Applications of Artificial Intelligence and Machine Learning

Applications of Artificial Intelligence and Machine Learning
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Artikel-Nr:
9789811630675
Veröffentl:
2021
Einband:
eBook
Seiten:
738
Autor:
Ankur Choudhary
Serie:
778, Lecture Notes in Electrical Engineering
eBook Typ:
PDF
eBook Format:
Reflowable eBook
Kopierschutz:
Digital Watermark [Social-DRM]
Sprache:
Englisch
Beschreibung:

The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security. This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications.

The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security. This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications.

Chapter 1: Building a Language Data Set in Telugu Using Machine    Learning Techniques to Address Suicidal Ideation and Behaviors in Adolescents.- Chapter 2: Feature Selection and Performance Comparison of Various Machine Learning Classifiers for Analyzing Students’ Performance Using Rapid Miner.- Chapter 3: Internet of Things (IoT) Based Automated Light Intensity Model using NodeMcu ESP 8266 Microcontroller.- Chapter 4: Handwritten Mathematical Symbols Classification Using WEKA.- Chapter 5: Enhancing Socio-Cultural Learning Using Hyperlocal Experience.- Chapter 6: Subsequent Technologies behind IoT and its Development Roadmap Towards Integrated Healthcare Prototype Models.- Chapter 7: Bug Assignment- Utilization of Metadata Features along with Feature Selection and Classifiers.

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