Machine Learning Monte Carlo Approaches and Statistical Physics Notions to Characterize Bacterial Species in Human Microbiota by Michele Bellingeri, Leonardo Mancabelli, Christian Milani, Gabriele Andrea Lugli, Roberto Alfieri, Massimiliano Turchetto, Marco Ventura, Davide Cassi

“…ML methods help analyze large datasets to uncover microbiota patterns and understand how these patterns affect human health. …”
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The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine learning by Beatrice Borsari, Mor Frank, Eve S. Wattenberg, Ke Xu, Susanna X. Liu, Xuezhu Yu, Mark Gerstein

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Deciphering Car Crash Dynamics in Greater Melbourne: a Multi-Model Machine Learning and Geospatial Analysis by Christopher JOHNSON, ZHOU Heng, Richard TAY, SUN Qian(Chayn)

“…In the continually evolving landscape of data-driven methodologies addressing car crash patterns, a holistic analysis remains critical to decode the complex nuances of this phenomenon. …”
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Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables by Amir Shahcheraghian, Adrian Ilinca

“…Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. …”
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Tropospheric NO₂: Anthropogenic Influence, Global Trends, Satellite Data, and Machine Learning Application by Valeria Ojeda-Castillo, Mario Alfonso Murillo-Tovar, Leonel Hernández-Mena, Hugo Saldarriaga-Noreña, María Elena Vargas-Amado, Enrique J. Herrera-López, Jesús Díaz

“…This investigation employs the Spectral Angle Mapper (SAM), a geometric machine-learning model, given its advantages in simplicity and computational efficiency, and OMI satellite measurements to carry out spatially supervised classification of tropospheric NO₂ global patterns from 2005 to 2021. …”
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Integrative multi-omics analysis and machine learning refine global histone modification features in prostate cancer by XiaoFeng He, QinTao Ge, QinTao Ge, WenYang Zhao, Chao Yu, HuiMing Bai, XiaoTong Wu, Jing Tao, WenHao Xu, WenHao Xu, Yunhua Qiu, Lei Chen, JianFeng Yang

“…The Comprehensive Machine Learning Histone Modification Score (CMLHMS) was developed to classify PCa into two distinct subtypes based on histone modification patterns. …”
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GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds by D. Oakley, D. Oakley, C. Loiselet, T. Coowar, T. Coowar, V. Labbe, J.-P. Callot

“…In this paper, we present automated workflows for detecting geological folds from map data using both unsupervised and supervised machine learning. For the unsupervised case, we use regular expression matching to identify map patterns suggestive of folds along lines crossing the map. …”
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A Comprehensive Review of Transformer Winding Diagnostics: Integrating Frequency Response Analysis with Machine Learning Approaches by Meysam Beheshti Asl, Issouf Fofana, Fethi Meghnefi, Youssouf Brahami, Joao Pedro Da Costa Souza

“…However, traditional FRA interpretation relies heavily on visual and subjective comparison of frequency response curves, which can introduce human bias and lead to inconsistent results. Integrating Machine Learning (ML) with FRA can significantly enhance fault diagnosis by automatically identifying complex patterns within the data that are difficult to detect using through human analysis. …”
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Machine learning approaches for spatial omics data analysis in digital pathology: tools and applications in genitourinary oncology by Hojung Kim, Hojung Kim, Jina Kim, Jina Kim, Su Yeon Yeon, Sungyong You, Sungyong You

“…Recent advances in spatial omics technologies have enabled new approaches for analyzing tissue morphology, cell composition, and biomolecule expression patterns in situ. These advances are promoting the development of new computational tools and quantitative techniques in the emerging field of digital pathology. …”
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ML‐UrineQuant: A machine learning program for identifying and quantifying mouse urine on absorbent paper by Warren G. Hill, Bryce MacIver, Gary A. Churchill, Mariana G. DeOliveira, Mark L. Zeidel, Marcelo Cicconet

