Direct Research Journal of Engineering and Information Technology
Direct Research Journal of Engineering and Information Technology (DRJEIT) (ISSN 2354-4155) is an international open-access journal. The journal is currently accepting papers for publication in its monthly issue in English in all areas of Engineering, physical science, and Information Technology. We are looking forward to strict compliance with the modern age standards in all these fields. Submit your manuscript using the submission link Submit Manuscript ARTICLE PROCESSING COST = 655USD FOR AUTHOR OTHER COUNTRIES OR 75000 NAIRA FOR NIGERIA. AUTHORS CAN APPLY FOR PARTIAL WAIVER TO THE JOURNAL EDITORIAL AT editorialoffice@directresearchpublisher.org Indexed in JISC Scilit
Bio-stimulation with Fresh Avocado Seed for crude oil Remediation with a Bio-kinetic Modelling
The remediation of crude oil-contaminated soils requires sustainable and cost-effective solutions. This study investigates the performance of fresh avocado seed (Persea americana) as a biostimulant for the remediation of Bonny Light crude oil-contaminated soil from the Kalarugbana community, Niger Delta. A 56-day laboratory-scale experiment was conducted using bioreactors containing 1 kg of soil contaminated with 100 ml of crude oil and treated with different volumes of fresh avocado seed slurry (0 ml as control, 100 ml, 200 ml, and 300 ml). Microbial growth (CFU/g) and Total Petroleum Hydrocarbon (TPH) depletion (mg/kg) were monitored weekly. Results demonstrated a strong positive correlation between inoculants volume and efficacy. Microbial counts increased from 5,000 CFU/g to 114,000, 135,000, and 170,000 CFU/g for the 100 ml, 200 ml, and 300 ml treatments, respectively. This enhancement directly drove TPH depletion from an initial 3,729.7 mg/kg to 1,202.4, 1,038.1, and 647.7 mg/kg, corresponding to degradation efficiencies of 67.76%, 72.17%, and 75.33%. The control reactor showed only minimal natural attenuation (38.3%). A biokinetic model was developed from first principles, relating substrate depletion to remediator concentration. Model simulations in MATLAB, using an equilibrium rate constant (K-eq) of 0.0083 week⁻¹, produced results in strong agreement with the experimental data. This work confirms that fresh avocado seed is a highly effective, low-cost biostimulant, with its efficiency directly scalable to the volume applied. The validated model provides a predictive tool for optimizing remediation strategies.
Biostimulation, Avocado Seed, Phytoremediation, Total Petroleum Hydrocarbon (TPH), Biokinetic Model, Niger Delta.
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Instructional Methods in Two-dimensional Creative Arts Education among Primary School Teachers in the Asante Akim North Municipality
Two-dimensional Creative Arts provides opportunities for learners to develop their creativity‚ visual literacy and practical problem-solving skills․ However‚ little is known about how the subject is organised‚ taught and learnt in primary schools in the Asante Akim North Municipality․ This research investigated teachers' pedagogical practices in teaching the two-dimensional Creative Arts․ The descriptive mixed-methods study involved headteachers‚ Creative Arts teachers and Basic Six pupils․ The questionnaires‚ interviews‚ classroom observations and a learner performance test were analysed using frequencies‚ percentages‚ means‚ standard deviations and thematic analysis․ The results indicated that drawing was the most frequently used approach to teaching of the 13 teachers (12 teachers or 92․3 percent)‚ while collage and printmaking were never used (0․0 percent)․ All 13 teachers reported using pencils‚ crayons‚ and markers (100%)‚ which indicates a narrow use of art materials on the part of teachers․ Pen and paper assessment was mostly theoretical‚ used by 75% of teachers in quizzes and tests․ Creativity‚ experimentation and skills were rarely assessed․ Four out of ten teachers did not use other forms of multimedia‚ whereas 46․2% of teachers reported using them occasionally․ It was demonstrated that the teaching of Creative Arts as a two-dimensional subject in the municipality is constrained by poor teacher preparedness‚ limited resource materials‚ poor exposure to practical work‚ and poor authentic assessment practices․ In-service training‚ art resource provision‚ and instructional supervision should be improved‚ while assessment recommendations should focus on creativity‚ process‚ originality‚ and learner involvement in all participating primary school classrooms․
Experimental Investigation of Water Absorption Behaviour of Coconut Shell Aggregate Concrete
The increasing demand for sustainable construction materials has stimulated interest in the utilization of agricultural waste as alternative concrete constituents. This study investigated the water absorption characteristics of concrete produced with coconut shell as a partial replacement for conventional coarse aggregate by volume. Four concrete mixtures containing 0%, 10%, 20%, and 30% coconut shell replacement were evaluated. A total of twenty (24) concrete cube specimens measuring 100 mm × 100 mm × 100 mm were cast, with eight specimens allocated to the total water absorption test, eight for absorption capacity and the remaining eight to the capillary water absorption test, giving two replicate specimens for each mixture in each test. Capillary water absorption test was determined in accordance with ASTM C1585 while the total water absorption test was evaluated using modified full-immersion procedure adapted from the weighing protocol of ASTM C1585, in which concrete cubes were completely submerged in water and weighed after 1,3,5,15 and 30 minutes, and after 1, 4, 24, and 48 hours to monitor water uptake. The absorption capacity was determined in accordance with ASTM C642. The results showed that both the absorption capacity, total and capillary water absorption increased with increasing coconut shell content due to the higher porosity and water absorption capacity of coconut shell compared with conventional granite aggregate. Statistical evaluation using two-way analysis of variance (ANOVA) revealed that both coconut shell replacement level and exposure time had statistically significant effects (p < 0.05) on the water absorption behavior of the concrete. Despite the increase in water absorption, the measured values remained within acceptable ranges reported for structural lightweight concrete. The findings indicate that coconut shell can be used as a sustainable partial replacement for coarse aggregate while maintaining acceptable durability characteristics, particularly at moderate replacement levels.
