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.

As a result of the introduction of digital technologies in all spheres of society, various latest technologically conditioned risks and threats have become various. It requires the formation and implementation of a legal mechanism for further development and operation of the system ensuring information security, taking into account the effects of digitalization and transformation of society. The purpose of this study is development of scientifically substantiated proposals and recommendations for implementation legal mechanisms for information security in the context of digitalization. The relevance of this study is due to the needlegal regulation of relations in the information sphere, formation of a system of protection and counteraction to cybercrime. To achieve the goal of the study, methodological principles and approaches of legal science were used. The results of the analysis showed that the most significant mutual influence is demonstrated by a group of indicators of the state's institutional capacity and a group of indicators of the digital capacity of the national economy and cybersecurity. This study substantiates the main directions of accelerating the optimization of the institutional system of cybersecurity in Ukraine, which provides for two key areas: legal and organizational. The legal mechanisms include further improvement and harmonization of the regulatory framework, the formation of relevant legal norms, state policy in the field of information security. The organizational measures are aimed at improving the efficiency of responsible institutional structures - the subjects of cybersecurity - by increasing their capabilities, eliminating duplication in the exercise of their powers, taking into account the best practices of international and European experience. The core of the integrated information security system is the National Cyber Security Coordination Center.

Limited energy capacity, physical distance between two nodes and the stochastic link quality are the major parameters in the selection of routing path in the internet of things network. To alleviate the problem of stochastic link quality as channel gain reinforcement based Q-learning energy balanced routing is presented in this paper. Using above mentioned parameter an optimization problem has been formulated termed as reward or utility of network. Further, formulated optimization problem converted into Markov decision problem (MDP) and their state, value, action and reward function are described. Finally, a QRL algorithm is presented and their time complexity is analyses. To show the effectiveness of proposed QRL algorithm extensive simulation is performed in terms of convergence property, energy consumption, residual energy and reward with respect to state-of-art-algorithms.

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.

The infestation of the world pandemic has significantly changed the landscape of teaching and learning experience in Malaysia.  Particularly for the institution under study, E-learning, previously used as the support medium to face-to-face classroom teaching, has become the primary medium of teaching and learning amid Covid-19.  E-learning platform has replaced the face-to-face classes. Even though it is only a temporary approach, less is known in terms of its effectiveness.  The present paper intends to examine the factors contributing to E-learning's effectiveness in a Higher Education Institution in Malaysia amid the COVID-19 crisis. The Technology Acceptance Model underpinned the conceptual framework.   This study investigated the relationship between the independent variables (perceived usefulness, perceived ease of use, and self-motivation) and E-learning effectiveness.  The data were collected through an online self-administrated questionnaire using Google form and received a total of 354 respondents.  The data were analyzed using SPSS and Smart PLS.  The findings revealed that perceived usefulness, perceived ease of use, and self-motivation positively influenced E-learning's effectiveness.  This paper concluded that regardless of whether as the support medium or the primary medium in the teaching and learning process, the determinant to E-Learning effectiveness is still perceived the same. This study also supports the TAM proposition while incorporate self-motivation in measuring learning effectiveness.

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.

Persian language is one of the most widely used languages in the Web environment. Hence, the Persian Web includes invaluable information that is required to be retrieved effectively. Similar to other languages, ranking algorithms for the Persian Web content, deal with different challenges, such as applicability issues in real-world situations as well as the lack of user modeling. CF-Rank, as a recently proposed learning to rank data, aims to deal with such issues by the classifier fusion idea. CF-Rank generates a few click-through features, which provide a compact representation of a given primitive dataset. By constructing the primitive classifiers on each category of click-through features and aggregating their decisions by the use of information fusion techniques, CF-Rank has become a successful ranking algorithm in English datasets. In this paper, CF-Rank is customized for the Persian Web content. Evaluation results of this algorithm on the dotIR dataset indicate that the customized CF-Rank outperforms baseline rankings. Especially, the improvement is more noticeable at the top of ranked lists, which are observed most of the time by the Web users. According to the NDCG@1 and MAP evaluation criteria, comparing the CF-Rank with the preeminent baseline algorithm on the dotIR dataset indicates an improvement of 30 percent and 16.5 percent, respectively.

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.