Research Articles in Computer Science

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Content Management System (CMS) Evaluation and Analysis
(Journal of Technical Science and Technologies, 2012-02-02) Moussa Mahamat Boukar
Content management systems (CMS) provide an optimal solution by organizing information and, mostly, creating and managing an enterprise’s knowledge. Nevertheless there is a big confusion about the functionalities that characterize CMS and about the differences with less performing products such as web content management systems, document and records management systems and enterprise content management systems. This paper aims to show the mismatches between companies’ needs and those information management products, which are often called CMS even if they are not. For this reason I first made a theoretical comparison between the functionalities of CMS and those of the systems that are often confused with. Then I showed the results of an empirical research on 22 products offered by international vendors. By using an original scheme, enterprises’ needs in terms of information collection, management and publication of knowledge management are compared with the functionalities of the aforementioned systems. The result consists of performing definitions for CMS and the other systems for managing information. Content Management products are analyzed, compared and evaluated by using a special table created to point out the actual functionalities of the products offered on the market, despite vendors’ declarations. The paper conclusions show how, on the demand side, companies’ needs are growing in a confused framework; at the same time the supply side keeps on feeding this confusion, reducing company satisfaction in regard to knowledge and information management
Developing Interactive Course Material for Volterra Integral Equations of Second Kind
(IEEE, 2014-02-02) Moussa Mahamat Boukar
The main purpose of this paper is to propose a developed interactive course material which is contained course materials, solution methods with examples and interactive environment for Volterra Integral Equations (VIEs) of Second Kind. It is possible to learn what VIE of Second Kind is, to make practice with interactive part of the material and, also how a VIE can be converted to an equivalent Initial Value Problem (IVP).
WEB Services
(IEEE, 2014-02-02) Moussa Mahamat Boukar
WEB Services convert the applications into a WEB application, which can publish its function or message to the rest of world. The basic WEB Services platform is XML + HTTP. The types of WEB Services have been explained. WEB Services platform elements are illustrated. The ScalabelVector Graphics (SVG) is explained in detail. The advantages of SVG are illustrated using practical examples, such as SVG-Ellipse, SVG-Line, SVG-Rect.
An Interactive Application (Maplet) for II-Order Ordinary Differential Equations
(IEEE, 2014-02-02) Moussa Mahamat Boukar
The main purpose of this paper is to propose a Maplet interactive application that is used to find general solutions, to find Initial Value Problems (IVPs) and to depict 2-D and 3-D graphics as well of the II-Order Ordinary Homogeneousl Non-homogeneous Differential Equations (ODEs). Furthermore, to make 2-D, 3-D graphing of solutions and how they can be used as effective educational tools for both students and instructors.
BASIC DEPENDENCY PARSING IN NATURAL LANGUAGE INFERENCE
(IEEE, 2017-02-02) Aleshinloye Abass Yusuf; Nnanna Agwu Nwojo; Moussa Mahamat Boukar
Parsing is the process of analyzing a sentence for it structure, content and meaning, this process uncover the structure, articulate the constituents and the relation between the constituents of the input sentence. This paper described the importance of parsing strategy in achieving entailment in natural language inference. Parsing is the basic task in processing natural language and it is also the basis for all natural language applications such as machine learning, question answering and information retrieval. We have used the parsing strategy in natural language inference to achieve entailment through an approach called normalization approach where entailment is achieved by removing or replacing some nodes as well as relations in a tree. This process requires a detailed understanding of the dependency structure, in order to generate a tree that does not contain nodes and relations that are irrelevant to the inference procedure. In order to achieve this, the dependency trees are transformed by applying some rewrite rules to the dependency tree
Medical Tool for Assisting Patients in Kazakhstan Polyclinics
(IEEE, 2017-02-02) Moussa Mahamat Boukar
The healthcare system in developing countries facing many challenges due to factors such as lack of doctors, medical equipment, overwhelmed hospitals, and increased number of refugees. The World Health Organization annually announces reports related to patients per doctor ratios, and according to reports even in many developed countries, it is low. The aim of this work was to develop a medical tool that will try to solve various issues and help assist patients as well as doctors. The tool is based on two machine learning algorithms for disease diagnosis which are rule-based method and decision tree algorithm. The tool also has several useful functionalities that help patients with their conditions. Using scikit-learn framework we were able to develop and integrate algorithms inside the tool. During the benchmarking study, the implemented machine learning algorithms achieved the following performance: an accuracy of 75% for the rule-based classifier, and 89% for the ID3 decision tree classifier.
