EdgeGuard: Decentralized Medical Resource Orchestration via Blockchain-Secured Federated Learning in IoMT Networks by Sakshi Patni, Joohyung Lee

“…We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. …”
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Model-Based Detection of Coordinated Attacks (DCA) in Distribution Systems by Nitasha Sahani, Chen-Ching Liu

“…In this paper, a novel proactive DCA strategy is proposed for early detection of CCA by establishing correlations among distinct attack events through model-based reinforcement learning that utilizes abductive reasoning to conclude the attacker goal. …”
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Machine learning for medical image classification by Gazi Husain, Jonathan Mayer, Molly Bekbolatova, Prince Vathappallil, Mihir Matalia, Milan Toma

“…It navigates through various ML methods utilized in healthcare, including Supervised Learning, Unsupervised Learning, Self-Supervised Learning, Deep Neural Networks, Reinforcement Learning, and Ensemble Methods. The challenge lies not just in the selection of an ML algorithm but in identifying the most appropriate one for a specific task as well, given the vast array of options available. …”
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A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks by Ashraf M. Etman, Mohamed S. Abdalzaher, Ahmed A. Emran, Ahmed Yahya, Mostafa Shaaban

“…This paper offers an extensive review of pertinent research emphasizing the use of supervised, unsupervised, and reinforcement learning approaches. The evaluation contains 234 peer reviewed articles from highly regarded academic journals and conferences covering the years 2017 through 2024 which depict the effectiveness of supervised techniques on WSNs in the field of SGs. …”
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Dissociating social reward learning and behavior in alcohol use disorder by Simon Jangard, Björn Lindström, Lotfi Khemiri, Nitya Jayaram-Lindström, Andreas Olsson

“…Finally, we applied reinforcement learning models to examine the computational properties of learning. …”
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Adaptive Handover Management in High-Mobility Networks for Smart Cities by Yahya S. Junejo, Faisal K. Shaikh, Bhawani S. Chowdhry, Waleed Ejaz

“…This paper presents an adaptive handover management scheme that utilizes reinforcement learning to optimize handover decisions in dynamic environments. …”
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Artificial Intelligence Methods Applied to Catalytic Cracking Processes by Fan Yang, Mao Xu, Wenqiang Lei, Jiancheng Lv

“…In the modeling stage, data-driven methods are often used to build the system model or rule base; In the optimization stage, heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base. …”
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Comparative analysis of Q-learning, SARSA, and deep Q-network for microgrid energy management by Sreyas Ramesh, Sukanth B N, Sri Jaswanth Sathyavarapu, Vishwash Sharma, Nippun Kumaar A. A., Manju Khanna

“…This research presents a novel application of Reinforcement Learning (RL) algorithms—specifically Q-Learning, SARSA, and Deep Q-Network (DQN)—for optimal energy management in microgrids. …”
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Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research by Babak Amiri, Nikan Shahverdi, Amirali Haddadi, Yalda Ghahremani

“…Factorial analysis uncovers thematic clusters, highlighting collaborative filtering, user experience, emotion identification, and reinforcement learning. A scientific mapping analysis classifies research themes in different historical periods, focusing on essential areas such as collaborative filtering, hybrid recommendation, sentiment analysis, and emotion identification. …”
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Smart Cognitive HMI With Automated Knowledge Extraction for Machine Tool by Jongsu Park, Jinho Son, Seongwoo Cho, Jumyung Um

“…It utilizes various AI technologies including optical character recognition, reinforcement learning, and natural language preprocessing technologies to minimize human intervention in the dataset construction. …”
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A Bibliometric Analysis of Agent-Based Systems in Cybersecurity and Broader Security Domains: Trends and Insights by Shreya Girish Savadatti, Kathiravan Srinivasan, Yuh-Chung Hu

“…Collaboration is prevalent, with important writers such as Zhang Y and Wang J driving cross-disciplinary research in fields like reinforcement learning and blockchain. To the best of our knowledge, this is the first work to conduct a comprehensive bibliometric analysis entirely focused on the intersection of agent-based systems and cybersecurity.…”
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Exploring the role of Energy Communities: A Comprehensive Review by M. Asim Amin, Renato Procopio, Marco Invernizzi, Andrea Bonfiglio, Youwei Jia

“…Hence, it can be inferred that Reinforcement Learning (RL) methodologies exhibit considerable potential in the control field. …”
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Deep Q-Networks for Minimizing Total Tardiness on a Single Machine by Kuan Wei Huang, Bertrand M. T. Lin

“…The advent of Deep Q-Networks (DQNs) within the reinforcement learning paradigm could be a viable approach to transcending these limitations, offering a robust and adaptive approach. …”
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A Review of the Self-Adaptive Traffic Signal Control System Based on Future Traffic Environment by Yizhe Wang, Xiaoguang Yang, Hailun Liang, Yangdong Liu

“…Finally, the article concluded that signal control based on multiagent reinforcement learning is a kind of closed-loop feedback adaptive control method, which outperforms many counterparts in terms of real-time characteristic, accuracy, and self-learning and therefore will be an important research focus of control method in future due to the property of “model-free” and “self-learning” that well accommodates the abundance of traffic information data. …”
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Learning enhances behaviorally relevant representations in apical dendrites by Sam E Benezra, Kripa B Patel, Citlali Perez Campos, Elizabeth MC Hillman, Randy M Bruno

“…Mice were trained to discriminate two orthogonal directions of whisker stimulation. Reinforcement learning, but not repeated stimulus exposure, enhanced tuft selectivity for both directions equally, even though only one was associated with reward. …”
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Digital Twin Framework Using Real-Time Asset Tracking for Smart Flexible Manufacturing System by Asif Ullah, Muhammad Younas, Mohd Shahneel Saharudin

“…The framework also utilized a deep reinforcement learning algorithm. This enables an Automated Guided Vehicle (AGV) to successfully navigate and avoid both static and mobile obstacles in a controlled laboratory setting. …”
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Design of an Integrated Model for Video Summarization Using Multimodal Fusion and YOLO for Crime Scene Analysis by Sai Babu Veesam, Aravapalli Rama Satish

“…Finally, a feedback-driven reinforcement learning framework named RL-HITL allows continuous improvement based on human input, which enhances the adaptability of the system over temporal instance sets. …”
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Exploring when to exploit: the cognitive underpinnings of foraging-type decisions in relation to psychopathy by D. V. Atanassova, J. M. Oosterman, A. O. Diaconescu, C. Mathys, V. I. Madariaga, I. A. Brazil

“…Abstract Impairments in reinforcement learning (RL) might underlie the tendency of individuals with elevated psychopathic traits to behave exploitatively, as they fail to learn from their mistakes. …”
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Object-to-Manipulation Graph for Affordance Navigation by Xinhang Song, Bohan Wang, Liye Dong, Gongwei Chen, Xinyun Hu, Shuqiang Jiang

“…Finally, a navigation policy is implemented (trained by reinforcement learning) to guide the navigation to the target places. …”
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Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game by Xiu Ji, Mingge Li, Zheyu Yue, Haifeng Zhang, Yizhu Wang

“…For the complexity of large-scale scheduling, this paper introduces the A2C (Advantage Actor-Critic) reinforcement learning algorithm, which combines the value network and the strategy network synergistically to optimize the real-time scheduling process. …”
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