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Utilize Deep learning to increase the performance of a Book recommender system using the Item-based Collaborative Filtering

Giap Cu Nguyen, Dieu Le Thi Huyen, Tuan Tran Manh, Ngan Tran Thi, Lan Luong Thi Hong

Annals of Computer Science and Information Systems

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2022

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Vol. 33

109--113

EN

Item-based Collaborative Filtering is a common and efficient approach for recommendation problems. In this study, we have investigated the power of deep learning in textual feature extraction and applied this advantage to a high-performance item-based collaborative filtering recommender system. The proposed approach has been experienced on book datasets added by texts collected from famous book review sites. The experiment proves that the proposed model has better performance thanks to the contribution of the new item profile process method based on Deep Learning.

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Chronic kidney disease diagnosis using Fuzzy Knowledge Graph Pairs : based inference in the extreme case

Chuan Pham Minh, Long Cu Kim, Lan Luong Thi Hong, Tuan Tran Manh, Hai Pham Van, Tan Nguyen Hong, Son Le Hoang

Annals of Computer Science and Information Systems

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2022

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Vol. 33

83--88

EN

Chronic kidney disease is one of the diseases with high morbidity and mortality, commonly occurring in the general adult population, especially in people with diabetes and hypertension. Scientists have researched and developed intelligent medical systems to diagnose chronic kidney disease. Nevertheless, healthcare services remain low in resource-limited areas, and general practitioners are very short of clinical experience. Identifying chronic kidney disease in clinical practice remains challenging, especially for the general practitioner. This study proposes a model to develop a model for improving the efficiency of differential diagnosis. This paper presents a model consisting of a fuzzy knowledge graph pairs-based inference mechanism by accumulating the new rules to enrich the fuzzy rule base. A real-world dataset is gathered in Dien Bien hospital to evaluate the performance of our proposed model.

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A New Approach of Question Answering based on Knowledge Graph in Traditional Medicine

Duong Pham Van, Trinh Tien-Dat, Pham Hai Van, Tuan Tran Manh, Son Le Hoang, Vu Huy-The, Nguyen Minh-Tien, Chuan Pham Minh

Annals of Computer Science and Information Systems

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2022

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Vol. 33

285--288

EN

In recent years, it has been great interest for Question Answering (QA) systems applied to many areas placing a high value on the community. The study and development of such QA systems through chatbot tools in medicine raise great needs for clinicians in their daily activities. Chatbots use the knowledge that could be retrieved from a database, but with limited inference capability. In this paper, we propose a new QA system based on Knowledge Graph (knowledge graph) for Traditional Medicine. Data of the knowledge graph is obtained from two sources including those from diagnostic of treatment diagrams and those collected on well-known medical websites through the Internet. The knowledge graph is then formed by combining the entities and relationships using the Named Entity Recognition (NER) model. Diagnosis is made via the node similarity algorithm in the knowledge graph for symptom identification. The effectiveness of the system is demonstrated through theoretical analysis and real-world experimental outcomes.