A Methodology for Acceleration Signals Segmentation During Forming Regular Reliefs Patterns on Planar Surfaces by Ball Burnishing Operation by Stoyan Dimitrov Slavov, Georgi Venelinov Valchev

“…In the present study, an approach for determining the different states of ball burnishing (BB) operations aimed at forming regular reliefs’ patterns on planar surfaces is introduced. The methodology involves acquiring multi-axis accelerometer data from CNC-driven milling machine to capture the dynamics of the BB tool and workpiece, mounted on the machine table. …”
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On Training Spiking Neural Networks by Means of a Novel Quantum Inspired Machine Learning Method by Jean Michel Sellier, Alexandre Martini

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Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review by Palpasa Shrestha, Bibek Shrestha, Jati Shrestha, Jun Chen

“…Images can be analyzed using machine learning, a pattern recognition method. The machine learning algorithm first computes the image characteristics deemed significant for making predictions or diagnoses about unseen images. …”
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Development of a High‐Latitude Convection Model by Application of Machine Learning to SuperDARN Observations by W. A. Bristow, C. A. Topliff, M. B. Cohen

“…Abstract A new model of northern hemisphere high‐latitude convection derived using machine learning (ML) is presented. The ML algorithm random forests regression was applied to a database of velocities derived from the Super Dual Auroral Radar Network (SuperDARN) observations processed with the potential mapping technique, Map‐Potential (Ruohoniemi & Baker, 1998, https://doi.org/10.1029/98ja01288). …”
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Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm by Fang Liu, Xiaoli Liu, Changyou Yin, Hongrong Wang

“…For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. …”
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Enhancing DDoS Attack Classification through SDN and Machine Learning: A Feature Ranking Analysis by Aymen AlAwadi, Kawthar Rasoul ALesawi

“…Due to the growing dependence of digital services on the Internet, Distributed Denial of Service (DDoS) attacks are a common threat that can cause significant disruptions to online operations and financial losses. Machine learning (ML) offers a promising way for early DDoS attack detection due to its ability to analyze large datasets and identify patterns. …”
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Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation by A. Srinivaas, N. R. Sakthivel, Binoy B. Nair

“…This paper concludes with a review of the progress in fault identification in ICE components and prospects, highlighted by an experimental investigation using 16 machine learning algorithms with seven feature selection techniques under three load conditions to detect faults in a four-cylinder ICE. …”
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Novel application of unsupervised machine learning for characterization of subsurface seismicity, tectonic dynamics and stress distribution by Mohammad Salam, Muhammad Tahir Iqbal, Raja Adnan Habib, Amna Tahir, Aamir Sultan, Talat Iqbal

“…Our study pioneers an innovative use of unsupervised machine learning, a powerful tool for navigating unclassified data, to unravel the complexities of subsurface seismic activities and extract meaningful patterns. …”
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Robust fault detection and classification in power transmission lines via ensemble machine learning models by Tahir Anwar, Chaoxu Mu, Muhammad Zain Yousaf, Wajid Khan, Saqib Khalid, Ahmad O. Hourani, Ievgen Zaitsev

“…This research introduces a novel approach for fault detection and classification by analyzing voltage and current patterns across transmission line phases. Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms—including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks—are evaluated. …”
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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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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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Enhancing decision-making on detractor-causing failures: an approach combining data mining and machine learning by Yuri A. V. da Silva, Geraldo Cardoso de Oliveira Neto, Gustavo Lima, Sidnei A. de Araújo, Rodrigo Neri Bueno da Silva, Francisco Elanio Bezerra, Marlene Amorim

“…The proposed approach employs Decision Tree (DT) algorithms to uncover patterns linked to service failures. …”
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Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach by Yehoon Jo, Mi-Yeon Shin, Sungkyoon Kim

“…This study used data from 2,960 participants in the Korean National Environmental Health Survey (KoNEHS) cycle 4 (2018–2020) to examine associations between environmental exposures and MetS risk through machine learning (ML) approaches. Eight ML algorithms were applied, with the multilayer perceptron (MLP) and random forest (RF) models identified as optimal predictors. …”
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Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects by Devendra Sahu, Ritesh Kumar Dewangan, Surendra Pal Singh Matharu

“…This research addresses these challenges by employing advanced signal processing techniques and machine learning algorithms. The study investigates and optimizes fault diagnosis of rolling element bearings using various machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP). …”
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Texas rural land market integration: A causal analysis using machine learning applications by Tian Su, Senarath Dharmasena, David Leatham, Charles Gilliland

“…Using quarterly transactional land value data from 1966 to 2017, this study uses cutting-edge machine learning algorithms and probabilistic graphical models to uncover causal interaction patterns of different land markets in Texas. …”
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Machine learning-based prediction of optimal antenatal care utilization among reproductive women in Nigeria by Jamilu Sani, Adeyemi Oluwagbemiga, Mohamed Mustaf Ahmed

“…After data preprocessing and feature selection, six supervised ML algorithms—Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and XGBoost—were applied using Python 3.9. …”
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Elucidating the role of KLRD1 in coronary atherosclerosis: harnessing bioinformatics and machine learning to advance understanding by Huan Liang, Xiao Liang, Man Zheng, Shoudong Wang

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Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber by T. Nageshkumar, Prateek Shrivastava, L. Ammayapan, Manisha Jagadale, L. K. Nayak, D. B. Shakyawar, Indran Suyambulingam, P. Senthamaraikannan, R. Kumar

“…In this study, a total of 432 patterns of input and output parameters obtained from laboratory experiments were used to develop machine learning algorithms (Random forest, support vector, and XGBoost). …”
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Evaluation of Machine Learning Models for Estimating Grassland Pasture Yield Using Landsat-8 Imagery by Linming Huang, Fen Zhao, Guozheng Hu, Hasbagan Ganjurjav, Rihan Wu, Qingzhu Gao

“…These data, combined with field-measured pasture yields, were employed to construct models using four machine learning algorithms: elastic net regression (Enet), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). …”
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Machine learning-based prediction of scale formation in produced water as a tool for environmental monitoring by Arash Tayyebi, Ali Alshami, Erfan Tayyebi, Ademola Owoade, MusabbirJahan Talukder, Nadhem Ismail, Zeinab Rabiei, Xue Yu, Glavic Tikeri

“…This is primarily due to the continuous variation in salt concentrations, temperature and pressure affecting inorganic scale composition. Machine learning (ML) as a data-driven method is a powerful tool for uncovering hidden patterns in experimental data necessary for decision-making on scale formation predictions by analyzing the complex relationships between mainly the water chemistry and the pH. …”
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