{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:40:30Z","timestamp":1763106030603,"version":"3.38.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T00:00:00Z","timestamp":1721433600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T00:00:00Z","timestamp":1721433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soc. Netw. Anal. Min."],"DOI":"10.1007\/s13278-024-01302-0","type":"journal-article","created":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T04:01:58Z","timestamp":1721448118000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Integrating neural network-driven customization, scalability, and cloud computing for enhanced accuracy and responsiveness for social network modelling"],"prefix":"10.1007","volume":"14","author":[{"given":"E.","family":"Aarthi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. Sahaya","family":"Sheela","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Vasantharaj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T.","family":"Saravanan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R. Senthil","family":"Rama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Sujaritha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,20]]},"reference":[{"issue":"1","key":"1302_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-024-01203-2","volume":"14","author":"E Aarthi","year":"2024","unstructured":"Aarthi E, Jagan S, Devi CP, Gracewell JJ, Choubey SB, Choubey A, Gopalakrishnan S (2024) A turbulent flow optimized deep fused ensemble model (TFO-DFE) for sentiment analysis using social corpus data. Soc Netw Anal Min 14(1):1\u201316","journal-title":"Soc Netw Anal Min"},{"key":"1302_CR2","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.comcom.2020.02.044","volume":"154","author":"M Alam","year":"2020","unstructured":"Alam M, Abid F, Guangpei C, Yunrong LV (2020) Social media sentiment analysis through parallel dilated convolutional neural network for smart city applications. Comput Commun 154:129\u2013137","journal-title":"Comput Commun"},{"key":"1302_CR3","first-page":"101846","volume":"35","author":"H Alimam","year":"2023","unstructured":"Alimam H, Mazzuto G, Tozzi N, Ciarapica FE, Bevilacqua M (2023) The resurrection of digital triplet: a cognitive pillar of human-machine integration at the dawn of industry 5.0. J King Saud Univ Comput Inf Sci 35:101846","journal-title":"J King Saud Univ Comput Inf Sci"},{"issue":"8","key":"1302_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-023-16382-x","volume":"83","author":"Z Amiri","year":"2023","unstructured":"Amiri Z, Heidari A, Navimipour NJ, Unal M, Mousavi A (2023) Adventures in data analysis: a systematic review of deep learning techniques for pattern recognition in cyber-physical-social systems. Multimed Tools Appl 83(8):1\u201365","journal-title":"Multimed Tools Appl"},{"key":"1302_CR5","doi-asserted-by":"publisher","first-page":"18431","DOI":"10.1007\/s00500-020-05049-6","volume":"24","author":"S Bairavel","year":"2020","unstructured":"Bairavel S, Krishnamurthy M (2020) Novel OGBEE-based feature selection and feature-level fusion with MLP neural network for social media multimodal sentiment analysis. Soft Comput 24:18431\u201318445","journal-title":"Soft Comput"},{"key":"1302_CR6","doi-asserted-by":"publisher","first-page":"121188","DOI":"10.1016\/j.eswa.2023.121188","volume":"236","author":"S Bansal","year":"2024","unstructured":"Bansal S, Gowda K, Kumar N (2024) Multilingual personalized hashtag recommendation for low resource Indic languages using graph-based deep neural network. Expert Syst Appl 236:121188","journal-title":"Expert Syst Appl"},{"key":"1302_CR7","first-page":"287","volume":"78","author":"IA Chikwendu","year":"2023","unstructured":"Chikwendu IA, Zhang X, Agyemang IO, Adjei-Mensah I, Chima UC, Ejiyi CJ (2023) A comprehensive survey on deep graph representation learning methods. J Arti Intell Res 78:287\u2013356","journal-title":"J Arti Intell Res"},{"volume-title":"Handbook of research on mobility and computing: evolving technologies and ubiquitous impacts: evolving technologies and ubiquitous impacts","year":"2011","key":"1302_CR8","unstructured":"Cruz-Cunha MM, Moreira F (eds) (2011) Handbook of research on mobility and computing: evolving technologies and ubiquitous impacts: evolving technologies and ubiquitous impacts. IGI Global, Harrisburg, PA"},{"key":"1302_CR9","doi-asserted-by":"crossref","unstructured":"Gan W, Wan S, Philip SY (2023) Model-as-a-service (MaaS): a survey. In: 