Conference Papers http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/2 2026-10-01T20:36:32Z 2026-10-01T20:36:32Z Abdulsalam, Z. O Alemede, I. C Ndagunu, S. N Muhammed, R.A Olaniyan, S.T Odede, B.O http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/32003 2026-10-01T04:30:55Z 2022-03-17T00:00:00Z

Title: Growth performance of weaner rabbits fed garlic and ginger supplemented basal diets Authors: Abdulsalam, Z. O; Alemede, I. C; Ndagunu, S. N; Muhammed, R.A; Olaniyan, S.T; Odede, B.O Abstract: This study was carried out to investigate the growth performance of rabbits fed diets supplemented with garlic and ginger. Thirty-six (36) female Dutch weaner rabbits were randomly allotted into four dietary treatments of 9 rabbits and three replicates with three (3) rabbits per in a completely randomized design. The dietary treatments were designated as follows; control containing 0 g garlic and ginger (T1), 100 g garlic per 100 kg feed (T2), 100 g ginger per 100 kg feed (T3) and 50 g garlic + 50 g ginger per 100 kg feed (T4). Data obtained were subjected to analysis of variance and significant differences were separated using Duncan Multiple Range Test. The result showed that there were no significant differences (P>0.05) in the values obtained for average initial body weights. However, there were significant (P<0.05) differences in the values obtained for average final body weights, average body weight gains and feed conversion ratio. The highest average final body weight was observed on rabbits in T4 (1403.70 g), follow by T3 (1374.00 g) while the least value was observed on rabbits in T1 (1314.30 g). Rabbits in T4 had the highest daily feed consumed (53.29 g) while the least value was observed on rabbits in T1 (50.69 g). The best feed conversion ratio was recorded on rabbit in T4 (03.84), while the least value was observed on rabbits in T1 (04.25). In conclusion, garlic and ginger could be used up to 0.1 % inclusion in rabbit diets without any adverse effect on growth performance.

2022-03-17T00:00:00Z Joseph, Christiana Ojeniyi, Joseph Noel, Moses Ahmad, Suleiman Olusanjo, Fasola Anyaora, Peter http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31990 2026-09-27T02:21:45Z 2025-09-16T00:00:00Z

Title: ARTIFICIAL INTELLIGENCE – POWERED CYBER WARFARE: RESOURCES, METHODS, WEAPONISATION, FORENSICS, THREAT INTELLIGENCE AND DEFENSES: A SYSTEMATIC REVIEW Authors: Joseph, Christiana; Ojeniyi, Joseph; Noel, Moses; Ahmad, Suleiman; Olusanjo, Fasola; Anyaora, Peter Abstract: Artificial 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 different areas, focusing on resources, methods, weaponisation, forensics, threat intelligence, defenses and research directions. This study uses PRISMA-ScR methodology to analyse relevant publications published between 2018 and 2025. The findings indicate that AI technologies are increasingly being utilised for reconnaissance, automated cyberattacks, 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.

2025-09-16T00:00:00Z Okechukwu, Chukwuemeka Nwodo, Chinweoke Anyaora, Peter Gungbias, Hosea Subairu, Sikiru Ojeniyi, Joseph http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31989 2026-09-27T01:52:10Z 2024-04-22T00:00:00Z

Title: Enhanced Convolutional Neural Network Model Using Bidirectional Encoder Representations from Transformers and Long Short-Term Memory for Hate Speech Detection Authors: Okechukwu, Chukwuemeka; Nwodo, Chinweoke; Anyaora, Peter; Gungbias, Hosea; Subairu, Sikiru; Ojeniyi, Joseph Abstract: Hate speech has become a prevalent and concerning issue in this digital age as a result of the adoption of social media as a means of communication, social interaction, and driving social discussion. This trend has opened a novel front in cybercrime, cyberbullying, privacy violations, and exposure of the people component of information security to violence, subsequently violating the governance and internal security of nations. The detection and mitigation of hate speech on online platforms have gained significant attention due to the growing influence of social media and the internet as communication channels and the adoption of the same as a means of propagating hate and hate crimes. This thesis offers a model for the more accurate detection of hate speech on an online platform using Bidirectional Encoder Representations from Transformers (BERT) to enhance the feature selection layer of Convolutional Neural Networks (CNN) and a hybrid of CNN and Long-Short-Term Memory (LSTM) to detect hate speech. The proposed BERT-CNN-LSTM model achieved an accuracy of 96.1%, which is an increase of over 0.45% from 95.63% in the benchmark paper, thereby contributing a novel and more accurate tool that, when adapted into the security policies of organisations and governments, will help safeguard the rights and privacy of users, thereby protecting them from both physical and psychological harm resulting from hate propagation over the internet.

2024-04-22T00:00:00Z Anyaora, Peter Adebayo, Olawale Ismalia, Idris Ojeniyi, Joseph Olalere, Morufu http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31988 2026-09-26T00:40:00Z 2023-03-21T00:00:00Z

Title: Systematic Literature Review on Android Malware Detection Authors: Anyaora, Peter; Adebayo, Olawale; Ismalia, Idris; Ojeniyi, Joseph; Olalere, Morufu Abstract: Users of Android-powered smartphones and tablets have multiplied dramatically. Thanks to Android third-party apps, the essential applications, such as banking and healthcare, are accessible on Android smartphones. There are new threats to be taken into account about harmful programs when these applications are utilized and embraced more broadly. This research performs a systematic literature review using the prima framework and Kitchenham statement to apply on android malware detection and analysis of different methodology of publishing research that have been used for android malware detection for the last past five years. Using the keyword” Android malware detection” the research had seen over 610 published articles on” Android Malware detection”. It was narrowed down to 142 published research papers due to it between the year 2018 to 2022 that was looked at, sixty-five articles (65) were finally selected for investigation after inclusion and exclusion. One of the research key findings is the performance of Machine Learning (ML) algorithms which were relatively higher than others.

2023-03-21T00:00:00Z