Hybrid Deep Learning Approach for Accurate Detection and Multiclass Classification of Broken Conductor Faults in Power Distribution Systems by Firas Saadoon Mohammed Al-Jumaili, Mustafa Onat

“…It is shown that the proposed method has higher fault detection and classification accuracy compared to three traditional classification approaches, namely, Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and three state-of-the-art methods: 1) Stockwell transform +SVM, 2) Fast Fourier Transform + SVM, and 3) Hilbert-Huang transform of vibration data and power spectral density + Artificial Neural Network. …”
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An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data by Beom Joon Kim, Jeong Hyeon Mun, Dae Hwan Hwang, Dong In Suh, Changwon Lim, Kyunghoon Kim

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Bootstrapping BI-RADS classification using large language models and transformers in breast magnetic resonance imaging reports by Yuxin Liu, Xiang Zhang, Weiwei Cao, Wenju Cui, Tao Tan, Yuqin Peng, Jiayi Huang, Zhen Lei, Jun Shen, Jian Zheng

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Development of Machine Learning Models to Categorize Life Satisfaction in Older Adults in Korea by Suyeong Bae, Mi Jung Lee, Ickpyo Hong

“…Additionally, we assessed the significance of variable importance as indicated by the final classification models. Results Out of the 1411 older adults living alone, 45.3% expressed satisfaction with their lives. …”
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A Hybrid Attention-Based Transformer Model for Arabic News Classification Using Text Embedding and Deep Learning by Md. Mithun Hossain, Md. Shakil Hossain, Mejdl Safran, Sultan Alfarhood, Meshal Alfarhood, M. F. Mridha

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Contrast-enhanced CT-based deep learning model assists in preoperative risk classification of thymic epithelial tumors by Xuhui Zhao, Lingyu Zhang, Li Liang, Qi Zhang, Wencan Wang, Junlin Li, Hua Zhang, Chunhai Yu, Lingjie Wang

“…Six DL models (DenseNet 121, ResNet 101, Inception V3, VGG 11, MobileNet V2, and ShuffleNet V2) were developed and evaluated using venous-phase CT images, alongside a traditional radiomic model using a support vector machine (SVM) for comparison. …”
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Rice Phenology Classification Model Based on Sentinel-1 Using Machine Learning Method on Google Earth Engine by Hengki Muradi, Dede Dirgahayu Domiri, I Made Parsa, I Kadek Yoga, Alhadi Bustamam, Anisa Rarasati, Sri Harini, R. Johannes Manalu, Mokhamad Subehi

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HECM-Plus: Hyper-Entropy Enhanced Cloud Models for Uncertainty-Aware Design Evaluation in Multi-Expert Decision Systems by Jiaozi Pu, Zongxin Liu

“…Experimental validation demonstrates three key advances: (1) Fuzziness–Randomness discrimination: HECM-Plus achieves balanced conceptual differentiation (δ<i>C₁</i>/<i>C₄</i> = 1.76, δ<i>C₂</i> = 1.66, δ<i>C₃</i> = 1.58) with linear complexity outperforming PDCM and HCCM by 10.3% and 17.2% in differentiation scores while resolving <i>He</i>-induced biases in HECM/ECM (<i>C₁</i>–<i>C₄</i> similarity: 0.94 vs. 0.99) critical for stochastic dispersion modeling; (2) Robustness in time-series classification: It reduces the mean error by 6.8% (0.190 vs. 0.204, *<i>p</i>* < 0.05) with lower standard deviation (0.035 vs. 0.047) on UCI datasets, validating noise immunity; (3) Design evaluation application: By reclassifying controversial cases (e.g., reclassified from a “good” design (80.3/100 average) to “moderate” via cloud model using HECM-Plus), it resolves multi-expert disagreements in scoring systems. …”
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Advancing Nutritional Status Classification With Hybrid Artificial Intelligence: A Novel Methodological Approach by Md. Moddassir Alam, Asif Irshad Khan, Aasim Zafar, Mohammad Sohail, Mohammad Tauheed Ahmad, Rezaul Azim