Coconut shell concrete; Total water absorption; Capillary water absorption; Sustainable concrete; ASTM C642; ASTM C1585; ANOVA
Triple Bottom Line Drivers of Sustainable Construction Performance in Nigeria: Evidence from Abuja Construction Firms
Although sustainability awareness in the construction sector has grown substantially over the last decade, empirical studies that simultaneously test the environmental, social, and economic pillars of the Triple Bottom Line (TBL) within a single, statistically validated model remain scarce in the Nigerian context; this study addresses that gap by providing the first integrated, firm-level evidence from Abuja. The growing environmental, social, and economic challenges associated with construction activities have intensified global calls for sustainable construction practices, particularly in developing countries such as Nigeria, where implementation remains inconsistent despite increasing awareness. This study examined the determinants of sustainable construction in Nigeria from a Triple Bottom Line perspective, focusing on environmental sustainability (EnvS), social sustainability (SS), and economic sustainability (EcoS) among construction firms operating in Abuja. A quantitative, cross-sectional survey design was adopted, targeting construction professionals and selected community stakeholders within five purposively selected construction firms. Using the Krejcie and Morgan (1970) table, a minimum sample size of 240 was established; 240 structured, five-point Likert-scale questionnaires were administered using stratified random sampling, and 221 valid responses (92.1% response rate) were retained for analysis. Data were analyzed with SPSS version 27 using descriptive statistics and multiple regression analysis at the 0.05 significance level. The regression model was statistically significant, F (3, 217) = 6.142, p < .001, and jointly explained 7.8% of the variance in sustainable construction performance (R² = .078, Adjusted R² = .065). All three dimensions exerted a significant positive influence on sustainable construction performance: environmental sustainability (β = .121, p = .049), social sustainability (β = .097, p = .031), and economic sustainability (β = .134, p = .046), with economic sustainability emerging as the strongest determinant, followed by environmental sustainability, while social sustainability recorded the weakest influence. The results further indicate that sustainable construction practices in Abuja are still unevenly implemented, with greater emphasis placed on economic and environmental dimensions than on social sustainability. The study concludes that achieving sustainable construction in Nigeria requires a balanced integration of the Triple Bottom Line dimensions, supported by stronger institutional enforcement, improved stakeholder engagement, and targeted policy incentives that promote holistic sustainability adoption across the construction sector.
Adaptive Frame Sampling for CPU-Based Multi-Camera Person Re-ID
Person re-identification (Re-ID) in multi-camera surveillance is typically designed for GPU hardware, limiting deployment in the CPU-only environments common across resource-constrained regions. We propose an adaptive frame sampling framework for multi-camera person Re-ID that operates in real-time on CPU-only systems while preserving identity tracking accuracy. The proposed system integrates a motion-triggered adaptive sampling mechanism with a YOLOv8n person detection model and a lightweight CNN-based Re-ID module. Rather than executing deep learning inference on every captured frame, the system selectively triggers inference when a frame differencing-based motion intensity score exceeds a defined threshold, skipping redundant frames during static or near-static scenes. The detection model was trained on the UCSD Pedestrian 1 dataset over 50 epochs, achieving a final mAP50 of 0.979 and a peak F1 score of 0.95. The Re-ID module extracts 256-dimensional L2-normalised appearance embeddings from detected person crops and performs identity assignment via cosine similarity matching against a rolling gallery. Comparative evaluation across three independent video experiments demonstrated that the adaptive sampling strategy reduced processed inference frames by an average of 89.3% and total processing time by 71.1%, while achieving an effective frame rate of up to 56.5 FPS on CPU hardware, with 100% identity consistency maintained across all conditions. These findings demonstrate that high-accuracy multi-camera person Re-ID is achievable at a fraction of the computational cost of conventional approaches, making real-time surveillance feasible in low-resource settings and expanding access to advanced video analytics beyond GPU-equipped deployments.
person re-identification, adaptive frame sampling, YOLOv8, multi-camera surveillance, CPU deployment, resource-constrained systems, motion detection, appearance embeddings
Knowledge and Utilization of Artificial Intelligence Tools among Undergraduate Students: Implications for Educational Technology Adoption
Artificial Intelligence (AI) is increasingly integrated into higher education, yet its adoption among students in Ghana remains underexplored. This study investigated undergraduate students’ knowledge, familiarity, and utilization of AI tools at the University of Ghana. A descriptive quantitative survey design was employed, with purposive sampling of 400 students (203 males, 197 females; ages 16–28). Data was collected via an online questionnaire and analyzed using descriptive statistics, cross-tabulations, and chi-square tests. Findings revealed that most respondents (93.5%) were aged 16–20, reflecting a youthful and digitally engaged population. In terms of AI knowledge, 40% defined AI as a technology enhancing learning and teaching, 37% as applications supporting problem-solving, and 23% as computer systems performing tasks. Familiarity levels were moderate for 54% of students, while 44.75% reported being familiar and 1.25% not familiar. Awareness was highest for ChatGPT (52.5%), followed by Snap AI (22.5%) and Meta AI (21.75%). Regarding usage, 53.25% reported frequent use of AI tools in academic research, 46.25% occasional use, and 0.5% no use. Students engaged critically with AI-generated responses, with 50% proofreading, 25% cross-checking, and smaller proportions editing (5%) or paraphrasing (4.5%). Chi-square analysis showed no significant gender difference in AI knowledge (χ² = 3.17, p = .205), but a significant association between gender and AI usage (χ² = 9.84, p = .007), with male students reporting higher frequency of use. These results highlight AI’s growing role as an educational aid, while underscoring gaps in deep familiarity and gender disparities in usage. Expanding AI literacy programs and promoting inclusive digital engagement are recommended to optimize the benefits of emerging technologies in higher education.
