667 papers found .

661. Determination of molds isolated from the patient materials, based on their microscopic morphological characteristics

ICIST 2021 Proceedings Part II, 178-182
Milanović Mina, Milosavljević Aleksandar
Abstract: Based on literature data in recent years the incidence of invasive fungal infection (IFI) caused by molds is on the rise. Diagnostics of those infections can sometimes be inefficient; they require a longer period of time in laboratory procedures and sometimes may lead to late diagnosis or misdiagnosis, which can result in patient’s critical condition or even mortality. Goal of this research is to train a classifier, a neural network model that can be used for classification of molds from patient materials, which can drastically improve the process of diagnosis and prevent fatal cases. Using a ResNet-50 deep convolutional neural network (CNN) and images obtained from Department of Microbiology and Immunology, Medical faculty, University of Niš, Serbia, archives, a classifier has been developed, displaying promising results, which show that with wider dataset it will be possible to train a model that can be used in diagnostics.

662. Ensuring the Durability and Reliability of Data in Smart Health Services Using Blockchain Technologies

ICIST 2021 Proceedings Part II, 183-186
Avdić Aldina, Marovac Ulfeta, Janković Dragan
Abstract: The use of modern technologies in all spheres of life offers the possibility of collecting and processing citizens' data in smart cities to improve the quality of their lives through creating smart health services. The collected data should be protected from misuse. This paper describes e-health services in smart cities, as well as the problems of ensuring durability and reliability of patients' data. As a solution to this problem, the use of blockchain technology has been proposed and ways for its application have been given.

663. An Analysis of the JSON Functionalities Evolution Across Different Versions of Oracle Relational DBMS

ICIST 2021 Proceedings Part II, 187-190
Bjeladinović Srđa, Asanović Marko, Gospić Nataša
Abstract: Modern web applications, as a necessity, require fast, efficient, and accurate data exchange, which all participants in communication can uniformly interpret. Consistency in interpreting exchanged data can be achieved using text, self-describing and platform-independent file formats such as XML and, recently, the even more popular JSON. JSON represents a common model for NoSQL databases. Also, it has been supported for years in relational databases from leading manufacturers, such as Oracle. This paper systematises, analyses, and compares the functionalities for working with JSON across different versions of Oracle DBMS, starting with version 12c R1, which introduced support for native JSON, concluding with the recently introduced version. For selected use cases, experimental tests were performed, which demonstrated the impact on performance achieved by the supported JSON functionalities from version 12c R1 to 19c of Oracle relational database management systems (DBMSs).

664. Creation of Exercise Plan Using Rule-based System

ICIST 2021 Proceedings Part II, 191-196
Travica Milica, Nikolić Siniša, Vejnović Mina, Luburić Nikola
Abstract: Nowadays, the modern world strives to live life healthier. With the influence of the virus COVID-19 on everyday life, it is difficult to maintain motivation and training routine. By using technology, overcoming those difficulties can be easier. This paper presents the implementation of the system for helping users during exercise, filling the role of a digital personal trainer. The system mimics the reasoning of a human expert in proposing a suitable exercise plan based on the current user's condition. Also, the system supports the process of monitoring the current user's status during exercise, acts on changes, and shows certain messages concerning the displayed status. The system uses rules to determine which exercises are most suitable for a particular user, as well as the messages that will be shown during the exercise. The system is implemented using a system based on rules.

665. Applying Blockchain Technology in eLearning systems: Overview, Analysis and Potential Solutions

ICIST 2021 Proceedings Part II, 197-201
Dimitrijević Nikola, Bogdanović Milena
Abstract: Technological innovations such as M-Learning, automatic graded systems, virtual and augmented reality, online classes, and the gamification of learning are rapidly being integrated in the field of contemporary education. One of these technologies is Blockchain, which has already proved to be a disruptive in several fields, such as finance, healthcare, human resources and advertising. Multiple organizations from different industries are developing blockchain-based systems, but the application of Blockchain in education is still novel. The goal of this paper is to present an overview of existing literature in this field, and the potential of application of Blockchain technology in education. We present a simple solution of an eLearning system for learning programming on the basis of automatic grading developed in the language Python.

666. Reinforcement learning usage in the development of recommender systems

ICIST 2021 Proceedings Part II, 202-205
Cincović Jelica, Drašković Dražen
Abstract: Recommender systems are used for predicting user preferences mostly for commercial purposes. As the need for recommender systems has increased significantly, due to the rapid lifestyle we all lead, machine learning has also found its wide application in the development and improvement of these systems. Reinforcement learning emerged as one of the newer machine learning techniques, which can make recommender systems more flexible using user interactions. This paper focuses on analyzing and comparing existing implementations of recommender systems based on reinforcement learning, and drawing final conclusions when combining these two techniques, as well as defining possible further upgrades.

667. Comparison of five outlier detection methods in case of OpenClick data set

ICIST 2021 Proceedings Part II, 206-211
Mitrović Vladimir, Mišić Dragan, Trajanović Miroslav, Vitković Nikola
Abstract: In data analysis, outlier detection is very important for tasks such as filtering data or finding points of special interest. Finding outliers can be quite challenging task, especially when data set represents human behavior or certain action, which can be hard to describe or predict. This is also case in OpenClick project, a platform which provides various tests where human behavior can be analyzed through human-computer interaction. For a single test type on OpenClick project, an individual test can be invalid for various reasons, e.g., a disturbance occurred during a test (such as ringing of a phone) or perhaps a test subject cheated in some way. One way how could we distinguish invalid test from valid is through detection of outliers in data set using outlier detection methods. For this purpose, we chose five outlier detection methods, which we use to detect suspicious data points and to evaluate tests in terms of how much outliers bring disturbance in test results. In addition, we give some observations on the form of the data set of a single test and propose possible way on how to approach a task of labeling tests as valid or not.

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