|
|
Pilot proficiency classification from gaze
Ruta, Dominik ; Vlk, Jan (oponent) ; Chudý, Peter (vedoucí práce)
This work deals with the classification of pilot proficiency level and basic flight maneuvers from gaze. The goal is to provide additional valuable tool for aviation instructors to evaluate proficiency of pilot students and provides them with feedback. This idea is based on results of numerous relevant studies, which discovered correlation between effective scanning patterns and domain performance. This thesis considers two proficiency levels~---~amateur and experienced. This work utilizes common analysis metrics of visual scanning and machine-learning classification techniques. The Support Vector Machine algorithm is used for the proficiency classification and Hidden Markov Models are utilized in basic flight maneuvers classification. The result of this thesis is a high accuracy proficiency classification and good ability to distinguish between individual basic flight maneuvers performed by pilots.
|
|
| |
|
|
Active Learning pro zpracování archivních pramenů
Hříbek, David ; Zbořil, František (oponent) ; Rozman, Jaroslav (vedoucí práce)
V teto praci je reseno vytvoreni systemu, ktery umoznuje nahrani a anotaci skenu historickych dokumentu a nasledne aktivni doucovani modelu pro rozpoznavani znaku (OCR) na dostupnych anotacich (vyznacenych radcich a jejich prepisech). V praci je popsan proces, klasifikovany techniky a uveden existujici system pro rozpoznavani znaku. Predevsim je kladen duraz na metody strojoveho uceni. Dale jsou vysvetleny metody aktivniho uceni a navrhnut zpusob doucovani OCR modelu z anotovanych skenu. Zbytek prace se zabyva konkretnim navrhem, implementaci, dostupnymi datasety, vyhodnocenim uspesnosti rozpoznavani znaku vlastnorucne vytvoreneho OCR modelu a testovanim celeho systemu.
|
|
|
Strojové učení v přirozeném jazyce
Otrusina, Lubomír ; Šilhavá, Jana (oponent) ; Smrž, Pavel (vedoucí práce)
Tato práce se zabývá zjednoznačňováním slovních významů pomocí metod strojového učení. Čtenář je krátce seznámen s danou problematikou a jejím historickým vývojem. Jsou zde popsány nejpoužívanější metody a přístupy, speciálně pak naivní Bayesův klasifikátor, který je implementován v systému. Je zde uveden i názorný příklad pro tento klasifikátor. V praktické části je popsán návrh systému využívající tohoto klasifikátoru včetně popisu různých algoritmů použitých v systému. Na závěr je uvedeno vyhodnocení výsledků systému a jejich analýza. Implementovaný systém se zúčastnil soutěže v rámci mezinárodní konfernce sémantického vyhodnocování SemEval-2007.
|
|
|
Optimization of DDoS Mitigation Rule Inference
Carasec, Elena ; Grégr, Matěj (oponent) ; Žádník, Martin (vedoucí práce)
This thesis discusses the possibility of using machine learning algorithms for DDoS protection. For classical and incremental (online) learning are considered explainable supervised learning methods, particularly decision trees. Furthermore, some possible optimisations are introduced to increase traffic classification accuracy and decrease the amount of blocked legitimate traffic.
|
|
|
Traffic analysis using on machine learning
Zelený, Ondřej ; Slanina, Martin (oponent) ; Frýza, Tomáš (vedoucí práce)
The aim of this thesis is to investigate the problematic of object detection and classification for traffic analysis. The theoretical part of the paper takes insight on numerous methods and techniques of object detection and classification. Further the paper discuses popular frameworks and programming languages for implementation of convolutional neural networks as well as multi-object tracking and communication with IoT server. The practical part shows implementation of chosen model and additional functionalities, object trackers and communication with selected IoT platform as well as data processing in cloud and visualization.
|
|
|
Traffic analysis using on machine learning
Zelený, Ondřej ; Slanina, Martin (oponent) ; Frýza, Tomáš (vedoucí práce)
The aim of this thesis is to investigate the problematic of object detection and classification for traffic analysis. The theoretical part of the paper takes insight on numerous methods and techniques of object detection and classification. Further the paper discuses popular frameworks and programming languages for implementation of convolutional neural networks as well as multi-object tracking and communication with IoT server. The practical part shows implementation of chosen model and additional functionalities, object trackers and communication with selected IoT platform as well as data processing in cloud and visualization.
|
|
|
Pilot proficiency classification from gaze
Ruta, Dominik ; Vlk, Jan (oponent) ; Chudý, Peter (vedoucí práce)
This work deals with the classification of pilot proficiency level and basic flight maneuvers from gaze. The goal is to provide additional valuable tool for aviation instructors to evaluate proficiency of pilot students and provides them with feedback. This idea is based on results of numerous relevant studies, which discovered correlation between effective scanning patterns and domain performance. This thesis considers two proficiency levels~---~amateur and experienced. This work utilizes common analysis metrics of visual scanning and machine-learning classification techniques. The Support Vector Machine algorithm is used for the proficiency classification and Hidden Markov Models are utilized in basic flight maneuvers classification. The result of this thesis is a high accuracy proficiency classification and good ability to distinguish between individual basic flight maneuvers performed by pilots.
|
|
|
Optimization of DDoS Mitigation Rule Inference
Carasec, Elena ; Grégr, Matěj (oponent) ; Žádník, Martin (vedoucí práce)
This thesis discusses the possibility of using machine learning algorithms for DDoS protection. For classical and incremental (online) learning are considered explainable supervised learning methods, particularly decision trees. Furthermore, some possible optimisations are introduced to increase traffic classification accuracy and decrease the amount of blocked legitimate traffic.
|
|
|
Traffic analysis using on machine learning
Zelený, Ondřej ; Slanina, Martin (oponent) ; Frýza, Tomáš (vedoucí práce)
The aim of this semestral thesis is to investigate the problematic of object detection and clasification for traffic analysis. The theoretical part of the paper takes insight on numerous methods and techniques of object detection and clasification. Further the paper discuses popular frameworks and programming languages for implementation of convolutional neural networks. The practical part shows implementation of chosen model and hardware selection for real system.
|
host ::
RSS.