http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/83 SICT Thu, 14 May 2026 18:37:29 GMT 2026-05-14T18:37:29Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31085 Title: Predicting the Impact of Socio-Demographic Risk Factors on COVID-19 Based on Hybrid ANN-CNN Model Authors: Yahaya Mohammed Sani, Yahaya Benjamin Davou Pam and Mamman Adamu Abstract: —Global health has been greatly influenced by the COVID-19 pandemic, especially in low- and middle-income nations like Nigeria. Despite the catastrophic effects of the pandemic, little is known about how sociodemographic risk factors that affects the number of COVID-19 infections and deaths in Nigeria. Using Spearman heat map correlation analysis, this study examined these parameters and developed a hybrid ANN-CNN model to forecast the influence of sociodemographic characteristics against COVID-19 confirmed cases and mortality cases in Nigeria. The Nigerian COVID-19 confirmed and death cases data from May 1, 2020, to April 30, 2021, as well as sociodemographic risk factor statistics, were the datasets used in this study. The experiment was completed by training and testing the models, and based on MAEs and RMSEs models performance evaluation metrics, the developed Hybrid ANN-CNN model outperformed the other five state-of the-art machine learning models involving Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Least Absolute Shrinkage and Selection Operator (LASSO). With mean absolute errors of (0.0157) and (0.0181) for confirmed and death cases, respectively, the developed Hybrid ANN-CNN model outperformed the others. Similarly, with RMSEs of (0.0842) and (0.0813) for confirmed and death cases, respectively, the developed Hybrid ANN-CNN model fared better than other models. The developed Hybrid ANN-CNN model can be helpful in tracking and containing pandemic outbreaks, both in the present and the future. Sun, 01 Jan 2023 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31085 2023-01-01T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/30914 Title: Artifi cial Intelligence Enabled Ransomware: A Systematic Review Authors: Osin, O.J; Isah, Abdulkadir O.; Subairu, Sikiru O.; Ahmad, Suleiman; Noel, Moses Dogonyaro Abstract: Ransomware has been one of the most severe cyber threats, hobbled operations as well as bottom lines worldwide. Normally, ransomware attacks have been based on the idea of encrypting or blocking the data itself and paying a ransom for it to be released, yet the introduction of AI now also leaves many traditional detection methods out of date. This survey aims to answer three main research questions about how AI-empowered ransomware are growing as a threat: What are these emerging threats, state of-the-art detection methodologies to detect them and what are the related forthcoming research directions? Applying the PRISMA 2020 methodology, the retrieved papers between 2020 and 2025 were systematically reviewed, where only the ones of high quality and focus were included. What’s clear is that AI has turned ransomware from something that was statically built to something that’s now intelligent, work rounding, complex. However, there is hope on the horizon for detecting and defending against such attacks due to the advances in AI-based detection and response systems. In the following, we reflect on remaining challenges identified in the study and the importance of further work to bridge the gap and make AI-based defenses more impactful. Description: N/A Thu, 18 Sep 2025 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/30914 2025-09-18T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/30913 Title: Artifi cial Intelligence – Powered Cyber Warfare: Resources, Methods, Weaponisation, Forensics, Threat Intelligence and Defenses: A Systematic Review Authors: Joseph, Christiana C.; Ojeniyi, Joseph Adebayo; Noel, Moses Dogonyaro; Ahmad, Suleiman; Fashola, O.O; Anyaora, Peter C. Abstract: Artifi cial Intelligence (AI) is gradually changing how cyber warfare works, creating new opportunities but also posing threats. This systematic literature review analyses how AI is used in diff erent areas, focusing on resources, methods, weaponisation, forensics, threat intelligence, defences and research directions. This study uses PRISMA-ScR methodology to analyse relevant publications published between 2018 and 2025. The fi ndings indicate that AI technologies are increasingly being utilised for reconnaissance, automated cyberatt acks, and enhanced threat detection. It also increases defensive measures by implementing intelligent systems that ensure faster response and mitigation. However, the increased reliance on AI raises serious concerns about ethical concerns, algorithmic bias, data privacy issues, and the high risk of autonomous weaponisation. The study highlights the need to improve AI-based defense systems and forensic methods to keep up with changing cyber threats. The paper ends by recommending more research in AI, stronger cybersecurity systems, and greater international cooperation to ensure AI is used responsibly in cyber warfare. Description: N/A Tue, 16 Sep 2025 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/30913 2025-09-16T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/30583 Title: PRIVACY-CENTRIC SELF-SOVEREIGN IDENTITY FRAMEWORK DESIGN FOR PUBLIC BLOCKCHAINS Authors: Muhammad, Idris; Oluwaseun, Adeniyi Ojerinde; Muhammad, Muhammed Kudu Abstract: Identity management has evolved over time, yet centralized and federated models remain vulnerable to large-scale data breaches and privacy concern. This paper presented a privacy- centric Self-Sovereign Identity (SSI) framework for public blockchains that incorporates advanced cryptographic method, such as ephemeral Decentralized Identifiers (DIDs), BBS+ selective disclosure signatures, and accumulator-based revocation. The framework was designed and evaluated using a Design Science Research (DSR) methodology, it encompassing architectural design, prototype implementation, and performance testing on the Ethereum testnet. With a slight performance overhead of 10–15% in latency and throughput, experimental results show an 80% reduction in linkability risk when compared to baseline SSI models. These results demonstrate that blockchain-based identity systems can successfully balance privacy, scalability, and usability. The study contributes a deployable, empirically validated architecture that advances privacy-by- design principles for next-generation digital identity infrastructures. Wed, 01 Oct 2025 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/30583 2025-10-01T00:00:00Z