{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T05:48:09Z","timestamp":1750225689844},"reference-count":42,"publisher":"National Library of Serbia","issue":"4","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:p>Computer vision (CV) has been successfully used in picture categorization applications in various fields, including medicine, production quality control, and transportation systems. CV models use an excessive number of photos to train potential models. Considering that image acquisition is typically expensive and time-consuming, in this study, we provide a multistep strategy to improve image categorization accuracy with less data. In the first stage, we constructed numerous datasets from a single dataset. Given that an image has pixels with values ranging from 0 to 255, the images were separated into pixel intervals based on the type of dataset. The pixel interval was split into two portions when the dataset was grayscale and five portions when it was composed of RGB images. Next, we trained the model using both the original and newly constructed datasets. Each image in the training process showed a non-identical prediction space, and we suggested using the topthree prediction probability ensemble technique. The top three predictions for the newly created images were combined with the corresponding probability for the original image. The results showed that learning patterns from each interval of pixels and ensembling the top three predictions significantly improve the performance and accuracy, and this strategy can be used with any model.<\/jats:p>","DOI":"10.2298\/csis230223056a","type":"journal-article","created":{"date-parts":[[2023,8,15]],"date-time":"2023-08-15T20:56:42Z","timestamp":1692133002000},"page":"1503-1517","source":"Crossref","is-referenced-by-count":2,"title":["Ensemble of top3 prediction with image pixel interval method using deep learning"],"prefix":"10.2298","volume":"20","author":[{"given":"Abdulaziz","family":"Anorboev","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javokhir","family":"Musaev","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarvinoz","family":"Anorboeva","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeongkyu","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeong-Seok","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"Ngoc","given":"Thanh","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland + Faculty of Information Technology, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dosam","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"S. Vega-Pons and J. Ruiz-Shulcloper, \u201cA survey of clustering ensemble algorithms,\u201d International Journal of Pattern Recognition and Artificial Intelligence, vol. 25, no. 3, pp. 337-372, May 2011.","DOI":"10.1142\/S0218001411008683"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Zhou, ZH. (2009). Ensemble Learning. In: Li, S.Z., Jain, A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA.","DOI":"10.1007\/978-0-387-73003-5_293"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Lappalainen, H., Miskin, J.W. (2000). Ensemble Learning. In: Girolami, M. (eds) Advances in Independent Component Analysis. Perspectives in Neural Computing. Springer, London.","DOI":"10.1007\/978-1-4471-0443-8_5"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"T. Alqurashi and W. Wang, \u201cClustering ensemble method,\u201d International Journal of Machine Learning and Cybernetics, vol. 10, no. 6, pp. 1227-1246, Jun. 2019.","DOI":"10.1007\/s13042-017-0756-7"},{"key":"ref5","unstructured":"A. Krogh, \u201cNeural Network Ensembles, Cross Validation, and Active Learning.\u201d"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"O. Sagi and L. Rokach, \u201cEnsemble learning: A survey,\u201d Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4. Wiley-Blackwell, Jul. 01, 2018.","DOI":"10.1002\/widm.1249"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, \u201cA survey on ensemble learning,\u201d Frontiers of Computer Science, vol. 14, no. 2. Higher Education Press, pp. 241-258, Apr. 01, 2020.","DOI":"10.1007\/s11704-019-8208-z"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"G. I. Webb and Z. Zheng, \u201dMultistrategy ensemble learning: reducing error by combining ensemble learning techniques,\u201d in IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 8, pp. 980-991, Aug. 2004.","DOI":"10.1109\/TKDE.2004.29"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"K. Faceli, A. de Carvalho, M. Carlos, and P. de Souto, \u201cMulti-objective clustering ensemble. Classical Weightless Neural Systems View project Feature Extraction and Selection Analysis in Biological Sequences View project SEE PROFILE,\u201d 2007. [Online]. Available: