Showing 501 - 520 results of 2,006 for search 'decision three classification model', query time: 0.20s Refine Results
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    An explainable AI-based blood cell classification using optimized convolutional neural network by Oahidul Islam, Md Assaduzzaman, Md Zahid Hasan

    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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    Capturing the Characteristics of Car-Sharing Users: Data-Driven Analysis and Prediction Based on Classification by Jun Bi, Ru Zhi, Dong-Fan Xie, Xiao-Mei Zhao, Jun Zhang

    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 by Xuejun Zhang, Yehui Liu, Ganxin Ouyang, Wenkang Chen, Aobo Xu, Takeshi Hara, Xiangrong Zhou, Dongbo Wu

    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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    Behavioral Biases in Investor Decision-Making: A Comparative Meta-Analysis of Behavioral Finance Research by Seyed Amir Sabet, Saeed Aibaghi esfahani, Abdolmajid Abdolbaghi Ataabadi

    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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