Prediction of contact resistance of electrical contact wear using different machine learning algorithms by Zhen-bing Cai, Chun-lin Li, Lei You, Xu-dong Chen, Li-ping He, Zhong-qing Cao, Zhi-nan Zhang

“…Random forest (RF), support vector regression (SVR) and BP neural network (BPNN) algorithms were used to establish RF, SVR and BPNN models, respectively, and the experimental data were trained and tested. …”
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Mitigating Sinkhole Attacks in MANET Routing Protocols using Federated Learning HDBNCNN Algorithm by Sherril Sophie Maria Vincent

“…In this study, Federated Learning is utilized to improve the security and privacy through empowering nodes to train the model deprived of distribution complex data. …”
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Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete by Muhammad Izhar Shah, Shazim Ali Memon, Muhammad Sohaib Khan Niazi, Muhammad Nasir Amin, Fahid Aslam, Muhammad Faisal Javed

“…Particle swarm optimization (PSO) algorithm was used to fine-tune the hyperparameter of the proposed MEP. …”
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Improving the Quality of Reshaped EoL Components by Means of Accurate Metamodels and Evolutionary Algorithms by Antonio Piccininni, Angela Cusanno, Gianfranco Palumbo, Giuseppe Ingarao, Livan Fratini

“…Data were then fitted by accurate Response Surfaces trained by means of interpolant Radial Basis Functions and anisotropic Kriging algorithms, subsequently used to carry out a virtual optimization managed by multi-objective evolutionary algorithms (MOGA-II and NSGA-II). …”
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Management and prediction of river flood utilizing optimization approach of artificial intelligence evolutionary algorithms by Rana Muhammad Adnan Ikram, Mo Wang, Hossein Moayedi, Atefeh Ahmadi Dehrashid

“…Effective flood susceptibility mapping (FSM) has become crucial for mitigating flood risks, especially in urban areas. This study evaluates the performance of artificial neural network (ANN) algorithms for FSM using machine learning classification. …”
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A Machine Learning Algorithm to Predict Medical Device Recall by the Food and Drug Administration by Victor Barbosa Slivinskis, Isabela Agi Maluli, Joshua Seth Broder

“…The algorithm was trained using 400 randomly selected devices and then tested using 100 unique random devices. …”
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Evaluation of hydraulic fracturing using machine learning by Ali Akbari, Ali Karami, Yousef Kazemzadeh, Ali Ranjbar

“…Key statistical metrics, including mean, median, variance, skewness, and quartiles, were used to explore data distribution and inform model training. Additionally, the study uniquely evaluates model robustness across varying train/test data ratios (from 0.1 to 0.9), providing deeper insights into algorithm performance stability. …”
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Development of Robot Feature for Stunting Analysis Using Long-Short Term Memory (LSTM) Algorithm by Muhammad Rahadian Abdurrahman, Halim Al Aziz, Farras Adhani Zayn, Muhammad Agus Purnomo, Heru Agus Santoso

“…The evaluation results show training accuracy of 96.65% and training validation of 96.61%, with precision, recall and f1-score varying with relevance to the f1-score and support value. …”
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Significance of Machine Learning-Driven Algorithms for Effective Discrimination of DDoS Traffic Within IoT Systems by Mohammed N. Alenezi

“…Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time.…”
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Optimized TD3 algorithm for robust autonomous navigation in crowded and dynamic human-interaction environments by Husam A. Neamah, Oscar Agustin Mayorga Mayorga

“…Finally, the results obtained by training the algorithm correspond nearly 0 for the actor and critic training, with a training of 12,000 episodes, and with an evaluation results 92 % of effectivity of our algorithm, based on 772 steps performed by the rob ten in a time of 11 s.…”
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An Improved HM-SAC-CA Algorithm for Mobile Robot Path Planning in Unknown Complex Environments by Ting Jiao, Conglin Hu, Lingxin Kong, Xihao Zhao, Zhongbao Wang

