Olumide Owolabi

University of Abuja

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Exploring structural equations modelling on the use of modified UTAUT model for evaluating online learning

Educational Technology Quarterly (2024)

Stephen Gbenga Fashoto Yinusa Akintoye Faremi Elliot Mbunge Olumide Owolabi

The sudden shift from traditional face-to-face classes to online learning during the COVID-19 pandemic has created a need to understand how well online learning is crucial and being accepted, particularly in developing countries. The Internet has enabled international communication and interaction, removing distance and space barriers between Lecturers and students. In some higher education institutions, technology has been gradually integrated into their teaching methods, utilising Learning Management Systems (LMS). This study aims to assess the factors that influence students' intention and use behaviour of online resources using the Unified Theory of Acceptance and Use of Technology (UTAUT). The results show that effort expectancy positively influences students’ behavioural intention to use online learning platforms such as Moodle, but facilitating conditions, performance expectancy, and social influence do not. Finally, results in this study also show that students’ behavioural intention positively influences students’ user behaviour to use the online learning platform. This study suggests that decision-makers should recommend and implement policies to address the challenges students learning from home might face during pandemics to ensure they can continue their education without unnecessary obstacles. This is particularly important in countries like Eswatini, where the cost of internet connectivity is high.

10.55056/etq.734

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Data Mining Approaches in the Study of the Nigerian Informal Sector

International Journal of Advances in Scientific Research and Engineering (2019)

Ernest Paul Olumide Owolabi

The vast amounts of economic data currently being generated and collected calls for the application of new methodologies in order to make sense of them. It is for this reason that we have undertaken the analysis of some of data collected on the informal sector of the Nigerian economy using data mining and machine learning techniques. This is different from the traditional bare statistical methods hitherto being used in such analysis. In this work, we used data gathered by the National Salaries, Incomes and Wages Commission (NSIWC) on the informal sector of the Nigerian economy between the year 2014 and 2016. These data were subjected to analysis using WEKA data mining/machine learning tools and the Random Forest ensemble algorithm. The results show that the modal population age group of Nigerians working in the informal sector is in the age bracket of 30-44 years. Similarly, it was discovered that wholesale business is the dominant activity in the informal sector and women's participation in informal sector business is still low compared to their male counterparts. This study shows the relevance or implication of utilizing data mining methodologies over the conventional statistical analysis of data.

Nigerians

Informal sector

Business sector

Relevance

10.31695/ijasre.2019.33565

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A New Facial Image Deviation Estimation and Image Selection Algorithm (Fide-Isa) for Facial Image Recognition Systems: The Mathematical Models

Hassan Opotu Siyaka Olumide Owolabi Hashim Ibrahim Bisallah

Deep learning models have been successful and shown to perform better in terms of accuracy and efficiency for facial recognition applications. However, they require huge amount of data samples that were well annotated to be successful. Their data requirements have led to some complications which include increased processing demands of the systems where such systems were to be deployed. Reducing the training sample sizes of deep learning models is still an open problem. This paper proposes the reduction of the number of samples required by the convolutional neutral network used in training a facial recognition system using a new Facial Image Deviation Estimation and Image Selection Algorithm (FIDE-ISA). The algorithm was used to select appropriate facial image training samples incrementally based on their facial deviation. This will reduce the need for huge dataset in training deep learning models. Preliminary results indicated a 100% accuracy for models trained with 54 images (at least 3 images per individual) and above.

Sample (material)

10.1109/icmeas52683.2021.9739810

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Knowledge and attitudes towards cervical cancer and human papillomavirus: a Nigerian pilot study.

PubMed (2010)

Obiageli Nnodu Nnodu Layi Erinosho Mustapha Jamda O Olaniyi Rabi Adelaiye

This study aimed to ascertain the knowledge and attitudes of urban and rural dwellers to cervical cancer and HPV in Gwagwalada Area Council of Nigeria. 400 participants aged 15-45 years were selected from Gwagwalada town and the adjourning Giri village to respond to a multi-choice-free response questionnaire designed to obtain information on respondents' biodata, knowledge of STIs, human papilloma virus and cervical cancer, health and communication resources in their communities. This was supplemented by focus group discussions among religious and tribal groups within the urban and rural communities. We found a low level of awareness about HPV and cervical cancer which majority felt could not be prevented. Although awareness of STDs was high in both urban and rural dwellers, condom use was low. The study underscores the need for a well planned and implemented health communication and education program on STIs, HPV and cervical cancer in Nigeria.

