Fault Diagnosis of Vehicle Gearbox based on Support Vector Machine Optimized by Improved Beetle Antennae Search by Wenshan Qiao, Jin Hua, Meihong Chen

“…Aiming at the fact that the performance of support vector machine （SVM） in vehicle gearbox fault diagnosis is greatly affected by parameters，a new method of vehicle gearbox fault diagnosis based on improved SVM is proposed based on the research of beetle antennae search （BAS）. …”
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On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning by Nansha Gao, Mou Wang, Xiao Liang, Guang Pan

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Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques by Satish Kumar, Sameer Sayyad, Arunkumar Bongale

“…The collected data are used to train the machine learning (ML) and deep learning (DL) classification models to classify the variation in printing parameters. …”
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Machine learning enhanced metal 3D printing: high throughput optimization and material transfer extensibility by Yuanjie Zhang, Cheng Lin, Yuan Tian, Jianbao Gao, Bo Song, Hao Zhang, Min Wang, Kechen Song, Binghui Deng, Dezhen Xue, Yonggang Yao, Yusheng Shi, Kun Kelvin Fu

“…Meanwhile, the “optimized” yet fixed parameters largely limit possible extensions to new designs and materials. …”
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Predicting the Relative Density of Stainless Steel and Aluminum Alloys Manufactured by L-PBF Using Machine Learning by José Luis Mullo, Iván La Fé-Perdomo, Jorge Ramos-Grez, Ángel F. Moreira Romero, Alejandra Ramírez-Albán, Mélany Yarad-Jácome, Germán Omar Barrionuevo

“…In addition, since experimental designs are costly, one solution is using machine learning algorithms that allow the effects of variations in the processing parameters on the resulting density of the additively manufactured components to be anticipated. …”
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Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors by Ran Zhang, Guo Chen, Shasha Gao, Lu Chen, Yongchao Cheng, Xiuquan Gu, Yue Wang

“…Consequently, this paper introduced a novel screening approach that integrates first principles with machine learning (ML) to rapidly predict the gas sensitivity of materials. …”
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Investigation of Machine Tool Developed Settings Influence on Productivity and Quality of Simultaneous Double-Sided Lens Processing by A. S. Kozeruk, Y. L. Malpica, A. A. Sukhotzkiy, M. I. Filonova, V. O. Kuznechik

“…The most advantageous values of the machine-tool setting parameters with various combinations have been proposed with the purpose to eliminate errors in the form of common as “knoll” and “hole” with due account of processing productivity and accuracy. …”
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Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning by Sangwon Lee, Yongha Hwang, Yan Jin, Sihyeong Ahn, Jaewan Park

“…Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. …”
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Review of machine learning applications for predicting the quality of biomass briquettes for sustainable and low-carbon energy solutions by Constance Nakato Nakimuli, Fred Kaggwa, Johan De Greef, David Kilama Okot, Julien Blondeau, Simon Kawuma

“…This paper reviews literature on various Machine Learning models applied for predicting and optimizing briquette quality parameters, including combustion, physical, and emission properties. …”
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Laser powder bed fusion process optimization of CoCrMo alloy assisted by machine-learning by Haoqing Li, Bao Song, Yizhen Wang, Jingrui Zhang, Weihong Zhao, Xiaoying Fang

“…Gaussian process regression (GPR) model of machine learning method was employed to identify the optimal process window for high-performance CoCrMo alloy in laser powder bed fusion (LPBF), considering density (≥99%) and surface roughness (≤7 μm) as key parameters. …”
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Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector Machine by Deliang Liu, Biao Lu, Wenping Wu, Wei Zhou, Wansu Liu, Yiye Sun, Shilong Wu, Guolong Shi, Leiming Yuan

“…The SVM parameters were optimized using grid search, particle swarm optimization (PSO), and genetic algorithm (GA), yielding the optimal parameters (C and gamma). …”
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A machine learning model for the computation of thermophysical properties of WCO biodiesel mixed with multiwalled carbon nanotubes by Hussain Syed Sameer, Ali Syed Abbas, Husain Dilawar, Sharma Manish

