The emergence of the coronavirus disease 2019 (COVID-19) has brought unprecedented impacts on the global tourism industry. Starting in 2020, the virus spread from Wuhan, China, where it was first detected, to more than 216 countries around the world (WHO, 2021). To date, there has been more than 240 million confirmed cases and more than 4 million deaths arising from the virus (WHO, 2021). To reduce the spread of the disease, most countries initiated nationwide or community lockdowns, border closures, suspension of flights, or closure of public spaces and restrictions of public gatherings. These restrictions have had negative ramifications for the tourism industry, which stands as an important economic sector in most countries. In pre-pandemic 2019, international global tourist arrivals clocked 1.5 billion and the industry contributed US$3.5 trillion to the total direct gross domestic product (TDGDP) (UNWTO, 2022). However, by 2021, international tourist arrivals had dropped to 415 million and the TDGDP had also dropped to US$1.9 trillion (UNWTO, 2022). Because of the fragility of the tourism industry arising from the pandemic, there is a need for tourism organizations to effectively manage their destinations.
Chatibura, D. M. and Motshegwa, N. (2023) ‘Developing Virtual Tourism in the Wake of COVID-19: a Critical Function of Tourism Destination Management Organizations’, CABI Books. CABI International. doi: 10.1079/9781800621022.0002. Developing Virtual Tourism in the Wake of COVID-19: a Critical Function of Tourism Destination Management Organizations
Under-five mortality remains a global health concern as many countries have failed to achieve the United Nations Millennium Development Goal 4 (MDG 4). Children under five (under-fives) continue to perish to preventable deaths globally. Zimbabwe is amongst the Sub-Saharan African countries that failed to achieve the MDG 4 on under-five mortality. Regardless of evidence from other regions that emerging technologies help eliminate preventable deaths among under-fives, Zimbabwe’s adoption of such technologies in public health facilities remains nascent.
Batani, J. and Maharaj, M.S., 2023, March. A Framework for the Adoption of Emerging Technologies to Reduce Under-Five Mortality in Zimbabwe. In 2023 Conference on Information Communications Technology and Society (ICTAS) (pp. 1-6). IEEE.
Android applications are indispensable resources that facilitate communication, health monitoring, planning, data sharing and synchronization, social interaction, business and financial transactions. However, the rapid increase in the smartphone penetration rate has consequently led to an increase in cyberattacks. Smartphone applications use permissions to allow users to utilize different functionalities, making them susceptible to malicious software (malware). Despite the rise in Android applications’ usage and cyberattacks, the use of deep learning (DL) models to detect emerging malware in Android applications is still nascent. Therefore, this review sought to explain DL models that are applied to detect malware in Android applications, explore their performance as well as identify emerging research gaps and present recommendations for future work. This study adopted the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to guide the review. The study revealed that convolutional neural networks, gated recurrent neural networks, deep neural networks, bidirectional long short-term memory, long short-term memory (LSTM) and cubic-LSTM are the most prominent deep learning-based malicious software detection models in Android applications. The findings show that deep learning models are increasingly becoming an effective technique for malicious software detection in Android applications in real-time. However, monitoring and tracking information flow and malware behavior is a daunting task because of the evolving nature of malware and human behavior. Therefore, training mobile application users and sharing updated malware datasets is paramount in developing detection models. There is also a need to detect malicious software before downloading mobile applications to improve the security of Android smartphones.
Elliot M,Benhildah M., Batani J. c, Nobuhle M ( 2023) A review of deep learning models to detect malware in Android applications. Cyber Security and Applications
Volume 1, December 2023, 100014
Educational data mining has become an effective tool for exploring the hidden relationships in educational data and predicting students’ academic performance. The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. To achieve qualitative education standard, several attempts have been made to predict the performance of the student, but the prediction accuracy is not acceptable. The main purpose of this research is significantly predict the student performance to improve the academic results. In order to accomplish the prediction with supplementary exactness, XGBoost based methods have been adopted. This work introduces a novel hybrid Lion-Wolf optimization algorithm to solve the problem of feature selection. Two level overlap improves the exploitation part. First phase overlap is used for feature selection and second phase used for adding some more important information and improve the classification accuracy. The XGBoost classifier improved the classification accuracy which is most famous classifier based on wrapper method. XGboost model using two different parameter adjustment methods are compared. XGBoost based on hybrid Lion-Wolf optimization performs better than traditional XGBoost on training accuracy and efficiency. Experiments are applied using the dataset and results prove that proposed algorithm outperform and provide better results.
Viswanathan, S. Vengatesh K (2023) A NOVEL APPROACH FOR ANALYSIS AND PREDICTION OF STUDENTS ACADEMIC PERFORMANCE USING MACHINE LEARNING ALGORUTHMS. Semiconductor Optoelectronics, Vol. 42 No. 1 (2023), 674-68
Many elderly people have hypertension, and health systems are faced with the challenge of responding to the needs of this population. The availability of Nutrition Management Service (NMS) in Outpatient Department (OPD) services is one of the strategies that have the potential to control these patients’ blood pressure. The study sought to investigate the availability of NMS for the hypertensive outpatient elderly in Thamae Health Centre and Khubetsona Health Centre. The study was a descriptive cross-sectional study employing both qualitative and quantitative data collection methods. NMS was found to be available in the facilities, but incomplete. Of all the patients followed, 16.2% received nutrition counselling and 9.5% received nutrition follow-up, but none of them received nutrition screening. NMS is available in the facilities, but it is incomplete, due to many factors which originate from the planners’ failure.
