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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書目名稱Intelligent Systems and Data Science影響因子(影響力)




書目名稱Intelligent Systems and Data Science影響因子(影響力)學(xué)科排名




書目名稱Intelligent Systems and Data Science網(wǎng)絡(luò)公開度




書目名稱Intelligent Systems and Data Science網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Intelligent Systems and Data Science被引頻次




書目名稱Intelligent Systems and Data Science被引頻次學(xué)科排名




書目名稱Intelligent Systems and Data Science年度引用




書目名稱Intelligent Systems and Data Science年度引用學(xué)科排名




書目名稱Intelligent Systems and Data Science讀者反饋




書目名稱Intelligent Systems and Data Science讀者反饋學(xué)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