Monitoring the dynamics of coastal wetlands ecosystems in Brittany (France) using LANDSAT time series and machine learning by Adrien Le Guillou, Simona Niculescu

“…The results revealed contrasting spatial patterns. The Audierne Bay has experienced significant urban expansion, with a 24% increase upstream, as well as coastal erosion reaching 1.63 m/year locally, with a retreat of approximately 50 m in the most affected areas during the period 1990–2020. …”
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Machine-learning unravels spatial shifting in homogeneous rainfall subregions in Central Africa under global warming by Alain T Tamoffo, Fernand L Mouassom, Torsten Weber

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Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors by Ana González-Castro, José Alberto Benítez-Andrades, Rubén González-González, Camino Prada-García, Raquel Leirós-Rodríguez

“…Models were trained using accelerometric data (movement patterns) and non-accelerometric data (demographic and clinical variables). …”
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Statistical modelling and forecasting of HIV and anti-retroviral therapy cases by time-series and machine learning models by Abdullah M. Almarashi

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Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy by Alex Noh, Sabrina Xin Zi Quek, Nuraini Zailani, Juin Shin Wee, Derrick Yong, Byeong Yun Ahn, Khek Yu Ho, Hyunsoo Chung

“…High-quality spectra (800–3300 cm⁻¹) revealed distinct patterns: adenocarcinoma tissues had higher intensities below 3150 cm⁻¹, while benign tissues exhibited higher intensities between 3150 and 3290 cm⁻¹ (p < 0.001). …”
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Identification of potential diagnostic markers and molecular mechanisms of asthma and ulcerative colitis based on bioinformatics and machine learning by Chenxuyu Zhang, Chenxuyu Zhang, Zheng Luo, Liang Ji

“…Gene Set Enrichment Analysis (GSEA) explored pathway alterations, while immune infiltration patterns were analyzed using the CIBERSORT algorithm. …”
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Two Decades of Coastal Development across the Arabian Gulf: Insights from Remote Sensing and Machine Learning by B. Dahy, R. Sousa, M. Sultan, J. A. Burt

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Elegante+: A Machine Learning-Based Optimization Framework for Sparse Matrix–Vector Computations on the CPU Architecture by Muhammad Ahmad, Sardar Usman, Ameer Hamza, Muhammad Muzamil, Ildar Batyrshin

“…However, due to the sparsity patterns of matrices and the diverse configurations of hardware, accurately modeling the performance of SpMV remains a complex challenge. …”
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Comparative Analysis of Machine Learning Algorithms and Statistical Techniques for Data Analysis in Crop Growth Monitoring with NDVI by M. Arunachalam, S. Sekar, A. M. Erdmann, V. V. Sajith Variyar, R. Sivanpillai

“…We assessed the potential of Machine Learning (ML) for mapping crop growth in three flood irrigated fields. …”
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Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas by Yue Hu, Xin Cao, Hongyi Chen, Daoying Geng, Kun Lv

“…The biological interpretability of selected features reflects distinct neuroplasticity patterns between LGGs and HGGs, advancing understanding of glioma-network interactions.…”
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Antifungal Susceptibility Testing in HIV/AIDS Patients: a Comparison Between Automated Machine and Manual Method by Erni J Nelwan, Evi Indrasanti, Robert Sinto, Farida Nurchaida, Rustadi Sosrosumihardjo

“…Resistant patterns for C. glabrata to fluconazole, voriconazole and amphotericin B were 52.4%, 23.8%, 23.8% vs. 9.5%, 9.5%, 4.8% respectively between manual diffusion disc methods and Vitek2 machine. …”
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Deciphering the proteome of Escherichia coli K-12: Integrating transcriptomics and machine learning to annotate hypothetical proteins by Sagarika Chakraborty, Zachary Ardern, Habibu Aliyu, Anne-Kristin Kaster

