Faculty of Computing
Permanent URI for this communityhttps://repository.nileuniversity.edu.ng/handle/123456789/13
Browse
14 results
Search Results
Item Test Case Generation Approach for Android Applications using Reinforcement Learning(Engineering, Technology & Applied Science Research, 2024-04-27) Moussa Mahamat Boukar; Muhammed Aliyu Suleiman; Ibrahim Anka SalihuMobile applications can recognize their computational setting and adjust and respond to actions in the context. This is known as context-aware computing. Testing context-aware applications is difficult due to their dynamic nature, as the context is constantly changing. Most mobile testing tools and approaches focus only on GUI events, adding to the deficient coverage of applications throughout testing. Generating test cases for various context events in Android applications can be achieved using reinforcement learning algorithms. This study proposes an approach for generating Android application test cases based on Expected State-Action-Reward-State-Action (E-SARSA), considering GUI and context events for effective testing. The proposed method was experimentally evaluated on eight Android applications, showing 48- 96% line of code coverage across them, which was higher than Q-testing and SARSA.Item Improving the Accuracy of Animal Species Classification in Camera Trap Images Using Transfer Learning(International Conference on Artificial Intelligence, Computer, Data Sciences and Application, 2024-02-02) Moussa Mahamat BoukarUnderstanding biodiversity, monitoring endangered species, and estimating the possible effect of climate change on particular regions all rely on animal species identification. Closed-circuit television (CCTV) cameras, which can collect huge volumes of video data, are an excellent environmental monitoring tool. However, manually evaluating these massive datasets is time-consuming, difficult, and expensive, emphasizing the need for automated ecological analysis.Deep learning models have transformed computer vision, handling problems such as object and species detection. Their cutting-edge performance qualifies them for this application. The purpose of this work was to create and test machine learning models for distinguishing diverse animal species using camera trap images. On VGG19, GoogLeNet (InceptionV3), ResNet50, and DenseNet121, we used transfer learning. The best multi- classification accuracy was attained by GoogLeNet (87%), followed by ResNet50 (83%), DenseNet (81%), and VGG19 (53%). This evidence suggests that transfer learning outperforms training models from scratch for this task.Item The Impact of Artificial Intelligence (AI) on Content Management Systems (CMS)(International Journal of Intelligent Systems and Applications in Engineering, 2024-02-02) Moussa Mahamat BoukarThe dynamic nature of the global environment is always changing, with technology playing a pivotal role in propelling these shifts. The emergence of artificial intelligence (AI) has fundamentally transformed the manner in which we oversee and engage with our digital data. The potential of integrating artificial intelligence (AI) with content management systems (CMS) holds significant promise for future advancements. Artificial intelligence (AI) has the potential to bring about substantial changes in the manner in which information is managed and shared on the internet. It can enhance search functionalities and streamline numerous processes via automation. Individuals engaged in website ownership, content generation, and marketing are required to acquaint themselves with the most recent advancements in content management systems (CMS) and artificial intelligence (AI). The objective of this article is to provide a comprehensive examination of the influence of artificial intelligence (AI) on content management systems (CMS), along with an analysis of emerging AI methodologies and their practical use within a corporate environment.Item A Few-shot custom CNN Model for Retinal Nerve Fibre Layer Thickness Measurement in OCT Images of Epilepsy(Proc. of International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (, 2024-02-01) Ruqayya Muhammad; Moussa Mahamat Boukar; Steve Adeshina; Senol DaneThis study aims to assess the effectiveness of employing deep learning models for measuring retinal nerve fiber layer (RNFL) thickness in optical coherence tomography (OCT) scans of epilepsy patients. Conventional OCT scan segmentation methods typically rely on supervised learning, demanding substantial data for training and assuming fixed network weights post-training. To mitigate these challenges, we explore the applicability of few-shot learning (FSL) in CNN architectures, allowing dynamic fine-tuning of network weights with minimal additional data. Experimental results demonstrate enhanced segmentation accuracy, with the proposed Few shot Custom CNN achieving a notable 91% accuracy, surpassing both the Custom CNN (86%) and the OCT machine data. This suggests the superiority of the few-shot Custom CNN model in segmentation performance compared to OCT scans.Item A Review of Fraudulent Practices in Healthcare Insurance and Machine Learning-Based Investigation Approaches(IEEE, 2023-02-02) Aishat Salau; Nwojo Agwu Nnanna; Moussa Mahamat BoukarHealthcare insurance fraud is a complex and costly problem that has become a concern globally. Traditional methods of detecting fraudulent claims and requests are time-consuming and often ineffective. Machine learning methods offer potential solutions to this problem by improving fraud investigation and prevention in health insurance systems. This paper presents a comprehensive review of machine learning-based approaches for addressing healthcare insurance fraud, as well as associated challenges and limitations. Despite limitations, our findings suggest that fraud could be effectively tackled by addressing the challenges identified. Areas for further research were also highlighted.Item The Impact of Artificial Intelligence (AI) on Content Management Systems (CMS)(International Journal of Intelligent Systems and Applications in Engineering, 2024-02-02) Moussa Mahamat BoukarThe dynamic nature of the global environment is always changing, with technology playing a pivotal role in propelling these shifts. The emergence of artificial intelligence (AI) has fundamentally transformed the