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Number of results: 8
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Ensemble of classifiers based on CNN for increasing generalization ability in face image recognition

Robert Szmurło Stanislaw Osowski
Bulletin of the Polish Academy of Sciences Technical Sciences | 2022 | 70 | 3 | e141004 | DOI: 10.24425/bpasts.2022.141004
Keywords CNN ensemble of classifiers face recognition feature selection
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Abstract

The paper considers the problem of increasing the generalization ability of classification systems by creating an ensemble of classifiers based on the CNN architecture. Different structures of the ensemble will be considered and compared. Deep learning fulfills an important role in the developed system. The numerical descriptors created in the last locally connected convolution layer of CNN flattened to the form of a vector, are subjected to a few different selection mechanisms. Each of them chooses the independent set of features, selected according to the applied assessment techniques. Their results are combined with three classifiers: softmax, support vector machine, and random forest of the decision tree. All of them do simultaneously the same classification task. Their results are integrated into the final verdict of the ensemble. Different forms of arrangement of the ensemble are considered and tested on the recognition of facial images. Two different databases are used in experiments. One was composed of 68 classes of greyscale images and the second of 276 classes of color images. The results of experiments have shown high improvement of class recognition resulting from the application of the properly designed ensemble.
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Authors and Affiliations

Robert Szmurło
1
ORCID: ORCID
Stanislaw Osowski
2
ORCID: ORCID
  1. Faculty of Electrical Engineering, Warsaw University of Technology, Koszykowa 75, 00-662 Warszawa, Poland
  2. Faculty of Electronic Engineering, Military University of Technology, gen. S. Kaliskiego 2, 00-908 Warszawa, Poland

Speech Emotion Recognition Based on Sparse Representation

Jingjie Yan Xiaolan Wang Weiyi Gu LiLi Ma
Archives of Acoustics | 2013 | vol. 38 | No 4 | 465-470 | DOI: 10.2478/aoa-2013-0055
Keywords speech emotion recognition sparse partial least squares regression (SPLSR) feature selection and dimensionality reduction
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Abstract

Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.
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Authors and Affiliations

Jingjie Yan
Xiaolan Wang
Weiyi Gu
LiLi Ma

Brain-computer interface as measurement and control system the review paper

Remigiusz J. Rak Marcin Kołodziej Andrzej Majkowski
Metrology and Measurement Systems | 2012 | No 3 | 427-444 | DOI: 10.2478/v10178-012-0037-4
Keywords EEG brain-computer interface feature extraction feature selection measurement and control.
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Abstract

In the last decade of the XX-th century, several academic centers have launched intensive research programs on the brain-computer interface (BCI). The current state of research allows to use certain properties of electromagnetic waves (brain activity) produced by brain neurons, measured using electroencephalographic techniques (EEG recording involves reading from electrodes attached to the scalp - the non-invasive method - or with electrodes implanted directly into the cerebral cortex - the invasive method). A BCI system reads the user's “intentions” by decoding certain features of the EEG signal. Those features are then classified and "translated" (on-line) into commands used to control a computer, prosthesis, wheelchair or other device. In this article, the authors try to show that the BCI is a typical example of a measurement and control unit.

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Authors and Affiliations

Remigiusz J. Rak
Marcin Kołodziej
Andrzej Majkowski

Electrodermal activity measurements for detection of emotional arousal

M. Kołodziej P. Tarnowski A. Majkowski R.J. Rak
Bulletin of the Polish Academy of Sciences Technical Sciences | 2019 | 67 | No. 4 | 813-826 | DOI: 10.24425/bpasts.2019.130190
Keywords electrodermal activity galvanic skin response skin conductance response feature selection arousal valence classification regression
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Abstract

In this article, we present a comprehensive measurement system to determine the level of user emotional arousal by the analysis of electrodermal activity (EDA). A number of EDA measurements were collected, while emotions were elicited using specially selected movie sequences. Data collected from 16 participants of the experiment, in conjunction with those from personal questionnaires, were used to determine a large number of 20 features of the EDA, to assess the emotional state of a user. Feature selection was performed using signal processing and analysis methods, while considering user declarations. The suitability of the designed system for detecting the level of emotional arousal was fully confirmed, throughout the number of experiments. The average classification accuracy for two classes of the least and the most stimulating movies varies within the range of 61‒72%.

