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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.

The purpose of this study is to explore and determinants the risk management and prevention in credit card business of Malaysian commercial banks. There were five factors derived through the literature and experimental investigation that are affecting the banks due to credit card which are External Fraud Risk, Internal Operational Risk, Credit Risk of Cardholders, Science and Technology and Intermediary Agency Trades Risk. Hence, 250 respondents participated in this study from state of Selangor and Federal Capital Kuala Lumpur. The data was collected and analyzed by SPPS software and it was concluded that that External fraud risk, Internal operational risk, Credit risk of Cardholder, Science and technology are positively correlated with the Credit card business risks of commercial Banks in Malaysia. These papers have highlighted suggestion for future research to develop this study further by considering the data derived from this study by using Delphi Technique

The study is aimed at assessing the strategic importance of a business processes for achieving sustainable competitive advantage, therefore, in this article the theoretical approach for measuring the strategic contribution of a business process to an enterprise’s business system is presented.  For evaluating of a business process strategic importance the study proposes the system of economic and managerial indicators, which includes the process’ contribution to the added value, its compliance to critical success factors, and its organizational involvement. Сombining these three indicators into one integral allows it to be used in different types of matrix analysis to make decisions on improving of a company’s business system.

In today's competitive environment, one of the new tools in the field ofinformation technology is business or organizational dashboards that are as abackup in the process of strategic management of organizations. The aim ofthis study is building a prototype of a hospital dashboard on the principles andguidelines of dashboards and evaluating it based on End UserComputing Satisfaction (EUCS). The prototype of a simple dashboard was prepared for evaluation, using experimental research through a questionnaire according to the end-user computing satisfaction model. The test results to compare the average of research variables consisted of satisfaction of the dashboard format, being up to date, ease of use, accuracy and content with average status showed a significant difference between the average of all variables and their average value. Results concluded that among the variables, the format which has had the highest satisfaction rate and accuracy has had the lowest levels of satisfaction among users. Also, the level of users’ satisfaction of all factors of the dashboard is equal.

The most important aspects of image processing are image enhancement. The visual form of an image can be enhanced by using image enhancement techniques for better human interpretation. In this research paper we discuss an   outline and analysis of commonly used image enhancement techniques using finger vein image or personal authentication. Also, experiments are carried out to compare performance of various types of filters for removal of noise from the noisy images through evaluation performance parameter such as mean square error (MSE), peak signal to noise ratio (PSNR) values and structural similarity (SSIM). It was found that application of max filter technique ensures an improved quality of the finger vein image. The mean filter is most advanced in de-noising the images. Mean filter is most efficient in eliminating the salt and pepper noise. From the experiments performed on finger vein image using SDUMLA-HMT database, it is proven that Weiner filters are outstanding for elimination of Gaussian Speckle and Poisson noises and thus, Weiner filter is found to be most appropriate and well-suited for eliminating nearly all types of noise.

Learning effective segment depictions and resemblance measures are fundamental to the recuperation execution of a substance based picture recuperation (CBIR) structure. Regardless of wide research tries for a significant long time, it stays one of the most testing open gives that broadly impedes the achievements of real-world CBIR structures. The key test has been credited to the extraordinary "semantic hole" subject that happens between low-level photo pixels got by technologies and raised close semantic thoughts saw by a human. Among various techniques, AI has been successfully analyzed as a possible course to interface the semantic gap in the whole deal. Impelled by late triumphs of significant learning techniques for PC vision and various applications, in this paper, we try to address an open issue: if significant learning is a longing for spreading over the semantic gap in CBIR and how much updates in CBIR endeavors can be cultivated by exploring the front line significant learning methodology for learning feature depictions and likeness measures. Specifically, we explore a structure of significant learning with application to CBIR assignments with a wide game plan of definite examinations by investigating front line significant learning methodologies for CBIR endeavors under moved settings. From our exploratory examinations, we find some encouraging results and compress some huge bits of information for upcoming research.