manner in which we oversee and engage with our digital data. The potential of integrating artificial intelligence (AI) with content management systems (CMS) holds significant promise for future advancements. Artificial intelligence (AI) has the potential to bring about substantial changes in the manner in which information is managed and shared on the internet. It can enhance search functionalities and streamline numerous processes via automation. Individuals engaged in website ownership, content generation, and marketing are required to acquaint themselves with the most recent advancements in content management systems (CMS) and artificial intelligence (AI). The objective of this article is to provide a comprehensive examination of the influence of artificial intelligence (AI) on content management systems (CMS), along with an analysis of emerging AI methodologies and their practical use within a corporate environment.Item Intrusion Detection System Using Initialization- based Few-shot Learning(International Conference on Multidisciplinary Engineering and Applied Science, 2023-02-02) Nurudeen M. Ibrahim; Moussa Mahamat BoukarAn Intrusion Detection System (IDS) has become an essential means of ensuring the security of a network. It is a system that monitors the state of the traffic running in the network. Few-shot learning is a novel machine learning (ML) approach that has the ability of recognizing novel objects from very few examples. Conventional ML models require substantial amount of data in order to train the model whereas the IDS dataset is imbalanced and is lacking in various categories. In this research, we proposed and implemented a method of using initialization-based few-shot learning (IB-FSL) to improve the performance of the IDS by initializing the weights and by “learning to fine-tune” the data as it detects intrusion. To accomplish our set objectives, we implemented a ResNet-50 on the proposed IB-FSL which gave us a model that initializes the weights such that classifiers for novel classes could be learned from very few labeled examples and a minimal number of gradient descent steps before the application of fine-tuning. The experiment on UNSW-NB15 showed a good performance with an accuracy of 85.96% on less than 2% of the training dataset. We also obtained a precision of 96.18% and F1-score of 92% while other methods such as Decision Tree (DT), Logistic regression (LR), Naïve Bayes (NB) and Expectation Maximization (EM) clustering used 100% of the training dataset but achieved an accuracy of 85.56%, 83.15%, 82.07% and 78.47% respectively. This model could be used to detect abnormal data intrusions into the network traffic with low false alarm rate leading to improved network securityItem Network Intrusion Detection using a Hybridized Harmony Search and Random Forest(International Conference on Multidisciplinary Engineering and Applied Science, 2023-02-02) Nurudeen M. Ibrahim; Moussa Mahamat BoukarIntrusion Detection Systems are used to find security holes in a system. However, a number of factors, including irrelevant information, contribute to intrusion detection system's low detection accuracy. This work presents a hybrid intrusion detection system (IDS) that combines the Random Forest algorithm and Harmony Search to address this issue and increase IDS detection accuracy. The proposed method was analyzed using NSL-KDD, and the experiment results show that the model functions effectively.Item Reinforcement Learning for Testing Android Applications(International Conference on Multidisciplinary Engineering and Applied Science, 2023-02-02) Moussa Mahamat Boukar; Muhammed Aliyu Suleiman; Ibrahim Anka SalihuThis paper offers a review of current research studies that use reinforcement learning (RL) to test Android applications. The primary purpose of this study is to simplify future research by collecting and investigating the current state of Android app testing approaches using the RL technique. We provide a well-defined criterion comprising of seven key points. The key points are: addressed problems, reasons for using the RL technique, RL algorithms, supported events, testing techniques, validation, and evaluation methods. In the literature, we have analyzed various techniques to evaluate their efficiency. This study showed that model-based testing is the most commonly used testing technique. Q-learning is the best algorithm in terms of predictive accuracy. We identified that code coverage is the most widely used evaluation metric and comparison with other tools and techniques is the preferred validation approach.Item A Machine Learning Led Investigation Predicting the Thermos‑mechanical Properties of Novel Waste‑based Composite in Construction(Waste and Biomass Valorization, 2024-05-04) Assia Aboubakar Mahamat; Moussa Mahamat Boukar; Ifeyinwa Ijeoma Obianyo; Nurudeen M. IbrahimThe study explores the potential of machine learning (ML) in predicting the thermal and mechanical properties of earth-based composites reinforced with natural Borassus fruit fiber. The limited availability of large datasets for accurate predictions is a challenge in material science research, which this study addresses. The authors collected data on thermal conductivity, compressive and flexural strength through experiments and employed four ML techniques suitable for small datasets: linear regression (LR), random forest (RF), decision tree regressor (DTR), and gradient boosting (GB). Evaluation metrics were used to assess the performance of the ML techniques. Linear regression emerged as the most efficient, exhibiting significantly lower error values compared to the others (e.g., RMSE of 0.066 for thermal conductivity, 0.119 for compressive strength, and 0.04 for flexural strength), followed by random forest and decision tree. However, gradient boosting showed relatively poor predictive accuracy. This study demonstrates the successful application of ML for predicting the properties of earth-based composites with limited data, which could significantly reduce the cost and time associated with developing new building materials and products. Manufacturers can gain a competitive edge by using ML to streamline material development, leading to lower costs, faster innovation, and the creation of more environmentally friendly building materials for a greener construction sector.