Artificial Intelligence, higher education, Ghana, digital literacy, technology adoption
Comparative Analysis of Agile and Traditional Software Development Methodologies: A Systematic Review of Project Success, Flexibility, and Organizational Performance
Software Development Methodologies have a very important impact on the success of the project, software quality, user satisfaction, and organisation performance. This study conducts a systematic qualitative review of the literature and industry evidence relating to Agile and traditional waterfall software development methodologies from 2010 to 2025, with particular focus on studies published in the last five years (2020–2025).The data were gathered from the databases including Scopus, IEEE Xplore, SpringerLink, ScienceDirect and Google Scholar. Both methodologies are assessed on criteria such as project success rates, adaptability, stakeholder participation, risk management, schedule and cost performance, and outcomes of software quality. The results showed that in a dynamic and a rapidly changing project environment, Agile methodologies are consistently more effective than Waterfall methodologies. Research has demonstrated that the success rate for Agile projects is about 40%, with about 10% failure rate, while Waterfall projects have a success rate of about 15% and failure rate of about 30%. Agile practices show greater adaptability, ongoing engagement of stakeholders, incremental risk management, and responsiveness and flexibility to changing needs. However, structured and predictable processes make Waterfall methodologies effective in projects that have static requirements, limited budgets, strict documentation requirements and regulatory compliance requirements. The review also reveals gaps in current research, such as limited longitudinal studies, lack of evidence to assess the outcomes of defect management and maintenance, publication bias, and self-reported metrics. The research findings suggest that there is no single best practice methodology, rather an appropriate development approach should match project complexity, organizational culture and the need for operations. For enterprise software that is large scale, the combination of Agile flexibility and Waterfall governance has been recommended and is known as hybrid frameworks.
Agile development; Waterfall model; Software development life cycle; Project success; Stakeholder engagement; Hybrid methodologies
Design and Performance Evaluation of Cassava Fibre Removal Machine for Fufu Production
This research work was carried out to design and evaluate the performance of a cassava fibre removal machine capable of efficiently removing insoluble fibres from fermented cassava and retaining the starch slurry used for the production of a fufu or akpu (cassava dough) a common staple food in West and Central Africa. Traditional cassava processing methods is labour-intensive, time-consuming, inefficient and often leave residual fibers in the cassava slurry, reducing product quality and increasing processing time. Processing of cassava is necessary for several reasons: it reduces or removes the potentially dangerous cyanogenic glucosides found in raw cassava and provide more options for consumer’s diets. Materials of right strength and sizes for the machine parts were determined and selected based on the design analysis and calculations carried out. Drum, conical water chamber, frustum screen, gear box, stirrer shaft, frame and discharge chute are the main components of the machine. Several cassava processing machines and method of removing insoluble fibre from cassava have been developed globally; however, some are not cost-effective for use in small-scale operations but rather for large industrial scale; others have negligible efficiency, low throughput capacity, high percentage of loss, and contamination of the food due to mild steel’s propensity to rust over time and subsequently degrade processed product quality. In this research, the developed machine performance evaluation was based on the effect of the insoluble cassava fiber removal in the drum, and retention of the cassava slurry in the conical chamber, encompassing different fermented cassava mash variation (5, 10, 21, 30 and 35kg) and residence time on key performance indicators (proportioned by weight of fermented mash, separation efficiency and throughput capacity). The machine operated at a reduced speed achieved by a 2.6Hp prime mover through a reduction gear mechanism, exhibited commendable performance, resulting in an average fiber separation efficiency of 89.4% and average throughput capacity of 191.4kg/h under the tested operating conditions (5 – 35kg feed loads). The inclusion of a conical water chamber into the design facilitated efficient fractionation and creating of slurry that washes the fine starch granules down through the sieve mesh, ensuring a smooth, lump-free texture for fufu/akpu production, leaving the insoluble fiber, bran and chaff out. The results also unveiled that the developed machine is efficient, economical, user friendly, reduce drudgery and fatigue than the traditional methods commonly used by Nigerian fufu/akpu processors in the removal of insoluble fiber from fermented cassava.
Cassava, Insoluble fibre removal, Fermentation, Machine efficiency, Food processing
Effect of Mentorship Training on Generative AI Adoption among Academic Staff in a Nigerian Health Sciences University: A Quasi-Experimental Study
The rapid emergence of Generative Artificial Intelligence (GenAI) has created a growing awareness-adoption gap in Nigerian higher education, where lecturers are broadly familiar with tools such as ChatGPT and Gemini but lack the structured training needed to integrate them competently and ethically into academic work. This study investigated the effect of a structured six-week mentorship training programme on GenAI competence, confidence, and adoption among academic staff of David Umahi Federal University of Health Sciences (DUFUHS), Uburu, Ebonyi State, Nigeria, and examined whether gender and academic rank moderate training outcomes. A quantitative quasi-experimental pre-test/post-test design was employed. Using multistage sampling, 250 academic staff were recruited from eight faculties, yielding 238 usable responses (response rate: 95.2%). Participants were assigned to an experimental group (n = 125) that received the mentorship intervention and a control group (n = 113) that did not. Data were collected using a validated, researcher-developed instrument, with a Cronbach's alpha reliability coefficient of 0.89. Descriptive statistics, paired-samples t-tests, independent-samples t-tests, and Analysis of Covariance (ANCOVA) were used for analysis. Pre-training, 82.4% of respondents were aware of ChatGPT, yet only 44.9% had actively used any GenAI tool, and a mere 11.8% had applied it for mentoring. Following the intervention, the experimental group recorded a composite mean competence gain of 2.04 points on a five-point scale (pre-test M = 2.11; post-test M = 4.15), with all paired-samples t-test comparisons significant at p < .001 (overall t = 38.47). Post-intervention, active GenAI usage rose from 44.9% to 91.2%. The largest competence gains were in Ethical AI Use (+2.26) and AI-Enabled Mentoring (+2.22). Independent-samples t-tests confirmed significantly higher post-test scores in the experimental group compared to controls across all domains (p < .001). ANCOVA revealed that gender did not significantly moderate outcomes (F = 2.29, p = .133), whereas academic rank was a significant moderator (F = 4.53, p = .005, η² = .10), with professors recording the highest adjusted post-test means. Structured mentorship training significantly and meaningfully enhanced GenAI competence, confidence, and adoption among academic staff in a Nigerian health sciences university, directly bridging the awareness-adoption gap. Training outcomes were equitable across gender but were moderated by academic rank. Universities and regulatory bodies such as the National Universities Commission (NUC) should institutionalize structured GenAI mentorship programmes as a standard component of faculty development and accreditation requirements. Future research should assess long-term retention of training outcomes, scale interventions across multiple Nigerian universities, and develop context-specific AI governance frameworks responsive to the infrastructural and sociocultural realities of Nigerian higher education.
