Design of intelligent energy management system for electric vehicles based on multi-objective optimization by Xinyan Wang, Yichao Li

“…The method involves creating an energy management strategy based on multi-objective optimization that incorporates the Pontryagin minimum principle and deep Q-Network. …”
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An artificial neural network and Levenberg-Marquardt training algorithm-based mathematical model for performance prediction by Farheen Bano, Suhail H. Serbaya, Ali Rizwan, Mohammad Shabaz, Faraz Hasan, Hany S. Khalifa

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Insight mechanism of ANN model for denitrification in spouted bed bioreactor by Keshava Joshi, Lokeshwari Navalgund, Nabisab Mujawar Mubarak, Vinayaka B. Shet

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VR-Aided Ankle Rehabilitation Decision-Making Based on Convolutional Gated Recurrent Neural Network by Hu Zhang, Yujia Liao, Chang Zhu, Wei Meng, Quan Liu, Sheng Q. Xie

“…The results were compelling: the optimized convolutional gated recurrent neural network outperformed both alternatives, boasting an average accuracy of 99.16% and a Macro-F1 score of 0.9786. …”
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Path planning algorithm for WCE with joint energy replenishment and data collection based on multi-objective optimization by Zhenchun WEI, Renhao SUN, Zengwei LYU, Jianghong HAN, Lei SHI, Junyi XU

“…Considering limited energy of the wireless charging equipment (WCE) in wireless rechargeable sensor network,an energy replenishment strategy and a data collection strategy are designed.On the basis of these,a path planning model for WCE with functions of joint energy replenishment and data collection based on multi-objective optimization is constructed with two optimization objectives,maximizing the total energy utility of WCE and minimizing the average delay of data transmission of all the sensor nodes in the network.To deal with it,a multi-objective ant colony optimization algorithm based on elitist strategy was proposed,where the state transition strategy and the pheromone updating strategy were improved.Then,the Pareto set was obtained in terms of this multi-objective optimization problem.The parameter setting of ant colony algorithm’s effects on the proposed algorithm were analyzed under 20 sensor nodes.50 groups of contrastive experiments show that the average number of energy utilization obtained by ES-MOAC algorithm is 4.53% higher than that of NSGA-II algorithm.The average number of average delay of all node data transmission obtained by ES-MOAC algorithm is 5.12% lower than that of NSGA-II algorithm.…”
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Artificial neural networks-based multi-objective optimization of immersion cooling battery thermal management system using Hammersley sampling method by Muhammed Donmez, Mehmet Ihsan Karamangil

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Optimized customer churn prediction using tabular generative adversarial network (GAN)-based hybrid sampling method and cost-sensitive learning by I Nyoman Mahayasa Adiputra, Paweena Wanchai, Pei-Chun Lin

“…However, these methods have not performed well with classical machine learning algorithms. Methods To optimize the performance of classical machine learning on customer churn prediction tasks, this study introduces an extension framework called CostLearnGAN, a tabular generative adversarial network (GAN)-hybrid sampling method, and cost-sensitive Learning. …”
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An Optimized Hybrid Framework Based on Long-Short Term Memory Neural Networks and Fourier SynchroSqueezed Transform for Photovoltaic Power Forecasting by Samer Rajah, Francisco J. Munoz, Alejandro Rodriguez

“…Additionally, Bayesian Optimization is used to determine the most relevant hyperparameters of the Long Short-Term Memory network. …”
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A Hybrid Scale-Up and Scale-Out Approach for Performance and Energy Efficiency Optimization in Systolic Array Accelerators by Hao Sun, Junzhong Shen, Changwu Zhang, Hengzhu Liu

“…We use mapping space exploration in a multi-tenant application environment to assign DNN operations to specific systolic array modules, thereby optimizing performance and energy efficiency. Experiments show that our proposed hybrid systolic array accelerator reduces energy consumption by up to 8% on average and improves throughput by up to 57% on average, compared to TPUv3 across various DNN models.…”
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Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network by Fengli Yue, Zhuo Sha, Hongyun Sun, Dayong Chen, Jinsong Liu

“…To address the problem of the large number of finite element model meshes and low solution efficiency, the wall thickness uniformity was predicted using a radial basis function (RBF) neural network, and parameter optimization was performed using the particle swarm optimization (PSO) algorithm. …”
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Sensor-Generated In Situ Data Management for Smart Grids: Dynamic Optimization Driven by Double Deep Q-Network with Prioritized Experience Replay by Peiying Zhang, Siyi Li, Dandan Li, Qingyang Ding, Lei Shi

