Multiphysics Feature-Based State-of-Energy Estimation for LiFePO4 Batteries Using Bidirectional Long Short-Term Memory and Particle Swarm-Optimized Kalman Filter by Zhengpu Wu, Xu He, Haisen Chen, Lu Lv, Jiuchun Jiang, Lujun Wang

“…A fusion model that integrates a bidirectional long short-term memory (BiLSTM) network, particle swarm optimization (PSO), and Kalman filter (KF) algorithm is proposed for SOE estimation. …”
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Dynamic Adaptive Environmental Flows (DAE‐Flows) to Reconcile Long‐Term Ecosystem Demands With Hydropower Objectives by Ana Paula Dalcin, Guilherme Fernandes Marques, Amaury Tilmant, Marcelo Olivares

“…Even under a drier climate change scenario, this allowed maintenance and improvement of environmental performance in most years, so during severe droughts the water could still be reallocated to hydropower but at a lesser cost to the environment.…”
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Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE by Muhammad Sarmad Mahmood, Tariq Ali, Inamullah Inam, Muhammad Zeeshan Qureshi, Syed Salman Ahmad Zaidi, Muwaffaq Alqurashi, Hawreen Ahmed, Muhammad Adnan, Abdul Hakim Hotak

“…To further enhance performance, XGB was optimized using Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO). …”
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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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Real-Time Multi-Vehicle Scheduling in Tasks With Dependency Relationships Using Multi-Agent Reinforcement Learning by Shupei Zhang, Huapeng Shi, Wei Zhang, Ying Pang, Pengju Sun

“…Moreover, traditional optimization algorithms make it difficult to achieve timeliness in real-time changing traffic conditions. …”
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The integration of artificial intelligence in assisted reproduction: a comprehensive review by Pragati Kakkar, Shruti Gupta, Kasmiria Ioanna Paschopoulou, Ilias Paschopoulos, Ioannis Paschopoulos, Vassiliki Siafaka, Orestis Tsonis

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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

“…Currently, most support generation algorithms are for the conventional 2.5D printing, which are not applicable to multi-axis printing. …”
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Anomaly Detection Using Machine Learning in Hydrochemical Data From Hot Springs: Implications for Earthquake Prediction by Ruijie Zhu, Fengtian Yang, Xiaocheng Zhou, Jiao Tian, Yongxian Zhang, Miao He, Jingchao Li, Jinyuan Dong, Ying Li

“…Despite limitations such as the inability to differentiate pre‐earthquake anomalies from post‐earthquake anomalies and pinpoint the precise location of earthquakes, this study successfully showcases the potential of machine learning algorithms in earthquake prediction, paving the way for further research and improved prediction methods.…”
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An extreme forecast index-driven runoff prediction approach using stacking ensemble learning by Zhiyuan Leng, Lu Chen, Binlin Yang, Siming Li, Bin Yi

“…The stacking ensemble learning framework comprises four base-models and a meta-model, and model hyperparameters are re-optimized using the particle swarm optimization algorithm. …”
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State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models by Zuriani Mustaffa, Mohd Herwan Sulaiman, Jeremiah Isuwa

“…This study proposes a novel approach for SoC estimation in BMW EVs by integrating a metaheuristic algorithm with deep neural networks. Specifically, teaching-learning based optimization (TLBO) is employed to optimize the weights and biases of the deep neural networks model, enhancing estimation accuracy. …”
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Seasonally Adaptive VMD-SSA-LSTM: A Hybrid Deep Learning Framework for High-Accuracy District Heating Load Forecasting by Yu Zhang, Keyong Hu, Lei Lu, Qingqing Yang, Min Fang

“…To improve the accuracy of heating load forecasting and effectively address the energy waste caused by supply–demand imbalances and uneven thermal distribution, this study innovatively proposes a hybrid prediction model incorporating seasonal adjustment strategies. …”
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A Novel Metaheuristic-Based Methodology for Attack Detection in Wireless Communication Networks by Walaa N. Ismail

“…In this study, we explore the application of an adaptive optimized machine learning-based framework to improve intrusion detection system (IDS) performance in wireless network access scenarios. …”
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Novel concept for the healthy population influencing factors by Yuhao Shen, Jichao Wang, Lihua Ma, Huizhe Yan

“…The random forest model improves the accuracy and universality of predictions. …”
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Advancing overbreak prediction in drilling and blasting tunnel using MVO, SSA and HHO-based SVM models with interpretability analysis by Yulin Zhang, Jian Zhou, Jialu Li, Biao He, Danial Jahed Armaghani, Shuai Huang

“…To address these limitations, this research proposes three innovative hybrid models that integrate metaheuristic optimization algorithms with support vector machine (SVM): multi-verse optimizer-SVM (MVO-SVM), salp swarm algorithm-SVM (SSA-SVM), and Harris’s Hawk optimization-SVM (HHO-SVM). …”
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Design of Dual-Channel Supply Chain Network Based on the Internet of Things Under Uncertainty by Hamed Nozari, Hossein Abdi, Agnieszka Szmelter-Jarosz, Seyyed Hesamoddin Motevalli

“…In this paper, a mathematical model of a dual-channel supply chain network (DCSCN) based on the Internet of Things (IoT) under uncertainty is presented, and its solution using algorithms based on artificial intelligence such as genetic algorithm (GA), particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and gray wolf optimizer (GWO). …”
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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

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Enhanced Transmission Expansion Planning With a High Confidence Strategy Considering Fluctuations in Solar PV Generation and Electricity Demand by Siripat Somchit, Niphon Kaewdornhan, Rongrit Chatthaworn

“…To address this issue, this work proposes an enhanced TEP-based 24-hour situation optimized by an improved Binary Differential Evolution (BDE) algorithm assisted by the logistic map for adjusting the mutation factor and crossover rate. …”
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Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques by Abbas Jafar, Myungho Lee

“…The ant colony optimization method identified the most relevant feature subsets using a clinical dataset, reducing model complexity while preserving predictive accuracy. …”
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SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud Computing by Ashwin Singh Slathia, Abhiram Sharma, P. B. Krishna, Saksham Anand, Ayush Rathi, Linda Joseph, Xiao-Zhi Gao

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Random PWM Technique Based Two-State Markov Chain for Permanent Magnet Synchronous Motor Control by Zhiqiang Wang, Xinyuan Liu, Xuefeng Jin, Guozheng Zhang, Zhichen Lin

“…Secondly, to address the problem of insufficient random performance in the traditional RPWM technique, an innovative optimization scheme is proposed, i.e., the introduction of a two-state Markov chain and, based on the immune algorithm for transition probability and random gain, the optimization of two key parameters. …”
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