https:\/\/www.researchgate.net\/publication\/220515994","DOI":"10.1109\/HIS.2006.264934"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"H. M. Gomes, J. P. Barddal, A. F. Enembreck, and A. Bifet, \u201cA survey on ensemble learning for data stream classification,\u201d ACM Computing Surveys, vol. 50, no. 2. Association for Computing Machinery, Mar. 01, 2017.","DOI":"10.1145\/3054925"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"S. Qummar et al., \u201cA Deep Learning Ensemble Approach for Diabetic Retinopathy Detection,\u201d IEEE Access, vol. 7, pp. 150530-150539, 2019.","DOI":"10.1109\/ACCESS.2019.2947484"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"D. P. Gaikwad and R. C. Thool, \u201cIntrusion detection system using bagging ensemble method of machine learning,\u201d in Proceedings - 1st International Conference on Computing, Communication, Control and Automation, ICCUBEA 2015, Jul. 2015, pp. 291-295.","DOI":"10.1109\/ICCUBEA.2015.61"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"S. Hamori, M. Kawai, T. Kume, Y. Murakami, and C. Watanabe, \u201cEnsemble Learning or Deep Learning? Application to Default Risk Analysis,\u201d Journal of Risk and Financial Management, vol. 11, no. 1, p. 12, Mar. 2018.","DOI":"10.3390\/jrfm11010012"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Y. Zhao, J. Li, and L. Yu, \u201cA deep learning ensemble approach for crude oil price forecasting,\u201d Energy Economics, vol. 66, pp. 9-16, Aug. 2017.","DOI":"10.1016\/j.eneco.2017.05.023"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Z. Yu et al., \u201cIncremental Semi-Supervised Clustering Ensemble for High Dimensional Data Clustering,\u201d IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 3, pp. 701-714, Mar. 2016.","DOI":"10.1109\/TKDE.2015.2499200"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"H. Sarmadi, A. Entezami, B. Saeedi Razavi, and K. V. Yuen, \u201cEnsemble learning-based structural health monitoring by Mahalanobis distance metrics,\u201d Structural Control and Health Monitoring, vol. 28, no. 2, Feb. 2021.","DOI":"10.1002\/stc.2663"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"L. Yu, S. Wang, and K. K. Lai, \u201cCredit risk assessment with a multistage neural network ensemble learning approach,\u201d Expert Systems with Applications, vol. 34, no. 2, pp. 1434-1444, Feb. 2008.","DOI":"10.1016\/j.eswa.2007.01.009"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Y. Xiao, J.Wu, Z. Lin, and X. Zhao, \u201cA deep learning-based multi-model ensemble method for cancer prediction,\u201d Computer Methods and Programs in Biomedicine, vol. 153, pp. 1-9, Jan. 2018.","DOI":"10.1016\/j.cmpb.2017.09.005"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"T. Zhou, H. Lu, Z. Yang, S. Qiu, B. Huo, and Y. Dong, \u201cThe ensemble deep learning model for novel COVID-19 on CT images,\u201d Applied Soft Computing, vol. 98, Jan. 2021.","DOI":"10.1016\/j.asoc.2020.106885"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"A. Galicia, R. Talavera-Llames, A. Troncoso, I. Koprinska, and F. Mart\u00ednez-\u00c1lvarez, \u201cMultistep forecasting for big data time series based on ensemble learning,\u201d Knowledge-Based Systems, vol. 163, pp. 830-841, Jan. 2019.","DOI":"10.1016\/j.knosys.2018.10.009"},{"key":"ref21","unstructured":"D. M\u00fcller, I. Soto-Rey, and F. Kramer, \u201cAn Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks.\u201d"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"B. T. Pham et al., \u201cEnsemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers,\u201d Geocarto International, 2020.","DOI":"10.1080\/10106049.2020.1737972"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"D. P. Gaikwad and R. C. Thool, \u201cIntrusion detection system using bagging ensemble method of machine learning,\u201d in Proceedings - 1st International Conference on Computing, Communication, Control and Automation, ICCUBEA 2015, Jul. 2015, pp. 291-295.","DOI":"10.1109\/ICCUBEA.2015.61"},{"key":"ref24","unstructured":"I. Tolstikhin et al., \u201cMLP-Mixer: An all-MLP Architecture for Vision,\u201d May 2021."},{"key":"ref25","unstructured":"A. Krogh, \u201cNeural Network Ensembles, Cross Validation, and Active Learning.\u201d"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"A. Anorboev, M. Javokhir, J. Hong, N. T. Nguyen and D. Hwang, \u201dInput Image Pixel Interval method for Classification Using Transfer Learning,\u201d 2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Biarritz, France, 2022.","DOI":"10.1109\/INISTA55318.2022.9894179"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"R. G. F. Soares, H. Chen, and X. Yao, \u201cA Cluster-Based Semisupervised Ensemble for Multiclass Classification,\u201d IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 1, no. 6, pp. 408-420, Dec. 2017.","DOI":"10.1109\/TETCI.2017.2743219"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Y. Chen, Y. Wang, Y. Gu, X. He, P. Ghamisi, and X. Jia, \u201cDeep Learning Ensemble for Hyperspectral Image Classification,\u201d IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 6, pp. 1882-1897, Jun. 2019.","DOI":"10.1109\/JSTARS.2019.2915259"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"F. Chollet, \u201cXception: Deep Learning with Depthwise Separable Convolutions,\u201d Oct. 2016.