The emergence of Internet of Things (IoT) has undoubtedly transformed safety, automation, and monitoring systems across various sectors, particularly in smart home environments. This study presents the design and implementation of an Internet of Things (IoT)-based real-time kitchen monitoring and automation system aimed at enhancing safety, efficiency, and intelligent control within kitchen environments. The system was developed to address the limitations of manual monitoring processes, which are often labor-intensive, error-prone, and unreliable in emergency situations. The system integrates multiple sensors to enable real-time alerts and automated responses. Wireless communication is achieved through a Wi-Fi network connected to the Blynk IoT platform, allowing remote monitoring via smartphones and laptops. Upon detection of hazardous conditions such as gas leakage, fire outbreak, abnormal temperature levels, motion intrusion, or low water levels, the system triggers local alarms and transmits notifications to users through the IoT dashboard. Sensor fusion techniques were applied to enhance data accuracy and reduce false alarms. Experimental evaluation was conducted under simulated real-world kitchen scenarios to assess responsiveness, accuracy, and reliability. The results demonstrated an overall system accuracy of 91.8%, with high detection reliability across all integrated sensors and minimal false positives.

]]> Internet of Things (IoT), Real-Time Monitoring, Kitchen Automation, Smart Kitchen Research Article Mon, 24 Aug 2026 00:00:00 IST Marine petroleum spills caused serious ecological degradation. It is urgent for automation early oil spill detection and identification. Although SAR is an active microwave sensor providing continuous day-and-night operation in adverse conditions such as cloudy days or night, the difficulty for differentiating between oil slicks and natural ocean look-alike has increased since their SAR backscatter properties are quite similar. This paper presents an interpretable deep neural network model for pixel-level oil slick segmentation that leverages a U-Net with a ResNet50 (pre-trained) feature extractor and Spatial/Channel Squeeze-and-Excitation (SCSE) attention modules. Gradient-weighted Class Activation Mapping (Grad-CAM) was used in the meantime to improve the visual transparency of automatic decisions to find important input regions by showing where and why the decision was made (for example, a pixel-level region of an oil spill region was found because its internal convolutional layer recognized a particular pattern). Through utilizing an official Sentinel-1 SAR dataset for testing and validation, the model reached an overall accuracy of 98.65% and mean Intersection over Union (mIoU) of 0.894, showing that accurate and reliable remote sensing-based oil spill monitoring is improved by the combination of attention processes and transfer learning.

]]> ResNet50, Explainable Artificial Intelligence, Semantic Segmentation, Transfer Learning, Oil Spill Detection, Synthetic Aperture Radar (SAR) Research Article Thu, 20 Aug 2026 00:00:00 IST Traditional multimedia projectors in Nigerian classrooms are frequently affected by technical failures, poor maintenance, and outright unavailability, disrupting lessons and reducing student engagement. This study developed an offline classroom presentation system that uses WebRTC technology over a local wireless network as a practical alternative to traditional projectors. The system comprises a presenter device, a Node.js signalling server built with Express.js and Socket.io, a Mediasoup Selective Forwarding Unit (SFU), and multiple viewer devices, all connected over a classroom local area network (LAN) without internet access. The presenter uploads a single media stream to the SFU, which forwards it independently to each connected viewer, eliminating the bandwidth and CPU scaling constraints associated with a peer-to-peer topology. The frontend was built with HTML, CSS, and vanilla JavaScript, requiring only a modern browser for access, while the server enforced HTTPS using pre-generated self-signed certificates to satisfy the browser security requirements of the screen-capture API. System performance was evaluated using latency, CPU and RAM usage, scalability, and cross-browser compatibility metrics. Results showed stable, low-latency screen and audio sharing to up to twenty simultaneous viewers, with average latency remaining below 100 ms and consistent performance across Chrome, Edge, and Firefox. The findings confirm the technical feasibility of the system and its ability to replicate the core functionality of a projector without dependence on internet connectivity or costly hardware. The study concludes that WebRTC-based LAN systems represent a viable, scalable, and cost-effective alternative to traditional classroom projectors, with meaningful implications for educational technology deployment in resource-constrained environments.

