درخت حوزه‌های تخصصی

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.

Dimensionality reduction is among the data mining process that is used to reduce the noise and complexity of features in various datasets. Feature selection (FS) is one of the most commonly used dimensionalities that reduces the unwanted features from the datasets. FS can be either wrapper or filter. Wrappers select subsets of the feature with better classification performance but are computationally expensive. On the other hand, filters are computationally fast but lack feature interaction among selected subsets of features which in turn affect the classification performance of the chosen subsets of features. This study proposes two concepts of information theory mutual information (MI). As well as entropy (E). Both were used together with binary cuckoo optimization algorithm BCOA (BCOA-MI and BCOA-EI). The target is to improve classification performance (reduce the error rate and computational complexity) on eight datasets with varying degrees of complexity. A support vector machine classifier was used to measure and computes the error rates of each of the datasets for both BCOA-MI and BCOA-E. The analysis of the results showed that BCOA-E selects a fewer number of features and performed better in terms of error rate. In contrast, BCOA-MI is computationally faster but chooses a larger number of features. Comparison with other methods found in the literature shows that the proposed BCOA-MI and BCOA-E performed better in terms of accuracy, the number of selected features, and execution time in most of the datasets.

Polling system is not trusted everywhere around the world it is very important in this modern world to replace the traditional polling system with the new technology. Some countries like United States, Japan, and India suffer from corrupted polling system. Major issues are faced by current polling systems like system hacking, vote rigging, vote manipulation, distributed denial of service attack, and online polling booth capturing. This paper will lead to the problems faced by the traditional polling system and how the new technology will provide the solution to that problem. Also, our purpose is to check the feasibility of the system by recording the transaction fees and evaluate the right way to spend the amount of gas in the transaction. This will highlight blockchain frameworks including blockchain as a service and polling system which is on blockchain that addresses all constraint introducing ethereum which is a blockchain-based distributed computing platform. Ethereum is open source, and publicly available with a system featuring smart contracts. It provides the cryptocurrency wallets that let you make cheap, instant payments with gas in the form of ethers. The ethereum community is the most active and largest blockchain community in the world. There is no centralized organization that controls ethereum.

The article examines the multiaspected problem of ensuring economic security. An analysis of challenges and threats to Ukraine's economic security during 2010-2019. Strategic goals of economic security have been set. The study of the relationship between public debt, foreign direct investment, and economic growth allows assessing the characteristics of economic security. A system of structural equations is proposed to study the relationship between GDP, public debt, and foreign direct investment. The result of economic security, which can be achieved by achieving the goal, can be considered increased resilience to internal and external factors.

Healthcare service institutions (HSIs) have sought ways to motivate patient loyalty in response to surging rates of medical tourism. Previous research indicates that Hospital Information System (HIS) is essential for HSIs to gather, measure, and analyze the massive amounts of data required to generate patient loyalty. There is currently no consensus on the factors that comprise HIS specifically geared towards motivating patient loyalty (HISPL). Furthermore, HIS requires full adoption by HSI staff to be effective. Thus, to reduce wastage of HSI resources, it is necessary to predict whether a given HIS specifically geared towards motivating patient loyalty is likely to be adopted. The purpose of this study is to reveal the factors that comprise HISPL and to modify the Unified Theory of Acceptance and Use of Technology (UTAUT) model to help predict the likelihood of an HISPL to be fully adopted by HSI staff. The results revealed that pertinent HISPL factors are capability, configurability, ease of use/help desk availability and competence (EU), and accessibility/shareability (AS). Using these factors, the UTAUT model was modified to fit the specific needs of HISPL. The modifications are theoretical and will have to be validated in future empirical studies.

