Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) by Gordan Mimić, Amit Kumar Mishra, Miljana Marković, Branislav Živaljević, Dejan Pavlović, Oskar Marko

“…In this study, the determination and prediction of harvest dates for different crops were performed by applying machine learning techniques on C-band synthetic aperture radar (SAR) data. …”
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Random Reflectance: A New Hyperspectral Data Preprocessing Method for Improving the Accuracy of Machine Learning Algorithms by Pavel A. Dmitriev, Anastasiya A. Dmitrieva, Boris L. Kozlovsky

“…This study employs machine learning (ML) algorithms, specifically Random Forest (RF) and Gradient Boosting (GB), to analyse the performance of RR in comparison to Min–Max Normalisation (MMN) and Principal Component Analysis (PCA). …”
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A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities by Xu Pengru, Zhou Junhui, Kausar Nasreen, Lin Chunlei, Lu Qianqian, Ghaderpour Ebrahim, Pamucar Dragan, Zadeh Ardashir M.

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An Automated Framework for Lane Closure Detection on Highway Using Connected Vehicle Data and Machine Learning Models by Ashutosh Dumka, Raghupathi Kandiboina, Aparna Joshi, Skylar Knickerbocker, Neal Hawkins, Anuj Sharma

“…This study introduces an innovative real-time lane closure detection approach using connected vehicle (CV) data and machine learning techniques. Our methodology analyzes CV data metrics such as speed variations and lateral waypoint positioning relative to road reference lines, comparing these across road segments with and without closures. …”
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Machine learning-based feature selection for ultra-high-dimensional survival data: a computational approach by Nahid Salma, Majid Khan Majahar Ali, Raja Aqib Shamim

“…This study evaluates machine learning (ML)-based feature selection methods to address limitations in scalability, feature redundancy, and predictive accuracy in UHD RCC survival data. …”
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Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance by Beytullah Eren, İdris Cesur

“…This study presents a comparative analysis of machine learning models for predicting carbon monoxide (CO) emissions in automotive engines. …”
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A Predictive Model for Perinatal Brain Injury Using Machine Learning Based on Early Birth Data by Ga Won Jeon, Yeong Seok Lee, Won-Ho Hahn, Yong Hoon Jun

“…Various machine learning models, including gradient boosting, were trained using early birth data to predict perinatal brain injury. …”
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Hierarchical Information-Extreme Machine Learning of Hand Prosthesis Control System Based on Decursive Data Structure by Anatolii Dovbysh, Vladyslav Piatachenko, Mykyta Myronenko, Mykyta Suprunenko, Julius Simonovskiy

“…The method was developed within the information-extreme intelligent data analysis technology framework to maximize the system’s information capacity during machine learning. …”
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Interpretable Machine Learning for Thermospheric Mass Density Modeling Using GRACE/GRACE‐FO Satellite Data by Qian Pan, Chao Xiong, ShunZu Gao, Zhou Chen, Artem Smirnov, Chunyu Xu, Yuyang Huang

“…In this study we propose a machine‐learning approach, the bidirectional gated recurrent unit with multi‐head attention mechanism (BGMA), for modeling and predicting the TMD, based on the Gravity Recovery and Climate Experiment (GRACE) satellite data. …”
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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

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Analysis of signals from air conditioner compressors with ordinal patterns and machine learning by Keila Barbosa, Alejandro C Frery, George DC Cavalcanti

“…Additionally, Ordinal Patterns allow for precise and understandable visualization of operational data, making interpreting results more accessible for professionals who may not be experts in data analysis. …”
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Innovative defect cluster analysis algorithm and tool powered by machine learning techniques by Shuai Ren, Huizhao Li, Dandan Chen, Lingyu Dong, Changjun Hu

