|
|
Human behaviour monitoring system based on smartphone and bracelet data analysis
Mikulec, Marek ; Zvončák, Vojtěch (referee) ; Mekyska, Jiří (advisor)
There has been established new technological field using smart phones and wearable devices for medical research since the arrival of health 4.0. The main goal of this work is to design, implement and test new system for monitoring people´s behaviour using smart phone and wearable device. These smart compoments should oblige requirements of health~4.0. This work uses open source software AWARE Framework and data from Fitbit API. The final system enables gathering and sharing 36 measurable metrics from smart phone and wearable device. Furthermore it secures efective access to gathered data and puts particular emphasis on the security of the system. Finally the system was used to examine a patterns of REM (Rapid Eye Movement) sleep behaviour disorder.
|
|
|
Integration of advanced artificial intelligence methods with log management security systems
Sedláček, Jiří ; Mikulec, Marek (referee) ; Safonov, Yehor (advisor)
Cyber security is a very important aspect of everyone’s daily life. With the ever-expanding cyberspace and its growing influence on the real world, the issue of cyber security is all the more important. The theoretical part of the thesis describes the basic aspects of security monitoring. Also, the process of collecting event logs and their management is briefly described. An important means of security monitoring is the management of security information and events. Its advantages, disadvantages and possible improvements with artificial intelligence are discussed. Security orchestration, automation and response functions are also mentioned in the theoretical part. Machine learning techniques such as neural networks and deep learning are also mentioned. This section also focuses on cyber operations centres in terms of improving the efficiency of human ”manual” labour. A survey of possible machine learning techniques for this use case has been conducted, as the lack of human resources is a critical issue within security operations centres. The practical part of the thesis involves setting out a goal (text sequence classification) that could make the work considerably easier in terms of manually categorizing event logs according to their source. For this set task, security monitoring related data was collected from different log sources. In the practical part, the methods for processing this data are also described in detail. Subsequently, a suitable neural network model was selected and its technical description was performed. Finally, the final data processing and the process of training, validating and testing the model are described. Three scenarios were developed for this process, which are then described in detail in the measurement results.
|
|
|
Comparison of specialized actigraphs with wearable devices in quantitative sleep analysis
Čech, Vladimír ; Zvončák, Vojtěch (referee) ; Mikulec, Marek (advisor)
Actigraphy and smart wearable devices provide similar functionality in quantitative sleep analysis. This work aims to verify how common wearable devices stand in comparison with a specialized actigraph used in clinical practice. The actigraph used in this work was Geneactiv Original and it was used to compare the sleep analysis results of eleven commercially available devices. The measurements for nine of the measured devices lasted for one week. Two devices were measured for six days. During sleep, the device was worn on the same hand as the actigraph, and at the same time a sleep diary was kept, in which data on the time of lying down, the time of awakening and the number of awakenings per night were recorded. After a week of measurement, the data obtained from the actigraph were evaluated by software and then compared with the data provided by the second device. In this work, data of total sleep time, sleep onset latency, number of awakenings during the night and sleep efficiency are compared. Ten of the eleven devices measured higher total sleep time and higher sleep efficiency than the actigraph. These devices measured higher total sleep time by 1.21 % – 12.06 % and measured higher sleep efficiency by 2.86 % - 13.86 %. One device, namely Fossil Sport, measured lower total sleep time by 9.02 % and lower sleep efficiency by 9.13 %. Sleep onset latency was the most distinct parameter. Wearable devices measured higher sleep time by 86.72 % – 1225.95 %. Neither device nor actigraph could reliably determine the number of awakenings during the night unless it was a significant physical activity during the night. From the results of the work, it is not possible to say in general that all commercially available devices would be a reliable substitute for actigraphy.
|
|
|
Online database for secure data collection
Kopec, Peter ; Mezina, Anzhelika (referee) ; Mikulec, Marek (advisor)
This bachelor thesis deals with the design and implementation of a secure online database for data collection, which is accessible from the Internet. A database that is accessible from the Internet and contains personal data or other valuable data must be well secured, because we do not want this data to be misused by an unauthorized person. To begin with, we select the appropriate applications for our system and analyze their functionality. The applications are selected based on the features they provide, the overall complexity and support of their online community. Part of the work is devoted to the analysis of data leaks from medical facilities in 2019 and 2020 and a few other leaks from other industries. Thanks to this analysis, we know the reasons for the data leakage and we are able to focus more on these weaknesses and point out the problems. The next part of the work is devoted to the design and implementation of a practical solution using applications that we selected at the beginning. In our case it is a MYSQL database, FLASK backend with Gunicorn WSGI and NGINX web server. Finally, we analyze the security of this solution using the most common vulnerabilities according to OWASP and the NMAP network scanner.
|
|
|
Classification of thorax diseases on chest X-ray images using artificial intelligence
Pijáček, Štěpán ; Mikulec, Marek (referee) ; Mezina, Anzhelika (advisor)
Tato práce se zabývá výzkumem použitelných řešení pro problém klasifikace onemocnění hrudníku na rentgenových snímcích hrudníku pomocí umělé inteligence. Pro lepší pochopení problému jsou v prvních kapitolách vysvětleny základní konvoluční neuronové sítě a jejich výhody a nevýhody. Na základě těchto prvních vysvětlení jsou vybrány dvě neuronové sítě, které rozšiřují koncept konvoluční neuronové sítě. Těmito sítěmi jsou kapslová síť a reziduální síť, obě jsou dále vysvětleny v příslušných kapitolách s jejich výhodami a nevýhodami. Reziduální síť a kapslová síť jsou poté implementovány pomocí programovacího jazyka python a frameworku TensorFlow s knihovnou Keras, obě se svými příslušnými kapitolami. Na konci práce jsou uvedeny výsledky a závěr.
