Dr.  Mohamed  Ezzat,

Tanta University, Faculty of Science, Mathematics Department
 

Summary of His Talk

About Dr. Mohamed  Ezzat

Dr. Abd El-Monem M.Kozae               

Tanta University, Faculty of Science, Mathematics Department
 

Summary of His Talk

  From topological point of view, the granular structure used in original model of rough set theory is a special type of topological structures. In the theory    of topological spaces, this stricture is known by cloopen, quasi discrete, zero dimensional or discrete in the sense of P. Alexandroof, in the context of RST this  topology is known by Pawlak topology. This view directed the attention to construct generalized rough set models using general topological structures . The             purpose of the talk is to indicate the importance of topological thinking as a basic tool for problem solving through brief exploration. Of topology history, its            branches and applications. The talk aims also to give an account on the directions for topological generalization of rough set models.

About Dr. A.M Kozae

  Dr. Aboul Ella Hassanien

Cairo University, Faculty of Computer and Information, IT Department
 

Summary of His Talk

Hybridization of intelligent systems is a promising research field of modern Artificial Intelligence concerned with the development of the next generation of intelligent systems. The objective of this talk  is to introduce a hybrid intelligent system that combines the advantages of different intelligent systems; fuzzy sets, rough sets and rough neural networks in conjunction with statistical feature extraction techniques. An application of, breast cancer imaging has been chosen and hybridization soft computing techniques have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: cancer or non-cancer. The system starts with fuzzy image processing as a pre-processing stage to enhance the contrast of the whole image; to extracts the region of interest and then to enhance the edges surrounding the region of interest. Next, subsequently extract features from the extracted regions characterizing the underlying texture of the interested regions using the gray-level co-occurrence matrix. A rough set approach to attribute reduction and rule generation is presented. Finally, rough neural networks are designed for discrimination for different regions of interest to test whether they are cancer or nun-cancer. A rough neuron can be viewed as a pair of neurons. One neuron corresponds to the upper bound and the other corresponds to the lower bound. Upper and lower neuron exchange information with each other during the calculation of their outputs. To evaluate the performance of the system, different images from the Mammographic Image Analysis Society (MIAS) database were selected. The experimental results show that the hybrid intelligent systems applied in this study perform well reaching over 98% in overall accuracy with 154 minimal number of generated rules.

About Dr. Aboul Ella Hassanien

Aboul Ella Hassanien   received his B.Sc. with honors in 1986 and M.Sc degree in 1993, both from Ain Shams University, Faculty of Science,   Pure Mathematics and Computer Science Department, Cairo, Egypt. On September 1998, he received his doctoral degree from the Department of Computer Science, Graduate School of Science & Engineering, Tokyo Institute of Technology, Japan. He is an associated  professor at Cairo University, Faculty of Computer and Information, information  Technology Department. Currently, he is a visiting professor at Kuwait University, College of Business Administration, Quantitative and Information System Department. He has served as the review committee chair, program committee member, and reviewer for various international conferences on artificial intelligence, soft computing, image processing and data mining. He has organized many conference sessions at many international conferences on the topics of rough sets,  Wavelet in signal/image processing, soft computing in image processing, rough neural hybridization techniques, and web mining. He has  received the excellence younger researcher award from Kuwait university for the academic year 2003/2004.  He serve in  Technical Committee on   Image Processing  and  Signal Processing for the term 2004-2007 in the International Association of Science and Technology for Development (IASTED). He is editing several special issues for many international journals like  Soft Computing journal, Informatica - journal, GVIP journal, International Journal of Hybrid Intelligent Systems on the topics of rough sets, wavelets, soft computing in image processing, multimedia mining, and rough neural hybridization: A new trend in decision making. He has directed many funded research projects. Currently, he is a member of the Interim Advisory Board committee of the International Rough Set Society.  His research interests include, rough set theory, wavelet theory, X-ray Mammogram analysis, medical image analysis, fuzzy image processing and multimedia data mining.  Homepage: http://www.cba.edu.kw/abo