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All Journal Mikailalsys Journal of Advanced Engineering International
Dadi Jonathan Abba
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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AI-Driven Strategies for Rebuilding Food Security in Post-Conflict Northern Nigeria: Opportunities, Challenges, and Policy Implications Dadi Jonathan Abba; Mafeng Jamima Dudari; Raliyah Umar Alkaleri; Jimmy Nirat Jakawa; Habibu Aminu Sani
Mikailalsys Journal of Advanced Engineering International Vol 3 No 2 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i2.9388

Abstract

Years of conflict in Northern Nigeria have displaced communities, disrupted agricultural production, and weakened market systems, creating urgent challenges for food security recovery. Traditional rehabilitation approaches alone are insufficient to address these multidimensional problems. This article aims to examine the potential of artificial intelligence (AI) as a transformative tool for rebuilding food security in post-conflict settings in Northern Nigeria. The study analyzes how AI-enabled technologies, including predictive modeling, climate monitoring, automated crop assessment, and data-driven supply-chain management, can support agricultural productivity, timely decision-making, and food system resilience. The findings indicate that AI can contribute to post-conflict recovery by strengthening early-warning systems, improving agricultural planning, enhancing supply-chain coordination, and supporting more sustainable food security interventions. However, the effective adoption of AI remains constrained by inadequate infrastructure, limited technological skills, and governance challenges. The study concludes that responsible, context-sensitive, and locally adapted AI strategies can accelerate food system recovery and contribute to sustainable food security in Northern Nigeria. This article contributes to the discourse on digital agriculture and post-conflict reconstruction by highlighting the strategic role of AI in strengthening resilience, improving recovery planning, and supporting evidence-informed food security interventions in fragile contexts.
Promoting the Effective Use of AI in Learning: A Smart Student’s Perspective at Karl Kumm University, Vom Dadi Jonathan Abba; Adamu Ahmed Yarma; Mafeng Jamima Dudari
Mikailalsys Journal of Advanced Engineering International Vol 3 No 1 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i1.8015

Abstract

Artificial intelligence (AI), with its capacity to support individualized learning, efficient research, and enhanced academic productivity, has become a disruptive force in higher education. However, limited understanding, low levels of digital literacy, and ethical concerns prevent many students from harnessing AI effectively. This study examines strategies for promoting the efficient and responsible use of AI in education from the perspective of “smart students” at Karl Kumm University, Vom. Using a mixed-methods design, data were collected from 200 undergraduate students through surveys and interviews to explore AI awareness, adoption patterns, perceived benefits, and perceived challenges. The findings indicate that students recognize AI’s potential to improve learning and engagement, yet its optimal use is constrained by inadequate technical skills, fears of over-reliance, and unresolved ethical issues. The study proposes practical interventions, including mentorship schemes, curriculum integration, structured training programs, and clear ethical use guidelines, to foster more responsible and effective adoption of AI in learning. Overall, the results provide actionable insights for higher education institutions seeking to leverage AI to improve academic outcomes and to cultivate an innovative, self-directed learning culture by enabling students to become discerning and competent AI users.
Artificial Intelligence in Early Disease Detection: Trends, Applications, and Challenges Dadi Jonathan Abba; Mafeng Jamima Dudari; Jimmy Nirat Jakawa; Habibu Aminu Sani; Kudyo Deborah Yona
Mikailalsys Journal of Advanced Engineering International Vol 3 No 2 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i2.9226

Abstract

Artificial intelligence (AI) is transforming healthcare by improving diagnostic precision, reducing clinician workload, and supporting early disease detection. Early diagnosis is essential for improving patient outcomes, reducing mortality, and lowering healthcare costs. This study examines current developments in AI-assisted diagnostics, with particular attention to applications in cancer, cardiology, neurology, infectious diseases, and personalized medicine. It discusses how AI, through machine learning, deep learning, and predictive analytics, can process large-scale medical datasets, analyze medical images, and support physicians in clinical decision-making. The findings indicate that AI offers substantial benefits for healthcare practice, including improved diagnostic accuracy, enhanced patient monitoring, reduced clinical errors, and more efficient decision support. However, major barriers remain, including algorithmic bias, high implementation costs, data privacy concerns, inadequate physician training, and unresolved ethical issues. The study concludes that the effective adoption of AI in early disease diagnosis requires collaborative research, robust policy frameworks, ethical governance, and practical integration strategies. These insights contribute to current discussions on AI-enabled healthcare by highlighting both its diagnostic potential and the institutional, technical, and ethical conditions needed to optimize its implementation in healthcare delivery.
Co-Authors Adamu Ahmed Yarma Habibu Aminu Sani Jimmy Nirat Jakawa Kudyo Deborah Yona Mafeng Jamima Dudari Raliyah Umar Alkaleri