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Integrating Artificial Intelligence into Fast-Moving Consumer Goods
Bagi, Juraj ; Hříbek, David (referee) ; Rozman, Jaroslav (advisor)
Accurate sales forecasting is pivotal for operational efficiency in the Fast-Moving Consumer Goods (FMCG) sector. This thesis explores the application of Long Short-Term Memory (LSTM) models, a specialized form of recurrent neural networks, to enhance the precision of sales predictions. Unlike traditional statistical methods, LSTMs are adept at capturing temporal dependencies within sales data, potentially offering more accurate forecasts. By applying LSTM models to historical sales data from a food industry company, this research demonstrates improvements over conventional forecasting techniques. The findings suggest that LSTMs can significantly help FMCG companies in optimizing inventory management and demand planning, thereby contributing valuable insights into artificial intelligence applications in supply chain management. These results emphasize the practical implications for FMCG stakeholders to embrace advanced artificial intelligence technologies to remain competitive in a dynamic market environment.
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Use of neural networks for estimation of dynamic variables
Dufek, Martin ; Repka, Martin (referee) ; Zháňal, Lubor (advisor)
The aim of the thesis is to verify the applicability of neural networks to predict vehicle dynamic variables. Some vehicle dynamic variables are difficult to measure or need to be calculated, and measuring such quantities can be very expensive. However, If neural networks could predict values with acceptable error, this would be a more affordable and economical method. Verification was performed by creating two recurrent neural networks to estimate the quantities of directional deviation angle and longitudinal forces on all wheels of the car. The paper describes the steps of network creation from processing the input data to evaluating the network predictions. The results show that neural networks can be used to determine dynamic quantities and replace expensive measurements for some purposes. Finally, important insights gained during the creation of neural networks are formulated that can help with the creation of new networks for the estimation of automotive dynamic quantities, and further possible improvements of the created neural networks are outlined.
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Model-Based Reinforcement Learning for POMDPs
Smíšková, Lucie ; Andriushchenko, Roman (referee) ; Češka, Milan (advisor)
Markovské rozhodovací procesy s částečným pozorováním nám umožňují modelovat systémy obsahující stavovou neurčitost. Jsou užitečné, pokud máme pouze částečné informace o stavech (tak zvaná pozorování). Cílem této práce bylo vyvinout metodu kombinující induktivní syntézu a zpětnovazebné učení k vytvoření co nejlepšího konečně stavového kontroléru. Tato metoda poté byla implementována jako rozšíření nástroje PAYNT.
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Using artificial intelligence to monitor the state of the machine
Popara, Nikola ; Bražina, Jakub (referee) ; Kovář, Jiří (advisor)
This thesis is focus on monitoring state of machine parts that are under the most stress. Type of artificial intelligence used in this work is recurrent neural network and its modifications. Chosen type of neural network was used because of the sequential character of used data. This thesis is solving three problems. In first problem algorithm is trying to determine state of mill tool wear using recurrent neural network. Used method for monitoring state is indirect. Second Problem was focused on detecting fault of a bearing and classifying it to specific category. In third problem RNN is used to predict RUL of monitored bearing.
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Convolutional Networks for Handwriting Recognition
Sladký, Jan ; Kišš, Martin (referee) ; Hradiš, Michal (advisor)
This thesis deals with handwriting recognition using convolutional neural networks. From the current methods, a network model was chosen to consist of convolutional and recurrent neural networks with the Connectist Temporal Classification. The Vertical Attention Module, which selects the relevant information in each column corresponding to the text in the figure was subsequently implemented in such a model. Then, this module was compared with other possibilities of vertical aggregation between convolutional and recurrent networks. The experiments took place on a data set containing over 80,000 lines of text from Czech letters from the 20th century. The results show that the Vertical Attention Module almost always achieves the best results on all used types of convolution networks. The resulting network achieved the best result with 8,9% of the character error rate. The contribution of this work is a neural network with a newly introduced element that can recognize lines of text.