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Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems by Mohamed Medani, Ghada Moh. Samir Elhessewi, Mohammed Alqahtani, Somia A. Asklany, Sulaiman Alamro, Da’ad Albalawneh, Menwa Alshammeri, Mohammed Assiri

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Comparative analysis of convolutional neural networks and transformer architectures for breast cancer histopathological image classification by Bo Yuan, Yudie Hu, Yan Liang, Yutong Zhu, Lingyu Zhang, Shimin Cai, Rui Peng, Xianbin Wang, Zheng Yang, Jinhui Hu

“…Recent advances in deep learning demonstrate promising potential to improve diagnostic accuracy, reduce false positives/negatives, and alleviate radiologists’ workload, thereby enhancing clinical decision-making in breast cancer management.MethodsThis study trains and evaluates 14 deep learning models, including AlexNet, VGG16, InceptionV3, ResNet50, Densenet121, MobileNetV2, ResNeXt, RegNet, EfficientNet_B0, ConvNeXT, ViT, DINOV2, UNI, and GigaPath on the BreakHis v1 dataset. …”
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Application of Machine Learning in Fault Detection And Classification in Power Transmission Lines by Michel Evariste Tshodi, Nathanael Kasoro, Freddy Keredjim, ALbert Ntumba Nkongolo, Jean-Jacques Katshitshi Matondo, Paul Mbuyi Balowe, Laurent Kitoko

“…Six fault categories were found in the dataset: No-Fault (2365 occurrences), Line A Line B to Ground Fault (1134 occurrences), Three-Phase with Ground (1133 occurrences), Line-to-Line AB (1129 occurrences), Three-Phase (1096 occurrences) and finally Line-to-Line with Ground BC (1004 occurrences).…”
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A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers by Mehtab Ur Rahman, Cem Direkoglu

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Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis by Mostafa Alizade-Harakiyan, Amin Khodaei, Ali Yousefi, Hamed Zamani, Asghar Mesbahi

“…Key predictive features included V40 (volume receiving 40 Gy), V60, and average esophageal dose. The model generated interpretable decision rules, with V60 ≥ 2.3 strongly indicating Grade 3 esophagitis. …”
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MISTIC: a novel approach for metastasis classification in Italian electronic health records using transformers by Livia Lilli, Mario Santoro, Valeria Masiello, Stefano Patarnello, Luca Tagliaferri, Fabio Marazzi, Nikola Dino Capocchiano

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Association rules of fuzzy soft set based classification for text classification problem by Dede Rohidin, Noor A. Samsudin, Mustafa Mat Deris

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Enhancing Health Mention Classification Through Reexamining Misclassified Samples and Robust Fine-Tuning Pre-Trained Language Models by Deyu Meng, Tshewang Phuntsho, Tad Gonsalves

“…This approach allows for continuous learning from errors. It improves the model&#x2019;s ability to distinguish subtle semantic differences, significantly outperforming existing state-of-the-art and baseline models across three HMC datasets. …”
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A fear detection method based on palpebral fissure by Rawinan Praditsangthong, Bhattarasiri Slakkham, Pattarasinee Bhattarakosol

“…This pattern was used to classify the emotions using a decision tree technique that led to the development of an emotional classification model. …”
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Development, deployment, and feature interpretability of a three-class prediction model for pulmonary diseases by Zhenyu Cao, Gang Xu, Yuan Gao, Jianying Xu, Fengjuan Tian, Hengfeng Shi, Dengfa Yang, Zongyu Xie, Jian Wang

“…Conclusion The XGBoost model outperforms RF in the three-class classification of lung diseases. …”
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Portable NIR Spectroscopy Combined with Machine Learning for Kiwi Ripeness Classification: An Approach to Precision Farming by Giuseppe Altieri, Sabina Laveglia, Mahdi Rashvand, Francesco Genovese, Attilio Matera, Alba Nicoletta Mininni, Maria Calabritto, Giovanni Carlo Di Renzo

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