| 1 |
Author(s):
Chidozie Managwu, Ibrahim Kushchu, Daniel Matthias.
Page No : 1-7
|
Browser-Based Object Detection System for Isolating Plastic Bottles using the COCO-SSD Model.
Abstract
Plastic waste, especially in urban environments and water bodies, poses a significant environmental threat. This paper presents a browser-based object detection system for identifying and isolating plastic bottles using state-of-the-art machine learning models. The system leverages TensorFlow.js, ML5.js, and P5.js libraries along with the COCO-SSD model to detect plastic bottles in real time using a mobile camera interface. By employing a browser-based architecture, the system offers cross-platform functionality, eliminating the need for server-based computations or specialized hardware. Experimental evaluation showed high detection accuracy across various environments, underscoring the potential for real-world applications in waste management and recycling efforts.
| 2 |
Author(s):
Umoh Enoima Essien, Sylvester I. Ele.
Page No : 8-26
|
Cuckoo Sandbox and Process Monitor (Procmon) Performance Evaluation in Large-Scale Malware Detection and Analysis.
Abstract
Malware has grown to be an intricate and dynamic threat to cybersecurity. Researchers and cybersecurity specialists use a range of methods to analyze and comprehend malware in order to effectively counter this threat. The malware sandbox is one of the most crucial instruments in this battle. Insights gained by evaluating malware in a sandbox aid in the creation of effective detection. Finding a sandbox that is both highly precise, efficient and affordable is a challenging task. This study compares the effectiveness of Cuckoo Sandbox and Procmon, two of the most popular sandboxes, in the efficient implementation of malware analysis and detection. A Windows 10 Pro window-based computer with a 4 GHz CPU, 16 GB RAM, 8 cores, and a 320 GB hard drive (HDD) was set up. An Oracle virtual machine (VM) for guests was set up and launched. Using the Oracle VM, a virtual operating system (Windows 10 Pro). Furthermore, Yara-Python was deployed and JSON reports, a system built on Python was created. The results show that Cuckoo consistently outperforms Procmon in terms of execution time, completing much more quickly and steadily over each of the ten process runs. Procmon has significantly longer and more fluctuating execution times, peaking at 989 seconds, while Cuckoo maintains execution durations around 530 seconds, suggesting superior efficiency and consistency. Six (6) machine learning-based methods for classifying and detecting malware that used Cuckoo sandbox and process monitor were surveyed. Different performance indicators were found in the six-machine learning-based malware detection and classification studies that Process Monitor was used to survey. A review of six machine learning-based malware detection and classification studies using both Process Monitor and Cuckoo Sandbox indicated that Cuckoo Sandbox consistently delivered better performance. The findings show that machine learning-based malware detection conducted with Cuckoo attained a higher average accuracy of 99.35% compared to 94.48% with Procmon, along with a superior ROC value of 0.97 (97%) versus 0.91 (91%) for Procmon.
| 3 |
Author(s):
Ernest E. Onuiri, Adeyemi John, Kelechi C. Umeaka.
Page No : 27-46
|
MRI-Based Brain Tumour Classification Using Convolutional Neural Networks: A Systematic Review and Meta-Analysis
Abstract
This study systematically reviewed advancements in brain tumor classification using convolutional neural networks (CNNs) with MRI data, aiming to assess the effectiveness and potential for future enhancements. An analysis of 37 studies demonstrated the extensive use of CNN architectures and pre-processing techniques, achieving high classification accuracy rates. However, challenges such as class imbalances and model interpretability were identified. To address these issues, the study recommends further exploration of advanced deep learning techniques, ensemble methods, and the inclusion of more diverse datasets. A maximum accuracy of 98.80% was reported on a dataset comprising 154 MRI brain images, demonstrating the effectiveness of CNNs in brain cancer detection. Additionally, a five-year meta-analysis (2018-2022) on MRI scan cases across different demographic groups revealed important patterns in healthcare resource allocation. This research not only provides a comprehensive evaluation of CNN usage in MRI-based brain tumor classification but also outlines directions for future research to enhance diagnostic capabilities.
| 4 |
Author(s):
Stephen-Orok Duke, Atte Enyinghi Okwong, Emmanuel U. Oyo-Ita.
