Multi-Objective Optimal Power Flow of Integrated Transmission and Distribution Network Considering Node Carbon Emission Intensity by ZHANG Xianglong, YUAN Zhaoxiang, DONG Shufeng, LIU Ying, XIAO Zhihong, TIAN Yuzhou

“…[Methods] To address these challenges， a collaborative distributed optimal power flow model based on a heterogeneous decomposition algorithm was proposed. …”
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Intelligent algorithm-based model for predicting mass transfer performance in CO2 absorption within a rotating packed bed by Wei Zhang, Hao Chen, Ke Huang, Xing Shu, Cheng Fu, Bin Huang

“…Using dimensional analysis, key factors are transformed into dimensionless numbers, which are then input into models integrating least squares support vector machine (LSSVM) with genetic algorithm (GA), particle swarm optimization (PSO), and a hybrid GA-PSO (HGAPSO). …”
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Advanced Computational Methods for Mitigating Shock and Vibration Hazards in Deep Mines Gas Outburst Prediction Using SVM Optimized by Grey Relational Analysis and APSO Algorithm by Xiang Wu, Zhen Yang, Dongdong Wu

“…Moreover, adaptive particle swarm optimization (APSO) was used to optimize the penalty factor and kernel parameters of the support vector machine to improve the global search ability and avoid the occurrence of the local optimal solutions. …”
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Optimization of Vessel Traffic Scheduling in a Compound Channel of an Estuarine Port with Opposing Distribution of Inner Anchorages and Terminals by Bolin Yin, Chengji Liang, Yu Wang, Xiaojie Xu, Yue Zhang

“…The entropy-weighted technique for order preference by similarity to ideal solution (TOPSIS) is used to calculate the objective weights of the Pareto frontier and identify the optimal solution. Experimental results show that the proposed model and algorithm yield optimal port entry and exit scheduling solutions. …”
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Optimized feature selection and advanced machine learning for stroke risk prediction in revascularized coronary artery disease patients by Yong Si, Armin Abdollahi, Negin Ashrafi, Greg Placencia, Elham Pishgar, Kamiar Alaei, Maryam Pishgar

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Learning‐based tracking control of AUV: Mixed policy improvement and game‐based disturbance rejection by Jun Ye, Hongbo Gao, Manjiang Hu, Yougang Bian, Qingjia Cui, Xiaohui Qin, Rongjun Ding

“…By combining prior dynamic knowledge and actual sampled data, the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP algorithm. …”
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Wireless sensor network positioning technology based on improved sampling box and fuzzy reasoning by Ningyun Dan, Lun Zhao, Wenjin Pan, Yan Cai

“…By using a fuzzy clustering algorithm based on time series optimization and a multidimensional Gaussian model to optimize the sampling box, the accuracy and efficiency of localization are significantly improved. …”
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Towards Automated Cadastral Map Improvement: A Clustering Approach for Error Pattern Recognition by Konstantinos Vantas, Vasiliki Mirkopoulou

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Multi-objective optimization of current-assisted splitting spinning of small module tooth-shaped part based on the combination of BP neural network and NSGA-II algorithm by Zhou Haoyang, Xia Qinxiang, Xiao Gangfeng, Chen Can

“…The results show that the material at the tooth tip along the radial direction is in the state of tensile stress along radial and compressive stresses along tangential and axial directions, which promotes the radial flowing of the material and is beneficial the tooth filling of SMTSPs; the tooth saturation increases obviously under pulse current comparing without pulse current; the BPNN combined with the NSGA-II algorithm can reliably optimize the process parameters of the CASS, improving the forming quality of SMTSPs; experiments verified the feasibility of the process and the accuracy of the predictive model based on the optimization results.…”
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Optimization analysis of air cooled open cathode proton exchange membrane fuel cell flow channel structure by Zhijun Deng, Qingwei Wang, Xun Fang, Yaru Han, Kunxiang Liu, Chen Zhao

“…In addition, in order to further improve the power density of the fuel cell, two agent models, support vector regression and Gaussian process regression, are constructed and trained, and a genetic algorithm is used to find the parameter optimization for the bending angle, width and height of the cathode channel. …”
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Mod Tanh‐Activated Physical Neural Network MPPT Control Algorithm for Varying Irradiance Conditions by Khuong Nguyen‐Vinh, Surender Rangaraju, Michal Jasinski

