International Workshop on New Approaches for


Multidimensional Signal Processing


NAMSP 2020

Technical University of Sofia, Sofia, Bulgaria

July 09-11, 2020

Invited Speakers


Prof. Abdel-Badeeh M. Salem, Prof. of Computer Science, Faculty of Computer and Information Sciences, Head of Artificial Intelligence and Knowledge Engineering Research Labs, Ain Sham University, Egypt, E-mail: abmsalem@yahoo.com, absalem@cis.asu.edu.eg


Title of Lecture: Computational Intelligence for Brain Tumors


Abstract: Magnetic resonance imaging (MRI) is an imaging technique that plays a vital role in detection and diagnosis of brain tumors in both research and clinical care for providing detailed information about the brain structure and its soft tissues. The image processing techniques can provide great help in analyzing the tumor area. Computer-aided detection (CAD) has been developing fast in the last two decades. The main idea of CAD is to assist radiologists in interpreting medical images by using dedicated computer systems to provide ‘second opinions’. The current research reveals the CAD systems of human brain MRI images are still an open problem.
On the other side, advance of computational intelligence (CI) techniques, computer-aided detection attracts more attention for brain tumor detection. It has become one of the major research subjects in medical imaging and diagnostic radiology. In the area of processing the brain images, Computer Aided-Diagnosis (CAD) systems are basically relied on different CI techniques in all its stages to implement a system that can help the radiologists by providing a second opinion that can assist in detection and diagnosis of brain tumors based on imaging techniques that are widely used in clinical care.
This talk is devoted to discussion of current research of the CI techniques for developing smart CAD Systems. We proposed a hybrid intelligent technique for automatic detection of brain tumor through MRI. The technique is based on the following CI methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward back-propagation neural network to classify inputs into normal or abnormal. Examples of the research performed by the author and his associates are discussed.


Biographical Notes: Prof. Abdel-Badeeh Salem is a full Professor of Computer Science since1989 at Faculty of Science, Ain Shams University, Egypt. His research includes intelligent computing, biomedical informatics, and intelligent e-learning, knowledge engineering, big data analytics and Biometrics. He has published around 500 papers (103 of them in Scopus). He has been involved in more than 550 International conferences and workshops as; a keynote and plenary speaker, member of Program Committees, workshop/invited session organizer, Session Chair and Tutorials.. In addition he was a member of many international societies. In addition he is a member of the Editorial Board of 50 international and national Journals Also, He is member of many Int. Scientific Societies and associations Elected member of Euro Mediterranean Academy of Arts and Sciences, Greece. Member of Alma Mater Europaea of the European Academy of Sciences and Arts, Belgrade and European Academy of Sciences and Arts, Austria. Member of international Engineering and Technology Institute - (IETI) , Hong Kong , May 2018.Member of Board of Trustees of Crown University Intl. Chartered Inc. (Ghana, Americas and partner campuses worldwide). E-mail: abmsalem@yahoo.com, absalem@cis.asu.edu.eg



Prof. Mariofanna Milanova, University of Arkansas at Little Rock, USA


Title of Lecture: Visual Intelligence and Deep Learning models: What we can learn from hierarchies in the primate's Visual Cortex?


Abstract: Video Analytics is a prominent field in multimedia research with numerous fundamental applications, including video surveillance, patient monitoring systems, law enforcement, video indexing, and human computer interaction. Various kinds of deep learning neural network architectures are the main technical basis for the current state of the art for the anticipation of human body motions from a video. This presentation will briefly cover the main concepts of Deep Learning, NVIDIA Developers Program and then focus on applications in video analytics healthcare. Recent advances in the field of human gait analysis systems using 2D single camera and muscle synergies from electromyography (EMG) signals will also be highlighted.


Biographical Notes: Mariofanna Milanova is a Professor of the Computer Science Department at the University of Arkansas at Little Rock, USA since 2001. She received a M.Sc. in Expert Systems and Artificial Intelligence and Ph.D. in Telecommunications from the Technical University, Sofia, Bulgaria. Dr. Milanova conducted post-doctoral research in visual perception at the University of Paderborn, Germany. Dr. Milanova has extensive academic experience at various academic and research organizations worldwide.
Dr. Milanova is IEEE Senior Member, Fulbright U.S. Scholar, and NVIDIA Deep Learning Institute University Ambassador. Dr. Milanova’s work is supported by NSF, NIH, DARPA, DoD, Homeland Security, NATO, Nokia Bell Lab, NJ, USA and NOKIA, Finland. Prof. Milanova’s research areas of interest are: Artificial Intelligence, Machine Learning, Image/Video Processing, Brian-like Computing and Computer Graphics. Dr. Milanova serves as a book editor for two books and as associate editor for various international journals.
She has published more than 120 publications, over 53 journal papers, 35 book chapters, and numerous conference papers and has 2 patents.