| 1 |
Author(s):
Ele. B. I..
Page No : 1-17
|
An Enhanced Mechanism for Protecting Web Applications from Cross Site Request Forgery (CSRF)
Abstract
Cross-Site Request Forgery (CSRF) is considered one of the top vulnerabilities in today’s web, where an untrusted website can force the user browser to send an unauthorized valid request to the trusted site. Legitimate users will lose their integrity over the website when the CSRF takes place. So far, many solutions have been proposed for the CSRF attacks such as the referrer HTTP header, custom HTTP header, origin header, client site proxy, a browser plug-in, and random token validation, but in this research, the use of random token validation to solve the problem of CSRF attacks was implemented. The proposed solution in the study used a concept known as Nonce which is a type of random token attached to requests sent over a server. This solution proved to be effective enough to protect and prevent web applications from CSRF attacks. Although no system can be secured against attacks, this study recommends that the random token validation (Nonce) approach of protecting web applications should be adopted by all web applications developers and users to improve on the already established solutions to reduce the chances of attacks on web applications to a very slim percentage, if not eliminated.
| 2 |
Author(s):
Ele B. I., Obono I. O., Iwinosa A. A..
Page No : 18-24
|
Problem Spaces and Algorithms in Data Mining
Abstract
Data mining has been described severally as the best thing to have happened to data and information management, especially these days that the cost of computing technologies and storage media are falling, data gathering tools becoming varied and very efficient and the boom in network computing becoming very rewarding. The challenges presented by the management and meaningful usage of large data sets have stimulated so much research in data mining. Consequently, the birth of a number of algorithms to provide insights to these big data has equally presented more complications in information processing computing. Therefore, this paper presents different problem spaces in data mining, available algorithms to mine these data and then mapping specific algorithms to specific problem spaces. Analysis of datasets from a typical financial institution suggests that no one algorithm is necessarily better than the other, but all have strengths and weaknesses depending on the particular problem spaces in use.
| 3 |
Author(s):
Ele B. I., Ele A. A., Agaba F..
Page No : 25-46
|
Intelligent-Based Job Applicants’ Assessment and Recruitment System
Abstract
In recent years, companies faced the challenge of sorting resumes from job applicants, assessing, recruiting, and notifying applicants manually. This study is centered on creating a system that will automate this process and resolve the problems associated with manual recruitment processes. The waterfall model was employed in the analysis and design of the system in this study due to its simplicity and sequential nature. The system was built using PHP programming language and the database was designed using MySQL. In this study, an intelligent-based job applicants’ assessment and recruitment system was developed using artificial intelligence techniques. It brings phenomenal success to Human Resource Management in a very short time. The recruitment process, in general, would experience a foremost modification, delivering rapid, efficient, and cost-effective methods of discovering prospective workers. Numerous opportunities were identified when utilizing this technology in recruitment, which include speeding up the staffing procedure, computerization of responsibilities, and rising detachment. Results obtained during the evaluation display that this method enhances the correctness of toning the right applicants with the right jobs. However, this system is still open to upgrading in the future for any researcher who finds this to be of interest.
| 4 |
Author(s):
Helena Kravarikova.
Page No : 47-57
|
Verification of the Analytical Solution and Numerical Simulation for Tension – Pressure
Abstract
The proposed constructions have to meet their use in terms of shape, dimensions and above all, strength. Parts of the structure are loaded by external forces. The load on the structure is examined in all parts and directions. Parts of the structure are stressed by a combination of basic types of stress. One of the basic types of stress is pull or pressure. The investigation of the part of the structure under tensile-compression stress is important because of the deformation, stability and strength of the structure as a whole. Analytical solution, classical experiment or modelling FEM can be used to predict the stress of structural parts. Currently, the most preferred solution is FEM numerical simulation. Numerical simulation has a more purposeful use in the solution of structural stability. The article contains the solution, tensile stresses for a steel rod, numerical simulation using the FEM method in the ANSYS program and analytically.
| 5 |
Author(s):
Gideon Uchechukwu Nwafor, PhD.
