Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review by Boris I, Kseniia Barashok, Yongjoon Choi, Yeongil Choi, Mohammed Aslam, Jaesun Lee

“…However, conventional ultrasonic approaches face challenges in analyzing complex signals, limiting their accuracy and efficiency in certain applications. The advent of machine learning (ML) has revolutionized ultrasonic data analysis by utilizing advanced data mining and pattern recognition capabilities to decode intricate signal patterns. …”
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An architectural framework for information integration using machine learning approaches for smart city security profiling by Adnan Abid, Ansar Abbas, Adel Khelifi, Muhammad Shoaib Farooq, Razi Iqbal, Uzma Farooq

“…This research aims to provide a generic architectural framework to semi-automatically accumulate law-and-order-related news through different news portals and classify them using machine learning approaches. The proposed architectural framework discusses all the important components that include data ingestion, preprocessor, reporting and visualization, and pattern recognition. …”
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Heat treatment control technology of high-strength steel gears based on support vector machine by Yanzhong Wang, Libin Zhang, Yulu Su, Hai Liu, HaiLong Yang, Yanyan Chen

“…In this study, with the help of machine learning, a support vector machine prediction model of gear tissue distribution is constructed based on heat treatment parameters, and the radial basis functions kernel function is selected as the kernel function of the support vector machine to improve the accuracy of model prediction by optimizing the kernel parameters. …”
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A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023 by Shalini Kumari, Chander Prabha, Asif Karim, Md. Mehedi Hassan, Sami Azam

“…These techniques are categorized further into machine learning (ML), deep learning (DL), and federated learning (FL). …”
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Exploring the Main Driving Factors for Terrestrial Water Storage in China Using Explainable Machine Learning by Xinjing Ma, Haijun Huang, Jinwen Chen, Qiang Yu, Xitian Cai

“…In this study, we employed a robust machine learning model to capture the spatial patterns of TWS in China and further applied the Shapley Additive Explanations (SHAP) method to disentangle the individualized effects of hydroclimatic variables. …”
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A Comparative Analysis of Machine Learning and Deep Learning Techniques for Accurate Market Price Forecasting by Olamilekan Shobayo, Sidikat Adeyemi-Longe, Olusogo Popoola, Obinna Okoyeigbo

“…This study compares three machine learning and deep learning models—Support Vector Regression (SVR), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM)—for predicting market prices using the NGX All-Share Index dataset. …”
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MODAL ANALYSIS OF CARRIER SYSTEM FOR HEAVY HORIZONTAL MULTIFUNCTION MACHINING CENTER BY FINITE ELEMENT METHOD by Yu. V. Vasilevich, S. S. Dovnar, I. I. Shumsky

“…The aim of the paper is to reveal and analyze resonance modes of a large-scale milling-drilling-boring machine. The machine has a movable column with vertical slot occupied by a symmetrical carriage with horizontal ram. …”
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Functional Diagnostic System for Multichannel Mine Lifting Machine Working in Factor Cluster Analysis Mode by Zimovets V. I., Shamatrin S. V., Olada D. E., Kalashnykova N. I.

“…Therefore, the creation of the basics of information synthesis of a functional diagnosis system (FDS) based on machine learning and pattern recognition is a topical task. …”
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Prediction of Reservoir Flow Capacity in Sandstone Formations: A Comparative Analysis of Machine Learning Models by Micheal Ayodeji Ogundero, Taiwo Adelakin, Kehinde Orolu, Isaac Femi Johnson, Theophilus Akinfenwa Fashanu, Kingsley Abhulimen

“…Given a large number of input variables that enclose geological and environmental factors, the study set the correlation of these conditions to provide profound analysis and reveal profound patterns within the data. With the following supervised machine learning algorithms: Random Forest, Artificial Neural Network (ANN) and Support Vector Regression (SVR); the study modeled RFC. …”
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Study on the Influence Mechanism of Machine-Learning-Based Built Environment on Urban Vitality in Macau Peninsula by Chen Pan, Jiaming Guo, Haibo Li, Jiawei Wu, Nengjie Qiu, Shengzhen Wu

“…The methodological integration of RAGA-PPM and SHAP advances the innovative paradigm of applying interpretable machine learning to the study of urban form.…”
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Impaired interhemispheric synchrony in patients with iridocyclitis and classification using machine learning: an fMRI study by Yan Tong, Zhi Wen, Xin Huang