A Holistic Approach to HVAC Design: Balance Load Calculation and Piping for Comfort and Sustainability
This study presents an integrated approach to HVAC (Heating, Ventilation, and Air Conditioning) system design, combining precise load calculation with efficient piping design to achieve thermal comfort, indoor air quality, and energy efficiency. The methodology was executed in two phases. First, detailed load calculations were performed using HEVACOMP software, accounting for building orientation, insulation, occupancy, and internal heat gains. These calculations established heating and cooling requirements, guiding optimal equipment sizing. Second, piping design was developed using AutoCAD to ensure effective distribution of conditioned water and air, minimizing pressure losses and maintaining proper flow. Considerations included pipe sizing, layout optimization, insulation, and compliance with standards. Control strategies, zoning, and air distribution effectiveness were integrated to enhance comfort and operational efficiency. The study identified a peak load of 220 kW and designed a piping system tailored to departmental needs. Results demonstrate that coupling accurate load assessment with meticulous piping design yields reliable, energy-efficient HVAC systems, aligning with sustainability goals and reducing operational costs.
Emerging Deep Learning Architectures for Heart Failure Prediction and Cardiovascular Disease Monitoring
Decentralized Cardiovascular diseases (CVDs) remain the leading cause of global mortality, accounting for nearly 20 million deaths annually, while heart failure (HF) continues to impose substantial clinical and economic burdens across both developed and emerging healthcare systems. Recent advances in artificial intelligence (AI), particularly deep learning (DL), have transformed predictive cardiology through automated feature extraction, multimodal clinical integration, and real-time physiological monitoring. This narrative review critically synthesizes recent evidence published between 2022 and 2026 regarding emerging DL architectures for HF prediction and cardiovascular monitoring. The review evaluates the clinical utility of convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) models, transformers, graph neural networks (GNNs), and multimodal fusion systems in electrocardiography (ECG), imaging diagnostics, remote monitoring, and personalized cardiovascular care. Particular attention is given to explainable AI (XAI), federated learning, wearable technologies, and digital twin systems that increasingly support precision cardiology. The review further identifies persistent challenges involving data heterogeneity, limited external validation, algorithmic bias, interpretability limitations, and computational scalability. Evidence indicates that transformer-based and multimodal DL systems now outperform conventional machine learning approaches in several cardiology applications, especially arrhythmia detection, mortality prediction, and HF readmission forecasting. However, clinical deployment remains constrained by regulatory uncertainty, interoperability limitations, and insufficient prospective validation across diverse populations. The review concludes that future cardiovascular AI systems will increasingly rely on explainable, privacy-preserving, and patient-specific architectures capable of integrating imaging, biosignals, genomics, and longitudinal electronic health records (EHRs). Interdisciplinary collaboration among clinicians, AI scientists, biomedical engineers, and regulatory agencies remains essential for translating DL innovations into safe and scalable cardiovascular healthcare solutions.
Deep learning, heart failure prediction, cardiovascular disease monitoring, ECG analysis, transformer models, graph neural networks, explainable AI, precision cardiology
Rheological Evaluation of Mucuna sloanei (Ukpo) as a Sustainable Bio-Polymer Viscosifier for Water-Based Drilling Fluids in Nigeria
The stability of drilling fluid rheology under varying operational conditions is critical for efficient petroleum drilling. Conventional water-based drilling fluids (WBDFs) often suffer instability under high-temperature and saline environments, necessitating sustainable alternatives. This study investigates Mucuna sloanei (Ukpo), an indigenous Nigerian bio-polymer, as a viscosifier for WBDFs, with Gum Arabic (GA) serving as the control. Laboratory experiments were conducted using API rotary viscometer protocols to evaluate viscosity and gel strength of bentonite-based muds modified with incremental concentrations (1–7 g/L) of Mucuna sloanei and GA. Tests were performed under ambient conditions, elevated temperatures (35–120°C), and saline environments (NaCl contamination). Comparative analyses were carried out to assess rheological performance and operational suitability. At ambient conditions, Mucuna sloanei significantly enhanced viscosity (24 cp to 49 cp at 600 rpm) and gel strength (9 lb/100 ft² to 31 lb/100 ft² at 10 sec), outperforming GA in thickening capacity. However, MS exhibited sharp declines in viscosity and gel strength at elevated temperatures, while GA demonstrated superior thermal stability (23 cp to 20 cp at 600 rpm across 35–120°C). Under saline conditions, MS showed strong viscosity retention (20 cp to 44 cp at 600 rpm) but with instability at higher dosages, whereas GA maintained controlled rheological behavior (23 cp to 35 cp at 600 rpm) with steady gel strength increases.This is the first comparative study of Mucuna sloanei and Gum Arabic under combined concentration, temperature, and salinity stress conditions. Findings reveal that Mucuna sloanei is a potent viscosifier suitable for low-to-moderate temperature drilling environments requiring high gel strength, while GA offers greater resilience in high-temperature and saline operations. The study underscores the potential of indigenous bio-polymers as eco-friendly alternatives to synthetic additives, supporting sustainable drilling fluid development in Nigeria and advancing green petroleum engineering practices.
Mucuna Sloanei, Gum Arabic, drilling fluids, rheology, viscosity, gel strength, bio-polymers, sustainability
Finite Element Analysis of Aluminium Alloy and Carbon-Fibre-Reinforced ABS Conveyor Idler Roller Materials
This study presents a finite element analysis of aluminium alloy and carbon-fibre-reinforced Acrylonitrile Butadiene Styrene (CF-ABS) materials for conveyor idler roller applications. A flat type idler roller was designed in accordance with CEMA standards and modelled using SolidWorks, while structural simulations were conducted in ANSYS Workbench under static loading conditions. The performance of aluminium alloy was compared with CF ABS composites containing varying carbon fibre contents (70%, 80%, and 90%). Key response parameters including total deformation, von Mises stress, strain, and bearing reaction forces were evaluated under trough angles ranging from 0° to 60°. Results showed that aluminium alloy exhibited the lowest deformation (8.40 × 10⁻⁵ m), while CF ABS composites recorded higher deformation values between 2.61 × 10⁻³ m and 3.24 × 10⁻³ m depending on fibre content. However, stress levels in CF ABS increased with fibre proportion, reaching up to 8.43 × 106 Pa at 90% carbon fibre content. Bearing reaction forces remained nearly constant at approximately 2538.7 N across all cases, indicating independence from material variation and trough angle. CF-ABS composites therefore demonstrated a favourable balance between lightweight characteristics and acceptable mechanical performance, suggesting their potential as sustainable alternatives to aluminium alloy in conveyor idler roller design.
