Machine learning-based predictive analysis of energy efficiency factors necessary for the HIFU treatment of adenomyosis by Ziyan Liu, Ziyi Liu, Yuan Wang, Xiyao Wan, Xiaohua Huang

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Dementia ascertainment in India and development of nation‐specific cutoffs: A machine learning and diagnostic analysis by Danny Maupin, Hongxin Gao, Emma Nichols, Alden Gross, Erik Meijer, Haomiao Jin

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Racial Disparities in Comorbidity Patterns of Early-Onset Liver Cancer: A Machine Learning Analysis by Bingya Ma MSc, Kai Zheng PhD, Fa-Chyi Lee MD, Yunxia Lu MD, PhD

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SECURITY ASSESSMENT OF MOODLE-BASED DISTANCE LEARNING SYSTEM COMPONENTS USING STATIC ANALYSIS TOOLS by Vladislav K. Kuchmin, Grigory O. Krylov

“…The article presents a methodological approach to assessing the security of software components within the Moodle-based distance learning system using automated static source code analysis methods. …”
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Integrating radiomic texture analysis and deep learning for automated myocardial infarction detection in cine-MRI by Wang Xu, Xiangjiang Shi

“…This study proposes a hybrid framework combining radiomic texture analysis with deep learning-based segmentation to enhance MI detection on non-contrast cine cardiac magnetic resonance (CMR) imaging.The approach incorporates radiomic features derived from the Gray-Level Co-Occurrence Matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM) methods into a modified U-Net segmentation network. …”
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Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants by Aikaterini-Artemis Agiomavriti, Maria P. Nikolopoulou, Thomas Bartzanas, Nikos Chorianopoulos, Konstantinos Demestichas, Athanasios I. Gelasakis

“…The objectives of the current review were (i) to describe the most widely applied spectroscopy-based and supervised machine learning methods utilized for the evaluation of milk components, origin, technological properties, adulterants, and drug residues, (ii) to present and compare the performance and adaptability of these methods and their most efficient combinations, providing insights into the strengths, weaknesses, opportunities, and challenges of the most promising ones regarding the capacity to be applied in milk quality monitoring systems both at the point-of-care and beyond, and (iii) to discuss their applicability and future perspectives for the integration of these methods in milk data analysis and decision support systems across the milk value-chain.…”
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Joint Learning of Underwater Terrain Matching and Suitability Analysis via SO(2) Elevation Transformer by Yan Han, Gang Fan, Qichen Yan, Pengyun Chen, Xiaolong Yan, Tinghai Yan, Guoguang Chen

“…The SEET encoder is pre-trained through self-supervised contrastive learning on underwater elevation data, eliminating the need for manual labeling. …”
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Predictive Performance of Machine Learning with Evoked Potentials for SCI and MS Prognosis: A Meta-Analysis by Constantinos Koutsojannis, Dionysia Chrysanthakopoulou

“…Evoked potentials (EPs), including somatosensory evoked potentials (SSEPs) and motor evoked potentials (MEPs), are used to assess neural conduction in spinal cord injury (SCI) and multiple sclerosis (MS), conditions marked by demyelination, inflammation, and axonal damage. Machine learning (ML), using data-driven algorithms, enhances EPs’ prognostic utility, but evidence synthesis is limited. …”
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Pesticide Residue Coverage Estimation on Citrus Leaf Using Image Analysis Assisted by Machine Learning by Adarsh Basavaraju, Edwin Davidson, Giulio Diracca, Chen Chen, Swadeshmukul Santra

“…As of today, there are no guiding digital tools available for citrus growers to provide pesticide residue leaf coverage analysis after foliar applications. Herein, we are the first to report software assisted by lightweight machine learning (ML) to determine the Kocide 3000 and Oxytetracycline (OTC) residue coverage on citrus leaves based on image data analysis. …”
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Indoor Air Quality Prediction in Sick Building Using Machine and Deep Learning: Comparative Analysis by Hayder Qasim Flayyih, Jumana Waleed, Amer M. Ibrahim