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Academic performance prediction using Machine Learning algorithms

Journal of Applied Science Information and Computing (2024)

Olumide Owolabi R. Obadaki

An excellent secondary school education becomes evident in students’ performance after they graduate or further their education. No matter their career choice, they can genuinely excel if they can identify areas that require them to put in more effort to have an overall excellent performance in school. In Nigeria, several solutions address students' learning needs or the administrative needs of the schools. Still, no systems cater to analysing and monitoring students’ performance, causing failures that can be averted. This dissertation reviews five different machine learning algorithms using data from students in public and private schools in rural and urban Nigeria to identify which algorithm performs best in predicting students’ performance using the Waikato Environment for Knowledge Analysis tool for modelling and the cross-industry standard process for data mining (CRISP-DM) research methodology. The result shows the Decision Tree as the algorithm with the best performance for the dataset. It is recommended that the findings be used to build a system embedded into a school management or learning management software to enable students, parents, and teachers to channel the right resources into areas where it has been predicted that the student will underperform to change the narrative.

10.59568/jasic-2024-5-1-06

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HANDOVER MANAGEABILITY AND PERFORMANCE MODELING IN MOBILE COMMUNICATION NETWORKS

Imeh Umoren Prince Oghenekaro Asagba Olumide Owolabi

In cellular Networks, a mobile station (MS’s) move from one cell region to another on seamless Communicationscheduling.. Handoff or Handover is an essential issue for the seamless communication. Several approaches havebeen proposed for handoff performance analysis in mobile communication systems. In Code-Division Multiple-Access (CDMA) systems with soft handoff, mobile stations (MS’s) within a soft-handoff region (SR) use multipleradio channels and receive their signals from multiple base stations (BS’s) simultaneously. Consequently, SR’sshould be investigated for handoff analysis in CDMA systems. In this paper, a model for soft handoff in CDMAnetworks is developed by initiating an overlap region between adjacent cells facilitating the derivation of handoffmanageability performance model. We employed an empirical modelling approach to support our analyticalfindings, measure and investigated the performance characteristics of typical communication network over a specificperiod from March to June, 2013 in an established cellular communication network operator in Nigeria. Theobserved data parameters were used as model predictors during the simulation phase. Simulation results revealedthat increased system capacity degrades the performance of the network due to congestion, dropping and callblocking, which the system is most likely to experience, but the rate of those factors could be minimized by properlyconsidering the handoff probabilities level. Comparing our results, we determined the effective and efficientperformance model and recommend it to network operators for an enhanced Quality of Service (QoS), which willpotentially improve the cost-value ratio for mobile users and thus confirmed that Soft Handoff (SH) performancemodel should be carefully implemented to minimize cellular communication system defects. Keywords : CDMA, QoS, optimization, Handoff Manageability, Congestion, Call Blocking and Call Dropping, .

Soft handover

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A modified UTAUT model for the acceptance and use of digital technology for tackling COVID-19

Sustainable Operations and Computers (2021)

Boluwaji Akinnuwesi Faith‐Michael E. Uzoka Stephen Gbenga Fashoto Elliot Mbunge Adedoyin Odumabo

COVID-19 pandemic expedites the development of digital technologies to tackle the spread of the virus. Several digital interventions have been deployed to reduce the catastrophic impact of the pandemic and observe preventive measures. However, the adoption and utilization of these technologies by the affected populace has been a daunting task. Therefore, this study carried out exploratory investigation of the factors influencing the behavioural intention (BI) of people to accept COVID-19 digital tackling technologies (CDTT) using the UTAUT (Unified Theory of Acceptance and Use of Technology) framework. The study applied principal components analysis and multiple regression analysis for hypotheses testing. The study revealed that performance expectancy (PE), facilitating conditions (FC) and social influence (SI) are the best predictors of people's BI to accept CDTT. Also, organizational influence and benefit (OIB) and government expectancy and benefits (GEB) influence the people's BI. However, variables such as age, gender and voluntariness to use CDTT have no significance to influence BI because the CDTT is still nascent and not easily accessible. The results show that the decision-makers and regulators should consider inciting variables such as PE, FC, SI, OIB and GEB, that motivate the acceptance and use of CDTT. Furthermore, the populace must be sensitized to the availability and use of CDTT in all communities. Also, the path diagram and hypothesis testing results for CDTT acceptance and use, will help government and private organizations in planning and responding to the digitalization of COVID-19 protective measures and hence revise the COVID-19 health protection regulation.

Pandemic

Voluntariness

10.1016/j.susoc.2021.12.001

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Hybrid Methods for Credit Card Fraud Detection Using K-means Clustering with Hidden Markov Model and Multilayer Perceptron Algorithm

British Journal of Applied Science & Technology (2015)

Stephen Gbenga Fashoto Olumide Owolabi Oluwafunmito Adeleye Joshua Wandera

Credit card fraud

Multilayer perceptron

10.9734/bjast/2016/21603

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Citations (21)

Implementing newborn screening for sickle cell disease as part of immunisation programmes in Nigeria: a feasibility study

The Lancet Haematology (2020)