Aluminium alloy, ANSYS simulation, Carbon-fibre-reinforced ABS, Composite materials, Conveyor idler roller, Finite element analysis
Cloud Infrastructure for Big Data Processing and Analytics
The rapid expansion of data-driven applications has intensified the need for scalable infrastructures capable of processing large and complex datasets. Cloud computing has emerged as a critical enabler of big data analytics, offering flexible and cost-efficient computational resources. However, existing research remains fragmented across domains and technologies. This study aims to provide a comprehensive synthesis of high-impact literature on the integration of cloud computing and big data analytics, identifying key trends, technological advancements, challenges, and application domains. A systematic review methodology was employed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to ensure transparency and reproducibility. The study adopts a mixed-methods analytical approach based on John W. Creswell’s convergent design, integrating qualitative thematic analysis with quantitative descriptive evaluation. Literature published between 2018 and 2026 was retrieved from major academic databases, including Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar. Following a rigorous screening and eligibility process, 68 peer-reviewed studies were included for analysis. The findings indicate a clear shift from traditional centralized systems to distributed, hybrid, and cloud–edge architectures. Increasing integration of artificial intelligence and Internet of Things (IoT) technologies is evident across sectors such as healthcare, finance, retail, and smart cities. While cloud-based big data analytics enhances scalability, efficiency, and real-time processing, critical challenges persist, including data security, privacy, latency, and system interoperability. This study provides a holistic, evidence-based synthesis of cloud-enabled big data analytics without relying on primary data or experimental methods. It advances theoretical understanding, highlights research gaps, and offers strategic insights for future research and practical implementation in data-driven environments.
Cloud computing; Big data analytics; Systematic review; PRISMA; Scalability; Data security; Cloud-edge computing; Artificial intelligence; IoT
Design and Implementation of an Artificial Intelligence-based Mobile Application for Diabetes Risk Prediction
The rising global prevalence of diabetes, coupled with its severe complications and the burden it places on healthcare systems, has created an urgent need for innovative solutions that can provide early risk detection and preventive intervention. Traditional diagnostic methods are often reactive, relying on clinical tests conducted after symptoms appear, which limits their effectiveness in minimizing disease progression. This paper presents an artificial Intelligence-based mobile application for health risk prediction, specifically focusing on diabetes mellitus. The mobile application is implemented to enable individuals and healthcare providers to access accurate and proactive health risk assessment. The mobile application incorporates relevant individual health information and lifestyle data, such as medical records, demographic information, and behavioral factors, into the predictive model, and evaluates the system using a probability risk score. The system integrates a module that enables users to register and authenticate their details, input their personal and behavioral information required to develop the artificial intelligence model, predict diabetes risk score, and recommend appropriate lifestyle changes to minimize risk factors. The system adopted Object-Oriented Software Development Methodology and data modelling approach encompassing data preprocessing, feature selection, and learning analytics, and a mobile application was developed in Python and Next.js. The Behavioral Risk Factor Surveillance System (BRFSS) dataset was used for model training using the Light Gradient Boosting model (Light GBM. Results showed that Light GBM achieved an optimal performance, producing a precise risk score. The findings confirmed that AI-based systems can effectively support preventive medical intervention, offering a scalable and practical tool for modern healthcare.
Design and Implementation of a Smart Solar-powered Microgrid Monitoring and Fault Detection System for Sustainable Energy Management
Decentralized solar microgrids are key to sustainable electrification, but they require robust monitoring and rapid fault detection to ensure reliability and protect equipment. This paper describes the design and implementation of a smart, low-cost microgrid monitoring and fault detection system using an ESP32 microcontroller, INA219 current/voltage sensors, voltage dividers for higher voltages, data acquisition modules, and edge/cloud analytics architecture. The system implements real-time monitoring, local rule-based fault detection covering overcurrent, islanding, and battery undervoltage conditions, and machine-learning-ready feature extraction for fault classification. A prototype was developed and tested on a small solar microgrid comprising a photovoltaic (PV) array, battery bank, inverter, and connected loads. Results demonstrate effective early detection of common faults and clear visualization via a web and Android dashboard. The system provides a replicable framework for rural and campus microgrids, enabling efficient monitoring and maintenance that enhances reliability and reduces downtime in similar energy systems.
Design and Implementation of a Smart Multi Tank Water Filling and Monitoring System with Android APP Integration for Sustainable Water Resource Management
Water scarcity and inefficient domestic/institutional water management remain major problems in many urban and peri-urban communities. This paper presents the design, implementation, and evaluation of a low-cost smart multi-tank water filling and monitoring system using an ESP32 microcontroller, ultrasonic and float sensors, flow measurement, relayed pump control, and an Android mobile application. The system supports prioritized tank filling, automatic pump control with anti-dry-run protection, remote monitoring via Wi-Fi/GSM, and historical consumption logs on a cloud backend. A prototype was built and tested in a campus environment. Results show accurate tank level detection (±2 cm), reliable automatic switching between tanks, reduced overflow incidents, and pump run-time through optimized control logic. The system contributes to sustainable water-resource management by reducing wastage and enabling data-driven conservation measures. Key contributions are: (1) a modular hardware and software architecture for multi-tank control; (2) practical algorithms for prioritized and demand-driven filling; and (3) an Android UI for real-time control and historical analytics. Relevant sensor choices, energy budgeting, and cybersecurity considerations are discussed.
