MLPNN and Ensemble Learning Algorithm for Transmission Line Fault Classification by Tanbir Rahman, Talab Hasan, Arif Ahammad, Imtiaz Ahmed, Nainaiu Rakhaine

“…In the IEEE 3-bus system, all of the learning types achieve approximately 99% accuracy in imbalanced and noisy data states, respectively, except CatBoost and decision tree, in the classification of line to line, line to line to line, line to line to ground, line to ground types of faults, and no fault. …”
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Integrating CEUS Imaging Features and LI-RADS Classification for Postoperative Early Recurrence Prediction in Solitary Hepatocellular Carcinoma: A Machine Learning-Based Prognostic... by Liang L, Pang J, Zhang B, Que Q, Gao R, Wu Y, Peng J, Zhang W, Bai X, Wen R, He Y, Yang H

“…Feature importance analysis identified LI-RADS classification, MVI, and tumor size as the top three prognostic indicators, while KM survival analysis confirmed the model’s ability to stratify patients into distinct risk groups (training cohort: p < 0.001; validation cohort: p = 0.003).Conclusion: The GBM-based ML model integrating CEUS imaging features and LI-RADS classification demonstrates potential for predicting early postoperative recurrence of HCC, which may assist in guiding follow-up strategies. …”
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Deep learning and explainable AI for classification of potato leaf diseases by Sarah M. Alhammad, Doaa Sami Khafaga, Walaa M. El-hady, Farid M. Samy, Khalid M. Hosny

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Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification by Bauke Arends, Melle Vessies, Dirk van Osch, Arco Teske, Pim van der Harst, René van Es, Bram van Es

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Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees by Ünal Kızıl, Hakkı Fırat Altınbilek, Sefa Aksu, Hakan Nar

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Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches by Hamed Ghaderi, Nasibe Alipour, Hossein Safari

“…We classify the GZ2 samples, first into the galaxies and nongalaxies and second, galaxies into spiral, elliptical, and odd objects (e.g., ring, lens, disturbed, irregular, merger, and dust lane). The two models include the support vector machine (SVM) and 1D convolutional neural network (1D-CNN), which use ZMs, compared with the other three classification models of 2D-CNN, ResNet50, and VGG16 that apply the features from original images. …”
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Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study by Ryo Hidaka, Kenta Matsuda, Takashi Igari, Shu Takeuchi, Yuichi Imoto, Satoshi Yagi, Hirotaka Kawano

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Emotion Classification from Electroencephalographic Signals Using Machine Learning by Jesus Arturo Mendivil Sauceda, Bogart Yail Marquez, José Jaime Esqueda Elizondo

“…This study aimed to evaluate the performance of three neural network architectures—ShallowFBCSPNet, Deep4Net, and EEGNetv4—for emotion classification using the SEED-V dataset. …”
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Optimized ensemble learning for non-destructive avocado ripeness classification by Panudech Tipauksorn, Prasert Luekhong, Minoru Okada, Jutturit Thongpron, Chokemongkol Nadee, Krisda Yingkayun

“…Five machine learning models Random Forest, Decision Tree, XGBoost, Gradient Boosting, and Gaussian Mixture Model were trained separately and then merged into an ensemble. …”
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Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation by Xiaoli Li, Longlong Zhao, Hongzhong Li, Luyi Sun, Pan Chen, Ruixia Jiang, Jinsong Chen

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Interpretable multimodal classification for age-related macular degeneration diagnosis. by Carla Vairetti, Sebastián Maldonado, Loreto Cuitino, Cristhian A Urzua

“…The classification model is able to achieve an accuracy of 0.94, performing better than other unimodal alternatives. …”
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Kernel Negative ε Dragging Linear Regression for Pattern Classification by Yali Peng, Lu Zhang, Shigang Liu, Xili Wang, Min Guo

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Multicriteria decision making-based approach to classify loose-leaf teas by Eszter Benes, Attila Gere

“…For such, multicriteria decision making models (and especially SRD) is strongly advised.…”
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Klasifikasi Mahasiswa HER Berbasis Algoritma SVM dan Decision Tree by Jajang Jaya Purnama, Hendri Mahmud Nawawi, Susy Rosyida, Ridwansyah Ridwansyah, Risnandar Risnandar

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Weakly supervised learning in thymoma histopathology classification: an interpretable approach by Chunbao Wang, Chunbao Wang, Xianglong Du, Xiaoyu Yan, Xiali Teng, Xiaolin Wang, Zhe Yang, Hongyun Chang, Yangyang Fan, Caihong Ran, Jie Lian, Chen Li, Hansheng Li, Lei Cui, Yina Jiang

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K-Ortalama Kümelerinin Sınıf Bilgisi Olarak Karar Ağacı Oluşturmada Kullanılması ve Glokom Çoklu Sınıflandırılmasında Başarıma Etkisi by Sait Can Yücebaş, Ahmet Cumhur Kınacı

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Schizophrenia Detection and Classification: A Systematic Review of the Last Decade by Arghyasree Saha, Seungmin Park, Zong Woo Geem, Pawan Kumar Singh

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Risk prediction and analysis of gallbladder polyps with deep neural network by Kerong Yuan, Xiaofeng Zhang, Qian Yang, Xuesong Deng, Zhe Deng, Xiangyun Liao, Weixin Si

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Method of troubleshooting in the neural network environment of intellectual decision supporting systems by О.І. Тимочко, С.В. Осієвський, О.О. Тімочко, П.В. Бєляєв

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Machine learning and explainable artificial intelligence to predict and interpret lead toxicity in pregnant women and unborn baby by Priyanka Chaurasia, Pratheepan Yogarajah, Abbas Ali Mahdi, Sally McClean, Mohammad Kaleem Ahmad, Tabrez Jafar, Sanjay Kumar Singh

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