Papers by STEPHEN FASHOTO
Optimizing Digital Solutions for Hyper-Personalization in Tourism and Hospitality, 2022
This chapter aims to review the tech evolution in hospitality, from services to eServices, that w... more This chapter aims to review the tech evolution in hospitality, from services to eServices, that will provide hyper-personalization in the hospitality field. In the past, the services were provided by hotels through diligent staff and supported by standardized and weak technology that was not allowed to provide personalized services by itself. Therefore, the study applied K-means and FCM clustering algorithms to cluster online travelers' reviews from TripAdvisor. The study shows that K-means clustering outperforms fuzzy c-means in this study in terms of accuracy and execution time while fuzzy c-means converge faster than K-means clustering in terms of the number of iterations. K-means achieved 93.4% accuracy, and fuzzy c-means recorded 91.3% accuracy.
Of late, pressure and need to manage emergencies in production systems with multiple objective fu... more Of late, pressure and need to manage emergencies in production systems with multiple objective functions are mounting. Information and Communication Technology (ICT) is one system where optimal response is needed for high level management amidst complex conflicting selection criteria. This paper designs an approach for solving this class of complexity in Multi-Objective Linear Programming problems (MOLP) with fuzzy objective functions. We take advantage of the Embedding theorem for fuzzy numbers to measure complex functions with several quantities within a production channel. Using C++ (Netbeans) with MATLAB, some design comparison and analysis with Nearest Interval Approximation (NIA) based defuzzification works are carried out. The gains of the design presented here are demonstrated in some benchmark problems solved for ease of application.
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2022
The overarching aim of this study is to develop a soft-computing system for the differential diag... more The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become 'confusable'. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians' initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of...
Healthcare insurance, delivered via the National Health Insurance Scheme (NHIS) is a veritable to... more Healthcare insurance, delivered via the National Health Insurance Scheme (NHIS) is a veritable tool for making quality healthcare available to the majority of Nigerian citizens, irrespective of income status. This scheme, however, faces imminent collapse if an effective way of grouping health insurance claims is not found. Health insurance claims account for a significant portion of all claims received by insurers amounting to billions of naira annually. Thus, this study focused on application of data mining techniques that could help to drastically reduce the time spent on segmenting health insurance claims in health insurance administration in Nigeria. The specific objectives were to: i) develop and implement "real-time assignment" K-means clustering algorithm on health insurance claims and ii) develop and implement "modified initialize scheme" K-means clustering algorithm on health insurance claims. The proposed algorithms for the improved K-Means Clustering a...
SSRN Electronic Journal, 2021
The spread of COVID-19 pandemic continues to burden education sectors globally leading to the can... more The spread of COVID-19 pandemic continues to burden education sectors globally leading to the cancellation of face-to-face teaching and temporary closure of schools and universities in some countries. To ensure that teaching and learning continue during national lockdowns and other COVID-19 measures, schools and universities resort to online learning or distance-learning exacerbating existing inequalities. Such transformation mostly affected students pursuing practicum oriented professions such as science, technology, engineering, technology and mathematics (STEM). Such professions require rigorous training and lab sessions, especially in computer programming courses. However, computer programming courses are perceived as difficult, complex and challenging especially in higher education. Therefore, the study sought to investigate factors influencing science, technology, engineering and mathematics students’ academic performance in first-year computer programming courses in higher education. The study revealed that besides the impact of COVID-19 pandemic, students’ academic performance in computer programming courses is influenced several factors including computer programming stereotype, digital literacy level, learning environment, pedagogical approaches, student’s cognitive learning style, the complexity of programming course setup, instructors’ competence to teach programming subject, availability and access to internet and computers, online programming materials, student’s coding background, self-discipline, student’s engagement in programming lessons, motivation, eagerness to get good marks in computer programming, students’ higher-order programming and thinking skills, students’ lecture and lab attendance.
Sustainable Operations and Computers, 2021
COVID-19 pandemic expedites the development of digital technologies to tackle the spread of the v... more 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 utilisation 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.
Data Science and Management, 2021
Computational Intelligence is not just about robots. It is also about understanding the nature of... more Computational Intelligence is not just about robots. It is also about understanding the nature of intelligent thought and action using computers as experimental devices. New applications using computational intelligence are still being developed, although computational intelligence is an established field. The essence of this keynote address is to give a general picture of the research directions which may give an insight into the future of this research area. Meanwhile, an attempt to comprehensively address how computational intelligence may enhance the progress of global solar radiation can be addressed in near future.
International Journal of Physical Sciences, 2016
The inability of a region to access a webpage, because of the ban being placed on users from that... more The inability of a region to access a webpage, because of the ban being placed on users from that region as a result of its location policy, has led to this study. This problem is often solved by anonymizing web traffic by using The Onion Router (TOR). These tools, however, suffer from the problem of exposure of identity and also lack the ability to monitor web users. This study describes in detail a web proxy server service solution within the context of a tertiary institution in Nigeria and explains how this service improves the user experience. An identity management system using a web proxy server was developed to tackle these problems. The new system proxy was designed using a transparent proxy model with some additional translational features where no modification was done to the response or request of resources, other than the addition of its identification information or that of the server from which the message was recovered, and mediation of resources. Redeemer’s Universit...
The healthcare industry today has grown rapidly and emphasizing the efficiency and effectiveness ... more 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 i...
