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The study has two main objectives: firstly, to examine the opportunity of the Momentum and Contrarian investment strategy for three different monetary systems to trade currencies in Forex markets using Symlet wavelet decomposition approach. Secondly, to examine the co-movements between the three monetary systems using wavelet coherence analysis. The findings indicate that an investor with momentum strategy can consider investing in Bitcoin and Gold market, while the contrarian investment strategy is more advisable for the fiat money market during crisis period. Furthermore, the wavelet coherence analysis indicates that Bitcoin currency is the most leading monetary system during the Covid-19 pandemic crisis, followed by gold. However, US dollar mostly leads Bitcoin during non-crisis periods, while Gold is found to lead the US dollar throughout the sample period of the study. This suggests that the cryptocurrency system or gold standard should be considered as the alternative monetary system for better economic stability specially during the crisis period. Moreover, Bitcoin and gold had an anti-phase correlation before the Covid-19 pandemic crisis, which implies better benefits of hedging in the non-crisis period, while during a crisis they are moving together across different horizons. In contrast, Bitcoin and Fiat Money are strongly correlated during non-crisis periods, while during covide-19 pandemic crisis the correlation is statistically insignificant. Overall, the outcomes offer significant guidance for policymakers in understanding which monetary system leads to better economic stability during the crisis period and provides many implications for market players such hedging and Diversification investments strategy in forex markets.

Falls are a serious concern among the elderly due to being a major cause of harm to their physical and mental health. Despite their potential for harm, they can be prevented with proper care and monitoring. As such, the motivation for this research is to implement an algorithmic solution to the problem of falls that leverages the benefits of Machine Learning to detect falls in the elderly. There are various studies on fall detection that works on one dataset: wearable, environmental, or vision. Such an approach is biased against low fall detection and has a high false alarm rate. According to the literature, using two datasets can result in high accuracy and lower false alarms. The purpose of this study is to contribute to the field of Machine Learning and Fall Detection by investigating the optimal ways to apply common machine and deep learning algorithms trained on multimodal fall data. In addition, it has proposed a multimodal approach by training two separate classifiers using both Machine and Deep Learning and combining them into an overall system using sensor fusion in the form of a majority voting approach. Each trained model outputs an array comprising three percentage numbers, the average of the numbers in the same class from both arrays is then computed, and the highest percentage is the classification result. The working system achieved results were 97% accurate, with the highest being achieved by the Convolutional Neural Network algorithm. These results were higher than other state-of-the-art research conducted in the field.

Equity is the basic structural component of the development of any business, so it is important for an industrial enterprise to develop tools to determine its effective management. It it is impossible without conducting qualitative financial analysis, timely and effective adaptation of valuation approaches in the modern conditions of functioning of enterprises. The effective functioning of enterprises is impossible without the use of modern complex methods, which include not only a methodology, a system of indicators, but also modern technologies for their assessment, planning and control. Information management is one of these technologies. To solve the problems of the study we used the method of structural-logical analysis, methods of theoretical generalization and comparative analysis, synthesis and method of grouping of absolute and relative values, graphical method and information management methods. The result of the study is the formation of a comprehensive approach to the financial analysis of the equity of the enterprise on the formed system of four groups of indicators. These indicators take into account the state of equity, its efficiency, ensuring risks in the process of the enterprise and directions of financial analysis.

Over the last few decades, both developed and developing countries have an increasing trend in technology but with an enormous gap, particularly in the education sector. The e-learning following institutions can achieve benefits by evaluating their e-learning readiness through up-front analysis. Moreover, several models have been introduced to measure e-learning readiness for developed countries but these are not adequate for developing countries.  This paper introduced the e-learning readiness evaluation model for the developing country, Libya, by considering the primary education sector. Furthermore, this study examines the e-learning level of readiness in the staff of the primary school. The purpose of this study is to evaluate the e-learning readiness of staff by directing factors of e-learning readiness i.e. cultural readiness, content readiness, and technology readiness. To achieve this objective, this paper collects data through questionnaires, and respondents are 110 staff member of primary schools in Yefren, Libya. Therefore, the multivariate analysis shows that the e-learning readiness factors have e significant relationship with the adoption of e-learning because most teachers are well prepared and ready. Likewise, results indicate that technology is the most significant factor instead of other e-learning readiness factors. According to the views of staff, there should be more content development training required for primary school staff. Thus, the demographic structure is inadequate to enhance the e-learning but the staff is ready for e-learning. Consequently, this study emphasizes the significance of cultural readiness and its relationship with the adoption of e-learning in primary education sectors’ development in Yefren, Libya.