Page No : 58-71
|
Using Data Mining Techniques to Develop Climate Change Mitigation and Adaptation Strategies in Nigeria.
Abstract
Climate change is a severe and growing challenge facing Nigeria, with significant impacts on the country's economy, environment, and society. To cope with this challenge, there is a need for climate change mitigation and adaptation strategies that are evidence-based and tailored to the country's unique vulnerabilities and opportunities. This study aimed to develop such strategies using data mining techniques. A range of climate and non-climate data sources were explored to identify key drivers of climate change in Nigeria and their associated impacts on different sectors, such as agriculture, water resources, and energy. Subsequently, data mining algorithms, including decision trees, clustering, and association rule mining, were applied to model and analyze the complex relationships between these drivers and their impacts on the sectors. The study found that a combination of mitigation and adaptation measures could be effective in reducing the severity of climate change impacts in Nigeria. These measures include promoting the use of renewable energy sources, improving water-use efficiency, and developing climate-smart agricultural practices. The data mining techniques used in this study proved useful for identifying these measures and for predicting their effectiveness under different scenarios. The results of this study provide important insights to policymakers and practitioners in Nigeria and other countries facing similar climate change challenges. It also highlights the potential of data mining techniques for developing climate change mitigation and adaptation strategies that are evidence-based, effective, and tailored to specific local contexts. Overall, this study contributes to the growing body of literature on leveraging data mining and machine learning techniques for addressing complex environmental and societal challenges.
| 6 |
Author(s):
Adegbite Ismaila Olawale, Odetayo Tajudeen Adewale.
Page No : 72-83
|
Optimal Scaling Appraisal of Maturity Status of Nigerian Polytechnic Education in Industry 4.0
Abstract
The first industrial revolution in the 18th century is related to the transformation to mechanization by using hydro power and steam power to second, third and a new concept referred to as Cyber-Physical Systems (CPS) that combine Internet of Things (IoT) technologies with the manufacturing which is seen as a significant paradigm shift in industrial manufacturing, named as Industry 4.0 (4IR). This study explored the perspectives of people on the lingering issue of maturity of Nigerian Polytechnics in terms of 4th industrial revolution; thus, the paper aimed at appraising the maturity status of Nigerian polytechnics in the industry 4.0 using Optimal Categorical Scaling Regression (Catreg). Data collection was cross-region in Nigeria. This research used the stratified sampling method to ensure a proper spread and it was designed in line with descriptive survey, systematically examining the maturity status of their institution on 4IR through the primary data collected with the aid of questionnaire and focus group discussion from the stakeholders in private and public Nigerian polytechnics. The data were sourced from 18 Polytechnics, 1 each from federal, state and private in each geographical zone. Inferential statistical tools of optimal scaling regression analysis were deployed. The analysis inferred that eagerness of leaders and competence as well as modern ICT and mobile devices are fully mature but implementation of 4IR road-map, decentralization, modelling and simulation, sharing knowledge and open innovation, ICT competence and openness to new technology are still maturing. It is recommended that the educational policy and a blueprint of education for Industry 4.0 should be formulated and implemented to meet the global standard of education.
| 7 |
Author(s):
Ogba Paul Onu, Bello Muriana.
Page No : 84-92
|
Rough Set Theory and its Applications in Data Mining
Abstract
One method for handling imprecise, ambiguous, and unclear data is rough set theory. Rough set theory offers a practical method for making decisions during data extraction. The practice of analyzing vast amounts of data to extract useful information from a larger collection of raw data is known as data mining. This paper discusses consistent data with rough set theory, covering blocks of attribute-value pairs, information table reductions, decision tables, and indiscernibility relations. It also explains the basics of rough set theory with a focus on applications to data mining. Additionally, rule induction algorithms are explained. The rough set theory to inconsistent data is then introduced, containing certain and potential rule sets along with lower and upper approximations (Skowron, et al, 2018). Finally, a presentation and explanation of rough set theory to incomplete data is given. This includes characteristic sets, characteristic relations, and blocks of attribute-value pairs.
| 8 |
Author(s):
Yeser Ali Almilby, Rami N. Alkhawaji (Ph.D.).
