The book is essential for anyone seeking to understand and leverage the transformative power of intelligent automation technologies, providing crucial insights into current trends, challenges, and effective solutions that can significantly enhance operational efficiency and decision-making within organizations.
Intelligent automation systems, also called cognitive automation, use automation technologies such as artificial intelligence, business process management, and robotic process automation, to streamline and scale decision-making across organizations. Intelligent automation simplifies processes, frees up resources, improves operational efficiencies, and has a variety of applications. Intelligent automation systems aim to reduce costs by augmenting the workforce and improving productivity and accuracy through consistent processes and approaches, which enhance quality, improve customer experience, and address compliance and regulations with confidence. Handbook of Intelligent Automation Systems Using Computer Vision and Artificial Intelligence explores the significant role, current trends, challenges, and potential solutions to existing challenges in the field of intelligent automation systems, making it an invaluable guide for researchers, industry professionals, and students looking to apply these innovative technologies.
Readers will find the volume:
- Offers comprehensive coverage on intelligent automation systems using computer vision and AI, covering everything from foundational concepts to real-world applications and ethical considerations;
- Provides actionable knowledge with case studies and best practices for intelligent automation systems, computer vision, and AI;
- Explores the integration of various techniques, including facial recognition, natural language processing, neuroscience and neuromarketing.
Audience
The book is designed for AI and data scientists, software developers and engineers in industry and academia, as well as business leaders and entrepreneurs who are interested in the applications of intelligent automation systems.
Rupali Gill, PhD is an associate professor and Dean at the Chitkara University Institute of Engineering and Technology. She has published over 40 technical research papers in leading journals, as well as a patent granted. Her interests include image processing, cloud computing, artificial intelligence, and machine learning.
Susheela Hooda, PhD is an associate professor and Head of Academic Delivery at Chitkara University Institute of Engineering and Technology with over 15 years of teaching and research experience. She has published over thirty technical research papers in leading international journals and conferences and more than ten international patents. Her research interests include software engineering, aspect-oriented software development, software testing, cloud computing, artificial intelligence, and machine learning.
Durgesh Srivastava, PhD is an associate professor and Head of Academic Operations at Chitkara University Institute of Engineering and Technology with over 15 years of research and academic experience. He has published over 30 papers in reputed national and international journals and conferences, as well as several patents and copyrights in the field of computer software. His research interests include machine learning, soft computing, pattern recognition, and software engineering, modeling, and design.
Shilpi Harnal, PhD is an assistant professor at Chitkara University, Punjab. She specializes in cloud computing with over 13 years of teaching experience. She has published over 30 research papers in various national and international peer-reviewed journals, books, and conferences. Her research interests include fog computing, underwater wireless sensor networks (UWSN), and artificial intelligence.
