Conflict evidence combination rule based on multi-dimensional weighted evidence optimization method and its applications in pattern recognition by Fuhai Xi, Mengli Mei, Shi Yang, Hang Guo, Min Yu

“…Abstract In complex pattern recognition scenarios, multi-source information fusion faces critical challenges in handling conflicting evidence. …”
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Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models by Benedictor Alexander Nguchu, Benedictor Alexander Nguchu, Yifei Han, Yanming Wang, Peter Shaw

“…The features were specifically the gray matter volume and dopaminergic features of the neostriatum, i.e., the caudate, putamen, and anterior putamen. We use machine learning (ML) algorithms, including Random Forest, Logistic Regression, and Support Vector Machine, to evaluate the diagnostic power of the brain features and network patterns in differentiating the PD subtypes and distinguishing PD from HC. …”
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Machine Learning-Based Identification of Phonological Biomarkers for Speech Sound Disorders in Saudi Arabic-Speaking Children by Deema F. Turki, Ahmad F. Turki

“…SHAP analysis revealed that articulation patterns and phonological patterns were the most influential features for distinguishing between Atypical and TD categories. …”
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Machine Learning-Driven Prediction of Offshore Vessel Detention: The Role of Neural Networks in Port State Control by Zlatko Boko, Tatjana Stanivuk, Nenad Radanović, Ivica Skoko

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Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning by Sangwon Lee, Yongha Hwang, Yan Jin, Sihyeong Ahn, Jaewan Park

“…Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. …”
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Predicting Weather Disruptions for the ICC Champions Trophy 2025 in Pakistan Using Machine Learning and Data Analytics by Syeda Faiza Nasim, Umm-e-Kulsoom, Syeda Alishba Fatima, Salka Naushad

“…It is vital for event planners to comprehend local climate dynamics since this variability causes unpredictable weather patterns, such as monsoon rains, intense heat waves, and droughts. …”
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Advancing Cybersecurity Through Machine Learning: A Scientometric Analysis of Global Research Trends and Influential Contributions by Kamran Razzaq, Mahmood Shah

“…This scientometric study aims to comprehensively analyse the study patterns and key contributions at the nexus of cybersecurity and machine learning. …”
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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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Improving the Efficiency of Screen Type Potato Sorting Machines by a Modification of the Sieve Drive Movement Algorithm by A. G. Ivanov, M. N. Erokhin, S. P. Kazantsev, P. V. Dorodov, I. I. Khuzyakhmetov, I. T. Khakimov

“…(Research purpose) To upgrade the sizing machine drive by modifying the sieve movement patterns for improving its operation efficiency. …”
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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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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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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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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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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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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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