{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T18:32:32Z","timestamp":1782325952251,"version":"3.54.5"},"reference-count":70,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T00:00:00Z","timestamp":1744588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Human activity recognition (HAR) is the task of classifying sequences of data into defined movements. Taking advantage of deep learning (DL) methods, this research investigates and optimizes neural network architectures to effectively classify physical activities from smartphone accelerometer data. Unlike traditional machine learning (ML) methods employing manually crafted features, our approach employs automated feature learning with three deep learning architectures: Convolutional Neural Networks (CNN), CNN-based autoencoders, and Long Short-Term Memory Recurrent Neural Networks (LSTM RNN). The contribution of this work is primarily in optimizing LSTM RNN to leverage the most out of temporal relationships between sensor data, significantly improving classification accuracy. Experimental outcomes for the WISDM dataset show that the proposed LSTM RNN model achieves 96.1% accuracy, outperforming CNN-based approaches and current ML-based methods. Compared to current works, our optimized frameworks achieve up to 6.4% higher classification performance, which means that they are more appropriate for real-time HAR.<\/jats:p>","DOI":"10.3390\/jsan14020042","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T06:18:36Z","timestamp":1744611516000},"page":"42","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Enhancing Sensor-Based Human Physical Activity Recognition Using Deep Neural Networks"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0417-6586","authenticated-orcid":false,"given":"Minyar","family":"Sassi Hidri","sequence":"first","affiliation":[{"name":"Computer Department, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0222-8562","authenticated-orcid":false,"given":"Adel","family":"Hidri","sequence":"additional","affiliation":[{"name":"Computer Department, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4699-6432","authenticated-orcid":false,"given":"Suleiman Ali","family":"Alsaif","sequence":"additional","affiliation":[{"name":"Computer Department, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4461-8446","authenticated-orcid":false,"given":"Muteeb","family":"Alahmari","sequence":"additional","affiliation":[{"name":"Computer Department, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3288-4271","authenticated-orcid":false,"given":"Eman","family":"AlShehri","sequence":"additional","affiliation":[{"name":"Computer Department, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,14]]},"reference":[{"key":"ref_1","first-page":"2161","article-title":"What factors distinguish overlapping Data job postings? Towards ML-based models for job category\u2019s factors prediction","volume":"18","author":"Hidri","year":"2024","journal-title":"Intell. Decis. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kollathodi, M.A. (2023). A comprehensive comparison and analysis of machine learning algorithms including evaluation optimized for geographic location prediction based on twitter tweets datasets. Cogent Eng., 10.","DOI":"10.1080\/23311916.2023.2232602"},{"key":"ref_3","unstructured":"Madabhushi, A., and Aggarwal, J. (1999, January 26). A bayesian approach to human activity recognition. Proceedings of the Second IEEE Workshop on Visual Surveillance (VS), Fort Collins, CO, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Patel, A.T., and Shah, J.H. (2019, January 13\u201315). Performance analysis of supervised machine learning algorithms to recognize human activity in ambient assisted living environment. Proceedings of the IEEE 16th India Council International Conference (INDICON), Rajkot, India.","DOI":"10.1109\/INDICON47234.2019.9030353"},{"key":"ref_5","first-page":"579","article-title":"Multilayer perceptron and neural networks","volume":"8","author":"Popescu","year":"2009","journal-title":"WSEAS Trans. Circuits Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"012059","DOI":"10.1088\/1742-6596\/1631\/1\/012059","article-title":"Human activity recognition using gaussian naive bayes algorithm in smart home","volume":"1631","author":"Shen","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"15673","DOI":"10.1007\/s00521-018-3437-x","article-title":"Comparison of offline and real-time human activity recognition results using machine learning techniques","volume":"32","author":"Suto","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1504\/IJCAT.2022.123237","article-title":"SiNoptiC: Swarm intelligence optimisation of convolutional neural network architectures for text classification","volume":"68","author":"Ferjani","year":"2022","journal-title":"Int. J. Comput. Appl. Technol."},{"key":"ref_9","unstructured":"Lee, S.-M., Yoon, S.M., and Cho, H. (2017, January 13\u201316). Human activity recognition from accelerometer data using convolutional neural network. Proceedings of the IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju, Republic of Korea."