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Amutha, J., Sharma, S., & Sharma, S. K. (2021). Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: Review, taxonomy, research findings, challenges and future directions. Computer Science Review, 40, 100376.Search in Google Scholar
Ifzarne, S., Tabbaa, H., Hafidi, I., & Lamghari, N. (2021). Anomaly detection using machine learning techniques in wireless sensor networks. In Journal of Physics: Conference Series (Vol. 1743, No. 1, p. 012021). IOP Publishing.Search in Google Scholar
Alshinina, R. A., & Elleithy, K. M. (2018). A highly accurate deep learning based approach for developing wireless sensor network middleware. IEEE Access, 6, 29885-29898.Search in Google Scholar
Mittal, M., De Prado, R. P., Kawai, Y., Nakajima, S., & Muñoz-Expósito, J. E. (2021). Machine learning techniques for energy efficiency and anomaly detection in hybrid wireless sensor networks. Energies, 14(11), 3125.Search in Google Scholar
Mamdouh, M., Elrukhsi, M. A., & Khattab, A. (2018, August). Securing the internet of things and wireless sensor networks via machine learning: A survey. In 2018 International Conference on Computer and Applications (ICCA) (pp. 215-218). IEEE.Search in Google Scholar
Radhika, S., & Rangarajan, P. (2019). On improving the lifespan of wireless sensor networks with fuzzy based clustering and machine learning based data reduction. Applied Soft Computing, 83, 105610.Search in Google Scholar
Saeed, U., Jan, S. U., Lee, Y. D., & Koo, I. (2021). Fault diagnosis based on extremely randomized trees in wireless sensor networks. Reliability engineering & system safety, 205, 107284.Search in Google Scholar
Hajjej, F., Hamdi, M., Ejbali, R., & Zaied, M. (2020). A distributed coverage hole recovery approach based on reinforcement learning for Wireless Sensor Networks. Ad Hoc Networks, 101, 102082.Search in Google Scholar
Zidi, S., Moulahi, T., & Alaya, B. (2017). Fault detection in wireless sensor networks through SVM classifier. IEEE Sensors Journal, 18(1), 340-347.Search in Google Scholar
Noshad, Z., Javaid, N., Saba, T., Wadud, Z., Saleem, M. Q., Alzahrani, M. E., & Sheta, O. E. (2019). Fault detection in wireless sensor networks through the random forest classifier. Sensors, 19(7), 1568.Search in Google Scholar
Abhale, A. B., & Manivannan, S. S. (2020). Supervised machine learning classification algorithmic approach for finding anomaly type of intrusion detection in wireless sensor network. Optical Memory and Neural Networks, 29(3), 244-256.Search in Google Scholar
Kumar, M., Mukherjee, P., Verma, K., Verma, S., & Rawat, D. B. (2021). Improved deep convolutional neural network based malicious node detection and energy-efficient data transmission in wireless sensor networks. IEEE Transactions on Network Science and Engineering, 9(5), 3272-3281.Search in Google Scholar
Otoum, S., Kantarci, B., & Mouftah, H. T. (2019). On the feasibility of deep learning in sensor network intrusion detection. IEEE Networking Letters, 1(2), 68-71.Search in Google Scholar
Cauteruccio, F., Fortino, G., Guerrieri, A., Liotta, A., Mocanu, D. C., Perra, C., ... & Vega, M. T. (2019). Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance. Information Fusion, 52, 13-30.Search in Google Scholar
Singh, A., Amutha, J., Nagar, J., Sharma, S., & Lee, C. C. (2022). AutoML-ID: Automated machine learning model for intrusion detection using wireless sensor network. Scientific reports, 12(1), 9074.Search in Google Scholar
Mohanty, S. N., Lydia, E. L., Elhoseny, M., Al Otaibi, M. M. G., & Shankar, K. (2020). Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks. Physical Communication, 40, 101097.Search in Google Scholar
Puri, D., & Bhushan, B. (2019, October). Enhancement of security and energy efficiency in WSNs: Machine Learning to the rescue. In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 120-125). IEEE.Search in Google Scholar
Muhammed, T., & Shaikh, R. A. (2017). An analysis of fault detection strategies in wireless sensor networks. Journal of Network and Computer Applications, 78, 267-287.Search in Google Scholar
Jan, S. U., Lee, Y. D., & Koo, I. S. (2021). A distributed sensor-fault detection and diagnosis framework using machine learning. Information Sciences, 547, 777-796.Search in Google Scholar
Soni, S., & Shrivastava, M. (2018). Novel learning algorithms for efficient mobile sink data collection using reinforcement learning in wireless sensor network. Wireless Communications and Mobile Computing, 2018(1), 7560167.Search in Google Scholar
Poornima, I. G. A., & Paramasivan, B. (2020). Anomaly detection in wireless sensor network using machine learning algorithm. Computer communications, 151, 331-337.Search in Google Scholar
Regin, R., Rajest, S., & Singh, B. (2021). Fault detection in wireless sensor network based on deep learning algorithms. EAI Endorsed Transactions on Scalable Information Systems, 8(32).Search in Google Scholar
Nayak, P., Swetha, G. K., Gupta, S., & Madhavi, K. (2021). Routing in wireless sensor networks using machine learning techniques: Challenges and opportunities. Measurement, 178, 108974.Search in Google Scholar
Ahmad, R., Wazirali, R., & Abu-Ain, T. (2022). Machine learning for wireless sensor networks security: An overview of challenges and issues. Sensors, 22(13), 4730.Search in Google Scholar
Kim, T., Vecchietti, L. F., Choi, K., Lee, S., & Har, D. (2020). Machine learning for advanced wireless sensor networks: A review. IEEE Sensors Journal, 21(11), 12379-12397.Search in Google Scholar
Kumar, D. P., Amgoth, T., & Annavarapu, C. S. R. (2019). Machine learning algorithms for wireless sensor networks: A survey. Information Fusion, 49, 1-25.Search in Google Scholar
Zakeria Shnizai,Yuki Matsushi & Hiroyuki Tsutsumi. (2020). Late Pleistocene slip rate of the Chaman fault based on 10 Be exposure dating of offset geomorphic surfaces near Kabul, Afghanistan. Tectonophysics(prepublish).Search in Google Scholar
William H. Nesse,Kelsey L. Clark & Behrad Noudoost. (2024). Information representation in an oscillating neural field model modulated by working memory signals. Frontiers in Computational Neuroscience1253234-1253234.Search in Google Scholar
Wilayat Shah,Junfei Chen,Irfan Ullah,Muhammad Haroon Shah & Irfan Ullah. (2024). Application of RNN-LSTM in Predicting Drought Patterns in Pakistan: A Pathway to Sustainable Water Resource Management. Water(11).Search in Google Scholar
Peng Juan Juan,Chen Xin Ge,Tan Hao,Sun Jing Yi,Long Qing Qi & Jiang Luo Luo. (2024). A heterogeneous picture fuzzy SWARA-MARCOS evaluation framework based on a novel cross-entropy measure. International Journal of Systems Science(8),1528-1552.Search in Google Scholar