TMS: Ensemble Deep Learning Model for Accurate Classification of Monkeypox Lesions Based on Transformer Models with SVM by Elsaid Md. Abdelrahim, Hasan Hashim, El-Sayed Atlam, Radwa Ahmed Osman, Ibrahim Gad

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Water Depth Correction-Based Classification Combination Method for Extracting Shallow Sea Reef Geomorphological Information: A Case Study of Xisha Chau and Zhaoshu Island by Zhigang Lu, Weidong Zhu, Daxing Lei, Yi Zhu, Yifan Chen, Zilin Yue, Zhongqiang Wu

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Risk-based evaluation of machine learning-based classification methods used for medical devices by Martin Haimerl, Christoph Reich

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Multi-Feature Fusion-Based Speech Disorder Classification Using MobileNetV3-EfficientNetB7, Linformer-Performer, and SHAP-Aware XGBoost by Abdul Rahaman Wahab Sait, Suresh Sankaranarayanan, P. Gouthaman

“…Thus, the proposed study introduces a novel image-based SD classification model to classify healthy and pathological speech with high accuracy and robustness. …”
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Intelligent method for supporting decision-making on software security using hybrid models by Оксана Сітнікова, Маргарита Мельник, Олена Сирота, Сергій Семенов

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Automated classification of chondroid tumor using 3D U-Net and radiomics with deep features by Tuan Le Dinh, Seungeun Lee, Hyemin Park, Sungwon Lee, Hyeondeok Choi, Keum San Chun, Joon-Yong Jung

“…In this study, we propose a hybrid approach that integrates deep learning and radiomics for chondroid tumor classification. First, we performed tumor segmentation using the nnUNetv2 framework, which provided three-dimensional (3D) delineation of tumor regions of interest (ROIs). …”
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An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network by Zhiling Liu, Ye Chen, Xinrui Dong, Jing Liu

“…This study aims to improve the ability to capture spatiotemporal dynamics of brain activity by proposing an advanced framework. (2) Methods: This study proposes an ASD recognition method that combines 3D Convolutional Neural Networks (3D-CNNs) and segmented temporal decision networks. …”
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Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN by Syed Waqad Ali, Muhammad Munaf Rashid, Muhammad Uzair Yousuf, Sarmad Shams, Muhammad Asif, Muhammad Rehan, Ikram Din Ujjan

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Machine Learning-Based Classification of Suspension Droplet-Solid Wall Impacts for Control of Droplet Fragmentation by Mikhail Vulf, Dmitry Zharikov, Dmitry Kolomenskiy, Dmitry Eskin, Pavel Osinenko

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Advancing blood cell detection and classification: performance evaluation of modern deep learning models by Shilpa Choudhary, Sandeep Kumar, Pammi Sri Siddhaarth, Guntu Charitasri, Monali Gulhane, Nitin Rakesh, Feslin Anish Mon, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

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CFP-AL: Combining Model Features and Prediction for Active Learning in Sentence Classification by Keuntae Kim, Yong Suk Choi

“…Therefore, a more detailed active learning strategy is needed beyond simply finding data near the decision boundary or data with high uncertainty. Based on this analysis, we propose CFP-AL, which considers the model’s feature space, and it demonstrated the best performance across six tasks and also outperformed others in three Out-Of-Domain (OOD) tasks. …”
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Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia Classification by Hai-Long Nguyen, Van Su Pham, Hai-Chau Le

“…Experiments conducted with seven diverse machine learning algorithms (Adaptive Boosting, Extreme Gradient Boosting, Decision Trees, k-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machine) demonstrate that the proposed blending solution, utilizing an LR meta-model with three optimal base models, achieves a superior classification accuracy of 96.48%, offering an effective tool for clinical decision support.…”
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Enhancing Network Security: A Study on Classification Models for Intrusion Detection Systems by Abeer Abd Alhameed Mahmood, Azhar A. Hadi, Wasan Hashim Al-Masoody

“…The meta-ensemble learning model does better at sub-multiclass classification than decision trees, random forests, and extreme gradient boosting. …”
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MPMFFT based DCA-DBT integrated probabilistic model for face expression classification by Kapil Juneja

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A risk prediction model for gastric cancer based on endoscopic atrophy classification by Yadi Lan, Weijia Sun, Shen Zhong, Qianqian Xu, Yining Xue, Zhaoyu Liu, Lei Shi, Bing Han, Tianyu Zhai, Mingyue Liu, Yujing Sun, Hongwei Xu

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Dual-Scale Complementary Spatial-Spectral Joint Model for Hyperspectral Image Classification by Huayue Chen, Yue Sun, Xiang Li, Bochuan Zheng, Tao Chen

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Enhancing Medical Image Classification with Unified Model Agnostic Computation and Explainable AI by Elie Neghawi, Yan Liu

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Mobile based deep CNN model for maize leaf disease detection and classification by Getnet Tigabie Askale, Achenef Behulu Yibel, Belayneh Matebie Taye, Gashaw Desalegn Wubneh

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COMPARATIVE ANALYSIS OF CLASSIFICATION MODELS FOR DETERMINING THE QUALITY OF WINE BY ITS CHEMICAL COMPOSITION by Vladimir S. Repkin, Artemy V. Li, Grigory Yu. Semenov, Nikita I. Sermavkin, Alexander S. Kovalenko, Nikolai S. Egoshin

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Application of Quantitative Interpretability to Evaluate CNN-Based Models for Medical Image Classification by Nuan Cui, Yingjie Wu, Guojiang Xin, Jiaze Wu, Liqin Zhong, Hao Liang

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