“…First, based on the SAC maximum entropy framework, a deep reinforcement learning algorithm with clipped automatic entropy adjustment is proposed to improve the quality of policy learning by suppressing entropy evaluation. …”
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Research on the prediction algorithm of tread wear for locomotive wheels based on GA-ridge regression analysis by FANG Xin, LIU Tong, CHENG Yaping, SUN Yuduo, WANG Feier

“…In the second step, data was integrated into datasets, and a time-sliding window was created for the training set data. The ridge regression algorithm was used to train the training set data for regression analysis, and the model parameters were tuned using a combination of the genetic algorithm and the validation set data to improve the prediction accuracy. …”
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Advancing sustainable renewable energy: XGBoost algorithm for the prediction of water yield in hemispherical solar stills by Salwa Ahmad Sarow, Hasan Abbas Flayyih, Maryam Bazerkan, Luttfi A. Al-Haddad, Zainab T. Al-Sharify, Ahmed Ali Farhan Ogaili

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Spatial prediction of landslide susceptibility using the data-mining algorithm (case study: Kamyaran county) by Osama Ashraf Mohammed, Hemn Kamal Hamaamin, Jwanro Azad, Sabri Rasooli, Houzhi Li

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Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and Validation by Hongwei Zhang, Mengyuan Diao, Sheng Zhang, Peifeng Ni, Weidong Zhang, Chenxi Wu, Ying Zhu, Wei Hu

“…We extracted 34 features for analysis and modeling using a 2-hour time compensation, 2 action features (mean arterial pressure and temperature), and 1 outcome feature (survival status at 28 d). We used an RL algorithm called weighted dueling double deep Q-network with embedded human expertise to maximize cumulative returns and evaluated the model using a doubly robust off-policy evaluation method. …”
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Localization algorithm for large-scale wireless sensor networks based on FCMTSR-support vector machine by Fang Zhu, Junfang Wei

“…Through the simulations, the performance of localization based on FCMTSR-support vector machine is evaluated. The results prove that the localization precision is improved 2%, the training time is reduce 55% than existing localization algorithm based on support vector machine without FCMTSR. …”
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Automatic Neural Architecture Search Based on an Estimation of Distribution Algorithm for Binary Classification of Image Databases by Erick Franco-Gaona, Maria Susana Avila-Garcia, Ivan Cruz-Aceves

“…Convolutional neural networks (CNNs) are widely used for image classification; however, setting the appropriate hyperparameters before training is subjective and time consuming, and the search space is not properly explored. …”
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Solar Spectrum Simulation Algorithms Considering AM0G and AM1.5G by Junjie Yang, Guoyu Zhang, Bin Zhao, Dongpeng Yang, Ke Zhang, Yu Zhang, Jian Zhang, Zhengwei Ren, Jingrui Sun, Lu Wang, Xiaoxu Mo, Taiyang Ren, Dianwu Ren, Zeng Peng, Songzhou Yang, Jiabo Lv

“…This strategy integrates a multi-objective genetic algorithm to generate training datasets and a neural network for solar spectrum simulation. …”
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Prediction of Body Mass of Dairy Cattle Using Machine Learning Algorithms Applied to Morphological Characteristics by Franck Morais de Oliveira, Patrícia Ferreira Ponciano Ferraz, Gabriel Araújo e Silva Ferraz, Marcos Neves Pereira, Matteo Barbari, Giuseppe Rossi

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Comparison of Artificial Intelligence Algorithms and Remote Sensing for Modeling Pine Bark Beetle Susceptibility in Honduras by Omar Orellana, Marco Sandoval, Erick Zagal, Marcela Hidalgo, Jonathan Suazo-Hernández, Leandro Paulino, Efrain Duarte

“…However, gaps remain in the evaluation and comparison of these algorithms when modeling susceptibility to bark beetle outbreaks in tropical conifer forests using Google Earth Engine (GEE). …”
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