“…A Machine Learning (ML) model has been developed to compute the thermophysical properties of Waste Cooking Oil (WCO) biodiesel dispersed with MultiWalled Carbon NanoTubes (MWCNTs). …”
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A multi-biomarker machine learning approach for early prediction of interstitial lung disease in rheumatoid arthritis by Jiaojiao Xu, Wei Zhang, Weili Bai, Nannan Gai, Jing Li, Yunqi Bao

“…The ILD group exhibited significantly elevated levels of inflammatory markers and specific biomarkers, particularly KL-6 (826.4 ± 458.2 vs. 285.6 ± 124.8 U/ml, P < 0.001), alongside distinct patterns in hematological parameters. Conclusion Machine learning approaches, particularly XGBoost, demonstrate promising potential for early RA-ILD prediction. …”
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Machine learning model based on survey assessment of sleep quality in chronic obstructive pulmonary disease patients. by Miraç Öz, Banu Eriş Gülbay, Barış Bulut, Elif Akıncı Aydınlı, Aslıhan Gürün Kaya, Öznur Yıldız, Turan Acıcan, Sevgi Saryal

“…Patients were categorized into two groups: good sleep quality and poor sleep quality. Parameters for the best model were selected from a total of 61 clinical and laboratory parameters using recursive feature elimination (RFE) and the Bayesian Information Criterion (BIC). …”
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Design and Analysis of Single Stack AFPM Machines with and without Air gap Between Rotor and Magnetic Poles by Abdul Majeed Shaikh, Umar Abdul Majeed, Muhammad Fawad Shaikh, Sheeraz Ahmed, Muhammad Bux

“… Permanent Magnet (PM) machines are widely used due to low cost, light weight, small size and better operating efficiency. …”
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Machine Learning-Based Prediction of In-Hospital Mortality in Severe COVID-19 Patients Using Hematological Markers by Rongrong Dong, Han Yao, Taoran Chen, Wenjing Yang, Qi Zhou, Jiancheng Xu

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STRUCTURE ANALYSIS AND MULTI-OBJECTIVE OPTIMIZATION DESIGN OF A DEWATERING BUCKET FOR A PULSATOR WASHING MACHINE by CAI Yun, PENG Liang, LIU Xue, CHENG ZhiWen

“…In order to suppress the vibration noise of a tumble dryer（ internal barrel） in a pulsator washing machine,and to optimize the internal barrel for better performance,firstly,a parameterized finite element model of the internal barrel was established by Solidworks software and imported into ANSYS Workbench software. …”
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Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review by Sathish J., Ramash Kumar K., Saraswathi D.

“…The battery management system (BMS) is the main part that is often in need of data processing of battery parameters and diagnosis of the problem. This paper explores the comprehensive literature review on machine learning and deep learning approaches for BMS in EVs. …”
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Experimental validation of machine learning for contamination classification of polluted high voltage insulators using leakage current by Umer Amir Khan, Mansoor Asif, Muhammad Hamza Zafar, Luai Alhems

“…The Bayesian optimization technique was used to optimize the parameters of Machine Learning Models. The models demonstrated exceptional performance, with accuracies consistently exceeding 98 %. …”
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Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models by Guoli Huang, Sarah Kanaan Hamzah, Pinank Patel, T. Ramachandran, Aman Shankhyan, A. Karthikeyan, Dhirendra Nath Thatoi, Deepak Gupta, S. AbdulAmeer, Mariem Alwan, Zahraa Saad Abdulali, Mahmood Jasem Jawad, Hiba Mushtaq, Mohammad Mahtab Alam, Hojjat Abbasi

“…Abstract Predicting the rate of penetration (ROP) is critical for optimizing drilling performance, yet it remains a complex task due to the interplay of multiple geological and operational parameters. This study comprehensively evaluates machine learning models, utilizing a real-time, high-resolution dataset from drilling operations in southeast Iraq. …”
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