淡江大學人工智慧學系

年度	書名
2024	Tzu-Chia Chen, Text Mining Techniques and Natural Language Processing, Text mining is an important branch of data mining that is used to analyze the text data. Text data is any type of data like structured data, unstructured data, and semi-structured data. All types of data are collected from different sources such as multimedia applications, mobile apps, digital systems, etc. These data are beneficial to get a good insight, meaningful results. We use data mining techniques like Support Vector Machine (SVM), Random Forest (RF), Multilayer Perception (MLP), Naive Bayes (NB), etc. to analyze the hidden relationship between data. We use three kinds of data types in text mining., John Wiley & Sons
2023	Chen, Tzu-chia, Towards Applicability of Machine Learning in Business Analytics for Sales Prediction, In the field of data processing and analytics, machine learning has emerged as a topic of significant interest in the recent years. When it comes to decision-making, less structured retailers rely on their gut feeling, whereas more structured retailers make use of business intelligence. Predicting future retail sales is a common practise in the industry of organised retail, where it is tremendously helpful for making timely and strategic decisions in the face of intense competition. Predictions about future sales are often arrived at by doing an analysis of previous data, making use of a variety of statistical and mathematical methodologies, etc. This study is an endeavour to communicate with retail business owners, managers, and policymakers the accuracy of data derived from retail sales forecasting using machine learning algorithms. In this study, we apply the machine learning techniques known as Artificial Neural Network and Random Forest to a dataset that is often used for training purposes., CRC Press
2023	Chen, Tzu-chia, Optimized Support Vector Machine for Early and Accurate Heart Disease Detection, Many academics use data mining to predict diseases. Some approaches can predict one sickness, while others can predict several. Sickness prediction may be improved. This article provides an overview of the numerous data categorization methods available today. Algorithms represent most commonly. Classifying data involves a lot of computation. To create a disease-fighting plan that works, enormous amounts of data must be analysed. Early diagnosis, severity assessment, and prognosis are frequent. Doing so may postpone disease development, improve quality of life, and lower medical costs. This approach uses machine learning. This article classifies and predicts cardiovascular disease data using machine learning. SVM, ANN, and RF classify heart disease data. Accuracy-wise, SVM is better for heart disease classification and detection., CRC Press
2023	Chen, Tzu-chia, Conversational Artificial Intelligence-Ch29, In this day and age of smartphones and computers, the prevalence of mobile applications has skyrocketed. They have numerous applications, including but not limited to communication, social media, news, messaging, shopping, making payments, watching videos and transmissions, and engaging in online gaming. When it comes to mobile devices, Android is presently the operating system with the most users worldwide. The Android platform has emerged as the most popular mobile operating system, with an increasing number of applications developed especially for Android mobile devices. Simultaneously, there has been a rise in the number of incidents. Attackers exploit vulnerable areas in mobile apps to introduce potentially malicious code into the system and steal confidential data. When creating an app for a mobile device, it is critical to prioritize the security and protection of users’ data. Only by thoroughly understanding the various vulnerabilities that could be introduced into their code can mobile app developers successfully fight potential security threats. This manuscript provides an in-depth study of vulnerability assessment and penetration testing in mobile applications. This manuscript also presents a mitigation plan for encountering vulnerabilities in mobile applications. A security framework is also proposed to enhance the security of mobile applications., John Wiley & Sons, Inc.
2023	Chen, Tzu-chia, Conversational Artificial Intelligence-Ch20, An intrusion detection system, often known as IDS, is primarily used to gather and analyze data regarding security events that occur in computer systems and networks. Its subsequent purpose is to either prevent these events from happening or notify them to the administrator of the system. As a result of the increasing number of attacks carried out by attackers, the users’ level of mistrust on the Internet has increased. Attacks that cause denial of service are a major violation of security. This article presents a particle swarm optimization and AdaBoost-based intrusion detection system. In this system, chatbot receives network traffic as input, and features of input dataset are selected using particle swarm optimization algorithm. A classification model is trained and tested. AdaBoost, KNN, and naïve Bayes algorithm are used to classify and detect malware-related records. NSL KDD dataset is used in the experimental work. PSO-AdaBoost achieves the highest accuracy, precision, and recall for intrusion detection and classification. The output of a chatbot is a language that is either normal or benign., John Wiley & Sons, Inc.
2021	張志勇; 廖文華; 石貴平; 王勝石; 游國忠, AI人工智慧, 人工智慧相關的議題歷史悠久，本書詳盡敘述人工智慧過往的發展和遇到的瓶頸，並說明近年來為何又開始一波新的熱潮，在這波熱潮中，本書內容貼近產業應用，說明AI如何應用在各大產業、服務以及新商品與革新。此外，本書亦透過AI技術的發展與創新，引導讀者瞭解，隨著人工智慧持續發展，AI對人們的未來生活可能帶來衝擊與影響。　　本書巧妙的運用範例、圖例及影片(QR Code)講解人工智慧的理論與技術，使理論架構變得淺顯易懂，不再因為艱澀難懂的數學公式抹滅了學習的興趣及成就，本書藉由邏輯清晰的『訓練資料』來訓練讀者，使其能夠越讀越明白，越學越有成就。　　本書適用於科大資工、電機及電子系「人工智慧」課程使用。, 新北市：全華
2020	張志勇; 廖文華; 石貴平; 王勝石; 游國忠, 人工智慧：素養及未來趨勢, 工智慧相關的議題歷史悠久，本書將詳盡敘述人工智慧過往的發展和遇到的瓶頸，並說明近年來為何又開始一波新的熱潮，在這波熱潮中，本書內容貼近產業應用，說明AI如何應用在各大產業、服務以及新商品與革新。此外，本書亦透過AI技術的發展與創新，引導讀者瞭解，隨著人工智慧持續發展，AI對人們的未來生活可能帶來衝擊與影響。　　本書巧妙的運用範例、圖例講解人工智慧的理論，使架構變得淺顯易懂，不再因為艱澀難懂的數學公式抹滅了學習的興趣及成就。, 新北市：全華
2020	張志勇, 機器學習與深度學習教材, ,
2020	廖文華; 張志勇; 蒯思齊, 雲端運算概論（2版）, 本書集結作者多年雲端教學經驗和心得，內容深入淺出，理論與實務兼備，適合當作大專院校「雲端運算」相關課程的教科書，也適合業界人士當成自學工具書。本書每章節都有豐富的圖文和範例，使讀者易於了解，每章最後都有習題可供練習，讓讀者檢視了解的程度。如果想更深入每章節的議題，參考文獻亦提供詳盡豐富的參考資料，可供進一步研讀。本書包含十五個章節，以循序漸進的方式介紹雲端運算的概念、架構、應用平台與技術外，還包括時下熱門的物聯網、大數據、行動App、軟體定義網路、雲端運算的關聯性與整合應用。期望讀者能透過研讀本書的內容，撥雲見日，開啟雲端運算的一扇窗，成為具備雲端運算背景知識與專業知識的專家。, 台北市：五南
2019	張志勇; 廖文華; 石貴平; 王勝石; 游國忠, 人工智慧, 人工智慧相關的議題歷史悠久，本專書詳盡敘述人工智慧過往的發展和遇到的瓶頸，並說明近年來為何又開始一波新的熱潮，在這波熱潮中，本書內容貼近產業應用與技術創新，尤其是作者本身參與的產業專案所融入的技術，以實例來說明AI技術如何應用在各大產業、服務以及新商品與革新。此外，本書亦透過AI技術的發展與創新，說明人工智慧的持續發展，對人們的未來生活可能帶來衝擊與影響。, 全華圖書

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