درخت حوزه‌های تخصصی

The Analytic visualization suggests representing knowledge during a visual type that may be charts, graphs, lists, or maps. The COVID 19 detection and analysis of spreading is very important for countries. Database management with respect to virus deep analysis is a critical task to the researcher through conventional algorithms. The RNA, DNA, and biological data are helping to the bio-inspired algorithm but its implementation can be complex by software tools. Therefore, an effective technique is required to cross over the above limitations. So that covid 19 pandemic data analysis is performed through FCNN (Fully conventional Neural Network) pre-training network. The dataset is collected from social media, Kaggle, and GitHub databases. At 1st stage, the auto stack encoding process is applied later same data is processed with FCNN deep learning classifier. In this research work, covid-pandemic affects parameters like infected persons, deaths, active cases, and recovering cases. The FCNN is take care of feature extraction, training, testing, and classification. Finally using a confusion matrix accuracy of 98.34%, sensitivity 97.63%, Recall 98.26%, and F measure 98.83% had been estimated.

Due to the rapid changes and developments caused by technology in the digital age and the growing importance of achieving sustainability for the survival of businesses, the need to create the capabilities needed to innovate in the business model of organizations has become inevitable. This research is trying to provide a model for business model innovation with the aim of sustainability by considering the external factor of digitalization (digital transformation) and the internal factor of dynamic capabilities. The method of this research is mixed so that in the first stage the qualitative method of Meta-Synthesis is used and in the second stage the quantitative method of Interpretive Structural Modeling (ISM) is used. In this research, based on the systematic review of previous researches in scientific databases, 402 related articles were identified and then 46 final articles were selected during the screening process. Based on the thematic analysis, a total of 4 main categories and 14 factors were extracted. In the second stage of the research, an interpretive structural model (ISM) is developed to show the inter‐relationship of different factors and their levels of importance in the food industry.   The 8-level hierarchy model of factors influencing the sustainable business model innovation in the digital age with a dynamic capabilities approach was proposed. Results show that a new component called network value has been added to the traditional business model components. Furthermore, the MICMAC approach has been used to categorise factors according to their driving power and dependence. finally, digital technologies and Sensing were found to be the factors with the highest independence and the highest driving power, and “Economic Sustainability” was identified as the factor with the highest dependence and the least driving power

Management of innovation processes is one of the functions of local governments and, therefore, they should be the initiators and moderators of communication between research organizations and enterprises. The program formation of agglomeration innovative development involves the creation of promoting innovation body, which allows to achieve the maximum involvement degree of all the participants in the innovation process. The article is devoted to the research of urban agglomeration innovative development, the need to create a special body or center for innovation, which will form a set and interconnected, and will be integrated into the urban agglomeration and carry out innovation and technological activities as part of research and production infrastructure. The article develops a method for predicting the effectiveness of the advancement of this body through the digital space using trend models. It is expected to receive three forecasts: optimistic, realistic and pessimistic. This will accelerate the establishment of links between the players of the regional innovation market and contribute to a qualitative change in the spatial and functional structure of urban agglomerations. The development of information and communication technologies allows to create effective systems that will stimulate the agglomerations innovative development . Therefore, the communicative activities of regional governments should be carried out through the use of information and communication technologies. Thus, the urgency of developing a methodology for assessing the increase of the innovative component of agglomeration economic development is due to the low percentage of implementation research results, low science-intensive gross value added in the Ukrainian regions, the possibility of using information and communication technologies. Increasing the number of targeted visits will simplify and speed up the process of establishing links between innovation market players at the agglomeration spatial level in both the short and long term

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.

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 is devoted to the problem of designing recycling in sustainable logistics systems in the field of processing wastewater treatment waste - municipal sewage sludge (MSS) in the focus of information technology management . The essence of the developed and proposed wastewater treatment project is based on its integration into the city's public utilities system, which will be a single basis for a closed-type logistics system that is managed on the basis of the introduction of a unified information system. This approach to the design of sustainable logistics systems ensures the formation of a closed material and informational logistics flow according to the "supply of raw materials - production - product distribution". This allows solving two problems: ensuring the creation of a closed logistics system that is managed by a single information base and developing a new type of fertilizer. The solutions proposed in this project in the implementation of the MSS processing project are complex and allow implementing all areas of the sustainable development strategy in the aspect of implementing modern information systems. The design technology makes it possible to completely close the logistics cycle for the supply and processing of MSS into finished products in the form of organic fertilizers

This study assessed the use of social media application significantly influence the students' decision-making to enrol at private higher education institutions (HEIs) within the Malaysian context. The underpinning theory applied in this study was the Theory of Reasoned Action (TRA). A quantitative method, stratified sampling technique and systematic sampling technique applied in this study to test the proposed hypotheses based on a sample of 500 first-year students in their first academic semester at 23 selected private HEIs in Kuala Lumpur and Selangor. Data analysis was carried out using smartPLS software. The analysis outcome of the focal study had expounded a significant positive relationship between the use of social media application and students' decision-making as a direct relationship. Thus, analysis of the structural model had further verified that hypotheses h1 of the focal study were accepted. The result shows a  significant positive relationship between social media application and students' decision-making indicated by a path coefficient of  0.630, t-value of 24.103, and p-value of 0.000.

