Department of Computer Science

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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. Ibrahim
The 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.
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 Boukar
An 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 security
Machine Learning Approaches for Prediction of the Compressive Strength of Alkali Activated Termite Mound Soil
(MDPI, 2021-05-22) Moussa Mahamat Boukar; Nurudeen M. Ibrahim
Earth-based materials have shown promise in the development of ecofriendly and sustainable construction materials. However, their unconventional usage in the construction field makes the estimation of their properties difficult and inaccurate. Often, the determination of their properties is conducted based on a conventional materials procedure. Hence, there is inaccuracy in understanding the properties of the unconventional materials. To obtain more accurate properties, a support vector machine (SVM), artificial neural network (ANN) and linear regression (LR) were used to predict the compressive strength of the alkali-activated termite soil. In this study, factors such as activator concentration, Si/Al, initial curing temperature, water absorption, weight and curing regime were used as input parameters due to their significant effect in the compressive strength. The experimental results depict that SVM outperforms ANN and LR in terms of R2 score and root mean square error (RMSE
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 Boukar
Intrusion 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.