A Proposed Method for Deep Learning-Based Automatic Tracking with Minimal Training Data for Sports Biomechanics Research by Daichi Yamashita, Minoru Matsumoto, Takeo Matsubayashi

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Insights into gender-equity in healthcare accessibility in northern Nigeria: descriptive and predictive approaches by Chika Yinka-Banjo, Olasupo Ajayi, Mary Akinyemi, David Tresner-Kirsch, Adekemi Omotubora

Subjects: “…Data analysis…”
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Application of Machine Learning Models for Baseball Outcome Prediction by Tzu-Chien Lo, Chen-Yin Lee, Chien-Lin Chen, Tsung-Yu Hsieh, Che-Hsiu Chen, Yen-Kuang Lin

“…Data science has become an essential component in professional sports, particularly for predicting team performance and outcomes. …”
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Unveiling Public Sentiments in Crisis: The EHDSAF Approach to Dark Social Media Data during the 2010 Pakistan Flood by Fazal Tariq, Muhammad Tufail, Taj Rehman

Subjects: “…Dark Data, Big Data, Machine Learning, Deep Learning, Sentiment Analysis, NLP, VADER.…”
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Data Augmentation Techniques for fMRI Data: A Technical Survey by Valentina Sanchez, Cicek Guven, Gonzalo Napoles, Marie Safar Postma

“…The application of machine learning to fMRI data classification, prediction, and analysis tasks has experienced rapid growth in recent years. …”
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Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction by Fabian Obster, Monica I. Ciolacu, Andreas Humpe

“…This paper investigates the empirical relationship between predictive performance, often called predictive power, and interpretability of various Machine Learning algorithms, focusing on bicycle traffic data from four cities. …”
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StarNet: Indian star gooseberries dataset for quality and maturity assessment(Mendeley Data). by Pushpa B․ R․, Manohar N․, N. Shobha Rani

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Cold-start visualization recommendation driven by large language models for ocean data analysis by Xin Li, Jixiu Liao, Wen Liu, Yu Miao, Leyu Wang, Shuqing Sun

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Lithological Classification Using ZY1-02D Hyperspectral Data by Means of Machine Learning and Deep Learning Methods in the Kohat–Pothohar Plateau, Khyber Pakhtunkhwa, Pakistan by Waqar Ahmad, Lei Liu, Zhenhua Guo, Yasir Shaheen Khalil, Nazir Ul Islam, Fakhrul Islam

“…The principal component analysis (PCA) obtained the highest eigenvalues and provided a significant discrimination of lithologies, particularly with hyperspectral data. …”
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Optimized deep learning models for stress-based stroke prediction from EEG signals by Sivasankaran Pichandi, Gomathy Balasubramanian, Venkatesh Chakrapani, J. Samuel Manoharan

Subjects: “…Stress analysis…”
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Chopped and minibars reinforced high-performance concrete: machine learning prediction of mechanical properties by Nikolai Ivanovich Vatin, Mohammad Hematibahar, Tesfaldet Hadgembes Gebre

“…In this study, the compressive strength and flexural strength are predicted via different types of machine learning models. Experiments carried out in the laboratory under standard controlled settings at 7, 14, and 28-day curing periods yielded sample data for analysis and model development. …”
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Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods by Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani

“…In addition, the graphical-based and statistical-based evaluations illustrate that AdaBoost and ensemble learning models outperforms all other developed data-driven intelligent models as these two models are associated with lowest values of mean square error (adaptive boosting: 0.017 and ensemble learning: 0.021 based on unseen test data) and largest values of coefficient of determination (adaptive boosting: 0.986 and ensemble learning: 0.983 based on unseen test data).…”
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Identifying Behaviours Indicative of Illegal Fishing Activities in Automatic Identification System Data by Yifan Zhou, Richard Davies, James Wright, Stephen Ablett, Simon Maskell

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ANALYSIS OF THE APPLICATIONS OF THE DATA-DRIVEN APPROACH IN EVALUATING THE THERMAL-PHYSICAL PROPERTIES OF COMPOSITES by Ruslan Lavshchenko, Gennadiy Lvov

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Enhancing customer retention with machine learning: A comparative analysis of ensemble models for accurate churn prediction by Payam Boozary, Sogand Sheykhan, Hamed GhorbanTanhaei, Cosimo Magazzino

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Quantum algorithms and complexity in healthcare applications: a systematic review with machine learning-optimized analysis by Agostino Marengo, Vito Santamato

“…This approach revealed two primary research directions: (1) quantum computing for artificial intelligence in healthcare, and (2) quantum computing for healthcare data security. We highlight the theoretical advances underlying these domains, from novel quantum machine learning algorithms for biomedical data to quantum cryptographic protocols for securing medical information. …”
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An Improved Machine Learning-Based Method for Unsupervised Characterisation for Coral Reef Monitoring in Earth Observation Time-Series Data by Zayad AlZayer, Philippa Mason, Robert Platt, Cédric M. John

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Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology by Hakim Benkirane, Maria Vakalopoulou, David Planchard, Julien Adam, Ken Olaussen, Stefan Michiels, Paul-Henry Cournède

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spCLUE: a contrastive learning approach to unified spatial transcriptomics analysis across single-slice and multi-slice data by Xiang Wang, Wei Vivian Li, Hongwei Li

“…We introduce spCLUE, a comprehensive framework combining multi-view graph network, contrastive learning, attention mechanisms, and a batch prompting module to learn informative spot representations and integrate data from both aligned and unaligned samples. spCLUE outperforms nine single-slice and seven multi-slice methods when tested on diverse datasets and reveals biologically relevant domains across different tissues and conditions. spCLUE offers a powerful solution to spatial domain analysis and integration in spatial transcriptomics, enabling more accurate and interpretable studies of tissue organization.…”
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Accelerating domain-aware electron microscopy analysis using deep learning models with synthetic data and image-wide confidence scoring by M. J. Lynch, R. Jacobs, G. A. Bruno, P. Patki, D. Morgan, K. G. Field

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