A Linear Regression Prediction-Based Dynamic Multi-Objective Evolutionary Algorithm with Correlations of Pareto Front Points by Junxia Ma, Yongxuan Sang, Yaoli Xu, Bo Wang

“…Specifically, when the DMOP environment changes, this paper first constructs a spatio-temporal correlation model between various key points of the PF based on the linear regression algorithm; then, based on the constructed model, predicts a new location for each key point in the new environment; subsequently, constructs a sub-population by introducing the Gaussian noise into the predicted location to improve the generalization ability; and then, utilizes the idea of NSGA-II-B to construct another sub-population to further improve the population diversity; finally, combining the previous two sub-populations, re-initializing a new population to adapt to the new environment through a random replacement strategy. …”
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Safety Helmet Wearing Detection Based on Jetson Nano and Improved YOLOv5 by Zaihui Deng, Chong Yao, Qiyu Yin

“…Finally, the YOLOv5-SN network is obtained by improving the YOLOv5 model, and the optimized model is deployed on Jetson Nano for testing. …”
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An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior. by Fanlong Zeng, Jintao Wang, Chaoyan Zeng

“…The framework integrates an Improved Hunter-Prey Optimization (IHPO) algorithm, an eXtreme Gradient Boosting (XGBoost) model, and SHapley Additive exPlanations (SHAP) theory to predict and interpret corporate ESG greenwashing behavior. …”
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Multi-objective operation optimization method of microgrid considering the influence of electric vehicle by Tiefeng Xu, Xiaofang Meng, Fangfang Zheng, Yiduo Zhang, Xin Wu, Mingyang Li

“…Taking the minimum total operating cost and the minimum peak-valley difference of the microgrid in one day as the optimization objective, and considering many constraints such as power balance constraints and output constraints of distributed generation units, the multi-objective optimization function is transformed into a single-objective optimization function by linear weighting method, and the model is solved by particle swarm optimization algorithm. …”
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Optimal scheduling of BIES with multi-energy flow coupling based on deep RL by XIA Xuhua, YANG Jiandi, SHI Yongtao

“…Secondly， the state space， action space， and reward function for the operational dispatch strategy are designed using deep RL， and a low-carbon economic and optimal dispatch framework is constructed using the ​soft actor-critic （SAC） algorithm.​ …”
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Dynamic balance optimization method for aero-engine rotor without trial weight by Lingli Jiang, Changzhi Shi, Xuejun Li, Hui Ma, Yiming Cao

“…Subsequently, equilibrium equations are established to relate the unbalanced response to rotor amplitude. A multi-strategy improved sparrow search algorithm is then applied. …”
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Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement by Sri Rossa Aisyah Puteri Baharie, Sugiyarto Surono, Aris Thobirin

“…Advancements in machine learning have enabled the development of more accurate and efficient health prediction models. This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. …”
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Optimization Path and Design of Intelligent Logistics Management System Based on ROS Robot by Ruijie Wu

“…Therefore, this paper aimed to design an intelligent logistics management system based on ROS robot and proposed to use the A-star algorithm to calculate the shortest path of the robot so as to achieve the optimal path. …”
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Deep Reinforcement Learning-Based Motion Control Optimization for Defect Detection System by Yuhuan Cai, Liye Zhao, Xingyu Chen, Zhenjun Li

“…To address these challenges, this study proposes a deep reinforcement learning-based control scheme, leveraging DRL’s capabilities to optimize system performance. Specifically, the TD3 algorithm, featuring a dual-critic structure, is employed to enhance control precision within predefined state and action spaces. …”
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APG mergence and topological potential optimization based heuristic user association strategy by Zhirui HU, Meihua BI, Fangmin XU, Meilin HE, Changliang ZHENG

“…Methods:The network scalable degree was designed as a measure of scalability,and then a user association strategy to improve network scalable degree was studied by using optimization theory. 1) For modelling the optimization problem, firstly, the network coupling degree, representing the degree of association among nodes, was constructed to establish the mathematical relationship between the network scalable degree and AP group (APG).Thus,the problem of improving the network scalable degree was modeled as the problem of minimizing the network coupling degree.Then,a multi-objective optimization problem of minimum network coupling degree and maximum user rate was established to find the balance between network scalable degree and network service quality. 2) For solving the optimization problem,to avoid the high computational complexity,a heuristic user association strategy based on APG mergence and topological potential optimization was proposed.With the proposed algorithm,the number of APG could be reduced by APG mergence,and the number of APG that AP belongs to could be reduced by AP exiting APG. …”
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Artificial intelligence for smart irrigation: Reducing water consumption and improving agricultural output by Arlanova A. A., Hojamkuliyeva B. A., Babanazarov N. Sh., Arlanov M. S.

