Enterprise Digital Transformation

Digital transformation (DT) has become a buzzword. Every industry segment across the globe is consciously jumping toward digital innovation and disruption to get ahead of their competitors. In other words, every aspect of running a business is being digitally empowered to reap all the benefits of the digital paradigm. All kinds of digitally enabled businesses across the globe are intrinsically capable of achieving bigger and better things for their constituents. Their consumers, clients, and customers will realize immense benefits with real digital transformation initiatives and implementations. The much-awaited business transformation can be easily and elegantly accomplished with a workable and winnable digital transformation strategy, plan, and execution.

 

There are several enablers and accelerators for realizing the much-discussed digital transformation. There are a lot of digitization and digitalization technologies available to streamline and speed up the process of the required transformation. Industrial Internet of Things (IIoT) technologies in close association with decisive advancements in the artificial intelligence (AI) space can bring forth the desired transitions. The other prominent and dominant technologies toward forming digital organizations include cloud IT, edge/fog computing, real-time data analytics platforms, blockchain technology, digital twin paradigm, virtual and augmented reality (VR/AR) techniques, enterprise mobility, and 5G communication. These technological innovations are intrinsically competent and versatile enough to fulfill the varying requirements for establishing and sustaining digital enterprises.

 

Enterprise Digital Transformation: Technology, Tools, and Use Cases features chapters on the evolving aspects of digital transformation and intelligence. It covers the unique competencies of digitally transformed enterprises, IIoT use cases, and applications. It explains promising technological solutions widely associated with digital innovation and disruption. The book focuses on setting up and sustaining smart factories that are fulfilling the Industry 4.0 vision that is realized through the IIoT and allied technologies.

1. Get Technology to Contribute to Business Strategy
Pradeep Henry

2. Introduction to Computer Vision
A. Vijayalakshmi

3. Essentials of the Internet of Things (IoT)
Nancy Jasmine Goldena

4. The Internet of Things (IoT) Architectures and Use Cases
Jinsi Jose and Deepa V. Jose

5. Challenges of Introducing Artificial Intelligence (AI) in Industrial Settings
Ardhendu G. Pathak

6. Blockchain-Based Circular Fused Encryption
K.Sathya and P. L.Chithra

7. Security Challenges and Attacks in MANET-IoT System
Serin V. Simpson ^, and G. Nagarajan

8. Machine and Deep Learning (ML/DL) Algorithms for Next-Generation Healthcare Applications
V. Pavithra and V. Jayalakshmi

9. A Review of Neuromorphic Computing -- A Promising Approach for IoT-Based Smart Manufacturing
R. Joshua Arul Kumar, S.Titus, and B. Janet

10. Text Summarization for Automatic Grading of Descriptive Assignments: A Hybrid Approach
Rachel Royan , Christina Jayakumaran, and Thompson Stephan

11. Building Autonomous IIoT Networks Using Energy Harvesters
Rathishchandra R. Gatti, Shruthi H. Shetty, and Ashwath Rao

12. An Interactive TUDIG Application for Tumor Detection in MRI Brain Images Using Cascaded CNN with LBP Features
G. Dheepa and P.L. Chithra

13. Virtual Reality in Medical Training, Patient Rehabilitation, and Psychotherapy-Applications and Future Trends
M. Karthigha and Madhumathi Ramasamy

14. Complexity Measures of Machine Learning Algorithms for Anticipating the Success Rate of IVF Process
A. Mercy Rani and A. Ranichitra

15. Commuter Traffic Congestion Control Evasion in IoT based VANET Environment
A. Ranichitra and A. Mercy Rani

16. Dyad Deep Learning-Based Geometry and Color Attributes Codec for 3D Airborne LiDAR Point Clouds
A. Christoper Tamilmathi and P.L. Chithra

17. Digital Enterprise Software Productivity Metrics and Their Business Impacts Using Machine Learning
Vipul Gaurav and Savita Choudhary

Dr. Sathyan Munirathinam is working as a Cloud Solution Architect Manager for ASML Corporation. He is responsible for developing a cloud strategy and roadmap for addressing dynamic cloud solutions as well as works on machine learning, deep learning, and IoT issues. He has a PhD in Machine Learning and more than 20 years of experience in working with business intelligence and 15 years of experience in manufacturing. His research interests include artificial intelligence, equipment health monitoring, IoT and big data analysis, statistical machine learning and data mining, ubiquitous computing, and human computer interaction. He holds a Master of Science from Illinois Institute of Technology and is a Certified Business Intelligence Professional. Dr. Peter Augustine has been working as an Associate Professor in the Department of Computer Science, CHRIST University, Bangalore. He has a PhD in Medical Image Processing in Cloud Environment, with more than eight years of experience in cloud computing and five years in Big Data Analytics. He has collaborated with St. John's Medical Research Institute in a research project to diagnose lung diseases using cutting-edge AI and Machine Learning. His research interests include artificial intelligence, IoT and big data analysis in the area of healthcare, data mining, and human computer interaction. Dr. Pethuru Raj has been working as the chief architect in the Site Reliability Engineering (SRE) division of Reliance Jio Infocomm Ltd. (RJIL), Bangalore. He previously worked as a cloud infrastructure architect in the IBM Global Cloud Center of Excellence (CoE), IBM India Bangalore for four years. Prior to that, he had a long stint as TOGAF-certified enterprise architecture (EA) consultant in Wipro Consulting Services (WCS) Division. He has also worked as a lead architect in the corporate research division of Robert Bosch, Bangalore. In total, he has gained more than 17 years of IT industry experience and eight years of research experience.