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      • Development of an AI Chatbot to Support Admissions and Career Guidance for Universities

        Le Hoanh Su,Truong Dang-Huy,Tran Thi-Yen-Linh,Nguyen Thi-Duyen-Ngoc,Ly Bao-Tuyen,Nguyen Ha-Phuong-Truc ASCONS 2020 INTERNATIONAL JOURNAL OF EMERGING MULTIDISCIPLINAR Vol.4 No.2

        The vocational guidance and advising education enrollment are one of the most important tasks in the enrollment process and promote the quality and reputation of the University. Admissions counseling offices at Universities and Colleges play a major role in vocational guidance and advising education enrollment. However, the support of these units is limited by office hours, speed and advisory efficiency, and besides, handling and answering questions process may also encounter obstacles such as: overload, misinformation, problem with the transmission, language barriers, expressions, limited time, support resources,… Thus we decided to do research to understand this situation. Then creating a dataset supports vocational guidance and advising education enrollment activities. We also design and integrate chatbot into the school system to support the admissions counseling process.

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        Effect of the Cultivation Technology on the Yield of Paddy Straw Mushroom (Volvariella volvacea)

        Thi-Thuy-Hai Luu,Dang-Khoa Bui,Nga Huynh,Truc-Linh Le,Iain David Green 한국균학회 2022 韓國菌學會誌 Vol.50 No.3

        The edible paddy straw mushroom Volvariella volvacea (Bull. ex F.) Singer has high nutritional and medicinal values. They are grown on a wide variety of agricultural byproducts using different several methods. The result of this present study showed that type of bed and cultivation condition (outdoor/indoor) had effects on the yield of paddy straw mushrooms grown on the spent oyster mushroom sawdust. The treatment of circular compact bed under indoor cultivation condition (CYIC) had the best yield and biological efficiency (B.E.) with 2,119.2 g/bed and 14.5%, respectively, followed by the treatment of conventional bed under outdoor cultivation condition (COOC) with the yield and B.E. of 1,935.5 g/bed and 13.2%, respectively, but the lowest yield and B.E. were observed in the treatment of conventional bed under indoor cultivation condition (COIC) with 1,226.1 g/bed and 8.4%, respectively. Paddy straw mushrooms cultivated on spent oyster mushroom sawdust should be proper in both outdoor and indoor conditions by conventional and circular compact methods as well. However, in indoor conditions, paddy straw mushrooms should be grown in a circular compact bed to ensure the temperature of the bed is suitable for the mushroom growth.

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        An Application of RASA Technology to Design an AI Virtual Assistant: A Case of Learning Finance and Banking Terms in Vietnamese

        Thi My Ni PHAM,Thi Ngoc Thao PHAM,Ha Phuong Truc NGUYEN,Bao Tuyen LY,Truc Linh NGUYEN,Hoanh Su LE 한국유통과학회 2022 The Journal of Asian Finance, Economics and Busine Vol.9 No.5

        Banking and finance is a broad term that incorporates a variety of smaller, more specialized subjects such as corporate finance, tax finance, and insurance finance. A virtual assistant that assists users in searching for information about banking and finance terms might be an extremely beneficial tool for users. In this study, we explored the process of searching for information, seeking opportunities, and developing a virtual assistant in the first stages of starting learning and understanding Vietnamese to increase effectiveness and save time, which is also an innovative business practice in Use-case Vietnam. We built the FIBA2020 dataset and proposed a pipeline that used Natural Language Processing (NLP) inclusive of Natural Language Understanding (NLU) algorithms to build chatbot applications. The open-source framework RASA is used to implement the system in our study. We aim to improve our model performance by replacing parts of RASA’s default tokenizers with Vietnamese tokenizers and experimenting with various language models. The best accuracy we achieved is 86.48% and 70.04% in the ideal condition and worst condition, respectively. Finally, we put our findings into practice by creating an Android virtual assistant application using the model trained using Whitespace tokenizer and the pre-trained language m-BERT.

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