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The purpose of this study is to study the indicators for evaluating the effectiveness of the implementation of investment processes on the securities market, taking into account the scientific foundations of information management systems and analysis of indicators of financial efficiency of the investment function of the securities market in Ukraine. The relevance of this study is due to the growing importance of management information systems in all sectors of the Ukrainian economy, in particular, the provision of solutions to the problems of activating investment processes in the securities market of Ukraine by analyzing and reassessing the effectiveness of investment processes at this level, taking into account the scientific basis of management information systems.  A set of indicators that best reflect the implementation of the investment function of the Ukrainian securities market is proposed. A matrix of characteristics of investment processes in the securities market is proposed. It is argued why domestic and foreign investors prefer local securities market indices when making investment decisions. Through the implementation of correlation-regression models, it has been proven that, on average, 87% of changes in investments in securities are due to changes in the number of licensed entities, which on the Chedoch scale indicates a close relationship between the indicators. The results obtained using statistical inference methods indicate a high impact of both external macroeconomic factors that inhibit the development of the securities market and internal, which in turn is reflected in the indicators of assessing the effectiveness of investment processes in the securities market.

Diabetes is a type of metabolic disorder with a high level of blood glucose. Due to the high blood sugar, the risk of heart-related diseases like heart attack and stroke got increased. The number of diabetic patients worldwide has increased significantly, and it is considered to be a major life-threatening disease worldwide. The diabetic disease cannot be cured but it can be controlled and managed by timely detection. Artificial Intelligence (AI) with Machine Learning (ML) empowers automatic early diabetes detection which is found to be much better than a manual method of diagnosis. At present, there are many research papers available on diabetes detection using ML techniques. This article aims to outline most of the literature related to ML techniques applied for diabetes prediction and summarize the related challenges. It also talks about the conclusions of the existing model and the benefits of the AI model. After a thorough screening method, 74 articles from the Scopus and Web of Science databases are selected for this study. This review article presents a clear outlook of diabetes detection which helps the researchers work in the area of automated diabetes prediction.

Human Identification has been widely implemented to enhance the efficiency of surveillance systems, however, systems based on common CCTV (closed-circuit television) cameras are mostly incompatible with the advanced identification algorithms which aim to extract the facial features or speech of an individual for identification. Gait (i.e., an individual’s unique walking pattern/style) is a leading exponent when compared to first-generation biometric modalities as it is unobtrusive (i.e., it requires no contact with the individual), hence proving gait to be an optimal solution to human identification at a distance. This paper proposes an automatic identification system that analyzes gait to identify humans at a distance and predicts the strength of the match (i.e., probability of the match being positive) between two gait profiles. This is achieved by incorporating computer vision, digital image processing, vectorization, artificial intelligence, and multi-threading. The proposed model extracts gait profiles (from low-resolution camera feeds) by breaking down the complete gait cycle into four quarter-cycles using the variations in the width of the region-of-interest and then saves the gait profile in the form of four distinct projections (i.e., vectors) of length 20 units each, thus, summing up to 80 features for each individual’s gait profile. The focus of this study revolved around the speed-accuracy tradeoff of the proposed model where, with a limited dataset and training, the model runs at a speed of 30Hz and yields 85% accurate results on average. A Receiver Operating Characteristic Curve (ROC) is obtained for comparison of the proposed model with other machine learning models to better understand the efficiency of the system

The worldwide Covid-19 epidemic while affecting numerous places, has had a profound effect on virtual advertising and advertising and, globally, in the provinces and at the neighborhood level. except, this effect for the most element become positive, in contrast to what has seen in exclusive sectors which include economy, human sources, etc., whilst contamination reasons a variety of incapacity amongst clients and advertisers alike, in phrases of welfare, social work, inflation, business, and many others., the equal shifted conduct goes as a long way as running. , investing strength, getting into self-schooling, adopting new programs from the internet, expanding social and hygiene concerns, retaining distance strategies from complete regions, internet-based media willpower, get right of entry to online sources, etc., and this has greatly impacted the display and endorsed efforts. The moral movement has moved past the PC and digital international, which places open doors for advertisers and products to connect with clients more efficiently than ever before. With the arrival of expanded online media and the call for pc-generated content material, the evolved Media have given advertisers a part of the monetary freedom. At the equal time, this has, in turn, enabling advertisers to be extra proactive and to engage with the public at the same time as appearing excessive excellent demonstration programs. The purpose of this study is to explore, investigate, and recognize the effect of coronavirus on the digital market and businesses.

