XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning by Ansel Y. Rodríguez González, Roberto E. López Díaz, Shender M. Ávila Sansores, María G. Sánchez Cervantes

“…Reinforcement Learning (RL) enables agents to learn optimal policies through environment interaction, with Q-learning being a fundamental algorithm for Markov Decision Processes (MDPs). …”
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Lightweight UAV Detection Method Based on IASL-YOLO by Huaiyu Yang, Bo Liang, Song Feng, Ji Jiang, Ao Fang, Chunyun Li

“…To address this, we propose the IASL-YOLO algorithm, which optimizes the YOLOv8s model to enhance detection accuracy and lightweight efficiency. …”
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Ultra-High Spatial Resolution Clinical Positron Emission Tomography (PET) Systems by Myungheon Chin, Muhammad Nasir Ullah, Derek Innes, Craig S. Levin

“…A key strategy involves organ-specific, or loco-regional, scanner configurations that optimize photon detection efficiency (PDE) while balancing the trade-off between spatial resolution and image signal-to-noise ratio (SNR). …”
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Efficient structure learning of gene regulatory networks with Bayesian active learning by Dániel Sándor, Péter Antal

“…We evaluate scalable structure learning algorithms within an active learning framework to optimize intervention selection. …”
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Pig Aggression Tracking and Analysis Application Based on A RPMeMOTR Method by Shuqin Tu, Haoxuan Ou, Aqing Yang, Yun Liang, Jiaying Du, Yuefei Cao, Ruilin He, Fang Yuan

“…The management of pig aggression in group-housed environments is crucial for ensuring animal welfare and optimizing production efficiency in the global agricultural industry. …”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…A k-fold cross-validation technique was employed to optimize hyperparameters. Results indicate that LightGBM and CatBoost outperform other models, achieving the lowest mean squared error and highest R² values. …”
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Convergence of nanotechnology and artificial intelligence in the fight against liver cancer: a comprehensive review by Manjusha Bhange, Darshan Telange

“…We highlight how AI-powered algorithms can optimize nanocarrier design, facilitate real-time monitoring of treatment efficacy, and enhance clinical decision-making. …”
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Heterogeneity-aware device selection for efficient federated edge learning by Yiran Shi, Jieyan Nie, Xingwei Li, Hui Li

“…This rigorous selection process ensures that the participating device set is optimally positioned to balance the dual objectives of training efficiency and model accuracy. …”
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Pseudolite Self-Interference Cancellation Without Additional GNSS Signal Distortion by Lichao Gao, Jin He, Yuanxin Wu, Wenxian Yu

“…The simulation test results show that the algorithm can filter self-interference without additional distortion of the satellite navigation signals. …”
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Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study by Shiying Shen, Wenhao Qi, Sixie Li, Jianwen Zeng, Xin Liu, Xiaohong Zhu, Chaoqun Dong, Bin Wang, Qian Xu, Shihua Cao

“…Future efforts should prioritize algorithm optimization and multisource data integration to advance clinical applications.…”
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A YOLOv8n-T and ByteTrack-Based Dual-Area Tracking and Counting Method for Cucumber Flowers by Liyang Su, Shujuan Zhang, Hongtu Zhang, Xiangsen Meng, Xiongkui He

“…The YOLOv8n-T incorporates a Coordinate Attention (CA) mechanism and lightweight modules while optimizing the loss function, thereby improving floral feature extraction capabilities and reducing computational complexity. …”
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Energy-Efficient Route and Velocity Planning for Electric Vehicles: A Hierarchical Eco-Driving Framework Integrating Traffic and Road Information by Dong Xie, Jianhua Guo, Yu Jiang, Zhuoran Hou, Jintao Deng

“…Then, an energy-efficient route is efficiently planned using the proposed multi-heuristic A* algorithm. Finally, based on the route information from the upper level, along with traffic, kinematic, and road information, a convex optimization algorithm is employed to achieve accurate and efficient velocity planning. …”
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Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments by Yang Lei, Jian Hou, Peixin Ma, Mingze Ma

“…In the lower layer, navigation planning is achieved using A* and Timed Elastic Band (TEB) algorithms, while an improved Cartographer algorithm is used to construct a joint map for the multi-robot system. …”
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Deep Reinforcement Learning-Based Impact Angle-Constrained Adaptive Guidance Law by Zhe Hu, Wenjun Yi, Liang Xiao

“…We introduce a parameter to the super-twisting algorithm and subsequently improve an intelligent parameter-adaptive algorithm grounded in the Twin-Delayed Deep Deterministic Policy Gradient (TD3) framework. …”
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Enhancing high pressure pulsation test bench performance: a machine learning approach to failure condition tracking by Aslı Aksoy, Ömer Haki

“…The objective of this study is to enhance the efficiency of HPPT benches by addressing specimen, bench, and test environment- based problems and to develop a failure condition tracking tool (FCTT) by using machine learning (ML) algorithms. The findings of this study provide a basis for the development of the company’s data-driven smart predictive maintenance applications while providing an increase in the operational efficiency of HPPT benches. …”
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Expansion Planning of Electrical Distribution Systems Considering Voltage Quality and Reliability Criteria by Marco Israel Zuñiga Villarreal, Alexander Aguila Téllez, Narayanan Krishnan, Marcelo García

“…A 5% annual load growth was simulated for each system, which involved analyzing key parameters, such as the power loss, voltage deviation, and average failure rate. An algorithm was developed to perform a multi-criteria analysis, which provided optimal solutions for the system behavior in response to increasing demand. …”
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Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state by Aydin Larestani, Sara Sahebalzamani, Abdolhossein Hemmati-Sarapardeh, Ali Naseri

“…A relevancy factor analysis quantified the influence of each input parameter on model outputs, whereas the Leverage technique identified outliers and defined the parameter ranges for optimal algorithm performance. While the ML models achieved high predictive reliability, their computational demands and complexity may limit deployment in resource-constrained environments and decision-critical applications. …”
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An online multi-camera multi-vehicle tracking using lightweight YOLO11 and improved association strategies by Feijiang Huang, Jialong Yao, Chengyue Su, Sheng Xu

“…For cross-camera trajectory association, a hierarchical clustering algorithm based on cosine distance and Dunn’s index automatically optimizes clustering parameters to enhance matching precision. …”
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Combination of Feature Selection and Learning Methods for IoT Data Fusion by V. Sattari-Naeini, Zahra Parizi-Nejad

“…In this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario,which are Relief and Perceptron (Re-P), Relief and Genetic Algorithm Particle Swarm Optimization (Re-GAPSO), Genetic Algorithm and Artificial Neural Network (GA-ANN), Rough and Perceptron (Ro-P)and Rough and GAPSO (Ro-GAPSO). …”
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Linear Continuous-Time Regression and Dequantizer for Lithium-Ion Battery Cells with Compromised Measurement Quality by Zoltan Mark Pinter, Mattia Marinelli, M. Scott Trimboli, Gregory L. Plett

“…Then, the Linear Continuous-Time Regression (LCTR) algorithm extracts exponential parameters from first-order or overdamped second-order systems, deducing ECM parameters and guaranteeing optimality with respect to RMSE. …”
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