"Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications is primarily written for graduate students, and academic researchers working in the fields of computer science and engineering, electrical engineering, and information technology. Seasonal Blurb: This reference text presents the most recent and advanced research on the application of artificial intelligence and machine learning on intelligent systems. It will discuss important topics such as business intelligence, reinforcement learning, supervised learning, and unsupervised learning in a comprehensive manner. It also includes case studies on smart transportation, smart home, smart agriculture, smart manufacturing, smart grid, smart education, and smart government. It is primarily written for graduate students, and academic researchers working in the fields of computer science and engineering, electrical engineering, and information technology"--
Intelligent systems are technologically advanced machines that perceive and respond to the world around them. Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications presents the evolution, challenges, and limitations of the application of machine learning and artificial intelligence to intelligent systems and smart communities.
- Covers the core and fundamental aspects of artificial intelligence, machine learning, and computational algorithms in smart intelligent systems
- Discusses the integration of artificial intelligence with machine learning using mathematical modeling
- Elaborates concepts like supervised and unsupervised learning, and machine learning algorithms, such as linear regression, logistic regression, random forest, and performance evaluation matrices
- Introduces modern algorithms such as convolutional neural networks and support vector machines
- Presents case studies on smart healthcare, smart traffic management, smart buildings, autonomous vehicles, smart education, modern community, and smart machines
Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications is primarily written for graduate students and academic researchers working in the fields of computer science and engineering, electrical engineering, and information technology. Seasonal Blurb: This reference text presents the most recent and advanced research on the application of artificial intelligence and machine learning on intelligent systems. It will discuss important topics such as business intelligence, reinforcement learning, supervised learning, and unsupervised learning in a comprehensive manner.
Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications presents the evolution, challenges, and limitations of the application of machine learning and artificial intelligence to intelligent systems and smart communities.
Chapter
1. A Detailed Study on deep learning versus machine learning approaches for Pest Classification in Field Crops.
Chapter
2. Integration of Artificial Intelligence (AI) and Other Edge Cutting Technologies.
Chapter
3. A detailed case study on various challenges in Vehicular Networks for Smart Traffic Control System using Machine Learning Algorithms.
Chapter
4. Revolutionizing Transportation: The Power of Artificial Intelligence.
Chapter
5. Smart Education using Explainable Artificial Intelligence (XAI).
Chapter
6. Smart Stock Prediction Techniques Using AI-ML.
Chapter
7. Smart Community: Concepts & Applications.
Chapter
8. Disseminating dynamic traffic information for sustainable mobility and transport.
T. V. Ramana is Professor of Computer Science and Engineering, JAIN University, Bangalore, India, where he heads the Cloud Technology and Information Security Group. He received his M.Tech. and Ph.D. from the Jawaharlal Nehru Technological University (JNTU), Hyderabad. His research interests include software engineering, computer system architecture, machine learning, and the Internet of Things (IoT). He is also Associate Dean of the Faculty of Engineering and Technology. He has researched extensively on cloud computing and computer system architecture. His current research focuses primarily on the IoT and machine learning.
G. S. Pradeep Ghantasala is currently a professor in the Department of Computer Science and Engineering, Alliance University, Bangalore, India. He has published various papers in reputed journals such as SCI, Scopus- indexed journals, UGC journals, conferences, books, book chapters, and national and international patents. He is also the editor and reviewer of various journals. His research interests include machine learning, deep learning, healthcare applications, and software engineering applications. Programming, machine learning, and artificial intelligence are just a few of the courses he has taught for both UG and PG students. He has published more than 30 manuscripts in scientific publications.
R. Sathiyaraj is currently working as an assistant professor in the Department of CSE, GITAM University, Bangalore. He received his B.Tech. in Information Technology from Anna University, Chennai, India, in 2009; M.Tech. from Vel Tech University, Chennai, India, in 2012; and Ph.D. in Computer Science and Engineering from Anna University, Chennai, India, in 2020. He has contributed to two books and is the lead editor of two books on various technologies. He has to his credit five patents and more than 20 articles and papers in various refereed journals and international conferences. His research interests include ML, big data analytics, and intelligent systems.
Mudassir Khan is currently working as an assistant professor and former Head in the Department of Computer Science at College of Science & Arts Tanumah, King Khalid University Abha, Saudi Arabia. He has more than 10 years of teaching experience at the King Khalid University of Saudi Arabia. He has published more than 25 papers in international journals (SCIE, ESCI, Scopus, UGC Care), presented in conferences of the IEEE and Springer Nature, and has to his credit one patent. He is a member of various technical/professional societies such as IEEE, UACEE, Internet Society, IAENG, and CSTA. His research interests include big data, IoT, deep learning, machine learning, eLearning, fuzzy logic, image processing, cyber security, and cloud computing.
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