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1

Classification of cognitive states using clustering-split time series framework

Ramakrishna J. Siva, Ramasangu Hariharan

Computer Assisted Methods in Engineering and Science

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2024

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Vol. 31, no. 2

241--260

EN

Over the last two decades, functional Magnetic Resonance Imaging (fMRI) has provided immense data about the dynamics of the brain. Ongoing developments in machine learning suggest improvements in the performance of fMRI data analysis. Clustering is one of the critical techniques in machine learning. Unsupervised clustering techniques are utilized to partition the data objects into different groups. Supervised classification techniques applied to fMRI data facilitate the decoding of cognitive states while a subject is engaged in a cognitive task. Due to the high dimensional, sparse, and noisy nature of fMRI data, designing a classifier model for estimating cognitive states becomes challenging. Feature selection and feature extraction techniques are critical aspects of fMRI data analysis. In this work, we present one such synergy, a combination of Hierarchical Consensus Clustering (HCC) and the Statistics of Split Timeseries (SST) framework to estimate cognitive states. The proposed HCC-SST model’s performance has been verified on StarPlus fMRI data. The obtained experimental results show that the proposed classifier model achieves 99% classification accuracy with a smaller number of voxels and lower computational cost.

2

Analysis of the possibility of mechanical devices supervision based on the measurement of the vibration level and SVM classifiers

Schossler Ryszard, Grochowina Marcin, Leniowska Lucyna

Vibrations in Physical Systems

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2022

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Vol. 33, nr 3

art. no. 2022316

EN

This paper presents the concept of diagnosing the technical condition of mechanical devices. The test is based on a non-invasive vibration analysis technique combined with the use of artificial intelligence method. The object of the research is an electric motor for which vibrations were recorded by a vibration sensor based on four 3-axis digital accelerometers and MPU-6050 gyroscopes. The effectiveness of classification methods using the two-class and one-class classification was compared. It has been shown that the use of an incomplete pattern of the vibration model and a single-class classifier allows for effective detection of anomalies in the operation of an induction motor. Satisfactory classification efficiency was achieved, despite the limitation of the teaching set only to the information obtained during the correct operation of the device. The described method is universal and can be used to diagnose the technical condition of many different types of technical devices.

3

Klasyfikator SVM w zastosowaniu do synchronizacji sygnału OFDM zniekształconego przez kanał wielodrogowy

Blok Marek, Gmiński Karol, Miklaszewski Patryk

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2022

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nr 4

412--415

PL

W pracy przedstawiono analizę przydatności klasyfikatora SVM bazującego na uczeniu maszynowym do estymacji przesunięcia czasowego odebranego symbolu OFDM. Przedstawione wyniki wykazują, że ten klasyfikator potrafi zapewnić synchronizację dla różnych kanałów wielodrogowych o wysokim poziomie szumu. Eksperymenty przeprowadzone w Matlabie z użyciem modeli modulatora i demodulatora wykazały, że w większości przypadków klasyfikator prawidłowo oceniał przesunięcie czasowe symbolu OFDM, nawet gdy był trenowany dla innego modelu kanału.

EN

The paper presents an analysis of capabilities of machine learning based SVM classifier to estimate the OFDM symbol time offset at the receiver. The presented results demonstrate classifier’s ability to provide synchronization for various multipath channel models with high noise levels. The research was conducted using OFDM modulator and demodulator models implemented in Matlab. It has been shown that in most cases the classifier was able to determine the time shift of the OFDM symbol with high confidence even if it was trained for a different channel model.

4

Automated quantification of ultrasonic fatty liver texture based on curvelet transform and SVD

Bharath R., Mishra P. K., Rajalakshmi P.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 1