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An improved beetle antennae search algorithm and its application in coverage of wireless sensor networks by Biao Yin, Liping Mo, Wei Min, Shan Li, Cunwei Yu

“…The simulation results of the algorithm applied to coverage optimization of wireless sensor networks with 30 nodes and 50 nodes reveal a marked improvement in both optimal and average coverage metrics relative to seven alternative algorithms. …”
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A Novel Diagnostic Framework with an Optimized Ensemble of Vision Transformers and Convolutional Neural Networks for Enhanced Alzheimer’s Disease Detection in Medical Imaging by Joy Chakra Bortty, Gouri Shankar Chakraborty, Inshad Rahman Noman, Salil Batra, Joy Das, Kanchon Kumar Bishnu, Md Tanvir Rahman Tarafder, Araf Islam

“…<b>Results:</b> A weighted average ensemble technique with a Grasshopper optimization algorithm has been designed and utilized to ensure maximum performance with high accuracy of 97.31%, precision of 97.32, recall of 97.35, and F1 score of 0.97. …”
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Optimization of social security patrol strategy based on graph theory and GGC algorithm by Jian Wang

“…The results indicated that the initial configuration method of intelligent patrol agents based on greedy algorithm reduced the global average idle time of patrol agents to about 200 s and 765 s respectively for the initial deployment of road networks A and B. …”
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Near-Field Nulling Control Beamfocusing Optimization for Multi-User Interference Suppression by Yuanzhe Gong, Mohammadhossein Karimi, Tho Le-Ngoc

“…Moreover, a constant-modulus beamfocusing scheme based on a perturbation-based nulling control beamfocusing algorithm employing particle swarm optimization is proposed. Using only phase shifters, an average gain difference of 26.1 dB between desired and undesired users can be achieved. …”
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Parameter Estimation for Line Impedance in Distribution Network Based on μPMU Data by Zitong ZHANG, Qun ZHOU, Yulin DIAN, Zichao GUAN, Yue YIN, Minrui LENG, Xueshan LIU

“…In order to solve the long standing issue of missing line impedance parameters in low voltage distribution network, this paper proposed a new approach based on measurement data collected from the micro-synchronous phasor measuring unit (μPMU). …”
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Optimal feedback control of dynamical systems via value-function approximation by Kunisch, Karl, Walter, Daniel

“…Based on universal approximation properties, existence, convergence and first order optimality conditions for optimal neural network feedback controllers are proved.…”
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A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis by Amal G. Diab, El-Sayed M. El-Kenawy, Nihal F. F. Areed, Hanan M. Amer, Mervat El-Seddek

“…The binary Greylag Goose (bGGO) optimizer was employed to perform this task, with an average fitness of 0.4137 and a best fitness of 0.3155. …”
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Smart adaptive learning and optimized feature clustering for enhanced image retrieval by P. Umaeswari, Sujata Patil, Parameshachari Bidare Divakarachari, Przemysław Falkowski-Gilski

“…The proposed SEGJO-EDCNN method is evaluated on the Corel 5 K and Oxford Flower datasets, and its performance is measured using precision, recall, F-score, mean average precision, and average recall. Comparative analysis with existing methods—ELNDP, SVM-CBIR, SPDNN, and DNN-SAR—demonstrates that SEGJO-EDCNN achieves a higher mean average precision of 97.595% on the Corel 5 K dataset, outperforming both ELNDP and DNN-SAR. …”
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Energy Efficient AR/VR Edge Processing: Architecture and Optimization by Azza E. A. Eltraify, Randa A. Thabit, Opeyemi O. Ajibola, Wafaa B. M. Fadlelmula, Ahmed A. M. Hassan, Ahrar N. S. Hamad, Abdelrahman S. Elgamal, Walter Z. Ncube, Harith S. Ibrahim, Mariam Elmirghani, Sanaa Hamid Mohamed, Louise Krug, Greg Mcsorley, Jaafar M. H. Elmirghani

“…The multi-objective function considers minimizing power consumption and minimizing end-to-end delay within the network architectures. We compare hosting the AR/VR applications in C-PON and in Spine-and-Leaf in terms of the power consumption, the average delay in links, and the end-to-end delay per user. …”
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