Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators by Hoa Thi Nguyen, Roland Olsson, Oystein Haugen

“…Analyzing time series data in industrial settings demands domain knowledge and computer science expertise to develop effective algorithms. AutoML approaches aim to automate this process, reducing human bias and improving accuracy and cost-effectiveness. …”
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Decentralized Nonstationary Fuzzy Neural Network with Meta-Learning-Net by Zhen Zhang, Meiling Yu, Hui Jia

“…To address this limitation, we propose a Decentralized NFNN (DNFNN) that overcomes the obstacles in the backward update process and enhances the efficiency of large-scale optimization. Additionally, we demonstrate improved computational efficiency and establish the linear convergence of the proposed decentralized algorithm. …”
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Joint Service Caching and Computing Offloading Strategies for Electrical Equipment Intelligent IoT Platform by Yi SUN, Shaonan CHANG, Kai CHEN, Qiang CUI, Weijie SHEN

“…Further more, we proposed a generalized Benders decomposition algorithm combined with particle swarm optimization to solve the mixed integer programming efficiently. …”
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NSGA-II-based load resource management for frequency and voltage support by Yaxin Wang, Zhihang Zhu, Zhihong Yu, Zigan Wang

“…Compared to the conventional optimal load control or using LRM alone, the improved NSGA-II-based LRM demonstrates superior performance. …”
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Analysis of Weak Links in the Mechanized Mining of Underground Metal Mines: Insights from Machine Learning and SHAP Explainability Models by Chengye Yang, Keping Zhou, Jielin Li

“…By leveraging data from 88 stopes at Guangxi Tongkeng Mine over a decade, we constructed a comprehensive dataset encompassing drilling, charging, blasting, ventilation, support, ore drawing, and maintenance. The XGBoost algorithm was employed to model factors influencing stope production capacity (PC), with its parameters optimized using the Marine Predator Algorithm (MPA). …”
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Machine Learning-Driven Prediction of CO₂ Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach by Seyed Hossein Hashemi, Farshid Torabi, Paitoon Tontiwachwuthikul

“…In this study, Gaussian Process Regression (GPR) with eight different kernels was optimized using the Grey Wolf Optimizer (GWO) algorithm to model this important phase behavior. …”
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Physics-Informed Decoupled Calibration for Fourier Ptychographic Microscopy by Mingdi Liu, Junzhao Liang, Yanxiong Wu, Zicong Luo, Rui Xie, Jiaxiong Luo, Lisong Yan

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Metaheuristic Prediction Models for Kerf Deviation in Nd-YAG Laser Cutting of AlZnMgCu1.5 Alloy by Arulvalavan Tamilarasan, Devaraj Rajamani

“…Optimization using GTO resulted in a 6.71% improvement in kerf deviation prediction accuracy compared to experimental values, while ZOA achieved a 2.37% improvement. …”
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Grid Integration of PV Systems With Advanced Control and Machine Learning Strategies by Venkata Reddy Kota, Bapayya Naidu Kommula, Asif Afzal, Mohammad Asif, Liew Tze Hui

“…The primary objective is to optimize voltage output from PV system while concurrently maximizing power using a novel Modified Zeta-Cuk converter, coupled with Hybrid Maximum Power Point Tracking (MPPT) algorithm combining Incremental Conductance and Bat Optimization Algorithm (InC-BOA). …”
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A Diagnostic and Performance System for Soccer: Technical Design and Development by Alberto Gascón, Álvaro Marco, David Buldain, Javier Alfaro-Santafé, Jose Victor Alfaro-Santafé, Antonio Gómez-Bernal, Roberto Casas

“…Results indicate high accuracy rates for detecting ball-striking events and CoDs, with improvements in algorithm performance achieved through adaptive thresholds and ensemble neural network models. …”
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A hybrid framework for heart disease prediction using classical and quantum-inspired machine learning techniques by Ankur Kumar, Sanjay Dhanka, Abhinav Sharma, Rohit Bansal, Mochammad Fahlevi, Fazla Rabby, Mohammed Aljuaid