2023 IEEE international conference on big data (BigData), IEEE, pp 4636\u20134645","DOI":"10.1109\/BigData59044.2023.10386351"},{"issue":"3","key":"1302_CR10","doi-asserted-by":"publisher","first-page":"493","DOI":"10.3390\/jcp3030025","volume":"3","author":"A Giannaros","year":"2023","unstructured":"Giannaros A, Karras A, Theodorakopoulos L, Karras C, Kranias P, Schizas N, Tsolis D (2023) Autonomous vehicles: sophisticated attacks, safety issues, challenges, open topics, blockchain, and future directions. J Cybersecur Priv 3(3):493\u2013543","journal-title":"J Cybersecur Priv"},{"key":"1302_CR11","doi-asserted-by":"crossref","unstructured":"Han Y, Karunasekera S, Leckie C (2020) Graph neural networks with continual learning for fake news detection from social media. arXiv preprint arXiv:2007.03316","DOI":"10.1007\/978-3-030-86340-1_30"},{"key":"1302_CR12","doi-asserted-by":"crossref","unstructured":"Jeon KE, She J, Wong S (2020) Extending BLE beacon lifetime by a novel neural network-driven framework. In: 2020 IEEE wireless communications and networking conference (WCNC), IEEE, pp 1\u20136","DOI":"10.1109\/WCNC45663.2020.9120555"},{"key":"1302_CR13","unstructured":"Jia J, Liang W, Liang Y (2023) A review of hybrid and ensemble in deep learning for natural language processing. arXiv preprint arXiv:2312"},{"key":"1302_CR14","doi-asserted-by":"publisher","first-page":"101566","DOI":"10.1016\/j.rineng.2023.101566","volume":"20","author":"H Kamyab","year":"2023","unstructured":"Kamyab H, Khademi T, Chelliapan S, SaberiKamarposhti M, Rezania S, Yusuf M, Ahn Y (2023) The latest innovative avenues for the utilization of artificial intelligence and big data analytics in water resource management. Results Eng 20:101566","journal-title":"Results Eng"},{"key":"1302_CR15","doi-asserted-by":"crossref","unstructured":"Kaplunovich A, Kaplunovich S (2023) Consolidating user data from social networks using machine learning and serverless cloud. In: 2023 international conference on intelligent computing, communication, networking and services (ICCNS), IEEE, pp 230\u2013236","DOI":"10.1109\/ICCNS58795.2023.10193182"},{"issue":"11","key":"1302_CR16","first-page":"464","volume":"14","author":"N Katayev","year":"2023","unstructured":"Katayev N, Altayeva A, Abduraimova B, Kurmanbekkyzy N, Madibaiuly Z, Kulambayev B (2023) Development of a framework for classification of impulsive urban sounds using BiLSTM network. Int J Adv Comput Sci Appl 14(11):464\u2013472","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"1","key":"1302_CR17","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s40537-023-00876-4","volume":"11","author":"B Khemani","year":"2024","unstructured":"Khemani B, Patil S, Kotecha K, Tanwar S (2024) A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions. J Big Data 11(1):18","journal-title":"J Big Data"},{"issue":"4","key":"1302_CR18","doi-asserted-by":"publisher","first-page":"576","DOI":"10.12928\/biste.v5i4.9668","volume":"5","author":"AK Lenson","year":"2023","unstructured":"Lenson AK, Airlangga G (2023) Comparative analysis of MLP, CNN, and RNN models in automatic speech recognition: dissecting performance metric. Bul Ilm Sarj Teknik Elektro 5(4):576\u2013583","journal-title":"Bul Ilm Sarj Teknik Elektro"},{"key":"1302_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2022.3179131","author":"MH Meng","year":"2022","unstructured":"Meng MH, Bai G, Teo SG, Hou Z, Xiao Y, Lin Y, Dong JS (2022) Adversarial robustness of deep neural networks: a survey from a formal verification perspective. IEEE Trans Dependable Secure Comput. https:\/\/doi.org\/10.1109\/TDSC.2022.3179131","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"1302_CR20","doi-asserted-by":"publisher","DOI":"10.18488\/journal.76.2021.81.1.7","volume-title":"Activity recognition and creation of web service for activity recognition using mobile sensor data using azure machine learning studio","author":"M Owais","year":"2021","unstructured":"Owais M, Pathan RN, Umar A, Bux R (2021) Activity recognition and creation of web service for activity recognition using mobile sensor data using azure machine learning studio. Conscientia Beam, Karachi, Pakistan"},{"issue":"8","key":"1302_CR21","doi-asserted-by":"publisher","first-page":"8846","DOI":"10.1109\/TITS.2023.3257759","volume":"24","author":"S Rahmani","year":"2023","unstructured":"Rahmani S, Baghbani A, Bouguila N, Patterson Z (2023) Graph neural networks for intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 