Robust Representation Learning Based on Deep Mutual Information for Scene Classification Against Adversarial Perturbations by Linjuan Li, Gang Xie, Haoxue Zhang, Xinlin Xie, Heng Li

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Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis by Gunjan Shandilya, Sheifali Gupta, Salil Bharany, Ateeq Ur Rehman, Upinder Kaur, Hafizan Mat Som, Seada Hussen

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A Two-Stream Deep Learning Framework for Robust Coral Reef Health Classification: Insights and Interpretability by Arslan Abdul Ghaffar, Gyu Sang Choi

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Application of neural network technology for public opinion analysis by Kyrylo Perevoznyk, Yurii Parzhyn

“…The resulting datasets were used to train several neural network models. The best classification results were obtained with the GPT-3.5-turbo model. …”
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Penerapan Decision Tree dan Random Forest dalam Deteksi Tingkat Stres Manusia Berdasarkan Kondisi Tidur by Sza Sza Amulya Larasati, Elok Nuraida Kusuma Dewi, Brahma Hanif Farhansyah, Fitra Abdurrachman Bachtiar, Fajar Pradana

“…Hyperparameter tuning is done using k-fold cross-validation, and the model is designed using the Decision Tree and Random Forest classification algorithms. …”
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BreNet: Attention-Enhanced Multi-Scale CNN Framework for Breast Cancer Classification in Histopathological Images by Helala M. Alshehri

“…The model achieves strong and consistent performance on both the BreakHis and IDC datasets, reaching classification accuracies of 99.96% and 88.26% respectively, while maintaining low false-positive and false-negative rates. …”
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Comparison of Radiomics and conventional SUVr methods for Alzheimer’s disease classification using AV45 PET imaging by Haiyan Gao, Arui Tan, Junhao Wu, Zhen Cao, Ziyang Zhu, Wei Zhang

“…Feature importance was ranked using a random forest algorithm, and three models were constructed: an SUVr model, a radiomics model, and a combined model. …”
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Accumulator Opposite-insertion Fault Diagnosis for Variable Refrigerant Flow (VRF) System based on Decision Tree Algorithm by Liu Jiahui, Liu Jiangyan, Li Shaobin, Hu Wenju, Li Jiong, Chen Huanxin

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Study on the automatic extraction of water body information from SPOT-5 images using decision tree algorithm by DENG Jin-song, WANG Ke, LI Jun, Dong Yun-qi

“…The results suggest that the model of decision tree was simple and effective and the precision of this approach was much higher than that of the supervised classification. …”
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High-accuracy lung sound classification for healthy versus unhealthy diagnosis using artificial neural network by Weiwei Zhang, Xinyu Li, Qiao Liu, Xiangyang Zheng, Yisu Ge, Xiaotian Pan, Xiaotian Pan, Yu Zhou

“…The dataset was obtained using a 3M Littmann® Electronic Stethoscope Model 3,200, employing three types of filters (Bell, Diaphragm, and Extended) to capture sounds across different frequency ranges. …”
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Automated classification of midpalatal suture maturation using 2D convolutional neural networks on CBCT scans by Mahshid Nik Ravesh, Nazila Ameli, Manuel Lagravere Vich, Hollis Lai

“…A 9-layer CNN architecture was trained and evaluated using standard classification metrics and receiver operating characteristic (ROC) curve analysis.ResultsThe model achieved a test accuracy of 96.49%. …”
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Visual/Near-Infrared Spectroscopy Combined with Linear Discriminant Analysis and Machine Learning for Classification of Apple Damage by ZHANG Yu, ZHANG Chongyang, DUAN Xinxin, MA Shaoge, ZHAO Fu, WANG Juxia

“…The results showed that the SVM model based on preprocessed spectra with Savitzky-Golay (SG) smoothing had the best classification performance, with an accuracy of 87.3%. …”
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EEG–fNIRS signal integration for motor imagery classification using deep learning and evidence theory by Mohammed E. Seno, Niladri Maiti, Maulik Patel, Mihirkumar M. Patel, Kalpesh B. Chaudhary, Ashish Pasaya, Babacar Toure

“…Experimental evaluation on the publicly available TU-Berlin-A dataset demonstrates the effectiveness of the proposed model, achieving an average accuracy of 83.26%, representing a 3.78% improvement over state-of-the-art methods. …”
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Optimized classification of potato leaf disease using EfficientNet-LITE and KE-SVM in diverse environments by Gopal Sangar, Velswamy Rajasekar

“…Computer vision is essential in precision agriculture, facilitating automated disease diagnosis and decision-making through real-time data. Inconsistent data in uncontrolled contexts undermines classic image classification techniques, hindering precise illness detection.MethodsWe present a novel model that integrates EfficientNet-LITE for enhanced feature extraction with KE-SVM Optimization for effective classification. …”
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A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology by null Samsuryadi, Rudi Kurniawan, Julian Supardi, null Sukemi, Fatma Susilawati Mohamad

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Hybrid-KANet: a hyperspectral remote sensing crop classification method based on the Kolmogorov–Arnold network by Weizhen Zhang, Zhihui Li, Tong Zhen

“…However, existing studies are usually difficult to adequately model the complex nonlinear feature distributions in hyperspectral data, which often limits the classification performance and affects the recognition accuracy. …”
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Enhanced Fault Detection in Photovoltaic Panels Using CNN-Based Classification with PyQt5 Implementation by Younes Ledmaoui, Adila El Maghraoui, Mohamed El Aroussi, Rachid Saadane

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Enhanced HS Code Classification for Import and Export Goods via Multiscale Attention and ERNIE-BiLSTM by Mengjie Liao, Lei Huang, Jian Zhang, Luona Song, Bo Li

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Classification of Oil Loss Levels in Palm Oil Processing Using Near-Infrared Spectroscopy with Machine Learning by Muhamad Ilham Fauzan, Jaka Adi Baskara, Wahyuningdiah Trisari Harsanti Putri, Quintin Kurnia Dikara

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Explainable multi-view transformer framework with mutual learning for precision breast cancer pathology image classification by Haewon Byeon, Mahmood Alsaadi, Richa Vijay, Purshottam J. Assudani, Ashit Kumar Dutta, Monika Bansal, Pavitar Parkash Singh, Mukesh Soni, Mohammed Wasim Bhatt

“…This mutual learning framework enhances performance while also producing interpretable attention maps and feature-level visualizations that reveal the decision-making process of the model—promoting transparency, trust, and clinical usability. …”
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