Titlebook: Intelligent Systems and Data Science; Second International Nguyen Thai-Nghe,Thanh-Nghi Do,Salem Benferhat Conference proceedings 2025 The E

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书目名称Intelligent Systems and Data Science影响因子(影响力)




书目名称Intelligent Systems and Data Science影响因子(影响力)学科排名




书目名称Intelligent Systems and Data Science网络公开度




书目名称Intelligent Systems and Data Science网络公开度学科排名




书目名称Intelligent Systems and Data Science被引频次




书目名称Intelligent Systems and Data Science被引频次学科排名




书目名称Intelligent Systems and Data Science年度引用




书目名称Intelligent Systems and Data Science年度引用学科排名




书目名称Intelligent Systems and Data Science读者反馈




书目名称Intelligent Systems and Data Science读者反馈学科排名

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Intelligent Systems and Data Science978-981-97-9616-8Series ISSN 1865-0929 Series E-ISSN 1865-0937

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Conference proceedings 2025er 9–10, 2024...The 38 full papers and 10 short papers were carefully reviewed and selected from 129 submissions. They were categorized under the topical sections as follows: AI in E-Commerce, Agriculture, and Aquaculture; AI in Health Care Analytics; Big Data, IoT, and Cloud Computing; and Natural Language Processing..

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Experimental Study on Spectrometric Features of Mud Crabs for Automatic Internal Quality Gradingat yield and ovarian fullness. Since they are currently evaluated and graded manually, there is a strong demand to automate them based on the objective spectrometric analysis. However, developing a practical spectrometric system and grading model with adequate performance and acceptable manufacturab

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Assessing Grain Size Variation Across Rice Panicles Using YOLOv8 and DeepLabv3 Models Grain morphology, characterized by length and width, is one of the key rice quality parameters. This characteristic could be influenced by grain position within the panicle. Numerous studies have employed image processing techniques and machine learning models for rice grain detection, counting, me

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BeLightRec: A Lightweight Recommender System Enhanced with BERTrs and decoders, particularly in recommendation systems utilizing collaborative filtering methods. Collaborative filtering exploits similarities between users and items from historical data. However, it overlooks distinctive information, such as item names and descriptions. The semantic data of item

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Enhancing the Efficiency of Lung Disease Classification Based on Multi-modal Fusion Modelutcomes and ensuring effective treatment. In this paper, we propose a multi-modal model that integrates both chest X-ray images and clinical information text to improve the efficiency of lung disease classification. Our proposed model trains a Support Vector Machine (SVM) model on top of the fine-tu