درخت حوزه‌های تخصصی

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.

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.

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.

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