The purpose of the article is to perform stress-testing technologies of the financial stability of an insurance company based on the constructed mathematical model of the insurance company's activity, which would meet the established requirements (adequate reproduction of the main parameters of the insurance company's functioning; taking into account the stochastic nature of insurance processes; flexible management of model parameters describing company's behaviour; the ability to influence the intensity of flows; suitability for algorithmization and construction of computational simulation model. The relevance of this study is due to the need to address the problem of changes and complications, the growing variety of strategies and products implemented by insurance companies. There is a need for innovative methods to assess and monitor the vulnerability of these institutions to various types of risks. One of these methods, which is gaining widespread recognition both among regulators and financial corporations, is stress testing. It has been established that stress testing as a risk management tool is used both to assess the insurance company's readiness for a crisis situation, and to develop a plan of adequate measures to counteract and eliminate its negative impact. The development and application of the proposed mathematical and simulation model of stress testing of the financial stability of the insurance company allows to solve issues of ensuring sufficiency of capital level, control of financial stability and solvency, reliability of efficiency of activities, taking into account the probabilistic nature of insurance activities, various typical insurance risks and time horizons.

The rapid growth and advancement of electronic devices and technologies in the FinTech industry empower new innovative products and services. The covid-19 pandemic could have a devastating effect on Malaysia’s economy, but it has offered additional opportunities for the E-wallet segment of the Fintech business to thrive. The E-wallet segment of FinTech is one of the latest innovations that is currently growing as there is a need for contactless payments during the pandemic situation. The main objective of the study is to examine the factors affecting e-wallet adoption among young adults in Malaysia. A sample of 200 responses was analyzed using Smart PLS 3.0. The findings revealed that the factors of “performance expectancy”, “effort expectancy”, “compatibility”, and “social influence” have a positive and significant impact during the pandemic; however, the factor of “facilitating conditions” has no significant impact on the adoption of the E-wallets. The study substantiates the key and important variables of adoption in order to develop and evolve E-wallet providers' existing services. Particularly, due to the increasing importance of e-commerce, E-wallet service providers are urged to focus on the system's interoperability, which encourages individuals or customers to use the strategy.  They should include unique features that allow customers to accept the service, trust its benefits and feel comfortable using the technology. The study is useful to the E-wallet providers to improve the existing services. The findings also guide the companies offering E-wallets to enhance the usage and adoption of their services.

Substitution-boxes (S-boxes) are very important nonlinear components used for achieving strong confusion for enhancing cryptographic security in most of the block ciphers. Designing cryptographically strong S-boxes has been a major research domain for the designers of symmetric crypto systems. In the proposed research work, Bat Algorithm based swarm technique is proposed to design strong S-boxes.  Cryptographic strong S-boxes are obtained by the developed swarm technique. Authors analyze cryptographic strength of the obtained S-box by evaluating properties like Bijectivity, Nonlinearity, Bit-Independence Criterion, Linear Probability and Differential Uniformity. The obtained performance parameters for the designed new S-box by the swarm technique are compared with some recently reported S-boxes in the literature. The designed S-box has good cryptographic strength. The designed S-box has good cryptographic strength like nonlinearity = 110.75 and average Strict Avalanche Criterion (SAC) value = 0.506. For the constructed S-box, most of the Differential uniformity components are 4 and shows uniform distribution approximately. The proposed new S-box is also free from the fixed points.

