Yakubu Bala Mohammed

This article presents a research investigation focusing on the effects of ChatGPT utilization on sustainable education and development. The study employed five machine learning (XGBoost, RF, SVM, GBDT, and ANN) models for predicting the impacts of ChatGPT usage in education, aiming at identifying the potential benefits of ChatGPT usage on learners, tutors, and possible future implications using the data collected via social networking sites. A total of 2,936 datasets concerning the impacts of ChatGPT utilization on sustainable education were analyzed. Four of the research AI-predictive models predicted the impacts of ChatGPT usage on sustainable education and development with greater accuracy with [ ] values of >.96. However, the XGBoost and RF models outperformed the other models with [ ] values >.98. The results indicate that the XGBoost model achieved the highest accuracy with [ ] > .98, training time(s) 8.4157, and testing time 0.0618 respectively. Furthermore, findings of the study revealed that utilization of ChatGPT increases students’ interest in learning, self-confidence, and ability to study independently. Also, the results of the study will provide valuable insights for education stakeholders in understanding the potential benefits, and future implications of ChatGPT usage on sustainable education, and provide direction for upcoming studies.

Expediency and suppleness were the main reasons for customers’ patronage of m-banking apps. However, data stored or transmitted in these apps are susceptible to different attacks, threats, and risks. Thus, the need for robust safety mechanisms to cope with these security and privacy challenges. The purpose of this research is to examine the different components of m-banking security that merit investigation, and the vulnerability of present authentication methods in order to propose a more robust verification technique. PRISMA preferred items reporting for Systematic Review and Meta-Analyses approach was used in this study. Six databases were utilized; IEEE-Explore, Scopus, EBSCOhost, Taylor & Francis, ScienceDirect, and Web of Science. About 1,149 articles were extracted from these databases out of which 38 articles met the review selection criteria, thus included in the review. Findings of the study highlight the efficacy of PRISMA method with regard to items reporting and identification of research gaps compared to the usual literature review. Also, the results of the study found intrusion via other apps stored on mobile devices, and device lost or theft were the main safety and privacy issues. Furthermore, the study findings discovered that the present authentication schemes used by banks are becoming weak and open to various attacks due to an increase in online fraud. Based on the review findings, an Artificial Intelligence-based user authentication and anomalies detection model was proposed which may provide direction for upcoming studies. Also, banks and other financial institutions can use the review results to improve m-banking security.

With recent advances in mobile and internet technologies, the digital payment market is an increasingly integral part of people’s lives, offering many useful and interesting services, e.g., m-banking and cryptocurrency. The m-banking system allows users to pay for goods, services, and earn money via cryptotrading using any device such as mobile phones from anywhere. With the recent trends in global digital markets, especially the cryptocurrency market, m-banking is projected to have a brighter future. However, information stored or conveyed via these channels is more vulnerable to different security threats. Thus, the aim of this study is to examine the influence of security and confidentiality on m-banking patronage using artificial intelligence ensemble methods (ANFIS, GPR, EANN, and BRT) for the prediction of safety and secrecy effects. AI models were trained and tested using 745 datasets obtained from the study areas. The results indicated that AI models predicted the influence of security with high precision (NSE > 0.95), with the GPR model outperformed the other models. The results indicated that security and privacy were key influential parameters of m-payment system patronage (m-banking), followed by service and interface qualities. Unlike previous m-banking studies, the study results showed ease of use and culture to have no influence on m-banking patronage. These study results would assist m-payment system stakeholders, while the approach may serve as motivation for researchers to use AI techniques. The study also provides directions for future m-banking studies.

Nowadays, movable banking apps are thriving in international pecuniary market due to its flexibility and convenience, especially during COVID-19 pandemic lock-down. Despites these benefits, its development continues to face a lot of challenges due to security, privacy, and authentication issues, especially in developing nations where elegant technologies and explicit cyberspace laws remain an issue. This study used PRISMA approach to systematically reviewed present m-banking studies with aims of identifying other security and privacy issues, and user authentication schemes challenges. Six scholarly databases of; IEEE Xplore, EBSCOhost, Science Direct, Scopus, Taylor and Francis, and Web of Science were searched. 38 articles were carefully read and analyzed meticulously. The study results exposed customers fear of third-party intrusion through other apps, device lost or theft with account information, financial loses, and absence of clear cyberspace laws to be the main safety and privacy issues. Also, the results found that the present authentication schemes used by banks are becoming weak and open to various attacks due to increase in online fraud. Thus, proposed two frameworks for investigating other dimensions of risk and trust factors, and for design of new user authentication scheme. Lastly, missing gaps in current studies, and direction for upcoming studies are mentioned.

Nowadays, mobile banking apps are becoming an integral part of people lives due to its suppleness and convenience. Despite these benefits, yet its growth in evolving states is beyond expectations. However, using mobiles devices to conduct financial transactions involved a lot of risk. This paper aims to investigate customers’ reasons for non-usage of the new conduits in developing countries with distinct interest in Nigeria. The study adopts two methods of analysis, artificial intelligence-based methods (AI), and structural equations modeling (SEM). A feed-forward neural network (FFNN) sensitivity examination technique was used to choose the most dominant parameters of mobile banking data collected from 823 respondents. Four algebraic directories were used to corroborate the study AI-based model. The study AI results found risk, trust, facilitating conditions, and inadequate digital laws to be the most dominant parameters that affect mobile banking growth in Nigeria, and discovered social influence and service quality to have no influence on Nigerians’ resolve to use moveable banking apps. Moreover, the results proved the superiority of AI-based models above the classical models. Government and pecuniary institutes can use the study outcomes to ensure secured services offering, and improve growth. Finally, the study suggests some areas for future studies.

Research has shown that effective and efficient learning management systems (LMS) were the main reasons for sustainable education in developed nations during COVID-19 pandemic. However, due to slow take-up of LMS many schools in developing countries, especially Africa were completely shut down due to COVID-19 pandemic. To fill this gap, 4 AI-based models; Support Vector Machine (SVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Boosted Regression Tree (BRT) were developed for prediction of LMS determinants. Nonlinear sensitivity analysis was employed to select the key parameters of the LMS determinants data obtained from 1244 schools’ students. Five statistical indices were used to validate the models. The performance results of the four developed AI models discovered facilitating conditions, attitude towards LMS, perceived enjoyment, users’ satisfaction, perceived usefulness, and ease of use to be the most significant factors that affect educational sustainability in Nigeria during COVID-19. Further, single model’s performance results comparison proved that SVM has the highest prediction ability compared to GPR, ANN, and BRT due to its robustness in handling data uncertainties. The study results identified the factors responsible for total schools’ closure during COVID-19. Future studies should examine the application of other linear and other nonlinear AI techniques.