Developing a Novel Adaptive Double Deep Q-Learning-Based Routing Strategy for IoT-Based Wireless Sensor Network with Federated Learning by Nalini Manogaran, Mercy Theresa Michael Raphael, Rajalakshmi Raja, Aarav Kannan Jayakumar, Malarvizhi Nandagopal, Balamurugan Balusamy, George Ghinea

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Machine Learning Tool for Analyzing Finite Buffer Queueing Systems by Attahiru Sule Alfa, Haitham Abu Ghazaleh

“…Some of these approximations usually give an oversimplified view of such systems; these approximations remain quite limited. In this paper, we present a machine learning tool that can potentially be used to analyze most finite buffer queues to obtain reasonable approximations for the mean number of items in such systems. …”
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Integrating Machine Learning and IoT for Effective Plant Disease Management by Bhoi Manjulata, Dubey Ahilya

“…Machine learning (ML) and Internet of Things (IoT) are bolting down the agriculture in the area of plant disease management, especially. …”
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Deep learning-driven IoT solution for smart tomato farming by Akshit Saxena, Aayushi Agarwal, Bhavya Nagrath, Carmel Sanjana Jayavanth, Shamita Thulasidoss, S. Maheswari, P. Sasikumar

“…Abstract The rising food demand and challenges with respect to the climate have made precision agriculture (PA) vital for sustainable crop production. This study presents an IoT-based smart greenhouse platform tailored for tomato farming, integrating environmental sensing and deep learning. …”
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Contrastive Learning‑based Simplified Graph Convolutional Network Recommendation by YU Yuchen, WU Siqi, ZHAO Qinghua, WU Xuhong, WANG Lei

“…[Purposes] Considering the problems of the existing Graph Convolutional Network (GCN) recommendation models, such as low model convergence efficiency, over-smoothing, and deteriorative recommendations for long-tail items caused by the effect of high-degree nodes on presentation learning, a Contrastive Learning-based Simplified Graph Convolutional Network recommendation algorithm (SGCN-CL) is presented. …”
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Synthesis of Fault-Tolerant Reliable Broadcast Algorithms With Reinforcement Learning by Diogo Vaz, David R. Matos, Miguel L. Pardal, Miguel Correia

“…To automate the process of developing such algorithms, this work presents a new approach that uses Reinforcement Learning to synthesize correct and efficient fault-tolerant distributed algorithms. …”
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Hybrid quantum enhanced federated learning for cyber attack detection by G. Subramanian, M. Chinnadurai

“…The presented model utilizes a hierarchical model aggregation procedure which dynamically groups nodes into regions based on the network condition and data similarity. …”
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Reservoir direct feedback alignment: deep learning by physical dynamics by Mitsumasa Nakajima, Yongbo Zhang, Katsuma Inoue, Yasuo Kuniyoshi, Toshikazu Hashimoto, Kohei Nakajima

“…Abstract The rapid advancement of deep learning has motivated various analog computing devices for energy-efficient non-von Neuman computing. …”
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Enhancing Incentive Schemes in Edge Computing through Hierarchical Reinforcement Learning by Gowtham R, Vatsala Anand, Yadati Vijaya Suresh, Kasetty Lakshmi Narasimha, R. Anil Kumar, V. Saraswathi

“…This work contributes to the advancement of edge learning by presenting a reliable and efficient solution for real-world applications. …”
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Ways of enhancing joyful teaching and learning in a graduate class of Nepal by Parbati Dhungana

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Symbiotic Positioning, Navigation, and Timing via Game Theory and Reinforcement Learning by Md Sadman Siraj, Eirini Eleni Tsiropoulou, Symeon Papavassiliou, Jim Plusquellic

“…The proposed framework establishes a cooperative interaction between targets with unknown positions and collaborator nodes with approximate location estimates. These interactions are supported by the RISs and the anchor nodes with known positions. …”
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A Multi-Task Based Clustering Personalized Federated Learning Method by Ao Xiong, Han Zhou, Yu Song, Dong Wang, Xu Wei, Da Li, Bo Gao

“…In response to the data heterogeneity issue, this paper presents a multi-task clustering-based personalized federated learning algorithm, which is applied to the prediction of carbon emissions in different regions and enterprises. …”
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Enhanced Distributed Computation for Machine Learning: Coded Strategies and Multidisciplinary Impact by Tian Xiaolin

“…This article explores methods to accelerate distributed computation, focusing on its application in machine learning. It discusses two primary concepts: coded multiplication and data shuffling, along with a non-linear core to Random Access Memory (RAM) approach, presenting new avenues for future research. …”
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Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions by Vishwa Amitkumar Patel, Pronaya Bhattacharya, Sudeep Tanwar, Rajesh Gupta, Gulshan Sharma, Pitshou N. Bokoro, Ravi Sharma

“…To overcome this, an emerging concept, i.e., federated learning (FL) is a viable solution. It performs training at client nodes and aggregates their results to train the global model. …”
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Optimizing Time-Sensitive Software-Defined Wireless Networks With Reinforcement Learning by Hyeontae Joo, Sangmin Lee, Seunghwan Lee, Hwangnam Kim

“…We implemented SDN-based TSN on a real testbed, consisting of real nodes as single board computers (SBCs) and an SDN controller, and applied RL-based network control solution to the network. …”
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Improving healthy food recommender systems through heterogeneous hypergraph learning by Jing Wang, Jincheng Zhou, Muammer Aksoy, Nidhi Sharma, Md Arafatur Rahman, Jasni Mohamad Zain, Mohammed J.F. Alenazi, Aliyeh Aminzadeh

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Trust-based Routing Optimization using Learning Automata in Wireless Sensor Network by Maryam Hajiee, Mehdi Fartash, Nafiseh Osati Eraghi

“…Learning automata identifies trusted nodes (to send data) and malicious nodes using the corresponding evaluation function. …”
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A deep-learning based method for accelerating dynamic reconfiguration of distribution networks by Yuxuan Wu, Tao Qian, Jingwen Ye, Qinran Hu, Qiangsheng Bu, Zhigang Ye

“…To address this issue, this paper presents a deep learning-based framework that converts the MIQP problem into a solvable quadratic program (QP), enabling accelerated DNR. …”
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Decentralized queue control with delay shifting in edge-IoT using reinforcement learning by Viacheslav Kovtun

“…Abstract The article presents an adaptive approach to modelling and managing the service process of requests at peripheral nodes of edge-IoT systems. …”
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Using Transformers and Reinforcement Learning for the Team Orienteering Problem Under Dynamic Conditions by Antoni Guerrero, Marc Escoto, Majsa Ammouriova, Yangchongyi Men, Angel A. Juan

“…This paper presents a reinforcement learning (RL) approach for solving the team orienteering problem under both deterministic and dynamic travel time conditions. …”
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