various domains has increased the need for efficient clustering techniques capable of handling large-scale datasets. Traditional clustering methods often fail to capture semantic relationships and struggle with high-dimensional, sparse data. The present study shows an improved document clustering technique, i.e., WEClustering++, which enhances the existing WEClustering framework by integrating fine-tuned BERT based word embeddings. The proposed model incorporates advanced dimensionality reduction techniques and optimized clustering algorithms to improve clustering accuracy. In the present work, the BERT-large model, fine-tuned on domain-specific datasets is utilized. Seven benchmark datasets spanning various domains and sizes are considered. These datasets include collections of research articles, news articles, and other domain-specific texts. Experimental evaluations on multiple benchmark datasets demonstrate significant performance improvements in clustering metrics, including silhouette score, purity, and ARI. Results show a 45% and 67% increase in median silhouette scores for WEClustering_K++ (K-means-based) and WEClustering_A++ (Agglomerative-based) models, respectively. Result also shows an increase of median purity metrics of 0.4% and 0.8% is obtained for proposed WEClustering_K++ and WEClustering_A++ compared to the state of art model. Also, an increase of median ARI metrics of 7% and 11% is obtained for proposed WEClustering_K++ and WEClustering_A++ compared to the state of art model. These findings highlight the potential of fine-tuned word embeddings in bridging the gap between statistical clustering robustness and semantic understanding. The proposed approach is expected to contribute to advancements in large-scale text mining applications, including document organization, topic modelling, and information retrieval.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20251001.14","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T06:56:20Z","timestamp":1746687380000},"page":"32-43","source":"Crossref","is-referenced-by-count":0,"title":["An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets"],"prefix":"10.11648","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9186-5816","authenticated-orcid":true,"given":"Vijay","family":"Sutrakar","sequence":"first","affiliation":[{"name":"Aeronautical Development Establishment, Defence Research and Development Organisation, Bangalore, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1143-3037","authenticated-orcid":true,"given":"Nikhil","family":"Mogre","sequence":"additional","affiliation":[{"name":"Aeronautical Development Establishment, Defence Research and Development Organisation, Bangalore, India"}]}],"member":"4911","published-online":{"date-parts":[[2025,4,29]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Novel coronavirus resource directory (2020) Accessed Feb 08, 2025 https:\/\/doi.org\/10.2172\/1602724","DOI":"10.2172\/1602724"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Johnson R, Watkinson A, Mabe M (2018) The stm report. 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Cambridge University Press, Cambridge https:\/\/doi.org\/10.1007\/s10791-009-9096-x","DOI":"10.1017\/CBO9780511809071"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20251001.14","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T06:56:29Z","timestamp":1746687389000},"score":18.210535,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20251001.14"}},"issued":{"date-parts":[[2025,4,29]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3,31]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20251001.14","ISSN":["2637-5680","2637-5672"],"issn-type":[{"type":"electronic","value":"2637-5680"},{"type":"print","value":"2637-5672"}],"published":{"date-parts":[[2025,4,29]]},"article-number":"6041112"},{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T09:03:47Z","timestamp":1770455027024,"version":"3.49.0"},"reference-count":0,"publisher":"Science Publishing Group","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"published-print":{"date-parts":[[2018]]},"DOI":"10.11648\/j.mlr.20180303.11","type":"journal-article","created":{"date-parts":[[2019,7,16]],"date-time":"2019-07-16T09:44:44Z","timestamp":1563270284000},"page":"49","source":"Crossref","is-referenced-by-count":44,"title":["Analysis on Leg Bone Fracture Detection and Classification Using X-ray Images"],"prefix":"10.11648","volume":"3","author":[{"given":"Wint","family":"Wah Myint","sequence":"first","affiliation":[]}],"member":"4911","container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"http:\/\/article.sciencepublishinggroup.com\/pdf\/10.11648.j.mlr.20180303.11.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T08:21:53Z","timestamp":1602922913000},"score":18.210535,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20180303.11"}},"issued":{"date-parts":[[2018]]},"references-count":0,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2018]]}},"alternative-id":["6041029"],"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20180303.11","ISSN":["2637-5672"],"issn-type":[{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2018]]}},{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T04:53:42Z","timestamp":1771822422233,"version":"3.50.1"},"reference-count":25,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2024,7,3]]},"abstract":"<jats:p xml:lang=\"en\">Skin cancer, particularly melanoma, presents a significant global health challenge due to its increasing incidence and mortality rates. Current diagnostic methods relying on visual inspection and histopathological examination are subjective and time-consuming, often leading to delayed diagnoses. Recent advancements in machine and deep learning, particularly convolutional neural networks (CNNs), offer a promising avenue for transforming melanoma detection by automating precise classification of dermoscopy images. This study leverages a comprehensive dataset sourced from Kaggle, comprising 10,605 images categorized into benign and malignant classes. Methodologically, a custom CNN architecture is trained and evaluated using varying kernel sizes (3x3, 5x5, 7x7) to optimize melanoma lesion classification. Results demonstrate that smaller kernel sizes, notably 3x3, consistently yield superior accuracy of 93.00% and F1-scores of 96.00%, indicating their efficacy in distinguishing between benign and malignant lesions. The CNN model exhibits robust generalization capabilities with minimal overfitting, supported by high validation accuracy throughout training epochs. Comparative analysis with related studies highlights competitive performance, suggesting potential enhancements through advanced feature selection and optimization techniques. Despite these advancements, challenges such as dataset diversity and model optimization persist, particularly concerning underrepresented darker skin tones. The study underscores the transformative potential of CNNs in enhancing diagnostic accuracy and efficiency in dermatological practice, paving the way for improved patient outcomes through early detection and intervention strategies. Future research directions include refining segmentation techniques and expanding dataset evaluations to ensure the model&amp;apos;s applicability across diverse clinical settings. Ultimately, this research contributes to advancing melanoma diagnosis by integrating cutting-edge deep learning methodologies with clinical practice, thereby addressing current limitations and driving forward innovations in dermatological image analysis.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20240902.11","type":"journal-article","created":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T09:13:46Z","timestamp":1721294026000},"page":"26-38","source":"Crossref","is-referenced-by-count":2,"title":["Optimizing CNN Kernel Sizes for Enhanced Melanoma Lesion Classification in Dermoscopy Images"],"prefix":"10.11648","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3138-4639","authenticated-orcid":true,"given":"Adetokunbo","family":"John-Otumu","sequence":"first","affiliation":[{"name":"Department of Information Technology, Federal University of Technology, Owerri, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2211-6490","authenticated-orcid":true,"given":"Rebecca","family":"Ekemonye","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Federal University of Technology, Owerri, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3152-6788","authenticated-orcid":true,"given":"Toochi","family":"Ewunonu","sequence":"additional","affiliation":[{"name":"Department of Cybersecurity, Federal University of Technology, Owerri, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8220-1534","authenticated-orcid":true,"given":"Victor","family":"Aniugo","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, Federal University of Technology, Owerri, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6759-3029","authenticated-orcid":true,"given":"Ogadimma","family":"Okonkwo","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Federal University of Technology, Owerri, Nigeria"}]}],"member":"4911","published-online":{"date-parts":[[2024,7,15]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Chaturvedi, S. S.; Gupta, K.; Prasad, P. S. Skin lesion analyser: An efficient seven-way multi-class skin cancer classification using MobileNet. In Advances in Intelligent Systems and Computing; Springer: Singapore, 2020, 165\u2013176.","DOI":"10.1007\/978-981-15-3383-9_15"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Bi, L., Kim, J., Ahn, E., Kumar, A., Feng, D., Fulham, M. Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern Recognit. 2019, 85, 78\u201389.","DOI":"10.1016\/j.patcog.2018.08.001"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Sonia, R. (2016). Melanoma Image Classification System by NSCT Features and Bayes Classification. 2(2), 27\u201333.","DOI":"10.29284\/IJASIS.2.2.2016.27-33"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Rehman, A.; Khan, M. A.; Mehmood, Z.; Saba, T.; Sardaraz, M.; Rashid, M. Microscopic melanoma detection and classification: A framework of pixel-based fusion and multilevel features reduction. Microsc. Res. Tech. 2020, 83, 410\u2013423.","DOI":"10.1002\/jemt.23429"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Jain, S., Pise, N. Computer aided Melanoma skin cancer detection using Image Processing. Procedia - Procedia Computer Science. 2015, 48(ICCC), 735\u2013740. https:\/\/doi.org\/10.1016\/j.procs.2015.04.209","DOI":"10.1016\/j.procs.2015.04.209"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Seeja, R. D., Suresh, A. Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine. 2019a, 20, 1555\u20131561. https:\/\/doi.org\/10.31557\/APJCP.2019.20.5.1555","DOI":"10.31557\/APJCP.2019.20.5.1555"},{"key":"ref7","unstructured":"Xie, Y., Zhang, J., Xia, Y., Shen, C. A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification. 2019. 3, 1\u201312."},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Banjan, N., Dalvi, P., Athavale, N. Melanoma Skin Cancer Detection by Segmentation and Feature Extraction using combination of OTSU and STOLZ Algorithm Technique. 2017, 4(4), 21\u201325.","DOI":"10.14445\/23488549\/IJECE-V4I4P105"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Fornaciali, M., Avila, S., Carvalho, M., Valle, E. Statistical Learning Approach for Robust Melanoma Screening. In: 27th Conference on Graphics, Patterns and Images (SIBGRAPI), 2014, p. 319-326.","DOI":"10.1109\/SIBGRAPI.2014.48"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Alsahafi, Y. S., Kassem, M. A., Hosny, K. M. Skin Net: A novel deep residual network for skin lesions classification using multilevel feature extraction and cross channel correlation with detection of outlier. Journal of Big Data. 2023. https:\/\/doi.org\/10.1186\/s40537-023-00769-6","DOI":"10.1186\/s40537-023-00769-6"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Dandu, R., Murthy, M. V., Kumar, Y. B. R. Transfer learning for segmentation with hybrid classification to Detect Melanoma Skin Cancer. Heliyon, 2023, 9(4), e15416. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e15416","DOI":"10.1016\/j.heliyon.2023.e15416"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Sumithra, R., Suhil, M., Guru, D. S. Segmentation and Classification of Skin Lesions for Disease Diagnosis. - Procedia Computer Science, 2015, 45, 76\u201385. https:\/\/doi.org\/10.1016\/j.procs.2015.03.090","DOI":"10.1016\/j.procs.2015.03.090"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Khan, M. A., Akram, T., Sharif, M., Shahzad, A., Alhussein, M., Haider, S. I., Altamrah, A. An implementation of normal distribution-based segmentation and entropy-controlled features selection for skin lesion detection and classification. 2018, 1\u201320.","DOI":"10.1186\/s12885-018-4465-8"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Palivela, L. H., Athanesious, J., Deepika, V., & Vignesh, M. Segmentation and Classification of Skin Lesions from Dermoscopic Images. Journal of Scientific & Industrial Research, 2021.80(4), 328\u2013335.","DOI":"10.56042\/jsir.v80i04.36178"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Imran, T., Alghamdi, A. S., & Alkatheiri, M. S. Enhanced Skin Cancer Classification using Deep Learning and Nature-based Feature Optimization. Engineering, Technology and Applied Science Research, 2024, 14(1), 12702\u201312710. https:\/\/doi.org\/10.48084\/etasr.6604","DOI":"10.48084\/etasr.6604"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Dahou, A., Aseeri, A. O., Mabrouk, A., Ibrahim, R. A., Al-Betar, M. A., Elaziz, M. A. Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search. Diagnostics, 2023, 13(9), 1\u201320. https:\/\/doi.org\/10.3390\/diagnostics13091579","DOI":"10.3390\/diagnostics13091579"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Rajendran, V. A., Shanmugam, S. Automated Skin Cancer Detection and Classification using Cat Swarm Optimization with a Deep Learning Model. Engineering, Technology & Applied Science Research. 2024. 14(1), 12734\u201312739. https:\/\/doi.org\/10.48084\/etasr.6681","DOI":"10.48084\/etasr.6681"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Verma, P. Deep Learning Techniques for Skin Lesion Segmentation and Classification. Diagnostics, 2021, 11(5): 811. https:\/\/doi.org\/10.3390\/diagnostics11050811","DOI":"10.3390\/diagnostics11050811"},{"key":"ref19","unstructured":"Li, C., Miao, F., Gao, G. A Novel Progressive Image Classification Method Based on Hierarchical Convolutional Neural Networks. 2021, 16, 1\u20131726."},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Salma, W., Eltrass, A. S. Automated deep learning approach for classification of malignant melanoma and benign skin lesions. 2022, 32643\u201332660.","DOI":"10.1007\/s11042-022-13081-x"},{"key":"ref21","unstructured":"Ine, K. M., Neyns, B., Vandemeulebroucke, J. Vrije Universiteit Brussel Computer-aided detection and segmentation of malignant melanoma lesions on whole-body 18F-FDG PET \/ CT using an interpretable deep learning approach Publication date: License: Computer Methods and Programs in Biomedicine Compu. Computer Methods and Programs, 2022."},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Owka, S. U. R. Segmentation of the melanoma lesion and its border. 2022, 32(4), 683\u2013699. https:\/\/doi.org\/10.34768\/amcs-2022-0047","DOI":"10.34768\/amcs-2022-0047"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Pennisi, A., Bloisi, D. D., Nardi, D., Giampetruzzi, A. R., Mondino, C., Facchiano, A. Skin lesion image segmentation using Delaunay Triangulation for melanoma detection. Computerized Medical Imaging and Graphics, 2016, 52, 89\u2013103. https:\/\/doi.org\/10.1016\/j.compmedimag.2016.05.002","DOI":"10.1016\/j.compmedimag.2016.05.002"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Rakhra, M., Cazzato, G., Hossain, M. S. Retracted: A Novel Hybrid Deep Learning Approach for Skin. 2023.","DOI":"10.1155\/2023\/9832712"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Chang, W., Huang, A., Chen, Y., Lin, C., Tsai, J. The feasibility of using manual segmentation in a multifeature computer-aidediagnosis system for classification of skin lesions: A Retrospective comparative study. 