درخت حوزه‌های تخصصی

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.

External factorsexert an enormous influence on the exigencies of changing the technological course of individualsectors. Digitalizationrepresents not a new milestone in the evolution of education, but its technological and methodological development. Due to a period of constraints, caused by COVID-19 pandemic, educational institutions at all levels in most countries have been forced to resort to digitalization of the learning process. Intelligent learning software provide their users with a variety of options to design and individualize the learning process, as well as independence from time and space. Teachers benefit from flexible content generation and just-in-time assessment of learners' progress, evaluation of large task sets, knowledge sharing with other teachers and knowledge institutions. Learners benefit from anytime availability of learning tools and just-in-time feedback. In our investigation, we consider the current state-of-the-art of educational software. Considering the results of quantitative empirical study and analysis of educational issues solvable by artificial intelligence, we present a developed and tested framework for selected educational problem.

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.

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

Despite the vital role of information in relational-oriented firms, there are limited studies on the impact of information quality on relationship marketing. To address this gap, this study develops a conceptual model to examine the impact of information quality on the successful implementation of relationship marketing by assessing the mediating role of salespeople's relational competency. The proposed model is tested using structural equation modeling (SEM) technique with data collected from 140 employee-customer pair sample of Shatel Group (Tehran, Iran). Stratified sampling is our applied research methodology, we also employed questionnaire for data collection. The results indicate that while there is no meaningful relationship between information quality and salespeople's relational competency, there is a significant relationship between salespeople's relational competency and relationship marketing. Accordingly, it could be concluded that salespeople's relational competency cannot act as a mediating variable and thus information quality cannot affect relationship marketing indirectly.

The present research examines the benefits of implementing knowledge management (KM) principles in the Jordanian banking sector to enhance performance. The study emphasizes the significance of Artificial Intelligence (AI) and how Jordanian banks utilize it to improve the quality of customer service they provide. This study targets managers at all levels and focuses on the Jordanian banking sector as its research environment. A questionnaire is created to gather information from a random sample to achieve the research's objectives. The study involves a sample of 250 managers. Additionally, the research adopts a descriptive methodology, and SPSS is used to analyze the data. The statistical findings provide robust evidence for the importance of performance expectations, social influence, and perceived risk in influencing consumer intentions. Marketers and decision-makers within the banking industry can leverage these insights to shape their long-term strategies for effectively utilizing and maximizing AI technology in the banking sector. Furthermore, by providing policymakers and practitioners of Jordanian commercial banks with insight into the variables influencing user satisfaction, the findings will help these complex institutions operate more effectively.

In this paper a novel digital watermarking algorithm is proposed. The proposed method comprises of a watermarking embedding and extraction algorithm using bio inspired optimization technique. Dragonfly algorithm (DA) is based on the static and dynamic swarming behaviors of dragonflies in nature. The dragonfly algorithm is used to optimize the scaling factor of the watermarking so that an optimal watermark is embedded. Watermarking algorithms take as input a cover image and the message. The cover image in the proposed method is decomposed into sub bands using discrete wavelet transform (DWT). Thereafter, it is converted to discrete cosine blocks (DCT). An optimal scaling factor is required for performing the watermarking. In this paper, DA is used for computing the scaling factor. The DA generated scaling factor is optimal and improves the performance of the watermarking. The inverse DWT and DCT are computed to extract the watermarked image from the cover image. The proposed method is applied on different images to evaluate the performance. The results obtained are compared with other state of the art methods.

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.

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

Online reviews have become essential aspect in E-commerce platforms due to its role for assisting customers’ buying choices. Furthermore, the most helpful reviews that have some attributes are support customers buying decision; therefore, there is needs for investigating what are the attributes that increase the Review Helpfulness (RH). This research paper proposed novel model called inclusive review helpfulnessmodel (IRHM) can be used to detect the most attributes affecting the RH and build classifier that can predict RH based on these attributes. IRHM is implemented on Amazon.com using collection of reviews from different categories. The results show that IRHM can detect the most important attributes and classify the reviews as helpful or not with accuracy of 94%, precision of 0.20 and had excellent area under curve close to 0.94.

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.

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.