Digital Transformation has not been foreign to people in emerging economic growth, especially SME entrepreneurs in developing countries such as Malaysia. Digital transformation adoption is a way for an enterprise to improve the use of technology in a better way. But as the digital revolution has been evolving, there are still a few entrepreneurs who neglect it. The purpose of the present study is to investigate the factors affecting the adoption which is (competitive pressure, technology advancement, cost minimization, and environmental influence) on digital transformation adoption among SMEs in Malaysia. The relationship also consists of a moderating effect which is environment shock (pandemic covid-19) between the independent variable and dependent variable. The study also provides some knowledge for a deeper understanding of digital transformation and a guide to all SME entrepreneurs and others.

The ability of now-casting and eventuality is the most crucial and vital achievement of big data analytics in the area of policy-making. To recognize the trends and to render a real image of the current condition and alarming immediate indicators, the significance and the specific positions of big data in policy-making are undeniable. Moreover, the requirement for policy-making institutions to produce a structured model based on big data analytics for now-casting and eventuality of predictive policies is growing rapidly. The literature review demonstrates that a comprehensive model to assist policy-making institutions by providing all components and indicators in now-casting of predictive policies based on big data analytics is not devised yet. The presentation of the model is the main finding of this research. This research aims to provide a comprehensive model of now-casting and eventuality of predictive policies based on big data analytics for policy-making institutions. The research findings indicate that the dimensions of the comprehensive model include: the alignment of now-casting strategies and the big data analytics’ architecture, now-casting ecosystem, now-casting data resources, now-casting analytics, now-casting model and now-casting skill. The results of using the model were analyzed and the recommendations were presented.

The article considers a method for solving an information asymmetry problem that is relevant for the banking sector. The authors compare bank products and services in which tariff packages are consist of different informational components. The principal feature of the proposed method is to focus on obtaining a subjective evaluation of the attractiveness of the Bank’s offers for specific target groups. This makes the proposed method relevant for Central and commercial banks, as well as for consumers of banking products. Since complex tariff packages of different banks contain not only many indicators but also different sets, it is impossible to directly compare them. The proposed method allows solving the main informational problems of traditional methods for comparing banking products. Using the Fuzzy Inference System (FIS) allows a flexible approach to the aggregation of singular indicators of tariff packages and simplifies the expert procedures. To make rules for fuzzy inference, linguistic constructions close to the natural language can be used. This makes it easier to fill the knowledge base. To solve the set tasks, a methodology for constructing hierarchical FIS is used. In comparison with non-hierarchical FIS it can reduce the requirements to the number of rules in fuzzy inference rule base and system implementation time. The experiments with the fuzzy inference system, which implements a corporate credit cards tariff packages comparison of four Ukrainian banks, showed the effectiveness of the proposed approach. The obtained numerical results are adequately interpreted for the highlighted groups of banking product consumers

The purpose of research is focused on the insight into the future of Bitcoin on the financial situation, its implications and challenges. The problem of study is to investigate how to deal with a new type of digital currencies (such Bitcoin) that does not have a physical presence and there is no specific body to issue. Thus, this study aims to identify the nature of Bitcoin currency and what are the challenges associated with it as well as exchange rates with some currencies, as the research hypothesized the main hypothesis that Bitcoin will contribute to financial development in the future, the research also used the analytical rooted approach to present concepts and data, using financial technical analysis to display the results The research also came up with a number of conclusions and recommendations. The results of paper significantly contribute to the literature through providing evidence from financial data of Bitcoin.

Focusing on the problems faced by blind people, this paper has come up with the technology solution for the assistance of blind people. The solution is based on the intelligent data transmission to the earphone of a person based on task associated. The solution consists of a jacket to detect the obstacles along with a wearable box with task priority switchs. The system helps in detection of the obstacle and its height, one-touch cab booking and support of relatives, Ambulance services, Police services, etc. in the case of emergency. Either wired and wireless headphones or speakers can be interfaced with the device (box) to get audio notifications. The various tasks are triggered using multiple switches. The system will use a definitive SOC (System on Chip) platform recognized as Rasp-Pi-Pi along with ultrasonic sensor HC-SR04, Neo-6M GPS (Global Positioning System) module, and different switches. The system uses a 20,000 mAh lion battery for the power supply. The voice signals can be provided in more than fifty languages. A fall detection system is also discussed in this paper. This system will be beneficial not only for blind but also for care of old aged people