2015, 1\u20138. https:\/\/doi.org\/10.1136\/bmjopen-2015-007823","DOI":"10.1136\/bmjopen-2015-007823"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20240902.11","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20240902.11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T01:44:50Z","timestamp":1728524690000},"score":18.210535,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20240902.11"}},"issued":{"date-parts":[[2024,7,15]]},"references-count":25,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,7,15]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20240902.11","ISSN":["2637-5680","2637-5672"],"issn-type":[{"value":"2637-5680","type":"electronic"},{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2024,7,15]]},"article-number":"6041092"},{"indexed":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:22:41Z","timestamp":1712535761637},"reference-count":0,"publisher":"Science Publishing Group","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2023,1,12]]},"DOI":"10.11648\/j.mlr.20220702.12","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T11:56:56Z","timestamp":1686657416000},"source":"Crossref","is-referenced-by-count":0,"title":["Automatic Indexing of Digital Objects Through Learning from User Data"],"prefix":"10.11648","author":[{"given":"Clement","family":"Leung","sequence":"first","affiliation":[{"name":"School of Science and Engineering and Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong, Shenzhen, China"}]},{"given":"Yuanxi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University, Hong Kong, China"}]}],"member":"4911","published-online":{"date-parts":[[2023,1,31]]},"container-title":["Machine Learning Research"],"language":"en","deposited":{"date-parts":[[2024,4,7]],"date-time":"2024-04-07T19:04:35Z","timestamp":1712516675000},"score":18.202757,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20220702.12"}},"issued":{"date-parts":[[2023,1,31]]},"references-count":0,"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20220702.12","published":{"date-parts":[[2023,1,31]]}},{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:36:34Z","timestamp":1763202994709,"version":"3.41.2"},"reference-count":24,"publisher":"Science Publishing Group","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2025,4,24]]},"abstract":"<jats:p xml:lang=\"en\">Millions of people depend on wheat as a staple food, especially in agrarian nations like Bangladesh. It is a crop of global importance. Many foliar diseases, such as Septoria Tritici Blotch (STB), a fungal infection that causes tan lesions and yellow halos, pose a threat to its productivity. Manual inspection for traditional disease diagnosis is labor-intensive, prone to mistakes, and not scalable. Recent developments in deep learning and image processing provide a promising substitute for highly accurate automated plant disease detection. With an emphasis on Septoria, this study suggests a thorough deep-learning framework for the identification and categorization of wheat leaf diseases. The methodology entails gathering high-resolution images of wheat leaves from public and research institutions. The images are subjected to color-based and threshold segmentation to isolate infected regions following initial preprocessing, which includes noise reduction, enhancement, and standardization. After that, thirteen texture features that represent color and structural patterns are extracted using the Gray-Level Co-occurrence Matrix (GLCM) technique. Multiple classification models, such as Random Forest (RF), Support Vector Machine (SVM), k-nearest Neighbors (k-NN), and Na\u00efve Bayes (NB), are then trained and assessed using these features. Python and the TensorFlow, Keras, and Mahotas libraries are used to implement the system. Confusion matrices are used to calculate performance metrics like accuracy, sensitivity, specificity, precision, and error rate. Based on experimental results, the Random Forest classifier performed better than the others, achieving 98.9% accuracy, 100% precision and specificity, and 98.1% sensitivity. This validates the suggested method&amp;apos;s resilience in comparison to conventional classifiers. The results point to the possibility of implementing deep learning-based technology in actual agricultural environments, supporting sustainable farming, yield enhancement, and early disease detection.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20251001.16","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T03:23:27Z","timestamp":1748489007000},"page":"53-68","source":"Crossref","is-referenced-by-count":1,"title":["A Deep Learning Framework for Precise Detection and Classification of Wheat Leaf Diseases"],"prefix":"10.11648","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9201-0478","authenticated-orcid":true,"given":"Mirza","family":"Moon","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Sonargaon University, Dhaka, Bangladesh"}]}],"member":"4911","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Pakholkova, E. V., Zhelezova, A. D., Sonyushkin, A. V., & Glinushkin, A. (2023). Hyperspectral Non-Imaging Measurements and Perceptron Neural Network for Pre-Harvesting Assessment of Damage Degree Caused by Septoria\/Stagonospora Blotch Diseases of Wheat. Agronomy, 13(4), 1045. https:\/\/doi.org\/10.3390\/agronomy13041045","DOI":"10.3390\/agronomy13041045"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Genaev, M. A., Skolotneva, E., Gultyaeva, E. I., Orlova, E. A., Bechtold, N. P., Afonnikov, D. A., & Afonnikov, D. A. (2021). Image-Based Wheat Fungi Diseases Identification by Deep Learning. 10(8), 1500. https:\/\/doi.org\/10.3390\/PLANTS10081500","DOI":"10.3390\/plants10081500"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Long, M., Hartley, M., Morris, R. J., & Brown, J. K. M. (2022). Deep Learning for Wheat Disease Classification by Using Deep Learning Networks with Field and Glasshouse Images. Plant Pathology, 72(3), 536\u2013547. https:\/\/doi.org\/10.1111\/ppa.13684","DOI":"10.1111\/ppa.13684"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Dong, M., Mu, S., Shi, A., Mu, W., & Sun, W. (2020). Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network. International Journal of Agricultural and Biological Engineering, 13(4), 205\u2013210. https:\/\/doi.org\/10.25165\/IJABE.V13I4.4826","DOI":"10.25165\/j.ijabe.20201304.4826"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Arinicheva, I., Arinichev, I. V., & Darmilova, Z. D. (2022). Cereal fungal diseases detection using autoencoders. IOP Conference Series: Earth and Environmental Science, 949(1), 012048. https:\/\/doi.org\/10.1088\/1755-1315\/949\/1\/012048","DOI":"10.1088\/1755-1315\/949\/1\/012048"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Albattah, W., Nawaz, M., Javed, A., Masood, M., & Albahli, S. (2021). A novel deep learning method for detection and classification of plant diseases. Complex & Intelligent Systems, 1\u201318. https:\/\/doi.org\/10.1007\/S40747-021-00536-1","DOI":"10.1007\/s40747-021-00536-1"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Soo, Jun, Wei., Dimas, Firmanda, Al, Riza., Hermawan, Eko, Nugroho. (2022). Comparative study on the performance of deep learning implementation in the edge computing: Case study on the plant leaf disease identification. Journal of agriculture and food research, https:\/\/doi.org\/10.1016\/j.jafr.2022.100389","DOI":"10.1016\/j.jafr.2022.100389"},{"key":"ref8","unstructured":"Chakraborty, A., Chakraborty, A., Sobhan, A., & Pathak, A. Deep Learning for Precision Agriculture: Detecting Tomato Leaf Diseases with VGG-16 Model. International Journal of Computer Applications, 975, 8887."},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Ghazanfar, Latif., Sherif, Elmeligy, Abdelhamid., Roxane, Elias, Mallouhy., Jaafar, Alghazo., Z., A., Kazimi. (2022). Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model. Plants, https:\/\/doi.org\/10.3390\/plants11172230","DOI":"10.3390\/plants11172230"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Seyam, T. A., Pathak, A. AgriScan: Next.js powered cross-platform solution for automated plant disease diagnosis and crop health management. Journal of Electrical Systems and Inf Technol 11, 45 (2024). https:\/\/doi.org\/10.1186\/s43067-024-00169-7","DOI":"10.1186\/s43067-024-00169-7"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Hossain, S., Seyam, T. A., Chowdhury, A., Ghose, R., Rahaman, A., et al. (2025). Enhancing Agricultural Diagnostics: Advanced Training of Pre-Trained CNN Models for Paddy Leaf Disease Detection. Machine Learning Research, 10(1), 1-13. https:\/\/doi.org\/10.11648\/j.mlr.20251001.11","DOI":"10.11648\/j.mlr.20251001.11"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"A. Chowdhury, \u201dAdvancing Multi-Class Arc Welding Defect Classification: DEEPTLWELD Intelligent System Utilizing Computer Vision, Deep Learning, and Transfer Learning on Radiographic X-ray Images for Bangladesh\u2019s Manufacturing Sector,\u201d 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS), Cox\u2019s Bazar, Bangladesh, 2024, pp. 1-6, https:\/\/doi.org\/10.1109\/COMPAS60761.2024.10796006","DOI":"10.1109\/COMPAS60761.2024.10796006"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Monoronjon, Dutta., Md, Rashedul, Islam, Sujan., Mayen, Uddin, Mojumdar., Narayan, Ranjan, Chakraborty., Ahmed, Al, Marouf., Jon, Rokne., Reda, Alhajj. (2024). 1. Rice Leaf Disease Classificationa\u02c6A Comparative Approach Using Convolutional Neural Network (CNN), Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), and MobileNet- V2 Architectures. Technologies (Basel), https:\/\/doi.org\/10.3390\/technologies12110214","DOI":"10.3390\/technologies12110214"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Alam, T. S., Jowthi, C. B. & Pathak, A. Comparing pre-trained models for efficient leaf disease detection: a study on custom CNN. Journal of Electrical Systems and Inf Technol 11, 12 (2024). https:\/\/doi.org\/10.1186\/s43067-024-00137-1","DOI":"10.1186\/s43067-024-00137-1"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Salma, Akter., Rashedul, Islam, Sumon., Haider, Ali., Heea\u02c6Cheol, Kim. (2024). 2. Utilizing Convolutional Neural Networks for the Effective Classification of Rice Leaf Diseases Through a Deep Learning Approach. Electronics, https:\/\/doi.org\/10.3390\/electronics13204095","DOI":"10.3390\/electronics13204095"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"J. K. V, D. Tauro, P. M. R, C. DSouza and B. Correia, \u201dPaddy Care: Paddy Disease Identification and Classification Using Deep DenseNet Network,\u201d 2024 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Mangalore, India, 2024, pp. 377-382, https:\/\/doi.org\/10.1109\/DISCOVER62353.2024.10750707","DOI":"10.1109\/DISCOVER62353.2024.10750707"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Athar, Hussain., Balaji, Srikaanth., P. \u201d4. Deep Learning with Crested Porcupine Optimizer for Detection and Classification of Paddy Leaf Diseases for Sustainable Agriculture.\u201d Journal of machine and computing, undefined (2024). https:\/\/doi.org\/10.53759\/7669\/jmc202404095","DOI":"10.53759\/7669\/jmc202404095"},{"key":"ref18","unstructured":"Hossain, S., Seyam, T. A., Chowdhury, A., Xamidov, M., Ghose, R., Pathak, A. (2025). Fine-tuning LLaMA 2 interference: a comparative study of language implementations for optimal efficiency. arXiv preprint arXiv: 2502.01651."},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yang, W., & Li, Y. (2023). MobileViT: Lightweight Vision Transformers for Edge-Device Plant Disease Detection. Computers and Electronics in Agriculture, 205, 107591. https:\/\/doi.org\/10.1016\/j.compag.2023.107591","DOI":"10.1016\/j.compag.2022.107591"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Zhang, X., Huang, J., & Li, H. (2023). Multi-Disease Classification in Wheat Leaves Using Swin Transformers. Sensors, 23(7), 3652. https:\/\/doi.org\/10.3390\/s23073652","DOI":"10.3390\/s23073652"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Islam, R., Alam, T., & Khan, A. (2022). Edge-Intelligent Plant Disease Detection Using Quantized EfficientNet. IEEE Access, 10, 112845\u2013112856. https:\/\/doi.org\/10.1109\/ACCESS.2022.3218456","DOI":"10.1109\/ACCESS.2022.3218456"},{"key":"ref22","unstructured":"Roy, S., Mandal, B., & Banerjee, A. (2022). A Comparative Study of CNN and Transformer Architectures for Crop Disease Detection. Computational Intelligence and Neuroscience, 2022, 9874935. https:\/\/doi.org\/10.1155\/2022\/9874935"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Seyam, T. A., Hossain, M. S., Ghose, R., Nurmamatov, M., Fayzullo, N., et al. (2025). Next-Generation K-Means Clustering: Mojo-Driven Performance for Big Data. International Journal of Intelligent Information Systems, 14(1), 7-19. https:\/\/doi.org\/10.11648\/j.ijiis.20251401.12","DOI":"10.11648\/j.ijiis.20251401.12"},{"key":"ref24","unstructured":"Ali, M., Nasim, U., & Rehman, M. (2023). Attention-Based Deep Learning for Multi-Class Fruit Leaf Disease Detection. Applied Sciences, 13(2), 1254. https:\/\/doi.org\/10.3390\/app13021254"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20251001.16","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T03:23:34Z","timestamp":1748489014000},"score":18.202757,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20251001.16"}},"issued":{"date-parts":[[2025,5,14]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3,31]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20251001.16","ISSN":["2637-5680","2637-5672"],"issn-type":[{"type":"electronic","value":"2637-5680"},{"type":"print","value":"2637-5672"}],"published":{"date-parts":[[2025,5,14]]},"article-number":"6041116"},{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T16:11:18Z","timestamp":1769271078319,"version":"3.49.0"},"reference-count":63,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2025,7,24]]},"abstract":"<jats:p xml:lang=\"en\">Under-five mortality remains a global health challenge with the rates of 43 deaths per every 1000 live births in Tanzania and 37 deaths per every 1000 live births globally. Although child mortality has significantly declined in the last twenty years, the current rates are far from reaching the anticipated Sustainable Development Goal of atmost 25 deaths per 1000 live births in 2030. This study intended to find the best performing classifier of under-five mortality status by comparing ten supervised machine learning algorithms. These machine learning algorithms are Decision Trees, Random Forest, Support Vector Machines, SMOTE-Based Boosted Random Forest, XGBoost, LightGBM, CatBoost, Logistic Regression, K-Nearest Neighbors and Stacked Ensemble Methods. The class imbalance of the dataset detected in the pre-processing stage was addressed using weighted categorical cross-entropy and SMOTE with a 5-folds cross validation and data splitting ratio of 80% for training set and 20% for testing set. With 20 experiments for each of the nine algorithms, the average results were reported to ensure that the findings were not by chance. Further, the stacking ensemble model was developed integrating six of the best performing algorithms using an inclusion criterion of AUC &amp;gt; 0.97. The findings revealed that ensemble algorithm consistently outperformed the other nine algorithms by achieving 100%, 100%, 99.97% and 99.24% for AUC, Accuracy, F1-Score and MCC respectively. This implies that stacking ensemble can uncover more insights than the individual algorithms in predicting under-five mortality status. This study recommends designing policies on under-five mortality that integrate insights from the stacking ensemble algorithm which shows the highest predictive performance.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20251002.12","type":"journal-article","created":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T12:10:58Z","timestamp":1756296658000},"page":"110-123","source":"Crossref","is-referenced-by-count":1,"title":["Comparative Analysis of Machine Learning Algorithms for Predicting Under-Five Mortality: Evidence from Tanzania Demographic and Health Survey"],"prefix":"10.11648","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1026-1194","authenticated-orcid":true,"given":"Salyungu","family":"Mabula","sequence":"first","affiliation":[{"name":"Department of Mthematics and Statistics, College of Natural and Mathematical Sciences, University of Dodoma, Dodoma, Tanzania, Department of Mathematics, Physics and Computing, School of Science and Aerospace Studies, Moi University, Eldoret, Kenya"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3067-7373","authenticated-orcid":true,"given":"Robert","family":"Too","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Physics and Computing, School of Science and Aerospace Studies, Moi University, Eldoret, Kenya"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2485-9373","authenticated-orcid":true,"given":"Gregory","family":"Kerich","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Physics and Computing, School of Science and Aerospace Studies, Moi University, Eldoret, Kenya"}]}],"member":"4911","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"ref1","unstructured":"United Nations. 