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Customer service Chatbot Enhanced with Conversational language Understanding and Knowledge Base 4

Customer Service Chatbot Enhanced with Conversational Langua...

引用

4th IEEE Eurasia Conference on IoT, Communication and Engineering, ECICE 2022

作者： Chang, Chin-Chih Cheng, Wen-shing Hsiao, sean Chung Hua University Bachelor's Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan

ISBN: (纸本)9781665482080

A customer service chatbot enhanced with conversational language understanding and knowledge base is developed. Here, we explore LUIs and QnA Maker which are unified as Azure cognitive service for language. LUIs is a cloudbased conversational AI service that responds a user intelligence. QnA maker is a knowledge base for custom question answering. This question-and-answer knowledge base is especially useful for customer dialogs. Hence, by combining the services, we provide a smart response and provide a knowledge base to understand and improve the service. We implement this chatbot on the LINE Bot platform. Users easily access it by simply adding the representative of this chatbot on LINE. In addition, we put marketing information through this chatbot. The experiments are conducted, and the results show the chatbot is feasible and has a high user acceptance. © 2022 IEEE.

关键词： Knowledge based systems

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stock Price Prediction and Recommendation Approach Based on Machine Learning 4

Stock Price Prediction and Recommendation Approach Based on ...

引用

4th IEEE Eurasia Conference on IoT, Communication and Engineering, ECICE 2022

作者： Chang, Chin-Chih Luo, Kei-Wen Hsiao, sean Chung Hua University Bachelor's Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan

ISBN: (纸本)9781665482080

Investment is a lifetime work. Investing in stocks is a popular measure. However, investing in stocks is not easy, and losing money is not uncommon. We investigate if there is a systematic way to find buyable stocks and more importantly when to buy and sell them. Nowadays, with the progress of hardwarel, artificial intelligence (AI) has become a feasible solution to many issues. Machine learning and deep learning are two widely adopted methods in AI. In this study, we show how to apply machine learning to stock prediction and recommendation. After trials, we found that LightGBM shows its potential to predict and recommend stocks. We used the data of the Taiwan stock market to validate the method. We compared the prediction and recommendation with the wellknown Taiwan ETFs 0050 and 0056. The results show that the method is feasible, effective, efficient, and has better performance than Taiwan ETFs 0050 and 0056. © 2022 IEEE.

关键词： Deep learning

来源：评论

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A Fitness Movement Evaluation system Using Deep Learning 15

A Fitness Movement Evaluation System Using Deep Learning

引用

15th International Conference on Knowledge and systems Engineering, KsE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Wu, Hao-Wei Hsiao, sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu City Taiwan Ph.D. Program in Engineering Science Chung Hua University Hsinchu City Taiwan Bachelor Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu City Taiwan

ISBN: (纸本)9798350329742

This paper proposes a fitness movement evaluation system using deep learning. The system uses a deep convolutional neural network (CNN) to extract features from pictures of fitness movements. The features are then used to classify the movements into different categories. The system is evaluated on a dataset of pictures of fitness movements. The results show that the system can accurately classify the movements into different categories. The system is designed to provide feedback to users on their fitness movements. The proposed system is a valuable tool for fitness enthusiasts. The main contribution of this paper is to propose a way to give users a score for their fitness movement. It can help users improve their fitness and track their progress over time. The system is also a valuable tool for fitness professionals. It can help professionals develop new fitness programs and provide feedback to their clients. © 2023 IEEE.

关键词： Convolutional neural networks

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An Application-Aware Clustering strategy in Vehicular Ad Hoc Network 6

An Application-Aware Clustering Strategy in Vehicular Ad Hoc...

引用

6th International Conference on Computing, Electronics and Communications Engineering, iCCECE 2023

作者： Wei, Chi-Hung Chang, Chin-Chih Chu, Chien-Hao Hsiao, sean Chung Hua University Ph.D. Program in Engineering Science Hsinchu City Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu City Taiwan Chung Hua University Bachelor Program in Applied Foreign Language and Smart Commerce Hsinchu City Taiwan

ISBN: (纸本)9798350340921

VANET (Vehicular Ad Hoc Network) was developed to address the unique challenges of communication in a mobile vehicular environment. This technology can enhance driving safety and traffic control. Moreover, similar principles can be applied to unmanned aerial vehicles and robotics. When transmitting data in moving vehicles, it is challenging as access points and base stations keep changing, requiring handovers between different access points and base stations. Clustering is a popular approach used to address this challenge. Common clustering strategies are based on the proximity, communication range, the density, the application requirements of the moving vehicles, or hybrid of them. In this paper, we propose a clustering strategy based on the application sublayer in the vehicle communication protocol. If two computing devices on the move run the same application, they can be grouped together. The contribution of this research is to develop a clustering mechanism based on the application with very low overhead. We also validate the proposed method through simulations. The simulation results indicate that the proposed method is feasible. © 2023 IEEE.

