Faculty of Computing

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Author: search.filters.author.Atoyebi, Temitope OlufunmiHas files: Yes

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    Systematic Review on the Impacts of Digital Transformation on Corporate Innovation Performance
    (FUDMA Journal of Sciences, 2026-01-02) Sauda Abdulhamid Isa; Atoyebi, Temitope Olufunmi; Ridwan Kolapo; Prema Kirubakaran; Eru Akwuma Nathaniel; Solomon Ahiaba
    ABSTRACT The influence of digital change on company innovation performance is meticulously reviewed in this study. An approach of shift in organizations known as "digital transformation," makes use of digital technologies to boost productivity. The study examined 107 journal papers released between 2020 and 2025 using a critical review approach. The study's findings demonstrate that enterprise success in innovation is significantly impacted by digital evolution, both positively and negatively. Increasing output, effectiveness, and competitiveness in the organization are indicators of that achievement improvement. The efficiency of organizations is not usually directly impacted by digital transformation. Organization size, industry, organizational culture, competitive edge, innovation in technology, and the capacity of organizations to manage the digital change are some of the variables that might affect the extent of digital transformation. As a result, the research advised firms to comprehend the variables that may affect the effects of the digital revolution and to exploit it to its fullest potential in order to boost efficiency.
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    Malaria Disease Prediction and Grading System
    (International Journal for Research in Applied Science & Engineering Technology, 2023-10-02) Atoyebi, Temitope Olufunmi; Rashidah Funke Olanrewaju; N. V. Blamah; Morufu Olalere
    Malaria disease is the number one cause of death all over the Sub-Sahara world. Data mining can help extract valuable knowledge from available data in the healthcare sector. This allows training a patient health prediction model faster than in a clinical trial. Various implementation of machine learning algorithms such as Bayesian Theorem, Logistic Regression, K-Nearest Neighbor, Support Vector Machine and Multinomial Naïve Bayes (MNB), etc. have been applied on Public Hospital Malaria Disease datasets but there has been a limit to modeling using Multinomial Naïve Bayes Algorithm. This research applied MNB modeling to discover the relationship between 15 relevant attributes of the Public Hospitals data collected from Bwari General Hospital in Bwari Area Council and Maitama Hospital in Abuja Municipal Area Council, Abuja, FCT, and Nigeria. The goal is to examine how dependencies between attributes affect the performance of the classifier. The MNB produces a reliable and transparent graphical representation between the attributes with the ability to predict new scenarios. The model has an accuracy of 97%. It was concluded that the model outperformed the GNB classifier which has an accuracy of 100% and RF which also has an accuracy of 100%.
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    Improving DDoS Detection in Software-Defined Networks Through a Hybrid Machine Learning Approach
    (IRE Journals, 2025-09-03) FRANCIS ONOJAH; PROF. PREMA KIRUBAKARAN; DR. RIDWAN KOLAPO; Atoyebi, Temitope Olufunmi; DR. R. RENUGA DEV
    (DDoS) Attacks remain a significant concern for network security, utilizing flood-like traffic at the volume, protocol, and application levels to exploit vulnerabilities in today's infrastructure. To lessen these risks, Software-Defined Networking (SDN) offers programmability and centralized control. However, current machine learning (ML)-based detection techniques have a high false positive rate, are not very flexible against zero-day attacks, and are ineffective when handling high-dimensional flow data. To enhance the detection of DDoS attacks in software-defined networks, this paper proposes a hybrid machine-learning approach. Tapping into SDNs broad view of all network flows, the system studies traffic in real time by merging supervised deep learning- in this case, Long Short-Term Memory- with unsupervised anomaly detection called Isolation Forest. The LSTM sorts incoming packets and learns new normal behavior, while the Isolation Forest flags any stray patterns that don’t fit.
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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.
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    GeoAI at the forefront of climate action
    (Global Journal of Engineering and Technology Advances, 2025-08-21) MAVISCLARA, OHAKA AMARACHI; Esekie, Jeffery Omozokpia; Atoyebi, Temitope Olufunmi; ATUMAH, Prayer Erumusele; Akadiri, Oluwatoyin Olawale; JIMOH, Rildwan Adekunle; IBRAHIM, ISIAKA OSHOBUGIE
    GeoAI, merging artificial intelligence with geospatial data, is transforming climate change mitigation and adaptation. This review synthesizes 2020–2025 advancements, focusing on deep learning models like convolutional neural networks (CNNs) and transformers, achieving 90–95% accuracy in flood prediction, carbon sequestration mapping, and urban heat mitigation. Key mitigation strategies include forest biomass estimation in the Amazon and renewable energy optimization in India, while adaptation efforts encompass real-time flood mapping in Bangladesh and coastal resilience modeling in the Pacific Islands. Despite successes, challenges persist, including data biases, computational costs, and ethical concerns like privacy in urban GeoAI applications. Public discourse on platforms like X highlights demand for equitable climate solutions, reflected in discussions on wildfires and Arctic rain. Future directions involve federated learning for privacy-preserving GeoAI and generative AI for climate scenario modeling. Aligning with Sustainable Development Goal 13, GeoAI offers transformative potential to enhance global climate resilience, necessitating investment in open-access tools and interdisciplinary collaboration to address research gaps and ensure inclusivity.