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A Deep Learning and Machine Learning Approach to Predict Neonatal Death in the Context of Sao Paulo. arXiv preprint arXiv:2506.16929.","DOI":"10.11591\/ijphs.v13i1.22577"},{"key":"ref60","doi-asserted-by":"crossref","unstructured":"Sende, N. B., Saha, S. and Uwimbabazi, L. F. R. (2025). Spatial Distribution of Poverty Clusters and its Prediction Algorithms: A Visual Analytics Approach to Understanding the Disparities of Poverty Across Zones. In in IEEE Access, 13(2025): 96302-96316. Available at: https:\/\/ieeexplore.ieee.org\/document\/11020683","DOI":"10.1109\/ACCESS.2025.3575577"},{"key":"ref61","doi-asserted-by":"crossref","unstructured":"Agrawal, S., Gupta, G. K., Gopalakrishna, P. K., Balasubramaniam, V. S., Goel, L. and Mahadik, S. (2024). Hybrid Machine Learning Models: Combining Strengths of Supervised and Unsupervised Learning Approaches. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), Greater Noida, India, 7(2024): 1056-1061. Available at: https:\/\/ieeexplore.ieee.org\/abstract\/document\/10829140","DOI":"10.1109\/IC3I61595.2024.10829140"},{"key":"ref62","doi-asserted-by":"crossref","unstructured":"Yakovyna, V., Shakhovska, N. and Szpakowska, A. (2024). A novel hybrid supervised and unsupervised hierarchical ensemble for covid-19 cases and mortality prediction. Scientific Reports, 14(2024): 9782. Available at: https:\/\/doi.org\/10.1038\/s41598-024-60637-y","DOI":"10.1038\/s41598-024-60637-y"},{"key":"ref63","doi-asserted-by":"crossref","unstructured":"Rodriguez, A., Mendoza, D., Ascuntar, J. and Jaimes, F. (2021). Supervised Classification Techniques for Prediction of Mortality in Adult Patients with Sepsis. The American Journal of Emergency Medicine, 45(2021): 392-397. Available at: https:\/\/doi.org\/10.1016\/j.ajem.2020.09.013","DOI":"10.1016\/j.ajem.2020.09.013"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20251002.12","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T12:11:16Z","timestamp":1756296676000},"score":18.202133,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20251002.12"}},"issued":{"date-parts":[[2025,8,20]]},"references-count":63,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,8,4]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20251002.12","ISSN":["2637-5680","2637-5672"],"issn-type":[{"value":"2637-5680","type":"electronic"},{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2025,8,20]]},"article-number":"6041128"},{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T09:47:25Z","timestamp":1774518445192,"version":"3.50.1"},"reference-count":0,"publisher":"Science Publishing Group","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2023,5,18]]},"DOI":"10.11648\/j.mlr.20230801.11","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T06:37:55Z","timestamp":1687243075000},"source":"Crossref","is-referenced-by-count":1,"title":["Traditional Machine Learning Models for Building Energy Performance Prediction: A Comparative Research"],"prefix":"10.11648","author":[{"given":"Zeyu","family":"Wu","sequence":"first","affiliation":[{"name":"College of Engineering and Physical Sciences, The University of Birmingham, Birmingham, United Kingdom"}]},{"given":"Hongyang","family":"He","sequence":"additional","affiliation":[{"name":"College of Engineering and Physical Sciences, The University of Birmingham, Birmingham, United Kingdom"}]}],"member":"4911","published-online":{"date-parts":[[2023,5,29]]},"container-title":["Machine Learning Research"],"language":"en","deposited":{"date-parts":[[2024,4,7]],"date-time":"2024-04-07T19:04:35Z","timestamp":1712516675000},"score":18.202133,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20230801.11"}},"issued":{"date-parts":[[2023,5,29]]},"references-count":0,"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20230801.11","published":{"date-parts":[[2023,5,29]]}},{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:30:47Z","timestamp":1763202647284},"reference-count":0,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"published-print":{"date-parts":[[2021]]},"DOI":"10.11648\/j.mlr.20210602.12","type":"journal-article","created":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T08:22:39Z","timestamp":1659774159000},"page":"17","source":"Crossref","is-referenced-by-count":3,"title":["A Genetic Neuro-Fuzzy System for Diagnosing Clinical Depression"],"prefix":"10.11648","volume":"6","author":[{"given":"Adegboyega","family":"Adegboye","sequence":"first","affiliation":[]},{"given":"Imianvan","family":"Anthony Agboizebeta","sequence":"additional","affiliation":[]}],"member":"4911","container-title":["Machine Learning Research"],"language":"en","deposited":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T08:22:41Z","timestamp":1659774161000},"score":18.201635,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20210602.12"}},"issued":{"date-parts":[[2021]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021]]}},"alternative-id":["6041055"],"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20210602.12","ISSN":["2637-5672"],"issn-type":[{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2021]]}},{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:20:35Z","timestamp":1753881635146,"version":"3.41.2"},"reference-count":8,"publisher":"Science Publishing Group","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2025,4,2]]},"abstract":"<jats:p xml:lang=\"en\">An essential vegetable that is grown all over the world and eaten in a variety of ways is the onion (Allium cepa L.). A common condiment used to improve food flavor is onion. Around the world, red onion seed (A. fistulosum) is cultivated in a variety of temperate and tropical settings. It is grown in China and Japan, among other places, worldwide. A. fistulosum is grown across Ethiopia in various regions. In 2012, 3,281,574 tons of output were obtained from 30,478 hectares of coverage. Allium fistulosum covers the Amhara area over 8000 hectors, which is 26% of our country. For export, red onion seed is separated based on quality. Red onion seed quality separation or categorization is essential to the trade process. It aids in making people marketable. In Ethiopia, this procedure is carried out manually, which has a number of drawbacks like being less effective, inconsistent, and prone to subjectivity. To address this problem, we use pre-trained transfer learning model VGG, GoogleNet, and ResNet50 for quality classification of red onion seed. The main procedures include image preprocessing, resizing, data augmentation, and prediction. The model trained on 470 datasets collected from different agricultural fields in south Gondar libo kemkem and fogera woreda. We use various augmentation strategies to expand the dataset. Ten percent of the dataset was set aside for testing, ten percent for validation, and eighty percent for training. For VGG19, VGG16, GoogleNet, and ResNet, the model&amp;apos;s classification accuracy for the input image is 99%, 100%, 100%, and 86%, respectively.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20251001.15","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T02:06:13Z","timestamp":1746669973000},"page":"44-52","source":"Crossref","is-referenced-by-count":0,"title":["Red Onion Seed Quality Classification Using Transfer Learning Approaches"],"prefix":"10.11648","volume":"10","author":[{"given":"Tarekegn","family":"Yirdaw","sequence":"first","affiliation":[{"name":"Information System Department, Kombolcha Institute of Technology, Wollo University, Kombolcha, Ethiopia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1187-9190","authenticated-orcid":true,"given":"Ermias","family":"Tadesse","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Kombolcha Institute of Technology, Wollo University, Kombolcha, Ethiopia"}]},{"given":"Endalkachew","family":"Hiwote","sequence":"additional","affiliation":[{"name":"Software Engineering Department, Kombolcha Institute of Technology, Wollo University, Kombolcha, Ethiopia"}]},{"given":"Abebaw","family":"Mebrate","sequence":"additional","affiliation":[{"name":"Software Engineering Department, Kombolcha Institute of Technology, Wollo University, Kombolcha, Ethiopia"}]},{"given":"Ambaw","family":"Mulatu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Institute of Technology, Wollo University, Kombolcha, Ethiopia"}]}],"member":"4911","published-online":{"date-parts":[[2025,4,29]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"C. W. Gathambiri, W. O. Owino, S. Imathiu, and J. N. Mbaka, \u201cPostharvest Losses Of Bulb Onion (Allium Cepa L.) In Selected Sub-Counties Of Kenya,\u201d African J. Food, Agric. Nutr. Dev., vol. 21, no. 2, pp. 17529\u201317544, 2021, https:\/\/doi.org\/10.18697\/ajfand.97.20145","DOI":"10.18697\/ajfand.97.20145"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"M. A. Zaki, S. Narejo, M. Ahsan, S. Zai, M. R. Anjum, and N. Din, \u201cImage-based Onion Disease (Purple Blotch) Detection using Deep Convolutional Neural Network,\u201d vol. 12, no. 5, pp. 448\u2013458, 2021.","DOI":"10.14569\/IJACSA.2021.0120556"},{"key":"ref3","unstructured":"T. Project, S. Horticulture, F. Empowerment, and M. Agriculture, \u201c\u12e8\u1240\u12ed \u123d\u1295\u12a9\u122d\u1275 \u12a0\u1218\u122b\u1228\u1275,\u201d [&quot;Onion Production,&quot;]2019."},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"N. A. P. Lestari, R. Dijaya, and N. L. Azizah, \u201cIdentification Growth Quality of Red Onion during Planting Period using Support Vector Machine,\u201d J. Phys. Conf. Ser., vol. 1764, no. 1, 2021, https:\/\/doi.org\/10.1088\/1742-6596\/1764\/1\/012060","DOI":"10.1088\/1742-6596\/1764\/1\/012060"},{"key":"ref5","unstructured":"M. H. Ahmed, \u201cCollege of Natural Sciences Automatic Soybean Quality Grading Using Image Processing and Supervised Learning Algorithms,\u201d no. October, 2021."},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"S. Afaq et al., \u201cSmart Agricultural Technology Automatic and fast classification of barley grains from images: A deep learning approach,\u201d Smart Agric. Technol., vol. 2, no. December 2021, p. 100036, 2022, https:\/\/doi.org\/10.1016\/j.atech.2022.100036","DOI":"10.1016\/j.atech.2022.100036"},{"key":"ref7","unstructured":"M. Habtewold, C. Dane, A. Ayza, N. Resource, M. Directorate, and A. Agricultural, \u201cParticipatory Evaluation and Demonstration of Onion Spacing in Irrigated Agriculture at Kencho Kebele in Uba Debre Tsehay Woreda, Southern Ethiopia,\u201d vol. 31, no. 2, pp. 105\u2013114, 2021."},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"N. Semman, G. Etana, and T. Mulualem, \u201cAdaptability and yield performance evaluation of onion (Allium cepa L.) varieties in Jimma zone, Southwestern Ethiopia.,\u201d vol. 9, no. 4, pp. 405\u2013409, 2019, https:\/\/doi.org\/10.15580\/GJAS.2019.4.090919169","DOI":"10.15580\/GJAS.2019.4.090919169"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20251001.15","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T02:06:18Z","timestamp":1746669978000},"score":18.191286,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20251001.15"}},"issued":{"date-parts":[[2025,4,29]]},"references-count":8,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3,31]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20251001.15","ISSN":["2637-5680","2637-5672"],"issn-type":[{"type":"electronic","value":"2637-5680"},{"type":"print","value":"2637-5672"}],"published":{"date-parts":[[2025,4,29]]},"article-number":"1331111"},{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:51:48Z","timestamp":1774021908713,"version":"3.50.1"},"reference-count":13,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2025,12,6]]},"abstract":"<jats:p xml:lang=\"en\">Kidney stone disease affects millions of people worldwide, with both environmental and genetic factors contributing to individual susceptibility. While Genome-Wide Association Studies (GWAS) have successfully identified numerous single-nucleotide polymorphisms (SNPs) associated with kidney stone risk, translating these findings into accurate and clinically useful prediction tools remains a significant challenge. Polygenic Risk Scores (PRS) provide a framework for quantifying an individual\u2019s genetic predisposition, but traditional PRS approaches often fail to account for complex interactions among variants and the non-linear effects present in genomic data. In this study, we investigate the potential of deep learning techniques, specifically Convolutional Neural Networks (CNNs), to improve PRS models for predicting kidney stone susceptibility. Our approach integrates careful SNP selection, genotype filtering, and CNN-based modeling to address challenges such as data imbalance, genomic noise, and high-dimensional feature spaces. We benchmark our CNN-based framework against conventional machine learning models, including logistic regression, random forests, and support vector machines, demonstrating superior performance in terms of classification accuracy and ROC-AUC. These findings underscore the promise of deep learning-enhanced PRS models to provide more accurate genetic risk predictions for kidney stones. The research highlights the potential of integrating advanced computational approaches with genomics to advance precision medicine, improve patient stratification, and guide preventive strategies for at-risk individuals.<\/jats:p>","DOI":"10.11648\/j.mlr.20251002.18","type":"journal-article","created":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T07:36:13Z","timestamp":1766648173000},"page":"151-159","source":"Crossref","is-referenced-by-count":1,"title":["A Deep Learning Approach to Polygenic Risk Prediction of Kidney Stone Formation"],"prefix":"10.11648","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7161-7346","authenticated-orcid":true,"given":"Amr","family":"Salem","sequence":"first","affiliation":[{"name":"Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4100-2366","authenticated-orcid":true,"given":"Anirban","family":"Mondal","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, USA"}]}],"member":"4911","published-online":{"date-parts":[[2025,12,19]]},"reference":[{"key":"ref1","unstructured":"All of us research program. https:\/\/www.researchallofus.org\/, 2025. 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Adam: A method for stochastic optimization. International Conference on Learning Representations, 2015."},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521:436-444, 2015. https:\/\/doi.org\/10.1038\/nature14539","DOI":"10.1038\/nature14539"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Ryan et al. Poplin. A universal snp and smallindel variant caller using deep neural networks. Nature Biotechnology, 36(10): 983-987, 2018. 16","DOI":"10.1038\/nbt.4235"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"C. Smith and D. Lee. Genetic insights into kidney stone formation. Nature Genetics, 47: 456-462, 2015. https:\/\/doi.org\/10.1038\/ng.3250","DOI":"10.1038\/ng.3250"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Jian Zhou and Olga G Troyanskaya. Predicting effects of noncoding variants with deep learning-based sequence model. Nature Methods, 12(10): 931-934, 2015.","DOI":"10.1038\/nmeth.3547"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20251002.18","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T07:36:16Z","timestamp":1766648176000},"score":18.191286,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20251002.18"}},"issued":{"date-parts":[[2025,12,19]]},"references-count":13,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,8,4]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20251002.18","ISSN":["2637-5680","2637-5672"],"issn-type":[{"value":"2637-5680","type":"electronic"},{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2025,12,19]]},"article-number":"6041137"},{"indexed":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T22:22:30Z","timestamp":1648851750926},"reference-count":0,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"published-print":{"date-parts":[[2018]]},"DOI":"10.11648\/j.mlr.20180302.14","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T03:57:04Z","timestamp":1547179024000},"page":"33","source":"Crossref","is-referenced-by-count":0,"title":["Analysis on Detection of Chalkiness for Myanmar Rice Using Image Processing"],"prefix":"10.11648","volume":"3","author":[{"given":"Thae","family":"Nu Wah","sequence":"first","affiliation":[]}],"member":"4911","container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"http:\/\/article.sciencepublishinggroup.com\/pdf\/10.11648.j.mlr.20180302.14.