Page No : 47-57
|
The Synopsis of Soft Computing.
Abstract
The traditional method of problem-solving, known as hard computing, is limited in its ability to handle modern digital technology and real-world problems accurately. Soft computing, a newer paradigm, offers a more versatile approach by utilizing multi-valued logic and human knowledge to solve complex, nonlinear problems efficiently. Unlike hard computing, soft computing can handle imprecise data and uncertainty effectively. This methodology has been successfully applied across various sectors, including scientific, industrial, and medical fields, providing more accurate results. Soft computing is good for its contributions to revolutionizing problem-solving techniques, being tolerant of imprecision, uncertainty, and linguistic variables, and offering approximate solutions to intricate problems.
| 5 |
Author(s):
Ndueso Udoetor, Godwin Ansa, Anietie Ekong, Anthony Edet.
Page No : 58-80
|
Intelligent System for Detection of Copyright-Protected Data for Enhanced Data Security.
Abstract
In the digital era, the proliferation of digital content has intensified concerns over intellectual property rights infringement, highlighting the need for robust copyright protection solutions. This paper presents a software solution designed to address these challenges by combining advanced algorithms with intuitive user interfaces for effective copyright enforcement. Central to the software’s functionality is the Most Significant Bit (MSB) embedding technique, which allows users to imperceptibly embed copyright or trademark information into digital images. This method modifies the MSB of pixel values to encode protection data while maintaining the visual integrity of the images. In the detection phase, the software employs Deep Convolutional Neural Networks (DCNN) to identify instances of unauthorized use or copyright infringement. By analyzing submitted images, the DCNNs use sophisticated pattern recognition algorithms to detect embedded copyright information or trademarks, promptly flagging infringements for further action. The software ensures a seamless user experience with an intuitive interface that guides users through image upload, copyright embedding, and infringement detection processes. This comprehensive approach provides a powerful tool for safeguarding intellectual property rights in the digital landscape, offering users an efficient means to protect and enforce copyright effectively.
| 6 |
Author(s):
Adesoji Adegbola, Akande Oyebola, Tunde-Idowu Inioluwa, Adebanjo Adedoyin, Adewuyi Oluwaseyi, Mgbeahuruike Emmanuel, Adediran Oluwaseyi.
Page No : 81-93
|
Development of a Web-Based Document Repository with Plagiarism Checker.
Abstract
In the digital age, managing vast volumes of documents and ensuring the originality of content has become a significant challenge. This paper presents the development of a web-based document repository integrated with a plagiarism checker, aimed at providing a comprehensive solution for storing, retrieving, and verifying the uniqueness of documents. The system allows users to upload, organize, and search documents efficiently while employing a robust plagiarism detection mechanism to ensure the integrity of content. By leveraging web technologies and plagiarism detection algorithms, this platform serves as a valuable tool for educational institutions, businesses, and content creators. The system enhances document management practices by offering a centralized, secure repository and reducing the risk of intellectual property infringement. This paper discusses the architecture, features, and implementation challenges of the system, along with its potential applications in various domain
| 7 |
Author(s):
Adesoji Adegbola, Akande Oyebola, Mgbeahuruike Emmanuel, Adebanjo Adedoyin, Adewuyi Oluwaseyi.
Page No : 94-102
|
A Convolutional Neural Network Model for Crop Disease Detection System.
Abstract
Crop diseases pose a significant challenge to global food security, adversely impacting agricultural output and resulting in considerable economic repercussions. The prompt and precise identification of these diseases is essential for effective intervention and sustainable agricultural practices. This study introduces a model based on Convolutional Neural Networks (CNNs) for the automated detection of crop diseases. The model employs advanced deep learning methodologies to recognize and categorize plant diseases through the analysis of leaf images. Our CNN framework is trained on an extensive dataset comprising both diseased and healthy plant images, employing multiple convolutional layers to extract intricate features, including texture, color variations, and patterns linked to specific diseases. The model demonstrates a high level of accuracy in identifying a variety of diseases across different crop species by learning from both overt symptoms and subtle cues. We evaluate the performance of the system using established metrics such as accuracy, and precision, thereby validating its efficacy in practical applications. The proposed system is designed for implementation in low-resource agricultural settings, offering farmers a cost-effective, dependable, and real-time solution for monitoring crop health.
| 8 |
Author(s):
Jackson Efiong Ante, Ubong Dominic Akpan, Godwin Odido Igomah, Christian Solomon Akpan, Udeme Emmanuel Ebere, Peter Obeten Okoi, Samuel Okon Essang.