The cybersecurity in the modern world has become global, and cyber attacks are becoming more complex and large-scale. In the system of civil and criminal justice, computer forensics helps to ensure the integrity of digital evidence presented in court cases. The purpose of this study is to develop scientifically sound proposals and recommendations for the implementation of tools for forensic research of computer tools and systems in the fight against cybercrime. The relevance of this study is due to the need to implement active ways to protect and combat cybercrime. To achieve the goal of the study, methodological principles and approaches of legal science were used. It is proposed to use computer forensic methods more widely research in the fight against cybercrime.This study identifies the types of computer forensics: forensics database; electronic forensics; malware forensics; criminology of memory; mobile forensics; network forensics. The authors foundlack of a regulatory mechanism to regulate cybersecurity, capture and use of digital evidence and the regulatory framework for international cooperation. To brought need in strengthening international cooperation and in developing appropriate policies and legislative initiatives of security and network and information systems, improvement legislation in the field countering cybercrime.

This study investigates the relationship between the user interface and problem-solving towards the continuous intention to use the services. New products or services will always face tough challenges for the customer, especially when the new procedures require them to learn and change some behaviors. Chatbots are also facing the same situation in Malaysia, where customers refuse to accept using chatbots to represent their physical presence. To understand customer behaviors, a quantitative survey was designed. Four hundred twenty-two data were collected from the online survey method. As per the results, the predictors of chatbot continuous intention are user interface and problem-solving. Apart from that, this study also measures the role of mediator, namely trust and customer satisfaction. This study contributes to unique academic and practical insights that can be used to explore the effectiveness of chatbots. The results revealed that both predictors were significant towards the continuous intentions. Besides, the role of the mediator was found to be significant and relevant in the relationship between trust and customer satisfaction and customer satisfaction and trust towards continuous attention.

This study investigates Ghanaian students' adoption of Mobile Learning (ML) by extending the technology acceptance model with a subjective norm variable. Specifically, this study focuses on the moderating effect of gender using the Measurement Invariance of Composite Models for the analysis. The study used a purposive sampling technique to collect the data for the study from sec-ond-year diploma students at the University of Professional Studies in Accra. SmartPLS 3.3.3 was used to analyze the data from 330 respondents. The findings of the study suggest that perceived ease of use, perceived usefulness, and subjective norm have a significant influence on the behavior-al intention to adopt mobile learning for the complete data set. In addition, the results suggest that the impact of the subjective norm was not significant for female students but for male students. Also, the impact of perceived ease of use and perceived usefulness on behavioral intention were insignificant. Furthermore, the findings suggest that behavioral intention influences students' actual use of mobile devices to access learning materials. Finally, gender moderates the relationship be-tween subjective norms and behavioral intention. The findings demonstrate group heterogeneity, therefore, investigations on technology adoption must always incorporate group dynamics to un-derstand how different groups respond to its adoption. The findings of the study hold significance for both policy and research implications.

This paper describes a series of experiments in using logistic regression machine learning as a method for entity resolution. From these experiments the authors concluded that when a supervised ML algorithm is trained to classify a pair of entity references as linked or not linked pair, the evaluation of the model’s performance should take into account the transitive closure of its pairwise linking decisions, not just the pairwise classifications alone. Part of the problem is that the measures of precision and recall as calculated in data mining classification algorithms such as logistic regression is different from applying these measures to entity resolution (ER) results.. As a classifier, logistic regression precision and recall measure the algorithm’s pairwise decision performance. When applied to ER, precision and recall measure how accurately the set of input references were partitioned into subsets (clusters) referencing the same entity. When applied to datasets containing more than two references, ER is a two-step process. Step One is to classify pairs of records as linked or not linked. Step Two applies transitive closure to these linked pairs to find the maximally connected subsets (clusters) of equivalent references. The precision and recall of the final ER result will generally be different from the precision and recall measures of the pairwise classifier used to power the ER process. The experiments described in the paper were performed using a well-tested set of synthetic customer data for which the correct linking is known. The best F-measure of precision and recall for the final ER result was obtained by substantially increasing the threshold of the logistic regression pairwise classifier.