http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/32 SEET 2026-06-10T02:00:36Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31544 Title: A Scientific Integrity Framework for Open-Set IoT Intrusion Detection with Device-Disjoint Splits Authors: Chikezie, Chekwas; Usman, Abraham Usman; David, Michael; Zubair, Sulieman; Ohize, Henry Ohiani; Ojeniyi, Joseph Abstract: Machine-learning-based intrusion detection for Internet of Things systems has often been evaluated through model-centered pipelines that use weakly governed partitioning, limited leakage auditing, and closed-set assumptions. Consequently, reported performance could reflect data-handling artifacts rather than reliable security intelligence. This paper introduces a scientific integrity framework that treats preprocessing as a primary research object for open-set Internet of Things intrusion detection. The framework integrated devicedisjoint split governance, feasibility-aware zero-day isolation, quantified leakage control, train-only preprocessing, shared-safe feature selection, diagnostic-harness verification, baseline split comparison, and auditable artifact generation. Applied to the CICIoT-DIAD 2024 corpus with Institute of Electrical and Electronics Engineers Organizationally Unique Identifier-based vendor enrichment, the protocol locked 28 canonical classes, eight semantic attack families, and five policy labels before constructing a device-disjoint, vendor-aware grouped split. When strict device-level zero-day holdout was infeasible, the framework activated an audited row-level fallback that preserved contamination-free holdout isolation without claiming strict device-novel zero-day evaluation. On 35,672,407 flows from 180 files, the accepted run achieved zero device overlap, zero flow-signature Jaccard leakage risk, 100 percent zero-day purity, a Feature Distribution Stability Score of 0.00518, a Device-Feature Dependency Index of 0.00000, an Attack Invariance Score of 0.92964, and an Attack Semantic Consistency Score of 0.90714. The diagnostic harness produced zero hard failures and zero warnings, while baseline comparison showed stronger preprocessing integrity than random stratified and simple device-disjoint splitting. This study did not claim downstream classifier superiority; rather, it established an auditable preprocessing substrate for later classifier-level experiments. 2026-05-27T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31484 Title: Blockchain for securing electronic voting systems: a survey of architectures, trends, solutions, and challenges Authors: Ohize, Henry O.; Onumanyi, Adeiza James; Umar, Buhari Ugbede; Ajao, Lukman A.; Isah, Omeiza Rabiu; Dogo, Eustace Manayi; Nuhu, Bello Kontagora; Olaniyi, Olayemi Mikail; Ambafi, James G.; Sheidu, Vincent B.; Ibrahim, Muhammad M. Abstract: Electronic voting (e-voting) systems are gaining increasing attention as a means to modernize electoral processes, enhance transparency, and boost voters’ participation. In recent years, significant developments have occurred in the study of e-voting and blockchain technology systems, hence reshaping many electoral systems globally. For example, real-world implementations of blockchain-based e-voting have been explored in various countries, such as Estonia and Switzerland, which demonstrates the potential of blockchain to enhance the security and transparency of elections. Thus, in this paper, we present a survey of the latest trends in the development of e-voting systems, focusing on the integration of blockchain technology as a promising solution to address various concerns in e-voting, including security, transparency, auditability, and voting integrity. This survey is important because existing survey articles do not cover the latest advancements in blockchain technology for e-voting, particularly as it relates to architecture, global trends, and current concerns in the developmental process. Thus, we address this gap by providing an encompassing overview of architectures, developments, concerns, and solutions in e-voting systems based on the use of blockchain technology. Specifically, a concise summary of the information necessary for implementing blockchain-based e-voting solutions is provided. Furthermore, we discuss recent advances in blockchain systems, which aim to enhance scalability and performance in large-scale voting scenarios. We also highlight the fact that the implementation of blockchain-based e-voting systems faces challenges, including cybersecurity risks, resource intensity, and the need for robust infrastructure, which must be addressed to ensure the scalability and reliability of these systems. This survey also points to the ongoing development in the field, highlighting future research directions such as improving the efficiency of blockchain algorithms and integrating advanced cryptographic techniques to further enhance security and trust in e-voting systems. Hence, by analyzing the current state of e-voting systems and blockchain technology, insights have been provided into the opportunities and challenges in the field with opportunities for future research and development efforts aimed at creating more secure, transparent, and inclusive electoral processes 2025-10-22T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31482 Title: Intelligent Evaporative Cooling Systems for Post-Harvest Fruit and Vegetable Preservation: A Systematic Literature Review Authors: Isah, Omeiza Rabiu; Nuhu, Bello Kontagora; Dogo, Eustace Manayi; Umar, Buhari Ugbede; Maliki, Danlami; Abdullahi, Ibrahim Mohammed; Olaniyi, Olayemi Mikail; Agajo, James Abstract: Post-harvest losses of fruits and vegetables are an important bottleneck in food systems of countries around the world, with 30–50% of perishable food items lost between farm and consumer, smallholder farmers in low-and-middle income countries (LMICs) with poor cold chain infrastructures facing a disproportionate burden. Evaporative cooling (EC) is a low-cost and energy-efficient alternative to mechanical refrigeration; however, traditional systems are operated in one position and are dependent on climate, which restricts its performance. The combination of Internet of Things (IoT) sensing, machine learning (ML), and the advanced control theory has made intelligent evaporative cooling systems (IECS) adaptive, data-driven platforms that can regulate the environment in real-time and optimise autonomously. This is a systematic literature review that was carried out according to PRISMA 2020, summarising 94 peer-reviewed articles published in 2018–2025 to map the technological landscape, performance indicators, and research directions of the field of post-harvest fruit and vegetable preservation using IECS. Findings indicate that IECS can considerably lower the storage temperatures, increase the shelf life by 50–200%, and reduce energy consumption by 75–90% compared to traditional refrigeration, and the payback period is as short as 1.2 years. In arid conditions, ML models are accurate in prediction with an R2 of 0.98. The gaps in the research identified are a lack of validation in wet climatic conditions, non-existent standardised Ag-IoT protocols, inadequate Food–Energy– Water (FEW) nexus calculation, and no explainable AI (XAI) interfaces. An example of a conceptual framework of four layers synthesised is proposed to direct next-generation research and implementation of the IECS 2026-02-24T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31481 Title: Development of an intelligent meat spoilage detection and grading system using particle swarm optimization-based convolutional neural network Authors: Isah, Omeiza Rabiu; Adegoke, Israel Adedolapo; Nuhu, Bello Kontagora Abstract: This research developed an intelligent meat spoilage and quality grading system using a particle swarm optimization-based convolutional neural network. It addressed the problems associated with the subjective manual assessment of meat quality and inefficient and expensive meat quality grading systems as well as the lack of a comprehensive dataset for meat quality detection. This research created a new dataset for meat spoilage and quality detection. Furthermore, a PSO-based convolutional neural network was trained with the new dataset for the classification and the grading of the meat. The Python code is then integrated into the Raspberry Pi 4 to make it a stand-alone system. Comparative analysis indicated that the PSO-based CNN performed better compared to the baseline CNN by 2.91% for accuracy, 2.49% for precision, 0.99% for F1-score, 1.87% for recall, 2.74% for specificity and 1.14% for sensitivity. The obtained results implied improved food safety in the food processing industry and retail environments. In addition, the intelligent system provides support to human experts for accurate assessment of meat quality. 2026-04-30T00:00:00Z