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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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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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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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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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

“…We developed a multi-model machine learning framework combining five traditional algorithms (ExtraTreesClassifier, RandomForestClassifier, LGBMClassifier, BernoulliNB, and BaggingClassifier) with a CNN deep learning model to identify potential RdRP inhibitors among FDA-approved drugs. …”
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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

“…Incorporating sophisticated data analysis and machine learning empowers precise predictions and comprehensive insights. …”
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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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A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE by Md. Shamshuzzoha, Tazkia Tasnim Bahar Audry, Md. Jahangir Alam, Zaheed Ahmed Bhuiyan, Md Motaharul Islam, Mohammad Mehedi Hassan

“…This study addresses these gaps by curating a unique social media dataset that captures seasonal patterns and employing advanced machine learning techniques for accurate SAD detection. …”
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Real-Time Depth Monitoring of Air-Film Cooling Holes in Turbine Blades via Coherent Imaging During Femtosecond Laser Machining by Yi Yu, Ruijia Liu, Chenyu Xiao, Ping Xu

“…The demonstrated system represents an advancement in non-destructive in-process monitoring for high-precision laser machining applications.…”
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Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds. by Fei Cai, Yuehua Wei, Daniel Kirchhofer, Andrew Chang, Yingnan Zhang

“…We hypothesized that specific sequence patterns within the peptide scaffolds played a crucial role in spontaneous folding into a stable topology, and thus, these sequences should not be subject to randomization in the original library design. …”
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Decoding corporate communication strategies: Analysing mandatory published information under Pillar 3 across turbulent periods with unsupervised machine learning. by Anna Pilková, Michal Munk, Lívia Kelebercová

“…This study explores the communication patterns of Slovak banks with stakeholders through mandatory disclosures mandated by Basel III's Pillar 3 framework and annual reports in 2007-2022. …”
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Development of a flexible electronic control unit for seamless integration of machine vision to CAN-enabled boom sprayers for spot application technology by Mozammel Bin Motalab, Ahmad Al-Mallahi

“…The first used UART protocol to parse machine vision messages, detect pest areas, and convert them into binary arrays for nozzle activation. …”
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Exploring the Streetscape Perceptions from the Perspective of Salient Landscape Element Combination: An Interpretable Machine Learning Approach for Optimizing Visual Quality of Str... by Wanyue Suo, Jing Zhao

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A hybrid model integrating recurrent neural networks and the semi-supervised support vector machine for identification of early student dropout risk by Huong Nguyen Thi Cam, Aliza Sarlan, Noreen Izza Arshad

“…Methods A hybrid prediction model DeepS3VM is designed by integrating a Semi-supervised support vector machine (S3VM) model with a recurrent neural network (RNN) to capture sequential patterns in student dropout prediction. …”
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Machine learning-based spatio-temporal assessment of land use/land cover change in Barishal district of Bangladesh between 1988 and 2024 by Walida Zaman, H Rainak Khan Real

“…The performance of four machine learning algorithms (Support Vector Machine, Classification and Regression Tree, K-Nearest Neighbor, and Random Forests) were evaluated to ensure classification reliability. …”
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A Cross-Machine Intelligent Fault Diagnosis Method with Small and Imbalanced Data Based on the ResFCN Deep Transfer Learning Model by Juanru Zhao, Mei Yuan, Yiwen Cui, Jin Cui

“…In this paper, we propose a cross-machine IFD method based on a residual full convolutional neural network (ResFCN) transfer learning model, which leverages the time-series features of monitoring data. …”
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Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains by Kaizhe Jin, Adrian Rubio-Solis, Ravi Naik, Daniel Leff, James Kinross, George Mylonas

“…Studies were included if they assessed cognitive states using physiological signals and applied machine learning (ML) or deep learning (DL) techniques in practical task settings. …”
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