درخت حوزه‌های تخصصی

This article discusses a device focused on images that enables users to recognise and detect many face-related features using the webcam. In this article, we are performing comprehensive and systemic studies to check the efficacy of these classic representation learning structures on class-imbalanced outcomes. We also show that deeper discrimination can be learned by creating a deep network that retains inter-cluster differences both and within groups. MobileNet, which provides both offline and real-time precision and speed to provide fast and consistent stable results, is the recently suggested Convolutional Neural network (CNN) model. The recently proposed Convolutional Neural Network (CNN) model is MobileNet, which has both offline and real-time accuracy and speed to provide fast and predictable real-time results. This also solved a problem related to the face that occurs in the identification and recognition of the face. This paper presents the different methods and models used by numerous researchers in literature to solve the issue of faces. They get a better result in using the highest number of layers. It is also noted that the combination of a machine learning approach with multiple image-based dataset increases the efficiency of the classifier to predict knowledge related to face detection and recognition

The steadily growing dependency over network environment introduces risk over information flow. The continuous use of various applications makes it necessary to sustain a level of security to establish safe and secure communication amongst the organizations and other networks that is under the threat of intrusions. The detection of Intrusion is the major research problem faced in the area of information security, the objective is to scrutinize threats or intrusions to secure information in the network Intrusion detection system (IDS) is one of the key to conquer against unfamiliar intrusions where intruders continuously modify their pattern and methodologies. In this paper authors introduces Intrusion detection system (IDS) framework that is deployed over KDD Cup99 dataset by using machine learning algorithms as Support Vector Machine (SVM), Naïve Bayes and Random Forest for the purpose of improving the precision, accuracy and recall value to compute the best suited algorithm.

World of technology provides everyone with a great outlet to give their opinion, using social media like Twitter and other platforms. This paper employs machine learning methods for text analysis to obtain sentiments of reviews by the people on twitter. Sentiment analysis of the text uses Natural language processing, a machine learning technique to tell the orientation of opinion of a piece of text. This system extracts attributes from the piece of writing such as a) The polarity of text, whether the speaker is criticizing or appreciating, b) The topic of discussion, subject of the text. A comparison of the work done so far on sentiment analysis on tweets has been shown. A detailed discussion on feature extraction and feature representation is provided. Comparison of six classifiers: Naïve Bayes, Decision Tree, Logistic Regression, Support Vector Machine, XGBoost and Random Forest, based on their accuracy depending upon type of feature, is shown. Moreover, this paper also provides sentiment analysis of political views and public opinion on lockdown in India. Tweets with ‘#lockdown’ are analysed for their sentiment categorically and a schematic analysis is shown.

Regional planning is an important element of the mechanism of market transformation in the economy of the region based on information technology management. The content of the plans is largely determined by the scenario of its development. However, it is necessary to determine the specifics of organizational support and the choice of a scenario for market transformation at the mesoscale. The objective of the research is to determine the specifics of organizational support and choose a scenario for market transformation at the mesoscale. Based on a study of the results of the implementation of the scenario of economic development, the main causes and trends of the regional economy are identified, and regulatory measures in the region are proposed. On the example of the regions of Ukraine, three possible scenarios of the scenario were formed in the near future: innovative, raw, paternalistic. The main criteria for all scenarios is the development of energy-saving technologies; the formation of a middle class that will ensure the modernization of social infrastructure, the development of education and health care, the improvement of the environment, the creation of an effective housing and communal economy and the beginning of the movement towards civil society.

The purpose of this paper is to identify and prioritise mobile wallet (m-wallet) key adoption factors (KAFs). 9 KAFs are proposed by authors on the basis of a systematic literature review. Authors have proposed one novel factor influencing adoption called 'contactless transactions' due to pandemic and new normal post-Covid-19. This has resulted in a total of 10 KAFs. The fuzzy TOPSIS approach is proposed to rank these key adoption factors (KAFs). Sensitivity analysis has also been conducted to check the robustness of the study. Examining the literature indicates that this study is among the first attempts to prioritise key adoption factors (KAFs) using fuzzy TOPSIS. The results show that perceived security, trust, performance expectancy, perceived ease of use and contactless transactions are among the top 5 adoption factors. The findings of this research will be beneficial for both academicians and practitioners. The key adoption factors (KAFs) proposed in the paper will help practitioners develop strategies for building a wider acceptance of m-wallet among customers. Moreover, the fuzzy TOPSIS technique discussed in this paper will apprise managers about critical factors to be focused upon. This study will provide an integrated framework for academicians to carry out further research in the field of mobile wallets.

Destination Image as a development which comprises of interrelated assessments converged with general impressions and convictions dependent on data preparing from an assortment of sources after some time; little research has concentrated on picture arrangement corresponding to this kind of goal, this article explores the mediating role of Online Image on the connection between E-WOM and  E-service quality, E-satisfaction, E- trust The result of Structural equation Modelling  (AMOS) path analysis affirmed that estimation model fulfilled the necessity and evidence the solidness of things, builds and correlation. All the four conditions for estimation model was met: things stacking above 0.7; unwavering composite quality (CR) above 0.7; Average Variance Extraction (AVE) above 0.5. The scholastics and industry will profit by the usage of this examination. Likewise, the tourist industry division can draw from these outcomes as references and proposals inside the impediments of the study. Future research headings were proposed to deal with the restrictions.

Due to the huge amount of data published on the Web, the Web search process has become more difficult, and it is sometimes hard to get the expected results, especially when the users are less certain about their information needs. Several efforts have been proposed to support exploratory search on the web by using query expansion, faceted search, or supplementary information extracted from external knowledge resources. However, these solutions are not well explored for the general web search in an open-domain setting. In addition, they mostly focus on supporting search in content expressed in English and Latin based languages. In this research, we propose a fully automated approach that aims to support exploratory search over the Arabic web content. It exploits the Arabic version of Wikipedia to extract complementary information that supports visual representation and deeper exploration of the search engine's results. Key Wikipedia entities are extracted from the text snippets produced by the search engine in response to the user's query. Entities are then filtered and ranked by using a novel ranking algorithm that extends the conventional PageRank algorithm. Finally, a graph is built and presented to the user to visually represent highly ranked topics and their relationships. The proposed approach was realized by developing ArabXplore, a system that integrates with the web browser to support the web search process by executing our approach in query time. It was assessed over a dataset of 100 Arabic search queries covering different domains, and results were assessed and rated by human subjects. The underlying ranking algorithm was also compared with the conventional PageRank.