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sassi Hidri, M. (2024, January 9\u201311). Multistep Time Series Forecasting of Energy Consumption Based on Stacked Deep LSTM Network Architecture. Proceedings of the 16th International Conference on Computational Collective Intelligencerocedia Computer Science (ICCCI), Leipzig, Germany.","DOI":"10.1007\/978-3-031-70248-8_11"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Babiker, M., Khalifa, O., Htike, K., Hashim, A., and Zaharadeen, M. (2017, January 28\u201330). Automated daily human activity recognition for video surveillance using neural network. Proceedings of the IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), Putrajaya, Malaysia.","DOI":"10.1109\/ICSIMA.2017.8312024"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liagkou, V., Sakka, S., and Stylios, C. (2022, January 23\u201325). Security and privacy vulnerabilities in human activity recognition systems. Proceedings of the 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Ioannina, Greece.","DOI":"10.1109\/SEEDA-CECNSM57760.2022.9932957"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sakka, S., Liagkou, V., and Stylios, C. (2023). Exploiting security issues in human activity recognition systems (HARSS). Information, 14.","DOI":"10.3390\/info14060315"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Singh, D., and Vishwakarma, D. (2019). Human Activity Recognition in Video Benchmarks: A Survey. Advances in Signal Processing and Communication, Springer.","DOI":"10.1007\/978-981-13-2553-3_24"},{"key":"ref_15","first-page":"54","article-title":"A depth video-based human detection and activity recognition using multi-features and embedded hidden markov models for health care monitoring systems","volume":"4","author":"Jalal","year":"2017","journal-title":"Int. J. Interact. Multim. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ogbuabor, G., and La, R. (2018, January 26\u201328). Human activity recognition for healthcare using smartphones. Proceedings of the 10th International Conference on Machine Learning and Computing (ICMLC), Macau, China.","DOI":"10.1145\/3195106.3195157"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Subasi, A., Radhwan, M., Kurdi, R., and Khateeb, K. (2018, January 28\u201329). IOT based mobile healthcare system for human activity recognition. Proceedings of the 15th Learning and Technology Conference (L&T), Gothenburg, Sweden.","DOI":"10.1109\/LT.2018.8368507"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"86411","DOI":"10.1109\/ACCESS.2020.2992584","article-title":"Human activity recognition based on improved bayesian convolution network to analyze health care data using wearable iot device","volume":"8","author":"Zhou","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Alizadeh, R., Savaria, Y., and Nerguizian, C. (2021, January 13\u201315). Human activity recognition and people count for a smart public transportation system. Proceedings of the IEEE 4th 5G World Forum (5GWF), Montreal, QC, Canada.","DOI":"10.1109\/5GWF52925.2021.00039"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sekiguchi, R., Abe, K., Shogo, S., Kumano, M., Asakura, D., Okabe, R., Kariya, T., and Kawakatsu, M. (2021, January 21\u201326). Phased human activity recognition based on GPS. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers, Virtual.","DOI":"10.1145\/3460418.3479382"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5379","DOI":"10.1109\/TVT.2019.2908425","article-title":"Driver activity recognition for intelligent vehicles: A deep learning approach","volume":"68","author":"Xing","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ye, J., Chen, W., Li, X., Zhang, Q., and Zhang, X. (2020). Deep learning-based human activity real-time recognition for pedestrian navigation. Sensors, 20.","DOI":"10.3390\/s20092574"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Akter, M., Ansary, S., Khan, M.A.-M., and Kim, D. (2023). Human activity recognition using attention-mechanism-based deep learning feature combination. Sensors, 23.","DOI":"10.3390\/s23125715"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e13256","DOI":"10.1111\/exsy.13256","article-title":"Human activity recognition based on multi-instance learning","volume":"40","author":"Birant","year":"2023","journal-title":"Expert Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"123143","DOI":"10.1016\/j.eswa.2024.123143","article-title":"Human activity recognition with smartphone-integrated sensors: A survey","volume":"246","author":"Dentamaro","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, X., and Zhang, S. (2023, January 17\u201319). Human activity recognition based on transformer in smart home. Proceedings of the 2nd Asia Conference on Algorithms, Computing and Machine Learning (CACML), Shanghai, China.","DOI":"10.1145\/3590003.3590100"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Morshed, M.G., Sultana, T., Alam, A., and Lee, Y.-K. (2023). Human action recognition: A taxonomy-based survey, updates, and opportunities. Sensors, 23.","DOI":"10.3390\/s23042182"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Parmar, D., Bhardwaj, M., Garg, A., Kapoor, A., and Mishra, A. (2023, January 20\u201321). Human activity recognition system. Proceedings of the international Conference on Computational Intelligence, Communication Technology and Networking (CICTN), Ghaziabad, India.","DOI":"10.1109\/CICTN57981.2023.10141250"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104674","DOI":"10.1016\/j.imavis.2023.104674","article-title":"Human activity recognition from UAV videos using a novel DMLC-CNN model","volume":"134","author":"Sinha","year":"2023","journal-title":"Image Vis. Comput."},{"key":"ref_30","unstructured":"Zheng, Y., Wong, W.