Page No : 103-117
|
On the Global Existence of Solution of the Comparison System and Vector Lyapunov Asymptotic Eventual Stability for Nonlinear Impulsive Differential Systems.
Abstract
This paper examines the existence of maximal solution of the comparison system as well as the asymptotic eventual stability of nonlinear impulsive differential equations using the vector Lyapunov functions, which is generalized by a class of piecewise continuous Lyapunov functions. Together with comparison results, sufficient conditions for the asymptotic eventual stability of the impulsive systems. In the paper, it was established that the maximal solution of the comparison system majorizes the vector form of the Lyapunov functions. Together with comparison results, sufficient conditions for the asymptotic eventual stability of impulsive differential systems are presented . Results obtained improves and extends existing results in the literature.
| 9 |
Author(s):
Taofeek A. Suleman.
Page No : 118-132
|
Architecture 5.0: Opportunities and Challenges in the Nigerian Construction Industry.
Abstract
The rapid advancement of artificial intelligence (AI), robotics, and other digital technologies (DTs) has often lacked a focus on human-centrism. Industry 5.0 emerged as a response to Industry 4.0’s digital revolution, emphasizing functional human-machine collaboration, sustainability, and resilience. However, the architecture, engineering, construction, and operations (AECO) sector, particularly in Nigeria, has been slow to seize the opportunities presented by Industry 5.0. This study investigates the opportunities and challenges associated with deploying Industry 5.0, specifically focusing on architecture within the Nigerian construction industry (NCI). A rapid literature review was conducted, analyzing relevant and indexed articles from reputable databases. The findings indicate that integrating AI into architectural design workflows can catalyze the adoption of other DTs, such as the Internet of Things (IoT), big data analytics, digital twins, cloud computing, Blockchain, and augmented/virtual reality. These technologies can potentially transform planning, operations, end-of-life management, and visualizations during the design phase of architectural services. The study emphasizes the importance of architectural professionals acquiring relevant technical skills through education and awareness initiatives. It also stresses the need for policies and programmes implemented by the government, regulatory agencies, and industry firms to accelerate the adoption of DTs. Effective strategies for leveraging AI’s potential are proposed to enhance design quality, speed, performance, and collaboration with allied design professionals. The findings offer valuable insights into adopting Architecture 5.0 within the NCI, particularly during the design stage.
| 10 |
Author(s):
Fathi Etaher Elbakoush, Aref M. Alkelani.
Page No : 133-138
|
Thermal Properties of Polyimide Film and Polyimide-Iron Composite Film .
Abstract
The thermogravimetric and the derivative weight loss of the Pi and Pi + Fe composite films. The weight loss resulted from the decomposition and burning. The peaks were observed and indicated the complete combustion of the Pi and Pi + Fe composite films. The prominent exothermic peak was observed at the Pi polyimide film at 550 ℃ and the Pi + Fe composite film at 530 ℃ indicating the complete combustion of the films. The enhanced thermal stability of the polyimide synthesized with iron can be attributed to the inherent thermal stability of these materials and the robust chemical bonding interactions between the polyimide and iron.
| 11 |
Author(s):
Nasir Sulaiman Muhammad, Sa’ad Shehu Janguza, Ma’aruf Abdulmumin Muhammad, Ramgopal Dhakar , Salim Rabiu, Ahmad Umar Labdo, Abdullahi Tijjani Abubakar.
Page No : 139-153
|
Impact of Improvised Instructional Materials on Teaching and Learning Biology Among Senior Secondary School Students in Dala Local Government Area, Kano State.