“…This paper makes four key contributions: (1) it employs the union-find algorithm to efficiently segment defect clusters in molecular dynamics data; (2) it integrates machine learning with material defect cluster characteristics, proposing a novel algorithm for defect cluster identification and classification, enabling detailed cluster visualization; (3) it introduces a defect data filtering method based on normal distribution, improving defect cluster classification accuracy; and (4) it develops software for analyzing defect clusters in nuclear reactor materials based on the proposed algorithms. …”
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Anomaly Detection Using Machine Learning in Hydrochemical Data From Hot Springs: Implications for Earthquake Prediction by Ruijie Zhu, Fengtian Yang, Xiaocheng Zhou, Jiao Tian, Yongxian Zhang, Miao He, Jingchao Li, Jinyuan Dong, Ying Li

“…Abstract This study explores the potential of machine learning algorithms for earthquake prediction, utilizing fluid chemical anomaly data from hot springs. …”
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Global air quality index prediction using integrated spatial observation data and geographics machine learning by Tania Septi Anggraini, Hitoshi Irie, Anjar Dimara Sakti, Ketut Wikantika

“…This study aims to detect and improve the accuracy of the Global Air Quality Index from Remote Sensing (AQI-RS) by integrating AQI from ground-based stations with driving factors such as meteorological, environmental, sources of air pollution, and air pollution magnitude from satellite observation parameters as independent variables using Geographics Machine Learning (GML). This study utilizes 425 air pollution stations and the driving factors data globally from 2013 to 2024. …”
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A comparative study of methods for dynamic survival analysis by Wieske K. de Swart, Marco Loog, Jesse H. Krijthe, Jesse H. Krijthe

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Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data by Quan Zhou, Lihua Zhang, Nan Xiang, Lele Zhang, Fuqiang Ma, Fengchang Yu, Shenhui Lv, Zhilin Lu, He-Rong Mao

“…Objectives To develop and validate a machine learning (ML) model to differentiate malignant from benign thyroid nodules (TNs) based on the routine data and provide diagnostic assistance for medical professionals.Setting A qualified panel of 1649 patients with TNs from one hospital were stratified by gender, age, free triiodothyronine (FT3), free thyroxine (FT4) and thyroid peroxidase antibody (TPOAB).Participants Thyroid function (TF) data of 1649 patients with TNs were collected in a single centre from January 2018 to June 2022, with a total of 273 males and 1376 females, respectively.Measures Seven popular ML models (Random Forest, Decision Tree, Logistic Regression (LR), K-Neighbours, Gaussian Naive Bayes, Multilayer Perception and Gradient Boosting) were developed to predict malignant and benign TNs, whose performance indicators included area under the curve (AUC), accuracy, recall, precision and F1 score.Results A total of 1649 patients were enrolled in this study, with the median age of 45.15±13.41 years, and the male to female ratio was 1:5.055. …”
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Machine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh. by Arman Hossain Chowdhury, Md Siddikur Rahman

“…Exploratory spatial analysis, spatial regression and tree-based machine learning models were utilized to analyze the data.…”
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Soft Robot Workspace Estimation via Finite Element Analysis and Machine Learning by Getachew Ambaye, Enkhsaikhan Boldsaikhan, Krishna Krishnan

“…This unique asymmetric design enables the soft robot to bend and curl in various ways. Machine learning is used to establish a forward kinematic relationship between the pressure inputs and the motion responses of the soft robot using data from FEA. …”
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Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review by Wenlong Wang, Zhe Wu, Dominic Peters, Berkay Citmaci, Carlos G. Morales-Guio, Panagiotis D. Christofides

“…The complexity of these systems – arising from coupled electrochemical reactions with mass, heat and charge transport phenomena – poses significant challenges in modeling, analysis, and control. Machine learning (ML) has emerged as a promising tool for addressing these challenges by providing data-driven solutions to complex process modeling, optimization, and advanced control. …”
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Genomics and integrative clinical data machine learning scoring model to ascertain likely Lynch syndrome patients by Ramadhani Chambuso, Takudzwa Nyasha Musarurwa, Alessandro Pietro Aldera, Armin Deffur, Hayli Geffen, Douglas Perkins, Raj Ramesar

“…We scored the clinicopathologic and somatic genomics data automatically using a machine learning model to discriminate between likely-LS and sporadic CRC cases. …”
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