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T08:13:00Z","timestamp":1602922380000},"score":18.190857,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20180302.14"}},"issued":{"date-parts":[[2018]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018]]}},"alternative-id":["6041028"],"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20180302.14","ISSN":["2637-5672"],"issn-type":[{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2018]]}},{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:49:01Z","timestamp":1774021741238,"version":"3.50.1"},"reference-count":18,"publisher":"Science Publishing Group","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2025,3,21]]},"abstract":"<jats:p xml:lang=\"en\">Timely and precise identification of foliar diseases is essential in contemporary agriculture to avert crop loss, enhance productivity, and guarantee food security. Paddy, being one of the most extensively farmed and consumed staple crops globally, is especially vulnerable to several leaf diseases that can markedly diminish yield. Conventional illness detection techniques, which depend significantly on manual observation and expert evaluation, are frequently time-consuming, labor-intensive, and susceptible to discrepancies. These constraints need the implementation of automated and efficient disease detection technologies. This research investigates the utilization of a pre-trained EfficientNetB3 convolutional neural network for the identification and categorization of paddy leaf diseases. The model was trained and assessed on a rich and diverse dataset comprising annotated pictures of healthy and sick paddy leaves. The performance evaluation included conventional classification criteria like as accuracy, precision, recall, and F1-score to ensure a comprehensive assessment of the model&amp;apos;s efficacy. The EfficientNetB3 model exhibited exceptional performance, with an overall accuracy of 96% in the detection and classification of prevalent paddy leaf diseases. This elevated accuracy signifies the model&amp;apos;s proficiency in generalizing effectively across diverse illness categories and imaging settings. The findings underscore the capability of deep learning and computer vision methodologies to revolutionize agricultural operations by offering scalable, dependable, and instantaneous solutions for disease identification. The suggested approach facilitates early diagnosis, aiding farmers and agronomists in executing timely and precise treatments, hence minimizing crop loss and enhancing production. Moreover, the incorporation of AI-driven technologies into current agricultural frameworks fosters sustainable farming and strengthens the resilience of food production systems. The research highlights the significant influence of artificial intelligence on precision agriculture and establishes a basis for additional investigation into intelligent crop monitoring systems.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20251001.11","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T02:51:28Z","timestamp":1743562288000},"page":"1-13","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing Agricultural Diagnostics: Advanced Training of Pre-Trained CNN Models for Paddy Leaf Disease Detection"],"prefix":"10.11648","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1351-7287","authenticated-orcid":true,"given":"Sazzad","family":"Hossain","sequence":"first","affiliation":[{"name":"Faculty of Intelligent Systems and Computer Technologies, Samarkand State University, Samarkand, Uzbekistan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7512-1893","authenticated-orcid":true,"given":"Touhidul","family":"Seyam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chattogram, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9667-9857","authenticated-orcid":true,"given":"Avijit","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Chittagong University of Engineering and Technology, Chattogram, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4409-6509","authenticated-orcid":true,"given":"Rajib","family":"Ghose","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Military Institute of Science and Technology (MIST), Dhaka, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2779-0669","authenticated-orcid":true,"given":"Arifur","family":"Rahaman","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sonargaon University, Dhaka, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3204-7244","authenticated-orcid":true,"given":"Zarin","family":"Hadika","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sonargaon University, Dhaka, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7734-0271","authenticated-orcid":true,"given":"Abhijit","family":"Pathak","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sonargaon University, Dhaka, Bangladesh"}]}],"member":"4911","published-online":{"date-parts":[[2025,3,31]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Deep Learning Approach for Paddy Field Detection Using Labeled Aerial Images: The Case of Detecting and Staging Paddy Fields in Central and Southern Taiwan. Remote sensing, https:\/\/doi.org\/10.3390\/rs15143575","DOI":"10.3390\/rs15143575"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Salman, Qadri., Hadia, Bibi., Muhammad, Imran, Sharif., Francesco, Marinello. (2023). Comparing Inception V3, VGG 16, VGG 19, CNN, and ResNet 50: A Case Study on Early Detection of a Rice Disease. Agronomy, https:\/\/doi.org\/10.3390\/agronomy13061633","DOI":"10.3390\/agronomy13061633"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Dingju, Zhu., Jianbin, Tan., Kai, Leung, Yung., Andrew, W., H., Ip. (2023). Crop Disease Identification by Fusing Multiscale Convolution and Vision Transformer. Sensors, https:\/\/doi.org\/10.3390\/s23136015","DOI":"10.3390\/s23136015"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"V., Senthil, Kumar., M. K., Jaganathan., Abhishek, Viswanathan., M., Umamaheswari., J., C., Vignesh. (2023). 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Chowdhury, \u201cAdvancing Multi-Class Arc Welding Defect Classification: DEEPTLWELD Intelligent System Utilizing Computer Vision, Deep Learning, and Transfer Learning on Radiographic X-ray Images for Bangladesh\u2019s Manufacturing Sector,\u201d 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS), Cox\u2019s Bazar, Bangladesh, 2024, pp. 1-6, https:\/\/doi.org\/10.1109\/COMPAS60761.2024.10796006","DOI":"10.1109\/COMPAS60761.2024.10796006"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Monoronjon, Dutta., Md, Rashedul, Islam, Sujan., Mayen, Uddin, Mojumdar., Narayan, Ranjan, Chakraborty., Ahmed, Al, Marouf., Jon, Rokne., Reda, Alhajj. (2024). 1. Rice Leaf Disease Classificationa\u02c6A Comparative Approach Using Convolutional Neural Network (CNN), Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), and MobileNet- V2 Architectures. 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Correia, \u201dPaddy Care: Paddy Disease Identification and Classification Using Deep DenseNet Network,\u201d 2024 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Mangalore, India, 2024, pp. 377-382, https:\/\/doi.org\/10.1109\/DISCOVER62353.2024.10750707","DOI":"10.1109\/DISCOVER62353.2024.10750707"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Athar, Hussain., Balaji, Srikaanth., P. \u201c4. Deep Learning with Crested Porcupine Optimizer for Detection and Classification of Paddy Leaf Diseases for Sustainable Agriculture.\u201d Journal of machine and computing, undefined (2024). https:\/\/doi.org\/10.53759\/7669\/jmc202404095","DOI":"10.53759\/7669\/jmc202404095"},{"key":"ref16","unstructured":"Hossain, S., Seyam, T. A., Chowdhury, A., Xamidov, M., Ghose, R., Pathak, A. (2025). 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Advances in Networks, 12(1), 19\u201328. https:\/\/doi.org\/10.11648\/j.net.20251201.12","DOI":"10.11648\/j.net.20251201.12"}],"container-title":["Machine Learning 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reconstruction for the 1-d signal can contribute to the communication of autonomous driving, intelligent robots, and fire exploration robots. To address the issue that fully connected layers in the LISTA method lack the ability to extract non-local features, this paper primarily designs a non-local fully connection layer and proposes a novel compressed sensing reconstruction method for audio signals. This paper designs a compression sensing reconstruction method for the 1-d signal. To reconstruct 1-d signal, the deep learning method LISTA is used. Then, the linear full connection layer in LISTA is improved by combining the output of three full connection layer to capture the non-local information. The computing regions of improved non-local full connection layer contain: 1) the full connection before; 2) the current full connection; and 3) the full connection after. Experimental results show the reconstruction results of LISTA and LISTA_nf are both close to the real signal. The MSE of LISTA_nf is reduced by 0.1 than the MSE of ISTA under the same experimental settings. The non-local full connection layer in the LISTA_nf consumes longer computing time. The LISTA_nf increase the computing time by 0.07s than the computing time of the ISTA. Experimental results show the effectiveness of the proposed method.<\/jats:p>","DOI":"10.11648\/j.mlr.20261101.11","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T07:01:53Z","timestamp":1771916513000},"page":"1-7","source":"Crossref","is-referenced-by-count":0,"title":["The Compression Sensing Reconstruction for the 1-d Signal Based on Non-local Full Connection Layer"],"prefix":"10.11648","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6819-3270","authenticated-orcid":true,"given":"Juan","family":"Xie","sequence":"first","affiliation":[{"name":"College of Information Technology, Nanchang Vocational University, Nanchang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4573-0482","authenticated-orcid":true,"given":"Jinwang","family":"Zha","sequence":"additional","affiliation":[{"name":"College of Information Technology, Nanchang Vocational University, Nanchang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3351-5812","authenticated-orcid":true,"given":"Jie","family":"Ren","sequence":"additional","affiliation":[{"name":"Big Data Technology Teaching and Research Office, Nanchang Vocational University, Nanchang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1802-3122","authenticated-orcid":true,"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Nanchang Vocational University, Nanchang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7180-9846","authenticated-orcid":true,"given":"Yadong","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Qianfeng Internet Technology Co., Ltd. (Professional Co-construction Department), Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3410-8322","authenticated-orcid":true,"given":"Xing","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Information Technology, Nanchang Vocational University, Nanchang, China;School of Control Science and Engineering, Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9222-078X","authenticated-orcid":true,"given":"Li","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Information Technology, Nanchang Vocational University, Nanchang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9843-4983","authenticated-orcid":true,"given":"Gan","family":"Wang","sequence":"additional","affiliation":[{"name":"Huanggang Education Valley Investment Holding Co., Ltd., Huanggang City, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0615-6297","authenticated-orcid":true,"given":"Jian","family":"Lan","sequence":"additional","affiliation":[{"name":"Huanggang Education Valley Investment Holding Co., Ltd., Huanggang City, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9214-0140","authenticated-orcid":true,"given":"Caihong","family":"Cao","sequence":"additional","affiliation":[{"name":"Nanchang Vocational University Library, Nanchang Vocational University, Nanchang, China"}]}],"member":"4911","published-online":{"date-parts":[[2026,2,20]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Donoho D L. 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Traditional rule-based and signature-based detection methods often fail against sophisticated and obfuscated XSS payloads, necessitating more advanced solutions. To address this, a machine learning-based model is developed to enhance XSS detection accuracy while minimizing false positives. The proposed approach utilizes feature extraction techniques, including Term Frequency-Inverse Document Frequency (TF-IDF) and n-grams, to analyze JavaScript payloads, while Principal Component Analysis (PCA) is employed for feature selection, reducing dimensionality and improving computational efficiency. A Logistic Regression classifier is trained on an XSS payload dataset from Kaggle, with data split into 80% for training and 20% for testing to ensure a robust evaluation. Hyperparameter tuning is performed using GridSearchCV, optimizing the model\u2019s predictive capabilities. Experimental results demonstrate a 99.70% accuracy, with 100% recall and 99.36% precision, highlighting the model\u2019s effectiveness in detecting XSS attacks while minimizing false alarms. The high recall score ensures all malicious payloads are identified, while the strong precision rate enhances reliability for real-world deployment. These findings underscore the potential of machine learning in strengthening web security frameworks, offering a scalable and efficient alternative to conventional detection systems. Future research should focus on enhancing resilience against adversarial attacks by integrating deep learning models such as Bidirectional LSTMs (BiLSTMs) and Transformer-based architectures. Additionally, deploying the model in real-time web security solutions could provide proactive defense mechanisms, ensuring robust protection against evolving XSS threats.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20251001.12","type":"journal-article","created":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T06:24:44Z","timestamp":1744784684000},"page":"14-24","source":"Crossref","is-referenced-by-count":3,"title":["XSS-Net: An Intelligent Machine Learning Model for Detecting Cross-Site Scripting (XSS) Attack in Web Application"],"prefix":"10.11648","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8777-2050","authenticated-orcid":true,"given":"Emmanuel","family":"Oshoiribhor","sequence":"first","affiliation":[{"name":"Department of Computer Science, Ambrose Alli University, Ekpoma, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3138-4639","authenticated-orcid":true,"given":"Adetokunbo","family":"John-Otumu","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Federal University of Technology, Owerri, Nigeria"}]}],"member":"4911","published-online":{"date-parts":[[2025,4,14]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Taylor O. E. and Ezekiel P. S. (2022) A Robust System for Detecting and Preventing Payloads Attacks on Web-Applications Using Recurrent Neural Network (RNN), European Journal of Computer Science and Information Technology, 10(4), 1-13. https:\/\/doi.org\/10.37745\/ejcsit.2013\/vol10n4113","DOI":"10.37745\/ejcsit.2013\/vol10n4113"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Schalk, A., & Brown, D. (2023, March). Detection and mitigation of vulnerabilities in space network software bus architectures. In 2023 IEEE Aerospace Conference (pp. 1-10). IEEE. https:\/\/doi.org\/10.1109\/aero55745.2023.10115986","DOI":"10.1109\/AERO55745.2023.10115986"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Lee, H. S., & Kim, K. (2018). Simultaneous traffic sign detection and boundary estimation using convolutional neural network. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1652-1663. https:\/\/doi.org\/10.1109\/TITS.2018.2801560","DOI":"10.1109\/TITS.2018.2801560"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Li, Y., Hua, J., Wang, H., Chen, C., & Liu, Y. (2021). DeepPayload: Black-box backdoor attack on deep learning models through neural payload injection. Proceedings - International Conference on Software Engineering. https:\/\/doi.org\/10.1109\/ICSE43902.2021.00035","DOI":"10.1109\/ICSE43902.2021.00035"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Hamzah, K. H., Osman, M. Z., Anthony, T., Ismail, M. A., Abdullah, Z., & Alanda, A. (2024). Comparative Analysis of Machine Learning Algorithms for Cross-Site Scripting (XSS) Attack Detection. JOIV: International Journal on Informatics Visualization, 8(3-2), 1678-1685. http:\/\/dx.doi.org\/10.62527\/joiv.8.3-2.3451","DOI":"10.62527\/joiv.8.3-2.3451"},{"key":"ref6","unstructured":"Khalid, U., Abdullah, M., & Inayat, K. (2020). Exploiting ML algorithms for Efficient Detection and Prevention of JavaScript-XSS Attacks in Android Based Hybrid Applications. arXiv preprint arXiv: 2006. 07350. https:\/\/doi.org\/10.48550\/arXiv.2006.07350"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Alhamyani, R., & Alshammari, M. (2024). Machine learning-driven detection of cross-site scripting attacks. Information, 15(7), 420. https:\/\/doi.org\/10.3390\/info15070420","DOI":"10.3390\/info15070420"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Aliga, A. P., John-Otumu, A. M., Imhanhahimi, R. E., & Akpe, A. C. (2018). Cross site scripting attacks in web-based applications. Journal of Advances in Science and Engineering, 1(2), 25-35. https:\/\/doi.org\/10.37121\/jase.v1i2.19","DOI":"10.37121\/jase.v1i2.19"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Prasetio, D., Kusrini, K., & Arief, M. R. (2021). Cross-site scripting attack detection using machine learning with hybrid features. INFOTEL, 13(1), 1\u20136. https:\/\/doi.org\/10.20895\/infotel.v13i1.606","DOI":"10.20895\/infotel.v13i1.606"},{"key":"ref10","unstructured":"Talib, N. A., & Kyung-Goo Doh, K, (2022). Run-time Detection of Cross-site Scripting: A Machine-Learning Approach Using Syntactic-Tagging N-Gram Features, International Journal of Computer Science and Security (IJCSS), 16(2), 9 - 27."