Emad Alsukhni

Bio: Emad Alsukhni is an academic researcher from Yarmouk University. The author has contributed to research in topics: Software bug & Computer-assisted translation. The author has an hindex of 5, co-authored 14 publications receiving 118 citations. Previous affiliations of Emad Alsukhni include University of Ottawa.

Natural selection methods for artificial bee colony with new versions of onlooker bee

Abstract: Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms simulate the foraging behavior of honey bees in their hive. ABC starts with a colony of artificial bees with sole aim of discovering the place of food sources with high nectar amount using the solution search equation in the employed bee and onlooker bee operators. However, the solution search equation is good in exploration and poor in exploitation. In this paper, the solution search equation of the onlooker bee is modified by using a value of the fittest food sources selected by a set of selection schemes inspired from the evolutionary algorithms. This is to guide the search process of onlooker bee toward the fittest food sources from the population in order to empower the exploitation capability and convergence. Four selection schemes are incorporated with the ABC algorithm to choose the fittest food sources in four versions as follows: global-best, tournament, linear rank, and exponential rank. For evaluation purposes, 10 classical benchmark optimization functions are used to study the sensitivity analysis of each ABC algorithm to its parameters. The performance of the proposed ABC versions is compared with the original ABC version in order to study the effectiveness of the modifications. In addition, a comparative evaluation of ABC algorithms is carried out against the state-of-the-art methods that worked on CEC2005 benchmark functions, CEC2015 benchmark functions, and two real-world cases of economic load dispatch problem. The experimental results show that the selection schemes incorporated within the search equation of the onlooker bee directly affects the performance of ABC algorithm.

A Non-convex Economic Dispatch Problem with Valve Loading Effect Using a New Modified $$\beta $$ β -Hill Climbing Local Search Algorithm

Abstract: In this paper, the problem of economic load dispatch (ELD) is tackled using a recently introduced local search-based method called $$\beta $$ -hill climbing optimizer. In a power system, the ELD problem is tackled by arranging a set of generation units’ outputs in a specific order to minimize the cost of the operating fuel and to match the power system load demand. This goal is achieved by satisfying all the power balance equality constraints and power output inequality constraints. $$\beta $$ -hill climbing algorithm is a new local search algorithm which uses an intelligent stochastic operator called $$\beta $$ -operator to escape the trap of local optima. The proposed method is evaluated using five real-world ELD benchmarks which vary in terms of complexity and size. The sensitivity analysis to study the effect of the proposed method parameters is conducted based on eight different convergence cases. The evaluation results of the proposed method are compared with 45 state-of-the-art methods using the same tested ELD benchmarks. Interestingly, the proposed method is able to produce a very closed-to-optimum result for almost all the tested ELD systems and the best result for one of them.

Efficient Assessment and Evaluation for Websites Vulnerabilities Using SNORT

Abstract: An endless number of methods or ways exists to access illegally a web server or a website. The task of defending a system (e.g. network, server, website, etc.) is complex and challenging. SNORT is one of the popular open source tools that can be used to detect and possibly prevent illegal access and attacks for networks and websites. However, this largely depends on the way SNORT rules are designed and implemented. In this paper, we investigated in details several examples of SNORT rules and how they can be tuned to improve websites protection. We demonstrated practical methods to design and implement those methods in such ways that can show to security personnel how effectively can SNORT rules be used. Continuous experiments are conducted to evaluate and optimized the proposed rules. Results showed their ability to prevent tested network attacks. Each network should try to find the best set of rules that can detect and prevent most network attacks while at the same time cause minimal impact on network performance.

