Load demand forecasting in air conditioning a rotor Hopfield neural network approach optimized by a new optimization algorithm by Mingguang Liu, Weibo Zhao, Ying Zhou, Mahdiyeh Eslami

“…The results showcase optimal performance, yielding an R2 value of 0.95, along with the lowest MSE, RMSE, and MAE values when compared to the other tested models. …”
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Environmental and Economic Dispatching of Fire-Wind Combined System Based on Improved MOPSO by Li Rongshuai, Du Ruiqi, Lu Yi, Jiang Shan

“…Then, an improved multi-objective particle swarm optimization algorithm, LRMOPSO, is proposed by combining multi-objective particle swarm optimization algorithm, Levy flight jamming strategy and reverse learning strategy. …”
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Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train by Chengda Yang, Kun Miao, Jieyuan Wang

“…To solve this model efficiently, an improved ant colony system algorithm with the difference edges (ACSd) is proposed, which takes the heuristic effect of the difference between the best solutions of two adjacent iterations, i.e., “the difference edges,” into account. …”
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A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTM by Pei Liu, Hao Gu, Chongshi Gu, Yanbo Wang

“…Analysis of the five evaluation criteria revealed that the RSA can better optimize the parameters of the VMD algorithm. Consequently, the proposed model demonstrates superior noise reduction capabilities and improved prediction accuracy.…”
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Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

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Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

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Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network by Kefei Cheng, Xiaoyong Guo, Xiaotong Cui, Fengchi Shan

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An Enhanced Generative Adversarial Network Prediction Model Based on LSTM and Attention for Corrosion Rate in Pipelines by Pujun Long, Mi Liang, Hongjian Chen, Qin Yang

“…To address the pervasive issue of internal pipeline corrosion in the oil and gas industry, this paper proposes a hybrid intelligent model for predicting corrosion rates. This model integrates an improved Generative Adversarial Network with Grey Wolf Optimization and Support Vector Regression (LAGAN-GWO-SVR). …”
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Research review on improving the efficiency of multimodal transportation based on technological solutions by M. I. Malyshev

“…Improving efficiency of multimodal transportation today is possible due to the optimization of the interaction system between the used modes of transport and transportation of goods. …”
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Improving SLICES crystal representation through CHGNet integration and parameter tuning by Bizhu Zhang, Kedeng Wu, Chang Zhang, Hang Xiao, Liangliang Zhu

“…To bridge this gap, we present an enhanced approach to the simplified line-input crystal-encoding system (SLICES) representation by incorporating the Crystal Hamiltonian Graph Neural Network (CHGNet) machine learning force field model. Comprehensive evaluations across multiple datasets (MP-20, MP21-40, and MOF) using different neighbor recognition algorithms (EconNN and CrystalNN) show that our approach outperforms the original in reconstructing structures with fewer than 20 atoms per unit cell, achieving up to a 1.34% improvement in reconstruction rate (from 92.55% to 93.89%). …”
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Fault diagnosis of HVCB via the subtraction average based optimizer algorithm optimized multi channel CNN-SABO-SVM network by Qingjun Song, Jiuxin Wang, Qinghui Song, Kai Li, Wenchao Hao, Haiyan Jiang

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Bio-inspired swarm intelligence for enhanced real-time aerial tracking: integrating whale optimization and grey wolf optimizer algorithms by GaoFeng Han

“…Abstract To address the decline in real-time tracking performance of moving targets in multi-UAV systems caused by uneven search coverage and low cooperation efficiency, the WGWO (Whale-Grey Wolf Optimization) model is proposed. The UAV swarm optimizes its flight paths using the spiral predation strategy of the WOA (Whale Optimization Algorithm) while employing a Kalman filter to process sensor data. …”
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Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction by Shweta Ashish Koparde, Sonali Kothari, Sharad Adsure, Kapil Netaji Vhatkar, Vinod V. Kimbahune

“…. • Addresses the data imbalance and improves model generalization, the oversampling technique is applied for data augmentation.The prediction model employs a Deep Recurrent Neural Network (DRNN) optimized by Fractional Addax Optimization 34 Algorithm (FAOA), a hybrid of Addax Optimization Algorithm (AOA) and Fractional Concept (FC), designed to perform 35 influenza virus host prediction. …”
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AI-Based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities With Spherical Fuzzy Decision Algorithm by Lin Yang

“…These models classify buildings based on energy consumption patterns, predicting energy needs and identifying areas for improvement. …”
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Design Parameter Optimization of Self-centering Pier Based on Deep Learning by Weike ZHANG, Zhengnan LIU, Xingchong CHEN, Jiawei TANG

“…A finite element structural agent model created through the deep learning method can incorporate random parameters related to geometric and material mechanical properties to improve the model’s robustness and the self-centering pier’s multi-objective optimization efficiency.…”
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Hybridization of metaheuristic algorithms for resource scheduling in distributed robotic control system by P. Anand Raj, M. Rajakumaran, S. Palani Murugan, S. Senthilkumar

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Improving the Integrity of a Voting Process with Biometric Authentication and Data Encryption by Walter M. Molina, Lino R. Mac Kay, Daniel Subauste

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YOLO-APDM: Improved YOLOv8 for Road Target Detection in Infrared Images by Song Ling, Xianggong Hong, Yongchao Liu

“…Replacing YOLOv8’s C2f module with C2f-DCNv3 increases the network’s ability to focus on the target region while lowering the amount of model parameters. The MSCA mechanism is added after the backbone’s SPPF module to improve the model’s detection performance by directing the network’s detection resources to the major road target detection zone. …”
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An Improved Unscented Kalman Filter Applied to Positioning and Navigation of Autonomous Underwater Vehicles by Jinchao Zhao, Ya Zhang, Shizhong Li, Jiaxuan Wang, Lingling Fang, Luoyin Ning, Jinghao Feng, Jianwu Zhang

“…Excessive noise interference may cause a decrease in filtering accuracy and is highly likely to result in divergence by means of the traditional Unscented Kalman Filter, resulting in an increase in uncertainty factors during submersible mission execution. An estimation model for system noise, the adaptive Unscented Kalman Filter (UKF) algorithm was derived in light of the maximum likelihood criterion and optimized by applying the rolling-horizon estimation method, using the Newton–Raphson algorithm for the maximum likelihood estimation of noise statistics, and it was verified by simulation experiments using the Lie group inertial navigation error model. …”
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Load balancing method of service cluster based on mean-variance by Xiaoan BAO, Xue WEI, Lei CHEN, Guoheng HU, Na ZHANG

“…When a large number of concurrent requests are allocated,the load scheduling mechanism is to achieve the load balancing of nodes in the network by minimizing the response time and maximizing the utilization ratio of nodes.In the load balancing algorithm based on genetic algorithm,the fitness function is designed to have an important influence on the load balancing efficiency.A service cluster load balancing method based on mean-variance was proposed to optimize the fitness function.The investment portfolio selection model mean-variance was used to minimize the response time,which was used to get the weight of each server's resource utilization,so as to obtain the optimal allocation combination.This method improves the accuracy and efficiency of the fitness function.Compared with other models in different service environment,the simulation results show that the load balancing algorithm makes the service cluster get a better balance performance in terms of node utilization and response time.…”
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