Kwaghe International Journal of Engineering and Information Technology
Kwaghe International Journal of Engineering and Information Technology aims to publish high-quality, peer-reviewed scholarship that advances engineering design, computational systems, digital technologies, and information-based innovation. The journal prioritizes contributions with clear technical novelty, sound validation, and practical or theoretical significance for engineering and information technology. • Engineering Innovation: promote rigorous research on engineering systems, design, modeling, optimization, and implementation. • Information Technology: support advances in software, computing, data systems, networking, and digital solutions. • Interdisciplinary Integration: encourage studies connecting engineering with information technology to solve complex real-world problems. • Applied Relevance: welcome research that demonstrates practical value, scalability, reliability, and technological impact. Submissions should clearly define the technical problem, explain the methodological approach precisely, report validation or evaluation transparently, and demonstrate a meaningful contribution to engineering and/or information technology scholarship. Scope KIJEIT welcomes original research papers, theoretical studies, system designs, and applied investigations in engineering and information technology. The journal considers work in both foundational and applied domains, provided the engineering or information-technology contribution is explicit, analytically sound, and well reported. • Computer and Information Technology: software engineering, information systems, databases, cloud computing, artificial intelligence, and data analytics. • Electrical and Electronic Engineering: electronics, instrumentation, embedded systems, signal processing, communications, and control systems. • Networks and Cybersecurity: network architecture, internet technologies, digital security, cryptography, and secure system implementation. • Automation and Intelligent Systems: robotics, IoT, smart systems, human–machine interaction, and technology-supported optimization. • Engineering Applications: computational modeling, simulation, algorithm development, system evaluation, and interdisciplinary engineering solutions. Priority is given to manuscripts with strong technical framing, transparent evaluation procedures, and conclusions that are logically supported by experimental, computational, or analytical evidence.
Articles
13 Documents
<![CDATA[Effect of Project-Based Instructional Strategy on Colleges of Education Students’ Achievement, and Retention in Electronics Technology in North-East Nigeria ]]>
<![CDATA[Ishaku Zechariah]]>;
<![CDATA[Ishaya Tumba]]>
<![CDATA[ Kwaghe International Journal of Engineering and Information Technology Vol 1 No 1 (2024): Kwaghe International Journal of Engineering and Information Technology ]]>
Publisher : <![CDATA[Darul Yasin Al Sys ]]>
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DOI: 10.58578/kijeit.v1i1.3829
<![CDATA[This study examined the Effect of Project-Based Instructional Strategy on Colleges of Education Students’ Achievement, and Retention in Electronics Technology in North-East Nigeria. College of Education is one of the tertiary institutions in Nigerian alongside other institutions like polytechnics, monotechnics etc. However, Colleges of Education are the institutions that are exclusively saddled with the responsibility of training teachers who will in turn teach at the Junior Secondary School level of education in Nigeria. They are expected to realise the objectives of NCE (T.) This implies that the graduates should invariably be technologist as well as agents of technological advancement both in the classroom and in the society. In the classroom, the NCE (T) teachers should keep in step with Educational Technology materials and strategies that are applicable to their discipline and level of training. Three research questions and three hypotheses were formulated to guide the study. The study adopted Quasi -experimental dsesign involving pre-test post-test control group. The population of the study was 73 Electronics Technology students in six Colleges of Education North-East Nigeria. The sample was 36 Electronics Technology students in three Colleges of Education. Digital Electronics Achievement Test (DEAT), Digital Electronics Retention Test (DERT) and Digital Electronics and DERT were tested for internal consistency using Pearson Product Moment Correlation. The reliability coefficients of the instruments were found to be 0.915 and 0.895 respectively Data were collected and analysed using SPSS the research questions were answered using mean, standard deviation while t-test, ANCOVA and ANOVA statistical tools were used to test the null hypotheses at 0.05 level of significance. The results showed that there was significant difference in achievement test scores between ProBaIS and TIS in favour of ProBaIS. More so, the results show significant differences in students’ achievement retention in favour of ProBaIS. Study also revealed that there was no significant difference in achievement test scores of male and female students when taught Digital Electronics Using ProBaIS. It was Recommended that ProBaIS should be encourage in Colleges of Education Electronics Lesson Delivery.]]>