]]> Offline Classroom, Presentation System, Local Area, Networks, Real-Time Screen Sharing Research Article Tue, 30 Jun 2026 00:00:00 IST Digital technology adoption in public sector organizations has become a strategic imperative for improving service delivery quality, administrative efficiency, and citizen satisfaction in developing economies. Despite Nigeria's National Digital Economy Policy 2022–2025 mandating digital transformation across all government ministries, departments, and agencies (MDAs), empirical evidence on the specific mechanisms through which digital technology dimensions translate into service delivery quality improvements particularly in North-Eastern state-level ministries remains limited. This study examined the adoption of digital technologies and their influence on service delivery quality at the Bauchi State Ministry of Science, Technology and Innovation (BMSTI), with digital adoption as a mediating variable. Grounded in the Technology-Organization-Environment (TOE) Framework and the New Public Management (NPM) theory, a quantitative cross-sectional survey design was adopted. A structured questionnaire was administered to 260 staff using stratified random sampling. IBM SPSS Statistics Version 29 was used for descriptive and reliability analysis. SmartPLS Version 4 was employed for PLS-SEM and mediation analysis. Python 3.11 (scikit-learn, matplotlib) was used for Decision Tree feature importance analysis and service delivery KPI visualization. Results showed that digital infrastructure (β = 0.334), e-service deployment (β = 0.278), staff digital capacity (β = 0.247), and leadership support (β = 0.221) each significantly influenced service delivery quality (all p ≤ 0.001), collectively explaining 63.8% of variance (R² = 0.638). Digital adoption mediated all four paths (VAF: 39.7%–42.6%). Python Decision Tree analysis identified digital infrastructure as the most important predictor (Gini importance = 0.312, Rank 1). Service delivery KPIs showed document retrieval time improved by 78.2% and digital service use rate grew by 265.8% between 2019 and 2023. All constructs demonstrated strong reliability (α > 0.82) and validity (AVE > 0.56).

]]> Digital Technologies, Service Delivery, Public Sector, PLS-SEM, TOE Framework, New Public Management, Bauchi State Research Article Tue, 30 Jun 2026 00:00:00 IST Nigeria's oil and gas sector, the bedrock of the national economy accounting for over 87% of foreign exchange earnings, suffers chronic infrastructure deterioration resulting in annual production losses estimated at $3–5 billion. Conventional preventive and reactive maintenance paradigms have proven inadequate for the scale and operational hazards of Nigerian petroleum infrastructure. This study develops, validates, and compares five AI-based predictive maintenance (PdM) models – Long Short-Term Memory (LSTM) networks, Random Forest, Support Vector Machine (SVM), XGBoost, and Temporal Fusion Transformer (TFT) – for fault detection and Remaining Useful Life (RUL) prediction, using a dataset of 52,840 multi-sensor time-series observations collected from operational Nigerian oil and gas installations over 2019–2024. The stacked ensemble model achieved the highest classification accuracy of 98.1% (F1-Score: 0.977; AUC-ROC: 0.994), outperforming the TFT (97.3%), LSTM (96.7%), and XGBoost (95.1%) individually, and substantially exceeding the conventional preventive maintenance baseline (71.4%). Economic impact analysis reveals that AI PdM deployment reduced annual unplanned downtime by 68.2% (from 4,218 to 1,342 hours), decreased maintenance expenditure by 47.3% (from $521.4M to $274.8M), and recovered an estimated $5.64 billion in previously lost production revenue annually. Macroeconomic modelling projects a 1.5 percentage point uplift in the oil sector's contribution to Nigeria's GDP. These findings establish a compelling evidence base for the systematic adoption of AI predictive maintenance across Nigeria's petroleum infrastructure and provide actionable policy recommendations for NUPRC, NNPC Limited, and international oil company (IOC) operators.

]]> Predictive Maintenance; Artificial Intelligence; LSTM; XGBoost; Transformer; Oil and Gas Infrastructure; Nigeria; Operational Efficiency; Economic Output; Industry 4.0 Research Article Tue, 30 Jun 2026 00:00:00 IST This paper presents a comparative analysis of five advanced Natural Language Processing (NLP) models: GPT-4, GPT-3.5, BERT, RoBERTa, and DistilBERT, specifically trained and evaluated for sentiment classification on Twitter data. The study emphasizes the development of these models and assesses their performance using standard metrics, including accuracy, precision, recall, and F1-score. The results indicate that BERT achieved the highest F1-score of 0.8% with a balanced focus on accuracy and efficiency, while DistilBERT delivered a competitive accuracy of 0.86% with significantly reduced inference times. Although GPT-based models excelled in contextual understanding, they exhibited higher latency. These findings highlight the trade-off between predictive accuracy and computational efficiency when deploying AI models for real-time sentiment analysis applications.

