AI-driven diagnosis and health management of autonomous electric vehicle powertrains: An empirical data-driven approach by Hicham El hadraoui, Adila El maghraoui, Oussama Laayati, Erroumayssae Sabani, Mourad Zegrari, Ahmed Chebak

“…The study focuses on identifying the most informative features from time, frequency, and wavelet domains, followed by dimensionality reduction using Principal Component Analysis (PCA) and Correlation Analysis (CA) to enhance classification performance, reduce complexity, and improve model interpretability. …”
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Supervised and unsupervised machine learning approaches for prediction and geographical discrimination of Iranian saffron ecotypes based on flower-related and phytochemical attribu... by Seid Mohammad Alavi-Siney, Jalal Saba, Alireza Fotuhi Siahpirani, Jaber Nasiri

“…Finally, considering the highest accuracy value of the superior classification model of Random forest, both feature subsets of “FFW, FDW, Picrocrocin, Safranal, and Crocin” and “SFW, FDW, Picrocrocin, Safranal, and Crocin” were nominated as the most powerful elements (comparing to the remaining 1021 feature subsets) to make accurate discrimination between Khorasan and non-Khorasan saffron ecotypes. …”
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Assisted Parkinsonism Diagnosis Using Multimodal MRI—The Role of Clinical Insights by Tobias Meindl, Alexander Hapfelmeier, Tobias Mantel, Angela Jochim, Jonas Deppe, Silke Zwirner, Jan S. Kirschke, Yong Li, Bernhard Haslinger

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Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis by Deepak Kumar, Brijesh Bakariya, Chaman Verma, Zoltán Illés

“…Background and Objective:: Accurate classification of liver disease stages provides crucial insights into patient prognosis, aiding in the prediction of disease outcomes and influencing clinical decision-making. …”
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Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages by Muhammad Kashif Nazir, Cm Nadeem Faisal, Muhammad Asif Habib, Haseeb Ahmad

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ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network by Xiao Feng, Xiao Feng, Zhongyuan Guo, Zhongyuan Guo, Sam Kwong

“…In addition, a fully connected layer with weight freezing is utilized to effectively suppress the negative influence of neurons on the model classification. With the global average pooling (GAP) layer incorporated in the residual shrinkage network structure, we introduce an EEG-based Class Activation Map (ECAM) interpretable method to enable visualization analysis of sample-wise learned patterns to effectively explain the model decision. …”
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Proposing a machine learning-based model for predicting nonreassuring fetal heart by Nasibeh Roozbeh, Farideh Montazeri, Mohammadsadegh Vahidi Farashah, Vahid Mehrnoush, Fatemeh Darsareh

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A Novel LSTM Architecture for Automatic Modulation Recognition: Comparative Analysis With Conventional Machine Learning and RNN-Based Approaches by Sam Ansari, Soliman Mahmoud, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny

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Classifying Flash Flood Disasters From Disaster‐Prone Environments to Support Mitigation Measures by Xiaoyan Zhai, Yongyong Zhang, Yongqiang Zhang, Ronghua Liu, Changjun Liu, Xiaoxiang Zhang, Yuehong Chen, Xiekang Wang, Nigel Wright

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Unlocking Retail Insights: Predictive Modeling and Customer Segmentation Through Data Analytics by Juan Tang

“…This research aims at examining the progress of retail demand forecasting and customer classification via regression models and RFM analysis in the retail chain industry. …”
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Enhancing predictive maintenance in automotive industry: addressing class imbalance using advanced machine learning techniques by Yashashree Mahale, Shrikrishna Kolhar, Anjali S. More

“…The on-board diagnostic dataset utilized has only 16.3% of the failure data, and to address this, 3 key approaches were explored: [i] synthetic minority oversampling technique (SMOTE), [ii] cost-sensitive learning, [iii] ensemble methods. …”
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Validity of the Updated Rx-Risk Index as a Disease Identification and Risk-Adjustment Tool for Use in Observational Health Studies by Widagdo I, Kerr M, Kalisch Ellett L, Schlegel C, Sadeqzadeh E, Wang A, Clarke AL, Pratt N

“…The Rx-Risk Index’s predictive validity for one-year mortality was also evaluated using logistic regression, with model fit assessed by AIC and c-statistic.Results: Data were analysed from 3,959 individuals in PLIDA and 157,709 individuals in NHDH. …”
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Matching Game Preferences Through Dialogical Large Language Models: A Perspective by Renaud Fabre, Daniel Egret, Patrice Bellot

“…This approach envisions personalizing LLMs by embedding individual user preferences directly into how the model makes decisions. The proposed D-LLM framework would require three main components: (1) reasoning processes that could analyze different search experiences and guide performance, (2) classification systems that would identify user preference patterns, and (3) dialogue approaches that could help humans resolve conflicting information. …”
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The analysis of motion recognition model for badminton player movements using machine learning by Xuanmin Zhu, Lizhi Liu, Jingshuo Huang, Genyan Chen, Xi Ling, Yanshuo Chen

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Association of insurance status with potentially avoidable transfers to an academic emergency department: A retrospective observational study by Megan K. Wright, Wu Gong, Kimberly Hart, Wesley H. Self, Michael J. Ward

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Debris-covered glaciers mapping based on machine learning and multi-source satellite images over Eastern Pamir by Aamir Feroz, Shuang Liang, Gunayu Li, Xinwu Li, Zahid Ur Rahman, Bojin Yang

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Optimizing Automated Negotiation: Integrating Opponent Modeling with Reinforcement Learning for Strategy Enhancement by Ya Zhang, Jinghua Wu, Ruiyang Cao

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Integrated radiomics and deep learning model for identifying medullary sponge kidney stones by Yubao Liu, Haifeng Song, Daxun Luo, Rui Xu, Zheng Xu, Bixiao Wang, Weiguo Hu, Bo Xiao, Gang Zhang, Jianxing Li

“…Radiomics features were extracted from manually delineated regions of interest (ROI) on nephrographic-phase CT images, while deep learning features were derived from a ResNet101-based model. Three diagnostic signatures—Radiomics (Rad), Deep Transfer Learning (DTL), and Deep Learning Radiomics (DLR)—were developed. …”
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A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…<h4>Results</h4>The logistic regression model emerged as the optimal model for predicting recurrence of NPM with machine learning, primarily utilizing three variables: FIB, bacterial infection, and CD4+ T cell count. …”
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A Deep Learning Segmentation Model for Detection of Active Proliferative Diabetic Retinopathy by Sebastian Dinesen, Marianne G. Schou, Christoffer V. Hedegaard, Yousif Subhi, Thiusius R. Savarimuthu, Tunde Peto, Jakob K. H. Andersen, Jakob Grauslund

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