During the COVID-19 pandemic, the usage of e-learning systems became the main challenge for many universities. E-learning has risen as cutting-edge method for promoting learning delivery. To ensure productive use, it is important to continue using e-learning. Numerous studies have shown that continued usage by the user is the indicator of success in e-learning, and in recent years, research on continued use of e-learning is being explored at a higher level than before. However, to date, there have been no attempts to systematically analyse these studies in order to provide researchers and practitioners with a picture of the current state of continued usage of e-learning. The aim of this research is to provide an in-depth look at the theory of continued use information systems in e-learning context. In this study, we used a systematic review approach to collect, evaluate, and synthesize data on the accuracy and value of previous articles published in digital databases between 2009 and 2019 that were based on this research area. To include all relevant research papers that were written during this period time, we used a Systematic Literature Review (SLR) approach to collect and review studies by following a predefined review process that included both automated and manual search strategies. We listed 87 primary studies from the review study that presented research on the continued use of e-learning. These studies were analysed using a comprehensive mapping method that collected relevant information to address a series of research questions. We summarized and analysed the published articles, which covered a wide range of research subjects, including the majority of factors that affect e-learning use. While research on the continued use of e-learning is growing and providing a promising new field of research, the systematic review found that a clearer understanding of the environment and path is not well reported. This research will contribute to a better understanding of the factors that affect e-learning use over time

The paper considers the possibilities of optimizing the search for agricultural information based on Shannon information entropy criterion. A comparison of the degree of uncertainty of information search through the Internet – search systems with similar indicators of Ukrainian agricultural websites. According to the number of hit count in Ukraine in July 2020 to the search engine, which consists of 10 sources, and to agricultural websites (43 sources) by estimating entropy, a conclusion was made about the significant degree of dispersion of agricultural information sources. It should be emphasized that the artificial division into 4 categories (AgroMedia, Agrotrade web resources, Information and advisory resources, Specialized web resources) did not improve the situation according to the degree of uncertainty compared to the search engine system. As for the entropy index, for almost all 4 categories of entropy indices (heterogeneity or diversity) is close to the case of uniform distribution, i.e., the same hit count of all possible resources (their number for all categories is approximately the same 10-11). As a result of the analysis of the potential client base of electronic consulting, it can be quantified as 4,700,000 of agricultural households and about 50,000 small and medium-sized farms. The transition to the land market is likely to lead to a substantial increase in the number of the latter. It was demonstrated that the current educational level of household owners indicates both the need to increase this indicator and the presence of a close relationship with the existing system of professional advisory in Ukraine, which operates in recent years through self-sufficiency and uncertain financial support from central and regional authorities. The existing official database of advisors should be restructured in line with the call of the times to reduce the degree of uncertainty in finding a specialist of the desired profile.

An Artificial Intelligence (AI) technique plays the most crucial factor to consider in energy utilization in a wireless sensor network (WSN). AI transforms industrial operations by optimizing the energy consumption in sensor nodes. As a result, it is crucial for improving sensor node location accuracy, particularly in unbalanced or Adhoc environments. Because of this, the purpose of this research is to improve the accuracy of the localization process in locations where sensor nodes encounter barriers or obstacles on a regular basis. The Bees Swarm Optimization (BSO) algorithm is used to segment sensor nodes in order to increase the accuracy of the Direction of Arrival (DoA) estimate between the anchor and unknown node pairs. Even in the presence of unbalanced conditions, the proposed DoA- BSO involving three separate bee colonies can identify plausible anchor nodes as well as segment nodes arranged in clusters. In order to obtain the intended result, the objective function is designed to take into consideration the hops, energy, and transmission distance of the anchor and unknown node pairs, among other factors. The studies are carried out in a large-scale WSN using sensor node pairs in order to determine the precision with which the DoA-BSO can be located. When comparing DoA-BSO to conventional approaches, the findings of the meta-heuristic algorithm show that it improves the accuracy and segmentation of nodes significantly

In recent scenario, women are suffering from breast cancer disease across the world. Mammography is one of the important methods to detect breast cancer early; that to reduce the cost and workload of radiologists. Medical image processing is a tremendous technique used to determine the disease in advance to reduce the risk factor. To predict the disease from 2-D mammography images for diagnosing and detecting based on advanced soft computing paradigm. Still, to get more accuracy in all coordinate axes, 3-D mammography imaging is used to capture depth information from all different angles. After the reconstruction of this process, a better quality of 3D mammography is obtained. It is useful for the experts to identify the disease in well advance. To improve the accuracy of disease findings, deep convolution neural networks (CNN) can be applied for automatic feature learning, and classifier building. This work also presents a comparison of the other state of art methods used in the last decades.