The main drivers of value creation in a ‘brand community’ are social networking, community engagement, impression management, and brand use. Marketers are therefore interested in determining which factors affect the value creation practices. This study examines the impact of the Interactivity of Electronic Word of Mouth (EWOM) systems on value creation practices in a brand community, which in turn influences the loyalty of the customers. In this regard, a conceptual model was developed and tested by the researchers of the current study. The results indicate that perceptions of the users regarding the interactivity of EWOM systems, highly impact only three of the four value creation practices including community engagement practices, impression management practices, and brand use practices. Furthermore, the researchers found that collective value creation practices could significantly and directly enhance brand loyalty. Several theoretical contributions and managerial implications were also discussed

Electronic word of mouth (eWOM) has been gaining popularity pertaining to its numerous benefits and ability to be applied in various fields. It helps consumers in making informed decisions and aids service providers in delivering an enhanced service or product. Despite all these benefits, dealing with the huge amounts of eWOM is a consistent problem. eWOM helpfulness comes handy in order to address this issue. In this study, we utilize 16699 hotels related eWOM written by 1099 reviewers which are collected from TripAdvisor.com. Our main objective is to analyze which factors impact eWOM helpfulness and how. For this purpose, eight unique variables belonging to three different categories are selected (eWOM length, eWOM subjectivity, eWOM polarity, eWOM readability, eWOM recency, hotel rating, reviewer badge and reviewer helpfulness) and are analyzed using econometric modelling. Our findings show that hotel rating as well as reviewer badge and helpfulness enjoy a positive significant relationship with eWOM helpfulness. It also suggests that eWOM length, readability and subjectivity positively influences eWOM helpfulness though eWOM polarity and recency are found to have an inverse relationship with the helpfulness of eWOM. Thus, our study reports that review, hotel and reviewer characteristics impact eWOM helpfulness in different ways. This study is summarized with the discussion of theoretical and practical implications.

Technology innovation affects both the public and private non-financial sectors, the zakat institution (Islamic social finance) included. The institution uses an online system to communicate with zakat payers and zakat recipients to ensure its effectiveness in terms of collection and distribution. This research focuses on the adoption of a technique known as AD/AC and ZP/ZR, which is mainly utilized by partially and non-privatized State zakat institutions in Malaysia. To this end, three (3) techniques pertaining to appraising the performance of Malaysia’s Zakat institutions in managing Zakat funds were established. The analysis also includes the adverse impact of Covid-19 pandemic on the performance of zakat collection of the respective zakat institutions in Malaysia. In this relation, a quantitative approach was adopted using the primary and secondary data collected from JAWHAR and various states’ zakat institutions. By utilizing a technique of online data collection and applying the AD/AC as well as ZP/ZR methods, the performance of Malaysia’s zakat institutions is appraised. Furthermore, with the help two (2) ratios and eight (8) scenarios the performance of the innovative technique of AD/AC and ZP/ZR for zakat collection and disbursement, efficiency and sustainability of zakat institutions in Malaysia were evaluated. The results show that most zakat institutions, particularly those that deal with zakat payments, have begun to use the technique AD/AC and ZP/ZR within their organizations. The performance of zakat institutions in all states in terms of zakat distribution and disbursement do not have a consistent trend for the period 2016-2020. However, there is still a dearth of technology being used for zakat disbursements and fund reporting. From the observed data, with 2016 being the exception, for each remaining year, a state in Malaysia is ranked as the best technology performer or the most efficient. Selangor - fully privatized state (2017), Pulau Pinang - fully privatized state (2018), Federal Territory - partially privatized state (2019) and Negeri Sembilan-partially privatized state (2020) are the most efficient for the respective year. The sustainability of each zakat institution in Malaysia in the presence of Covid-19 pandemic was ascertained as well. Interestingly, contrary to the efficiency result, all non-privatized states (Kedah, Sabah and Kelantan) and one partially privatized state (Negeri Sembilan) were sustainable in coping with Covid-19 pandemic, while all fully privatized states (Selangor and Pulau Pinang) were not. Overall, it was conjectured that the technique AD/AC as well as ZP/ZR would help to improve the operations of zakat institutions to become more efficient and successful in distributing zakat fund to the poor. However, as zakat collecting and distribution involved millions of records, its management is still debatable by all parties.

