Scientific Journal of Astana IT University
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USE OF VECTOR ALGEBRA TO ENSURE THE INTEGRITY OF THE COMPONENTS OF THE PROJECT-VECTOR MANAGEMENT MULTISYSTEM OF EDUCATIONAL ENVIRONMENTS
Get PDFThe article is devoted to the development of a mathematical model of the projectvector space of educational environments. Mathematical formalization of the project-vector space is performed. The main directions of application of vector algebra to ensure the integrity of the components of multisystem of project-vector management of educational environments are proposed. It is shown that in order to build an effective project management system, it is not so much the direction of movement of individual objects that is important, but the same or different vectors of their movement in the project-vector space. The same vectors mean that the movement of objects of different projects is equally conditioned. A model for calculating the distances between vectors and determining the optimal set of project groups (respectively, subsystems of the project management system) is proposed. Mathematical models have been developed for estimating the magnitude of the similarity of vectors over significant time intervals, as well as estimating the magnitude of the proximity of vectors specified by qualitative categories. These models show that the slower the objects of various projects move relative to each other, the more profitable it is to attribute them to one group and manage them on the basis of a single component of a multi-project management system
DEVELOPMENT OF THE STRUCTURE OF AN AUTOMATED SYSTEM FOR DIAGNOSING DISEASES
Get PDFToday, the importance of information support for various medical technologies has increased significantly. The use of modern information technologies is becoming a critical factor in the development of most branches of knowledge and areas of practice, so the development and implementation of information systems is an urgent task. The clinical decision support system provides clinicians and stakeholders with individualized patient assessments and recommendations to assist in the clinical decision-making process.
Knowledge-based information systems are widely used in medicine around the world. Modern technical capabilities make it possible to reach a qualitatively new level of presentation of the course of the disease, namely, based on appropriate mathematical models, to model the typical development of the pathological process in a particular disease, to speed up the process of diagnosing and receiving recommendations on treatment protocols.
In Kazakhstan, there is no variety of decision support systems in medicine, especially in the process of diagnosing diseases. The purpose of this study is to develop the structure of an automated system for diagnosing diseases. The Unified Modeling Language (UML) is used as a design tool. The structure of the automated system is presented; the main components and key terms are considered. A mathematical model of diagnostics is shown (in the example of diseases of intestinal and pancreatic insufficiency) based on decision-making methods with fuzzy initial data. A diagram of the data flows of the system is presented. Thus, the paper proposes the structure of an automated system that will contribute to high-quality diagnostics due to an effective method of system organization and the use of fuzzy set theory methods
INSPIRATIONAL INTUITION AND INNOVATION IN IT PROJECT MANAGEMENT
Get PDFThe role and place of inspiring intuition and creativity in the management of IT projects and development programs of organizations are considered. A conceptual model of the interaction of inspiring intuition and creativity in the processes of IT project management is presented. The influence of inspiring intuition and creativity on the life cycle of innovative projects for the development of knowledge and management technologies is determined. With the help of intuition, IT project managers can anticipate new products, management processes, business areas and development. Such promising actions usually cannot be planned purely rationally, but require an “intuitive feeling.” Vision and imagination open up opportunities for action beyond the paths. This is “inspiring intuition”. This inspiring dimension of intuition has a long-lasting, holistic and gradual effect. The key competencies and strategic priorities of the organization for the implementation of the strategy of sustainable development are considered. In the process of research, two models of sustainable development based on the use of innovative projects and programs were selected. The first model, the Strategic Sustainable Development Framework (FSSD), defines three levels of creative competencies - linear, literal, and holistic. Within the framework of this model, the qualitative influences of individual competencies on the formation of inspiring intuition are determined by example. The second model is related to the application of the system of knowledge and competencies for the management of IT projects and P2M programs. Within this model, priority competencies have been identified that shape the inspiring intuition of project managers. Within the framework of the evaluations, a matrix of qualitative influences on inspiring competence in the processes of implementation of innovative projects and programs was built
DEVELOPMENT OF AN INFORMATION AND EDUCATIONAL PORTAL OF DISTANCE LEARNING BASED ON EDUCATIONAL DATA MINING