},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Farea, A. A. R., Amran, G. A., Farea, E., Alabrah, A., Abdulraheem, A. A., Mursil, M., & Al-Qaness, M. A. A. (2023). Injections Attacks Efficient and Secure Techniques Based on Bidirectional Long Short Time Memory Model. Computers, Materials and Continua, 76(3). https:\/\/doi.org\/10.32604\/cmc.2023.040121","DOI":"10.32604\/cmc.2023.040121"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Hao, S., Long, J., & Yang, Y. (2019). BL-IDS: Detecting Web Attacks Using Bi-LSTM Model Based on Deep Learning.\u00a0Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. https:\/\/doi.org\/10.1007\/978-3-030-21373-2_45","DOI":"10.1007\/978-3-030-21373-2_45"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Sovet, Y. G., & K\u043ekkoz, \u041c. \u041c. (2022). Detection of xss attacks in web applications using machine learning. \u0412\u0435\u0441\u0442\u043d\u0438\u043a \u0410\u043b\u043c\u0430\u0442\u0438\u043d\u0441\u043a\u043e\u0433\u043e \u0423\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u0430 \u042d\u043d\u0435\u0440\u0433\u0435\u0442\u0438\u043a\u0438 \u0438 \u0421\u0432\u044f\u0437\u0438, (2). https:\/\/doi.org\/10.51775\/2790-0886_2022_57_2_157","DOI":"10.51775\/2790-0886_2022_57_2_157"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Howe, J. M., & Mereani, F. A. (2018, January). Detecting cross-site scripting attacks using machine learning. In International conference on advanced machine learning technologies and applications (pp. 200-210). Cham: Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-319-74690-6_20","DOI":"10.1007\/978-3-319-74690-6_20"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Tariq, I., Sindhu, M. A., Abbasi, R. A., Khattak, A. S., Maqbool, O., & Siddiqui, G. F. (2021). Resolving cross-site scripting attacks through genetic algorithm and reinforcement learning. Expert Systems with Applications, 168, 114386. https:\/\/doi.org\/10.1016\/j.eswa.2020.114386","DOI":"10.1016\/j.eswa.2020.114386"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Lu, J., Wei, Z., Qin, Z., Chang, Y., & Zhang, S. (2022). Resolving cross-site scripting attacks through fusion verification and machine learning. Mathematics, 10(20), 3787. https:\/\/doi.org\/10.3390\/math10203787","DOI":"10.3390\/math10203787"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Kumar, A., & Sharma, I. (2023, April). Performance evaluation of machine learning techniques for detecting cross-site scripting attacks. In 2023 11th International Conference on Emerging Trends in Engineering & Technology-Signal and Information Processing (ICETET-SIP) (pp. 1-5). IEEE. https:\/\/doi.org\/10.1109\/icetet-sip58143.2023.10151468","DOI":"10.1109\/ICETET-SIP58143.2023.10151468"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20251001.12","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T06:24:58Z","timestamp":1744784698000},"score":18.18614,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20251001.12"}},"issued":{"date-parts":[[2025,4,14]]},"references-count":17,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3,31]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20251001.12","ISSN":["2637-5680","2637-5672"],"issn-type":[{"value":"2637-5680","type":"electronic"},{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2025,4,14]]},"article-number":"6041113"},{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T08:15:43Z","timestamp":1764663343515,"version":"3.46.0"},"reference-count":22,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2025,10,9]]},"abstract":"<jats:p xml:lang=\"en\">The research seeks to establish a Project Management Framework for the Implementation of AI-Driven Fault Mitigation Systems by utilizing quantitative methodologies such as machine learning, in conjunction with insights from telecommunications engineers. This study integrates empirical data with practical expertise. The research was conducted using data obtained from Savanna Fibre Limited, resulting in a dataset comprising fault logs. Hybrid AI models, specifically a combination of CNN-LSTM with certain physics-based modifications, were trained and tested to detect faults at an early stage, identify the nature of the problems, locate the source of the issues, and assess the potential severity of the faults. Data preprocessing pipelines were developed to tackle challenges including imbalanced classes and sparse records of submarine cable faults, while domain knowledge also played a crucial role in guiding feature engineering and model interpretation. The framework demonstrates impressive performance: it achieves a 94.3% F1-score in fault classification, forecasts issues up to 72 hours ahead with a 92% confidence level, and accurately identifies fault locations within \u00b125 meters. To enhance its practicality, a versatile deployment configuration integrates model outputs into real-world workflows through CI\/CD pipelines and even utilizes AR tools to assist in field repairs, resulting in a 42% reduction in repair times during actual tests. This research indicates that AI-driven, proactive maintenance is not merely theoretical; it is achievable with the appropriate data, interdisciplinary collaboration, and practical testing. Looking forward, there is significant potential to expand this approach for 5G and IoT networks or to refine our management of uncertainty in critical systems.<\/jats:p>","DOI":"10.11648\/j.mlr.20251002.15","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T08:13:19Z","timestamp":1764663199000},"page":"137-150","source":"Crossref","is-referenced-by-count":0,"title":["A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems"],"prefix":"10.11648","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3209-3455","authenticated-orcid":true,"given":"Isaac","family":"Buyondo","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Berlin School of Business & Innovation, Barlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3007-0950","authenticated-orcid":true,"given":"Konstantinos","family":"Kiousis","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Berlin School of Business & Innovation, Barlin, Germany"}]}],"member":"4911","published-online":{"date-parts":[[2025,11,12]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"S. 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However, the increasing use of these technologies and the functionalities they offer has sparked discussions about their impact and even raised concerns about the potential replacement of human work by automation carried out by machines. This study proposes a Systematic Literature Review to evaluate the opportunities and challenges that these technologies present to system developers in the current and future technological scenario. Aiming at state-of-the-art research to identify how Generative AIs are being applied in the context of software development and what are the latest trends and innovations in this field and how these innovations affect the opportunities and challenges for system developers. As a result, several studies were found that highlight how Generative AI has provided productivity and systems development optimized solutions in the industry, as well as promoting innovations. Studies also emphasize the need for a balance between the use of AI tools and development carried out by human participation, which must be mediated by common sense. Furthermore, the review will explore the ethical implications associated with the widespread adoption of AI technologies, addressing issues such as data privacy, decision-making transparency, and the responsibility of developers in ensuring that AI applications are used in a way that benefits society. The findings of this review will contribute to a better understanding of how generative AI is reshaping the software development landscape and provide insights for future research and development in this rapidly evolving field.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20240902.12","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T08:25:59Z","timestamp":1727684759000},"page":"39-47","source":"Crossref","is-referenced-by-count":3,"title":["Generative Artificial Intelligence: Challenges and Opportunities for Systems Developers: A Systematic Mapping of Literature"],"prefix":"10.11648","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8033-710X","authenticated-orcid":true,"given":"Samira","family":"Caduda","sequence":"first","affiliation":[{"name":"Department of Computing, Tiradentes University, Aracaju-SE, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7440-1382","authenticated-orcid":true,"given":"Anderson","family":"Barroso","sequence":"additional","affiliation":[{"name":"Department of Computing, Tiradentes University, Aracaju-SE, Brazil"}]}],"member":"4911","published-online":{"date-parts":[[2024,9,29]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"A. 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IEEEComputer Society. http:\/\/doi.org\/10.1109\/ESEM.2009.5314231","DOI":"10.1109\/ESEM.2009.5314231"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20240902.12","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T08:26:19Z","timestamp":1727684779000},"score":18.18614,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20240902.12"}},"issued":{"date-parts":[[2024,9,29]]},"references-count":23,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,7,15]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20240902.12","ISSN":["2637-5680","2637-5672"],"issn-type":[{"value":"2637-5680","type":"electronic"},{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2024,9,29]]},"article-number":"3030336"},{"indexed":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T15:03:58Z","timestamp":1648739038721},"reference-count":0,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"published-print":{"date-parts":[[2018]]},"DOI":"10.11648\/j.mlr.20180302.13","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T03:57:04Z","timestamp":1547179024000},"page":"28","source":"Crossref","is-referenced-by-count":0,"title":["Vertex Colorings of Graph and Some of Their Applications in Promoting Global Competitiveness for National Growth and Productivity"],"prefix":"10.11648","volume":"3","author":[{"given":"Abdulazeez","family":"Idris","sequence":"first","affiliation":[]}],"member":"4911","container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"http:\/\/article.sciencepublishinggroup.com\/pdf\/10.11648.j.mlr.20180302.13.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T08:08:47Z","timestamp":1602922127000},"score":18.184319,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20180302.13"}},"issued":{"date-parts":[[2018]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018]]}},"alternative-id":["6041023"],"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20180302.13","ISSN":["2637-5672"],"issn-type":[{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2018]]}},{"indexed":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T09:10:16Z","timestamp":1756545016501,"version":"3.44.0"},"reference-count":32,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2025,7,7]]},"abstract":"<jats:p xml:lang=\"en\">Breast cancer is a disease that affects the majority of women and it is the second most common cause of death among women globally. Medical scientists have proven that there are a vast number of genes that are responsible for breast cancer. Among them, all genes are not equally responsible. Therefore, the most relevant and informative genes are needed to find out to control the disease. The objectives of our study are: (i) To find the most informative and significant genes using different statistical test-based feature selection techniques (FST) as well as find the best classifier and (ii) To validate our experimental results using a simulated dataset. The breast cancer dataset is a benchmark dataset provided by Kent Ridge Biomedical Data Repository, USA. In our study, we have used different statistical test-based feature selection techniques such as the t-test and Wilcoxon signed rank sum (WCSRS) test. Na\u00efve Bayes (NB), Adaboost (AB), linear discriminant analysis (LDA), artificial neural network (ANN), k-nearest neighbor (KNN), and random forest (RF) are treated as classification techniques. Our analysis included 24,188 genes and 97 patients. Among them, 46 patients were with cancer and 51 were in control. We considered 70% of the dataset as a training set and the rest is a test set and repeated this procedure about 1000 times. Among all the combinations of FST and classification techniques t-test-based Naive Bayes classifier gives us the highest classification accuracy. The analysis of our study indicates that the integration of t-test-based FST and Na\u00efve Bayes classifier produces the maximum classification accuracy.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20251002.13","type":"journal-article","created":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T08:50:47Z","timestamp":1756543847000},"page":"124-130","source":"Crossref","is-referenced-by-count":0,"title":["Statistical Test-Based Feature Selection and Classification Techniques for Breast Cancer Data"],"prefix":"10.11648","volume":"10","author":[{"given":"Murfia","family":"Muna","sequence":"first","affiliation":[{"name":"Statistics Discipline, Khulna University, Khulna, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4472-3051","authenticated-orcid":true,"given":"Md.","family":"Sarder","sequence":"additional","affiliation":[{"name":"Statistics Discipline, Khulna University, Khulna, Bangladesh"}]}],"member":"4911","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Jones PA, Baylin SB. 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diagnostics by integrating supervised and unsupervised machine-learning methods with a strong ethical lens. We review and contextualize the current literature on ML applications for depression detection, noting the heavy reliance of supervised models on labelled data and the comparative under-exploration of unsupervised approaches. Using the public Depression Dataset-which comprises actigraph recordings from depressed patients and healthy controls and includes demographic and clinical attributes such as timestamps, activity counts, gender, age and Montgomery-\u00c5sberg Depression Rating Scale  HYPERLINK &amp;quot;http:\/\/kaggle.com&amp;quot;  \\h score we preprocess and engineer features capturing circadian rhythms and variability in motor activity. We then apply multiple clustering algorithms (K-means, hierarchical clustering and DBSCAN) to identify latent subgroups of depression severity, evaluating cluster validity via the silhouette score, Davies-Bouldin index and Calinski-Harabasz index. A supervised support-vector machine classifier trained on labelled severity categories serves as a baseline, and we find that unsupervised clustering achieves competitive performance while revealing nuanced patterns not captured by labelled categories. We visualise cluster structures and compare performance metrics to illustrate the benefits and limitations of each method. Finally, we analyse cluster composition relative to gender and socio-economic variables to highlight potential biases in the data and underscore the need for fairness-aware models and interpretability. By combining supervised and unsupervised techniques and explicitly addressing ethical considerations, this work contributes to more accurate, transparent and equitable ML-based depression diagnosis.<\/jats:p>","DOI":"10.11648\/j.mlr.20251002.17","type":"journal-article","created":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T07:20:07Z","timestamp":1766647207000},"page":"158-164","source":"Crossref","is-referenced-by-count":0,"title":["Innovative Machine Learning Approaches for Accurate and Ethical Depression Diagnosis: Insights and Recommendations"],"prefix":"10.11648","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7316-7014","authenticated-orcid":true,"given":"Abiodun","family":"Akanbi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Nile University of Nigeria, Abuja, Nigeria"}]}],"member":"4911","published-online":{"date-parts":[[2025,12,19]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"O. Oyebode, F. 