“…Existing approaches to using artificial intelligence in agricultural technologies for predicting water needs and regulating irrigation are examined. A mathematical model based on machine learning algorithms is developed to predict the optimal water volume required for irrigation of agricultural crops. …”
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Study on the Evolutionary Characteristics of Spatial and Temporal Patterns and Decoupling Effect of Urban Carbon Emissions in the Yangtze River Delta Region Based on Neural Network... by Xichun Luo, Chaoming Cai, Honghao Zhao

“…To improve the performance of neural network models, the Aquila Optimizer (AO) algorithm is introduced to optimize the hyper-parameter values in the back-propagation (BP) neural network model in this research due to the appealing searching capability of AO over traditional algorithms. …”
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Drilling Parameter Control Based on Online Identification of Drillability and Multi-Objective Optimization by Jianbo Dai, Xilu Yin, Yan Zhang, Lei Si, Dong Wei, Zhongbin Wang, Longmei Zhao

“…A multi-objective optimization model of the optimal drilling parameters is established with the mechanical specific energy and drilling speed prediction model as the objective functions, and the NSGA-II algorithm and TOPSIS algorithm are used for solutions and decision-making. …”
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Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan by Lianbo Deng, Qi Peng, Li Cai, Junhao Zeng, Nava Raj Bhatt

“…Finally, the proposed model and algorithm are validated with the real data from the Guangzhou Metro Line 2. …”
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A hybrid Bayesian network-based deep learning approach combining climatic and reliability factors to forecast electric vehicle charging capacity by David Chunhu Li

“…This architecture uses extensive transaction data and climate analysis to build a detailed model of EV charging pile reliability. Additionally, two algorithms are designed to assess the usage and reliability of charging stations. …”
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A hybrid PKI and spiking neural network approach for enhancing security and energy efficiency in IoMT-based healthcare 5.0 by Dipalee D․Rane Chaudhari, Manisha S. Bhende, Aadam Quraishi, Azzah AlGhamdi, Ismail Keshta, Mukesh Soni, Brajesh Kumar Singh, Haewon Byeon, Mohammad Shabaz

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Disturbance Observer-Based Optimal Active Suspension Control for Vehicle-Trailer Systems by Yonghwan Jeong

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Numerical Investigation and Optimization of Transpiration Cooling Plate Structures with Combined Particle Diameter by Dan Wang, Yaxin Liu, Xiang Zhang, Mingliang Kong, Hanchao Liu

“…Further optimization with the multi-objective genetic algorithm (MOGA) determines the optimal structural parameters. …”
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EFFICACY OF OPTIMIZED REMINERALIZING THERAPY IN POST-COVID-19 PATIENTS: EVALUATION OF RESULTS by N.M. Savielieva, M.E. Diasamidze

“…To correct the identified disorders and prevent the occurrence and development of carious lesions, we applied an improved algorithm of prophylaxis of dental enamel diseases using remineralizing therapy. …”
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Extraction of Optimal Measurements for Drowsy Driving Detection considering Driver Fingerprinting Differences by Yifan Sun, Chaozhong Wu, Hui Zhang, Yijun Zhang, Shaopeng Li, Hongxia Feng

“…Finally, we selected measurements calculated by IDBCPs that can distinguish drowsy driving to constitute individual drivers’ optimal drowsiness-detection measurement set. To verify the advantages of IDBCPs, the measurements calculated by UCPs and IDBCPs were, respectively, used to build driver-specific drowsiness-detection models: DF_U and DF_I based on the Fisher discriminant algorithm. …”
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