Browsing by Author "Jimoh, R.G."
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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 TSDL: A Framework for Tip Spam Detection in Location Based Social Network.(Nigeria Computer Society, 2017) Adewole, K.S.; Isiaka, R.M.; Jimoh, R.G.; Ajiboye, A.R.In Web 2.0 systems, Location Based Social Network (LBSN) has become increasingly popular because of its ability for locating places, such as restaurants, hotels and nearby facilities that can render services to the users. Among such LBSN is Apontador, a popular social network introduced in Brazil a couple of years ago. However, despite the numerous opportunities offered by Apontador LBSN, it has become attractive social network for tips spam distribution. In this work, an intelligent machine learning framework is proposed to detect tips spam in Apontador LBSN. The proposed framework explored three classification algorithms: Random Forest, Multilayer Perceptron, and Decorate. Bio-inspired feature identification was studied using Evolutionary algorithm (EA) and Particle Swamp Optimization (PSO) to identify the discriminating features for tip spam detection in LBSN. Three experiments were conducted based on sixty (60), twenty-two (22), and ten (10) features to ascertain the performance of the proposed framework for tip spam detection. Based on the various experiments conducted, Random Forest classification algorithm produced accuracy and F-measure of 90.5% and ROC of 96.7% using 60 features extracted from Apontador LBSN. The algorithm also outperformed the two other classifiers during EA evaluation. However, Decorate classifier produced the best results during the PSO evaluation, achieving F-measure and ROC of 86.3% and 92.9% respectively. The experimental results show that the proposed framework improved in performance for tips spam detection in Apontador LBSN.