Benchmarking Machine Learning Algorithms for Bearing Fault Classification Using Vibration Data: A Deployment-Oriented Study by Prasanta Kumar Samal, R. Srinidhi, Pramod Kumar Malik, H. J. Manjunatha, Imran M. Jamadar

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Unsupervised Machine Learning Approaches for Test Suite Reduction by Anila Sebastian, Hira Naseem, Cagatay Catal

“…Therefore, the Test Suite Reduction (TSR) process is of great importance, contributing to the reduction of time and costs in executing test suites for complex software by minimizing the number of test cases to be executed. …”
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Advanced machine learning techniques for predicting compressive strength and ultrasonic pulse velocity of concrete incorporating industrial by-products by Ehsan Mohsennia, Alireza Javid, Vahab Toufigh

“…Among the models tested, the CatBoost (CB) algorithm, optimized with the Whale Optimization Algorithm (WOA), exhibited outstanding predictive performance. …”
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A Comparative Analysis of Buckling Pressure Prediction in Composite Cylindrical Shells Under External Loads Using Machine Learning by Hyung Gi Lee, Jung Min Sohn

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Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine Using WOA-XGBoost Model by Dan Li, Jiwei Qu, Delan Zhu, Zheyu Qin

“…The relation between meteorological parameters and solar irradiance is studied, and four different parameter combinations are formed and considered as inputs to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict solar radiation. …”
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Suitability of Mechanics-Based and Optimized Machine Learning-Based Models in the Shear Strength Prediction of Slender Beams Without Stirrups by Abayomi B. David, Oladimeji B. Olalusi, Paul O. Awoyera, Lenganji Simwanda

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Enhancing Intrusion Detection Systems with Dimensionality Reduction and Multi-Stacking Ensemble Techniques by Ali Mohammed Alsaffar, Mostafa Nouri-Baygi, Hamed Zolbanin

“…To overcome these limitations, this paper presents an innovative approach that integrates dimensionality reduction and stacking ensemble techniques. We employ the LogitBoost algorithm with XGBRegressor for feature selection, complemented by a Residual Network (ResNet) deep learning model for feature extraction. …”
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Association between the (neutrophil + monocyte)/albumin ratio and all-cause mortality in sepsis patients: a retrospective cohort study and predictive model establishment according... by Lulu Liu, Qian Ma, Guangzan Yu, Xuhou Ji, Hua He

“…Moreover, we employed Boruta algorithm to evaluate the predictive potential of the NMa ratio and established the prediction models utilizing machine learning algorithms. …”
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Predicting Gestational Diabetes Mellitus in the first trimester using machine learning algorithms: a cross-sectional study at a hospital fertility health center in Iran by Somayeh Kianian Bigdeli, Marjan Ghazisaedi, Seyed Mohammad Ayyoubzadeh, Sedigheh Hantoushzadeh, Marjan Ahmadi

“…Conclusion The results of this study demonstrate that ML algorithms, especially RF, have acceptable accuracy in the early prediction of GDM during the first trimester of pregnancy.…”
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Re-Imagining a 'We' Beyond the Gathering of Reductions by Lila Athanasiadou, Goda Klumbyte, Antoinette Rouvroy

“…While a lot of the discourses on algorithms and digital future invoke catastrophic imaginaries of totalizing control, this conversation works with a propositional format, teasing out affirmative politics by pointing to spaces of potentiality within the environmental, the social and the mental realm. …”
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A novel research on network security situation prediction based on iteratively optimized RBF-NN by Yuqin Wu, Congqi Shen, Shungen Xiao, Wei Feng, Yexian Fan, Xiuzhuang Zhou

“…Abstract Network security situation (NSS) prediction has attracted significant attention in recent years due to its potential to preemptively mitigate various types of network attacks. …”
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Construction cost prediction model for agricultural water conservancy engineering based on BIM and neural network by Kun Han, Tieliang Wang, Wenhe Liu, Chunsheng Li, Xiaochen Xian, Yingying Yang

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LSTM-ANN-GA A HYBRID DEEP LEARNING MODEL FOR PREDICTIVE MAINTENANCE OF INDUSTRIAL EQUIPEMENT by Farouk Noumich, Abouchabaka Jaafar, Amrani Ayoub

Subjects: “…predictive maintenance…”
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Simultaneous Energy Optimization of Heating Systems by Multi-Zone Predictive Control—Application to a Residential Building by Rina Bitar, Nicolas Youssef, Julien Chamoin, Fadi Hage Chehade, Didier Defer

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Retracted: Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm by Nitin Nandkumar Sakhare, Imambi S. Shaik, Suman Saha

“…Shaik, Suman Saha, Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm, IET Software 2023 (https://doi.org/10.1049/sfw2.12092)]. …”
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Springback Angle Prediction for High-Strength Aluminum Alloy Bending via Multi-Stage Regression by Enzhi Gao, Di Xue, Yiming Li

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Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm by Juncheng Fu, Zhengxiang Song, Jinhao Meng, Chunling Wu

“…Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is proposed using a deep hybrid kernel extreme learning machine (DHKELM) optimized by the improved black-winged kite algorithm (IBKA). …”
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Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions by Sunawar Khan, Tehseen Mazhar, Tariq Shahzad, Muhammad Amir Khan, Ateeq Ur Rehman, Mamoon M. Saeed, Habib Hamam

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Research on the Photovoltaic MPPT Method Based on Improved BP-SVM-ELM Combination Prediction by DAI Bowang, ZHAO Xianggui, ZHU Qiliang

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Enhancing Compressive Strength Prediction in Recycled Aggregate Concrete through Robust Hybrid Machine Learning Approaches by Samuel Keown, Dylan O’Dwyer

“…This investigation explores the integration of LSSVR with two innovative optimizers, namely the Giant Trevally Optimizer (GTO) and the Dingo Optimization Algorithm (DOA). The performance of the LSSVR model is enhanced through the utilization of these optimizers, resulting in its increased proficiency in predicting the f'c of RAC. …”
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