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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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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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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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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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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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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SPARQ: Efficient Entanglement Distribution and Routing in Space–Air–Ground Quantum Networks by Mohamed Shaban, Muhammad Ismail, Walid Saad

“…To solve the entanglement routing problem, a deep reinforcement learning (RL) framework is proposed and trained using deep Q-network (DQN) on multiple graphs of SPARQ to account for the network dynamics. …”
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Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor Networks by Xiaoping Yang, Quanzeng Wang, Bin Yang, Xiaofang Cao

“…We propose a deep reinforcement learning (DRL)–successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. …”
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Sensation seeking and risk adjustment: the role of reward sensitivity in dynamic risky decisions by Yin Qianlan, Chen Shou, Hou Tianya, Dong Wei, Taosheng Liu

“…By integrating the reinforcement learning model and neural measures obtained from dynamic risk-taking tasks, we aim to explore how these personality traits influence individual decision-making processes and engagement in risk-related activities. …”
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Robot Dynamic Path Planning Based on Prioritized Experience Replay and LSTM Network by Hongqi Li, Peisi Zhong, Li Liu, Xiao Wang, Mei Liu, Jie Yuan

“…To address the issues of slow convergence speed, poor dynamic adaptability, and path redundancy in the Double Deep Q Network (DDQN) within complex obstacle environments, this paper proposes an enhanced algorithm within the deep reinforcement learning framework. This algorithm, termed LPDDQN, integrates Prioritized Experience Replay (PER) and the Long Short Term Memory (LSTM) network to improve upon the DDQN algorithm. …”
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Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects by Kim Junhyeong, Kim Jae-Yong, Kim Jungmin, Hyeong Yun, Neseli Berkay, You Jong-Bum, Shim Joonsup, Shin Jonghwa, Park Hyo-Hoon, Kurt Hamza

“…Furthermore, we explore state-of-the-art deep learning techniques, involving discriminative models, generative models, and reinforcement learning. We also introduce and categorize several notable inverse-designed nanophotonic devices and their respective design methodologies. …”
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Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques by Waleed Bin Tahir, Shah Khalid, Sulaiman Almutairi, Mohammed Abohashrh, Sufyan Ali Memon, Jawad Khan

“…While this review highlights advancements in social media-based depression detection, it excludes alternative approaches like graph-based systems and reinforcement learning, and its focus on social media may limit its applicability to other domains.…”
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Multi-Objective Simulated Annealing for Efficient Task Allocation in UAV-Assisted Edge Computing for Smart City Traffic Management by Ahmed Shamil Mustafa, Salman Yussof, Nurul Asyikin Mohamed Radzi

“…While existing technologies provide solutions for data collection (UAVs), processing (computer vision), and control (reinforcement learning), the integration and resource optimization of these components remains a significant challenge. …”
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Adaptive Cut Selection in Mixed-Integer Linear Programming by Turner, Mark, Koch, Thorsten, Serrano, Felipe, Winkler, Michael

“…We present a reinforcement learning framework for selecting cuts, and train our design using said framework over MIPLIB 2017 and a neural network verification data set. …”
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PREDICTING THE LEARNING PATH TO LEARNER’S OPTIMUM COMPREHENSION by Ifeanyi Isaiah Achi, Chukwuemeka Odi Agwu, Christopher Chizoba Nnamene, Sylvester C. Aniobi, Ifebude Barnabas C., Kelechi Christian Oketa, Godson Kenechukwu Ezeh, John Otozi Ugah

“…This research paper adopted Reinforcement Learning and the Markov decision process, specifically the Markov Chain approach, in developing an improved model for prediction. …”
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A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless Communications by Getaneh Berie Tarekegn, Rong-Terng Juang, Belayneh Abebe Tesfaw, Hsin-Piao Lin, Huan-Chia Hsu, Robel Berie Tarekegn, Li-Chia Tai

“…The trajectory of the aerial base stations is then continuously adjusted through a centralized multi-agent deep reinforcement learning algorithm to optimize communication performance. …”
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