Get PDFCurrently, there is an increase in demand for distance education programs, which actualizes the problems of organizing the educational process at universities using these technologies. The article highlights and describes the characteristic features andprospects of using the analysis of educational data in the information and educational portal of distance learning, in order to implement adaptive learning and learning in accordance with dynamically formed individual trajectories. The task is to create a fundamentally new information system of the university using the results of the analysis of educational data. One of the functions of such a system is to extract knowledge from the data accumulated during operation. Creating own system of this type is an iterative and time-consuming process that requires preliminary research and step-by-step prototyping of modules. The novelty lies in the fact that there is currently no methodology for developing such systems in Kazakhstan, so a number of experiments were conducted in order to collect data, select suitable methods for studying the collected data, and then interpret them. As a result of the experiment, the authors identified the sources of educational data available for analysis in the information environment of the university. The data of semester academic performance obtained from the Toraighyrov University information system, data obtained as a result of independent work of students and data obtained using specially developed Google-forms were taken as a basis. Aninformation and educational portal was created for the automated collection, processing and analysis of educational data. Based on the study of students’ behavior, it becomes possible to form recommendations for teachers to improve the content and structure, as well as recommendations for the training of students. The data contained in the activity logs are examined to obtain information, search for dependencies by filtering relevant logs, structuring information from them and providing data in a form convenient for analysis and drawing conclusions. The data of the main types of events generated as a result of recording user actions in the learning management system and scenarios for using the results of the analysis of these data are considered. The elements of the software implementation of this system are described in detail, conclusions are made about the availability of the data sources used, and conclusions are drawn about the prospects for further development
DEFENDER-ATTACKER MODELS FOR RESOURCE ALLOCATION IN INFORMATION SECURITY
Get PDFToday, information security in defender-attacker game models is getting more attention from the research community. A game-theoretic approach applied in resource allocation study requires security in information for successive defensive strategy against attackers. For the defensive side players, allocating resources effectively and appropriately is essential to maintain the winning position against the attacking side. It can be possible by making the best response to the attack, i.e., by defining the most effective secure defensive strategy. This present work develops one defender – two attackers game model to determine the defensive strategy based on the Nash equilibrium and Stackelberg leadership equilibrium solutions of one defender-one attacker game model. Both game models are designed and studied in two scenarios: simultaneous and sequential modes. Game modes are defined according to the information that is available for attackers. In the first one, the defender is not aware of the attack and makes a simultaneous decision of how many resources should be allocated. Meanwhile, in the second mode, the defender knows about the entrance of attackers into a market and is assumed to commit a better strategy. The budget constraints are studied for both modes, all calculations and proof are presented in the work. According to obtained game mathematical models, it can be highlighted that network value of customers is important through the introduction of new variables in modeling and performing game theory equilibriums. This paper underlines the importance of information availability, budget limitations, and network value of customers in resource allocation through mathematical models and proofs; and focuses on modeling and studying defender-attacker games to define defensive strategy
SOLVING THE PROBLEM OF DETECTING PHISHING WEBSITES USING ENSEMBLE LEARNING MODELS
Get PDFDue to the popularity of the easiest way to obtain personal information among attackers, phishing detection is becoming a popular area for research aimed at countering the implementation of such attacks. Malicious website detection is essential to prevent the spread of malware and protect end users from victims. Unfortunately, malicious URL detection still needs to be better understood due to a lack of features and inaccurate classification. Possible sources were examined in order to investigate the subject. Based on the collected information from previous studies, this study is devoted to solving the problem of detecting phishing websites using Ensemble Learning. The aim of the work is to choose the most optimal algorithm for classifying phishing websites using gradient boosting algorithms. AdaBoost, CatBoost, and Gradient Boosting Classifier were chosen as Ensemble Learning algorithms and were used to improve the efficiency of classifiers. Practical studies of the parameters of each algorithm for finding the optimal classification model are given. Research and experiments were carried out on a dataset containing information extracted from the contents of a URL: main URL, domain, directory, and file. A thorough Exploratory Data Analysis (EDA) was carried out, as a result of which the main dependencies and patterns of determining phishing resources were identified using correlation analysis. ROC AUC Score was chosen as an evaluation metric for the algorithms. The best result for predicting phishing websites was demonstrated by the AdaBoost Classifier algorithm, with an average ROC AUC score of 99%. The results of the experiments were illustrated in the form of graphs and tables
RESEARCH OF QUANTUM KEY DISTRIBUTION PROTOCOLS: BB84, B92, E91