-K., Guan, X., and Trost, S. (2013, January 14\u201318). Physical activity recognition from accelerometer data using a multi-scale ensemble method. Proceedings of the Conference on Innovative Applications of Artificial Intelligence (IAAI), Bellevue, WA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Surek, G.A.S., Seman, L.O., Stefenon, S.F., Mariani, V.C., and Coelho, L.d.S. (2023). Video-based human activity recognition using deep learning approaches. Sensors, 23.","DOI":"10.3390\/s23146384"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Xue, Y. (2015, January 9\u201312). A deep learning approach to human activity recognition based on single accelerometer. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China.","DOI":"10.1109\/SMC.2015.263"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4405","DOI":"10.1007\/s11042-015-3177-1","article-title":"A survey of depth and inertial sensor fusion for human action recognition","volume":"76","author":"Chen","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Medrano, C., Plaza, I., Igual, R., Sanchez, A., and Castro, M. (2016). The effect of personalization on smartphone-based fall detectors. Sensors, 16.","DOI":"10.3390\/s16010117"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.dcan.2015.02.006","article-title":"A review on radio based activity recognition","volume":"1","author":"Wang","year":"2015","journal-title":"Digit. Commun. Netw."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Oleh, U., Obermaisser, R., and Ahammed, A.S. (2024). A Review of Recent Techniques for Human Activity Recognition: Multimodality, Reinforcement Learning, and Language Models. Algorithms, 17.","DOI":"10.3390\/a17100434"},{"key":"ref_37","first-page":"1","article-title":"Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities","volume":"54","author":"Chen","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Dua, N., Singh, S., Challa, S., Semwal, V., and Kumar, M. (2023, January 19\u201320). A Survey on Human Activity Recognition Using Deep Learning Techniques and Wearable Sensor Data. Proceedings of the 4th International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, Virtual Event.","DOI":"10.1007\/978-3-031-24352-3_5"},{"key":"ref_39","first-page":"1","article-title":"A survey on deep learning for human activity recognition","volume":"54","author":"Gu","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.procs.2022.08.009","article-title":"Investigation on human activity recognition using deep learning","volume":"204","author":"Sarveshwaran","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sousa Lima, W., Souto, E., El-Khatib, K., Jalali, R., and Gama, J. (2019). Human activity recognition using inertial sensors in a smartphone: An overview. Sensors, 19.","DOI":"10.3390\/s19143213"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Budisteanu, E.A., and Mocanu, I.G. (2021). Combining supervised and unsupervised learning algorithms for human activity recognition. Sensors, 21.","DOI":"10.3390\/s21186309"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3448074","article-title":"Unsupervised human activity representation learning with multi-task deep clustering","volume":"5","author":"Ma","year":"2021","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1109\/TII.2023.3264284","article-title":"Unsupervised human activity recognition learning for disassembly tasks","volume":"20","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_45","first-page":"487","article-title":"RF-based human activity recognition using signal adapted convolutional neural network","volume":"21","author":"Chen","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mubibya, G.S., and Almhana, J. (2022, January 16\u201320). Improving human activity recognition using ml and wearable sensors. Proceedings of the IEEE International Conference on Communications, Seoul, Republic of Korea.","DOI":"10.1109\/ICC45855.2022.9839267"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mohsen, S., Elkaseer, A., and Scholz, S.G. (2021, January 15\u201317). Human activity recognition using k-nearest neighbor machine learning algorithm. Proceedings of the 8th International Conference on Sustainable Design and Manufacturing (KES-SDM), Split, Croatia.","DOI":"10.1007\/978-981-16-6128-0_29"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, P., Zhang, Y., and Jiang, W. (2021, January 18\u201320). Application of k-nearest neighbor (KNN) algorithm for human action recognition. Proceedings of the 4th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China.","DOI":"10.1109\/IMCEC51613.2021.9482165"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Oniga, S., and Suto, J. (2014, January 28\u201330). Human activity recognition using neural networks. Proceedings of the 2014 15th International Carpathian Control Conference (ICCC), Velke Karlovice, Czech Republic.","DOI":"10.1109\/CarpathianCC.2014.6843636"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"030002","DOI":"10.1063\/5.0096708","article-title":"Human activity recognition utilizing svm algorithm with gridsearch","volume":"2453","author":"Kusuma","year":"2022","journal-title":"AIP Conf. Proc."