Faculty of Computing

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    Analysis of Bad Roads Using Smart phone
    (IEEE, 2019-02-02) Moussa Mahamat Boukar; Steve Adeshina
    Developing nations are faced with a lot of bad roads with potholes of different debt ranges, the maintenance and rehabilitation process by government agencies is an ongoing effort that requires periodic bad road inventory to guarantee safety. Bad roads are either identified by government agency’s survey teams or individual who volunteer to report these conditions to the authorities. Our research provided a simple but effective solution to aid in automatically reporting bad roads using smart-phones through measuring the pavement profile based on the vibration of a moving vehicle. In this article, we will explain how we used some a smart-phone in reading the vibration pattern, GPS location, speed and direction of a vehicle that drives through a pothole, these parameters are periodically streamed to a cloud application. We used standard deviation to measure the level of dispersion around a segmented set of streamed vehicle vibration to identify potholes of different sizes, we also used Artificial Intelligence - supervised learning algorithm (classification) to reduce the false positive error rates due to human behaviors. The final results show a distinct vibration levels between small pot-holes, speed bumps and big pot-holes, these values are displayed on map application to visualize the geographical locations of these pot-holes (Google maps)
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    Harnessing AI-Driven CRISPR Bioinformatics
    (Intenational Journal of Biological Pharmaceutical Sciences Archive, 2025-09-05) Ogochukwu Peace Chinedu-Nzereogu; Atoyebi, Temitope Olufunmi; Mercy Adedamola Adebayo; Ikenna Kenneth Maduike; Tinsae Alebel Dejene; Tochukwu Excellent Okechukwu; Yetunde Victoria Mene
    In a world grappling with the escalating crisis of antimicrobial resistance (AMR), claiming millions of lives annually, a revolutionary fusion of artificial intelligence (AI) and CRISPR bioinformatics ignites a beacon of hope, poised to redefine precision diagnostics. This review unveils the exhilarating potential of AI-driven CRISPR technologies, which deliver lightning-fast detection of AMR genes with a staggering 95% accuracy and slash diagnostic times by 70%, empowering clinicians to outpace deadly infections. Platforms like SHERLOCK and DETECTR, supercharged by AI’s computational prowess, unravel complex resistance mechanisms and pinpoint metabolic biomarkers with unparalleled precision, transforming chemical pathology into a cornerstone of personalized medicine. From bustling urban hospitals to remote rural clinics, these innovations promise to democratize diagnostics, offering scalable, cost-effective solutions that bridge global health disparities. Yet, technical hurdles, ethical challenges, and scalability barriers loom large, demanding bold, collaborative action. This article charts a thrilling path forward, exploring how AI-CRISPR synergy can conquer AMR, revolutionize biomarker profiling, and forge a future where precision diagnostics save lives across the globe, captivating researchers, clinicians, and policymakers alike.