Obiageli Nnodu Alayo Sopekan Uche Nnebe‐Agumadu Chinatu Ohiaeri Adeyemi Adeniran

BackgroundSickle cell disease is highly prevalent in sub-Saharan Africa, where it accounts for substantial morbidity and mortality. Newborn screening is paramount for early diagnosis and enrolment of affected children into a comprehensive care programme. Up to now, this strategy has been greatly impaired in resource-poor countries, because screening methods are technologically and financially intensive; affordable, reliable, and accurate methods are needed. We aimed to test the feasibility of implementing a sickle cell disease screening programme using innovative point-of-care test devices into existing immunisation programmes in primary health-care settings.MethodsBuilding on a routine immunisation programme and using existing facilities and staff, we did a prospective feasibility study at five primary health-care centres within Gwagwalada Area Council, Abuja, Nigeria. We systematically screened for sickle cell disease consecutive newborn babies and infants younger than 9 months who presented to immunisation clinics at these five centres, using a lateral flow immunoassay-based point-of-care test (HemoTypeSC). A subgroup of consecutive babies who presented to immunisation clinics at the primary health-care centres, whose mothers gave consent, were tested by the HemoTypeSC point-of-care test alongside a different immunoassay-based point-of-care test (SickleSCAN) and the gold standard test, high-performance liquid chromatography (HPLC).FindingsBetween July 14, 2017, and Sept 3, 2019, 3603 newborn babies and infants who presented for immunisation were screened for sickle cell disease at five primary health-care centres using the ELISA-based point-of-care test. We identified 51 (1%) children with sickle cell anaemia (HbSS), four (<1%) heterozygous for HbS and HbC (HbSC), 740 (21%) with sickle cell trait (HbAS), 34 (1%) heterozygous for HbA and HbC (HbAC), and 2774 (77%) with normal haemoglobin (HbAA). Of the 55 babies and infants with confirmed sickle cell disease, 41 (75%) were enrolled into a programme for free folic acid and penicillin, of whom 36 (88%) completed three visits over 9 months (median follow-up 226 days [IQR 198–357]). The head-to-head comparison between the two point-of-care tests and HPLC showed concordance between the three testing methods in screening 313 newborn babies, with a specificity of 100% with HemoTypeSC, 100% with SickleSCAN, and 100% by HPLC, and a sensitivity of 100% with HemoTypeSC, 100% with SickleSCAN, and 100% by HPLC.InterpretationOur pilot study shows that the integration of newborn screening into existing primary health-care immunisation programmes is feasible and can rapidly be implemented with limited resources. Point-of-care tests are reliable and accurate in newborn screening for sickle cell disease. This feasibility study bodes well for the care of patients with sickle cell disease in resource-poor countries.FundingDoris Duke Charitable Foundation, Imperial College London Wellcome Trust Centre for Global Health Research, and Richard and Susan Kiphart Family Foundation.

Point-of-Care Testing

Gold standard (test)

Point of care

10.1016/s2352-3026(20)30143-5

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Decision support model for supplier selection in healthcare service delivery using analytical hierarchy process and artificial neural network

AFRICAN JOURNAL OF BUSINESS MANAGEMENT (2016)

G Stephen Akinnuwesi Boluwaji Olumide Owolabi Adelekan David

The healthcare industry today has grown rapidly and emphasizing the efficiency and effectiveness within the healthcare delivery systems has become a major priority in the field. In order to increase the satisfaction and safety of patient, hospitals must improve their overall performance. We established from our review that a number of models have been developed for supplier selection using diverse methods. Most of the models were used to evaluate the performance of healthcare service sector but there is little emphasis on suppliers of health service facilities. And also to the best of our search, we could not find research works on models for evaluating and selecting suppliers in the healthcare unit of tertiary institution. Hence our focus in this study is to develop a decision support model for evaluating and selecting suppliers in the healthcare service of universities. The use of manual techniques for supplier selection in healthcare unit of universities in developing countries is quite tedious and inefficient particularly when several criteria are taken into consideration. These make decision making difficult and also cause the health centre to frequently stock out. Moreover deciding when to order and how much to order is not very easy and hence not meeting patients’ demands adequately. This study focuses on investigating and developing a decision support model for evaluating and selecting suppliers in the healthcare service of tertiary institutions using analytical hierarchy process (AHP) and artificial neural network (ANN). Our case study is the health center of Redeemers University, Nigeria. According to the Overall Priority Vector, the priority values for the respective criteria are: Quality = 0.2192, Service = 0.2160, Delivery = 0.2102, Cost = 0.1968 and Risk = 0.1860. Our results revealed that the quality of product supply by the supplier is the most important criterion, while the risk on the supplies is the least important. To improve on the accuracy of these results, the AHP model was supplemented by a 3-layer artificial neural network, adding a learning component to the model. The result also shows that quality is the most important criterion, but with a high index of 0.6845 as opposed to 0.2192 for the AHP alone. This shows that the hybrid model is much better than the AHP alone. Key words: Supply chain management, AHP, ANN, decision making and supplier selection.

10.5897/ajbm2016.8030

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Citations (30)