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Neural Networks Classifier Design using Genetic Algorithm
Tomášek, Michal ; Vašíček, Zdeněk (referee) ; Mrázek, Vojtěch (advisor)
The aim of this work is the genetic design of neural networks, which are able to classify within various classification tasks. In order to create these neural networks, algorithm called NeuroEvolution of Augmenting Topologies (also known as NEAT) is used. Also the idea of preprocessing, which is included in implemented result, is proposed. The goal of preprocessing is to reduce the computational requirements for processing of benchmark datasets for classification accuracy. The result of this work is a set of experiments conducted over a data set for cancer cells detection and a database of handwritten digits MNIST. Classifiers generated for the cancer cells exhibits over 99 % accuracy and in experiment MNIST reduces computational requirements more than 10 % with bringing negligible error of size 0.17 %.
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Using of visually evoked potentials for assessment of visual acuity
Hnízdilová, Bohdana ; Svozilová, Veronika (referee) ; Mézl, Martin (advisor)
In this thesis is described the usage of visual evoked potentials. The theoretical part describes how evoked potentials are measured and recorded. In this thesis is described the usage of evoked potencials in ophtalmology to determine visual acuity. Furthermore, it explains flash stimuli, onset / offset stimuli, pattern-reversal stimuli, and their responses. Practical part deals with evoked response to pattern-reversal stimuli. Signals from encephalogram electrodes and lux meter were measured using Bitalino device. The signals are processed and evaluated by semi-automatic software, where individual sections of the signal are selected by the user. The output is the epoch of the selected section.
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Deep Neural Networks Used for Customer Support Cases Analysis
Marušic, Marek ; Ryšavý, Ondřej (referee) ; Pluskal, Jan (advisor)
Umelá inteligencia je pozoruhodne populárna v dnešnej dobe, pretože si dokáže poradiť s rôznymi veľmi komplexnými úlohami v odvetviach ako napr. spracovanie obrazu, spracovanie zvuku, spracovanie prirodzeného jazyka a podobne. Keďže Red Hat doteraz už vyriešil obrovksé množstvo zákazníckych požiadavkov počas podpory rôznych produktov. Preto bola navrhnutá myšlienka použiť umelú inteligenciu práve na tieto dáta a docieliť tak zlepšenie a zrýchlenie procesu riešenia zákaznícky požiadavkov. V tejto práci sú popísané použité techniky na spracovanie týchto dát a úlohy, ktoré je možné riešiť pomocou hlbokých neurónových sietí. Taktiež sú v tejto práci popísane rôzne modely, ktoré boli vytvorené počas riešenia tejto práce a snažia sa adresovať rôzne úlohy. Ich výkony sú porovnané na spomínaných úlohách.
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Analysis of neurite directionality
Plišková, Diana ; Čmiel, Vratislav (referee) ; Odstrčilík, Jan (advisor)
Práca je zameraná na navrhnutie vhodnej metódy analýzy smerovosti neuritov. Využité boli snímky neurónov z fluorescenčnej mikroskopie. Pred samotnou segmentáciou bolo potrebné snímky predspracovať, pričom sa postupne využila úprava kontrastu, ostrenie a adaptívna filtrácia pomocou Weinerovského filtru. Jednotlivé návrhy metód segmentácie pozostávali z prostého prahovania, narastaním oblastí a využitím morfologických operácií. Následná analýza smerovosti využívala smer gradientov v obraze. Navrhnutá metóda bola využitá aj ako klasifikátor, ktorý dokázal rozdeliť jednotlivé snímky do skupín podľa smeru rastu.
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Simplified Multiplication in Convolutional Neural Networks
Juhaňák, Pavel ; Jaroš, Jiří (referee) ; Sekanina, Lukáš (advisor)
This thesis provides an introduction to classical and convolutional neural networks. It describes how hardware multiplication is conventionally performed and optimized. A simplified multiplication method is proposed, namely multiplierless multiplication. This method is implemented and integrated into the TypeCNN library. The cost of the hardware solution of both conventional and simplified multipliers is estimated. The thesis also introduces software tools developed to work with convolutional neural networks and datasets used to test them in the image classification task. Test architectures and experimentation methodology are proposed. The results are evaluated, and both the classification accuracy and cost of the hardware solution are discussed.
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Bayesian and Neural Networks
Hložek, Bohuslav ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This paper introduces Bayesian neural network based on Occams razor. Basic knowledge about neural networks and Bayes rule is summarized in the first part of this paper. Principles of Occams razor and Bayesian neural network are explained. A real case of use is introduced (about predicting landslide). The second part of this paper introduces how to construct Bayesian neural network in Python. Such an application is shown. Typical behaviour of Bayesian neural networks is demonstrated using example data.
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Stochastic Prediction of Mean Monthly Flows in Selected Hydrometric Profile
Jansa, Jakub ; Menšík, Pavel (referee) ; Marton, Daniel (advisor)
The diploma thesis is focused on the average monthly flows forecast in the selected hydrometric profile. Aim of this work will be evaluation of the calculated values and the interpretation of the results in understandable form. The next step will be find an appropriate connection between randomly-generated inputs in the form of random real flow series using the standard hydrological prediction models. This models are based on the principles of artificial intelligence and probability model. The result of the work will be verification of procedures and compilation of mean monthly flow stochastic forecast in selected hydrometric profile, which would be used for a reservoirs management, respectively for water systems management.
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Visual Simulator of General Neural Networks
Herman, David ; Zbořil, František (referee) ; Martinek, David (advisor)
The subject of this bachelor thesis is the design of a general library of neural networks. Another subject is the implementation of a visual simulator, which would represent graphically, in a suitable manner, the algorithm of learning and the active dynamics of the network, in separate steps. This application also has to be platform independent.
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Usage of the MATLAB environment for neural networks
Lenk, Peter ; Atassi, Hicham (referee) ; Škorpil, Vladislav (advisor)
This bachelor thesis discusses the basic theory and modelling of neural networks in the software environment of MATLAB. The thesis can be divided into four parts. After an introduction into the thesis, the theoretical background of the neural netwoks is explained in the first chapter. This chapter features a brief history and a biological background of neural networks and deals with the basic network architectures and the training processes. The next part is the description of how to implement networks in a general way using the MATLAB enviroment, so it deals with preparation of data, creation, simulation and training of a neural network. The last part of the paper covers a design of two excersises created in order to introduce modelling of the neural networks in the MATLAB enviroment to the students.
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Utilization of inverse reliability analysis tools for probability based design of selected structural parameters
Lipowczan, Martin ; Novák, Drahomír (referee) ; Lehký, David (advisor)
This bachelor thesis deals with the application of methodology and tools of inverse analysis in regards to probabilistic design of selected design parameters of structure. The first step was to get familiar with the probabilistic design and analysis, then understanding of the inverse analysis methodology itself which is based on artificial neural networks. After researching the topic we could get to the actual issue. To put the theory in practice easier examples were used at first. These were mathematical functions and one practical-based example, whereas the results were known in advance. This simplified a process of checking achieved values. Using software tools and especially DLNNET software allowed us to take on practical exercises. Used exercises are chosen from earlier undergraduate studies at the Faculty of Civil Engineering, Brno. The first of these was a design of reinforced concrete slab, where desired parameters were slab’s height and area of reinforcement. The second one was a design of a diagonal truss screw connection, aimed to size the screw diameter and its quantity.
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