Data Dissemination via web Services for Distributed and Heterogeneous Data sources: An Enhancement of the Nigerian University Certificate Verification System
(IEEE, 2017-02-02) Salisu Ibrahim Yusuf; Moussa Mahamat Boukar
Harmonization of academic records between institutions will ease information sharing among institutions and reduce forgery of certifications and other academic qualifications. A solutions was proposed which collect relevant certificate information from Nigerian Universities’ databases via web service and make it publically available across all platforms via web service as a means for verifying certificate authenticity. One of the limitations of the proposed system is the limitation imposed on the data that can be retrieved from institutions by the defines JSON template, more relevant data might be neglected, also it was assumed that all universities use relational database, with the current trend it is possible in the nearest future a good number of institutions might move to NoSQL platform. In this study we proposed an enhancement of the initially proposed system to accommodate diversity of data and databases provided by institutions by using NoSQL platform and allowing institutions modify the template for the web service they will share their data, this improves the parsing time as data will not need to be structured as relational database. Hence an enhancement of the Nigerian Universities’ Certificate Verification was proposed.
An Error Analysis Algorithm for Approximate Solution of Linear Fredholm-Stieltjes Integral Equations with Generalized Trapezium Method
(IEEE, 2017-02-02) Moussa Mahamat Boukar
Integral 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.
User Define Time Based Change Pattern Dynamic Password Authentication Scheme
(IEEE, 2017-02-02) Salisu Ibrahim Yusuf; Moussa Mahamat Boukar
In 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.
Time Series Analysis and prediction of bitcoin using Long Short Term Memory Neural Network
(International Conference on Electronics Computer and Computation, 2019-02-02) Temiloluwa I. Adegboruwa; Steve Adeshina; Moussa Mahamat Boukar
Bitcoin is the first digital currency that uses decentralization to solve the issue of trust in performing the functions of a digital currency successfully. This digital currency has shown extraordinary growth and intermittent plunge in value and market capitalization over time. This makes it important to understand what determines the volatility of bitcoin and to what extent they are predictable. Long Short Term Memory Neural Networks (LSTM-NN) have recently grown popular for time series prediction systems but there has been no consensus on methods to model time series inputs for LSTMs, this paper proposes the need for this problem to be solved by conducting an experimental research on the efficacy of an LSTM-NN given the form of its time-series input features.
Analysis of Bad Roads Using Smart phone
(IEEE, 2019-02-02) Moussa Mahamat Boukar; Steve Adeshina
Developing nations are faced with a lot of bad roads with potholes of different debt ranges, the maintenance and rehabilitation process by government agencies is an ongoing effort that requires periodic bad road inventory to guarantee safety. Bad roads are either identified by government agency’s survey teams or individual who volunteer to report these conditions to the authorities. Our research provided a simple but effective solution to aid in automatically reporting bad roads using smart-phones through measuring the pavement profile based on the vibration of a moving vehicle. In this article, we will explain how we used some a smart-phone in reading the vibration pattern, GPS location, speed and direction of a vehicle that drives through a pothole, these parameters are periodically streamed to a cloud application. We used standard deviation to measure the level of dispersion around a segmented set of streamed vehicle vibration to identify potholes of different sizes, we also used Artificial Intelligence - supervised learning algorithm (classification) to reduce the false positive error rates due to human behaviors. The final results show a distinct vibration levels between small pot-holes, speed bumps and big pot-holes, these values are displayed on map application to visualize the geographical locations of these pot-holes (Google maps)
A framework for Poultry weather control with IoT in sub-Saharan Africa
(15th International Conference on Electronics Computer and Computation (ICECCO 2019), 2019-02-02) Nasiru Afeez; Steve Adeshina; Abdullahi Inci; Moussa Mahamat Boukar
Poultry farming in the sub-Saharan region of Africa is fraught with a lot of challenges among which are high temperature and humidity. In this paper, the authors proposed an Internet of Things (IoT) framework that will help in regulating the various climatic conditions that will help at providing a high yield of poultry products. This framework is aimed at providing proactive and preventive ways to avert or reduce the high mortality rate in a flock of birds as a result of heat stress. IoT which is a connected environment of monitoring sensors with high precision and an accurate decision taken would be presented in managing environmental conditions of poultry house that will gather information, analyze it and effect an action based on the predetermined weather conditions that are suitable for bird’s existence
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 Boukar
This 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.