Motsieloa, Lineo Florina, and Mpati Evelyn Fosa. “INVESTIGATING THE AVAILABILITY OF NUTRITION MANAGEMENT SERVICE FOR HYPERTENSIVE ELDERLY PEOPLE IN KHUBETSOANA AND THAMAE HEALTH CENTRES.” Pharmacology 3, no. 1 (2023): 103-123.
This study aims to explore the influence of surprise and delight on the loyalty intentions of retail banking customers in an emerging market context. This study also considers the moderating effect of trust on these relationships.
Svotwa, T.D., Makanyeza, C., Roberts-Lombard, M. and Jaiyeoba, O.O. (2023), “A relationship marketing perspective on delight, its antecedents and outcomes in a banking context”, European Business Review, Vol. 35 No. 3, pp. 306-336. https://doi.org/10.1108/EBR-09-2022-0170
Emmanuel Z, Molelekeng K., Thekiso M. (2023) Lessons From COVID-19: A Silver Lining For Teaching And Learning In Selected Lesotho Institutions Of Higher Learning. International Journal of All Research Writings Vol. 4 Issue. 8
There has been a myriad of negative implications on teaching and learning at tertiary level that came along with the emergence of Covid-19 pandemic. The purpose of this paper is to investigate the lessons and opportunities presented by the pandemic to the learning and teaching process in Higher Educational Institutions (HEIs). The study employed a survey design and used diary and semistructured questionnaires as key data generation tools. A sample of 80 lecturing staff was used from three HEIs in Lesotho adopting a stratified random sampling. The findings from the study suggest that the implementation of online teaching and learning brought positive implications to Lesotho’s HEIs as most of the sampled respondents supported the intervention. The study also revealed that the COVID19 pandemic exposed shortcomings such as a lack of staff capacity to teach online, adoption of blended learning, and teaching and infrastructural preparedness in tertiary institutions. The study further suggests that there is a need to redevelop and redesign the curriculum, harmonize policies of teaching and assessment, and align them to online learning and teaching. The study recommended an inclusive stakeholder approach at the national and institutional level to drafting and implementing online learning supportive policies. Further research is recommended to quality regulators of tertiary institutions to understand their insights and perceptions of adopting online teaching and learning in HEIs.
Government expenditure on health and economic growth in Botswana: Testing for cointegration and specification of deterministic components using the pantula principle. International Journal of Research in Business and Social Science (2147-4478), 12(2), pp.204-216.
This study examines the relationship between government expenditure on health and economic growth in Botswana. It seeks to test the existence of cointegration and specification of the deterministic components with special reference to the Pantula Principle. This helps to overcome the shortfall of the method by Johansen, which may lead to spurious results by omitting the presence of deterministic components in the analysis. The cointegration approach is used and tested using three methods by Engle and Granger (1987) or EG, a procedure suggested by Johansen (1988) and error correction model (ECM) approach proposed by Granger(1988) and short-run analysis is made using the pairwise granger causality tests. Findings show that the correct model specification for testing long-run relationships consists of one cointegrating vector with a constant which is the most restrictive hypothesis according to the Pantula principle. Using the Johansen approach, total health expenditure and recurring health expenditure have a cointegration relationship with growth while development health expenditure and growth are not cointegrated. The ECM and the approach by EG confirm a weak and/or no cointegration between the variables. Growth has no effect on government expenditure on health in the short run, but a cointegration relationship suggests that it may marginally contribute to an increase in health expenditure over the long term. The study clarifies the correct model to test for cointegration and specification for the deterministic component. It confirms the existence of a healthcare expenditure-led growth hypothesis.
Sinha, N. and Mbulawa, S., 2023. Government expenditure on health and economic growth in Botswana: Testing for cointegration and specification of deterministic components using the pantula principle. International Journal of Research in Business and Social Science (2147-4478), 12(2), pp.204-216.
This chapter introduces the concept of financial globalization and examines the factors driving financial globalization in emerging and developing market economies. The role of financial globalization in driving the development and strengthening of the financial sector, sustainable economic growth, and the nature of innovations are explored. On a broader scale, there is a need to understand the developments in global financial innovation and their implications for developing and emerging markets. The chapter explores the challenges, risks and benefits of financial globalization to emerging and developing markets and how they will shape future behavior and interactions by economic agents in these markets. Financial globalization can lead to different outcomes that include but not limited to domestic capital flight and potential effects on net capital flows, investment, and growth; capital inflows and higher investment and growth; or volatile capital flows and unstable domestic financial markets. The chapter discusses the measurement issues of financial openness. These all need to be explored in this context and consider the rise in innovations in the financial sector.
Applied Intelligence, pp.1-38.
HUIM (High utility itemsets mining) is a sub-division of data mining dealing with the task to obtain promising patterns in the quantitative datasets. A variant of HUIM is to discover the HAUIM (High average-utility itemsets mining) where average-utility measure is used to obtain the utility of itemsets. HAUIM is the refined version of FIM (Frequent itemset mining) problem and has various applications in the field of market basket analysis, bio-informatics, text mining, network traffic analysis, product recommendation and e-learning among others. In this paper, we provide a comprehensive survey of the state-of-the-art methods of HAUIM to mine the HAUIs (High average-utility itemsets) from the static and dynamic datasets since the induction of the HAUIM problem. We discuss the pros and cons of each category of mining approaches in detail. The taxonomy of HAUIM is presented according to the mining approaches. Finally,various extensions, future directions and research opportunities of HAUIM algorithms are discussed.
Singh, K., Kumar, R. and Biswas, B., 2021. High average-utility itemsets mining: a survey. Applied Intelligence, pp.1-38. High average-utility itemsets mining: a survey | SpringerLink