SSRN Electronic Journal, 2020
The outbreak of COVID-19 affects Zimbabwe’s education sector leading to disruption of teaching an... more The outbreak of COVID-19 affects Zimbabwe’s education sector leading to disruption of teaching and learning activities across the country. Though there are positive results from the implemented COVID-19 measures, however, higher institutions remained closed as cases continue escalating. COVID-19 poses tremendous challenges in higher institutions and opportunities to revise education policies and adopting emerging technologies for teaching and learning purposes are not yet explored in Zimbabwe. Therefore, this study aimed at providing a special reflection of the effects of COVID-19 in higher education and their responses to the pandemic to ensure safe re-opening. Also, the paper presents the critical role of emerging technologies in teaching and learning. To achieve that we applied literature search of memos, reports from the Ministry of Higher Education, COVID-19 reports and guidelines and articles from Google Scholar, Elsevier and Web of Science. The study revealed that there is significant progress in preventing the spread of COVID-19 and reopening is inevitable. The study recommends colleges and universities improve internet bandwidth, provision of psychosocial support services, create hybrid learning model, increase Wi-Fi access points, change of learning and pedagogical policies, implementation of phased learning approach and training of staff and students as institutions of learning reopen.
This maiden edition of the “Book of Abstracts” contains the abstracts of the various presentation... more This maiden edition of the “Book of Abstracts” contains the abstracts of the various presentations in the 2015 AUSTECH International conference by distinguished professors in the fields of Computer Science, Materials Science & Engineering and Petroleum Engineering. Each field as a track has different activities which includes poster presentations, PhD paper contests, technical sessions and panel discussions. These presentations were done by distinguished experts in the field from within Nigeria and across the globe. AUSTECH is an annual event by AUST. It focuses on current developments in Engineering technologies, scientific and industrial applications for development in Sub-Saharan Africa.
Informatics in Medicine Unlocked, 2021
Abstract Electronic Health Records (EHRs) hold symptoms of many diverse diseases and it is impera... more Abstract Electronic Health Records (EHRs) hold symptoms of many diverse diseases and it is imperative to build models to recognise these problems early and classify the diseases appropriately. This classification task could be presented as a single or multi-label problem. Thus, this study presents Psychotic Disorder Diseases (PDD) dataset with five labels: bipolar disorder, vascular dementia, attention-deficit/hyperactivity disorder (ADHD), insomnia, and schizophrenia as a multi-label classification problem. The study also investigates the use of deep neural network and machine learning techniques such as multilayer perceptron (MLP), support vector machine (SVM), random forest (RF) and Decision tree (DT), for identifying hidden patterns in patients’ data. The study furthermore investigates the symptoms associated with certain types of psychotic diseases and addresses class imbalance from a multi-label classification perspective. The performances of these models were assessed and compared based on an accuracy metric. The result obtained revealed that deep neural network gave a superior performance of 75.17% with class imbalance accuracy, while the MLP model accuracy is 58.44%. Conversely, the best performance in the machine learning techniques was exhibited by the random forest model, using the dataset without class imbalance and its result, compared with deep learning techniques, is 64.1% and 55.87%, respectively. It was also observed that patient’s age is the most contributing feature to the performance of the model while divorce is the least. Likewise, the study reveals that there is a high tendency for a patient with bipolar disorder to have insomnia; these diseases are strongly correlated with an R-value of 0.98. Our concluding remark shows that applying the deep and machine learning model to PDD dataset not only offers improved clinical classification of the diseases but also provides a framework for augmenting clinical decision systems by eliminating the class imbalance and unravelling the attributes that influence PDD in patients..
Sustainable Operations and Computers, 2021
Human Behavior and Emerging Technologies, 2021
Abstract Zimbabwe is among the countries affected with the coronavirus disease (COVID‐19) and imp... more Abstract Zimbabwe is among the countries affected with the coronavirus disease (COVID‐19) and implemented several infection control and measures such as social distancing, contact tracing, regular temperature checking in strategic entry and exit points, face masking among others. The country also implemented recursive national lockdowns and curfews to reduce the virus transmission rate and its catastrophic impact. These large‐scale measures are not easy to implement, adhere to and subsequently difficult to practice and maintain which lead to imperfect public compliance, especially if there is a significant impact on social and political norms, economy, and psychological wellbeing of the affected population. Also, emerging COVID‐19 variants, porous borders, regular movement of informal traders and sale of fake vaccination certificates continue to threaten impressive progress made towards virus containment. Therefore, several emerging technologies have been adopted to strengthen the health system and health services delivery, improve compliance, adherence and maintain social distancing. These technologies use health data, symptoms monitoring, mobility, location and proximity data for contact tracing, self‐isolation, and quarantine compliance. However, the use of emerging technologies has been debatable and contentious because of the potential violation of ethical values such as security and privacy, data format and management, synchronization, over‐tracking, over‐surveillance and lack of proper development and implementation guidelines which impact their efficacy, adoption and ultimately influence public trust. Therefore, the study proposes ethical framework for using emerging technologies to contain the COVID‐19 pandemic. The framework is centered on ethical practices such as security, privacy, justice, human dignity, autonomy, solidarity, beneficence, and non‐maleficence.
Journal of Computer Science and Its Application, 2021
The Nigeria ports plays a vital role in socio-economic growth by being a cheap mode of conveying ... more The Nigeria ports plays a vital role in socio-economic growth by being a cheap mode of conveying shipments for importation and exportation. The number of vessels coming into the Nigerian ports every year is on the average of about 4,900. A well flourishing and efficient ports and cargo management will in no doubt put a developing economy such as Nigeria in a leading pedestal with developed nations. Thus, stakeholders in container terminals are concerned about discharging containers as fast as possible, with the purpose of saving terminal costs. This study is driven to minimize the time being used up by ships in container terminal using genetic algorithm (GA) and thus attain maximum efficiency. The limited berth space in the wharf lead to berth allocation problem (BAP) and an optimal solution is required. Moreover, high berth occupancy results in congestion where vessels are queuing to be served. This leads to high turn-around time and results in bad service for the container termin...
Public Health in Practice, 2021
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Papers by STEPHEN FASHOTO