Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods by Mehmet Kızıldağ, Fatih Abut, Mehmet Fatih Akay

“…The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. …”
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Turning local anisotropy for macroscopic auxeticity: Design auxetic meta-laminae via systematic finite element simulations and machine learning approach by Shujing Dong, Ammar Batwa, Yaning Li

“…This investigation delves into patch pattern-property relationships, machine learning methods, and inverse design approaches tailored for specific properties. …”
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Real‐time object detection for unmanned vehicles in Bangladesh: Dataset, implementation and evaluation by Muhammad Liakat Ali, Topu Biswas, Shahin Akter, Mohammed Farhan Jawad, Hadaate Ullah

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Estimated Disease Progression Trajectory of White Matter Disruption in Unilateral Temporal Lobe Epilepsy: A Data-Driven Machine Learning Approach by Daichi Sone, Noriko Sato, Yoko Shigemoto, Iman Beheshti, Yukio Kimura, Hiroshi Matsuda

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The dynamic linkage between covid-19 and nutrition: a review from a probiotics perspective using machine learning and bibliometric analysis by Christos Stefanis, Christina Tsigalou, Ioanna Bezirtzoglou, Gregoria Mitropoulou, Chrysoula Voidarou, Elisavet Stavropoulou

“…This study attempts to detect the relationship between dietary patterns and the disease of COVID-19 and emphasizes research on probiotics by mapping the knowledge produced during the pandemic until 2024.MethodsIn addition to bibliometrics, a machine-learning framework, ASReview, was used to structure the literature search. …”
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Machine learning-based construction of a programmed cell death-related model reveals prognosis and immune infiltration in pancreatic adenocarcinoma patients by Bing Wang, Zhida Long, Xun Zou, Zhengang Sun, Yuanchu Xiao

“…High-risk patients exhibited worse prognosis and immunosuppressive infiltration patterns. Furthermore, consensus clustering identified two PAAD molecular subtypes with distinct PCDRGs expression patterns and survival outcomes. …”
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QCAE-QOC-SVM: A hybrid quantum machine learning model for DoS and Fuzzy attack detection on autonomous vehicle CAN bus by Meghana R, Sowmyashree Sakrepatna Ramesha, Adwitiya Mukhopadhyay

“…Our approach is a combination of a Quantum Convolutional Autoencoder (QCAE) and a Quantum Orthogonal Classifier based on Support Vector Machines (QOC-SVM). The method effectively extracts patterns from CAN bus traffic with the help of quantum-powered classification for accurate anomaly detection. …”
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Using modern clustering techniques for parametric fault diagnostics of turbofan engines by I. J. Buraimah

“…Differences in cluster groupings/patterns between healthy engine and engine with degraded performance are compared and used as the bases for defining faults. …”
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Integrating Metabolomics and Machine Learning to Analyze Chemical Markers and Ecological Regulatory Mechanisms of Geographical Differentiation in <i>Thesium chinense</i> Turcz by Cong Wang, Ke Che, Guanglei Zhang, Hao Yu, Junsong Wang

“…This study integrates metabolomics, machine learning, and ecological factor analysis to elucidate the geographical variation patterns and regulatory mechanisms of secondary metabolites in <i>T. chinense</i> Turcz. from Anhui, Henan, and Shanxi Provinces. …”
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Evaluating climatic variability's impact on milk yield across climate zones: A machine learning-based comparative study of Switzerland and Thailand by Boonyarat Phadermrod, Varunya Attasena

“…Across all scenarios, previous milk yield is a stronger predictor than short-term meteorological variables, suggesting that recent production trends already reflect key weather effects. This pattern also holds within homogeneous sub-datasets. …”
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Application of flexible sensor multimodal data fusion system based on artificial synapse and machine learning in athletic injury prevention and health monitoring by XiaoLan Gai

“…The system achieves a 92.1% accuracy rate in the detection of improper motion patterns and prediction of injury risks, much higher than traditional methods. …”
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Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060 by Joshua Veli Tampubolon, Rinaldy Dalimi, Budi Sudiarto

“…To forestall instability, we developed a predictive simulation based on long short-term memory (LSTM) networks that combines historical generation and consumption patterns with models of EV population growth and initial charging-time (ICT). …”
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A Multi-Model Machine Learning Framework for Identifying Raloxifene as a Novel RNA Polymerase Inhibitor from FDA-Approved Drugs by Nhung Thi Hong Van, Minh Tuan Nguyen

“…The molecular dynamics simulation confirmed stable binding with RMSD values of 0.12–0.15 nm for the protein–ligand complex and consistent hydrogen bonding patterns. Our findings suggest that raloxifene may possess RdRP inhibitory activity, providing a foundation for its experimental validation as a potential broad-spectrum antiviral agent.…”
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State of Health Estimation for Lithium-Ion Batteries Using Electrochemical Impedance Spectroscopy and a Multi-Scale Kernel Extreme Learning Machine by Jichang Peng, Ya Gao, Lei Cai, Ming Zhang, Chenghao Sun, Haitao Liu

“…While electrochemical impedance spectroscopy (EIS) effectively characterizes LIBs degradation patterns, the high dimensionality of EIS data poses challenges for an efficient analysis. …”
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Uncovering the epigenetic regulatory clues of PRRT1 in Alzheimer’s disease: a strategy integrating multi-omics analysis with explainable machine learning by Fang Wang, Ying Liang, Qin-Wen Wang

“…Utilizing interpretable machine learning models and ELMAR analysis, we dissected the complex relationships between these epigenetic signatures and gene expression patterns, revealing novel regulatory elements and pathways. …”
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An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data by Amena Mahmoud, Eiko Takaoka

“…Our method addresses the challenges of high dimensionality and complex patterns in genomic data to improve diagnostic accuracy and interpretability. …”
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IoT-Enabled Machine Learning for Comprehensive Water Quality Assessment in the Mahanadi River: A Multibelt Analysis of Seasonal Contamination and Predictive Modeling by Suprava Ranjan Laha, Binod Kumar Pattanayak, Saurav Kumar, Mitrabinda Ray, Saumendra Pattnaik

“…Comparative evaluations highlight the superiority of the proposed method in seasonal patterns, the calculation of the water quality index (WQI), and belt-wise comparisons. …”
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A Cloud-Based Framework for Creating Scalable Machine Learning Models Predicting Building Energy Consumption from Digital Twin Data by Elham Mahamedi, Alaeldin Suliman, Martin Wonders

“…., sensors and IoT devices), enabling real-time representation of physical building states in a digital environment. Although machine learning (ML) techniques are increasingly used to predict building energy consumption from this DT data, existing approaches often lack scalability in handling data growth (data scalability) and/or adapting to evolving data patterns (model scalability). …”
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Double-diffusive magnetoconvection in a tilted porous parallelogrammic domain with discrete heated-cooled segments: Leveraging machine learning and CFD approach by P. Ravindra, M. Sankar, Suresh Rasappan, Wardah Abdullah Al Majrafi, Pugalarasu Rajan, S. Sanal Kumar

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Geospatial analysis of short sleep duration and cognitive disability in US adults: a multi-state study using machine learning techniques by Tue T. Te, Alex A. T. Bui, Constance H. Fung, Mary Regina Boland

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