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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

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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

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Strategies of Collaborative Filtering Recommendation Mechanism Using a Deep Learning Approach 3

Strategies of Collaborative Filtering Recommendation Mechani...

引用

3rd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2021

作者： Chang, Chin-Chih Chang, Huan-Ming Chung Hua University Becholar Program in Applied Foreign Language and Smart Commerce Hsinchu30012 Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu30012 Taiwan

ISBN: (纸本)9781665445160

Nowadays, recommendation systems are widely used to help users locate the items they want. Collaborative filtering (CF) is a commonly used method for the recommendation. CF techniques use user-item ratings for prediction but suffer from the problems of data sparsity, cold start, and scalability. Though the Matrix Factorization (MF) techniques like Singular Value Decomposition (SVD) or Principal Component Analysis (PCA) overcome the above-mentioned problems, these methods are possible to deliver unmeaningful results in the condition of a low ranked approximation and denser singular vectors. In this paper, we review strategies of collaborative filtering recommendation mechanisms and propose an approach based on an autoencoder of convolutional neural network. Autoencoders are unsupervised learning methods in which neural networks are supported for the task of representation learning. We identify the user's features through learning, and then use these features to combine the collaborative filtering algorithm to recommend items. The experimental results show that the convolutional autoencoder can effectively reduce the computations when the amount of data is huge and benefited from the performance of its convolutional neural network. © 2021 IEEE.

关键词： Recommender systems

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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)

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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

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A Shoe Shopping System based on Convolutional Neural Network Image Recognition 5

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

引用

5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Chen, Chen-Sheng Chen, Jian-An Yeh, Shih-Hsien Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Program in Engineering Science Hsinchu Taiwan Chang Gung University Department of Computer Science and Information Engineering Taoyuan Taiwan Chung Hua University Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

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. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system

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Meal Image Recognition and Healthy Meal Combination Recommendation System Integrated with Generative Artificial Intelligence 6

Meal Image Recognition and Healthy Meal Combination Recommen...

引用

6th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2024

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

ISBN: (纸本)9798350396133

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. © 2024 IEEE.

关键词： Generative adversarial networks

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Cuisine Image Recognition and Recommender System using Convolutional Neural Network 5

Cuisine Image Recognition and Recommender System using Convo...

引用

5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Wu, Kuan-Yun Kao, Hao-Xiang Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Ph.D. Program in Engineering Science Hsinchu Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan Chung Hua University Program of Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

The fusion of technology and culinary exploration has allowed for the emergence of advanced online customer service systems. We developed a novel approach to enhance the dining experience. We used a Cuisine Image Recognition and Recommender System (CIRRS) powered by Convolutional Neural Network (CNN) to identify and suggest diverse cuisines based on visual inputs. CIRRS swiftly identified diverse cuisines from user-captured images, offering information on origin, ingredients, and variations by crawling websites. The system enhanced dining experiences by suggesting personalized menus based on individual preferences and past selections. Extensively tested across various culinary genres, CIRRS consistently demonstrated accuracy and adaptability. User feedback validated its potential to simplify dining choices and elevate satisfaction. This innovative system enriched dining experiences as a valuable tool for culinary enthusiasts, chefs, and restaurateurs, bridging the gap between technology and gastronomy, It also offers a unique way to explore Taiwanese cuisines. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system web crawler

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