Elastic Optimization for Stragglers in Edge Federated Learning by Khadija Sultana, Khandakar Ahmed, Bruce Gu, Hua Wang

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Short-term salinity prediction for coastal areas of the Vietnamese Mekong Delta using various machine learning algorithms: a case study in Soc Trang Province by Le Thi Thanh Dang, Hiroshi Ishidaira, Ky Phung Nguyen, Kazuyoshi Souma, Jun Magome

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Research on optimization of basic rail top bending prediction model by Chunjiang Liu, Zhikui Dong, Long Ma, Xinyu Hou, Nanbing Qiao

“…The results showed that the top bend prediction optimization model established in this study had high feasibility and met the machining accuracy requirements.…”
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Unconfined Compressive Strength Prediction of Rocks Using a Novel Hybrid Machine Learning Algorithm by Rafiqul Islam, Md. Arif Hossain

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Machine learning based shear strength prediction in reinforced concrete beams using Levy flight enhanced decision trees by Aybike Özyüksel Çiftçioğlu, Anıl Delikanlı, Torkan Shafighfard, Faramarz Bagherzadeh

Subjects: “…Hybrid machine learning framework…”
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Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm by El Assri Nasima, Ennejjar Mohammed, Jallal Mohammed Ali, Chabaa Samira, Zeroual Abdelouhab

“…In addition to predictive analysis, this study utilizes a Modified Whale Optimization Algorithm (MWOA) to optimize energy consumption. …”
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Comparing Decision Tree and Support Vector Machines in Hospital Satisfaction by Dinda Anggraini, Indra Gamayanto, Sasono Wibowo

“…This study compares the performance of Decision Tree and Support Vector Machine (SVM) in classifying patient satisfaction at Harapan Hospital Magelang for service optimization. …”
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Adaptive machine learning framework: Predicting UHPC performance from data to modelling by Yinzhang He, Shaojie Gao, Yan Li, Yongsheng Guan, Jiupeng Zhang, Dongliang Hu

“…The framework has several key modules: data preprocessing, feature selection, outlier detection, model training, hyperparameter optimization, and model interpretation. …”
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Adaptive lift chiller units fault diagnosis model based on machine learning. by Yang Guo, Zengrui Tian, Hong Wang, Mengyao Chen, Pan Chu, Yingjie Sheng

“…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …”
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An intelligent optimized object detection system for disabled people using advanced deep learning models with optimization algorithm by Marwa Obayya, Fahd N. Al-Wesabi, Menwa Alshammeri, Huda G. Iskandar

“…The temporal convolutional network (TCN) model is implemented for classification. Finally, the hyperparameter selection of the TCN model is implemented by the sparrow search optimization algorithm (SSOA) model. …”
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A novel ensemble support vector machine model for land cover classification by Ying Liu, Lihua Huang

“…In this article, a novel ensemble support vector machine model that uses AdaBoost approach is proposed to mitigate the influence of noises and error parameters with focus on application on land cover classification. …”
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Robust parameter design for EDM-based AL2024-T3 machining by Emre Ayhan, Semih Güner, Mustafa Yurdakul, Yusuf Tansel İç

“…This paper proposes a robust parameter optimization study of Al2024-T3 for EDM using a goal programming model. …”
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On the machine learning algorithm combined evolutionary optimization to understand different tool designs’ wear mechanisms and other machinability metrics during dry turning of D2... by Muhammad Sana, Muhammad Umar Farooq, Sana Hassan, Anamta Khan

“…In this study, three-step novel modelling approach for optimal prediction of dry turning parameters is proposed. …”
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Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning Models by Fei Wang, Kai Cui, Jinxiang Liu, Wenhai He, Qiuyu Zhang, Weihai Zhang, Tianshuai Wang

“…To address this issue, this study explores the prediction of electrostatic sensitivity in HTPB propellants using machine learning techniques. A dataset comprising 18 experimental formulations was employed to train and evaluate six machine learning models. …”
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Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review by Wenlong Wang, Zhe Wu, Dominic Peters, Berkay Citmaci, Carlos G. Morales-Guio, Panagiotis D. Christofides

“…The complexity of these systems – arising from coupled electrochemical reactions with mass, heat and charge transport phenomena – poses significant challenges in modeling, analysis, and control. Machine learning (ML) has emerged as a promising tool for addressing these challenges by providing data-driven solutions to complex process modeling, optimization, and advanced control. …”
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Scenario modeling of the drug prescription process for children: application of machine learning methods by А. А. Kondrashov, М. М. Kurashov, Е. Е. Loskutova

“…Optimization of the data and model increased the accuracy of predictions up to 85%.Conclusion. …”
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Hybrid extreme learning machine for real-time rate of penetration prediction by Abdelhamid Kenioua, Omar Djebili, Ammar Touati Brahim

“…Abstract This study presents a comparative analysis of hybrid Extreme Learning Machine (ELM) models optimized with metaheuristic algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO) for real-time Rate of Penetration (ROP) prediction in drilling operations. …”
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Fault Diagnosis in Power Generators: A Comparative Analysis of Machine Learning Models by Quetzalli Amaya-Sanchez, Marco Julio del Moral Argumedo, Alberto Alfonso Aguilar-Lasserre, Oscar Alfonso Reyes Martinez, Gustavo Arroyo-Figueroa

“…This work presents a comparative analysis of machine learning (ML) models for the generator fault diagnosis. …”
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Modeling women cyclists' perceived security: A comparison of machine learning techniques by Peyman Noorbakhsh, Navid Khademi, Phromphat Thansirichaisree

“…While prior studies have explored certain aspects of perceived security —mainly for pedestrians—the application of Machine Learning (ML) models to predict cyclists’ perceived security remains a relatively developing research area. …”
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Development and Comparison of Machine Learning and Deep Learning Models for Speech Audiometry Prediction by Jae sung Shin, Jun Ma, Mao Makara, Nak-Jun Sung, Seong Jun Choi, Sung yeup Kim, Min Hong

“…These findings suggest that machine learning models, particularly gradient boosting and XGBoost, outperform deep learning models in SA prediction. …”
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