مطالب

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.

The heavy trends of the Information Society are knowledge that requires the spirit of innovation, creativity, flexibility and critical thinking “more than benign introspection.” In addition, information about each other’s relationships with oneself, the other and the environment is crucial to enable individuals to understand the complex reality. The Moroccan school is part of this societal vision, by renovating its education and training programs, which claim the development of the quality of procedural, cognitive and metacognitive strategies. In this perspective this research work aimed at studying the engineering analysis of investigation approaches in the teaching of SVT for the implementation of a reflexive practice in lycian learners, generating the ability to process information and invest in mechanisms for understanding, conceptualizing and proceduralizing cognitive and metacognitive strategies. Informational investigative capacity is crucial because it informs learning decisions, which can lead to innovations in the delivery of cognitive styles. To do this, we chose to conduct our study, through interview surveys and semi-structured questionnaires. The first among teachers and the second for SVT inspectors from three provincial directorates of National Education. The results show that the majority of those involved in education are involved in the classical informational approach and the experimental approach. The use of investigative analysis approaches remains episodic in some learning units (scientific ecological output and DNA extraction). Teachers are keen to introduce new information technologies to create favourable conditions for learning about investigative approaches, in order to improve the power of analysis, treatment and critical thinking and coping with situations of authentic and novel learning. We are concerned about the ability to understand what and how each of its actors is further shaping.

The article is devoted to the research process of digitalization of vegetable development management as an important component of food security of the Black Sea region. Vegetables are necessary for the normal functioning of the human body, provide it with the necessary vitamins and trace elements. one of the conditions of good health. The annual volume of vegetables in food kits for the main social and demographic groups of the population of Ukraine is calculated. The dynamics of vegetable production of vegetable crops in the Black Sea region of Ukraine is studied. In addition, information and communication (digital) technologies in the agar sector are considered. It is proved that the digitization of agribusiness significantly saves the amount of materials and other resources, and execution time and production are optimized. Due to the systematization and grouping of data, the costs of document circulation are reduced, the process of accumulation and use of information is stimulated, the production and economic indicators of agricultural enterprises are improved, which will help increase their competitiveness in the long run.

The purpose of this study is to substantiate the basic principles and prerequisites for the formation of project maturity of public administration for the successful implementation of digital governance projects. The relevance of this study is due to the need to implement the task of digital transformation of Ukraine by solving the problem of modernization of public administration on the basis of constant increase of project maturity of the organizations.The study proved the existence of a direct dependence of prospects for the implementation of digital transformation projects on the level of project maturity of public management. A variant of the project maturity model adapted to the local self-government body is being developed. The model assumes three levels of maturity of local governments. It has established that the majority of the created united territorial communities of Ukraine were characterized by the first two levels of maturity. The implementation of the maturity model makes it possible to assess the current state of the system, choose adequate tools for staff training, create conditions for continuous improvement and realization of the potential of the local community for the implementation of digital transformation projects.

This study aims to test the relationship between knowledge generation and organizational creativity. The second method was surveyed (analytical) using exploratory research through the survey as a data collection tool. The validity coefficient was measured as a reasonable degree of stability was obtained for a sample of (46) individuals. The data was analyzed using statistical package (SPSS V25). Descriptive Statistic Among the findings is that CASNOS has a medium level of the knowledge generation process, and there is a statistically significant effect of knowledge generation on the organizational innovation of CASNOS (El-Oued), according to the dimensions adopted in the study model.