]]> Sentiment Analysis, Natural Language Processing, Transformer Models, OpenAI, GPT, Twitter Research Article Tue, 30 Jun 2026 00:00:00 IST The rapid expansion of modern digital environments and cyber-physical systems has significantly increased organizational exposure to insider threats. Unlike external actors, malicious insiders operate with legitimate credentials and system access, enabling them to easily bypass static perimeter controls and traditional intrusion detection systems. To address this challenge, this paper introduces a multi-tiered Behavioral Analytics Framework for Machine-Learned Insider Threat Detection. The proposed framework continuously ingests multi-modal telemetry spanning computer-mediated linguistic communications, system access logs, and physical badge records to construct dynamic user profiles and isolate subtle behavioral anomalies. While traditional linear classifiers struggle in this domain due to high false-positive rates, tree-based gradient boosting models provide exceptional discriminative capability. In particular, XGBoost achieves superior threat sensitivity with an F_1-score of 75.5% and an AUC of 98.5%, recording the fewest false negatives overall. By integrating these gradient boosting dynamics into an optimized voting ensemble core, the proposed framework achieves a peak classification accuracy of 98.3%, an AUC of 98.5%, and an F_1-score of 98.2%, while constraining False Acceptance (FAR) and False Rejection (FRR) rates to 3.1% and 2.8%, respectively. Operating with an average inference latency of 185 ms, predictive risk scores feed directly into an automated Zero-Trust enforcement engine. Augmented by Explainable AI (XAI) feature attribution modules and adversarial input defenses, the framework offers high operational transparency for analysts while maintaining resilience against insider manipulation and evasion.

]]> Insider Threats, Cyber security, Behavioural Analysis, Machine Learning, Anomaly Detection, Data Security Research Article Tue, 30 Jun 2026 00:00:00 IST The rapid growth of the Internet of Things (IoT) has significantly advanced its application across various sectors, particularly in safety and security systems. One of the critical applications of IoT-based solutions is in the development of an IoT-based intruder detection and monitoring system for transmission towers. This study aims to design and implement such a system to enhance security measures, enable real-time monitoring, and reduce the risk of vandalism, unauthorized access, and damage to transmission tower infrastructure. This study developed an IoT-based real-time intrusion detection system for transmission tower security, integrating PIR sensors, a microcontroller, wireless communication, and solar power for autonomous operation in remote areas. The system enables real-time monitoring and alerting, with performance evaluated using a confusion matrix across multiple test scenarios. Results show an accuracy of approximately 96% with fast response times (1.5–1.9 seconds) and minimal false alarms, though performance is occasionally affected by environmental and network factors. Overall, the system provides a reliable and sustainable solution for enhancing infrastructure security in off-grid environments with 96%.

]]> IoT, Monitoring System, Transmission Tower, Integrated Sensors, Microcontroller, Wireless Communication Research Article Tue, 30 Jun 2026 00:00:00 IST Rapid advancement happening information and communication technology, it has transformed the academic, economic, and social experiences of students globally. This research work investigates the influence of Internet on students at Ladoke Akintola University of Technology (LAUTECH), focusing on its role in academic performance, income generation, and social interactions. While the Internet provides numerous opportunities for research, online learning, and communication, challenges such as misinformation, unstable electricity, and high costs of access continue to hinder its effective utilization. Descriptive survey was employed for data collection among 71 undergraduate students across different levels at LAUTECH through a structured questionnaire. The results revealed that 90.1% of the respondents use the Internet daily, with smartphones being the most common access device (45.1%). Academically, 92.3% of students used the Internet for research, and 67.7% reported great improvement in performance due to online resources. Economically, 91.7% of students engaged in income-generating activities online, with 51.5% earning between ₦10,000 – ₦50,000 monthly, and 27.3% earning above ₦50,000. Socially, 81.4% agreed that the Internet enhanced their interactions, with WhatsApp being the most widely used platform (84.3%). However, barriers such as slow Internet speed (73.2%) and power failures (47.9%) were identified as major obstacles. The study concludes that the Internet serves as a multifaceted tool for LAUTECH students that will help them positively academically, socially and economic development.

]]> impact, internet, student, transformation, Nigeria Research Article Fri, 12 Jun 2026 00:00:00 IST Blockchain and Artificial Intelligence (AI) are two transformative technologies that are reshaping modern digital systems. While AI enables intelligent decision-making through data-driven models, blockchain provides decentralized, transparent, and tamper-resistant data management. Their integration has gained increasing attention as a means to address limitations inherent in each technology when deployed independently. This review paper presents a comprehensive survey of blockchain–AI integration, focusing on architectural models, enabling techniques, application domains, and current challenges. The study examines how blockchain enhances trust, data integrity, and accountability in AI systems, while AI improves scalability, efficiency, and automation within blockchain networks. Key application areas including healthcare, finance, Internet of Things, supply chain management, and smart cities are discussed. The paper further identifies open research challenges and future directions necessary to realize secure, scalable, and intelligent decentralized systems.