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Authors and Affiliations

M. Kołodziej
P. Tarnowski
A. Majkowski
R.J. Rak

Sleep Snoring Sound Recognition Based on Wavelet Packet Transform

Li Ding Jianxin Peng Xiaowen Zhang Lijuan Song
Archives of Acoustics | 2023 | vol. 48 | No 1 | 3-12 | DOI: 10.24425/aoa.2022.142906
Keywords snoring recognition wavelet packet transform feature selection machine learning
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Abstract

Snoring is a typical and intuitive symptom of the obstructive sleep apnea hypopnea syndrome (OSAHS), which is a kind of sleep-related respiratory disorder having adverse effects on people’s lives. Detecting snoring sounds from the whole night recorded sounds is the first but the most important step for the snoring analysis of OSAHS. An automatic snoring detection system based on the wavelet packet transform (WPT) with an eXtreme Gradient Boosting (XGBoost) classifier is proposed in the paper, which recognizes snoring sounds from the enhanced episodes by the generalization subspace noise reduction algorithm. The feature selection technology based on correlation analysis is applied to select the most discriminative WPT features. The selected features yield a high sensitivity of 97.27% and a precision of 96.48% on the test set. The recognition performance demonstrates that WPT is effective in the analysis of snoring and non-snoring sounds, and the difference is exhibited much more comprehensively by sub-bands with smaller frequency ranges. The distribution of snoring sound is mainly on the middle and low frequency parts, there is also evident difference between snoring and non-snoring sounds on the high frequency part.
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Authors and Affiliations

Li Ding
1
Jianxin Peng
1
Xiaowen Zhang
2
Lijuan Song
2
  1. School of Physics and Optoelectronics, South China University of Technology, Guangzhou, China
  2. State Key Laboratory of Respiratory Disease, Department of Otolaryngology-Head and Neck Surgery Laboratory of ENT-HNS Disease, First Affiliated Hospital, Guangzhou Medical University, Guangzhou, China

Ensemble of classifiers based on deep learning for medical image recognition

Fabian Gil Stanisław Osowski Bartosz Świderski Monika Słowińska
Metrology and Measurement Systems | 2023 | vol. 30 | No 1 | 139-156 | DOI: 10.24425/mms.2023.144400
Keywords breast cancer CNN deep learning ensemble of classifiers feature selection melanoma
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Abstract

The paper presents special forms of an ensemble of classifiers for analysis of medical images based on application of deep learning. The study analyzes different structures of convolutional neural networks applied in the recognition of two types of medical images: dermoscopic images for melanoma and mammograms for breast cancer. Two approaches to ensemble creation are proposed. In the first approach, the images are processed by a convolutional neural network and the flattened vector of image descriptors is subjected to feature selection by applying different selection methods. As a result, different sets of a limited number of diagnostic features are generated. In the next stage, these sets of features represent input attributes for the classical classifiers: support vector machine, a random forest of decision trees, and softmax. By combining different selection methods with these classifiers an ensemble classification system is created and integrated by majority voting. In the second approach, different structures of convolutional neural networks are directly applied as the members of the ensemble. The efficiency of the proposed classification systems is investigated and compared to medical data representing dermoscopic images of melanoma and breast cancer mammogram images. Thanks to fusion of the results of many classifiers forming an ensemble, accuracy and all other quality measures have been significantly increased for both types of medical images.
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Authors and Affiliations

Fabian Gil
1
Stanisław Osowski
1 2
Bartosz Świderski
3
Monika Słowińska
4
  1. Military University of Technology, Faculty of Electronics, Institute of Electronic Systems, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
  2. Warsaw University of Technology, Faculty of Electrical Engineering, pl. Politechniki 1, 00-661 Warsaw, Poland
  3. University of Life Sciences, ul. Nowoursynowska 166, 02-787 Warsaw
  4. Central Clinical Hospital Ministry of Defense, Military Institute of Medicine – National Research Institute, ul. Szaserów 128, 04-141 Warsaw, Poland

Design of Wearable EEG Device for Seizures Early Detection

Viktoriia Gaidar Oleksandr Sudakov
International Journal of Electronics and Telecommunications | 2021 | vol. 67 | No 2 | 187-192 | DOI: 10.24425/ijet.2021.135963
Keywords circuit design statistical sampling Hilbert-Huang transform feature selection
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Abstract

This paper presents the design of a wearable electroencephalography device and signal processing algorithm for early detection and forecasting of the epileptiform activity. The availability of the examination of functional brain activity for a prolonged period, outside of the hospital facilities, can provide new advantages in early diagnosis and intervention systems. In this study, the low-cost five-channel device is presented. The system consists of two main parts: the data acquisition and transmission units and processing algorithms. In order to create the robust epileptiform pattern recognition approach the application of statistical sampling and signal processing techniques are performed. The discrete wavelet and Hilbert- Huang transforms with principal component analysis are used in order to extract and select a low-dimension feature vector.
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Bibliography