<![CDATA[Effect of Blended Instructional Strategy on Colleges of Education Students’ Achievement, and Retention in Electronics Technology in North-East Nigeria ]]>
<![CDATA[Ishaku Zechariah]]>;
<![CDATA[Patrick Duhu Chinda]]>
<![CDATA[ Kwaghe International Journal of Engineering and Information Technology Vol 1 No 1 (2024): Kwaghe International Journal of Engineering and Information Technology ]]>
Publisher : <![CDATA[Darul Yasin Al Sys ]]>
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DOI: 10.58578/kijeit.v1i1.3830
<![CDATA[This study examined the Effect of Blended Instructional Strategy on Colleges of Education Students’ Achievement, and Retention in Electronics Technology in North-East Nigeria. College of Education is one of the tertiary institutions in Nigerian alongside other institutions like polytechnics, monotechnics etc. However, Colleges of Education are the institutions that are exclusively saddled with the responsibility of training teachers who will in turn teach at the Junior Secondary School level of education in Nigeria. They are expected to realize the objectives of NCE (T). This implies that the graduates should invariably be technologist as well as agents of technological advancement both in the classroom and in the society. Three research questions and eight hypotheses were formulated to guide the study. The study adopted Quasi -experimental design involving pre-test post-test control group. The population of the study was 73 Electronics Technology students in six Colleges of Education North-East Nigeria. The sample was 36 Electronics Technology students in three Colleges of Education. Digital Electronics Achievement Test (DEAT), Digital Electronics Retention Test (DERT) were developed by the researcher as the instruments for data collection. The validated DEAT and DERT were tested for internal consistency using Pearson Product Moment Correlation. The reliability coefficients of the instruments were found to be 0.915 and 0.895 respectively. Data were collected and analysed using SPSS the research questions were answered using mean, standard deviation while t-test, ANCOVA, ANOVA and Scheffe’s statistical tools were used to test the null hypotheses at 0.05 level of significance. The results showed that there was significant difference in achievement test scores between BIS and TIS strategy in favour BIS. More so, the results show significant differences in students’ achievement retention in favour of BIS. Study also revealed that there was no significant difference in achievement test scores of male and female students when taught Digital Electronics using BIS as well as retention. The study concluded that and BIS enhanced the academic achievement, and retention in Digital Electronics Technology Students,. It was Recommended that BIS should be encourage in Colleges of Education Electronics Lesson Delivery.]]>
<![CDATA[A Solution of Airy Differential Equation Via Elzaki Transform ]]>
<![CDATA[Nand Kishor Kumar]]>;
<![CDATA[Suresh Kumar Sahani]]>
<![CDATA[ Kwaghe International Journal of Engineering and Information Technology Vol 2 No 1 (2025): Kwaghe International Journal of Engineering and Information Technology ]]>
Publisher : <![CDATA[Darul Yasin Al Sys ]]>
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DOI: 10.58578/kijeit.v2i1.4534
<![CDATA[The Elzaki transform provides an elegant method to solve the Airy differential equation, leveraging its unique properties to handle the mixed derivative and variable coefficient terms. While the traditional methods remain standard, the Elzaki transform offers a powerful alternative, particularly when coupled with numerical techniques.]]>
<![CDATA[Development of Unmanned Aerial Vehicle for Agrochemical Spraying to Enhance Crop Protection ]]>
<![CDATA[Kadala F. Shalzim]]>;
<![CDATA[Ndana’acha Alfred]]>
<![CDATA[ Kwaghe International Journal of Engineering and Information Technology Vol 2 No 2 (2025): Kwaghe International Journal of Engineering and Information Technology ]]>
Publisher : <![CDATA[Darul Yasin Al Sys ]]>
