SICT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/35 2026-08-13T01:04:06Z 2026-08-13T01:04:06Z Attah, Tansi Arome Usman, Abdulsalam Ahmed Abduldayan, Fatimah Jibril http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31964 2026-07-30T14:31:02Z 2023-01-01T00:00:00Z

Title: Demographic Characteristics, Awareness, and Utilisation Patterns of Public Libraries in Nigeria: Implications for Promoting Access to Knowledge and Enhancing User Engagement in Nigeria Authors: Attah, Tansi Arome; Usman, Abdulsalam Ahmed; Abduldayan, Fatimah Jibril Abstract: This study is on public libraries' roles in helping achieve to achieve the United Nations Sustainable Development Goal (SDG) on Poverty Eradication in Nigeria. Poverty has long been a challenge that has faced humanity as many struggles to make ends meet and get decent living for themselves. We live in an 'Information Age' where people equipped with the right information are able to make better decisions and better their lives. Public libraries exist as an information household that can provide people with the right information to better their status quo and life as a whole. This study identified the demographic characteristics of public library users in Nigeria, investigated the level of awareness of public libraries. frequency of use of the library, identified the reasons for visiting public libraries and the types of resources and services that users consult, and finally, suggested strategies that could be employed by public libraries in achieving the SDG goals on poverty eradication in Nigeria. A survey was carried using online Google Forms as the data collection instrument. Questions were generated in line with the research objectives and a link to the form was shared on social media with 238 young adult Nigerians from 25 selected states cross the six geopolitical zones in Nigeria. Findings revealed that 54.9% of male students that falls within the age range of 15-25 years from tertiary institutions form the highest number of public library users in Nigeria. Despite half of the respondents being aware of public libraries, the majority do not visit, with only only a small percentage visiting on a daily basis. Borrowing books and other information materials as well as studying are the main reasons for visiting public libraries in Nigeria. The study recommended the importance of increasing awareness of public libraries, and partnership and collaboration with other libraries and organisations in rolling out programs on poverty eradication for youths.

2023-01-01T00:00:00Z Hamza, Ibrahim Datti Prof. Udensi, J. N Saka, K.A Adabara, N. U http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31958 2026-07-29T18:17:13Z 2026-01-01T00:00:00Z

Title: AN EMPIRICAL INVESTIGATION OF THE DETERMINANTS OF RESEARCH PRODUCTIVITY AMONG MEDICAL LECTURERS IN FEDERAL UNIVERSITIES IN NORTH-WEST NIGERIA Authors: Hamza, Ibrahim Datti; Prof. Udensi, J. N; Saka, K.A; Adabara, N. U Abstract: This study was carried out to empirically investigate the research productivity among medical lecturers in federal universities in North-West Nigeria with a view to find out how individual and institutional factors affect scholarly productivity. A quantitative methodology employing a descriptive correlational research design was used. The population of the study comprises 775 medical lecturers teaching in the five operational medical colleges in Federal Universities in North-West, Nigeria. A total of 264 respondents participated and 209 copies were retrieved and found usable yielding a 79.2% response rate. A questionnaire was used as the instrument for data collection and was validated and pilot-tested with a reliability coefficient of 0.86 using Cronbach’s Alpha. Data were analyzed using frequency, percentages, mean and standard deviation. Findings reveal that medical lecturers in the surveyed federal universities demonstrate a relatively high level of research productivity. This is shown by strong performance on important measures like book chapters, co-authored textbooks, postgraduate supervision, research grants, ongoing research initiatives, community service, citation visibility, and teaching duties. Together, these determinants show the importance of being actively involved in collaborative scholarship, keeping up with research, and having an impact on academia. The study recommends that medical lecturers should have more structured research assistance, better ways to get financing, ongoing training, and stronger ways to collaborate together to boost research output and make them more competitive on a worldwide scale.

