A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections by Yazan Alatoom, Abdallah Al-Hamdan

“…A comprehensive evaluation was undertaken across prediction accuracy, training-testing performance discrepancy, sensitivity to outliers, computational time cost, and model robustness. …”
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A Novel Dataset and Detection Method for Unmanned Aerial Vehicles Using an Improved YOLOv9 Algorithm by Depeng Gao, Jianlin Tang, Hongqi Li, Bingshu Wang, Jianlin Qiu, Shuxi Chen, Xiangxiang Mei

“…This dataset can be used in model training to help detectors distinguish UAVs from these nontarget objects and thereby improve their anti-interference capabilities. …”
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Enhancing Vocal Performance using Variational Onsager Neural Network and Optimized with Golden Search Optimization Algorithm by Lian Sun

“…The proposed SMATS-AI-VONN-GSOA stands out by incorporating the Variational Onsager Neural Network (VONN) alongside the Golden Search Optimization Algorithm (GSOA). This amalgamation tailors training routines based on the user’s progress and preferences, emphasizing the development of muscle memory and control for enhanced vocal performance. …”
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Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms. by Alemu Birara Zemariam, Biruk Beletew Abate, Addis Wondmagegn Alamaw, Eyob Shitie Lake, Gizachew Yilak, Mulat Ayele, Befkad Derese Tilahun, Habtamu Setegn Ngusie

“…The data was pre-processed, and 80% and 20% of the observations were used for training, and testing the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. …”
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Neural Network-Based Genetic Algorithm for Complex Circuit Design of High-Power Vacuum Electron Device by Dongyang Wang, Yonggang Che, Hongfei Yu, Yan Teng

“…This method avoids generating an extensive dataset for ANN pre-training before optimization. Testing on a dataset obtained by a simple genetic algorithm (SGA) shows that the ANN has good modeling capabilities for power, model evaluation, and tuning performance. …”
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A Comparative Study of Machine Learning Algorithms for Intrusion Detection Systems using the NSL-KDD Dataset by Rulyansyah Permata Putra, Amarudin Amarudin

“…The research methodology includes the collection of the NSL-KDD dataset, followed by data transformation, cleaning, normalization, and partitioning into training and testing sets. Each algorithm was trained using tuned parameters, and performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and an analysis of training and prediction time. …”
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Identifying liver cirrhosis in patients with chronic hepatitis B: an interpretable machine learning algorithm based on LSM by Xueting Bai, Chunwen Pu, Wenchong Zhen, Yushuang Huang, Qian Zhang, Zihan Li, Yixin Zhang, Rongxuan Xu, Zhihan Yao, Wei Wu, Mei Sun, Xiaofeng Li

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A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients by Alla Ahmad Hassan, Tarik A Rashid

“…Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. …”
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A Novel Black Widow Optimization Algorithm Based on Lagrange Interpolation Operator for ResNet18 by Peiyang Wei, Can Hu, Jingyi Hu, Zhibin Li, Wen Qin, Jianhong Gan, Tinghui Chen, Hongping Shu, Mingsheng Shang

“…Moreover, we evaluate LIBWONN’s effectiveness using 24 benchmark functions from CEC2017 and CEC2022 and compare it with nine advanced metaheuristic algorithms. …”
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Cultural Heritage Color Regeneration: Interactive Genetic Algorithm Optimization Based on Color Network and Harmony Models by Zhonghua Jiang, Qianlong Xia, Zhizhou Wang, Kaiwei Zhu, Qianyu Su, Jiajun Wang, Yirui Huang, Bo Wu, Yan Hong

“…Then, a dataset of 10,743 color and color rating data points is used to train 12 color harmony prediction models, with the most efficient stacking model selected to improve the efficiency of user evaluation of color schemes. …”
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Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms by Yonghan Luo, Wenrui Ding, Xiaotao Yang, Houxi Bai, Feng Jiao, Yan Guo, Ting Zhang, Xiu Zou, Yanchun Wang

