Browsing by Author "Mojeed, Hameed Adeleye"
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Item Comparative Analysis of Selected Heterogeneous Classifiers for Software Defects Prediction Using Filter-Based Feature Selection Methods(Faculty of Engineering, Federal University Oye-Ekiti, Ekiti., 2018-03-31) Akintola, Abimbola Ganiyat; Balogun, Abdullateef Oluwagbemiga; Lafenwa-Balogun, Fatimah; Mojeed, Hameed AdeleyeClassification techniques is a popular approach to predict software defects and it involves categorizing modules, which is represented by a set of metrics or code attributes into fault prone (FP) and non-fault prone (NFP) by means of a classification model. Nevertheless, there is existence of low quality, unreliable, redundant and noisy data which negatively affect the process of observing knowledge and useful pattern. Therefore, researchers need to retrieve relevant data from huge records using feature selection methods. Feature selection is the process of identifying the most relevant attributes and removing the redundant and irrelevant attributes. In this study, the researchers investigated the effect of filter feature selection on classification techniques in software defects prediction. Ten publicly available datasets of NASA and Metric Data Program software repository were used. The topmost discriminatory attributes of the dataset were evaluated using Principal Component Analysis (PCA), CFS and FilterSubsetEval. The datasets were classified by the selected classifiers which were carefully selected based on heterogeneity. Naïve Bayes was selected from Bayes category Classifier, KNN was selected from Instance Based Learner category, J48 Decision Tree from Trees Function classifier and Multilayer perceptron was selected from the neural network classifiers. The experimental results revealed that the application of feature selection to datasets before classification in software defects prediction is better and should be encouraged and Multilayer perceptron with FilterSubsetEval had the best accuracy. It can be concluded that feature selection methods are capable of improving the performance of learning algorithms in software defects prediction.Item MEMETIC APPROACH FOR MULTI-OBJECTIVE OVERTIME PLANNING IN SOFTWARE ENGINEERING PROJECTS(School of Engineering, Taylor’s University, Malaysia., 2019-12) Mojeed, Hameed Adeleye; Bajeh, Amos Orenyi; Balogun, Abdullateef Oluwagbemiga; Adeleke, HammidSoftware projects often suffer from unplanned overtime due to uncertainty and risk incurred due to changing requirement and attempt to meet up with time-to-market of the software product. This causes stress to developers and can result in poor quality. This paper presents a memetic algorithmic approach for solving the overtime-planning problem in software development projects. The problem is formulated as a three-objective optimization problem aimed at minimizing overtime hours, project makespan and cost. The formulation captures the dynamics of error generation and propagation due to overtime using simulation. Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA) specifically designed for overtime planning is applied to solve the formulated problem. Empirical evaluation experiments on six real-life software project datasets were carried out using three widely used multi-objective quality indicators. Results showed that MOSFLA significantly outperformed the existing traditional overtime management strategies in software engineering projects in all quality indicators with 0.0118, 0.3893 and 0.0102 values for Contribution (IC), Hypervolume (IHV) and Generational Distance (IGD) respectively. The proposed approach also produced significantly better IHV and IGD results than the state of the art approach (NSGA-IIV ) in 100% of the project instances. However, the approach could only outperform NSGA-IIV in approximately 67% of projects instances with respect to IC.Item Multiple Ceaser Cipher Encryption Algorithm(Mathematical Association of Nigeria (MAN)., 2017-12) Balogun, Abdullateef Oluwagbemiga; Sadiku, Peter Ogirima; Mojeed, Hameed Adeleye; Raifu, Hameed AdetunjiThe Caesar cipher has always been the major reference point when cryptographic algorithms (also called ciphers) are discussed. This, probably, is due to its being an age-long cipher. It may also be owing to the belief that the Caesar cipher was the first cipher used ever. Caesar cipher operation is based on shift-by-3 rule which makes its breaking obviously easy since an exhaustive key search of the other 25 keys can be conveniently performed. Ipso facto, an investigation into an enhancement of this too-simple-to-crack cipher is invariably necessary and ultimately important. This study is, therefore, concerned with developing a new enhanced model of Caesar cipher for a better security using multiple encryption technique, whereby an already-encrypted message is encrypted one or more times using the same or different algorithm. The new model works by wrapping a plaintext message in three crypto-wrappers and each encryption/decryption phase uses a different shift key from the other. The model supports both uppercase and lowercase characters. However, the model does not encrypt/decrypt numbers, special characters, whitespace, and file types such as word document, binary, or pdf files, but only text files. Most importantly, the new enhanced model is able to provide a better security of message by encrypting a plaintext message three times; in this way, brute forcing or an exhaustive key search will be difficult to perform; thus, making cryptanalysis almost a mirage!