Smart Water System, Multi-tank Control, Electronic Valves, ESP32, Internet of things, Android App and Sustainable Water Management
Artificial Intelligence and Managerial Decision-Making in Manufacturing Firms in Delta State
This study examined the impact of artificial intelligence (AI) on managerial decision-making in manufacturing firms in Delta State, Nigeria, focusing on machine learning and robotic process automation (RPA). A quantitative cross-sectional survey design was adopted, and data were collected from 234 managers and supervisors using structured questionnaires. The data were analyzed using descriptive statistics, Pearson correlation, and multiple regression analysis. Findings revealed that machine learning significantly enhances managerial decision-making by improving data analysis, forecasting accuracy, and decision quality. Similarly, RPA positively influences decision-making by automating routine tasks, ensuring data accuracy, and providing real-time operational insights. The regression results indicated that both variables significantly predict managerial decision-making effectiveness. The study concludes that AI technologies are critical for improving decision speed, accuracy, and strategic responsiveness in manufacturing firms. It recommends that firms invest in AI infrastructure and integrate AI tools into managerial processes to enhance performance and competitiveness. This study contributes to knowledge by providing empirical evidence on AI-driven decision-making in the Nigerian manufacturing sector.
Design and Development of an AI-Driven Mobile Application for Smart Menstrual Cycle Tracking Using Agile Methodology
In developing countries such as Nigeria, the unavailability of secure and user-friendly menstrual tracking tool presents an all-pervasive problem for young women. Stable menstrual health condition significantly contributes to maintaining public composure and coordinated mental interactions among the women folk. Mobile app-based smart menstrual tracker represents a cutting-edge innovation for enhancing menstrual health. This study developed a model of mobile app that leverages user-friendly interfaces, advanced artificial intelligence algorithms to monitor, analyze and predict menstrual cycles. The app offers personalized insights into cycle patterns, ovulation and fertility windows, as well as track symptoms such as cramps, mood changes and energy level. The study adopted agile methodology software development process to facilitate integration of predictive algorithm for identifying cycle patterns and forecasting likely future occurrence using previous data. This approach harnessed critical hardware and software tools such as visual studio code editor, React.js with Vite, Node.js with Express and MongoDB to develop and implement both the frontend and backend functionalities of the app. It explored AI models to accurately identify menstrual cycle phases using last short-time memory (LSTM) algorithm to advance privacy-preserving reproductive health monitoring. The app was tested using locally accessed real user data assigned pseudo-names, summary results were presented as test-case I, test-case II and test-case III. The results showed optimal performance where the date-based computation model provided accurate outputs that matched the expected results. This study contributes to women’s health by enhancing flexible user-centric and personalized menstrual health information management system for improved, secure and precision oriented cycle tracking.
Design, Fabrication, and Experimental Validation of a Low-cost Neuromuscular Electrical Stimulator for the Prevention of Skeletal Muscle Atrophy
Individuals with limited mobility are at a significantly higher risk of muscle atrophy due to prolonged sedentary states. In rural Nigeria, the inconsistent electrical supply often renders contemporary electrical muscle stimulators (EMS) redundant. To address this, the current study aims to design and fabricate and experimentally validate a low cost, battery-powered muscle stimulator that ensures readily available therapy in resource-constrained environments. A user-centered design approach was adopted, involving the construction of a portable power management unit and a muscle stimulation circuit. The system’s output parameters were verified using Proteus 8 simulation software to ensure voltage and current ranges aligned with established clinical standards. The hardware features an LCD interface that displays real-time frequency and stimulation duration. To evaluate clinical efficacy, handgrip strength gains were measured using a dynamometer and compared against a standard commercial EMS unit. The test was approved by the ethics committee of the Federal University of Science and Technology, Owerri. Nigeria. A paired-sample t-test conducted on 30 young adults between ages 18 – 30 revealed that the mean strength gain for the fabricated system (3.995 ± 0.483) did not differ significantly from the standardized EMS (3.947 ± 0.782), with t (29) = 0.516 and p = 0.610 (two-tailed). Furthermore, a strong positive correlation (r = 0.7899) indicated highly similar response patterns between the two modalities. These results demonstrate that the low-cost, battery-operated stimulator is a viable, high-performance alternative for muscle rehabilitation in areas lacking stable power infrastructure.
Muscle atrophy, muscle stimulator, battery-powered
Modeling and Dynamic Analysis of a 9 MW DFIG-Based Grid-Connected Wind Energy Conversion System
The global shift toward low-carbon energy sources has elevated the role of wind power in sustainable electricity generation. This study presents the design and MATLAB/Simulink-based simulation of a 9 MW grid-connected doubly fed induction generator (DFIG) wind energy conversion system. The system comprises a horizontal-axis wind turbine, back-to-back converters, and a DC-link, with decoupled vector control used to regulate active and reactive power. The wind farm is connected to a 120 kV point of common coupling and interfaced with a 575 kV transmission network through step-up transformer stages. System performance is evaluated under normal operating conditions and symmetrical grid faults, with emphasis on transient and steady-state response as well as fault ride-through capability. Sensitivity and correlation analyses identify grid frequency and converter switching frequency as the most influential parameters affecting system performance. The results demonstrate stable operation, effective power quality control, and satisfactory dynamic response, confirming the suitability of DFIG-based wind energy systems for large-scale grid integration. The findings provide practical guidance for optimizing wind power systems and support the deployment of reliable and resilient renewable energy infrastructure in developing regions.
An Empirical Assessment of Cloud Computing Adoption in University Libraries in Niger State, Nigeria
Cloud computing has emerged as a transformative force in libraries, offering scalable, on-demand access to digital resources and enabling operational efficiency. The study examined the adoption of cloud computing for service delivery in academic libraries in Niger State. Three objectives were formulated to guide the study. The study adopted descriptive survey design and structured questionnaire was used to collect data for the study. Data collected was analyzed and presented in tables, frequencies, percentage and mean. The results of the findings revealed high level of agreement on cloud computing improves access to library materials from anywhere (4.39), allows remote and convenient access to services (4.36) and enhances speed and efficiency of service delivery (4.34). However, the finding also identified challenges affecting adoption of cloud computing which included poor internet connectivity, financial constraints and erratic power supply. The study concluded that cloud computing offers substantial benefits in enhancing library services, however, its success in adoption depends on addressing internet connectivity, financial, erratic power supply and other challenges identified.