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DOI: 10.58578/kijeit.v2i2.5281
<![CDATA[The use of autonomous or guided unmanned aerial vehicle (UAV) is beginning to have enormous benefits in Agriculture by modernizing the ways in which farmers work not only to improve the yield and maximize profit but also the need to be healthy by avoiding direct contact with the agrochemicals used in our farmlands. The aim of this paper is to present an on-going research on the local development of agricultural spraying unmanned aerial vehicle for enhancing crop production and protection in small and medium scale maize, rice, and sugar cane plantation farmers in the Nigeria. The objective is to design and develop a practical UAV sprayer based solutions that would enable the adoption of the precision agriculture farming technique by Nigerian farmers. The proposed method involved the development of a 850 mm hexa-rotor UAV platform with an air bone spray system, the UAV uses a pixhawk flight controller, iRotor 5010/360KV motors with a1555 propellers,40A ESC and 5200mAh/ 22.2volts battery. It telemetry operates on a frequency of2.4GHz using flysky transmitter and receiver, with an ultrasonic sensor for capturing the unmanned aerial vehicle height above the ground level. In this research thrust analysis was performed tos determine the lift capacity of four propeller, the 1245, 1447, 1555 and 1655 inches in combination with 360Kv motor. And it was determined that the 1555 inches propeller out performs the other three propellers as shown in their performance plots in Spyder-Python –Anaconda. The UAV Sprayer system was implemented using the best optimal performing components, with the overall intention of developing sustainable methods in crop production and protection that would increase the crop yield that will build a resilient economy and minimize human exposure to the hazardous chemicals while optimizing both crop production and protection.]]>
<![CDATA[Development of a Framework for Cybersecurity Risk Assessment in the Maritime Industry Using Machine Learning Techniques ]]>
<![CDATA[Bartholomew Idoko]]>;
<![CDATA[Kenneth Nwankwo]]>
<![CDATA[ Kwaghe International Journal of Engineering and Information Technology Vol 2 No 2 (2025): Kwaghe International Journal of Engineering and Information Technology ]]>
Publisher : <![CDATA[Darul Yasin Al Sys ]]>
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DOI: 10.58578/kijeit.v2i2.5342
<![CDATA[This study assesses the level of cybersecurity risk inherent in the maritime industry in order to improve process in the sector. The maritime sector has continued to witness cyber incidents due to its importance to national economy. Also, the growing dependence of the sector on information and communications technology (ICT), as a result of increased automation, has greatly exacerbated the threats. The underlying cyber infrastructure with its expanding threat landscape and vulnerabilities have also further exacerbated the risk landscape in the sector. More so, the dearth of empirical studies in this domain is an indication of knowledge gap occasioned by non-availability of empirical data on how organizations in this sector manage cybersecurity risk. That is, how organizational operations and technological assets, individuals and processes affect the sector. Thus, the study has identified and established the cybersecurity risks specific to the maritime sector and gauged the gap based on people, process and technology elements of cybersecurity. This study uses Artificial Intelligence, machine learning model in particular to carry out the assessment. The study identified how organizations applied security controls in the sector using the metrics of people, process and technology. The risk was analysed and graded into very high, high, moderate, low and very low from the established risk factors like threat and vulnerabilities. We used k-nearest neigbour and factorization methods for model training and risk ratings. The findings showed that the maritime sector has a high cybersecurity risk rating. This knowledge and the recommendations that followed, will help deepen the understanding of cybersecurity risks in the maritime sector as well as improve maritime process, its potential effects on service delivery, national security and economic wellbeing of the nation.]]>
<![CDATA[Swarm Intelligence-Based Intrusion Detection Framework Using Neural Network & Bee Colony Optimiation ]]>
<![CDATA[Kenneth Nwankwo]]>;
<![CDATA[Bartholomew Idoko]]>
<![CDATA[ Kwaghe International Journal of Engineering and Information Technology Vol 2 No 2 (2025): Kwaghe International Journal of Engineering and Information Technology ]]>
Publisher : <![CDATA[Darul Yasin Al Sys ]]>