Internet of Things could be current paradigm, with promising recent wireless communication. Simultaneously as its will likely deliver compelling and effective arrangement, security of the gadgets and organization could be challenge issue. Therefore security has arisen as quite difficult for the IoT during this paper is expect to create security challenges of the IoT, structural design of IoT through handling security troubles at every layer of the design, key advancements in IoT and use of IoT.

The special issue aims to cover the latest research topics in designing and deploying visual information analytics and management techniques using deep learning. It is intended to serve as a platform to researchers who want to present research in deep learning. The special issue focuses explicitly on deep learning and its application in visual computing and signal processing. It emphasizes on the extent to which Deep Learning can help specialists in understanding and analyzing complex images and signals. The field of Visual Information Analytics and Management is considered in its broadest sense and covers both digital and analog aspects. This involves development of techniques for image analysis, understanding and restoration. Deep learning techniques are effective for visual analytics. Deep learning is a fast growing area and is gaining impetus for application in various fields. Therefore, in this special issue, the objective is to publish articles related to deep learning in various problems of visual information analytics and management.

The purpose of this study is the development of IT-consulting in the system of foreign economic activity of the Black Sea region. The relevance of this study is due to the need to address the development of consulting services in the system of foreign economic activity of the agro-industrial complex in the region. It is established that today many companies are interested in reducing the risks associated with running their own business. This has led to a rapid growth of consulting companies. The most popular are IT consulting services, market research, financial consulting and auditing with the largest market share, while environmental consulting is only gaining momentum. Institutional priorities of IT-consulting services in the system of foreign economic development of agro-food production of the region are established. The mechanism of development of the system of regional IT-consulting for stimulation of foreign economic activity of agro-food sphere is offered. The basic elements of development and functioning of the international infrastructure of regulation and self-regulation of IT-consulting services are investigated. It is determined that in the long run the services will be developed on the basis of innovative concepts and management tools to ensure the strategic competitiveness of customers. The consumers served are metallurgy, energy, construction, telecommunications, chemical industry, food industry, trade and distribution, media companies and the financial sector. In general, the consulting industry in Ukraine has some space, and domestic consulting companies also have a significant share of reserves in the market of consulting services. The estimated value of exports of the Black Sea region according to the models in 2022 is projected at the level of 2989.242 million dollars, and in 2023 - at the level of 3146.893 million dollars. It is proved that for further intensification of foreign economic activity of enterprises two main problems should be solved: to overcome excessive dependence on export of plant products (and other products with low added value) and to intensify export capacities of Kherson region.

Agovernmentshouldusesocialmediaforcommunicatingwithitscitizen.Theengagement index score is one of the methods for assessing the rate of governmental success in using social media as a tool in establishing interactive relationships with its citizen. In general, the engagement index score is obtained by calculating the number of posts, number of likes and comments, and so forth on a single social media account. Therefore, we propose an integrated engagement index score for three social media: Facebook, Youtube and Twitter. In this work, we carry out a study for local governments in Indonesia. The engagement index score was adopted from the previous research. However, we modified the formula to get a better distribution score.Our modified formula generates the same ranking sequences with previous research.Also, Facebook and Youtubes’ reaction are considered in this work to analyzes the quality of sentiments to a Facebook fan page and Youtube channel of localgovernments.

This paper presents an approach that uses both genetic algorithm (GA) and fuzzy inference system (FIS), for feature selection for descriptor in a quantitative structure activity relationships (QSAR) classification and prediction problem. Unlike the traditional techniques that employed GA, the FIS is used to evaluate an individual population in the GA process. So, the fitness function is introduced and defined by the error rate of the GA and FIS combination. The proposed approach has been implemented and tested using a data set with experimental value anti-human immunodeficiency virus (HIV) molecules. The statistical parameters q2 (leave many out) is equal 0.59 and r (coefficient of correlation) is equal 0.98. These results reveal the capacity for achieving subset of descriptors, with high predictive capacity as well as the effectiveness and robustness of the proposed approach.