Dr. Milan Tuba

Singidunum University, Belgrade, Serbia

Brief Profile:

Milan Tuba is the Vice Rector for International Relations, Singidunum University, Belgrade, Serbia and was the Head of the Department for Mathematical Sciences at State University of Novi Pazar and the Dean of the Graduate School of Computer Science at John Naisbitt University. He is listed in the World's Top 2% Scientists by Stanford University in 2020 and 2021. Prof. Tuba is the author or coauthor of more than 250 scientific papers (cited more than 5000 times, h-index 42) and editor, coeditor or member of the editorial board or scientific committee of number of scientific journals and conferences. He was invited and delivered around 60 keynote lectures at international conferences. He received B. S. in Mathematics, M. S. in Mathematics, M. S. in Computer Science, M. Ph. in Computer Science, Ph. D. in Computer Science from University of Belgrade and New York University. From 1983 to 1994 he was in the U.S.A. first at Vanderbilt University in Nashville and Courant Institute of Mathematical Sciences, New York University and later as Assistant Professor of Electrical Engineering at Cooper Union School of Engineering, New York. During that time he was the founder and director of Microprocessor Lab and VLSI Lab, leader of the NSF scientific projects and theses supervisor. From 1994 he was Assistant Professor of Computer Science and Director of Computer Center at University of Belgrade, from 2001 Associate Professor, Faculty of Mathematics, University of Belgrade, from 2004 also a Professor of Computer Science and Dean of the College of Computer Science, Megatrend University Belgrade. Prof. Tuba was the principal creator of the new curricula and programs at the Faculty of Mathematics and Computer Science at the University of Belgrade and later at John Naisbitt University where he was the founder and practically alone

Title of Talk:

Recent Advances in Digital Image Classification

Abstract of Talk:

Digital images introduced big changes in the world. Besides using digital images in everyday life, they became an irreplaceable part of numerous scientific areas such as medicine, security, agriculture, etc. One of the usual tasks in applications with digital images is digital image classification. Great progress in solving this task was made in recent years by convolutional neural networks. Convolutional neural networks represent a special class of deep neural networks that take a spatial correlation of input data into consideration. The results achieved by CNN are significantly better in comparison with the other classification methods. To use a CNN for a specific classification problem it is necessary to find the optimal network architecture and set various hyperparameters such as the number of different layers, number of neurons in each layer, optimization algorithm, activation functions, kernel size, optimization algorithm, etc. Frequently, CNN’s configuration is based on some previous experience or knowledge or set by guessing and estimating (guestimating) better values for the hyperparameters. Recent studies achieved promising results when using swarm intelligence algorithms for CNN’s hyperparameters tuning. A few examples of using swarm intelligence algorithms for convolutional neural network hyperparameter tuning will be presented.

Dr. Sandeep Singh Sengar

Cardiff Metropolitan University, United Kingdom

Brief Profile:

Dr. Sandeep Singh Sengar is a Lecturer in Computer Science at Cardiff Metropolitan University, United Kingdom. Before joining this position, he worked as a Postdoctoral Research Fellow at the Machine Learning Section of Computer Science Department, University of Copenhagen, Denmark (a rank #1 university of Denmark). He holds a Ph.D. degree in Computer Science and Engineering from Indian Institute of Technology (ISM), Dhanbad, India and an M. Tech. degree in Information Security from Motilal Nehru National Institute of Technology, Allahabad, India. He has more than seven years of research and teaching experience (excluding PhD duration). Dr. Sengar’s current research interests include Medical Image Segmentation, Motion Segmentation, Visual Object Tracking, Object Recognition, and Video Compression. His broader research interests include Machine/Deep Learning, Computer Vision, Image/Video Processing and its applications. He has published several research articles in reputed international journals and conferences in the field of Computer Vision and Image Processing. He is a Reviewer of several reputed International Transactions, Journals, and conferences including IEEE Transactions on Systems, Man and Cybernetics: Systems, Pattern Recognition, Neural Computing and Applications, Neurocomputing. He has also served as a Technical Program Committee member in many reputed International Conferences. He has organized several special sessions and given keynote presentations at International Conferences. In addition to these, he has also given many expert talks in reputed organizations. He always believes in collaborative opportunities.

Title of Talk:

Intelligent Biomedical Image Analysis

Abstract of Talk:

Medical image segmentation is the part of computer vision and its target is to label each pixel of an object of interest in medical images. An end to end deep learning approach, Convolutional Neural Networks have shown state-of-the-art performance for automated medical image segmentation. However, it doesn’t perform well in case of complex environments. U-Net is another popular deep learning architecture especially for biomedical imaging. In this talk, a concise overview of the modern deep learning model applied in computer vision specifically in medical image analysis is provided and the key tasks performed by deep learning model, i.e., classification and segmentation are shown. Furthermore, we will discuss the thoroughly evaluated deep learning framework for segmentation of arbitrary medical image volumes. Our discussed framework requires no human interaction, no task-specific information, and is based on a fixed model topology and a fixed hyper parameter set.