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State Evaluation and Risk Assessment for Relay Protection System by WANG Shenghe, YE Yuanbo, LIU Hongjun, XIE Min, CONG Lei, CHEN Jun

“… In order to improve the evaluation accuracy of the relay protection system and the adaptability of the evaluation method,a risk evaluation model of the relay protection system based on the semi-supervised Mahalanobis distance machine learning algorithm is proposed.First，according to the network topology of the smart substation， the evaluation indicators of the relay protection system are analyzed; second， on the basis of the analytic hierarchy process，the evaluation results are used as the training set for fuzzy comprehensive evaluation. …”
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Multiclass Sentiment Analysis of Electric Vehicle Incentive Policies Using IndoBERT and DeBERTa Algorithms by Muhammad Bayu Nugroho, Akhmad Khanif Zyen, Nur Aeni Widiastuti

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A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems by Zijian Jiang, Chaoli Sun, Xiaotong Liu, Hui Shi, Sisi Wang

“…Furthermore, the number of unlabelled solutions participating in model training is adaptively determined based on the objective evaluations conducted. …”
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Evaluation of Techniques for Sim2Real Reinforcement Learning by Mahesh Ranaweera, Qusay Mahmoud

“…Custom API was created to induce noise, manipulate in-game physics, dynamics, and lighting conditions, and perform domain randomization to improve generalization. Two RL algorithms; Q-Learning and Actor-Critic were implemented to train the agent and to evaluate the performance in bridging the reality gap. …”
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A steady state micro genetic algorithm for hyper-heuristic generation in one-dimensional bin packing by Julio Juárez, Jesús Guillermo Falcón-Cardona, José Carlos Ortiz-Bayliss

“…Our experimental results showcase the effectiveness of the SS $$\mu$$ GA across multiple training and testing datasets for the 1DBPP. Compared to other evolutionary methodologies, also used as generative HH methods (i.e., generational GA, steady-state GA, and generational $$\mu$$ GA), the SS $$\mu$$ GA consistently achieves higher fitness values within the same number of evaluations, on the training set. …”
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Adaptive Hybrid PSO-Embedded GA for neuroevolutionary training of multilayer perceptron controllers in VSC-based islanded microgrids by Yared Bekele Beyene, Getachew Biru Worku, Lina Bertling Tjernberg

“…By effectively reducing entrapment in local minima and balancing exploration and exploitation, AHPEGA improves the quality of neural controller design. The algorithm’s performance is evaluated against conventional optimization methods, demonstrating significant improvements in accuracy, convergence speed, and consistency across multiple runs. …”
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Comparison of Deep Learning Models and Optimization Algorithms in the Detection of Scoliosis and Spondylolisthesis from X-Ray Images by Abdullah Erhan Akkaya, Cengiz Hark, Harun Güneş

“…While working on these deep architectures, each model has been evaluated using different optimization algorithms. …”
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Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms by Hossein Moayedi, Azfarizal Mukhtar, Nidhal Ben Khedher, Isam Elbadawi, Mouldi Ben Amara, Quynh TT, Nima Khalilpoor

“…The study found that TLBO and VS outperform other algorithms in CO2 emission computation accuracy. TLBO has a higher training MSE (3.6778) and lower testing MSE (4.4673), suggesting larger squared errors on training data and lower testing MSE, suggesting less overfitting due to better generalization to the testing set.…”
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Body Weight Estimation in Holstein × Zebu Crossbred Heifers: Comparative Analysis of XGBoost and LightGBM Algorithms by Jose Herrera‐Camacho, Cem Tırınk, Rosa Inés Parra‐Cortés, Lütfi Bayyurt, Rashit Uskenov, Karlygash Omarova, Aizhan Makhanbetova, Kadyrbai Chekirov, Alfonso Juventino Chay‐Canul

“…ABSTRACT This study evaluates the effectiveness of XGBoost and LightGBM algorithms for estimating the live weight of Holstein×Zebu crossbred heifers. …”
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