Investigating the Use of Machine Learning Algorithms in Detecting Gender of the Arabic Tweet Author

Abstract: Twitter is one of the most popular social network sites on the Internet to share opinions and knowledge extensively. Many advertisers use these Tweets to collect some features and attributes of Tweeters to target specific groups of highly engaged people. Gender detection is a sub-field of sentiment analysis for extracting and predicting the gender of a Tweet author. In this paper, we aim to investigate the gender of Tweet authors using different classification mining techniques on Arabic language, such as Naive Bayes (NB), Support vector machine (SVM), Naive Bayes Multinomial (NBM), J48 decision tree, KNN. The results show that the NBM, SVM, and J48 classifiers can achieve accuracy above to 98%, by adding names of Tweet author as a feature. The results also show that the preprocessing approach has negative effect on the accuracy of gender detection. In nutshell, this study shows that the ability of using machine learning classifiers in detecting the gender of Arabic Tweet author.

Test Case Reduction Using Data Mining Technique

Abstract: Software testing is a process of ratifying the functionality of software. It is a crucial area which consumes a great deal of time and cost. The time spent on testing is mainly concerned with testing large numbers of unreliable test cases. The authors' goal is to reduce the numbers and offer more reliable test cases, which can be achieved using certain selection techniques to choose a subset of existing test cases. The main goal of test case selection is to identify a subset of the test cases that are capable of satisfying the requirements as well as exposing most of the existing faults. The state of practice among test case selection heuristics is cyclomatic complexity and code coverage. The authors used clustering algorithm which is a data mining approach to reduce the number of test cases. Their approach was able to obtain 93 unique effective test cases out a total of 504.

An overview of AspectJ

Abstract: Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter.

Abstract: This overview presents the framework and the results of the Author Profiling task at PAN 2017. The objective of this year is to address gender and language variety identification. For this purpose a corpus from Twitter has been provided for four different languages: Arabic, English, Portuguese, and Spanish. Altogether, the approaches of 22 participants are evaluated.

Overview of the 6th Author Profiling Task at PAN 2018: Multimodal Gender Identification in Twitter.

Abstract: This overview presents the framework and the results of the Author Profiling shared task at PAN 2018. The objective of this year’s task is to address gender identification from a multimodal perspective, where not only texts but also images are given. For this purpose a corpus with Twitter data has been provided, covering the languages Arabic, English, and Spanish. Altogether, the approaches of 23 participants are evaluated.

From ants to whales: metaheuristics for all tastes

Abstract: Nature-inspired metaheuristics comprise a compelling family of optimization techniques. These algorithms are designed with the idea of emulating some kind natural phenomena (such as the theory of evolution, the collective behavior of groups of animals, the laws of physics or the behavior and lifestyle of human beings) and applying them to solve complex problems. Nature-inspired methods have taken the area of mathematical optimization by storm. Only in the last few years, literature related to the development of this kind of techniques and their applications has experienced an unprecedented increase, with hundreds of new papers being published every single year. In this paper, we analyze some of the most popular nature-inspired optimization methods currently reported on the literature, while also discussing their applications for solving real-world problems and their impact on the current literature. Furthermore, we open discussion on several research gaps and areas of opportunity that are yet to be explored within this promising area of science.

Island bat algorithm for optimization

Abstract: Structured population in evolutionary algorithms is a vital strategy to control diversity during the search. One of the most popular structured population strategies is the island model in which the population is divided into several sub-populations (islands). The EA normally search for each island independently. After a number of predefined iterations, a migration process is activated to exchange specific migrants between islands. Recently, bat-inspired algorithm has been proposed as a population-based algorithm to mimic the echolocation system involved in micro-bat. The main drawback of bat-inspired algorithm is its inability to preserve the diversity during the search and thus the prematurity can take place. In this paper, the strategy of island model is adapted for bat-inspired algorithm to empower its capability in controlling its diversity concepts. The proposed island bat-inspired algorithm is evaluated using 25 IEEE-CEC2005 benchmark functions with different size and complexity. The sensitivity analysis for the main parameters of island bat-inspired algorithm is well-studied to show their effect on the convergence properties. For comparative evaluation, island bat-inspired algorithm is compared with 17 competitive methods and shows very successful outcomes. Furthermore, the proposed algorithm is applied for three real-world cases of economic load dispatch problem where the results obtained prove considerable efficiency in comparison with other state-of-the-art methods.