Research on bearing fault diagnosis based on improved northern goshawk algorithm optimizing SVM by WU Xiaojun, LI Quwei

“…An improved northern goshawk optimization (INGO) algorithm was proposed to address the local optimization problem that swarm intelligence algorithms often encounter when optimizing support vector machine (SVM) models, and it was applied to fault diagnosis of rolling bearings. …”
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Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms? by Khabat Khosravi, Aitazaz A. Farooque, Sayed M. Bateni, Changhyun Jun, Dorsa Mohammadi, Zahra Kalantari, James R. Cooper

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Electrical Fault Diagnosis Method for Tunneling Equipment Based on Fault Simulation and Optimized Particle Swarm Algorithm by Huan Zou, Xueping Zhang, Xin Wang, Yu Li

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An Innovative Proposal for Developing a Dynamic Urban Growth Model Through Adaptive Vector Cellular Automata by Ahmet Emir Yakup, Ismail Ercument Ayazli

“…During the calibration phase, the model was trained using three machine learning algorithms: Random forest, support vector machine, and multi-layer perceptron. …”
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Comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter (CDOM) from Sentinel-2/MSI images in the coastal waters of the Persian Gulf by Bonyad Ahmadi, Mehdi Gholamalifard, Seyed Mahmoud Ghasempouri, Tiit Kutser

“…Initial CDOM retrieval algorithms yielded suboptimal accuracy (MAE = 1.16, RMSLE = 1.2). …”
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Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A SHAP-Driven Framework to Accelerate Urban Carbon Neutrality by Zhihui Zhao, Minjuan Wang, Jin Wei, Xiao Cen, Shengnan Du, Ziwen Wu, Huanying Liu, Weiqiang Wang

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Forest age estimation using UAV-LiDAR and Sentinel-2 data with machine learning algorithms- a case study of Masson pine (Pinus massoniana) by Jinjin Chen, Xuejian Li, Zihao Huang, Jie Xuan, Chao Chen, Mengchen Hu, Cheng Tan, Yongxia Zhou, Yinyin Zhao, Jiacong Yu, Lei Huang, Meixuan Song, Huaqiang Du

“…In this study, Sentinel-2 remote sensing data, UAV-LiDAR data, and combined Sentinel-2 and LiDAR data are used as data sources. Three machine learning algorithms, Adaptive Boosting (AdaBoost), Random Forest (RF), and Extreme Random Tree (ERT), are used to predict forest age in a Masson pine (Pinus massoniana Lamb.) forest. …”
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A Lightweight Kernel Density Estimation and Adaptive Synthetic Sampling Method for Fault Diagnosis of Rotating Machinery with Imbalanced Data by Wenhao Lu, Wei Wang, Xuefei Qin, Zhiqiang Cai

“…The model employs the Kernel Density Estimation Adaptive Synthetic Sampling (KDE-ADASYN) algorithm for oversampling to balance the data, applies fast Fourier transform (FFT) to convert time-domain signals into frequency-domain signals, and utilizes a 1D-MobileNet network enhanced with a Squeeze-and-Excitation (SE) block for feature extraction and fault diagnosis. …”
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Optimizing anomaly detection models for edge IIoT with an enhanced firefly algorithm-based hyperparameter tuning strategy by Mohemmed Yousuf Rahamathulla, Mangayarkarasi Ramaiah

“…Security issues in the Industrial Internet of Things (IIoT) have grown more serious as industrial automation rises as these networks are especially prone to cyberattacks. By means of adaptive attack detection models, machine learning (ML) presents a potential solution. …”
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Enhancing UAV Security Against GPS Spoofing Attacks Through a Genetic Algorithm-Driven Deep Learning Framework by Abdallah Al-Sabbagh, Aya El-Bokhary, Sana El-Koussa, Abdulrahman Jaber, Mahmoud Elkhodr

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K-Gen PhishGuard: an Ensemble Approach for Phishing Detection with K-Means and Genetic Algorithm by Ali Al-Hafiz, Adnan Jabir, Shamala Subramaniam

“…In the second phase, the best set of features in each group is identified through the Genetic algorithm to enhance the classification process. Finally, a voting ensemble technique is applied, in which the Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Adaptive boosting (AdaBoost) models are combined. …”
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A Multi-Task Based Clustering Personalized Federated Learning Method by Ao Xiong, Han Zhou, Yu Song, Dong Wang, Xu Wei, Da Li, Bo Gao

“…Simulation experiments conducted on carbon emission prediction data demonstrate that the proposed algorithm performs better in various evaluation metrics compared with the Federated Averaging (FedAvg) algorithm and traditional clustering personalized federated learning algorithm. …”
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A Survey on Multi-UAV Path Planning: Classification, Algorithms, Open Research Problems, and Future Directions by Mamunur Rahman, Nurul I. Sarkar, Raymond Lutui

“…After detailing classification, we compare various multi-UAV path planning algorithms based on time consumption, computational cost, complexity, convergence speed, and adaptability. …”
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A Bayesian Optimization Approach for Tuning a Grouping Genetic Algorithm for Solving Practically Oriented Pickup and Delivery Problems by Cornelius Rüther, Julia Rieck

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Enhanced Binary Kepler Optimization Algorithm for effective feature selection of supervised learning classification by Amr A. Abd El-Mageed, Amr A. Abohany, Khalid M. Hosny

“…The algorithm showed rapid convergence, minimal feature selection, and scalability, making it a robust and adaptable tool for enhancing FS in machine learning, validated through the Wilcoxon rank-sum test.…”
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Salp Navigation and Competitive based Parrot Optimizer (SNCPO) for efficient extreme learning machine training and global numerical optimization by Oluwatayomi Rereloluwa Adegboye, Afi Kekeli Feda, Ghanshyam G. Tejani, Aseel Smerat, Pankaj Kumar, Ephraim Bonah Agyekum

“…Abstract Metaheuristic optimization algorithms play a crucial role in solving complex real-world problems, including machine learning parameter tuning, yet many existing approaches struggle with maintaining an effective balance between exploration and exploitation, leading to premature convergence and suboptimal solutions. …”
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Extending WSN Lifetime with Enhanced LEACH Protocol in Autonomous Vehicle Using Improved K-Means and Advanced Cluster Configuration Algorithms by Cheolhee Yoon, Seongsoo Cho, Yeonwoo Lee

“…Our proposed method first constructs the cluster’s configuration and then elects the CH applying an improved K-means clustering algorithm—one of the machine learning methods—integrated with a proposed IK-MACHES mechanism. …”
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A machine learning approach to assess the climate change impacts on single and dual-axis tracking photovoltaic systems by Udit Mamodiya, Indra Kishor, Priyam Ganguly, Isha Mukherjee, Nithesh Naik

“…Conventional fixed-tilt, single-axis, and dual-axis tracking techniques are not real-time adaptive, resulting in energy loss. This paper introduces COMLAT (Climate-Optimized Machine Learning Adaptive Tracking), an AI solar tracking system that employs climate prediction using CNN-LSTM for climate prediction, XGBoost for estimation of energy yield, and Deep Q-Learning (DQL) for real-time tracking control for solar efficiency optimization. …”
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Enhancing Security in CPS Industry 5.0 using Lightweight MobileNetV3 with Adaptive Optimization Technique by Mohammed A. Aleisa

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A Novel LSTM Architecture for Automatic Modulation Recognition: Comparative Analysis With Conventional Machine Learning and RNN-Based Approaches by Sam Ansari, Soliman Mahmoud, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny

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