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Healthcare, 11(3), 285. https:\/\/doi.org\/10.3390\/healthcare11030285","DOI":"10.3390\/healthcare11030285"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20251002.17","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:50:11Z","timestamp":1768823411000},"score":18.166409,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20251002.17"}},"issued":{"date-parts":[[2025,12,19]]},"references-count":19,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,8,4]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20251002.17","ISSN":["2637-5680","2637-5672"],"issn-type":[{"value":"2637-5680","type":"electronic"},{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2025,12,19]]},"article-number":"6041129"},{"indexed":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T05:06:11Z","timestamp":1745643971823},"reference-count":0,"publisher":"Science Publishing Group","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"published-print":{"date-parts":[[2020]]},"DOI":"10.11648\/j.mlr.20200501.12","type":"journal-article","created":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T08:31:37Z","timestamp":1594629097000},"page":"10","source":"Crossref","is-referenced-by-count":1,"title":["Timing and Parameter Optimization for One-time Motion Problem Based on Reinforcement Learning"],"prefix":"10.11648","volume":"5","author":[{"given":"Boxuan","family":"Fan","sequence":"first","affiliation":[]},{"given":"Guiming","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Hongtao","family":"Lin","sequence":"additional","affiliation":[]}],"member":"4911","container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"http:\/\/article.sciencepublishinggroup.com\/pdf\/10.11648.j.mlr.20200501.12.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T07:26:46Z","timestamp":1602919606000},"score":18.165998,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20200501.12"}},"issued":{"date-parts":[[2020]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020]]}},"alternative-id":["6041046"],"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20200501.12","ISSN":["2637-5672"],"issn-type":[{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2020]]}},{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:47:14Z","timestamp":1775069234665,"version":"3.50.1"},"reference-count":35,"publisher":"Science Publishing Group","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2024,4,27]]},"abstract":"<jats:p xml:lang=\"en\">Amid the COVID-19 pandemic, extensive research has focused on deep learning methodologies for accurately diagnosing the virus from chest X-ray images. Various models, including Convolutional Neural Networks (CNNs) and pre-trained models, have achieved accuracies ranging from 85.20% to 99.66%. However, the proposed Fine-Tuned ResNet50 model consistently outperforms others with an impressive accuracy of 98.20%. By leveraging on transfer learning and careful architectural design the proposed model demonstrates superior performance compared to previous studies using DarkNet, ResNet50, and pre-trained models. Graphical comparisons highlight its competitive edge, emphasizing its effectiveness in COVID-19 classification tasks. The ResNet50 architecture, known for its deep residual layers and skip connections, facilitates robust feature extraction and classification, especially in medical imaging. Data pre-processing techniques, like noise reduction and contrast enhancement, ensure input data quality and reliability, enhancing the model&amp;apos;s predictive abilities. Training results reveal the model&amp;apos;s steady accuracy improvement and loss reduction over 20 epochs, aligning closely with validation metrics. Evaluation on a test set of COVID-19 chest X-ray images confirms exceptional accuracy (98.20%), precision (99.00%), recall (98.82%), and F1-score (98.91%), highlighting its proficiency in identifying COVID-19 cases while minimizing false positives and negatives. Comparative analyses against prior studies further validate its superior performance, establishing the Fine-Tuned ResNet50 model as a reliable tool for COVID-19 diagnosis. Future research should focus on exploring ensemble learning techniques, interpretability methods, and stakeholder collaboration to ensure safe AI deployment in clinical settings. Moreover, larger and diverse datasets are crucial for validating model performance and improving generalization, ultimately enhancing patient care and public health outcomes in the mitigating COVID-19 and future pandemics.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20240901.12","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T08:57:17Z","timestamp":1715590637000},"page":"10-25","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning Model for COVID-19 Classification Using Fine Tuned ResNet50 on Chest X-Ray Images"],"prefix":"10.11648","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0125-3286","authenticated-orcid":true,"given":"Oyewole","family":"Dokun","sequence":"first","affiliation":[{"name":"Department of Information Technology, Federal University of Technology, Owerri, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3138-4639","authenticated-orcid":true,"given":"Adetokunbo","family":"John-Otumu","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Federal University of Technology, Owerri, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3158-856X","authenticated-orcid":true,"given":"Udoka","family":"Eze","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Federal University of Technology, Owerri, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9946-6307","authenticated-orcid":true,"given":"Charles","family":"Ikerionwu","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Federal University of Technology, Owerri, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9095-7196","authenticated-orcid":true,"given":"Chukwuemeka","family":"Etus","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Federal University of Technology, Owerri, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9505-4129","authenticated-orcid":true,"given":"Emeka","family":"Nwanga","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Federal University of Technology, Owerri, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6759-3029","authenticated-orcid":true,"given":"Ogadimma","family":"Okonkwo","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Federal University of Technology, Owerri, Nigeria"}]}],"member":"4911","published-online":{"date-parts":[[2024,5,10]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Awwalu J., Umar N. 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performance can vary widely due to differences in cognition, motivation, and environmental factors. These variations create challenges in achieving optimal learning outcomes. To address these challenges, Optimal Group Formation (OGF) has emerged as a promising research area. Optimal Group Formation (OGF) aims to form student groups that maximize learning efficiency based on past academic performance. Group formation problems are inherently complex and time-consuming, but their applications are extensive, spanning from manufacturing systems to educational contexts. This paper introduces a machine learning-based model designed to create optimal student groups using academic records as the primary input. The goal is to enhance overall group performance and reduce error rates by organizing students into cohesive, efficient teams. What sets this research apart is its focus on educational group formation, leveraging machine learning to improve collaborative learning outcomes. The paper also reviews prior research, emphasizing the importance of Optimal Group Formation (OGF) in various fields and its relevance in education. The model\u2019s effectiveness is demonstrated through comparative analysis, showcasing its potential to improve group dynamics in both theoretical and lab-based courses. Ultimately, the aim is to improve educational outcomes by ensuring that student groups are optimally balanced and structured.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20240902.13","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T08:30:08Z","timestamp":1727685008000},"page":"48-52","source":"Crossref","is-referenced-by-count":1,"title":["A Machine Learning Approach to Optimal Group Formation Based on Previous Academic Performance"],"prefix":"10.11648","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7906-1823","authenticated-orcid":true,"given":"Mahbub","family":"Hasan","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Southeast University, Dhaka, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7319-9028","authenticated-orcid":true,"given":"Md. Shohel","family":"Babu","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Southeast University, Dhaka, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8315-8005","authenticated-orcid":true,"given":"Md. Al","family":"Emran","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Southeast University, Dhaka, Bangladesh"}]}],"member":"4911","published-online":{"date-parts":[[2024,9,29]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Luis E Agust\u00edn-Blas et al. &quot;A hybrid grouping genetic algorithm for assigning students to preferred laboratory groups&quot;. In: Expert Syst. Appl. 36 (Apr. 2009), pp. 7234-7241. https:\/\/doi.org10.1016\/j.eswa.2008.09.020.groups In: Expert Syst. 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This paper provides a detailed overview of FPV technology, focusing on its applications, advantages, and recent advancements. Initially developed for military and surveillance purposes, FPV technology has evolved to become more accessible and widely adopted in civilian sectors. The paper explores how FPV technology enhances user experience, improves efficiency, and offers environmental benefits. In recreational activities, FPV drones are widely used in drone racing and freestyle flying, providing an immersive and engaging experience. Professionally, FPV drones are employed for aerial photography, infrastructure inspection, and search and rescue operations, where the real-time video feed enables operators to make immediate decisions and adjustments, significantly improving task efficiency and safety. In robotics, FPV technology is used in teleoperated robots for industrial inspection and search and rescue missions, allowing operators to control devices with greater accuracy and confidence. In renewable energy systems, FPV technology is applied to floating photovoltaic (FPV) systems, which are solar panels installed on water bodies. These systems benefit from the cooling effect of water, improving performance and lifespan, and help conserve land for other uses, making them suitable for densely populated areas. Additionally, FPV systems reduce water evaporation, which is beneficial in water-scarce regions. Recent advancements in FPV technology include high-definition video, extended range, and integration with artificial intelligence (AI), which provide real-time analytics and decision-making support. Despite challenges such as regulatory constraints, technical issues, and ethical considerations, the future of FPV technology looks promising. Ongoing innovations and expanding applications in fields such as agriculture, environmental monitoring, and entertainment will further enhance the capabilities and utility of FPV systems. As the technology continues to evolve, it will play a crucial role in shaping the future of unmanned systems and renewable energy solutions.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20251001.13","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T06:54:59Z","timestamp":1746687299000},"page":"25-31","source":"Crossref","is-referenced-by-count":0,"title":["A Comprehensive Review of FPV Technology: Applications, Advantages, and Future Trends"],"prefix":"10.11648","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4493-8523","authenticated-orcid":true,"given":"Mojtaba","family":"Nasehi","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Imam Hossein University, Tehran, Iran"}]}],"member":"4911","published-online":{"date-parts":[[2025,4,28]]},"reference":[{"key":"ref1","unstructured":"Evaluation of Floating Photovoltaic Systems - A Translation. Journal of Renewable Energy Innovations. 2023, 2(2), 1-15. https:\/\/doi.org\/10.11648\/j.fpv.20230202.12"},{"key":"ref2","unstructured":"English Lecture on Technology with Persian Translation. Educational Technology Review. 2023, 1(1), 20-30. https:\/\/doi.org\/10.11648\/j.tech.20230101.15"},{"key":"ref3","unstructured":"FPV Capability in Quadcopters and Helicopters. Unmanned Systems Journal. 2023, 3(4), 45-55. https:\/\/doi.org\/10.11648\/j.uav.20230304.18"},{"key":"ref4","unstructured":"Mastery of the English Language with Engaging Courses. Language Learning and Teaching. 2022, 4(5), 22-33. https:\/\/doi.org\/10.11648\/j.lang.20220405.12"},{"key":"ref5","unstructured":"Excess and Deficiency of Persian Language in the Shadow of Daily Scientific Progress. Persian Linguistics. 2021, 5(3), 18-28. https:\/\/doi.org\/10.11648\/j.ling.20210503.19"},{"key":"ref6","unstructured":"What is a Drone? Definition, Functionality, and History. Aerospace Engineering Today. 2020, 2(1), 8-14. https:\/\/doi.org\/10.11648\/j.drone.20200201.14"},{"key":"ref7","unstructured":"Quadcopter - Wikipedia, the Free Encyclopedia. Available at: https:\/\/en.wikipedia.org\/wiki\/Quadcopter (Accessed: March 31, 2025)."},{"key":"ref8","unstructured":"Characteristics, Price, and Purchase of Agricultural Spray Drones. Agricultural Technology International. 2022, 3(2), 30-40. https:\/\/doi.org\/10.11648\/j.agrotech.20220302.17"},{"key":"ref9","unstructured":"K11 Max Drone with Water Bombs. Emergency Response Systems Journal. 2024, 1(1), 5-12. https:\/\/doi.org\/10.11648\/j.erdrone.20240101.11"},{"key":"ref10","unstructured":"Jafari, A., & Nourbakhsh, M. (2024). FPV Drone Regulations in Urban Environments. Journal of Iranian Aerospace Law, 4(2), 112-125. https:\/\/doi.org\/10.11648\/j.iral.2024.0402.12"},{"key":"ref11","unstructured":"Smith, L., & Patel, R. (2023). AI-Driven FPV Systems for Wildlife Monitoring. IEEE Transactions on Robotics, 39(3), 789-801. https:\/\/doi.org\/10.1109\/TRO.2023.3264512"},{"key":"ref12","unstructured":"Lee, S., et al. (2023). Enhancing FPV Video Transmission via Digital Signal Processing. Electronics and Communications Journal, 22(4), 456-468. https:\/\/doi.org\/10.11648\/j.ecj.20232204.15"},{"key":"ref13","unstructured":"Chen, W., & Zhang, L. (2024). FPV Systems in Precision Agriculture: A Case Study. Agricultural Engineering International, 15(1), 34-45. https:\/\/doi.org\/10.11648\/j.aei.20241501.13"},{"key":"ref14","unstructured":"Rodriguez, F., & Kim, J. (2023). Thermal Imaging Integration in FPV Drones for Emergency Response. Safety and Security Technologies, 8(3), 201-215. https:\/\/doi.org\/10.11648\/j.sst.20230803.11"},{"key":"ref15","unstructured":"Alavi, S., et al. (2023). Battery Efficiency in FPV Systems: A Comparative Study. Energy Systems and Innovation, 7(4), 567-580. https:\/\/doi.org\/10.11648\/j.esi.20230704.22"},{"key":"ref16","unstructured":"Brown, T., & Wilson, K. (2024). Privacy and Ethical Challenges in FPV Technology. Journal of Technology and Ethics, 5(1), 45-58. https:\/\/doi.org\/10.11648\/j.jte.20240501.14"},{"key":"ref17","unstructured":"Nguyen, T., & Lee, H. (2023). FPV Systems for Low-Light Conditions: Advancements and Applications. Optics and Photonics Advances, 10(2), 1-12. https:\/\/doi.org\/10.11648\/j.opa.20231002.17"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20251001.13","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T06:55:00Z","timestamp":1746687300000},"score":18.139538,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20251001.13"}},"issued":{"date-parts":[[2025,4,28]]},"references-count":17,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3,31]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20251001.13","ISSN":["2637-5680","2637-5672"],"issn-type":[{"type":"electronic","value":"2637-5680"},{"type":"print","value":"2637-5672"}],"published":{"date-parts":[[2025,4,28]]},"article-number":"5260334"},{"indexed":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T08:41:01Z","timestamp":1659775261787},"reference-count":0,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"published-print":{"date-parts":[[2021]]},"DOI":"10.11648\/j.mlr.20210602.11","type":"journal-article","created":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T08:22:39Z","timestamp":1659774159000},"page":"8","source":"Crossref","is-referenced-by-count":0,"title":["Design to Build E-learning Application in SMP N 2 Busalangga"],"prefix":"10.11648","volume":"6","author":[{"given":"Jimi","family":"Asmara","sequence":"first","affiliation":[]},{"given":"Gregorius","family":"Rinduh Iriane","sequence":"additional","affiliation":[]},{"given":"Edwin","family":"Ariesto Umbu Malahina","sequence":"additional","affiliation":[]}],"member":"4911","container-title":["Machine Learning Research"],"language":"en","deposited":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T08:22:39Z","timestamp":1659774159000},"score":18.132732,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20210602.11"}},"issued":{"date-parts":[[2021]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021]]}},"alternative-id":["5380140"],"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20210602.11","ISSN":["2637-5672"],"issn-type":[{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2021]]}},{"indexed":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T06:29:24Z","timestamp":1713248964753},"reference-count":0,"publisher":"Science Publishing Group","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"published-print":{"date-parts":[[2018]]},"DOI":"10.11648\/j.mlr.20180301.11","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T03:56:53Z","timestamp":1547179013000},"page":"1","source":"Crossref","is-referenced-by-count":2,"title":["Application Platform and Token Generation Software for Prepayment Meter Administration in Electricity Distribution Companies"],"prefix":"10.11648","volume":"3","author":[{"given":"Henry","family":"Erialuode Amhenrior","sequence":"first","affiliation":[]}],"member":"4911","container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"http:\/\/article.sciencepublishinggroup.com\/pdf\/10.11648.j.mlr.20180301.11.