Justice O. Emuoyibofarhe

Work place: Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Nigeria

E-mail: eojustice@gmail.com

Website: https://orcid.org/0000-0003-1211-5182

Research Interests: Mobile Computing, Medicine & Healthcare, Computational Intelligence


Justice Emuoyibofarhe is a Professor of Computing at Ladoke Akintola University of Technology. He received his PhD in 2004. He specialises in neuro-fuzzy computing computational optimisation. He had post-doctoral fellowship at the Centre of Excellence for Mobile e-service, University of Zululand, South Africa in 2006. He is a member of the IEEE Computational Intelligence Society. He is also a Visiting Researcher at the Hasso Plattner Institute, University of Potsdam, Germany. His present research area is in the application of mobile computing and wireless communication to e-health and telemedicine.

Author Articles
A Trust Management System for the Nigerian Cyber-health Community

By Ifeoluwani Jenyo Elizabeth A. Amusan Justice O. Emuoyibofarhe

DOI: https://doi.org/10.5815/ijitcs.2023.01.02, Pub. Date: 8 Feb. 2023

Trust is a basic requirement for the acceptance and adoption of new services related to health care, and therefore, vital in ensuring that the integrity of shared patient information among multi-care providers is preserved and that no one has tampered with it. The cyber-health community in Nigeria is in its infant stage with health care systems and services being mostly fragmented, disjointed, and heterogeneous with strong local autonomy and distributed among several healthcare givers platforms. There is the need for a trust management structure for guaranteed privacy and confidentiality to mitigate vulnerabilities to privacy thefts. In this paper, we developed an efficient Trust Management System that hybridized Real-Time Integrity Check (RTIC) and Dynamic Trust Negotiation (DTN) premised on the Confidentiality, Integrity, and Availability (CIA) model of information security. This was achieved through the design and implementation of an indigenous and generic architectural framework and model for a secured Trust Management System with the use of the advanced encryption standard (AES-256) algorithm for securing health records during transmission. The developed system achieved Reliabity score, Accuracy and Availability of 0.97, 91.30% and 96.52% respectively.

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Development and Evaluation of Mobile Telenursing System for Drug Administration

By Justice O. Emuoyibofarhe Joseph O. ADIGUN Emuoyiborfarhe N. Ozichi

DOI: https://doi.org/10.5815/ijieeb.2020.04.05, Pub. Date: 8 Aug. 2020

Mobile telenursing is an emerging sub-field of mobile health that proposes remote nursing care provision with the aim of reducing stress on nurses by enabling them to spend more time on direct patient care rather than indirect care. Most existing mobile telenursing systems are limited to a simple phone call and Short Message Service (SMS) alert thereby rendering them inadequate to support indirect patient cares such as remote prescription based on consultant’s advice and monitoring of drug usage adherence. Hence, this research developed a Mobile Telenursing and Drug Administration System (MTS) characterized by support for the aforementioned indirect patient cares. Domain-Driven Design was employed in designing MTS client-server architecture and its framework. The framework was implemented by developing an SMS reminder system and client mobile application which maintains real-time communication with the webserver. The MTS, which supports real-time consultation between nurse-patient and nurse-consultant, was developed for mobile devices running the Android operating system using the framework implemented. The MTS was programmed using JavaScript and Pusher real-time messaging library of the Android development Kits. The performance of the MTS was evaluated through users’ assessment by administering a set of questionnaire on purposively selected 64 nurses; 53 patients chosen from four (4) purposively selected hospitals (General Hospital, New Bussa; Bowen University Teaching Hospital, Ogbomoso; Alimosho General Hospital, Lagos and Ifako-Ijaiye General Hospital, Lagos). The responses obtained from the questionnaires were statistically evaluated to determine MTS suitability for drug usage monitoring, remote prescription, palliative care, injection administration, patients’ clean-up and follow-up treatment using a correlation test. Also, mean effectiveness and mean acceptability of MTS were evaluated using a t-test at 0.05 level of significance. The MTS was deployed on mobile devices running the Android operating system. The results of performance evaluation revealed that MTS recorded suitability values of drug usage monitoring (r = 0.656), remote prescription (r = 0.829), palliative care (r = 0.925), injection administration (r = -0.772), patients’ clean-up (r = -0.841) and follow-up treatment (r = 0.868) of patients at (p < 0.05). Similarly, MTS recorded mean effectiveness and acceptability values of 3.596 and 3.32, 3.770 and 3.36 at (p < 0.05) for nurses and patients, respectively. The research developed MTS which is suitable for the provisioning of remote nursing care and drug administration without breaking basic medical ethics. The system can be adopted for mobile telenursing and drug administration system.

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Early Skin Cancer Detection Using Deep Convolutional Neural Networks on Mobile Smartphone

By Justice O. Emuoyibofarhe Daniel Ajisafe Ronke S. Babatunde Meinel Christoph

DOI: https://doi.org/10.5815/ijieeb.2020.02.04, Pub. Date: 8 Apr. 2020

Malignant melanoma is the most dangerous kind of skin cancer. It is mostly misidentified as benign lesion. The chance of surviving melanoma disease is high if detected early. In recent years, deep convolutional neural networks have attracted great attention owing to its outstanding performance in recognizing and classifying images. This research work performs a comparative analysis of three different convolutional neural networks (CNN) trained on skin cancerous and non-cancerous images, namely: a custom 3-layer CNN, VGG-16 CNN, and Google Inception V3.
Google Inception V3 achieved the best result, with training and test accuracy of 90% and 81% respectively and a sensitivity of 84%. This work contribution is mainly in the development of an android application that uses Google Inception V3 model for early detection of skin cancer.

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Fuzzy-Based System for Determining the Severity Level of Knee Osteoarthritis

By Justice O. Emuoyibofarhe Taiwo K.F

DOI: https://doi.org/10.5815/ijisa.2012.09.06, Pub. Date: 8 Aug. 2012

The task of medical diagnosis, unlike other diagnostic processes is more complex because a lot of vagueness, linguistic uncertainty, subjectivity, measurement imprecision, natural diversity are all prominently present in medical diagnosis. Osteoarthritis (OA) of the knee is a major public health issue causing chronic disability and reduction in quality of life; it is reported to be associated with a significant decline in function and causes a higher rate of disability than any other chronic condition. Osteoarthritis (OA) exacts a cost in terms of pain, limited mobility, and decreased function among a wide range of individuals. With improvement in science and technology, intelligent computing has been used to assist in enhancing qualitative services.

This paper reports the development of a fuzzy-based system to determine the level of severity of knee osteoarthritis, given some input conditions. The system was implemented and simulated using MATLAB Fuzzy Logic Toolbox. The results are entrusting and promising based on the flexibility and case of adaptability.

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