Faculty of Computing
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Item Remote Sensing Image Classification for Land Cover Mapping in Developing Countries(IJCSNS International Journal of Computer Science and Network Security, 2022-02-05) Nwojo Agwu Nnanna; Moussa Mahamat BoukarConvolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.Item Developing a Digital Interactive Course Material on Automated Management System (AMS) Moodle for Partial Differential Equations (PDEs) Course(IEEE, 2019-02-02) Moussa Mahamat BoukarMany physical, engineering problems from some areas like fluid mechanics, heat transfer, rigid body dynamics and elasticity are modelled by Partial Differential Equations (PDEs). That’s why, PDEs course is the main course in the higher educations. The aim of this work is to develop an interactive digital course material for some kind of PDEs. A digital question bank is developed using Wildcard technology on the automated management system Moodle.Item An Error Analysis Algorithm for Approximate Solution of Linear Fredholm-Stieltjes Integral Equations with Generalized Trapezium Method(IEEE, 2017-02-02) Moussa Mahamat BoukarIntegral equations and their solutions are very important for various areas like physics, engineering, biology and other. Fredholm-Stieltjes integral equations are some of the integral equations. Sometimes it is possible to find exact solutions for some of the integral equations.The main purpose of this paper is to propose an error analysis algorithm for approximate solution of linear Fredholm-Stieltjes integral equations of second kind with Generalized Trapezium Method. Firstly, the theory of error analysis is given. Then the implementation of algorithm is done with Maple software and examples are given with graphics.Item User Define Time Based Change Pattern Dynamic Password Authentication Scheme(IEEE, 2017-02-02) Salisu Ibrahim Yusuf; Moussa Mahamat BoukarIn this paper a novel time based dynamic password was presented to the overcome challenge of using a third party such as one-time password email, test and token device system for authentication in dynamic password authentication systems, user will set an initial password define how the password will be changing over a defined time, we found that the system retains the strength of the dynamic password and improves the usability of the system in terms of availability.Item DEVELOPMENT OF ROAD ANOMALY DATA TRANSMISSION USING ANT COLONY OPTIMIZATION ALGORITHM IN A VEHICLE-TO-VEHICLE COMMUNICATION(IEEE, 2019-02-02) Muktar Othman; Steve Adeshina; Moussa Mahamat BoukarThis study aim is to design a road anomaly transmission Algorithms using Ant Colony Optimization(ACO) based Technique in a Vehicle-to-Vehicle (V2V) and Vehicle to Infrastructure (V2I) Communication. The developed VACO also uses the features of VANET to find out the optimal path by considering a minimum number of nodes and cost parameters, which provides information related to accidents, speed of neighbouring vehicle and weather to help users in making informed decisions. Vehicle routing protocol based on ACO (VACO) also ensures to mitigate issues by combining the reactive and proactive approach and considers the parameters affecting the Quality of Service (QoS) such as latency, bandwidth, and delivery ratio in evaluating the Algorithms.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 Evaluation of Collision Resolution Methods Using Asymptotic Analysis(IEEE, 2021-02-02) Saleh Abdullahi; Moussa Mahamat Boukar; Salisu Ibrahim YusufItem Four-Factors Authentication Algorithm For Preventing Fake Attendance(International Conference on Electronics Computer and Computation, 2019-02-02) Asim Balarabe Yazid; Moussa Mahamat Boukar; Salisu Ibrahim YusufTaking attendance is a day-to-day chore for every organization, human resources, and class teachers, traditionally people take attendance manually either by calling out names or allowing the user to sign the attendance sheets or clock in and out. The problem, however, people most likely sign the attendance on behalf of their colleagues that are absent. This makes the traditional method very vulnerable and may affect the integrity of the system. Researchers come up with different ideas and methods of minimizing fake attendance to improve efficiency in terms of integrity, time and cost. After reviewing the existing system’s strength and vulnerabilities, we are proposing a multi-factor authentication algorithm which makes use of QR code, GPS, and Facial recognition. The user will make use of their personal mobile phone. The research of this proposed is still ongoing, we are hoping the proposed technique can be applied to various attendance systems such as schools, universities, and organizations.Item Graphic User Interface for Hausa Text-to-Speech System(IEEE, 2022-02-02) Umar Adam Ibrahim; Moussa Mahamat Boukar; Muhammed Aliyu SuleimanNatural language processing and Digital signal processing are broadly used methods used to enable systems to understand commands and manipulate speech or text. Most of the Text-to-speech done was for major languages such as English, French and others, with no or little for African languages like Hausa, which are termed under resource languages. In this paper, we developed a graphical user interface for the Hausa Text-to-Speech system. This system converts Hausa text to Hausa audio sound, by processing and analyzing it using natural language processing and Digital Signal Processing. Our graphical user interface, aid in converting entered Hausa language text into Hausa speech.Item Hepatitis C Stage Classification with hybridization of GA and Chi2 Feature Selection(IJCSNS International Journal of Computer Science and Network Security, 2022-02-02) Steve Adeshina; Moussa Mahamat BoukarIn metaheuristic algorithms such as Genetic Algorithm (GA), initial population has a significant impact as it affects the time such algorithm takes to obtain an optimal solution to the given problem. In addition, it may influence the quality of the solution obtained. In the machine learning field, feature selection is an important process to attaining a good performance model; Genetic algorithm has been utilized for this purpose by scientists. However, the characteristics of Genetic algorithm, namely random initial population generation from a vector of feature elements, may influence solution and execution time. In this paper, the use of a statistical algorithm has been introduced (Chi2) for feature relevant checks where p-values of conditional independence were considered. Features with low p-values were discarded and subject relevant subset of features to Genetic Algorithm. This is to gain a level of certainty of the fitness of features randomly selected. An ensembled-based learning model for Hepatitis has been developed for Hepatitis C stage classification. 1385 samples were used using Egyptian-dataset obtained from UCI repository. The comparative evaluation confirms decreased in execution time and an increase in model performance accuracy from 56% to 63%.