Abstract
Biology education is crucial for students to understand living organisms and their interactions with the environment. However, many schools struggle with limited access to teaching materials. This study investigated the impact of improvised instructional materials on biology education among senior secondary school students in Dala Local Government Area, Kano State. The research involved two schools, SSII classes from each school were assigned to control and experimental groups, totaling 346 students. Data was collected through surveys, classroom observations, and student assessments. Biology teachers were trained to design and use improvised materials in their curriculum. The findings revealed that improvised instructional materials improved teaching and learning in biology. Students in the experimental group showed better learning outcomes, higher engagement, and enhanced comprehension and retention of concepts. Quantitative data from post-tests were statistically analyzed to compare learning outcomes between the groups, while qualitative data from surveys were thematically analyzed to understand perceptions, challenges and benefits of using improvised materials. This study underscores the cost-effectiveness and practicality of improvised instructional materials, advocating for their adoption in schools. It provides insights for educators and policymakers to enhance biology education, innovative and student-centered teaching methods that foster a deeper understanding of life sciences among students.
| 12 |
Author(s):
Okwong Atte Enyenihi, Duke Stephen Orok.
Page No : 154-164
|
Business Mobile Application Using Technology Acceptance Model (Tam) for Hospitality Industry.
Abstract
The adoption of business mobile applications (BMAs) in the hospitality industry has surged globally, facilitated by the rapid growth of smartphones and mobile technologies. Using the Technology Acceptance Model (TAM) as a theoretical lens, this study examines the factors influencing the adoption of BMAs in Nigeria and South Africa's hospitality sectors. TAM posits that perceived ease of use (PEOU) and perceived usefulness (PU) are critical factors that determine user acceptance of technology. In the context of the hospitality industry, BMAs enhance customer experience by streamlining booking processes, improving customer service, and offering tailored services through personalized applications. In Nigeria, the increasing penetration of mobile devices and digital payment platforms like Flutterwave and Paystack has driven BMA adoption, though infrastructural challenges and data privacy concerns remain. South Africa, on the other hand, has seen a rapid adoption of BMAs due to government initiatives promoting the digital hospitality industry despite language and cultural barriers affecting user engagement. In South Africa, high internet penetration and an established digital ecosystem have accelerated BMA adoption in hospitality, with an emphasis on enhancing customer engagement through artificial intelligence and data analytics, leading to mobile technology adoption, driven by innovations in mobile payment systems like Alipay and ChatPay, enabling seamless integration of BMAs into the hospitality sector. However, government regulations around data security pose significant challenges. Across these diverse regions, the study concludes that cultural, infrastructural, and regulatory factors play pivotal roles in shaping BMA adoption. Despite contextual differences, TAM remains a useful model to understand the adoption patterns of BMAs, suggesting that improving PEOU and PU will drive further technological integration in the hospitality industry. The adoption of business mobile applications (BMAs) in the hospitality industry has surged globally, facilitated by the rapid growth of smartphones and mobile technologies. Using the Technology Acceptance Model (TAM) as a study, examines the factors influencing the adoption of BMAs in the hospitality sectors of Nigeria and South Africa. TAM posits that perceived ease of use (PEOU) and perceived usefulness (PU) are critical factors that determine user acceptance of technology. In the context of the hospitality industry, BMAs enhance customer experience by streamlining booking processes, improving customer service, and offering tailored services through personalized applications. In Nigeria, the increasing penetration of mobile devices and digital payment platforms like Flutterwave and Paystack has driven BMA adoption, though infrastructural challenges and data privacy concerns remain. South Africa, on the other hand, has seen a rapid adoption of BMAs due to government initiatives promoting the digital hospitality industry despite language and cultural barriers affecting user engagement. In South Africa, high internet penetration and an established digital ecosystem have accelerated BMA adoption in hospitality, with an emphasis on enhancing customer engagement through AI and data analytics. However, government regulations around data security pose significant challenges. Across these diverse regions, the study concludes that cultural, infrastructural, and regulatory factors play pivotal roles in shaping BMA adoption. Despite contextual differences, TAM remains a useful model to understand the adoption patterns of BMAs, suggesting that improving PEOU and PU will drive further technological integration in the hospitality industry.