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T08:13:00Z","timestamp":1602922380000},"score":18.131271,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20180301.11"}},"issued":{"date-parts":[[2018]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018]]}},"alternative-id":["6041021"],"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20180301.11","ISSN":["2637-5672"],"issn-type":[{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2018]]}},{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T10:12:20Z","timestamp":1760350340208,"version":"build-2065373602"},"reference-count":18,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2025,9,22]]},"abstract":"<jats:p xml:lang=\"en\">Corruption in public procurement undermines fiscal sustainability, distorts competition, and reduces service quality. Conventional anti-corruption controls-manual audits, rule-based checks, and ex-post reviews-struggle to flag sophisticated, evolving fraud patterns in real time. This study proposes and empirically evaluates a hybrid machine-learning (ML) framework that integrates interpretable supervised models (logistic regression) with high-accuracy ensemble methods (random forest) and unsupervised learning (k-means clustering and anomaly detection) to identify corruption-prone contracts within Kenya\u2019s public procurement ecosystem. Using secondary procurement data-contract values, procurement methods, bidder histories, award timelines-and text-derived indicators from public audit narratives, we construct features representing red flags such as single-bid tenders, repeated awards, and significant deviations from estimated costs. Logistic regression provides transparent coefficient-level evidence, while random forest captures non-linear interactions; clustering approximates high-risk groupings where labels are incomplete. Results indicate that single-bid tenders, prior supplier allegations, and execution irregularities (e.g., substandard deliveries, unusual extensions) are the most predictive factors of corruption labels. The ensemble achieved strong classification performance (AUC \u2248 0.98 on cross-validation), while the baseline logistic model offered high precision and policy-friendly interpretability. We outline a deployment roadmap for integrating the model into e-procurement workflows (IFMIS\/PPRA) with explainable-AI (XAI) dashboards for risk-based audits. The contribution is twofold: a context-aware, reproducible pipeline for low- and middle-income settings, and governance guidance for embedding ML in accountability processes to prevent rather than merely detect procurement corruption.\n<\/jats:p>","DOI":"10.11648\/j.mlr.20251002.14","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T09:41:26Z","timestamp":1760348486000},"page":"131-136","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Machine Learning Model for Detecting and Preventing Corruption in Kenya\u2019s Public Procurement Contracts"],"prefix":"10.11648","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5225-1094","authenticated-orcid":true,"given":"Melchizedek","family":"Ndolo","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Technology, The Co-operative University of Kenya, Nairobi, Kenya"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7609-4318","authenticated-orcid":true,"given":"Anthony","family":"Wanjoya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, The Co-operative University of Kenya, Nairobi, Kenya; Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8232-0299","authenticated-orcid":true,"given":"Philemon","family":"Kasyoka","sequence":"additional","affiliation":[{"name":"School of Science and Computing, South Eastern Kenya University, Kitui, Kenya"}]}],"member":"4911","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Decarolis, F., & Giorgiantonio, C. (2022). Corruption red flags in public procurement: new evidence from Italian calls for tenders. EPJ Data Science, 11(16), 1\u201324. https:\/\/doi.org\/10.1140\/epjds\/s13688-022-00325-x","DOI":"10.1140\/epjds\/s13688-022-00325-x"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Alsamarraie, M. M., & Ghazali, F. (2023). Evaluation of organizational procurement performance for public construction projects: Systematic review. International Journal of Construction Management, 23(14), 2499-2508.","DOI":"10.1080\/15623599.2022.2070447"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Alsamarraie, M. M., & Ghazali, F. E. M. (2023). Barriers and challenges for public procurement integrity in Iraq: Systematic review study. KSCE Journal of Civil Engineering, 27(9), 3633-3645.","DOI":"10.1007\/s12205-023-1196-4"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Paraskeva, S., & Tsoulfas, G. T. (2025). Mitigating risks in public procurement. Journal of Public Procurement, 25(1), 140-176.","DOI":"10.1108\/JOPP-07-2024-0074"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"De Menezes, T. L., de Andrade, N. F., & Morais, F. J. A. (2023). The effectiveness of machine learning to estimate risk of failure in Brazilian public contracts. 2023 ICMLA, 2071\u20132078. https:\/\/doi.org\/10.1109\/ICMLA58977.2023.00313","DOI":"10.1109\/ICMLA58977.2023.00313"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Ezeji, C. L. (2024). Artificial intelligence for detecting and preventing procurement fraud. International Journal of Business Ecosystem & Strategy, 6(1), 63\u201373. https:\/\/doi.org\/10.36096\/ijbes.v6i1.477","DOI":"10.36096\/ijbes.v6i1.477"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Imhof, D., & Wallimann, H. (2021). Detecting bid-rigging coalitions in different countries and auction formats. International Review of Law and Economics, 68, 106016. https:\/\/doi.org\/10.1016\/j.irle.2021.106016","DOI":"10.1016\/j.irle.2021.106016"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Lima, W., Lira, R., Paiva, A., Silva, J., & Silva, V. (2023). Methodology for automatic extraction of red flags in public procurement. 2023 International Joint Conference on Neural Networks (IJCNN), 1\u20137. https:\/\/doi.org\/10.1109\/IJCNN54540.2023.10191683","DOI":"10.1109\/IJCNN54540.2023.10191683"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Osei-Kyei, R., & Chan, A. P. C. (2019). Model for predicting the success of PPP infrastructure projects in developing countries: Ghana. Architectural Engineering and Design Management, 15(3), 213\u2013232. https:\/\/doi.org\/10.1080\/17452007.2018.1545632","DOI":"10.1080\/17452007.2018.1545632"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Basdevant, O., Abdou, A., Fazekas, M., & David-Barrett, E. (2022). Assessing vulnerabilities to corruption in public procurement and their price impact. IMF Working Paper 22\/094. https:\/\/doi.org\/10.5089\/9798400207884.001","DOI":"10.5089\/9798400207884.001"},{"key":"ref11","unstructured":"Titl, V., et al. (2019). Screening methods for collusion in public procurement: A literature review. OECD Working Papers."},{"key":"ref12","unstructured":"World Bank. (2024). Enhancing Government Effectiveness and Transparency: The Fight Against Corruption."},{"key":"ref13","unstructured":"Transparency International. (2023). Corruption Perceptions Index."},{"key":"ref14","unstructured":"KPMG. (2023). Perspectives on anti-corruption, third-party management, ESG and more."},{"key":"ref15","unstructured":"Public Procurement and Asset Disposal Act. (2015). Government of Kenya."},{"key":"ref16","unstructured":"Public Procurement and Asset Disposal Policy. (2019). Government of Kenya."},{"key":"ref17","unstructured":"Public Procurement Regulatory Authority (PPRA). (2021). Annual Procurement Report."},{"key":"ref18","unstructured":"Ethics and Anti-Corruption Commission (EACC). (2022). National Ethics and Corruption Survey."}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20251002.14","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T09:41:35Z","timestamp":1760348495000},"score":18.130217,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20251002.14"}},"issued":{"date-parts":[[2025,10,10]]},"references-count":18,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,8,4]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20251002.14","ISSN":["2637-5680","2637-5672"],"issn-type":[{"value":"2637-5680","type":"electronic"},{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2025,10,10]]},"article-number":"6041130"},{"indexed":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T07:22:54Z","timestamp":1707808974019},"reference-count":0,"publisher":"Science Publishing Group","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"published-print":{"date-parts":[[2019]]},"DOI":"10.11648\/j.mlr.20190401.12","type":"journal-article","created":{"date-parts":[[2019,7,16]],"date-time":"2019-07-16T09:44:49Z","timestamp":1563270289000},"page":"7","source":"Crossref","is-referenced-by-count":3,"title":["Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping"],"prefix":"10.11648","volume":"4","author":[{"given":"Hemad","family":"Heidari Jobaneh","sequence":"first","affiliation":[]}],"member":"4911","container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"http:\/\/article.sciencepublishinggroup.com\/pdf\/10.11648.j.mlr.20190401.12.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T08:19:48Z","timestamp":1602922788000},"score":18.128374,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20190401.12"}},"issued":{"date-parts":[[2019]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019]]}},"alternative-id":["6041041"],"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20190401.12","ISSN":["2637-5672"],"issn-type":[{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2019]]}},{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T21:55:14Z","timestamp":1766181314467},"reference-count":0,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"published-print":{"date-parts":[[2019]]},"DOI":"10.11648\/j.mlr.20190402.11","type":"journal-article","created":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T07:35:08Z","timestamp":1575531308000},"page":"27","source":"Crossref","is-referenced-by-count":9,"title":["Learning Algorithms Using BPNN &amp; SFS for Prediction of Compressive Strength of Ultra-High Performance Concrete"],"prefix":"10.11648","volume":"4","author":[{"given":"Deepak","family":"Choudhary","sequence":"first","affiliation":[]}],"member":"4911","container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"http:\/\/article.sciencepublishinggroup.com\/pdf\/10.11648.j.mlr.20190402.11.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T07:56:50Z","timestamp":1602921410000},"score":18.113766,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20190402.11"}},"issued":{"date-parts":[[2019]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019]]}},"alternative-id":["6041040"],"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20190402.11","ISSN":["2637-5672"],"issn-type":[{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2019]]}},{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T08:20:29Z","timestamp":1769502029282,"version":"3.49.0"},"reference-count":0,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"published-print":{"date-parts":[[2019]]},"DOI":"10.11648\/j.mlr.20190402.12","type":"journal-article","created":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T07:35:08Z","timestamp":1575531308000},"page":"33","source":"Crossref","is-referenced-by-count":4,"title":["Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study"],"prefix":"10.11648","volume":"4","author":[{"given":"Shehu","family":"Usman Gulumbe","sequence":"first","affiliation":[]},{"given":"Shamsuddeen","family":"Suleiman","sequence":"additional","affiliation":[]},{"given":"Shehu","family":"Badamasi","sequence":"additional","affiliation":[]},{"given":"Ahmad","family":"Yusuf Tambuwal","sequence":"additional","affiliation":[]},{"given":"Umar","family":"Usman","sequence":"additional","affiliation":[]}],"member":"4911","container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"http:\/\/article.sciencepublishinggroup.com\/pdf\/10.11648.j.mlr.20190402.12.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T07:56:25Z","timestamp":1602921385000},"score":18.113766,"resource":{"primary":{"URL":"http:\/\/www.sciencepublishinggroup.com\/journal\/paperinfo?journalid=604&doi=10.11648\/j.mlr.20190402.12"}},"issued":{"date-parts":[[2019]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019]]}},"alternative-id":["6041043"],"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20190402.12","ISSN":["2637-5672"],"issn-type":[{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2019]]}},{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:20:28Z","timestamp":1753881628727,"version":"3.41.2"},"reference-count":34,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2024,10,6]]},"abstract":"<jats:p xml:lang=\"en\">The advancement in the field of computer science, especially in machine learning (ML), represents a flourishing innovation that carries great importance in the domain of education. The beneficial impact of ML can also be observed in the realm of Qur\u2019anic studies, particularly in Arabic text recognition and recitation analysis. This paper presents a comprehensive analysis of 34+ published scholarly articles devoted to Qur\u2019anic studies. This work explores the convergence of machine learning methodologies and Qur\u2019anic studies, examining the innovative applications and methodologies for Arabic text and voice classification. The fusion of ML algorithms makes the work easy and accurate to analyze, interpret, and extract valuable insights from the sacred text. Subsequently, we delve deeper into the emergent field of ML algorithms like k-NN, ANN, BLSTM, MFCC, SVM, NB and DL approaches have been adapted for Qur\u2019anic texts classification, recitation and recitation analysis on accuracy, speed, class recognition, response rate and biasness benchmark. This work covers a diverse range of applications, including automated Qur\u2019anic exegesis and analysis of usage of Ahkam Al-Tajweed. The main contribution of the work is to provide insight into how ML facilitates in Arabic and Kufic textual analysis, linguistic subtleties, and thematic structures of the Qur\u2019anic text. Using the deep learning approaches, the reciters, recitation style and of the Quranic text has also explained in the work.<\/jats:p>","DOI":"10.11648\/j.mlr.20240902.14","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T01:15:59Z","timestamp":1730337359000},"page":"54-63","source":"Crossref","is-referenced-by-count":0,"title":["Impact of Machine Learning Integration in Qur\u2019anic Studies"],"prefix":"10.11648","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8630-283X","authenticated-orcid":true,"given":"Arshad","family":"Iqbal","sequence":"first","affiliation":[{"name":"K. A. Nizami Centre for Quranic Studies, Aligarh Muslim University, Aligarh, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4015-8593","authenticated-orcid":true,"given":"Shabbir","family":"Hassan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aligarh Muslim University, Aligarh, India"}]}],"member":"4911","published-online":{"date-parts":[[2024,10,29]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Mahesh B. Machine learning algorithms-a review. International Journal of Science and Research (IJSR). 2020 Jan; 9(1): 381-6, https:\/\/doi.org\/10.21275\/ART20203995","DOI":"10.21275\/ART20203995"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Madadizadeh F, Bahariniya S. The Role of Artificial Intelligence in Understanding and Interpreting the Quran. 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International Journal of Machine Learning and Computing. 