The purpose of the article is to substantiate the fiscal role of the excise tax by studying its information and functional potential and to model the dynamics of its payment by the brewing industry. Excise tax occupies a special place in a tax system of each state because, in addition to significant fiscal importance, it has a considerable regulatory impact on the production and consumption of certain categories of goods. Based on information systems in the article analyses and monitors the indicators of the excise tax payments on goods produced in Ukraine on the example of a particular enterprise in the brewing industry. By means of the initial data analysis of autocorrelation functions of volumes’ indicators of the accrued excise taxes on beer the expediency of modelling realization of such indicator dynamics on the basis of ARIMA model is proved. The analytical and statistical approaches to the formation of models for the implementation of forecast for the calculation of excise tax on beer of brewing industry enterprises are improved. The proposed approach is based on the values of autocorrelation of balances and partial autocorrelation, as well as methods of analysis of time series with gaps, which allows to use it in the economic activity of enterprises to make forecasts for the calculation and payment of the excise tax. This will produce financial effects for the brewing industry in terms of cost optimization and minimization of the excise tax risks.

Co-Saliency object detection is the process of identifying common and repetitive objects from the group of images. Earlier studies have looked over several state-of-art deep neural network methodologies for co-saliency detection approach. The Deep CNN approaches rely heavily on co-saliency detection due to their potent feature extraction capabilities both deep and wide. This article assess the performance of several state-of-art deep learning model (VGG19, Inceptionv3, modifiedResNet, MobileNetV2 and PoolNet) for the purpose of co-saliency detection among images from benchmark datasets. All the models were trained on   70% part of the dataset and remaining were used for testing purpose. Experimental results show that modified ResNetmodel outperforms getting 96.53% accuracy as compared to other state-of-the-art deep neural network models.

Blockchain technology is one of the revolutionary technologies in service-based industries. E-health services are considered as a major economic and social activity in the world in which patients, the medical community and service providers such as insurance organizations play a key role. In Iran, e-health services have been introduced for more than a decade, but it is seriously lagging behind the leading countries. According to the Blockchain Opportunity window, a technological and economic catch-up is possible in e-health services in the country. Decentralization, data transparency, security and privacy are the key features of Blockchain technology. Therefore, in this research, through case study strategy and using documents, scientific articles, evidence, statistics and events and opinions of experts, policy factors were presented to catch-up through the Blockchain technology opportunity window. The dimensions of the proposed policy framework were presented in 4 categories: macro policies, market, financing, and technology learning and development. This study presents technological catch-up policy framework by Blockchain technology to policymakers for using in e-health services.

Trust establishment (TE) among sensor nodes has become a vital requirement to improve security, reliability, and successful cooperation. Existing trust management approaches for large scale WSN are failed due to their low cooperation (i.e., dependability), higher communication and memory overheads (i.e., resource inefficient). This paper provides a new and comprehensive hybrid trust estimation approach for large scale WSN employing clustering to improve cooperation, trustworthiness, and security by detecting selfish sensor nodes with reduced resource (memory, power) consumption. The proposed scheme consists of unique features like authentication based data trust, scheduler based node trust, and attack resistant by giving the high penalty and minimum reward during node misbehavior. A task scheduling mechanism is employed for scheduling the significant task to reduce computation overhead. The proposed trust model would be capable to provide security against blackhole attack, grey hole attack, and badmouthing attack. Moreover, the proposed trust model feasibility has been tested with MATLAB. Simulation results exhibit the great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes with the help of analyzing consistency in trust values and communication overhead.

The Architecture, Engineering, and Construction (AEC) industry rely heavily on Building Information Modeling (BIM). BIM is the collection of Information and Communication Technologies (ICT), interacting policies, and procedures. BIM is a tool for managing digital project data during the life cycle of a building. Despite the many benefits and features of BIM, the Malaysian construction industry's proliferation is confronted with adoption issues. Therefore, this research study intends to find the effect of BIM adoption factors in Malaysian AEC. Quantitative data collection from construction firms is gathered. The proposed model's theoretical foundations are based on Technology, Organization Environment framework. The model is tested and validated with the Smart PLS tool. The study's findings indicate that Perceived benefits, Organizational Capabilities, and Trialability are drivers of BIM adoption. Perceived cost and Insecurity are the barriers to BIM adoption. Perceived ease of use and compatibility does not affect BIM adoption. Finally, this study performs Importance Performance Map Analysis to provide recommendations to AEC stakeholders to address the BIM adoption issues for enhancing its diffusion in Malaysia.