Naser El-Bathy

Intelligent Extended Clustering Genetic Algorithm

In this paper, the problem of clustering intelligent web using K-means algorithm has been analyzed and the need for a new data clustering algorithm such as Genetic Algorithm (GA) is justified. We propose an Intelligent Extended Clustering Genetic Algorithm (IECGA) using Business Process Execution Language (BPEL) to be an optimal solution for data clustering. It improves the efficiency and performance for retrieving a proper information results that satisfy users needs. The proposed IECGA uses several mutation operators simultaneously to produce next generation. This series of random mutation process depend on chromosome best fitness in the population and rely on high relevancy as well. The mutation operation will guarantee the success of IECGA for data clustering since it expands the search. So the highly effective mutation operators the greater effects on the genetic process. Finally, IECGA for data clustering gives the user needed documents based on similarity between query matching and relevant document mechanism. The results obtained from the web intelligent search engine are optimal.

Intelligent surveillance lifecycle architecture for epidemiological data clustering using Twitter and novel genetic algorithm

Social media has spread globally. Over the past few years, Twitter has climbed to over one billion active users. On average, they send out 340 million tweets per day. That produces a massive amount of information, calculated to be in excess of 150 gigabytes of data per year. This research, as reported in this paper, focuses on creating an Intelligent surveillance lifecycle architecture for epidemiological data clustering using Twitter and novel genetic algorithm (ISLA/EDC). The purpose of the architecture is to develop concepts and techniques such that epidemiologists can be able to more efficiently and effectively determine the locations of disease outbreaks as they occur. They do not have to rely on the illnesses being directly reported to them. The technical solution of the architecture provides medical tasks that support strategic decision making and operational business processes. The design of the architecture is based on Service-Oriented Architecture (SOA). In a SOA environment, the technical solution develops new intelligent concepts of integrating approaches of search methodologies, information extraction, data clustering, genetic algorithm, and intelligent agents. A prototype is created and examined in order to validate the concepts.

Intelligent teaching models for STEM related careers using a service-oriented architecture and management science

The development of World Wide Web (WWW) a little more than a decade ago has caused an information explosion that needs an Intelligent Web (IW) for users to easily control their information and commercial needs. Therefore, engineering schools have offered a variety of IW courses to cultivate hands-on experience and training for industrial systems. In this study, Intelligent Teaching Models for STEM Related Careers Using Service-Oriented Architecture (SOA) and Management Science project course has been designed. The goal is to help students learn theoretical concepts of IW, practice advanced technical skills, and discover knowledge to solve problem. Undergraduate Science, Technology, Engineering and Mathematics (STEM) students involved in the development of innovative approaches and techniques. They are able to help solve the problems of disease misdiagnoses that medical and health care professionals experience. They co-authored and presented numerous research papers introducing the solution via scientific conferences and journals. This study provides the solution in the form of an Intelligent models using an integration of Service-Oriented Architecture and Management Science to decrease disease misdiagnosis in health care. Results show that this new course strengthens the capacity and quality of STEM undergraduate degree programs and the number of overall graduate student enrollment. It promotes a vigorous STEM academic environment and increases the number of students entering STEM careers. It expands the breadth of faculty and student involvement in research and development. It enhances and leverages the active engagement of faculty technology transfer and translational research. It improves and develops new relationships between educational institutions and research funding entities to broaden the universitys research portfolio and increase funding. The proposed project course is a software engineering research methodology, an educational tool, and a teaching technique is needed in future medical and health IT fields.

Intelligent data mining and machine learning for mental health diagnosis using genetic algorithm

Inappropriate diagnosis of mental health illnesses leads to wrong treatment and causes irreversible deterioration in the clients mental health status including hospitalization and/or premature death. About 12 million patients are misdiagnosed annually in US. In this paper, a novel study introduces a semi-automated system that aids in preliminary diagnosis of the psychological disorder patient. This is accomplished based on matching description of a patients mental health status with the mental illnesses illustrated in DSM-IV-TR, Fourth Edition Text Revision. The study constructs the semi-automated system based on an integration of the technology of genetic algorithm, classification data mining and machine learning. The goal is not to fully automate the classification process of mentally ill individuals, but to ensure that a classifier is aware of all possible mental health illnesses could match patients symptoms. The classifier/psychological analyst will be able to make an informed, intelligent and appropriate assessment that will lead to an accurate prognosis. The analyst will be the ultimate selector of the diagnosis and treatment plan.

An Automated Design Framework for Floating Point Scientific Algorithms using Field Programmable Gate Arrays (FPGAs) (Abstract Only)

This paper presents a reconfigurable computing environment while addressing the problem of porting High Performance Computing (HPC) applications directly to Field Programmable Gate Arrays (FPGAs)-based architectures. The objectives of this research are developing a comprehensive floating point library of essential functions for scientific applications; demonstrate order of magnitude speedup of reconfigurable computing applications, demonstrating the effectiveness of automated design framework for both development and test of scientific algorithms. The developed framework can be reused in various scientific applications which shares kernel functions. The study of this research has identified an exponential function as a kernel for cellular ophthalmoscopy camera processing, traffic monitoring and light wave simulation. The paper demonstrates 30x speedup of these kernels in three algorithms using its novel architecture and its automated toolset. Exponential kernel generation case study and its flexible hardware implementation on an FPGA has been validated onto a Xilinx LX-100 device and the Nallatech H101-PCIXM FPGA board.