[1] Stacey, William C. ”Seizure Prediction Is Possible–Now Let’s Make It Practical.”, EBioMedicine, No. 27,2018,pp. 3-4.
[2] Federico, P., Abbott, D. F., Briellmann, R. S., Harvey, A. S. ”Functional MRI of the pre-ictal state”, Brain vol. 128, 2005, pp. 1811 - 1817.
[3] Smith S. J. M. ”EEG in the diagnosis, classification, and management of patients with epilepsy”, Journal of Neurology, Neurosurgery & Psychiatry. no. 76, 2005, pp. 112 - 117.
[4] V. Mihajlovi´c, B. Grundlehner, R. Vullers and J. Penders ”Wearable, Wireless EEG Solutions in Daily Life Applications: What are we Missing?”, Journal of Biomedical and Health Informatics vol. 19, no. 1, 2015, pp. 6-21.
[5] Malmivuo, Plonsey, Jakko Malmivuo, Plonsey Robert, ”Bioelectromagnetism: principles and applications of bioelectric and biomagnetic fields”, Oxford University Press, USA, 1995.
[6] Sudakov O., Kriukova G.,Natarov R. et al., ”Distributed System for Sampling and Analysis of Electroencephalograms”, in Proc. 2017 IEEE 9th International conference IDAACS 2017, Bucharest, 21-23 September 2017, pp. 306-310
[7] Direito B. et al. ”A realistic seizure prediction study based on multiclass SVM.” International Journal of Neural Systems, vol. 27, no. 3 , 2017.
[8] Federico P., Abbott, D. F., Briellmann, R. S., Harvey, A. S. ”Functional MRI of the pre-ictal state”, Brain, vol. 128., 2005, pp. 1811-1817
[9] Obermaier B. et al. ”Hidden Markov models for online classification of single trial EEG data.”, Pattern recognition letters, No. 12, 2001, pp.1299-1309.
[10] Zaena J.V. ”Adaptive tracking of EEG oscilattiond”, Neuroscience Methods, 2010.
[11] Dilran S.W., Lakshitha P.W, Sudaraka M. ”Seizure prediction using Hilbert-Huang transform on field programmable gate array”, IEEE Global conference on signal and information processing, Orlando, 2015.
[12] Jin-De Zhu, Chin-Feng L. et al. ”Analysis of spike waves in epilepsy using Hilbert-Huang transform”, Journal of Medical Systems, 2015.
[13] Kshischang F. R.”The Hilbert transform”, University of Toronto, 2006.
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[15] Shaiens J.”A tutorial on Principal Analysis”, arXiv preprint, Cornell University, 2014.

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Authors and Affiliations

Viktoriia Gaidar
1
Oleksandr Sudakov
1
  1. Taras Shevchenko National University of Kyiv, Ukraine

An improved facial image retrieval using hybrid cheetah optimization algorithm

C Balasubramanian J Raja Sekar
Bulletin of the Polish Academy of Sciences Technical Sciences | 2024 | 72 | 2 | e148942 | DOI: 10.24425/bpasts.2024.148942
Keywords deep learning feature selection facial image retrieval cheetah optimization approximate nearest neighbor
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Abstract

With the advent of social media, the volume of photographs uploaded on the internet has increased exponentially. The task of efficiently recognizing and retrieving human facial images is inevitable and essential at this time. In this work, a feature selection approach for recognizing and retrieving human face images using hybrid cheetah optimization algorithm is proposed. The deep feature extraction from the images is done using deep convolutional neural networks. Hybrid cheetah optimization algorithm, an improvised version of cheetah optimization algorithm fused with genetic algorithm is used, to choose optimum features from the extracted deep features. The chosen features are used for finding the best-matching images from the image database. The image matching is performed by approximate nearest neighbor search for the query image over the image database and similar images are retrieved. By constructing a k-NN graph for the images, the efficiency of image retrieval is enhanced. The proposed system performance is evaluated against benchmark datasets such as LFW, MultiePie, ColorFERET, DigiFace-1M and CelebA. The evaluation results show that the proposed methodology is superior to various existing methodologies.
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Authors and Affiliations

C Balasubramanian
1
ORCID: ORCID
J Raja Sekar
1
  1. Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, India

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