Academic Libraries, Cloud Computing, Cloud Adoption, Niger State, University Libraries, Nigeria
Improved Human-Centric Cyber security Framework for Protecting Home users from Social Engineering Attacks
Social engineering attacks remain a major cybersecurity threat, especially among home users who do not have the well-developed security policies typically found in organizations. Despite the fact that current countermeasures are primarily focused on technical defences, there is increasing evidence that the strategies are no longer adequate in the light of attacks that rely on human cognition, emotion, and behaviour. The problem addressed in this study is the inefficiency of existing cybersecurity measures in preventing social engineering attacks against home users, necessitating the development of an improved cybersecurity approach to address gaps identified in the literature. The qualitative research design was chosen and semi-structured interviews with cybersecurity professionals were carried out using the purposive sampling method. Thematic analysis was used to identify existing threats, anticipated future risks, and mitigation strategies. Results indicate that phishing, vishing, and smishing are the most prevalent attack vectors affecting home users, and their sophistication has been increasing due to personalisation, artificial intelligence, and impersonation techniques. Experts argued that technical solutions alone are insufficient; continuous user awareness, behavioural adaptation, and contextual understanding are therefore imperative to improve resilience. The research develops a more human-centred cybersecurity framework that integrates cognitive, behavioural, psychological, and contextual variables with applicable technological controls. The framework does this by positioning home users as stakeholders in the fight against cybersecurity offences and by establishing a culture of adaptive cybersecurity, which provides a less inherently unsustainable means of addressing evolving social engineering challenges. Although the framework remains conceptual and has not yet been empirically validated in a home setting, it provides a systematic basis for further research and for the practice of home-user cybersecurity. It is suggested that future research be conducted to empirically validate the framework in large populations and in new threat situations.
Social engineering; Human-centric Cybersecurity; Home Users; Phishing; Vishing; Smishing; User awareness; Cybersecurity framework; Behavioral Security
Design and Implementation of an Internet of Things (IoT) Based Automatic Fire Fighting Sprinkler System
The frequent incidence of fire accidents in homes, work places, industrial buildings and the limitations of conventional fire suppressor system demand the development of intelligent and responsive fire-fighting solutions. This research work, design and Implementation of an IoT based automatic fire fighting sprinkler system is aimed at improving response time, reliability, and remote monitoring compared to conventional fire protection systems. The system employs a combination of temperature, smoke and flame sensor interfaced with a microcontroller (ESP8266) to continuously monitor environmental conditions. When fire indicators exceed preset safety thresholds of above 600C, the controller automatically triggers a sprinkler mechanism through a relay- driven solenoid valve to suppress the fire. In addition, the system transmits real-time alerts and sensor data to a cloud platform (Bylink) through a wireless communication module, allowing users to monitor conditions and receive notifications from remote locations. The prototype was developed using avoidable and energy efficient components and evaluated under controlled fire simulation. The result shows that the system responds promptly to fire events which activates the sprinkler effectively, and provides reliable real- time monitoring. The constructed IoT based fire- fighting sprinkler system demonstrates improved efficiency, scalability, and suitability for home and industrial fire safety applications.
Fire detection, Internet of things (IoT), Automatic fire sprinkler system, embedded system, GSM alert system
Multi doped Hydroxyapatite-Urea Nano fertilizer: Synthesis and Controlled Micronutrient Release
Hydroxyapatite HAp-urea composite based nano fertilizer constitutes a novel approach of incorporating variety of ionic species into HAp hexagonal lattice structure with control release capabilities. Evidently, it is a veritable and sustainable alternative to commercial fertilizer in enhancing plant yield in nutrient deficient soil. In this research, HAp doped with multi ionic species of Mg2+, Si4+ and Cu2+ (Ca10-w/2-x-ySiw+MgxCuy(PO4)6-w(OH)2-w) with nutrient and subsequently form a HAp-urea nano fertilizer composite via chemical precipitation route. The following HAp to nano-urea matrix-filler ratio 100 mL: 0.15 g was adopted. The crystallinity, chemical constituent and nanoparticle sizes was investigated using X-ray diffractometry (XRD), X-ray fluorescence (XRF), Transmission Electron Microscope (TEM), Brunauer–Emmett–Teller (BET) and Scanning Electron Microscopy (SEM). Micronutrient ion release of Ca2+, P5+, Mg2+ and Cu2+ was examined using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) in water sample and XRF in soil sample. Monitored plant foliage activities was analyzed by applying 10g of the produced HAp-urea nanofertilizer composite in comparison to 10g of commercial fertilizer on selected plants. Ultimately, the multidoped HAp-urea composite based nanofertilizer showed superior plant growth height (PGH) of HU: 54, HUMS: 58 and HUMSC: 56 cm as compared to commercial fertilizer with PGH of 36 cm.
Hydroxyapatite, urea, nanofertilizer, controlled release, nanocomposite, sustainable agriculture, ICP-OES
Multi-Objective Generation Expansion Planning with Renewable Integration and Uncertainty Modelling
The global energy landscape confronts unprecedented challenges in generation expansion planning, characterized by increasing renewable energy integration, complex technological uncertainties, and the critical need to simultaneously optimize economic, environmental, and reliability objectives. Traditional deterministic planning approaches have proven inadequate for capturing the dynamic and stochastic nature of modern power systems. This research aims to develop a comprehensive multi-objective generation expansion planning framework capable of effectively modelling renewable energy integration, quantifying deep uncertainties, and providing robust decision support mechanisms for strategic infrastructure planning. The study employs a hybrid stochastic-robust optimization approach, integrating advanced techniques such as Monte Carlo scenario generation, econometric load forecasting models, spatial-temporal correlation modelling, multi-criteria decision analysis, and sophisticated mathematical optimization algorithms. The proposed framework generated 147 non-dominated Pareto solutions, validated through extensive simulation and benchmarking on the IEEE 118-bus test system. Key achievements include revealing non-linear cost-emission trade-offs and identified critical renewable integration thresholds. Performance was measured using the hyper volume indicator (a metric that quantifies the extent of the objective space covered by Pareto-optimal solutions), achieving a value of 0.847, which demonstrates superior coverage compared to traditional models. Ultimately, the research presents a transformative approach to generation expansion planning, offering a sophisticated methodology that bridges technological complexity, uncertainty management, and strategic decision-making in the evolving global energy landscape.