|
|
|
Web application for displaying cyber attacks in local networks
Matušicová, Viktória ; Mikulec, Marek (referee) ; Safonov, Yehor (advisor)
The information sphere is constantly and rapidly developing. This expansion means an increase in the risks of the Internet use. Vulnerabilities and other threats are emerging that provide an opportunity for unauthorized users to penetrate the integrity of protected infrastructures. The main goal of the bachelor’s thesis is to create a tool that allows the system administrator to perform the analysis of end stations in the local network. With the help of the web application, the administrator is able to view all computer attacks performed on his computer infrastructure. That makes it possible for him to implement countermeasures which will improve performance and security of the entire infrastructure. From a theoretical point of view, the bachelor thesis is focused on the issue of computer attacks on the data layer and network layer of the ISO/OSI model. Subsequently, it is focused on the structure of workplace involvement and web application. In the last part the work is focused on the design of the web application and its integration into the experimental workplace. Emphasis during the practical part is placed on the implementation of the workplace and web application on the local network. The practical part is divided into two implementation groups. Initially the experimental workplace is implemented within the local network. Here the web application focuses on the development of the server side – working with databases. In the second phase of implementation the experimental workplace is transferred into a real form. Following application features are added: various graphical displays, filtering and a section for user settings. Large emphasis is placed onto the security of the entire application – login system, server and client configuration settings. After the experimental workplace is connected with the web application, the functionality of the entire solution is tested by three different computer attacks. At the end of the thesis a brief conclusion and summary of the bachelor’s thesis is established.
|
|
|
Identification of sleep disorders based on actigraphy data and sleep diaries
Molík, Miroslav ; Mekyska, Jiří (referee) ; Mikulec, Marek (advisor)
This master’s thesis deals with prediction of Parkinson's disease using sleep parameters from actigraphy and sleep diaries. The goal is to design a machine learning approach, which will be able to recognize pacients suffering from Parkinson's disease. For training dataset supplied by St. Anne's University Hospital Brno was used, which was variously modified for achieving best possible results. These adjustments were evaluated according to the results of the trained models and based on these results, two models (achieving test accuracies of 85 and 82%) were selected.
|
|
|
Automated diagnosis of sleep disorders using wearable devices
Sigmund, Jan ; Mekyska, Jiří (referee) ; Mikulec, Marek (advisor)
Sleep disorders induce many negative repercussions. Furthermore, research about their connection to cognitive health is increasing in numbers. This thesis concerns detection of poor sleep quality via raw actigraphy data. Existing method for assessing sleep was selected, it’s performance was validated against polysomnography on 27 patients. Used algorithm defines sleep as the absence of change in arm angle. Resulting 81 % sensitivity, 62 % specificity and 78 % accuracy is different from the outcome in the pilot study. Two approaches, to determine sleep quality were used. Both are based on comparing sleep features – first, with National Sleep Foundation recommendations and second, with control group without sleep disorders (7 persons). The goal was to pinpoint the remaining 19 patients with diagnosis. The recommendation for SOL, WASO, NA>5 and SE had higher sensitivity (75 %), lower specificity (71 %) and identical accuracy (74 %). These approaches were then also tested on 7-day actigraphy, consisting of 27 subjects, that are presumed to have prodromal dementia with Lewy bodies. Same principle was applied to try to predict LBD and thereby address the link between sleep quality and neurodegeneration. This resulted in 86 % sensitivity, 38 % specificity and 63 % accuracy. With regard to achieving solid sensitivity in all cases and good accuracy this could be used to indicate sleep quality.
|
|
|
System of secured actigraph data transfer and processing
Mikulec, Marek ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
The new Health 4.0 concept brings the idea of combining modern technologies from field of science and technology with research in healthcare and medicine. This work realizes a system of secured actigraph data transfer and preprocessing based on the concept of Health 4.0. The system is successfully designed, implemented, tested and secured. With the help of a non-invasive method of monitoring the movement and temperature of the subject using the GENEActiv actigraph allows the system to securely transfer, process and evaluate the subject's sleep data using the machine learning algorithm XGBoost. The proposed system is in accordance with the valid law of the Czech Republic and meets legal requirements.
|
|
|
Application of deep learning techniques for anomaly detection in computer networks using graphical representation of network traffic
Židovský, Patrik ; Mikulec, Marek (referee) ; Safonov, Yehor (advisor)
This thesis deals with the application of deep learning techniques for anomaly detection in computer networks. By selecting appropriate features of the communication network, a graphical representation of the network traffic has been created in order to train convolutional neural networks. The first trained model was used in a Raspberry Pi device with a Neural Compute Stick hardware accelerator. The second model was placed in a central location for additional control of the results. The aim of this work was to design and implement an automated anomaly detection system to be tested by three selected cyber attacks. Evaluate the results obtained and propose optimization options.
|
guest ::
RSS feed.