Improved Crack Detection and Recognition Based on Convolutional Neural Network by Keqin Chen, Amit Yadav, Asif Khan, Yixin Meng, Kun Zhu

“…Experimental results show that the Adam optimization algorithm and batch normalization (BN) algorithm can make the model converge faster and achieve the maximum accuracy of 99.71%.…”
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Validation of a teaching model instrument for university education in Ecuador through an artificial intelligence algorithm by Jenniffer Sobeida Moreira-Choez, Tibisay Milene Lamus de Rodríguez, Aracelly Fernanda Núñez-Naranjo, Ángel Ramón Sabando-García, María Belén Reinoso-Ávalos, Cynthia Michel Olguín-Martínez, Daniel Omar Nieves-Lizárraga, Julieta Elizabeth Salazar-Echeagaray

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Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging by Youngjin Han, Inwhee Joe

“…By integrating Principal Component Analysis (PCA)<i>,</i> hyperparameter optimization, and resampling methods, as well as combining Edited Nearest Neighbors (<i>ENN</i>) with the Synthetic Minority Oversampling Technique (SMOTE), the model significantly improves predictive accuracy and model generalization. …”
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An Innovative Smart Irrigation Using Embedded and Regression-Based Machine Learning Technologies for Improving Water Security and Sustainability by Abdennabi Morchid, Abdennacer Elbasri, Zahra Oughannou, Hassan Qjidaa, Rachid El Alami, Badre Bossoufi, Saleh Mobayen, Pawel Skruch

“…This study proposes an innovative approach to irrigation management, integrating real-time data and predictive models to improve irrigation efficiency. This study proposes an irrigation system based on embedded systems, using sensors and algorithms to collect and analyze data in order to optimize water management. …”
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Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques by Hamed Shokrnia, Ashkan KhodabandehLou, Peyman Hamidi, Fedra Ashrafzadeh

“…So, this study integrates the ANFIS (adaptive neuro-fuzzy inference system) and ELM (extreme learning machine) machine learning models with three optimization algorithms, i.e., WCA (water cycle algorithm), PSO (particle swarm optimization), and GWO (grey wolf optimizer) to precisely estimate the CS of fiber-reinforced concrete (FRC) containing SiO2. …”
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Comprehensive improvement of energy efficiency and indoor environmental quality for university library atrium—A multi-objective fast optimization framework by Shen Xu, Yongzhong Chen, Jianlin Liu, Jian Kang, JinFeng Gao, Yuchen Qin, Wenjun Tan, Gaomei Li

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Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer by Mohd Herwan Sulaiman, Zuriani Mustaffa

“…This paper presents an innovative approach using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model optimized by the Barnacles Mating Optimizer (BMO). …”
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Energy storage configuration considering user-shared costs in peak shaving auxiliary services with improved multi-objective particle swarm optimization by Yiyou Xing, Jin Shen, Xinru Li

“…Moreover, an improved particle swarm optimization algorithm, specifically adapted for this model, is developed. …”
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Effects of off-design performances and multiple market carbon trading mechanism on integrated energy systems with waste incineration power units by Jing Liu, Tong Zhao, Haolin Sui

“…Therefore, an optimal dispatching model of electricity-gas-heating-cooling IES with renewable energy and waste incineration power units (WIP) based on a novel multiple market carbon trading mechanism (MMCTM) is proposed. …”
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An improved hybrid approach involving deep learning for urban greening tree species classification with Pléiades Neo 4 imagery—A case study from Nanjing, Eastern China by Min Sun, Stephane G.P. Debulois, Zhengnan Zhang, Xiaolei Cui, Zhili Chen, Mingshi Li

“…Future work will integrate multi-source data, multi-seasonal observations, and adaptive algorithms to further enhance classification performance and improve model robustness across diverse urban environments.…”
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Dynamic weighted ensemble model for predictive optimization in green sand casting: Advancing industry 4.0 manufacturing by Rajesh V․ Rajkolhe, Dr. Sanjay S․ Bhagwat, Dr. Priyanka V․ Deshmukh

“…The gains were statistically significant (p < 0.05) based on paired t-test analysis, confirming that DWE offers superior prediction consistency.The proposed DWE model supports real-time optimization in green sand casting, helping reduce defects and improve quality outcomes. …”
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Mesosphere data assimilation based on the intelligent optimization of the uncertainty parameters in a theoretical model by Dexin Ren, Jiuhou Lei, Xuetao Chen, Tong Dang, Yu Liu

“…In this study, we conducted an intelligent optimization particle filtering algorithm to optimize the uncertainty parameters in a physics-based model, which was used to simulate the terrestrial mesosphere. …”
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Path Optimization Model for Urban Transportation Networks under the Perspective of Environmental Pollution Protection by Can Liu, Zongping Li, Yueyang Li

“…Finally, the improved particle swarm optimization algorithm is used to solve the two-objective model to obtain the Pareto front solution set, that is, the path scheme under real-time traffic conditions. …”
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Brown bear optimized random forest model for short term solar power forecasting by Rathika Senthil Kumar, P.S. Meera, V. Lavanya, S. Hemamalini

“…To further improve the accuracy of the RF model, the hyperparameters of the random forest model are tuned using brown bear optimization algorithm (BBOA). …”
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An improved Harris hawks optimizer with enhanced logarithmic spiral and dynamic factor and its application for predicting molten iron temperature in the blast furnace by Zhendong Liu, Yiming Fang, Le Liu, Shuidong Ma

“…Abstract In response to the problem of poor search performance and difficulty in escaping from the local optimum in the Harris hawks optimizer, an improved Harris hawks optimizer with enhanced logarithmic spiral and dynamic factor (IHHO‐ELSDF) is proposed in this paper. …”
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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…Finally, an SVM classification algorithm is employed for personnel detection. To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. …”
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Enhancing Lidar-Based 3D Classification Through an Improved Deep Learning Framework With Residual Connections by Shih-Lin Lin, Jun-Yi Wu

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The Application Based on Support Vector Machine Optimized by Particle Swarm Optimization and Genetic Algorithm by MAN Chun-tao, LIU Bo, CAO Yong-cheng

“…In order to improve the precision of the parameter optimization, the research integrates the Particle Swarm Optimization Algorithm with Support Vector Machine, and matches the experimental data, and then establishes a steadystate model of complex process system, which is based on Particle Swarm Optimization Algorithm and Support Vector Machine On the basis of this model, an improved Particle Swarm Optimization Algorithm introduced to Genetic Algorithm is proposed, in order to overcome the defects of Particle Swarm. …”
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Modeling and Optimization of Tensile Properties of Epoxy Biocomposites Reinforced with Washingtonia robusta Waste and Biochar Using Response Surface Methodology, Artificial Neural... by Messaouda Boumaaza, Ahmed Belaadi, Hassan Alshahrani, Ibrahim M. H. Alshaikh, Djamel Ghernaout

“…To model and optimize the mechanical behavior, Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and a Multi-Criteria Decision-Making (MCDM) method based on TOPSIS were applied. …”
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Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints by Daxing Lei, Yaoping Zhang, Zhigang Lu, Hang Lin, Yifan Chen

“…To address these challenges, this study proposes a hybrid ML model that integrates a multilayer perceptron (MLP) with the slime mold algorithm (SMA), termed the SMA-MLP model. …”
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