Sentiment Analysis or opinion mining is NLP's method to computationally identify and categorize user opinions expressed in textual data.  Mainly it is used to determine the user's opinions, emotions, appraisals, or judgments towards a specific event, topic, product, etc. is positive, negative, or neutral. In this approach, a huge amount of digital data generated online from blogs and social media websites is gathered and analyzed to discover the insights and help make business decisions. Social media is web-based applications that are designed and developed to allow people to share digital content in real-time quickly and efficiently.  Many people define social media as apps on their Smartphone or tablet, but the truth is, this communication tool started with computers. It became an essential and inseparable part of human life. Most business uses social media to market products, promote brands, and connect to current customers and foster new business. Online social media data is pervasive. It allows people to post their opinions and sentiments about products, events, and other people in the form of short text messages. For example, Twitter is an online social networking service where users post and interact with short messages, called "tweets." Hence, currently, social media has become a prospective source for businesses to discover people's sentiments and opinions about a particular event or product. This paper focuses on the development of a Multinomial Naïve Bayes Based social media data emotion analyzer and sentiment classifier. This paper also explains various enriched methods used in pre-processing techniques. This paper also focuses on various Machine Learning Techniques and steps to use the text classifier and different types of language models.

The article is devoted to a comprehensive analysis of peculiarities of the content types, which can be placed in social media and Ukrainian Internet media, its differences, types of manipulation, and methods of prevention, perception, and distribution of unreliable information, determination of difference between fact and judgment. The article also analyzes the statistics of social media usage in Ukraine and around the world, using Facebook as an example. Various kinds and types of media existing on the Internet and traditional, conducted a survey of the most popular content using the Starch method, it was possible to identify how a person perceives one content that is placed on different media. The relationship between content type and media type is established. The phenomenon of information restriction, arising as a result of modern algorithms embedded in the Internet companies and social media, their positive and negative aspects and why, therefore, we are deprived of a choice among a wide range of other topics, and learning some other points of view was considered. Examining the origin and meaning of the "Information Bubble" term, the authors propose to use a different term, "Membrane of Relevance", which, in their opinion, is more relevant to the essence of the said concept. The authors propose to use a taxonomy method to determine the impact of content on a particular type of media, which will greatly simplify the content analysis process.

The study has two main objectives: firstly, to examine the opportunity of the Momentum and Contrarian investment strategy for three different monetary systems to trade currencies in Forex markets using Symlet wavelet decomposition approach. Secondly, to examine the co-movements between the three monetary systems using wavelet coherence analysis. The findings indicate that an investor with momentum strategy can consider investing in Bitcoin and Gold market, while the contrarian investment strategy is more advisable for the fiat money market during crisis period. Furthermore, the wavelet coherence analysis indicates that Bitcoin currency is the most leading monetary system during the Covid-19 pandemic crisis, followed by gold. However, US dollar mostly leads Bitcoin during non-crisis periods, while Gold is found to lead the US dollar throughout the sample period of the study. This suggests that the cryptocurrency system or gold standard should be considered as the alternative monetary system for better economic stability specially during the crisis period. Moreover, Bitcoin and gold had an anti-phase correlation before the Covid-19 pandemic crisis, which implies better benefits of hedging in the non-crisis period, while during a crisis they are moving together across different horizons. In contrast, Bitcoin and Fiat Money are strongly correlated during non-crisis periods, while during covide-19 pandemic crisis the correlation is statistically insignificant. Overall, the outcomes offer significant guidance for policymakers in understanding which monetary system leads to better economic stability during the crisis period and provides many implications for market players such hedging and Diversification investments strategy in forex markets.

Buying online is a trend especially for young customers aged less than 30 years old. A lot of studies have been conducted to investigate this phenomenon but hitherto no cohesive model has been established. Therefore, the present study is meant to investigate online shopping factors that contribute to customers’ purchase decision. Five factors were identified to have potential influence on online purchase decision and one factor is expected to be a mediator to affect the relationship. From 384 data collected from online customers who visited popular online shopping sites, it was found that all five factors (perceived convenience, perceived risk, perceived competence, perceived benevolence, and perceived integrity) lead to trust and at the same time trust acts as a quasi mediator to affect the relationship between these factors and online purchase decision. The findings provide the actively sought after evidence to support the claims on the role of these factors in affecting online purchase decision. Online businesses should incorporate the findings in order to facilitate customers’ decision making process while visiting their websites. The websites must contain or portray all the elements as found in this study.

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.