The article proposes a methodical approach to assessing the competitiveness of enterprises in the logistics infrastructure of the Black Sea region in the context of the strategy of the development of the Black Sea. The article is presented is the author's interpretation of the steel development of the territory and injected into the logistic infrastructure. It was proposed of the methodical approach to assessing the competitiveness of enterprises-moving enterprises in the development of the territory, as one of the stages of the methodology for assessing the development of the territory in the region. It have been identified Indicators for assessing sustainable development in assessing the competitiveness of key enterprises in the region and in terms of using the TOPSIS model. The application of the TOPSIS model allows to assess the value of the aggregate indicator of sustainable development of a region or territory, taking into account the impact of the level of competitiveness of its key enterprises. In the context of this approach, it is studied and evaluated the level of competitiveness of powerful enterprises of the Black Sea region, which form the logistics infrastructure of the region. It was  determined their influence on the strategy of sustainable development of the territory. This approach allowed to expand the methodology for assessing the assessment of sustainable development of the territory through the methodology of assessing the competitiveness of enterprises-engines of development of the territory, as one of the stages of the overall methodology.

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.

Blockchain is one of the trending emerging technologies that enables immutable data storage over distributed ledgers. The fundamental features of blockchain technology, such as record and identity traceability and immutability makes it an attractive alternative for the state-of-the-art information systems. Thus, blockchain has found its way into various fields. In Iran, blockchain technology has been employed in different areas including: commercial mining platforms, secure exchange platforms, and innovative trustless projects. One of the main applications of blockchain is secure management of sensitive information, which makes it a suitable infrastructure for providing advanced control over data in police task forces. The motivation behind this research is to provide a conceptual framework for employing blockchain in law enforcement organizations by analysis and comparison of best practices in other countries and academic case-studies. We provide a systematic overview of case-studies by qualitative content analysis. Moreover, the reliability of our statistics is evaluated by Cohen's kappa coefficient roughly 0.64. Finally, the prioritized applications were evaluated by a 14 member experts panel through fuzzy Delphi method with a Kendall’s Coefficient of Concordance of W=0.711, indicating high rates of agreement among members. To this end, “trustless authentication systems”, “distributed data/resource/information management”, and “inter-department integrated information systems” were selected as most valuable applications of blockchain for police task forces and law enforcement agencies in Iran.

The economic events of recent years caused by the effects of the pandemic have contributed to rising farm costs. Determining the unit cost of current biological assets of crop production and detailing these costs by major categories is one way to optimize them and increase the efficiency of the main activities of the producer. It requires formation and implementation effective cost control mechanisms in crop production.The purpose of this study is development of scientifically sound proposals and recommendations for controlling the cost of current biological assets of crop production, identifying priority cost items at each stage of cost formation. The main standards for accounting for current biological assets of crop production in Ukraine are IAS 41 "Agriculture" and NP (S) FA 30 "Biological assets". Control is regulated by the company's accounting policy. It has established that it is possible to determine the actual cost of agricultural products only at the end of the year. Currently, in Ukraine there is no legal act that would give the right to reduce the time of determining the actual cost of agricultural products. To determine the financial result, there is a method of sequential closing of accounts of different types of agricultural production. In order to improve the system of control over the value of current biological assets of crop production in agricultural enterprises, it is proposed to strengthen internal control by means of information systems and technologies. Control of costs that form the cost of crop production should be carried out both at the planning stage and during the execution of works and at the stage of their completion.