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A mathematical model of the chemical process in the ideal mixing reactor on a bipartite graph is constructed. The type of mathematical model is shown depending on the conditions of the process — taking into account changes or without taking into account changes in the volume of the reaction mixture. Based on graph theory, the existence of a solution to the kinetic model of a chemical process is shown.

The passport of the Russian Federation is the main document of Russian citizens. A Russian passport is required for the vast majority of transactions in banks, government agencies, education, etc. This paper presents a neural network approach to data recognition from the Russian Federation passport. The use of two neural networks is proposed. The first neural network based on SOTA neural network YOLOv5 to determine the location of areas with text. The second convolutional neural network with the topology chosen by the authors to recognize the text in the previously selected blocks. A console application was implemented to demonstrate the approach. The accuracy of the first network is 78.6%, the second is 97.4%.

The development of information technologies requires the processing of an increasing amount of information. A great number of tools and DBMSs for working with big data have been created. Examples of DBMS are: MS SQL Server, Oracle Database, SciDB, VoltDB, PostgreSQL XL, Clusterix, etc. Most systems of the BigData class require serious computing power to ensure acceptable performance. This article discusses the TPC-H test and 22 of its queries, the execution of which allows you to analyze the performance of database management systems (DBMS). PostgreSQL, MySQL, and Microsoft SQL Server DBMS are considered and their performance is investigated based on TPC-H test queries with database (DB) of various sizes in the DbVisualizer Free version 11.0.6.

In 21st-century learning academic, where collaboration, digital literacy, critical thinking, and problem-solving are considered the core competencies to be enhanced and developed further.In a multinational country, such as the United Arab Emirates (UAE) which remains one of the fastest-growing countries on the planet in all domains including education, tourism and health there is a gap in matching students with different background. To accommodate different background, heritage and education systems for the incoming expats and their family and kids, there are different education systems (America, British, Local, Indian to name but a few) that are currently running in the UAE. However, the move from one system to another is not a straightforward process due to many reasons which can be categorized into three groups: admission stage, leveling stage, and class stage. The implementation of the proposed work will be integrated as a case study in one of the British school systems in Abu Dhabi, UAE. This paper considers the application of the robotics in Education providing the background for current use of the robots in the society.

Tracking students' attendance is a regular occurrence in most academic environments. The manual and semiautomated attendance systems are quite time-consuming, inefficient, as well as lacking in security. Thus, the objective of this research is to develop an efficient and secure attendance system that could be beneficial for all educational institutes. As outcomes, an integrated, embedded and fully automated attendance system is developed that makes the use of edge and cloud computing, biometric sensors, and real-time cloud database. The developed system was evaluated with 15 participants in a laboratory environment and found that the proposed system is comparatively more efficient, secure and propitious for educational institutes in tracking attendance.

Successful transformation of government public transactions to digital services depends on having a precisely thought national strategy which is informed by a context-aware information and communication technologies (ICTs). This study examines the level ICTs awareness of public sector employees and the extent to which they can support the digital transformation of government public transactions. An online questionnaire was adopted to gather data from employees of Iraq public sector employees. According to the results, employee participants were believed they have proper ICTs skills to support the digital shift of government services. The findings also indicate that employee participants were enthusiasts to the digital services, whereas they are less satisfied with the government national policy as well as the allocated financial resources to implement digital government principles.

The use of technology and Artificial Intelligence is becoming much more apparent in our lives, especially, in the many working environments that there are; FinTech, Medicine, Education, and more recently, Legal Tech. The paper provides the background for the use of AI in Law, explores the experience of existing applications. Based on the requirements gathered during the interviews and the observations made from the case study the decision support system waa developed that is injected with intelligence through the inclusion of a framework, which facilitates the data science element and in turn assists legal professionals with decision making, risk management and carrying out cumbersome legal research tasks.

Artificial intelligence, and especially Artificial Neural Networks (ANN), has gain a monumental growth and interest of healthcare providers to improve medical care while reduce cost. Applications of ANN for classification and prediction are well-established on numerous aspects of real-world applications. One of these many aspects is to improve healthcare delivery through influencing healthcare provider decisions. This study provides a systematic review of the applications of ANN to medical applications. We have screened 87 articles from several academic databases with coverage our cross-disciplinary query to identify matches in the literature based on the combinations following keywords; Artificial Neural Networks, Medicine, Healthcare, and Applications. Our systematic review process involves searching evidence from different sections while focusing on eligibility, rationale, objectives, evaluation and limitations. Reviewed studies have targeted the use of different ANN used including multilayer perceptron, convolutional and recurrent neural networks, along with deep learning approaches. Most of these studies informed classification of diseases or decision-making process. The commonest ANN architecture in this review was found to be the multilayer perceptron within a feed forward learning approach. Interpreting ANN final models was found to be the main challenge with medical applications.

The article covers the methods of Deep Learning for education data mining (EDM) and learning analytics (LA). The authors have reviewed the researches on EDM and LA that have employed the classical approaches of artificial neural networks (ANNS). The authors come to conclusion that LA and EDM present insufficient volume of research on the application of a new and more profound technology of artificial neural networks – Deep Learning. The new methods are described and the research on LA and EDM that employs Deep Learning is studied

Sign language is a main communication tool for hearing impaired people. The sign language recognition systems are designed to convert it into text or speech, breaking the interaction barrier for deaf person. The paper reviews the state of the art in sign language recognition. The study focuses on data acquisition methods, because they put the major restrictions on a further recognition process. We perform the sign language recognition using data collected by the Leap Motion Controller. The device detects user’s hands and transmits it into a three-dimensional model, providing hands and fingers location and posture. This paper presents a system that is capable of recognition and classification of the sign language using deep-convolutional neural network. We highlight the main features of the Leap Motion Controller that improves and simplifies feature extraction. Also, the paper discusses the problems we faced during the sign language recognition.

At present, the transition to personalized medicine is one of the main vectors of the development of medicine. The use of a neural network approach will allow you to determine the disease in the early stages, taking into account the collected medical statistics of the patient or the observations of the patient. This article describes the first step in creating a digital twin of a patient suffering from psoriasis. The application module helps to determine the presence or absence of a disease with a patient at the initial stage from a photograph.