]]> Blockchain, Artificial Intelligence, Distributed Ledger, Machine Learning, Smart Contracts, Decentralized Systems, Trustworthy AI Review Article Wed, 31 Dec 2025 00:00:00 IST Decentralized Finance (DeFi) represents a paradigm shift in financial services by leveraging blockchain technology and smart contracts to provide open, permissionless, and trust-minimized alternatives to traditional financial systems. Over the past few years, DeFi has experienced rapid growth, enabling decentralized exchanges, lending platforms, stablecoins, and synthetic assets without centralized intermediaries. Despite its transformative potential, DeFi faces significant challenges related to security vulnerabilities, systemic risks, governance limitations, and regulatory uncertainty. This review paper provides a comprehensive analysis of DeFi protocols, examines key technical and economic risks, and critically evaluates emerging regulatory responses across jurisdictions. The paper further identifies open research gaps and future directions necessary for the sustainable development of decentralized financial ecosystems.

]]> Decentralized Finance, Blockchain, Smart Contracts, DeFi Risks, Financial Regulation, Cryptocurrencies Review Article Wed, 31 Dec 2025 00:00:00 IST The current stage of quantum computing development is defined by Noisy Intermediate-Scale Quantum (NISQ) devices, which operate with a moderate number of qubits that are inherently susceptible to noise and decoherence. Despite the absence of full error correction, NISQ systems have demonstrated the ability to perform computational tasks that challenge classical simulation under certain conditions. This paper explores the concept of quantum advantage in the NISQ era by analysing prominent algorithmic approaches, benchmarking methodologies, and the practical constraints imposed by contemporary hardware. Hybrid quantum–classical algorithms, sampling-based experiments, and performance metrics are examined to assess the extent to which near-term quantum devices can outperform classical systems. The study finds that while progress toward quantum advantage is evident, significant technical and algorithmic limitations prevent its widespread realization. The paper concludes by identifying key research directions required to bridge the gap between experimental demonstrations and practical quantum computing applications.

]]> Quantum Advantage, NISQ Devices, Variational Algorithms, Quantum Benchmarking, Quantum Noise, Hybrid Quantum–Classical Computing Review Article Wed, 31 Dec 2025 00:00:00 IST Quantum computing has progressed from theoretical speculation to experimental realization, leading to milestone demonstrations that claim computational superiority over classical systems. Among these milestones, quantum supremacy experiments have attracted considerable attention by showcasing tasks that are infeasible for classical computation within reasonable time limits. However, the practical value of such demonstrations remains a subject of debate. This paper critically evaluates the distinction between quantum supremacy and quantum utility, emphasizing their conceptual differences, experimental foundations, and real-world relevance. By examining key experimental results, algorithmic developments, and benchmarking approaches, the study argues that quantum utility-defined by practical, application-oriented performance-provides a more meaningful metric for long-term progress. The paper highlights the limitations of supremacy-based demonstrations and discusses pathways toward achieving utility-driven quantum advantage in the near and long term.

]]> Quantum Supremacy, Quantum Utility, NISQ Devices, Quantum Advantage, Benchmarking, Hybrid Quantum Algorithms Review Article Wed, 31 Dec 2025 00:00:00 IST Blockchain technology has evolved significantly since its introduction as the underlying framework for cryptocurrencies. In recent years, it has expanded into a versatile distributed ledger technology supporting decentralized applications across finance, healthcare, supply chains, governance, and the Internet of Things. This review paper presents a comprehensive survey of recent advances in blockchain technology, focusing on architectural developments, consensus mechanisms, scalability solutions, security enhancements, and emerging application domains. The study critically examines current challenges such as performance limitations, energy consumption, interoperability, privacy concerns, and regulatory uncertainties. By analysing recent literature and technological trends, the paper identifies key research gaps and outlines promising directions for future investigation. The findings aim to support researchers and practitioners in understanding the current state of blockchain technology and its trajectory toward large-scale, sustainable adoption.

]]> Blockchain Technology, Distributed Ledger, Consensus Mechanisms, Scalability, Smart Contracts, Web3, Research Gaps Review Article Wed, 31 Dec 2025 00:00:00 IST