“…ABSTRACT The increasing adoption of solar photovoltaic systems necessitates efficient maximum power point tracking (MPPT) algorithms to ensure optimal performance. This study proposes a Mod tanh‐activated physical neural network (MAPNN)‐based MPPT control algorithm, which addresses inefficiencies in existing models caused by spectral mismatch and improper converter control. …”
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Fine Resolution Mapping of Forest Soil Organic Carbon Based on Feature Selection and Machine Learning Algorithm by Yanan Li, Jing Li, Jun Tan, Tianyue Ma, Xingguang Yan, Zongyang Chen, Kunheng Li

“…The performance of Boruta and SHAP (SHapley Additive exPlanations) in optimizing feature selection was evaluated. Ultimately, the optimal machine learning model and feature selection method were applied to map the SOC distribution, with variable contributions quantified using SHAP. …”
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A cross-chain model for warehouse receipts in port supply chain based on notary mechanism and ShangMi cryptographic algorithms by Yangbo Chen, Wei Ou, Mengxue Pang, Jianqiang Ma, Qiuling Yue, Wenbao Han

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DGCA3QM: DESIGN OF A DUAL GENETIC ALGORITHM BASED AUTOREGRESSION MODEL FOR CORRELATIVE PREDICTION OF AIR QUALITY METRICS by Harna M. Bodele, G. M. Asutkar, Kiran G. Asutkar

“…Due to incorporation of dual bioinspired optimizers with autoregressive correlation, the model is able to improve prediction accuracy by 8.5%, precision by 4.9%, recall by 1.5%, while reducing computational delay by 3.4% when compared with standard air quality analysis models. …”
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Improved deep learning model for accurate energy demand prediction and conservation in electric vehicles integrated with cognitive radio networks by V. Niranjani, Anandakumar Haldorai

“…To overcome this issue, this paper proposed a model of Empirical Mode Decomposition with CNN and optimized with Seagull Optimization Algorithm (EMD-CNN-SOA). …”
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A Two-Stage Optimization Method for Multi-Runway Departure Sequencing Based on Continuous-Time Markov Chain by Guan Lian, Yingzi Wu, Weizhen Luo, Wenyong Li, Yaping Zhang, Xiaoyue Zhang

“…Then, continuous-time Markov chains (CTMC) were employed to track aircraft state transitions in the taxiway queue, and a nested whale optimization algorithm was proposed to optimize both the pushback sequence and runway resource allocation. …”
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Evolutionary Synthesis of High-Capacity Reconfigurable Multilayer Road Networks Using a Multiagent Hybrid Clustering-Assisted Genetic Algorithm by Andranik S. Akopov, Levon A. Beklaryan

“…To achieve this goal, a new multiagent hybrid clustering-assisted genetic algorithm (MA-HCAGA). The proposed algorithm combines the use of binary-coded crossovers and mutations as genetic operators, and biobjective discrete particle swarm optimization (BODPSO) techniques to improve the evolutionary search process. …”
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Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization by Venkata Nagaraju Thatha, M. Ganesh Karthik, Venu Gopal Gaddam, D. Pramodh Krishna, S. Venkataramana, Kranthi Kumar Lella, Udayaraju Pamula

“…This study introduces an advanced deep learning framework for histopathological image classification, integrating AlexNet and Gated Recurrent Unit (GRU) networks, optimized using the Hippopotamus Optimization Algorithm (HOA). …”
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Gradient descent based dynamic optimization for VSG dominated microgrid by Yan Li, Decheng Wang, Qun Zhang, Jian Du, Qinshan Wang, Qiong Wang

“…This article proposes an optimization method based on the gradient descent algorithm to fine‐tune the control parameters of multiple VSGs in independent microgrids. …”
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Optimizing oil production forecasts in Iranian oil fields: a comprehensive analysis using ensemble learning techniques by Mohammad Ghodsi, Pouya Vaziri, Mahdi Kanaani, Behnam Sedaee

“…The inclusion of advanced optimization strategies, such as Genetic Algorithm (GA), Teaching-Learning-Based Optimization (TLBO), and Particle Swarm Optimization (PSO), ensures that each base model reaches its highest potential performance. …”
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