Browsing by Author "Mojeed, H.A."
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Item Comparative Analysis of Selected Heterogeneous Classifiers for Software Defects Prediction using Filter-Based Feature Selection Method.(FUOYE Journal of Engineering and Technology., 2018) Akintola, G.A.; Balogun, A.O.; Lafenwa-Balogun, F.; Mojeed, H.A.Classification 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 Expert System and Decision Support System for Electrocardiogram Interpretation and Diagnosis: Review, Challenges and Research Directions.(MDPI Publishers, Basel Switzerland, 2022) Adewole, K.S.; Mojeed, H.A.; Ogunmodede, James Ayodele; Gabralla, L.A.; FARUK, N; ABDULKARIM, A; et alAbstract: Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using electrodes that are attached to the body surface. The use of ECG in the diagnosis and management of cardiovascular disease (CVD) has been in existence for over a decade, and research in this domain has recently attracted large attention. Along this line, an expert system (ES) and decision support system (DSS) have been developed for ECG interpretation and diagnosis. However, despite the availability of a lot of literature, access to recent and more comprehensive review papers on this subject is still a challenge. This paper presents a comprehensive review of the application of ES and DSS for ECG interpretation and diagnosis. Researchers have proposed a number of features and methods for ES and DSS development that can be used to monitor a patient’s health condition through ECG recordings. In this paper, a taxonomy of the features and methods for ECG interpretation and diagnosis were presented. The significance of the features and methods, as well as their limitations, were analyzed. This review further presents interesting theoretical concepts in this domain, as well as identifies challenges and open research issues on ES and DSS development for ECG interpretation and diagnosis that require substantial research effort. In conclusion, this paper identifies important future research areas with the purpose of advancing the development of ES and DSS for ECG interpretation and diagnosis.Item HYBRID SFLA-TABU SEARCH ALGORITHM FOR OPTIMAL PROJECT SCHEDULING AND STAFFING(12th AICTTRA Conference Proceedings Ile Ife., 2019) Mojeed, H.A.; Jimoh, R.G.; Sadiku, P.O.; Salihu, S.A.Planning a large scale software project involves the objectives of optimal ordering of a set of activities and an allocation of staff to activities. Current adopted method presents difficulty in reaching optimal good solutions when the two objectives are combined. This study proposes a hybrid SFLA-TABU search algorithm to solve the project scheduling and staffing problem with the two objective combined. The hybrid algorithm retains the framework of SFLA algorithm but employs the neighborhood structure method of tabu search and its avoidance of already explored area in the solution space to move towards optimal solution within the local memetic evolution. The algorithm was applied on three randomly generated problem instances representing small, medium and large sized problems. Results showed that the proposed algorithm was able to produce good optimal solutions with average fitness values 0.44, 0.56 and 0.15 in small, medium and large sized problems respectively. The hybrid algorithm outperformed the baseline algorithms in 100% of the problem instances and findings from the experiment revealed theoretically, the scalability of the proposed approach in handling various sizes of software project.Item Performance Evaluation Of Manhattan And Euclidean Distance Measures For Clustering Based Automatic Text Summarization.(FUOYE Journal of Engineering and Technology, 2019) Salihu, S.A.; Onyekwere, I.P.,; Mabayoje, M.A.; Mojeed, H.A.In the past few years, there has been an explosion in the amount of text data from a variety of sources. This volume of text is a valuable source of information and knowledge which needs to be effectively summarized to be useful. In this paper, automatic text summarization with K-means clustering techniques is presented by employing two different distance measurement methods (Euclidean and Manhattan). The dataset extracted from African prose was preprocessed using stopwords removal and tokenization. The preprocessed document is converted into vector representation using tf-idf technique and k-means clustering is applied using Euclidean and Manhattan distance measures to generate summary. There are different distance measures for k-means which has been used in several works. However, there is dearth of work on performance evaluation of these distance measures in text summarization. The experimental analysis was performed on Waikato Environment for Knowledge Analysis. The results obtained showed that the Euclidean variation produced an extractive summary of sentences amounting to 72% from three different clusters while the Manhattan variation produced an extractive summary of sentences that made up 94% of the total document all in one cluster using compression ratio as the performance metric.Item A Shuffled Frog-Leaping Algorithm for Optimal Software Project Planning.(African Journal of Computing & ICTs, 2014) Oladele, R.O.; Mojeed, H.A.In recent time, software project management has received considerable attention from researchers in the field of Search Based Software Engineering (SBSE). This paper presents an approach to Search Based Software Project Planning based on Shuffled Frog-Leaping Algorithm (SFLA). Our approach seeks to optimize work package scheduling with a view to achieving early overall completion time. To evaluate the algorithm, it is tested on a set of randomly generated problems and it’s results are compared with those of Genetic Algorithm (GA). Results indicate that SFLA is significantly superior to GA.