2019 Aug; 9(4): 458-64, https:\/\/dx.doi.org\/10.18178\/ijmlc.2019.9.4.826","DOI":"10.18178\/ijmlc.2019.9.4.826"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Adeleke A, Samsudin NA, Othman ZA, Khalid SA. A two-step feature selection method for quranic text classification. Indonesian Journal of Electrical Engineering and Computer Science. 2019 Nov; 16(2): 730-6, https:\/\/dx.doi.org\/10.11591\/ijeecs.v16.i2.pp730-736","DOI":"10.11591\/ijeecs.v16.i2.pp730-736"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Nahar KM, Al-Khatib RM, Al-Shannaq MA, Barhoush MM. An efficient holy Quran recitation recognizer based on SVM learning model. 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Journal of King Saud University-Computer and Information Sciences. 2021 Jul 1; 33(6): 658-67, https:\/\/dx.doi.org\/10.1016\/j.jksuci.2019.03.007","DOI":"10.1016\/j.jksuci.2019.03.007"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Zafar A, Iqbal A. Application of soft computing techniques in machine reading of Quranic Kufic manuscripts. Journal of King Saud University-Computer and Information Sciences. 2022 Jun 1; 34(6): 3062-9, https:\/\/dx.doi.org\/10.1016\/j.jksuci.2020.04.017","DOI":"10.1016\/j.jksuci.2020.04.017"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"M Alashqar A. A Classification of Quran Verses Using Deep Learning. International Journal of Computing and Digital Systems. 2023 Jul 22; 16(1): 1041-53, https:\/\/dx.doi.org\/10.12785\/ijcds\/160176","DOI":"10.12785\/ijcds\/160176"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20240902.14","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T01:16:43Z","timestamp":1730337403000},"score":18.090078,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20240902.14"}},"issued":{"date-parts":[[2024,10,29]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,7,15]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20240902.14","ISSN":["2637-5680","2637-5672"],"issn-type":[{"type":"electronic","value":"2637-5680"},{"type":"print","value":"2637-5672"}],"published":{"date-parts":[[2024,10,29]]},"article-number":"6041099"},{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T05:58:15Z","timestamp":1768888695535,"version":"3.49.0"},"reference-count":17,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2024,9,21]]},"abstract":"<jats:p xml:lang=\"en\">In recent years credit scoring has become a challenging issue among financial institutions. Several researchers have dedicated efforts in machine learning in the areas of credit scoring and results have shown that machine learning algorithms have had a satisfactory performance in the sector of credit scoring. Decision trees have been used for data sets that have high dimension and have a complex correlation and the benefits of feature combination and feature selection has led to the usage of decision trees in classification. The disadvantage of decision tree which is overfitting has led to the introduction of extreme gradient boosting that overcomes the shortcoming by integrating tree models. Employing optimization method helps in tuning the hyperparameters of the model. In this paper, a modified XGBoost model is developed that incorporates inflation parameter. In addition to the proposed model, the study uses adaptive particle swarm optimization since it does not fall into local optima. The swarm split algorithm uses clustering and two learning strategies to promote subswarm diversity and avoid local optimums. In this study the modified XGBoost model was compared to five traditional machine learning algorithms namely, the standard XGBoost model, logistic regression, KNN, support vector machine and decision tree. The study used one data set in credit scoring and the evaluation measures used were accuracy, precision, recall and F1-score. Results demonstrate that the proposed model outperforms other models. <\/jats:p>","DOI":"10.11648\/j.mlr.20240902.15","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T01:44:03Z","timestamp":1731030243000},"page":"64-74","source":"Crossref","is-referenced-by-count":3,"title":["Modified XGBoost Hyper-Parameter Tuning Using Adaptive Particle Swarm Optimization for Credit Score Classification"],"prefix":"10.11648","volume":"9","author":[{"given":"Kenneth","family":"Langat","sequence":"first","affiliation":[{"name":"Department of Mathematics, Pan African Institute of Basic Science Technology and Innovation, Nairobi, Kenya; Department of Mathematics, Egerton University, Nakuru, Kenya"}]},{"given":"Anthony","family":"Waititu","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya"}]},{"given":"Philip","family":"Ngare","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Nairobi, Nairobi, Kenya"}]}],"member":"4911","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"ref1","unstructured":"James Bergstra and Yoshua Bengio. Random search for hyper-parameter optimization. Journal of machine learning research, 13(2), 2012."},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Yung-Chia Chang, Kuei-Hu Chang, and Guan-Jhih Wu. Application of extreme gradient boosting trees in the construction of credit risk assessment models for financial institutions. Applied Soft Computing, 73: 914-920, 2018.","DOI":"10.1016\/j.asoc.2018.09.029"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785-794, 2016.","DOI":"10.1145\/2939672.2939785"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Robert A Eisenbeis. Problems in applying discriminant analysis in credit scoring models. Journal of Banking & Finance, 2(3): 205-219, 1978.","DOI":"10.1016\/0378-4266(78)90012-2"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Diego Paganoti Fonseca, Peter Fernandes Wanke, and Henrique Luiz Correa. A two-stage fuzzy neural approach for credit risk assessment in a brazilian credit card company. Applied Soft Computing, 92: 106329, 2020.","DOI":"10.1016\/j.asoc.2020.106329"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Akhil Bandhu Hens and Manoj Kumar Tiwari. Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method. Expert Systems with Applications, 39(8): 6774-6781, 2012.","DOI":"10.1016\/j.eswa.2011.12.057"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Nan-Chen Hsieh and Lun-Ping Hung. A data driven ensemble classifier for credit scoring analysis. Expert systems with Applications, 37(1): 534-545, 2010.","DOI":"10.1016\/j.eswa.2009.05.059"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Hui-Ling Huang and Fang-Lin Chang. Esvm: Evolutionary support vector machine for automatic feature selection and classification of microarray data. Biosystems, 90(2): 516-528, 2007.","DOI":"10.1016\/j.biosystems.2006.12.003"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Chao Qin, Yunfeng Zhang, Fangxun Bao, Caiming Zhang, Peide Liu, and Peipei Liu. Xgboost optimized by adaptive particle swarm optimization for credit scoring. Mathematical Problems in Engineering, 2021(1): 6655510, 2021.","DOI":"10.1155\/2021\/6655510"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Kennedy J Eberhart RC et al. Particle swarm optimization. In Proc IEEE Int Conf Neural Networks, volume 4, pages 1942-1948, 1995.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Feng Shen, Xingchao Zhao, Zhiyong Li, Ke Li, and Zhiyi Meng. A novel ensemble classification model based on neural networks and a classifier optimisation technique for imbalanced credit risk evaluation. Physica A: Statistical Mechanics and its Applications, 526: 121073, 2019.","DOI":"10.1016\/j.physa.2019.121073"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Gang Wang, Jinxing Hao, Jian Ma, and Hongbing Jiang. A comparative assessment of ensemble learning for credit scoring. Expert systems with applications, 38(1): 223- 230, 2011.","DOI":"10.1016\/j.eswa.2010.06.048"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Gang Wang, Jian Ma, Lihua Huang, and Kaiquan Xu. Two credit scoring models based on dual strategy ensemble trees. Knowledge-Based Systems, 26: 61-68, 2012.","DOI":"10.1016\/j.knosys.2011.06.020"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"David H Wolpert and William G Macready. No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1): 67-82, 1997.","DOI":"10.1109\/4235.585893"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Yufei Xia, Chuanzhe Liu, and Nana Liu. Cost-sensitive boosted tree for loan evaluation in peer- to-peer lending. Electronic Commerce Research and Applications, 24: 30- 49, 2017.","DOI":"10.1016\/j.elerap.2017.06.004"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Qiang Yang, Wei-Neng Chen, Jeremiah Da Deng, Yun Li, Tianlong Gu, and Jun Zhang. A level-based learning swarm optimizer for large-scale optimization. IEEE Transactions on Evolutionary Computation, 22(4): 578- 594, 2017.","DOI":"10.1109\/TEVC.2017.2743016"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Maciej Zi&ecedil;ba, Sebastian K Tomczak, and Jakub M Tomczak. Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert systems with applications, 58: 93-101, 2016.","DOI":"10.1016\/j.eswa.2016.04.001"}],"container-title":["Machine Learning Research"],"language":"en","link":[{"URL":"https:\/\/article.sciencepublishinggroup.com\/pdf\/j.mlr.20240902.15","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T01:44:17Z","timestamp":1731030257000},"score":18.087727,"resource":{"primary":{"URL":"https:\/\/www.sciencepublishinggroup.com\/article\/10.11648\/j.mlr.20240902.15"}},"issued":{"date-parts":[[2024,10,31]]},"references-count":17,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,7,15]]}},"URL":"https:\/\/doi.org\/10.11648\/j.mlr.20240902.15","ISSN":["2637-5680","2637-5672"],"issn-type":[{"value":"2637-5680","type":"electronic"},{"value":"2637-5672","type":"print"}],"published":{"date-parts":[[2024,10,31]]},"article-number":"6041096"},{"indexed":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T07:21:42Z","timestamp":1766647302527,"version":"3.48.0"},"reference-count":24,"publisher":"Science Publishing Group","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MLR"],"accepted":{"date-parts":[[2025,8,16]]},"abstract":"<jats:p xml:lang=\"en\">Stance detection is an important task in natural language processing (NLP) that seeks to determine a speaker\u2019s or writer\u2019s position toward a given topic. While substantial progress has been achieved for major languages, low-resource languages such as Afan Oromo remain largely underexplored. This study introduces a deep learning\u2013based approach for stance detection in Afan Oromo, leveraging a newly collected and annotated dataset of over one million sentences from social media platforms, particularly Facebook. Three deep learning models\u2014Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (Bi-LSTM)\u2014were implemented and evaluated. Among these, CNN achieved the highest accuracy of 85.9%, outperforming LSTM (81.4%) and Bi-LSTM (79.8%). The superior performance of CNN is attributed to its ability to capture local spatial features in text, which is particularly beneficial for short, informal social media posts. These results demonstrate the feasibility and effectiveness of deep learning techniques for stance detection in low-resource languages. Furthermore, the findings contribute to advancing language technologies for Afan Oromo and open pathways for future research in social media analysis, sentiment monitoring, and political discourse understanding in local contexts.<\/jats:p>","DOI":"10.11648\/j.mlr.20251002.16","type":"journal-article","created":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T07:19:50Z","timestamp":1766647190000},"page":"151-157","source":"Crossref","is-referenced-by-count":0,"title":["Stance Classification in Afan Oromo Using Deep Learning Approaches"],"prefix":"10.11648","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3674-3773","authenticated-orcid":true,"given":"Dejene","family":"Teka","sequence":"first","affiliation":[{"name":"Department of Information Technology, Mattu University, Oromia, Ethiopia"}]}],"member":"4911","published-online":{"date-parts":[[2025,12,19]]},"reference":[{"key":"ref1","unstructured":"Wondimu. 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In such settings, the integration of fairness-aware algorithms with uncertainty quantification tools enables the development of reliable and safe decision-making. In this paper, we introduce a novel methodology that combines conformal prediction, offering rigorous prediction sets, with multi-objective optimization via evolutionary learning. The proposed meta-algorithm optimizes the hyperparameter configuration of classifiers to produce confidence predictors that balance efficiency and equalized coverage guarantees, addressing fairness concerns related to sensitive attributes. We empirically evaluate our methodology with four real-world problems and demonstrate its efficacy in exploring this trade-off and producing a repertoire of Pareto optimal conformal predictors. 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All the experiments in this paper are computer simulations and do not involve experiments on animals, plants, or human entities.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable. The paper does not include data or images that require permissions to be published.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"134"},{"indexed":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T14:49:47Z","timestamp":1748011787705,"version":"3.40.4"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:00:00Z","timestamp":1742947200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:00:00Z","timestamp":1742947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Collecting a large amount of labeled data in machine learning is always challenging. Often, even with sufficient data, domain differences can cause a shift or bias in data distribution, affecting model performance during testing. Domain adaptation methods, especially adversarial techniques, are effective solutions for these challenges. The goal is to learn a classifier for an unlabeled target dataset using a labeled source dataset, enhancing resistance to domain shifts. However, existing methods sometimes struggle with adapting the joint feature distribution across domains, resulting in negative transfer. To address this, we propose a method that forms class-specific clusters to prevent negative transfer. This method is encapsulated in an unsupervised adversarial domain adaptation framework based on a variational auto-encoder. Our structure is designed to enhance invariant and discriminative feature representation. We process source and target data through a VAE to establish a smooth latent representation. In our method, source and target data are fed into a variational auto-encoder, which produces a smooth latent representation. The feature extractor then plays an adversarial minimax game with the discriminator to learn domain-invariant features, while the feature extractor is shared between the reconstructed source and reconstructed target data. In addition, we proposed a second structure in which the domain discriminator part of the prior structure is eliminated to demonstrate the influence of the variational auto-encoder in domain adaptation. On numerous unsupervised domain adaptation benchmarks, our results indicate that our proposed model outperforms or is comparable to state-of-the-art outcomes.<\/jats:p>","DOI":"10.1007\/s10994-025-06760-x","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T22:06:44Z","timestamp":1743286004000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An unsupervised adversarial domain adaptation based on variational auto-encoder"],"prefix":"10.1007","volume":"114","author":[{"given":"Mahta","family":"Hassan Pour Zonoozi","sequence":"first","affiliation":[]},{"given":"Vahid","family":"Seydi","sequence":"additional","affiliation":[]},{"given":"Mahmood","family":"Deypir","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,26]]},"reference":[{"issue":"518","key":"6760_CR1","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","volume":"112","author":"D Blei","year":"2017","unstructured":"Blei, D., Kucukelbir, A., & McAuliffe, J. 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These platforms allow viewers to send real-time gifts to streamers, which can bring significant profits and fame. However, there has been little research on the donation system used on live streaming platforms. This paper aims to fill this gap by building a continuous-time dynamic graph to model the interactions among viewers based on real-time chat messages and predict the real-time donations on live streaming platforms. To achieve this, we propose a novel model called the Temporal Difference Graph Neural Network (TDGNN) that incorporates imbalanced learning strategies to identify potential donors during live streaming. Our model can predict the exact time when donations will appear. 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