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DOI: 10.58578/kijeit.v2i2.5452
<![CDATA[An Intrusion Detection System (IDS) serves as a critical defense mechanism for safeguarding networks against unauthorized activities and cyber attacks. However, the processing of sophisticated datasets with contemporary detection methodologies often presents challenges due to their intricate scale, complicating the identification of complex threats. This study aims to enhance IDS operational efficacy through the development of a novel method integrating Bee Colony Optimization (BCO) and Neural Networks (NN). Employing a quasi-experimental design, the research evaluates the system's performance, demonstrating that the integration of BCO significantly optimizes neural network functionality, thereby improving both the speed of attack detection and the accuracy of feature selection. Utilizing the NSL-KDD dataset, the proposed framework notably minimizes false alerts while augmenting overall detection accuracy levels. The findings underscore that advancements in cybersecurity systems can be achieved through the synergy of Neural Networks and Swarm Intelligence technology, providing effective solutions for real-time intrusion detection systems. This research not only contributes to the theoretical understanding of IDS optimization but also has practical implications for enhancing cybersecurity measures in various organizational contexts.]]>
<![CDATA[Tiktok Through AI Eyes: A Deep Learning Approach to Sentiment Analysis ]]>
<![CDATA[Hambali Moshood Abiola]]>;
<![CDATA[Ayo Iyanuoluwa]]>;
<![CDATA[Akinyemi Adesina A.]]>;
<![CDATA[Adamu Muhammed Gadafi]]>;
<![CDATA[Ashraf Ishaq]]>
<![CDATA[ Kwaghe International Journal of Engineering and Information Technology Vol 2 No 2 (2025): Kwaghe International Journal of Engineering and Information Technology ]]>
Publisher : <![CDATA[Darul Yasin Al Sys ]]>
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DOI: 10.58578/kijeit.v2i2.5485
<![CDATA[Background: The rapid growth of social media has transformed communication, with TikTok standing out among younger users for its short-form videos. Understanding user sentiment on these platforms is key to analyzing public opinion, trends, and engagement. Aim: This study explores sentiment analysis of TikTok user reviews using deep learning approaches, specifically Recurrent Neural Networks with Long Short-Term Memory (RNN-LSTM) and Deep Belief Networks (DBN). With over 144,000 reviews collected from Google Play and Apple App stores, the dataset was preprocessed using techniques such as lemmatization, tokenization, and GloVe word embeddings. The reviews were then classified into positive and negative sentiments. Both models were trained and evaluated based on metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Result: Experimental results revealed that the RNN-LSTM model outperformed the DBN, achieving an accuracy of 81.99% and an AUC of 0.8874, compared to DBN's 78.53% accuracy and 0.8577 AUC. The findings demonstrate the effectiveness of deep learning—particularly LSTM—in capturing sentiment from noisy, user-generated content on platforms like TikTok. This work contributes to the growing field of AI-driven sentiment analysis and provides a foundation for future improvements through hybrid or multimodal approaches.]]>
<![CDATA[The Role of Blockchain in Securing IoT Devices ]]>
<![CDATA[Abubakar Jibrin]]>;
<![CDATA[Ashraf Ishaq]]>;
<![CDATA[Aliyu Ahmed]]>;
<![CDATA[Adamu Muhammad Gadafi]]>
<![CDATA[ Kwaghe International Journal of Engineering and Information Technology Vol 2 No 2 (2025): Kwaghe International Journal of Engineering and Information Technology ]]>
Publisher : <![CDATA[Darul Yasin Al Sys ]]>
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DOI: 10.58578/kijeit.v2i2.5584
<![CDATA[The proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges, including data breaches, unauthorized access, and the exploitation of centralized network vulnerabilities. Traditional security architectures struggle to provide robust protection due to the distributed and resource-constrained nature of IoT environments. Blockchain technology, with its decentralized ledger, cryptographic security, and smart contract functionality, presents a promising approach to mitigating these risks. By ensuring data integrity, enabling secure authentication, and facilitating trustless interactions among IoT devices, blockchain can enhance the overall security framework of IoT ecosystems. This paper critically examines the role of blockchain in securing IoT networks, outlining its key benefits, potential real-world applications, and associated limitations. While blockchain addresses fundamental IoT security concerns, challenges such as scalability, computational overhead, and integration complexity hinder widespread adoption. The study underscores the need for further research into optimizing blockchain protocols for IoT environments and explores potential advancements in hybrid security models.]]>