2026-01-01T00:00:00Z Chinedu, Somtochukwu Uduimoh, Andrew A Anyaora, Peter Alhassan, John k Yusuf, Hadiza http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31937 2026-07-14T16:02:31Z 2025-01-01T00:00:00Z

Title: Development of a Hybrid Anomaly-Based Intrusion Detection System Using Autoencoder and Isolation Forest Authors: Chinedu, Somtochukwu; Uduimoh, Andrew A; Anyaora, Peter; Alhassan, John k; Yusuf, Hadiza Abstract: As cyberattacks advance in sophistication and fluidity, modern intrusion detection systems (IDS) must progress from static, signature-based models to adaptive models that can detect known and zero-day threats (Labonne, 2020; Ali et al., 2023). This study proposes a hybrid anomaly-based IDS that integrates an Autoencoder (AE) for deep feature representation with an Isolation Forest (IF) for statistical anomaly scoring. The objective is to enhance detection performance for both known and zero-day attacks within an unsupervised learning framework. The proposed model is an adaptation of the hybrid AE–IF framework devised by Mohammed and Telek (2023) and the fog-computing adaptation by Sadaf and Sultana (2020). The hybrid model deploys a logical fusion framework—Hybrid OR and Hybrid AND—dynamically balancing precision and recall in anomaly detection. The model is implemented in Python and trained using unsupervised learning on two benchmark datasets, NSL-KDD and CICIDS2017, so the model operates without any prior knowledge of attack signatures (Engelen et al., 2021). Experimental responses to model anomaly detection capabilities support that the Hybrid OR configuration of the model provided the most balanced anomaly detection performance scores recording F1-score of 0.85 and 0.69 on NSL-KDD and CICIDS2017 datasets respectively—both hybrid performance metrics outperforming the stand-alone AE and IF models. These results are in line with more recent evidence supporting the position that combining feature-reconstruction learning with statistical isolation increases the resilience and adaptability towards network intrusion detection (Elsaid & Binbusayyis, 2024; Alhassan et al., 2024). While performance declined on the more complex CICIDS2017 dataset, the hybrid approach demonstrated improved generalisation relative to individual models. The results suggest that logical fusion of representation learning and isolation-based scoring provides a lightweight and adaptable framework for network intrusion detection, although further validation in live network environments is required before operational deployment (Abuabed et al., 2023; Wang et al., 2023).

2025-01-01T00:00:00Z Mohammad, Umar Majigi Ismaila, Idris Shafii, Abdulhamid M. Richard, Adeyemi Ikuesan Uduimoh, Andrew Anogie http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31935 2026-07-14T15:41:11Z 2023-01-01T00:00:00Z

Title: Blockchain Based Zero Knowledge Proof Model for Secure Data Sharing Scheme in a Distributed Vehicular Networks Authors: Mohammad, Umar Majigi; Ismaila, Idris; Shafii, Abdulhamid M.; Richard, Adeyemi Ikuesan; Uduimoh, Andrew Anogie Abstract: The possibility of implementing advanced applications, such as improved driving safety, has increased with the rapid development of vehicular telematics, and existing vehicular services have been enriched through data sharing and analysis between vehicles. This research uses smart contracts and consortium blockchain zero knowledge proof to secure data sharing and storage in vehicular networks. The results indicate that, for message sizes (m), both data_ experiments _2 and 1 produce ciphertext of the same size, with the exception of 'gnfuv-temp-exp1-55d487b85b-5g2xh,' which generates ciphertext of 156 bits with the lowest decryption time of 26,865ms and a small decrease in encryption time between 28,620ms and 28,162ms. the proposed model validation shows that the model performed better than Advanced encryption standard in term of ciphertext size, encryption time and decryption time in comparison and it satisfies the good and robust blockchain-based zero knowledge proof model for secure data sharing and storage for distributed VANET. The scheme achieves high levels of security while operating with reasonable efficiency, reliability and availability according to numerical results.

2023-01-01T00:00:00Z