24(8):8846\u20138885","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"21","key":"1302_CR22","doi-asserted-by":"publisher","first-page":"16335","DOI":"10.1007\/s00500-020-04943-3","volume":"24","author":"R Ramani","year":"2020","unstructured":"Ramani R, Devi KV, Soundar KR (2020) MapReduce-based big data framework using modified artificial neural network classifier for diabetic chronic disease prediction. Soft Comput 24(21):16335\u201316345","journal-title":"Soft Comput"},{"issue":"9","key":"1302_CR23","doi-asserted-by":"publisher","first-page":"6121","DOI":"10.1109\/TIM.2020.2969588","volume":"69","author":"U Singh","year":"2020","unstructured":"Singh U, Determe JF, Horlin F, De Doncker P (2020) Crowd forecasting based on wifi sensors and lstm neural networks. IEEE Trans Instrum Meas 69(9):6121\u20136131","journal-title":"IEEE Trans Instrum Meas"},{"key":"1302_CR24","doi-asserted-by":"publisher","first-page":"79143","DOI":"10.1109\/ACCESS.2021.3082932","volume":"9","author":"J Skarding","year":"2021","unstructured":"Skarding J, Gabrys B, Musial K (2021) Foundations and modeling of dynamic networks using dynamic graph neural networks: a survey. IEEE Access 9:79143\u201379168","journal-title":"IEEE Access"},{"issue":"9","key":"1302_CR25","doi-asserted-by":"publisher","first-page":"2946","DOI":"10.3390\/s18092946","volume":"18","author":"M Syafrudin","year":"2018","unstructured":"Syafrudin M, Alfian G, Fitriyani NL, Rhee J (2018) Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors 18(9):2946","journal-title":"Sensors"},{"issue":"4","key":"1302_CR26","first-page":"1544","volume":"34","author":"DA Tedjopurnomo","year":"2020","unstructured":"Tedjopurnomo DA, Bao Z, Zheng B, Choudhury FM, Qin AK (2020) A survey on modern deep neural network for traffic prediction: trends, methods and challenges. IEEE Trans Knowl Data Eng 34(4):1544\u20131561","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1302_CR27","doi-asserted-by":"crossref","unstructured":"Thantharate P (2023) SCALE-IT: distributed and realistic simulation frameworks for testing cloud-based software. In: 2023 10th international conference on electrical engineering, computer science and informatics (EECSI), Palembang, Indonesia, pp 300\u2013306","DOI":"10.1109\/EECSI59885.2023.10295630"},{"issue":"17","key":"1302_CR28","doi-asserted-by":"publisher","first-page":"3554","DOI":"10.3390\/electronics12173554","volume":"12","author":"AE Topcu","year":"2023","unstructured":"Topcu AE, Alzoubi YI, Elbasi E, Camalan E (2023) Social media zero-day attack detection using TensorFlow. Electronics 12(17):3554","journal-title":"Electronics"},{"key":"1302_CR29","doi-asserted-by":"publisher","first-page":"110128","DOI":"10.1016\/j.epsr.2024.110128","volume":"229","author":"D Wu","year":"2024","unstructured":"Wu D, Du X, Peng F (2024) Multi-layer and multi-source features stacking ensemble learning for user profile. Electr Power Syst Res 229:110128","journal-title":"Electr Power Syst Res"},{"issue":"12","key":"1302_CR30","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.3390\/e21121227","volume":"21","author":"X Xue","year":"2019","unstructured":"Xue X, Feng J, Gao Y, Liu M, Zhang W, Sun X, Guo S (2019) Convolutional recurrent neural networks with a self-attention mechanism for personnel performance prediction. Entropy 21(12):1227","journal-title":"Entropy"}],"container-title":["Social Network Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-024-01302-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13278-024-01302-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-024-01302-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T05:42:22Z","timestamp":1740462142000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13278-024-01302-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,20]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1302"],"URL":"https:\/\/doi.org\/10.1007\/s13278-024-01302-0","relation":{},"ISSN":["1869-5469"],"issn-type":[{"type":"electronic","value":"1869-5469"}],"subject":[],"published":{"date-parts":[[2024,7,20]]},"assertion":[{"value":"8 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki. I consent to participate in the research project and the following has been explained to me: the research may not be of direct benefit to me. My participation is completely voluntary. My right to withdraw from the study at any time without any implications to me.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent"}}],"article-number":"139"}}