Osman Hegazy

Monitoring and synchronization for teamwork in GPGP

This paper addresses the problem of coordinating a group of agents involved in a team. To achieve flexible teamwork, agents should synchronize their work and monitor their performance to avoid redundant work. Generalized Partial Global Planning (GPGP) is one of the most common techniques used in coordinating cooperative agents, however, no technique is without limitations. Our work adopts some concepts of STEAM to overcome some of GPGP limitations. In particular, we suggest adding coordination mechanisms to GPGP and extending TAEMS, the model underlying GPGP, to facilitate such mechanisms. The work has successfully been implemented using JAF architecture. The coordination mechanisms are written as Soar rules where we implemented a JAF component that implements the Soar engine. Analysis of a case study is presented along with experimental results to illustrate the power of the proposed work.

Distance Similarity as a CBR Technique for Early Detection of Breast Cancer: An Egyptian Case Study

Case-based reasoning as a concept covers almost a lot of technologies and techniques including knowledge management, artificial intelligence, machine learning techniques as well as database technology. The usage of all these technologies can easily aid in early detection of breast cancer as well as help other decision makers take the right decision on time and all the times. Of the main hot topics nowadays concerning executive managers and decision makers is measuring the similarity between objects. For better performance most organizations are in need on semantic similarity and similarity measures. This article presents mathematically different distance metrics used for measuring the binary similarity between quantitative data within cases. The case study represents a quantitative data of breast cancer patients within Faculty of medicine Cairo University. The experimental results show that the squared chord distance yields better with a 96.76 % without normalization that correlate more closely with human assessments compared to other distance measures used in this study.

Case Selection Strategy Based on K-Means Clustering

Knowledge acquisition is considered as an extraordinary issue concerning organizations and decision makers nowadays. Learning from previous failures and successes saves plenty of time in understanding the problems and visualizing data. Case-based Reasoning (CBR) as a process is one of the most used methods to solve the problem of knowledge capture and data understanding. In this paper we proposed an approach for clustering theses documents based on CBR combined with lexical similarity and k-means algorithm for cluster-dependent keyword weighting. The cluster dependent keyword weighting help in partitioning and categorizing the theses documents into more meaningful categories. The proposed approach yield to 91.95 % increase of using CBR in comparison to human assessments.

A Keystroke Dynamics Based Approach for Continuous Authentication

In this paper, we present the application of keystroke dynamics for continuous user authentication in desktop platform. We show the differences between static and continuous systems based on keystroke dynamics in terms of creating the template and authentication phase. The key factor in the continuous authentication system is monitoring the genuineness of the user during the whole session, and not only at log-in. Moreover, we propose a general approach for continuous authentication based on keystroke dynamics. In our experiments, we use the email application as a case study to present the effectiveness and efficiency of our proposed approach. Our main conclusion is that using keystroke dynamics can serve as a feasible and acceptable measure for continuous user authentication. The investigations have shown that it is feasible to authenticate users based on keystroke dynamics for continuous authentication systems.

Early development of graphical user interface (GUI) in agile methodologies

The Agile methodologies have shown success over plan-driven methodologies especially in web development due to their ability to adapt to new requirement changes. Because of the importance of the development of Graphical User Interfaces (GUI), we have proposed a new agile practice, “Early User Interface Development” (EUID), for developing GUI at early stages in our previous paper [1,2]. To speed up the process of producing the output of our practice, we propose a framework for representing the GUI structure and behavior. Besides, the framework can be used for automating the development of GUI according to the EUID process.

Grey Wolf Optimizer and Case-Based Reasoning Model for Water Quality Assessment

This paper presents a bio-inspired optimized classification model for assessing water quality. As fish gills histopathology is a good biomarker for indicating water pollution, the proposed classification model uses fish gills microscopic images in order to asses water pollution and determine water quality. The proposed model comprises five phases; namely, case representation for defining case attributes via pre-processing and feature extraction steps, retrieve, reuse/adapt, revise, and retain phases. Wavelet transform and edge detection algorithms have been utilized for feature extraction stage. Case-based reasoning (CBR) has been employed, along with the bio-inspired Gray Wolf Optimization (GWO) algorithm, for optimizing feature selection and the k case retrieval parameters in order to asses water pollution. The datasets used for conducted experiments in this research contain real sample microscopic images for fish gills exposed to copper and water \(\textit{pH}\) in different histopathlogical stages. Experimental results showed that the average accuracy achieved by the proposed GWO-CBR classification model exceeded 97.2 % considering variety of water pollutants.

A mapreduce fuzzy techniques of big data classification

Due to the huge increase in the size of the data it becomes troublesome to perform efficient analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of data but in addition a need for a means to meet the computational requirements to process such huge volume of data. The objective of this research is to implement a map reduce paradigm using fuzzy and crisp techniques, and to provide a comparative study between the results of the proposed systems and the methods reviewed in the literature. In this paper four proposed system is implemented using the map reduce paradigm to process on big data. First, in the mapper there are two techniques used; the fuzzy k-nearest neighbor method as a fuzzy technique and the support vector machine as non-fuzzy technique. Second, in the reducer there are three techniques used; the mode, the fuzzy soft labels and Gaussian fuzzy membership function. The first proposed system is using the fuzzy KNN in the mapper and the mode in the reducer, the second proposed system is using the SVM in the mapper and the mode in the reducer, the third proposed system is using the SVM in the mapper and the soft labels in the reducer, and the fourth proposed system is using the SVM in the mapper and fuzzy Gaussian membership function in the reducer. Results on different data sets show that the fuzzy proposed methods outperform a better performance than the crisp proposed method and the method reviewed in the literature.

Case-Based Reasoning: A Knowledge Extraction Tool to Use

Case-based reasoning (CBR) is a relative newcomer to AI and is commonly described as an AI as well as KM technology. Case-Based Reasoning is considered as a methodology not a technology to use. Finding the similarities between objects as well as knowledge extraction sometimes is a complicated issue to handle concerning decision makers and executive managers. Learning from previous failures and successes saves plenty of time in understanding the problems and visualizing the data. CBR as a process is one of the most used methods to solve the problem of knowledge capture and data understanding. In this paper we show mathematically the usage of CBR in clustering documents and finding correlations between medical data by using CBR with DB technology as an application. Results yield to an increase in comparison to human assessments and not using CBR methods.

Enhanced Authentication Mechanisms for Desktop Platform and Smart Phones

With hundreds of millions using computers and mobile devices all over the globe, these devices have an established position in modern society. Nevertheless, most of these devices use weak authentication techniques with passwords and PINs which can be easily hacked. Thus, stronger identification is needed to ensure data security and privacy. In this paper, we will explain the employment of biometrics to computer and mobile platforms. In addition, the possibility of using keystroke and mouse dynamics for computer authentication is being checked. Finally, we propose an authentication scheme for smart phones that shows positive results.

Water quality classification approach based on bio-inspired Gray Wolf Optimization

This paper presents a bio-inspired optimized classification approach for assessing water quality. As fish liver histopathology is a good biomarker for detecting water pollution, the proposed classification approach uses fish liver microscopic images in order to detect water pollution and determine water quality. The proposed approach includes three phases; preprocessing, feature extraction, and classification phases. Color histogram and Gabor wavelet transform have been utilized for feature extraction phase. The Machine Learning (ML) Support Vector Machines (SVMs) classification algorithm has been employed, along with the bio-inspired Gray Wolf Optimization (GWO) algorithm for optimizing SVMs parameters, in order to classify water pollution degree. Experimental results showed that the average accuracy achieved by the proposed GWO-SVMs classification approach exceeded 95% considering a variety of water pollutants.