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 Yusuf
Taking 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.
Developing a Digital Interactive Course Material on Automated Management System (AMS) Moodle for Partial Differential Equations (PDEs) Course
(IEEE, 2019-02-02) Moussa Mahamat Boukar
Many 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.
Maplet for Linear Fredholm Integral Equations of Second Kind (FIESK)
(2020-02-02) Moussa Mahamat Boukar
The main purpose of this paper is to propose a Maplet application of the solution methods, Neumann, Eigen function, Adomian Decomposition Method (ADM), and Simple Method for Linear Fredholm Integral Equations of Second kind. Furthermore, to make 2-D, 3-D graphing of solutions and how they can be used as effective educational tools for both students and instructors.
Collision Resolution Techniques in Hash Table
(2021-02-02) Moussa Mahamat Boukar
Machine learning techniques versus classical statistics in strength predictions of eco-friendly masonry units
(IEEE, 2021-02-02) Assia Aboubakar Mahamat; Moussa Mahamat Boukar
Earth-based materials demonstrated promising characteristics in the development of eco-friendly, low cost and sustainable construction materials. However, their unconventional utilization in construction makes the assessment of their properties very difficult and inaccurate because they are assessed based on conventional materials procedures. Hence, the properties of earth-based materials are not well understood. The assessment of earth-based materials properties for sustainable construction is time-consuming, expensive, and inaccurate. To obtain more accurate properties, an artificial neural network and statistical linear regression analysis were used to predict the compressive strength of alkali-activated soil. Statistical linear regression analysis was carried out to compare the efficiency of the machine learning technique with the classical statistics model. Parameters such as Si/Al, activator level, curing temperature, water absorption, and weight were used as input parameters to predict the target variable. The coefficient of determination was used to examine the performance of the models. The results depict that artificial neural network outperformed statistical linear regression analysis with R2=0.74, RMSE=0.119 and R2 =0.48, RMSE=0.466 respectively. This indicates that statistical linear regression analysis is inefficient for prediction of the strength in alkali activated soils
Analysis of Prostate Cancer DNA Sequences Using Bi-direction Long Short Term Memory Model
(IEEE, 2021-02-02) Yusuf Aleshinloye Abass; Steve Adeshina; Nwojo Nnana Agwu; Moussa Mahamat Boukar
Machine and deep learning-based models are the emerging techniques in addressing prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that requires huge attention in the biomedical domain. These techniques have been shown to provide better accurate results when compared to the conventional regression-based models. Prediction of the gene sequence that leads to cancerous diseases such as prostate cancer is very crucial. Identifying the most important features in a gene sequence is one of the most challenging tasks and extracting the components of the gene sequence that can give an insight into the kind of mutation in the gene is very important, it will lead to effective drug design and promote the new concept of personalized medicine. In this work we have extracted the exons in the various prostate gene sequence that was used in the experiment, we built a bi-LSTM model using a k-mer encoding for the DNA sequence and one- hot encoding for the class label. The bi-LSTM model was evaluated on different classification metrics. Our experimental results show that the model prediction offers a training accuracy and validation accuracy of 95 percent and 91 percent respectively.
Evaluation of Collision Resolution Methods Using Asymptotic Analysis
(IEEE, 2021-02-02) Saleh Abdullahi; Moussa Mahamat Boukar; Salisu Ibrahim Yusuf