“…BackgroundThis study examined the interhemispheric integration function pattern in patients with iridocyclitis utilizing the voxel-mirrored homotopic connectivity (VMHC) technique. …”
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Rapid classification of rice according to storage duration via near-infrared spectroscopy and machine learning by Chen Zhai, Wenxiu Wang, Man Gao, Xiaohui Feng, Shengjie Zhang, Chengjing Qian

“…Subsequently, two-dimensional correlation spectroscopy and competitive adaptive reweighted sampling (CARS) were used to extract the characteristic spectra associated with storage time. Finally, three pattern recognition methods (K-nearest neighbor analysis, linear discriminant analysis, and least squares support vector machine (LS-SVM)) were compared for their effectiveness in constructing classification models. …”
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A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization by Md. Saddam Hossain, Md. Parvez Khandocar, Farzana Akter Riti, Md. Yeakub Ali, Prithbey Raj Dey, S M Jahurul Haque, Amira Metouekel, Atrsaw Asrat Mengistie, Mohammed Bourhia, Farid Khallouki, Khalid S. Almaary

“…Imaging scans leverage pattern recognition to predict outcomes, diagnose disorders, and suggest treatments. …”
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Model test on the effects of shield machine cutterhead vibration on tunnel face stability in sandy ground by Meng-Bo Liu, Jun-Hua Xiao, Shao-Ming Liao, Zhi-Yong Liu, Jun-Zuo He, Yan-Qing Men, Jia-Cheng Sun

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Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

“…Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. …”
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A Robust Behavioral Biometrics Framework for Smartphone Authentication via Hybrid Machine Learning and TOPSIS by Moceheb Lazam Shuwandy, Qutaiba Alasad, Maytham M. Hammood, Ayad A. Yass, Salwa Khalid Abdulateef, Rawan A. Alsharida, Sahar Lazim Qaddoori, Saadi Hamad Thalij, Maath Frman, Abdulsalam Hamid Kutaibani, Noor S. Abd

“…The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methodology has also been incorporated to obtain the most affected and valuable features, which are then fed as input to three different Machine Learning (ML) algorithms: Random Forest (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (KNN). …”
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Machine learning-based identification of efficient and restrictive physiological subphenotypes in acute respiratory distress syndrome by Gabriela Meza-Fuentes, Iris Delgado, Mario Barbé, Ignacio Sánchez-Barraza, Mauricio A. Retamal, René López

“…Data on physiological and ventilatory variables were collected during the first 24 h IMV. We applied machine learning techniques to categorize subphenotypes in ARDS patients. …”
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Adaptive optimization of natural coagulants using hybrid machine learning approach for sustainable water treatment by Pallavi Randive, Madhuri S. Bhagat, Mangesh P. Bhorkar, Rajesh M. Bhagat, Shilpa M. Vinchurkar, Sagar Shelare, Shubham Sharma, N. Beemkumar, S. Hemalatha, Parveen Kumar, Ankit Kedia, Ehab El Sayed Massoud, Deepak Gupta, Jasmina Lozanovic

“…Lastly, SOMs and MARS are used to identify pattern recognitions in tracing the crucial interaction among mixing parameters. …”
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Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han, Jianping Bao

“…By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. …”
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A machine learning approach to identifying key predictors of Peruvian school principals' job satisfaction by Luis Alberto Holgado-Apaza, Dany Dorian Isuiza-Perez, Nelly Jacqueline Ulloa-Gallardo, Yban Vilchez-Navarro, Ruth Nataly Aragon-Navarrete, Wilian Quispe Layme, Marleny Quispe-Layme, Danger David Castellon-Apaza, Remo Choquejahua-Acero, Jaime Cesar Prieto-Luna

“…Despite the significance of this issue, there is limited research on satisfaction predictors for these professionals, particularly using machine learning approaches. This study identified key predictors of job satisfaction among Peruvian school principals by applying an ensemble of feature selection methods and evaluating five machine learning algorithms (Random Forest, Decision Trees-CART, Histogram-Based Gradient Boosting, XGBoost, and LightGBM) with data from the 2018 National Survey of Directors. …”
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