Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm - Sheta, Alaa F.; Abdel-Raouf, Amal (International Journal of Advanced Computer Science and Applications (IJACSA), 2016)
In this age of technology, building quality software is essential to competing in the business market. One of the major principles required for any quality and business software product for value fulfillment is reliability. Estimating software reliability early during the software development life cycle saves time and money as it prevents spending larger sums fixing a defective software product after deployment. The Software Reliability Growth Model (SRGM) can be used to predict the number of failures that may be encountered during the software testing process. In this paper we explore the advantages of the Grey Wolf Optimization (GWO) algorithm in estimating the SRGM’s parameters with the objective of minimizing the difference between the estimated and the actual number of failures of the software system. We evaluated three different software reliability growth models: the Exponential Model (EXPM), the Power Model (POWM) and the Delayed S-Shaped Model (DSSM). In addition, we used three different datasets to conduct an experimental study in order to show the effectiveness of our approach.
Number: 4
Keywords: dept.csc; peer-reviewed

Big Data Framework - Abdel-Raouf, Amal (2016)
The objective of my sabbatical leave project was to propose a new scheduling algorithm that extends the current MapReduce model to improve system performance. MapReduce, which has been popularized by Google, is a scalable tool that enables the processing of massive volumes of data.
Keywords: dept.csc; faculty_collection; sabbatical_report

Authorship categorization of public domain literature - Boran, Tudor; Voss, John; Hossain, Shafaeat (Ubiquitous Computing, 2016)
We defined a set of quantifiable features for authorship categorization. We performed our experiments on public domain literature - all books analyzed were obtained in plain text format through Project Gutenberg's online repository of classic books. We tested three machine learning algorithms: Artificial Neural Network, Naïve Bayes Classifier, and Support Vector Machine with our features. We found that certain features, such as punctuation and various suffixes result in a higher accuracy. In addition, the Support Vector Machine classifier produces repeatedly higher accuracies than other classifiers and seems to be a far superior method of classification in terms of authorship categorization. © 2016 IEEE.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Authorship categorization; Bayes Classifier; Classification (of information); Learning algorithms; Learning systems; Mobile telecommunication systems; Neural networks; Online repositories; Plain text; Public domains; Support vector machine classifiers; Support vector machines; Ubiquitous computing; classification; dept.csc; public domain literature; support vector machine

On finding appropriate reject region in serial fusion based biometric verification - Hossain, Shafaeat (IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support, 2016)
We give the theoretical foundation for finding a reject region which gives the minimum equal error rate in serial fusion based biometric verification. Given a user-specified tolerance of x percent genuine score reject rate, we prove that there exists a unique reject region inside which the false alarm rate and impostor pass rate curves overlap, and this reject region gives the minimum equal error rate. Our theory leads to new algorithms for finding reject regions, which have two key advantages over the state-of-the-art: (1) the algorithms allow the system administrator to control the proportion of genuine scores that a reject region can erroneously reject and (2) the algorithms determine reject regions directly from the scores, without the need to estimate score distributions. Our proofs do not rely on data belonging to any particular distribution, which makes them applicable to a wide range of biometric modalities including face, finger, iris, speech, gait, and keystrokes. © 2016 IEEE.
3 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Biometric verification; Biometrics; Decision support systems; Equal error rate; False alarm rate; Genuine scores; Particular distribution; State of the art; System administrators; Theoretical foundations; dept.csc

User authentication and identification on smartphones by incorporating capacitive touchscreen - Rilvan, Mohamed Azard; Lacy, Kolby Isiah; Hossain, Shafaeat; Wang, Bing (International Performance, Computing, and Communications Conference, 2016)
Smartphones, while providing users ease of access to sensitive information on the go, also present severe security risks if an attacker is able to gain access to them. To strengthen the user authentication and identification in a smartphone, we develop a biometric authentication and identification system which uses the capacitive touchscreen that is featured in all current smartphones. Our methodology focuses on using the touchscreen as a sensor to capture the image of a user's ear, thumb or four fingers. We extract the capacitive raw data from the touched body part to obtain a capacitive image, and then use it to capture geometric features (e.g., length and width of a finger) and principal components. After that, we experiment with Support Vector Machine (SVM) and Random Forest (RF) classifiers to verify and also identify each user. We achieved the maximum authentication accuracy of 98.84% by four fingers with SVM, and maxinum identification accuracy of 97.61% by four fingers with RF. © 2016 IEEE.
4 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Authentication; Biometric; Biometric authentication; Biometrics; Decision trees; Geometric feature; Identification (control systems); Identification accuracy; Principal Components; Random forests; Security risks; Sensitive informations; Smartphones; Support vector machines; User authentication; authentication; capacitive touchscreen; dept.csc; identification; smart-phones