Copyright (c) 2025 Direct Research Journal of Engineering and Information Technology
Multi-Criteria Evaluation Framework for Deep Learning Architectures in Medical Image Segmentation
The rapid expansion of deep learning architectures for medical image segmentation presents a major challenge to selecting optimal model to implement in practice. While several traditional evaluation methods have been proposed to solving this selection problem, they cannot use criteria interdependencies and expert judgment to select deep learning architectures for medical image segmentation. This study, therefore, presents a multi-stage multi-criteria decision-making framework to address this knowledge gap. The framework contains an improved DEMATEL (Decision Making Trial and Evaluation Laboratory), intuitionistic fuzzy AHP (Analytic Hierarchy Process), and adaptive VIKOR (VIekriterijumsko Optimizacija Kompromisno Resenje). This study evaluated framework performance using six deep learning architectures and thirteen criteria. The DEMATEL-fuzzy AHP results showed sensitivity, specificity, and accuracy the most important criteria for evaluate the deep learning architectures for medical image segmentation. The enhanced VIKOR model identified Swin-Unet and nnU-Net as the optimal compromise and best solution, respectively, for the optimum compromise. Based on sensitivity analysis that was conducted, the VIKOR ranked architecture as UNETR >> nnU-Net >> Attention U-Net >> TransUNet >> Swin-Unet >> classic U-Net. This study’s findings have showed that the proposed framework can be used to support deep learning architectures for medical image segmentation decisions.
Deep learning architecture selection, medical image segmentation, multi-criteria decision-making, clinical decision support
Decentralization of Electronic Record Systems for Improved School Record Management in Nigeria
In Nigeria, particularly in Katsina State, implementing electronic record-keeping systems (ERKS) has been slow and uneven. While these systems offer benefits for information management and data retrieval, public schools face challenges including limited technology, inadequate training, and resistance to change. This study employed a mixed-methods approach combining qualitative interviews and quantitative surveys. The evaluation assessed ERKS decentralization's impact on record-keeping, while the survey collected data from 364 participants using stratified random sampling across 34 Local Government Areas. Questionnaires were administered online as well the interviews were conducted online to maximize response rates and data quality. Quantitative data were analyzed using descriptive statistics and inferential tests (Chi-square and log-likelihood ratio). Both tests showed statistical significance (p < 0.001), confirming that decentralized e-record management and improved information sharing significantly enhance record management efficiency. The Likert scale ranged from 1 (Strongly Disagree) to 5 (Strongly Agree). Findings revealed respondents occasionally use computers for work (mean = 2.77), but rarely use smartphones (2.07) or tablets (2.38), primarily due to limited device availability and infrastructure constraints. Despite these limitations, most respondents who had exposure to or training in electronic systems strongly agreed ERKS would improve work efficiency (4.35), accuracy (4.39), and time savings (4.45). Qualitative interviews with 19 stakeholders confirmed electronic records improve efficiency, accuracy, and productivity. The study concludes that ERKS decentralization and enhanced information sharing significantly improve school record management, enabling faster data access, better decision-making, and improved administrative outcomes. However, successful implementation requires substantial investment in infrastructure (electricity, internet connectivity) and capacity building. It is recommended that the Ministry of Education and State Universal Basic Education Board encourage phased ERKS adoption in all schools, prioritizing infrastructure development, and develop clear policies on data privacy, security, and compliance.
Electronic record-keeping systems, Decentralization, School record management, Digital transformation, Katsina State, Public Schools, Nigeria, Educational technology
Comparative Analysis of PCA-Transformed Soil Data and ML Models for Maize Yield Prediction in Nigeria
This study predicts maize yield using soil data from long-term trials by the International Institute of Tropical Agriculture (IITA) in Ibadan, Nigeria. Multi-year measurements from experimental and farmer-managed fields covered pH, organic matter, nitrogen, phosphorus, exchangeable cations, texture, and micronutrients. To manage multi-collinearity, variables were standardized and analysed using principal component analysis (PCA). Six principal components (PCs) explained over 80% of variance, capturing fertility and texture gradients. These PCs was used as predictors in three machine-learning models: Random Forest (RF), Gradient Boosting Machine (GBM), and Artificial Neural Network (ANN). RF achieved the highest accuracy (R² ≈ 0.89; RMSE ≈ 0.59), outperforming GBM (R² ≈ 0.50) and ANN (R² ≈ 0.36). PCA loadings and RF feature importance identified soil organic matter, nitrogen, cation exchange capacity, and texture as major yield drivers. Results confirm PCA improves data efficiency and interpretability, while RF provides robust, reliable predictions for maize yield, supporting precision agriculture in tropical systems.
Soil Fertility, Agronomic Modeling, Principal Component Analysis (PCA), Random Forest (RF, Artificial Neural Network (ANN), Precision Agriculture, Gradient Boosting Machine (GBM)
Awareness and Practices on Smartphone Battery Drainage: A Cross-Sectional Study in Dodoma City Higher Education Institutions, Tanzania
Smartphone battery drainage remains a remarkable challenge that negatively affects device performance and users’ daily activities. The challenge is more severe mainly to students who rely heavily on mobile devices for academic and social purposes. This study examined the level of awareness and practices related to smartphone battery drainage among students in higher education institutions (HEIs) in Dodoma City, Tanzania. A cross-sectional study design was employed, using a structured self-administered questionnaire distributed to 384 students selected through systematic sampling. Of these, 381 completed questionnaires were valid and included in the analysis. Data were analyzed using descriptive statistics and multiple linear regression analysis to determine the relationship between students’ awareness, charging behaviors, usage patterns, and experiences of smartphone battery drainage. The findings revealed a moderate level of awareness regarding the causes of battery drainage and battery-saving practices among students. However, inappropriate behaviors such as prolonged use of background applications, excessive screen brightness, and irregular charging patterns were prevalent and significantly associated with increased battery drainage. The multiple regression results further indicated that awareness level was a statistically significant predictor of effective battery drainage management practices (p < 0.05). These findings underscore the need for targeted awareness and educational programs within HEIs to promote optimal smartphone usage and charging practices, thereby enhancing battery longevity and improving overall device efficiency among students.
Smartphone battery drainage, user awareness, charging practices, higher education institutions, Dodoma city, Tanzania