Meet the editor

Dr. Dinesh G. Harkut is Associate Professor at Prof Ram Meghe College of Engineering & Management (PRMCEAM), Badnera, India, in the Computer Science and Engineering Department. He obtained a bachelor's degree, a master's of engineering (CSE), and a PhD (CSE) from SGBAU Amravati University, Maharashtra, India. He also holds a master's degree and PhD in Business Administration. His primary research interests are in artificial

intelligence, big data, analytics, embedded systems, and e-commerce. He has supervised eighteen master's degree and twenty-four bachelor's degree students. He has published forty-seven papers in refereed journals and published six books with international publishers. He has also organized various workshops, sessions, conferences, and trainings. He has two patents filed and published in his name in India. He is a member of the Board of Studies (Computer Science and Engineering) and a recognized PhD supervisor at SGBAU Amravati University, Maharashtra, India. He holds membership in various professional bodies including the Institution of Electronics and Telecommunication Engineers (IETE), New Delhi; International Society for Technology in Education (ISTE), New Delhi; Universal Association of Computer and Electronics Engineers (UACEE), USA; International Economics Development and Research Center (IEDRC), Hong Kong; International Association of Engineers (IAENG), Hong Kong; and the European Alliance for Innovation, Belgium.

Contents

*by Dinesh G. Harkut*

with Kalman Filter

Uncertainties

Introductory Chapter: Data Assimilation

*by Hong Son Hoang and Remy Baraille*

Learning with Industrial Application

*by Anand Raju and Shanthi Thirunavukkarasu*

*by Afef Salhi, Fahmi Ghozzi and Ahmed Fakhfakh*

Data Processing Using Artificial Neural Networks *by Wesam Salah Alaloul and Abdul Hannan Qureshi*

*by Yassine Zahraoui and Mohamed Akherraz*

**Preface XI**

**Chapter 1 1**

**Chapter 2 5**

**Chapter 3 27**

**Chapter 4 45**

**Chapter 5 61**

**Chapter 6 81**

Adaptive Filter as Efficient Tool for Data Assimilation under

Convolutional Neural Network Demystified for a Comprehensive

Estimation for Motion in Tracking and Detection Objects

Kalman Filtering Applied to Induction Motor State Estimation