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Convolutional Networks for Lip Reading
Kadleček, Josef ; Kišš, Martin (referee) ; Hradiš, Michal (advisor)
This thesis deals with current methods for automatic speech recognition and lip reading via neural networks. Furthermore it deals with similarities in the architectures of neural networks for audio and visual data and available datasets in the field of audiovisual automatic speech recognition. The main contribution of this thesis is set of experiments comparing different changes in neural network architecture and its impact on results. The thesis includes an implementation of a system for automatic speech recognition from audio (CER: 12.6 %) and visual (CER: 57,7 %) data. The architectures of both systems are based on features extraction via convolutional networks followed by recurrent layers LSTM, another layer of convolutions and loss function CTC.
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Neural network utilization for etwork traffic predictions
Pavela, Radek ; Mačák, Jaromír (referee) ; Kacálek, Jan (advisor)
In this master’s thesis are discussed static properties of network traffic trace. There are also addressed the possibility of a predication with a focus on neural networks. Specifically, therefore recurrent neural networks. Training data were downloaded from freely accessible on the internet link. This is the captured packej of traffic of LAN network in 2001. They are not the most actual, but it is possible to use them to achieve the objective results of the work. Input data needed to be processed into acceptable form. In the Visual Studio 2005 was created program to aggregate the intensities of these data. The best combining appeared after 100 ms. This was achieved by the input vector, which was divided according to the needs of network training and testing part. The various types of networks operate with the same input data, thereby to make more objective results. In practical terms, it was necessary to verify the two principles. Principle of training and the principle of generalization. The first of the nominated designs require stoking training and verification training by using gradient and mean square error. The second one represents unknown designs application on neural network. It was monitored the response of network to these input data. It can be said that the best model seemed the Layer recurrent neural network (LRN). So, it was a solution developed in this direction, followed by searching the appropriate option of recurrent network and optimal configuration. Found a variant of topology is 10-10-1. It was used the Matlab 7.6, with an extension of Neural Network toolbox 6. The results are processed in the form of graphs and the final appreciation. All successful models and network topologies are on the enclosed CD. However, Neural Network toolbox reported some problems when importing networks. In creating this work wasn’t import of network functions practically used. The network can be imported, but the majority appear to be non-trannin. Unsuccessful models of networks are not presented in this master’s thesis, because it would be make a deterioration of clarity and orientation.
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Machine Learning Strategies in Electronic Trading
Huf, Petr ; Kolář, Martin (referee) ; Černocký, Jan (advisor)
Úspěšné obchodování na trzích je snem mnoha lidí. Zajímavým odvětvím tohoto byznysu je elektronické obchodování, kde obchodní strategie běží na počítači bez jakéhokoliv zásahu člověka. Tento způsob obchodování poskytuje spoustu volného času a vysoké příjmy. Tato práce je zaměřena na využití neuronových sítí při stavbě takovéto obchodní strategie. Jako základ byla použita již existující rekurentní neuronová síť, která byla postupně modifikována podle potřeb pro obchodování. Výsledkem je neuronová síť předpovídající budoucí pohyby trhu. Obchodní strategie používající tuto neuronovou síť dokáže na burze úspěšně obchodovat.
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Adaptation of Neural Networks to Target Writer
Sekula, Jakub ; Hradiš, Michal (referee) ; Kohút, Jan (advisor)
This bachelor's thesis deals with the adaptation of neural networks to a specific writer with an aim to improve recognition of handwritten text of this specific writer. The method that I use is fast, requires small training dataset and uses regularization, which tries to keep the distribution of regularized weights in adaptation network similar to the one in the original network. I tested this method on dataset of printed text called IMPACT and dataset of handwritten text. When testing on dataset of handwritten text I was able to improve recognition on two diaries with pre adaptation recognition error rate of 10,82 % and 1,82 % to 8,48 % and 0,77 % with a small number of adaptation iterations and using small amount of training lines. When testing on IMPACT dataset I was able to improve recognition error rate from 32,88 % to 5,30 %.
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Usage of Public Business Information for Automatic Trading
Gráca, Martin ; Plchot, Oldřich (referee) ; Černocký, Jan (advisor)
In the era of modern technology and high performance computers, the classical trades model getting insufficient. For successful trading, generating stable profit, it is good to use modern technologies and opportunities. The main goal of this work is to develop a trading system based on modern technologies. This work uses public business data from Edgar database managed by U.S. Securities and Exchange Commission (SEC), historical share´s prices and recurrent neural network to create such model. The final system is able to trade successfully and generate profit.
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