<![CDATA[AI-Driven Mitigation of Cognitive Biases in Intelligent Personal Assistant Interactions: Evidence from African Contexts ]]>
<![CDATA[Abdullahi A. Shinkafi]]>;
<![CDATA[Steve Bassey]]>;
<![CDATA[Shammah Emmanuel Chaku]]>;
<![CDATA[Gilbert I. O. Aimufua]]>;
<![CDATA[Abraham D. Joseph]]>
<![CDATA[ Kwaghe International Journal of Engineering and Information Technology Vol 2 No 3 (2025): Kwaghe International Journal of Engineering and Information Technology ]]>
Publisher : <![CDATA[Darul Yasin Al Sys ]]>
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DOI: 10.58578/kijeit.v2i3.6480
<![CDATA[This paper presents a rigorous investigation into how artificial intelligence-driven features in Intelligent Personal Assistants (IPAs) can mitigate cognitive biases within the culturally diverse landscapes of African societies. Positioned at the intersection of cognitive psychology, artificial intelligence, and African cultural studies, the research examines how traditional decision-making patterns in West, Southern, and Central African contexts interact with AI-powered debiasing mechanisms. Grounded in Dual-Process Theory and indigenous knowledge systems, the study explores how IPAs can be culturally calibrated to address confirmation bias, anchoring, and availability heuristics as they uniquely manifest within African socio-cultural frameworks. Employing a sequential explanatory mixed-methods design, the study integrates survey data from 528 participants across eight countries with 40 in-depth interviews. The findings reveal that while AI-driven interventions significantly reduce cognitive biases, their effectiveness is deeply moderated by cultural dimensions such as power distance, uncertainty avoidance, and collectivist orientations—each varying distinctly across regions. Culturally contextualized nudges and interventions aligned with local values and communication norms yielded the strongest debiasing outcomes. This research offers essential empirical insights into the emerging field of culturally responsive AI design, emphasizing the need to recalibrate debiasing techniques to reflect and respect African cultural perspectives rather than applying Western-centric models of cognitive optimization.]]>
<![CDATA[Enhancing Academic Performance through Machine Learning: A Comprehensive Study of Student Academic Tracking Systems ]]>
<![CDATA[Samuel Olofu Owoicho]]>
<![CDATA[ Kwaghe International Journal of Engineering and Information Technology Vol 2 No 3 (2025): Kwaghe International Journal of Engineering and Information Technology ]]>
Publisher : <![CDATA[Darul Yasin Al Sys ]]>
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DOI: 10.58578/kijeit.v2i3.7541
<![CDATA[The rapid advancement of technology has created new opportunities to enhance education, with machine learning (ML) emerging as a transformative tool. This study presents the development and evaluation of a comprehensive academic tracking system designed to monitor and categorize students based on performance metrics, while also providing functionality beyond simple grade reporting. Unlike traditional systems that serve primarily as repositories for academic scores, the proposed system offers integrated tools for tracking attendance, monitoring academic progress, managing assignments, and generating early alerts for at-risk students. Developed using Python for backend logic, React for frontend implementation, and MySQL for secure data management, the web-based platform was designed to improve real-time access and usability for both students and educators. The system incorporates a multifaceted methodology to analyze a wide range of student-related factors, including demographic data (e.g., age, gender, socioeconomic background), academic performance (e.g., grades, attendance), and behavioral indicators (e.g., participation and assignment submissions). The model classifies students into low, average, and high-performing groups using machine learning techniques, enabling more targeted interventions. When tested with real academic data from tertiary institutions in Nigeria, the proposed system demonstrated superior accuracy and efficiency in tracking and predicting student performance compared to existing solutions. These findings underscore the system’s potential to support data-driven decision-making in educational environments and to enhance learning outcomes through early identification and personalized support strategies.]]>