“…We further provide experimental validation of in silico predicted functions for three HP-encoding genes (yhdN, yeaC and ydgH) as proof of concept, by analyzing growth patterns of deletion mutants compared to the wild type, as well as their transcriptional responses to specific conditions. …”
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Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach by Rahem Rahmati, Fatemeh Zarimeidani, Farnaz Ahmadi, Hannaneh Yousefi-Koma, Abdolreza Mohammadnia, Maryam Hajimoradi, Shadi Shafaghi, Elham Nazari

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Mathematics and Machine Learning for Visual Computing in Medicine: Acquisition, Processing, Analysis, Visualization, and Interpretation of Visual Information by Bin Li, Shixiang Feng, Jinhong Zhang, Guangbin Chen, Shiyang Huang, Sibei Li, Yuxin Zhang

“…Visual computing in medicine involves handling the generation, acquisition, processing, analysis, exploration, visualization, and interpretation of medical visual information. Machine learning has become a prominent tool for data analytics and problem-solving, which is the process of enabling computers to automatically learn from data and obtain certain knowledge, patterns, or input–output relationships. …”
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Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review by Daniel H. de la Iglesia, Carlos Chinchilla Corbacho, Jorge Zakour Dib, Vidal Alonso-Secades, Alfonso J. López Rivero

“…This systematic review presents a critical analysis of advanced machine learning (ML) and deep learning (DL) approaches for predicting the remaining useful life (RUL) of electric vehicle (EV) batteries. …”
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Automatic priority analysis of emergency response systems using internet of things (IoT) and machine learning (ML) by Abu S.M. Mohsin, Shadab H. Choudhury, Munyem Ahammad Muyeed

“…This paper presents a comprehensive framework for deploying an IoT, and ML-driven emergency response system (ERS), which uses real-time data analysis and predictive modelling to identify patterns and prioritise responses based on their expected impact, urgency, distance and available resources. …”
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Enhanced Gold Ore Classification: A Comparative Analysis of Machine Learning Techniques with Textural and Chemical Data by Fabrizzio Rodrigues Costa, Cleyton de Carvalho Carneiro, Carina Ulsen

“…Several supervised and unsupervised machine learning methods and applications integrate a wide variety of algorithms that aim at the efficient recognition of patterns and similarities and the ability to make accurate and assertive decisions. …”
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Identifying Diagnostic Biomarkers for Electroacupuncture Treatment of Rheumatoid Arthritis Using Bioinformatic Analysis and Machine Learning Algorithms by Sun Y, Dong G, Gao H, Yao Y, Yang H

“…A rat model of RA was established using Complete Freund’s Adjuvant (CFA), and quantitative real-time PCR was performed to confirm the differential expression of identified diagnostic biomarkers and assess the modulatory impact of EA on these genes.Results: Twenty-six genes were identified as differentially expressed following EA treatment. Three machine learning algorithms converged on ARHGAP17 and VEGFB as potential diagnostic biomarkers for RA, exhibiting robust diagnostic performance (AUC &gt; 0.75) and consistent expression patterns across multiple RA cohorts (GSE17755, GSE205962 and GSE93272). …”
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Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs by Kailei Liu, Boyue Xu, Changjea Kim, Jing Fu

“…The first method is the artificial neural network, through which we can analyze the big data from unconventional reservoirs to understand the underlying patterns and relationships. A bunch of factors is contained such as hydraulic fracture parameters, well completion, and production data. …”
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A multi-biomarker machine learning approach for early prediction of interstitial lung disease in rheumatoid arthritis by Jiaojiao Xu, Wei Zhang, Weili Bai, Nannan Gai, Jing Li, Yunqi Bao

“…The ILD group exhibited significantly elevated levels of inflammatory markers and specific biomarkers, particularly KL-6 (826.4 ± 458.2 vs. 285.6 ± 124.8 U/ml, P < 0.001), alongside distinct patterns in hematological parameters. Conclusion Machine learning approaches, particularly XGBoost, demonstrate promising potential for early RA-ILD prediction. …”
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