Get PDFThe proposed article is devoted to the investigation of quantum key distribution protocols. The idiosyncrasy of this theme lies within the truth that present day strategies of key distribution, which utilize classical computing at their center, have critical downsides, in contrast to quantum key distribution. This issue concerns all sorts of calculations and frameworks for scrambling mystery data, both symmetric encryption with a private key and deviated encryption with an open key. A case is that in a communication channel ensured by quantum key distribution, it is conceivable to distinguish an interceptor between two legitimate organize substances utilizing the standards laid down in quantum material science at the starting of the final century. Standards and hypotheses such as the Heisenberg guideline, quantum trap, superposition, quantum teleportation, and the no-cloning hypothesis. The field of ponder of this theme may be a promising and quickly creating zone within the field of data security and data security. There are as of now made commercial items with the usage of a few of the quantum key dispersion conventions. Numerous of the made items are utilized in different circles of human movement. The significance of applying quantum key distribution conventions beneath perfect conditions without taking into consideration blunders within the frame of quantum clamor is analyzed. The usage of three quantum key distribution conventions is illustrated, as well as the comes about of the appearance of keys and the likelihood of event of each of them. The purpose of the article is pointed at analyzing and investigating quantum key distribution conventions. The article examines the points of interest and impediments of the BB84, B92, and E91 quantum key distribution conventions
TRAFFIC SIGN RECOGNITION WITH CONVOLUTIONAL NEURAL NETWORK
Get PDFRecognizing road signs is one of the most important steps drivers can take to help prevent accidents. The purpose of the research work is to develop a recognition system, increasing the classification accuracy of the model, using deep learning methods of the road sign recognition system for drivers in real time on the road. Stages of road sign image classification were carried out, and other authors' solutions were analyzed. In addition, in this work, a convolutional neural network (CNN) was used for an autonomous traffic and road sign detection and recognition system. The proposed system works in real-time on the recognition of road signs images. In this paper, a model is trained using deep learning of 43 different road signs using existing datasets and collected local road signs. A traffic sign detection and recognition system is presented using an 8-layer convolutional neural network, which acquires different functions by training different types of traffic signs.
In previous studies, models were trained using simple machine learning algorithms, but the relevance of this study is that a CNN model was trained for a classification task based on convolutional neural networks using deep learning. As a result of the study, classification accuracy of 95% was obtained using deep learning methods. As a novelty of the work, it is possible to note the diversity of the convolutional network methods used to increase the efficiency of the used data set and model training algorithms, the variety of received road signs and algorithms for its recognition, as well as the achievement of a high accuracy rate. This allowed the system to overcome the limited accuracy and performance issues caused by environmental factors, and to be more versatile and accurate than most modern systems
DEEP LEARNING-BASED FACE MASK DETECTION USING YOLOV5 MODEL
Get PDFBased on the background of rapid transmission of novel coronavirus and various pneumonia, wearing masks becomes the best solution to effectively reduce the probability of transmission. For a series of problems arising from crowded public places and collective units, where face recognition is difficult to increase target density, a deep convolutional neural network is used for real-time mask detection and recognition.This paper presents the method based on YOLOv5 model for deep learning and mask detection in image recognition as well as a live camera to label the pedestrians without masks in time. This experiment will use LabelImg software to preprocess 5003 images and make lightweight improvements based on the original YOLOv5 model to generate the final face mask recognition model. The Mosaic method is added to merge the images effectively and process the images in batch, and secondly, the GIoU loss function is selected to calculate the bounding box regression loss by comparison, which improves the localization accuracy even more. According to the experimental detection results, analogized with the original model YOLOv5, the recall and accuracy are effectively improved. In this paper, YOLOv3, SSD, Fast-R-CNN detection algorithms are used for comparison, the detection results of this model have a high mAP value which is equal to 92.9, which are higher than the detection results of other models. Real-time target recognition based on this model combined with practical applications can be applied in hospitals and crowd-gathering places to achieve effective reduction of epidemic transmission probability in a short period of time
ANALYSIS OF THE EFFECTIVENESS OF THE USE OF ADAPTIVE TRAINING PLATFORMS IN SECONDARY VOCATIONAL EDUCATION
Get PDFThe article considers the tasks and features of mathematics training for students of secondary vocational education. Special attention is paid to the need to solve the problem of adaptation of students to the conditions of study in college and the organization of independent work. In this regard, the authors propose to make wider use of the practice of adaptive learning as innovative pedagogical tools. The article considers the concept of the effectiveness of adaptive personalized learning and suggests the directions by which it can be evaluated. As an example, the experience of implementing an adaptive educational course “Mathematics”, designed in the Articulate Storyline platform, is analyzed. The module is designed to organize and support adaptive learning of students of the Department of Information Systems by means of adaptive educational technologies. The results of the training are analyzed, and the possibilities of the Articulate Storyline platform in ensuring the independent work of students are presented. The main part of the article is devoted to evaluating the effectiveness of e-learning using an adaptive educational platform. With the help of questionnaires and tools of the Articulate Storyline platform, an assessment of the educational result achieved was made, the degree of motivation of students to master the discipline of mathematics was analyzed, and the attitude of students to the process of e-learning using an adaptive educational platform was investigated