درخت حوزه‌های تخصصی

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.

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.

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

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 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.

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.

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.

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.

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

Building Information Modeling (BIM) is the backbone of the Architecture, Engineering, and Construction (AEC) industry. BIM is the collection of Information and Communication Technologies (ICT), interacting policies, and procedures. BIM generates a methodology to manage the project data in digital format throughout the building's life-cycle. Despite the numerous benefits and features of BIM, its proliferation remains limited and facing adoption issues. Although many existing studies discussed BIM adoption from contextual lenses, discipline-focused, there is still a scarcity of a comprehensive overview of technology adoption models and frameworks in BIM research. The purpose of this Systemic Literature Review (SLR) is to evaluate the current status of BIM adoption, technology acceptance theories, models used and find the research challenges. Furthermore, to identify the roles of independent constructs, dependent construct, moderators, and mediators in BIM adoption research. Also, the findings provide an in-depth description of the different stages of BIM adoption. Finally, this SLR will help the researchers for further research in the field of BIM adoption.

Day by day demand for object tracing is increasing because of the huge scope in real-time applications. Object tracing is one of the difficult issues in the computer vision and video processing field. Nowadays, object tracing is a common problem in many applications specifically video footage, traffic management, video indexing, machine learning, artificial intelligence, and many other related fields. In this paper, the Enhanced Method of Object Tracing Using Extended Kalman Filter via Binary Search Algorithm is proposed. Initially, the background subtraction method was used for merge sort and binary search algorithm to identify moving objects from the video. Merge sort is to divide the regions and conquer the algorithm that arranges the region in ascending order. After sorting, the binary search algorithm detects the position of noise in sorted frames and then the next step extended the Kalman Filter algorithm used to predict the moving object. The proposed methodology is linear about the valuation of mean and covariance parameters. Finally, the proposed work considered less time as compared to the state of art methods while tacking the moving objects. Its shows less absolute error and less object tracing error while evaluating the proposed work.

In the era of internet technologies, to provide wireless communication and transfer the information in seconds from one place to another has arrived because of the need to consume information technologies. All users desire to quickly access the smart world’s life and interact with the entire world socially. This paper proposed an environment for the safe and secure smart patient’s room connected to the WSN, BAN, and RFID. All the data will be transferred to the session key, secure and contains the patient’s information. The network connected through WSN and data will be sent through the session key to make an smart hospital’s patient cabin. The small token is there that will be transferred throughout the network to get authenticated by each network. This proposed scheme is secure enough to overcome the drawbacks of the other protocol in such a way as to make the protocol more secure from the entire adversary’s attack may occur.

Heart disease is stated as the world's biggest killer. The risk factors of this deadly disease are due to some bad habits such as being overweight, bad eating diet, smoking, assumption of alcohol, etc. Nevertheless, patients can live a healthy lifestyle if they have the proper guidance of persuasive-emotional featured technologies. In line with this, this study focuses on developing an emotional-persuasive habit-change support mobile application called BeHabit to improve heart disease patients’ lifestyles. Persuasive-emotional features are two different features that are integrated with BeHabit to distinguish this application from the existing ones. The proposed system is designed, implemented, tested, and evaluated by 10 users. In conclusion, the users are satisfied to used BeHabit to change their bad habits. Emotional and persuasive features that are integrated into BeHabit are the key to help patients to change their bad habits. BeHabit and the integrated feature can be used as a guideline for healthcare developers and providers for the improvement of mHealth services.

The article explores approaches to assessing the quality of health care sites. The main components of evaluation are identified. The author's toolkit for health site evaluation is offered. It is based on the three branches of resource competitiveness (content, technical and marketing components). Twenty parameters of site quality calculation are defined, which have the scale and are supported by determining the weights of components. The scaling of the resulting indicator is proposed. It allows designation of the level of development of the particular site in the field of health. The practical approbation of the proposed method was carried out on the example of Ukrainian sites on the topic of rehabilitation and disease prevention. The diagram of the grouping of websites on a level of development of separate components is constructed to define the further directions of work with these sites to strengthen their competitive positions in the "Health" category. The author's typology of health sites depending on the calculated numerical combinations of the quality assessment level of components is given. It also describes the state of the "ideal" mix of components of the health site, which is formed based on the balance of individual elements.

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.

In recent times amount of data is increasing rapidly and analysis of data is a must to come up with business decisions, predictions etc. It’s not just text are numbers which has to be stored properly. Data Types these days varies from images, video, social media data, surveys to blogs etc. When this is the case its mandatory to deal with Bigdata and to safeguard those Bigdata. Technologies available in Bigdata and Bitcoin helps us in achieving this. Bigdata technologies helps in storing the unstructured data effectively and processing of such voluminous data is taken care of. Hashing algorithms are used in Blockchain to keep the data safe. Hashing algorithms like SHA 256 are used to make it more secure. Its almost not possible to break the data available in Blockchain. Let’s try to secure the data even more using encryption algorithms to make sure that proper data is used for analysis etc.