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Manosha Chathuramali, K.G., and Rodrigo, R. (2012, January 12\u201315). Faster human activity recognition with SVM. Proceedings of the International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, Sri Lanka.","DOI":"10.1109\/ICTer.2012.6421415"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.patrec.2009.09.019","article-title":"Recognition of human activities using svm multi-class classifier","volume":"31","author":"Qian","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ariza-Colpas, P.P., Vicario, E., Oviedo-Carrascal, A.I., Butt Aziz, S., Pieres-Melo, M.A., Quintero-Linero, A., and Patara, F. (2022). Human activity recognition data analysis: History, evolutions, and new trends. Sensors, 22.","DOI":"10.3390\/s22093401"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4560365","DOI":"10.1155\/2016\/4560365","article-title":"Two-layer hidden markov model for human activity recognition in home environments","volume":"12","author":"Kabir","year":"2016","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Manouchehri, N., and Bouguila, N. (2023). Human activity recognition with an HMM-based generative model. Sensors, 23.","DOI":"10.3390\/s23031390"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Xu, W., Pang, Y., Yang, Y., and Liu, Y. (2018, January 20\u201324). Human activity recognition based on convolutional neural network. Proceedings of the 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545435"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Pienaar, S., and Malekian, R. (2019, January 18\u201320). Human activity recognition using LSTM-RNN deep neural network architecture. Proceedings of the IEEE 2nd Wireless Africa Conference (WAC), Pretoria, South Africa.","DOI":"10.1109\/AFRICA.2019.8843403"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yu, T., Chen, J., Yan, N., and Liu, X. (2018, January 18\u201320). A multi-layer parallel lstm network for human activity recognition with smartphone sensors. Proceedings of the 10th International Conference on Wireless Communications and Signal Processing (WCSP), Hangzhou, China.","DOI":"10.1109\/WCSP.2018.8555945"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., and Jitpattanakul, A. (2021). LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes. Sensors, 21.","DOI":"10.3390\/s21051636"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Dirgov\u00e1 Lupt\u00e1kov\u00e1, I., Kubov\u010d\u00edk, M., and Posp\u00edchal, J. (2022). Wearable Sensor-Based Human Activity Recognition with Transformer Model. Sensors, 22.","DOI":"10.20944\/preprints202202.0111.v1"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Srivatsa, P., and Pl\u00f6tz, T. (2024). Using Graphs to Perform Effective Sensor-Based Human Activity Recognition in Smart Homes. Sensors, 24.","DOI":"10.3390\/s24123944"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Manjulalayam, R., Vyas, B., Patel, R., Goswami, A., Mistry, H., and Mavani, C. (2024, January 14\u201316). A Comparative Study of Deep Learning Architectures for Activity Recognition. Proceedings of the 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO), Phuket, Thailand.","DOI":"10.1109\/ICCMSO61761.2024.00032"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Sheng, M., Jiang, J., Su, B., Tang, Q., Yahya, A., and Wang, G. (2016, January 3\u20134). Short-time activity recognition with wearable sensors using convolutional neural network. Proceedings of the 15th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry (VRCAI), Zhuhai, China.","DOI":"10.1145\/3013971.3014016"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Barros, T., SouzaNeto, P., Silva, I., and Guedes, L.A. (2019). Predictive models for imbalanced data: A school dropout perspective. Educ. Sci., 9.","DOI":"10.3390\/educsci9040275"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Mohammed, R., Rawashdeh, J., and Abdullah, M. (2020, January 7\u20139). Machine learning with oversampling and undersampling techniques: Overview study and experimental results. Proceedings of the 11th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan.","DOI":"10.1109\/ICICS49469.2020.239556"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Varotto, G., Susi, G., Tassi, L., Gozzo, F., Franceschetti, S., and Panzica, F. (2021). Comparison of resampling techniques for imbalanced datasets in machine learning: Application to epileptogenic zone localization from interictal intracranial EEG recordings in patients with focal epilepsy. Front. Neuroinform., 15.","DOI":"10.3389\/fninf.2021.715421"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the 18th International Conference of Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_68","unstructured":"Agarap, A.F. (2018). Deep learning using rectified linear units (ReLU). arXiv."},{"key":"ref_69","unstructured":"Gulli, A., and Pal, S. (2017). Deep Learning with Keras, Packt Publishing Ltd."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Prasad, A., Tyagi, A.K., Althobaiti, M.M., Almulihi, A., Mansour, R.F., and Mahmoud, A.M. (2021). Human activity recognition using cell phone-based accelerometer and convolutional neural network. Appl. Sci., 11.","DOI":"10.3390\/app112412099"}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/14\/2\/42\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:13:56Z","timestamp":1760030036000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/14\/2\/42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,14]]},"references-count":70,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["jsan14020042"],"URL":"https:\/\/doi.org\/10.3390\/jsan14020042","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,14]]}}}