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    Engineering Resilience
    (World Journal of Advanced Engineering Technology and Sciences, 2025-10-21) Ohaka Amarachi Mavisclara; Ibrahim Isiaka Oshobugie; Atoyebi, Temitope Olufunmi; Suleiman Mustapha; Adeyinka Taslim Olabode; Ozuruoha Nkiruka Esther; Oyeboade Adekunle Yakub
    As climate change intensifies floods, heatwaves, and sea-level rise, global infrastructure faces unprecedented vulnerabilities, with damages reaching $360 billion in 2022. This review explores how smart technologies, artificial intelligence (AI), Internet of Things (IoT), digital twins, and resilient materials, transform climate-resilient infrastructure to withstand these escalating hazards. Synthesizing recent advancements from the last five years, it examines their applications in urban and rural contexts, drawing on case studies from the Netherlands’ IoT-enabled dikes, Singapore’s AI-driven urban planning, and Ghana’s cost-effective rural solutions. These innovations reduce maintenance costs by 15-25%, enhance flood response times by 40%, and align with Sustainable Development Goals (SDGs) for equitable, sustainable development. However, economic barriers, governance gaps, and equity challenges hinder global adoption, particularly in developing nations where connectivity and funding limit scalability. The review proposes future directions, including open-source platforms, public-private partnerships, and interdisciplinary research to address multi-hazard risks. By integrating data-driven engineering with green infrastructure, this study offers a roadmap for policymakers and engineers to build climate-proof infrastructure that ensures safety, sustainability, and resilience. This comprehensive synthesis underscores the urgent need for collaborative, inclusive strategies to safeguard global infrastructure against climate change, providing actionable insights for a resilient future.
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    Smart Health Care Delivery System
    (EasyChair Preprint, 2024-03-31) Lubem Gafa; Akwuma Nathaniel Eru; Atoyebi, Temitope Olufunmi
    The health sector in Nigeria, particularly the Secondary hospitals, is collapsing due to a number of factors, including inadequate referral systems, poor decision-making, excessive bureaucracy, subpar medical personnel, corruption, improper patient attention, improper documentation of patient records for continuity despite the population growth. The aforementioned issues with secondary hospital management have been demonstrated to arise from the health sector's underutilized and inaccessibility of smart technologies. The term "smart health delivery system" describes the integration of smart technology concepts and tactics within the healthcare industry. Additionally, it makes use of information and communication technologies (ICT) to guarantee that health care delivery keeps up with technological advancements. By using a patient-concentric approach, the tech-driven consolidated platform can automate the entire clinic process and update patient record management. As such, a smart healthcare delivery system was proposed using the spiral model, consisting of medical personnel module, administrative personnel module as well as patients' management module. Three experiments demonstrated the system's ease of administration as regards to the management of the secondary hospitals, proper patient documentation, the system also enables patient manage their appointments. It was concluded that the proposed system effectively addressed medical personnel recklessness, proper patient medical record management, and elimination of excessive bureaucracy. Thus, it is advised that the suggested system be put into place in Nigeria Secondary Hospital in order to enhance healthcare services delivery and to ensure long-term viability of the secondary medical field.
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    Efforts At Utilizing Ict Tools For Early Identification And Diagnosis Of Malaria Disease
    (South Eastern Journal of Research and Sustainable Development, 2023-06-02) Atoyebi, Temitope Olufunmi; Rashidah R. F. Olanrewaju; N.V. Blamah
    Malaria disease impose risk to human life and health status. In this paper, a historical background of some of the existing applications of ICT to predicting Malaria Disease is presented. Many organizations globally are involved in campaigning for reducing Malaria disease and equally controlling it. Almost all these efforts are focused on control at various stages of the lifecycle, while less or rare efforts are invested at terminal intervention by the end users. These basically revolve round the smart phone, mainly text parameters; with Internet technology it’s also mentioned. The paper is expected to provide background resource for an efficient and effective information system capable of predicting and/or minimizing the risks resulting from this dangerous illness. The ultimate goal is to develop products which will assist in early detection and for use by health-information related agencies