关键词： Vehicular ad hoc networks

来源：评论

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Customer service Chatbot Enhanced with Conversational language Understanding and Knowledge Base

Customer Service Chatbot Enhanced with Conversational Langua...

引用

IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)

作者： Chin-Chih Chang Wen-shing Cheng sean Hsiao Bachelor’s Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan

ISBN: (纸本)9781665482097

关键词： Customer services Knowledge based systems Chatbots Question answering (information retrieval)

来源：评论

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stock Price Prediction and Recommendation Approach Based on Machine Learning

Stock Price Prediction and Recommendation Approach Based on ...

引用

IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)

作者： Chin-Chih Chang Kei-Wen Luo sean Hsiao Bachelor’s Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan

ISBN: (纸本)9781665482097

关键词： Deep learning systematics stock markets Investment

来源：评论

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An Application-Aware Clustering strategy in Vehicular Ad Hoc Network

An Application-Aware Clustering Strategy in Vehicular Ad Hoc...

引用

International Conference on Computing, Electronics & Communications Engineering (iCCECE)

作者： Chi-Hung Wei Chin-Chih Chang Chien-Hao Chu sean Hsiao Ph.D. Program in Engineering Science Chung Hua University Hsinchu City Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu City Taiwan Bachelor Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu City Taiwan

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A Fitness Movement Evaluation system Using Deep Learning

A Fitness Movement Evaluation System Using Deep Learning

引用

International Conference on Knowledge and systems Engineering (KsE)

作者： Chin-Chih Chang Chi-Hung Wei Hao-Wei Wu sean Hsiao Department of Computer Science and Information Engineering Chung Hua University Hsinchu City Taiwan Ph.D. Program in Engineering Science Chung Hua University Hsinchu City Taiwan Bachelor Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu City Taiwan

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Meal Image Recognition and Healthy Meal Combination Recommendation system Integrated with Generative Artificial Intelligence

Meal Image Recognition and Healthy Meal Combination Recommen...

引用

IEEE Eurasia Conference on Biomedical Engineering, Healthcare and sustainability (ECBIOs)

作者： Chin-Chih Chang Chi-Hung Wei sean Hsiao Chyuan-Huei Thomas Yang Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Bachelor's Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu Taiwan School of Information Engineering Shandong Vocational and Technical University of International Studies Shandong China

ISBN: (数字)9798350396133

ISBN: (纸本)9798350396140

People increasingly prioritize a balanced diet to enhance well-being, yet making informed dietary choices remains challenging amidst the abundance of options. To address this, we developed a meal image recognition and healthy meal combination recommender system, integrating generative artificial intelligence (AI). Convolutional neural networks were used for precise meal image recognition to identify diverse food items. The generative AI-augmented recommendation engine offers personalized meal suggestions aligned with nutritional goals and dietary preferences, utilizing a nutrition knowledge base to ensure overall well-being. The system's feasibility was validated, illustrating its excellence in meal recognition accuracy, recommendation diversity, and user engagement. By integrating generative AI, the system shows its potential to enhance dietary recommendations and public health.

关键词： Image recognition Accuracy Generative AI Knowledge based systems Convolutional neural networks sustainable development Public healthcare Recommender systems Engines Biomedical engineering

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A shoe shopping system based on Convolutional Neural Network Image Recognition

A Shoe Shopping System based on Convolutional Neural Network...

引用

IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)

作者： Chin-Chih Chang Chi-Hung Wei Chen-sheng Chen Jian-An Chen shih-Hsien Yeh sean Hsiao Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Bachelor Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu Taiwan

In this article, we present an innovative approach to enhance the online shoe shopping experience. The convolutional neural network (CNN) image recognition technology was used to enhance shoe classification and recommendations. By training the CNN model on an extensive dataset, unique shoe features and styles were learned. Integrated into a user-friendly online platform, the system offers real-time image recognition, allowing users to snap a photo of a desired shoe for instant identification, including brand, price, and availability details. Moreover, the CNN-based recommendation engine provides personalized suggestions based on style, color, and customer preferences, enriching the shopping experience. Evaluation results confirmed the system's feasibility, and user feedback highlighted its effectiveness in simplifying the shopping process and enhancing satisfaction. This innovative system presents a significant leap in merging AI and e-commerce and shows the potential of image recognition to transform online marketplaces, benefiting consumers, offering valuable insights for retailers, and ultimately reshaping the future of online shoe shopping.

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