“…Subsequently, both classical and quantum-inspired models are trained and optimized. The classical models utilized Genetic Algorithms (CGA) and Particle Swarm Optimization (CPSO) for hyperparameter tuning, while the quantum-inspired models employed Quantum Genetic Algorithms (QGAs) and Quantum Particle Swarm Optimization (QPSO). …”
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Techno-economic analysis and energy management strategies for micro gas turbines: A state-of-the-art review by A.H. Samitha Weerakoon, Mohsen Assadi

“…The review critically examines the techno-economic analysis of Micro Gas Turbines, focusing on four major areas: integration with photovoltaic systems, configurations like cogeneration and trigeneration, optimization techniques, and hydrogen utilization. The review reveals that while standalone Micro Gas Turbines face economic challenges, their integration with photovoltaic and battery bank systems significantly improves cost-effectiveness, achieving a cost of electricity as low as $0.263/kWh. …”
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Sparse support path generation for multi-axis curved layer fused filament fabrication by Tak Yu Lau, Dong He, Yamin Li, Yihe Wang, Danjie Bi, Lulu Huang, Pengcheng Hu, Kai Tang

“…Firstly, the overhang regions are detected by identifying the overhang points given a multi-axis nozzle path. Then, an optimization framework for the support guide curve is proposed to minimize its total length while ensuring that overhang filaments can be stably supported. …”
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Research on Wind Power Grid Integration Power Fluctuation Smoothing Control Strategy Based on Energy Storage Battery Health Prediction by Bin Cheng, Jiahui Wu, Guancheng Lv, Zhongbo Li

“…The experimental results show that the GA-SVR algorithm can accurately predict the health of the energy storage system, and the MPC-based control strategy derived from health predictions can improve grid power stability while adaptively adjusting energy storage output according to different health levels.…”
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Microservice Workflow Scheduling with a Resource Configuration Model Under Deadline and Reliability Constraints by Wenzheng Li, Xiaoping Li, Long Chen, Mingjing Wang

“…Additionally, we propose a reliability microservice workflow scheduling algorithm (RMWS), which incorporates heuristic leasing and deployment strategies to ensure reliability while reducing cloud resource leasing cost. …”
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Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach by Vito Santamato, Caterina Tricase, Nicola Faccilongo, Massimo Iacoviello, Agostino Marengo

“…The scientific novelty of the study lies in its integrated approach, combining systematic review and predictive algorithms to provide a comprehensive understanding of AI’s role in improving healthcare management across different contexts. …”
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Online English teaching resource recommendation method design based on LightGCNCSCM by Jing Tang

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STARMAP: Spaceborne Target Acquisition Radar With Meta-RL Assisted Placement by Alireza Famili, Shihua Sun, Tolga Atalay, Angelos Stavrou

“…To address this, we have developed an advanced meta-reinforcement learning (meta-RL) algorithm, enhancing a double deep Q-network (DDQN) to optimize receiver placement. …”
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Energy-efficient artificial fish swarm-based clustering protocol for enhancing network lifetime in underwater wireless sensor networks by Puneet Kaur, Kiranbir Kaur, Kuldeep Singh, Kiran Saleem, Ateeq Ur Rehman, Rupesh Gupta, Seada Hussen Adem

“…EAFSCCIP leverages the collective behavior of artificial fish within a Bees algorithm framework, using a combination of heuristic and metaheuristic approaches for optimal cluster-head (CH) selection in each round. …”
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RLEAFS: Reinforcement Learning-Based Energy Aware Forwarding Strategy for NDN-Based IoT Networks by Naeem Ali Askar, Adib Habbal

“…To address these challenges, we propose Reinforcement Learning-based Energy-Aware Forwarding Strategy (RLEAFS), a novel strategy for NDN-based IoT communications that leverages reinforcement learning to optimize forwarding decisions. Our Strategy integrates Q learning algorithm into path selection procedure, focusing on minimizing energy consumption and extending network lifetime while maintaining efficient data delivery. …”
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