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"K. Faceli, A. de Carvalho, M. Carlos, and P. de Souto, \u201cMulti-objective clustering ensemble. Classical Weightless Neural Systems View project Feature Extraction and Selection Analysis in Biological Sequences View project SEE PROFILE,\u201d 2007. [Online]. Available: https:\/\/www.researchgate.net\/publication\/220515994","DOI":"10.1109\/HIS.2006.264934"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"T. Alqurashi and W. Wang, \u201cClustering ensemble method,\u201d International Journal of Machine Learning and Cybernetics, vol. 10, no. 6, pp. 1227-1246, Jun. 2019, doi: 10.1007\/s13042-017- 0756-7.","DOI":"10.1007\/s13042-017-0756-7"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"S. olah Abbasi, S. Nejatian, H. Parvin, V. Rezaie, and K. Bagherifard, \u201cClustering ensemble selection considering quality and diversity,\u201d Artificial Intelligence Review, vol. 52, no. 2, pp. 1311-1340, Aug. 2019, doi: 10.1007\/s10462-018-9642-2.","DOI":"10.1007\/s10462-018-9642-2"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"S. Vega-Pons and J. Ruiz-Shulcloper, \u201cA survey of clustering ensemble algorithms,\u201d International Journal of Pattern Recognition and Artificial Intelligence, vol. 25, no. 3, pp. 337-372, May 2011, doi: 10.1142\/S0218001411008683.","DOI":"10.1142\/S0218001411008683"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"R. G. F. Soares, H. Chen, and X. Yao, \u201cA Cluster-Based Semisupervised Ensemble for Multiclass Classification,\u201d IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 1, no. 6, pp. 408-420, Dec. 2017, doi: 10.1109\/TETCI.2017.2743219.","DOI":"10.1109\/TETCI.2017.2743219"},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"Z. Yu et al., \u201cIncremental Semi-Supervised Clustering Ensemble for High Dimensional Data Clustering,\u201d IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 3, pp. 701-714, Mar. 2016, doi: 10.1109\/TKDE.2015.2499200.","DOI":"10.1109\/TKDE.2015.2499200"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"J. Ruiz-Santaquiteria, A. Pedraza, N. Vallez, and A. Velasco, \u201cParasitic Egg Detection with a Deep Learning Ensemble,\u201d IEEE Xplore, Oct. 01, 2022. https:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?arnumber=9897858 (accessed Dec. 21, 2022).","DOI":"10.1109\/ICIP46576.2022.9897858"},{"key":"ref37","doi-asserted-by":"crossref","unstructured":"S. Shastri, K. Singh, M. Deswal, S. Kumar, and V. Mansotra, \u201cCoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19,\u201d Spatial Information Research, Jun. 2021, doi: 10.1007\/s41324-021-00408-3.","DOI":"10.1007\/s41324-021-00408-3"},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"Md. R. Islam and Md. Nahiduzzaman, \u201cComplex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach,\u201d Expert Systems with Applications, vol. 195, p. 116554, Jun. 2022, doi: 10.1016\/j.eswa.2022.116554.","DOI":"10.1016\/j.eswa.2022.116554"},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"K. L. Tan, C. P. Lee, K. M. Lim, and K. S. M. Anbananthen, \u201cSentiment Analysis With Ensemble Hybrid Deep Learning Model,\u201d IEEE Access, vol. 10, pp. 103694-103704, 2022, doi: 10.1109\/access.2022.3210182.","DOI":"10.1109\/ACCESS.2022.3210182"},{"key":"ref40","doi-asserted-by":"crossref","unstructured":"Nguyen N.T. (2006): Conflicts of Ontologies - Classification and Consensus-based Methods for Resolving. In: Proceedings of KES 2006, Lecture Notes in Artificial Intelligence 4252, 267-274","DOI":"10.1007\/11893004_34"},{"key":"ref41","doi-asserted-by":"crossref","unstructured":"Nguyen N.T., Sobecki J. (2003): Using Consensus Methods to Construct Adaptive Interfaces in Multimodal Web-based Systems. Journal of Universal Access in the Information Society 2(4), 342-358","DOI":"10.1007\/s10209-003-0050-1"},{"key":"ref42","unstructured":"Katarzyniak R., Nguyen N.T. (2000): Reconciling inconsistent profiles of agents\u2019 knowledge states in distributed multi-agent systems using consensus methods. System Science 26(4), 93-119"}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T09:28:12Z","timestamp":1722245292000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142300056A"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.2298\/csis230223056a","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}