Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation by A. Srinivaas, N. R. Sakthivel, Binoy B. Nair

“…The former uses physical models of engine components to diagnose defects, while the latter employs statistical analysis of sensor data to identify patterns indicating faults. Various methods for ICE fault identification, such as vibration analysis, thermography, acoustic analysis, and optical approaches, are reviewed. …”
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Machine learning and multi-omics analysis reveal key regulators of proneural–mesenchymal transition in glioblastoma by Can Xu, Jin Yang, Huan Xiong, Xiaoteng Cui, Yuhao Zhang, Mingjun Gao, Lei He, Qiuyue Fang, Changxi Han, Wei Liu, Yangyang Wang, Jin Zhang, Ying Yuan, Zhaomu Zeng, Ruxiang Xu

“…The Lasso, Cox, and Step machine learning algorithms were used to construct and screen the optimal risk assessment prognostic model. …”
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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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Detection of <i>Tagosodes orizicolus</i> in Aerial Images of Rice Crops Using Machine Learning by Angig Rivera-Cartagena, Heber I. Mejia-Cabrera, Juan Arcila-Diaz

“…This study employs RGB imagery and machine learning techniques to detect <i>Tagosodes orizicolus</i> infestations in “Tinajones” rice crops during the flowering stage, a critical challenge for agriculture in northern Peru. …”
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Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach by Carlos Alejandro Perez Garcia, Patrizia Tassinari, Daniele Torreggiani, Marco Bovo

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Enhancing hand-drawn diagram recognition through the integration of machine learning and deep learning techniques by Vanita Agrawal, MVV Prasad Kantipudi, Jayant Jagtap

“…Additionally, deep learning techniques, which are well known for their ability to find intricate patterns and features in data, are incorporated into the proposed system. …”
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An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction by Fabio Cassano, Anna Maria Crespino, Mariangela Lazoi, Giorgia Specchia, Alessandra Spennato

“…This research details the creation and evaluation of an EWS that incorporates deep learning methods, particularly using Long Short-Term Memory (LSTM) networks enhanced with attention layers to predict critical machine faults. The proposed system is designed to process time-series data collected from an industrial printing machine’s embosser component, identifying error patterns that could lead to operational disruptions. …”
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Shoreline dynamics prediction using machine learning models: from process learning to probabilistic forecasting by Afshar Adeli, Afshar Adeli, Ali Dastgheib, Ali Dastgheib, Dano Roelvink, Dano Roelvink, Dano Roelvink

“…Through comprehensive testing across one complex shoreline evolution scenario, this research identifies the ConvLSTM model—trained on 2D gridded data— as the optimal machine learning approach suited for addressing specific shoreline complexities and evolution 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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A Survey on Machine Learning Enhanced Integrated Sensing and Communication Systems: Architectures, Algorithms, and Applications by Mikael Ade Krisna Respati, Byung Moo Lee

“…This technology utilizes the same communication resources for communicating and sensing within the same framework, enabling more efficient use of resources. Currently, machine learning (ML) has been developed in the field of communications, including sensing and wireless communications, due to its ability to tackle complex optimization problems, estimate complex issues, and extract and exploit spatial/temporal patterns that can improve ISAC performance. …”
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Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data by Silvia Elaine Cardozo Macedo, Marina de Borba Oliveira Freire, Oscar Schmitt Kremer, Ricardo Bica Noal, Fabiano Sandrini Moraes, Mauro André Barbosa Cunha

“…Epidemiological, clinical, and laboratory data were processed by machine learning algorithms in order to identify patterns. …”
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The influence of digital transformation on the total factor productivity of enterprises: the intermediate role of human-machine cooperation by Qi Xiong, Jingyi Yang, Xiuwu Zhang, Yarui Deng, Yao Gui, Xiaoyang Guo

“…The findings reveal that (1) digital transformation significantly enhances TFP, a conclusion that remains valid after considering endogeneity issues and conducting a series of robustness checks, thereby refuting the productivity paradox associated with digital transformation; (2) furthermore, the enabling effect of digital transformation on TFP varies significantly across enterprises due to differences in ownership, factor intensity, asset size, degree of marketization, tax preference, and geographical location; (3) in terms of the impact mechanism, digital transformation promotes TFP by enabling efficient human–machine collaboration patterns. This study not only complements research on the influencing factors of microenterprise TFP, providing empirical evidence for improving enterprise production efficiency, but also offers insights for local governments to formulate differentiated digital policies.…”
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A Compact Dual-Band Antenna for Brain–Machine Interface and Skin-Implantable Biotelemetry Applications by Emtiaz Ahmed Mainul, Md. Faruque Hossain

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Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete by Samuel Olaoluwa Abioye, Yusuf Olawale Babatunde, Oluwafikejimi Abigail Abikoye, Aisha Nene Shaibu, Bailey Jonathan Bankole

“…Abstract This research examines the application of eight different machine learning (ML) algorithms for predicting the compressive strength of high-performance concrete (HPC). …”
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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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Predicting intensive care need in women with preeclampsia using machine learning – a pilot study by Camilla Edvinsson, Ola Björnsson, Lena Erlandsson, Stefan R. Hansson

“…In this study we aimed to develop a prediction model for severe outcomes using routine biomarkers and clinical characteristics.Methods We used machine learning models based on data from an intensive care cohort with severe preeclampsia (n=41) and a cohort of preeclampsia controls (n=40) with the objective to find patterns for severe disease not detectable with traditional logistic regression models.Results The best model was generated by including the laboratory parameters aspartate aminotransferase (ASAT), uric acid and body mass index (BMI) with a cross-validation accuracy of 0.88 and an area under the curve (AUC) of 0.91. …”
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High School and Undergraduate Student Volunteers as an Imperfect Solution to Machine Learning Geoscience Research Needs by Sarah E. Esenther, Neiv Gupta, Chanatip Vongkitbuncha, Mason N. Lee, Laurence C. Smith

“…We describe our experiences working with 20 early‐stage students to build a large training data set digitized from satellite images of meltwater drainage patterns on ice sheets. The intent of this Perspective is to share our experience and lessons learned with other machine learning researchers who, like us, may have minimal experience mentoring young volunteer researchers but may seek such partnerships for the first time in response to their machine learning training data set needs. …”
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Effects of Broken Rotor Bars on Three-Phase Induction Machines and Detection Methods – State of Arts by Boudmane Bilal, Rachid Aziz, Mortabit Idriss, Errifai Nidale

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Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events by Aratz Olaizola, Ibai Errekagorri, Elsa Fernández, Julen Castellano, John Suckling, Karmele Lopez-de-Ipina

“…This study aims to enhance the performance and in-game success in women’s football by developing machine learning (ML) models that predict match outcomes based on player and team behaviour following goals. …”
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Spatiotemporal Analysis of Sea-Surface pH in the Pacific Ocean Based on Interpretable Machine Learning by Minlong Huang, Jin Qi, Can Zhang, Yuanyuan Wang, Yijun Chen, Jian Shao, Sensen Wu

“…Therefore, this study provides a data-driven approach to gain deeper insights into the spatiotemporal patterns of pH and its influencing factors.…”
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