Social media has become a regular part of university life everywhere, including in countries like
Nigeria, Ghana, Cameroon and even in the European Countries. There are concerns about it
cutting into study time and lowering grades, which has prompted a lot of research. However, most
previous reviews have not used a formal framework, and very few have focused specifically on
developing countries. This review applies the PRISMA 2020 framework to examine four peer
reviewed studies from those four countries. Each of these studies explores the relationship between
social media use and students’ GPA or CGPA. Using PRISMA, I followed each step: identifying
studies, screening them, checking if they met criteria, and including them. I extracted details such
as sample size, student demographics, which aspects of social media use were considered, how
academic performance was measured, and the statistical findings. I also checked study quality
with a modified Newcastle-Ottawa Scale and assessed the risk of bias in selection, information,
and confounding. All four studies (total N = 573) reported the same result: more time spent on
social media is linked to poorer academic performance. The effect was not small, either. Pearson
correlations ranged from r = −0.239 in Nigeria to a negative relationship equal to R² = 0.374 in
Ghana. Regression coefficients were between β = −0.317 and β = −0.303. In Cameroon, six
predictors together explained just over half the variation in GPA (R² = .505; F = 16.142, p <
.001). Facebook was the main platform used across all studies. Factors like whether students used
social media at suitable times, addiction, the type of use, and feelings of social connection all
played a part. In summary, the message is clear: heavier social media use is associated with lower
grades among university students in these developing countries, but the specifics matter how,
when, and why students use social media influences the outcome. Universities should take this
seriously by teaching digital literacy and self-regulation, and by creating their own social media
platforms to encourage more productive student engagement.
Usman Haruna· International Journal of Com...· 0 citations
Plant diseases remain a threat to global agricultural productivity, food security and livelihoods,
especially in developing countries where the availability of experts in agriculture is still limited.
The recent progress in AI, particularly deep learning and computer vision, has ushered in new
possibilities for automated plant disease diagnosis, especially for plant image-based systems. This
paper provides a systematic review of the deep learning methods employed for plant disease
diagnosis, highlighting CNN-based methods, the application of transfer learning, explainable AI
(XAI) methods and deployment issues. In the framework of PRISMA 2020, the relevant peer
reviewed literature from 2016 to 2025 was systematically identified, screened and analysed on the
most important academic databases. The review compared some of the most popular architectures
such as GoogLeNet, DenseNet-121, MobileNetV2, EfficientNet, Attention-CNNs and Vision
Transformers. Results showed very high classification accuracy in controlled lab conditions with
DenseNet-121 achieving ~99.75% accuracy with good computational efficiency. But it also
revealed a big gap between the lab and the field, mainly due to environmental variations, domain
shifts, and dependence on datasets. Some innovative and emerging technologies like explainable
AI, hyperspectral imaging, few-shot learning, and lightweight mobile architectures showed
promise of enhancing the interpretability, early detection of disease, and the use of smart phones
in low-resource agricultural settings. In conclusion, the study suggests that in order to be
implementable in the field, future intelligent agricultural diagnosis systems must be able to
balance predictive accuracy, explainability, computational efficiency and field adaptability. The
results enrich the existing knowledge on precision agriculture and serve as useful information for
researchers, agricultural technologists, and policymakers working on the creation of AI-based
systems for crop protection.
Usman Haruna· Research Journal of Pure Sci...· 0 citations
The study concludes that rule-based approaches remain highly valuable for mission-critical 5G slicing applications and recommends their integration with adaptive techniques to enhance scalability and performance.
R. Paper, Zayyanu Yunusa, Usman Haruna· International Journal of Eme...· 0 citations