“…Abstract The void spot assay has gained popularity as a way of assessing functional bladder voiding parameters in mice, but analyzing the size and distribution of urine spot patterns on filter paper with software remains problematic due to inter‐laboratory differences in image contrast and resolution quality and non‐void artifacts. …”
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Using Machine Learning Approaches on Dynamic Patient-Reported Outcomes to Cluster Cancer Treatment-Related Symptoms by Nora Asper, Hans Friedrich Witschel, Louise von Stockar, Emanuele Laurenzi, Hans Christian Kolberg, Marcus Vetter, Sven Roth, Gerd Kullak-Ublick, Andreas Trojan

“…We aimed to examine whether the patterns in electronic patient-reported outcomes, without any additional clinician data input, are predictive of the underlying cancer type and reflect tumor- and treatment-associated symptom clusters (SCs). …”
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The Development of a Wearable-Based System for Detecting Shaken Baby Syndrome Using Machine Learning Models by Ram Kinker Mishra, Khalid AlAnsari, Rylee Cole, Arin Nazarian, Ilkay Yildiz Potter, Ashkan Vaziri

“…This study proposes an inertial measurement unit (IMU)-based detection system enhanced with machine learning to identify aggressive shaking patterns. …”
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Safeguarding against Cyber Threats: Machine Learning-Based Approaches for Real-Time Fraud Detection and Prevention by Vikas R. Shetty, Pooja R., Rashmi Laxmikant Malghan

“…In addition, deep learning techniques and artificial neural networks (ANN) are used to detect complex fraud patterns, while logistic regression is used to model the probability of fraudulent events. …”
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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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Machine Learning Insights into the Last 400 Years of Etna Lateral Eruptions from Historical Volcanological Data by Arianna Beatrice Malaguti, Claudia Corradino, Alessandro La Spina, Stefano Branca, Ciro Del Negro

“…Here, we applied a machine learning technique to automate the analysis of these datasets, handling intricate patterns that are not easily captured by explicit commands. …”
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A Comparative Study of Machine Learning Algorithms for Intrusion Detection Systems using the NSL-KDD Dataset by Rulyansyah Permata Putra, Amarudin Amarudin

“…In today’s digital era, cyberattacks are becoming increasingly complex, rendering traditional rule-based Intrusion Detection Systems (IDS) often ineffective in recognizing new attack patterns. The primary objective of this study is to design and implement a machine learning model for detecting network intrusions efficiently while minimizing latency, through a comparative analysis of several algorithms: Decision Tree, Random Forest, Support Vector Machine (SVM), and Boosting. …”
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Overview of deep learning and large language models in machine translation: a special perspective on the Arabic language by Sanaa Abou Elhamayed, Mohamed Nour

“…The bidirectional-encoder-representation from transformer (BERT) and LLMs are presented to utilize the big amount of textual data to learn translation patterns. The main measurable criteria that are used to evaluate the performance of MT and Arabic machine translation (AMT) are also presented. …”
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Optimizing a Machine Learning Algorithm by a Novel Metaheuristic Approach: A Case Study in Forecasting by Bahadır Gülsün, Muhammed Resul Aydin

“…Accurate sales forecasting is essential for optimizing resource allocation, managing inventory, and maximizing profit in competitive markets. Machine learning models are being increasingly used to develop reliable sales-forecasting systems due to their advanced capabilities in handling complex data patterns. …”
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Dynamic Dual-Phase Forecasting Model for New Product Demand Using Machine Learning and Statistical Control by Chien-Chih Wang

“…These findings underscore the framework’s resilience in cold-start situations and its capacity to adapt to evolving demand patterns, providing a viable solution for data-scarce and dynamic manufacturing environments.…”
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A Comprehensive Analysis of Imbalance Signal Prediction in the Japanese Electricity Market Using Machine Learning Techniques by Kaiyao Jiang, Yuji Yamada

“…This study comprehensively analyzes imbalance signal dynamics and develops practical forecasting tools using Machine Learning (ML) techniques. By incorporating a diverse range of features—including lagged imbalance data, weather forecast errors specific to Japan, and temporal patterns—we demonstrate that the prediction accuracy of imbalance signals is significantly improved compared to a baseline reflecting random forecasts based on class distribution observed during the initial training period. …”
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