Browsing by Subject "CNN"

Now showing 1 - 14 of 14

Access status: Open Access ,
An application of the »traffic lights« idea to crop control in integrated administration control system
(Wydawnictwa AGH, 2021) Hejmanowska, Beata; Twardowski, Mariusz; Żądło, Anna
The aim of the paper is to discuss the idea of marking agricultural parcels in the control of direct payments to agriculture. The method of using remote sensing to monitor crops and mark them according to the idea of »traffic lights« is introduced. Classification into a given »traffic lights« color gives clear information about the status of the parcel. The image classification was done on Sentinel-1 and Sentinel-2 datasets by calculating the NDVI and SIGMA time series in the season from autumn 2016 to autumn 2017. Two approaches are presented: semi-automated and automated classifications. Semi-automated classification based on NDVI_index and SIGMA_index. Automated classification was performed on NDVI by Spectral Angle Mapper method and on SIGMA by Artificial Neural Network (Multilayer Perceptron, MLP method). The following overall accuracy was obtained for NDVI_SAM: 70.35%, while for SIGMA_CNN it was: 62.01%. User accuracy (UA) values were adopted for traffic lights analysis, in machine learning: positive predictive value (PPV). The UA/PPV for rapeseed were in NDVI_index method: 88.1% (6,986 plots), NDVI_SAM: 85.0% (199 plots), SIGMA_index: 61.3% (4,165 plots) and in SIGMA_CNN: 88.9% (2,035 plots). In order to present the idea of »traffic lights«, a website was prepared using data from the NDVI_index method, which is a trade-off between the number of plots and UA/PPV accuracy.
Access status: Restricted ,
Analiza możliwości automatycznego doboru architektur sieci neuronowych z wykorzystaniem biblioteki numerycznej Microsoft/nni
(Data obrony: 2020-01-20) Jasiński, Dawid
Wydział Inżynierii Metali i Informatyki Przemysłowej
Access status: Open Access ,
Deep convolutional neural network using a new data set for berber language
(Wydawnictwa AGH, 2023) Mokrane, Kemiche; Sadou, Malika
Currently, handwritten character recognition (HCR) technology has become an interesting and immensely useful technology, it has been explored with impressive performance in many languages. However, few HCR systems have been proposed for the Amazigh (Berber) language. Furthermore, the validation of any Amazigh handwritten character-recognition system remains a major challenge due to the lack of availability of a robust Amazigh database. To address this problem, we first created two new data sets for Tifinagh and Amazigh Latin characters by extending the well-known EMNIST database with the Amazigh alphabet. Then, we proposed a handwritten character recognition system that is based on a deep convolutional neural network to validate the created data sets. The proposed convolutional neural network (CNN) has been trained and tested on our created data sets, the experimental tests showed that it achieves satisfactory results in terms of accuracy and recognition efficiency.
Access status: Restricted ,
DeepFake: algorithms for fake video detection
(Data obrony: 2020-12-10) Putyra, Andrzej
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
Access status: Open Access ,
Hybrid CNN-Ligru acoustic modeling using sincnet raw waveform for hindi ASR
(Wydawnictwa AGH, 2020) Kumar, Ankit; Aggarwal, Rajesh Kumar
Deep neural networks (DNN) currently play a most vital role in automatic speech recognition (ASR). The convolution neural network (CNN) and recurrent neural network (RNN) are advanced versions of DNN. They are right to deal with the spatial and temporal properties of a speech signal, and both properties have a higher impact on accuracy. With its raw speech signal, CNN shows its superiority over precomputed acoustic features. Recently, a novel first convolution layer named SincNet was proposed to increase interpretability and system performance. In this work, we propose to combine SincNet-CNN with a light-gated recurrent unit (LiGRU) to help reduce the computational load and increase interpretability with a high accuracy. Different configurations of the hybrid model are extensively examined to achieve this goal. All of the experiments were conducted using the Kaldi and Pytorch-Kaldi toolkit with the Hindi speech dataset. The proposed model reports an 8.0% word error rate (WER).
Access status: Restricted ,
Klasyfikacja obiektów z wykorzystaniem głębokich i impulsowych sieci neuronowych
(Data obrony: 2020-10-12) Orlikowski, Aleksander
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
Access status: Open Access ,
Optimized lossless audio compression using DCT energy thresholding and machine learning technique
(Wydawnictwa AGH, 2025) Debnath, Asish; Mondal, Uttam Kr.
This paper proposes a novel lossless audio compression technique, utilizing the Discrete Cosine Transform (DCT) coefficient-controlled technique based on energy thresholding, an XOR-based neural network compression model, and a CNN model. Initially, the DCT is applied to the input audio signal to achieve better energy compaction, followed by transforming selected DCT coefficients into a compressed binary stream. Subsequently, this binary stream is passed to two prediction-based optimized models: an XOR model and a CNN model for further compression.The binary stream is divided into two equal pieces, the data and the key. The XOR neural network model processes the data and key to produce an compressed XORed binary stream. Using a proposed CNN architecture, this stream is further compressed with latent space representations to produce compressed audio data. The simulation findings are analyzed using various statistical and robustness measures and compared with existing approaches.
Access status: Open Access ,
Preliminary study on artificial intelligence methods for cybersecurity threat detection in computer networks based on raw data packets
(Wydawnictwa AGH, 2025) Ogonowski, Aleksander; Żebrowski, Michał; Ćwiek, Arkadiusz; Jarosiewicz, Tobiasz; Klimaszewski, Konrad; Padee, Adam; Wasiuk, Piotr; Wójcik, Michał
potential of deep learning algorithms to directly extract features and patterns from raw packets. Moreover, it impedes real-time monitoring due to the necessity of waiting for the processing pipeline to complete and introduces dependencies on additional software components. In this paper, we investigate deep learning methodologies capable of detecting attacks in real-time directly from raw packet data within network traffic. Our investigation utilizes the CIC-IDS-2017 dataset, which includes both benign traffic and prevalent real-world attacks, providing a comprehensive foundation for our research.
Access status: Restricted ,
Rozpoznawanie gestów w czasie rzeczywistym za pomocą konwolucyjnych sieci neuronowych.
(Data obrony: 2020-10-07) Akin, Daria
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
Access status: Restricted ,
Rozpoznawanie ograniczonego zbioru osób metodami sztucznej inteligencji
(Data obrony: 2019-09-30) Bubula, Maksymilian
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
Access status: Restricted ,
System do analizy uszkodzeń poszycia statków powietrznych wykorzystujący konwolucyjne sieci neuronowe
(Data obrony: 2021-01-22) Pasternak, Aleksandra; Majerz, Emilia
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Restricted ,
System do analizy uszkodzeń poszycia statków powietrznych wykorzystujący konwolucyjne sieci neuronowe
(Data obrony: 2021-01-22) Majerz, Emilia; Pasternak, Aleksandra
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Restricted ,
Szacowanie wieku osoby na podstawie obrazów twarzy
(Data obrony: 2020-10-26) Kwarciak, Mateusz
Wydział Inżynierii Metali i Informatyki Przemysłowej
Access status: Restricted ,
Wykorzystanie gestów mimicznych twarzy do sterowania aplikacją
(Data obrony: 2020-12-07) Waśniewski, Mikołaj
Wydział Inżynierii Metali i Informatyki Przemysłowej