Page No : 93-107
|
Haram Lost and Found
Abstract
With its great religious significance, the Holy Mosque in Makkah welcomes millions of tourists every year. The problem of misplaced objects at this holy site poses a problem for both guests and guards. In order to tackle this issue and improve worshippers' and visitors' overall experience, this study project explores the ideation, creation, and testing of a state-of-the-art mobile application called the "Lost and Found Assistance" app. This software makes use of modern technologies to expedite the process of finding misplaced items inside the Holy Mosque.
The 'Lost and Found Assistance' app's main objective is to offer a workable answer to anyone who have misplaced their belongings inside the enormous Holy Mosque. It presents an easy-to-use and effective platform that enables users to report misplaced things, facilitating their prompt recovery. Additionally, it enables users to look for misplaced items, providing comfort to individuals who could have momentarily misplaced priceless items while visiting.
In order to provide insight on the Holy Mosque's hallowed legacy and importance in the Islamic world, this study initiative examines the historical significance of the building. It explores the issues surrounding misplaced objects at this hallowed location, highlighting the demand for an up-to-date, technologically advanced solution.
In addition, the research highlights the revolutionary role that mobile applications play in a variety of contexts as it examines the current state of lost and found services. Through an analysis of current applications and technological breakthroughs, it pinpoints chances to modify these advances to meet the particular difficulties presented by the Holy Mosque.
The entire development process of the 'Lost and Found Assistance' app is described, from the first conceptualization to the design and coding stages. The project emphasizes the value of intuitive design concepts and user-friendly interfaces in making sure that the software is useful and accessible to a variety of users.
An analysis of the app's usefulness in helping users find misplaced goods within the Holy Mosque completes the research endeavor. It talks about user pleasure and feedback, highlighting how the software improves users' experiences. It also looks at how widely the app might be used and offers ideas for improvements down the road.
The 'Lost and Found Assistance' app helps to maintain a calm and serene atmosphere inside the Holy Mosque by utilizing contemporary technologies. The ability of technology to solve long-standing issues and improve the pilgrimage and visitor experience for millions of worshippers and tourists who visit this holy site is demonstrated by this study effort.
| 9 |
Author(s):
Oni Oluwabunmi Ayankemi, Kabir Uthman Opeyemi, Bassir Abdullai Abiye, Lawrence Adeolu Sunday.
Page No : 108-114
|
Automation of a Complaint Management System Using RPA
Abstract
The aim of this research work is to design and implement an improved system which will contribute greatly in providing solutions to evaluate the impact of RPA on the complaint-handling process in a school complaint portal. The proposed Robotic Process Automation for a complaint management system and student registration system can help in saving time and automating repetitive tasks. The complaint management system can instantly allow the admin to create different categories instead of doing it manually which takes a lot of time, compared to the automated process which will get the total number of students and complete it within a few minutes. The student registration system saves the admin the stress of opening the browser and typing the login credentials every time a task needs to be done. The 200 students’ details were generated and turned into an Excel file within 4 minutes compared to the manual method which takes hours to complete.
| 10 |
Author(s):
Oni Oluwabunmi Ayankemi, Idowu Oluwaferanmi Ruth, Bassir Abdullai Abiye.
Page No : 115-121
|
Fake News Detection System Using Logistic Regression, Decision Tree and Random Forest.
Abstract
The purpose of this study is to design a fake news detection system with these three machine learning models, namely: Decision Tree, Random Forest, and Logistic Regression. These three different models were analysed to determine the most efficient model for accurately detecting fake news. The result obtained showcased Logistic Regression with an accuracy of 98.80%, Decision Tree with an accuracy of 99.64% and Random Forest with an accuracy of 99.23%. It is evident as deduction from the comparative analysis that our best model came out to be Decision Tree with an accuracy of 99.64%.