“…The artificial intelligence (AI) models presented in this work utilized Machine Learning (ML) and Deep Learning (DL) methodologies to train the available dataset. …”
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Machine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts of the Billion Tree Afforestation Project by Kaleem Mehmood, Shoaib Ahmad Anees, Sultan Muhammad, Fahad Shahzad, Qijing Liu, Waseem Razzaq Khan, Mansour Shrahili, Mohammad Javed Ansari, Timothy Dube

“…These results demonstrate the effectiveness of integrating machine learning with remote sensing as a framework to support data‐driven afforestation efforts and inform sustainable environmental management practices.…”
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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

“…We applied a comprehensive approach that integrates spatiotemporal and behavioural data during the transitional period following goals focusing on team dynamics, including chaotic and collective behavioural analysis with entropy and fractality, spatial area, movement trajectories, and locomotor patterns. …”
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Enhancing Multi-Disease Prediction with Machine Learning: A Comparative Analysis and Hyperparameter Optimization Approach by Mariam Kili Bechir, Ferhat Atasoy

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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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Deep learning-based video analysis for automatically detecting penetration and aspiration in videofluoroscopic swallowing study by Soyoung Kwak, Jeoung Kun Kim, Jun Sung Moon, Gun Woo Lee, Sungho Kim, Min Cheol Chang

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Leveraging environmental microbial indicators in wastewater for data-driven disease diagnostics by Gayatri Gogoi, Gayatri Gogoi, Sarangthem Dinamani Singh, Sarangthem Dinamani Singh, Devpratim Koch, Devpratim Koch, Emon Kalyan, Rashmi Rani Boro, Aradhana Devi, Hridoy Jyoti Mahanta, Hridoy Jyoti Mahanta, Pankaj Bharali, Pankaj Bharali

“…Unsupervised learning algorithms, including K-means and K-medoid clustering, were employed to categorize the data into four distinct clusters, revealing patterns of viral positivity and environmental conditions.ResultsCluster analysis indicated that seasonal variations and water quality characteristics significantly influenced SARS-CoV-2 positivity rates. …”
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Psychological Impact of Internet Blackouts: A Case Study With Machine Learning-Based Stress Analysis by Mohammad Ariful Islam, Tahidul Islam, Gahangir Hossain, M. Mofazzal Hossain

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Classification Analytics for Wind Turbine Blade Faults: Integrated Signal Analysis and Machine Learning Approach by Waqar Ali, Idriss El-Thalji, Knut Erik Teigen Giljarhus, Andreas Delimitis

“…This paper presents an approach to classify faults in wind turbine blades by leveraging well-known signals and analysis with machine learning techniques. The methodology involves a detailed feature engineering process that extracts and analyzes features from the time and frequency domains. …”
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Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection by Nuwan Madusanka, Hadi Sedigh Malekroodi, H. M. K. K. M. B. Herath, Chaminda Hewage, Myunggi Yi, Byeong-Il Lee

“…This study presents a novel framework that integrates Vision Graph Neural Networks (ViGs) with supervised contrastive learning for enhanced spectro-temporal image analysis of speech signals in Parkinson’s disease (PD) detection. …”
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A hybrid deep learning model for sentiment analysis of COVID-19 tweets with class balancing by Md. Alamin Talukder, Md. Ashraf Uddin, Suman Roy, Partho Ghose, Smita Sarker, Ansam Khraisat, Mohsin Kazi, Md Momtazur Rahman, Musawer Hakimi

“…Abstract The widespread dissemination of misinformation and the diverse public sentiment observed during the COVID-19 pandemic highlight the necessity for accurate sentiment analysis of social media discourse. This study proposes a hybrid deep learning (DL) model that integrates Bidirectional Encoder Representations from Transformers (BERT) for contextual feature extraction with Long Short-Term Memory (LSTM) networks for sequential learning to classify COVID-19-related sentiments. …”
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