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An explainable AI-based blood cell classification using optimized convolutional neural network
Published 2024-12-01“…Additionally, performance is further enhanced by experimenting with various architectural structures and hyperparameters to optimize the proposed model. A comparative evaluation is conducted to compare the performance of the proposed model with three transfer learning models, including Inception V3, MobileNetV2, and DenseNet201.The results indicate that the proposed model outperforms existing models, achieving a testing accuracy of 99.12%, precision of 99%, and F1-score of 99%. …”
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Land Cover Classification for Identifying the Agriculture Fields Using Versions of YOLO V8
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VR-Aided Ankle Rehabilitation Decision-Making Based on Convolutional Gated Recurrent Neural Network
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Automated classification of chest X-rays: a deep learning approach with attention mechanisms
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Capturing the Characteristics of Car-Sharing Users: Data-Driven Analysis and Prediction Based on Classification
Published 2020-01-01“…This manifestation is in line with the short-term lease of shared cars to complete short- and medium-distance travel design concepts. We also propose a model that predicts the driver cluster based on the decision tree. …”
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DermViT: Diagnosis-Guided Vision Transformer for Robust and Efficient Skin Lesion Classification
Published 2025-04-01“…Currently, skin lesion classification faces challenges such as lesion–background semantic entanglement, high intra-class variability, artifactual interference, and more, while existing classification models lack modeling of physicians’ diagnostic paradigms. …”
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Building occupancy type classification and uncertainty estimation using machine learning and open data
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Scalable and robust machine learning framework for HIV classification using clinical and laboratory data
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Distributed Denial of Service Attack Detection in Software-Defined Networks Using Decision Tree Algorithms
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Behavioral Biases in Investor Decision-Making: A Comparative Meta-Analysis of Behavioral Finance Research
Published 2025-12-01“…By understanding these biases, markets can develop tools to mitigate their effects and foster more rational decision-making, as recognizing behavioral biases in investment decisions proves crucial for both investors and policymakers to help mitigate irrational choices and avoid unexpected financial risks, while this research aligns with global behavioral finance studies in emphasizing the need for bias-aware strategies to enhance decision-making stability.MethodsThis meta-analysis synthesizes empirical research on investor behavioral biases through a rigorous four-step methodology: (1) systematic literature review to identify relevant studies, (2) effect size calculation using standardized metrics, (3) heterogeneity testing via Q-statistics and I² to assess consistency, and (4) model selection (fixed- or random-effects) based on heterogeneity levels, with inclusion criteria requiring studies to examine at least one of 12 key biases (e.g., overconfidence, loss aversion), report statistical outcomes (effect sizes, p-values), and cover diverse markets including traditional assets and cryptocurrencies. …”
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Machine Learning-Based Classification of Programming Logic Understanding Levels by Mouse-Tracking Heatmaps
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Concept learning based on improved FCM- BiLSTM for fuzzy data classification and fusion
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