145--157

EN

Fatty liver is a prevalent disease and is the major cause for the dysfunction of the liver. If fatty liver is untreated, it may progress into chronic diseases like cirrhosis, hepatocellular carcinoma, liver cancer, etc. Early and accurate detection of fatty liver is crucial to prevent the fatty liver progressing into chronic diseases. Based on the severity of fat, the liver is categorized into four classes, namely Normal, Grade I, Grade II and Grade III respectively. Ultrasound scanning is the widely used imaging modality for diagnosing the fatty liver. The ultrasonic texture of liver parenchyma is specific to the severity of fat present in the liver and hence we formulated the quantification of fatty liver as a texture discrimination problem. In this paper, we propose a novel algorithm to discriminate the texture of fatty liver based on curvelet transform and SVD. Initially, the texture image is decomposed into sub-band images with curvelet transform enhancing gradients and curves in the texture, then an absolute mean of the singular values are extracted from each curvelet decomposed image, and used it as a feature representation for the texture. Finally, a cubic SVM classifier is used to classify the texture based on the extracted features. Tested on a database of 1000 image textures with 250 image textures belonging to each class, the proposed algorithm gave an accuracy of 96.9% in classifying the four grades of fat in the liver.

5

Klasyfikacja przemieszczeń pionowych obiektów budowlanych z wykorzystaniem sieci neuronowej SVM

Mrówczyńska M.

Materiały Budowlane

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2016

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nr 11

44--45

PL

W artykule przedstawiono podstawowe zasady budowania i uczenia sieci neuronowych zwane metodą (techniką) wektorów podtrzymujących (ang. Support Vector Machine – SVM) wraz z możliwością aplikacji tego rodzaju sieci. Sieci nieliniowe SVM wykorzystano do klasyfikacji danych separowalnych liniowo w celu sformułowania modelu przemieszczeń punktów sieci pomiarowo-kontrolnej. Punkty sieci pomiarowo-kontrolnej zostały założone na obiekcie budowlanym posadowionym na gruntach ekspansywnych.

EN

The article presents the basic rules for constructing and training neural networks called the Support Vector Machine (SVM) method as well as possible applications for this kind of network. Non-linear SVM networks have been used for classifying linearly separable data with a view to formulating a model of displacements of points in a measurement-control network. The points of the measurement-control network were placed on a civil engineering object located on expansive soil.

6

Dobór cech diagnostycznych dla klasyfikatora SVM w zadaniu klasyfikacji stanu przetoki tętniczo-żylnej na podstawie sygnału akustycznego

Grochowina M., Leniowska L.

Acta Bio-Optica et Informatica Medica. Inżynieria Biomedyczna

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2016

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Vol. 22, nr 4

207--212

PL

W artykule przedstawiono proces selekcji cech diagnostycznych dla klasyfikatora SVM. Badania przeprowadzone zostały z użyciem zbioru danych zawierającego próbki sygnału dźwiękowego emitowanego przez przetokę tętniczo-żylną. Celem prac było stworzenie rozwiązania klasyfikacji wieloklasowej w oparciu o klasyfikator z rodziny SVM pozwalającego na skuteczną i wiarygodną ocenę stanu przetoki tętniczo-żylnej.

EN

The paper presents the process of selection diagnostic features for SVM classifier. The study was conducted with using a data set containing samples of the sound signal emitted by the arteriovenous fistula. The objective was to create a solution multi-class classification based on SVM classifier family allowing for an effective and reliable evaluation of the arteriovenous fistula state.

7

System rozpoznawania cyfr oparty na histogramie zorientowanych gradientów

Zacniewski A.

Logistyka

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2015

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nr 4

6921--6926, CD2

PL

W artykule pokazano możliwości użycia odręcznego pisma oraz potrzebę dalszych badań nad jego rozpoznawaniem. Przedstawiono krótką charakterystykę deskryptorów obrazu HOG oraz liniowych klasyfikatorów SVM wraz z przykładowymi zastosowaniami. Przedstawiony został algorytm, który pozwala na rozpoznanie i klasyfikację odręcznie zapisanych cyfr za pomocą wymienionych metod wraz z technikami takimi jak algorytm progowania Otsu i algorytm detekcji krawędzi Canny’ego. Pokazano praktyczny przebieg procesu uczenia i klasyfikacji przy zastosowaniu omawianych narzędzi. Wskazane zostały wady i zalety zastosowanej metody oraz pokazano możliwości poprawy i rozwoju algorytmu rozpoznawania odręcznego pisma poprzez zmodyfikowanie parametrów zastosowanych technik uczenia maszynowego i rozpoznawania obrazów.

EN

In article the possibility of using handwriting and the need for further research on its recognition were shown. The characteristics of the image HOG descriptors and linear SVM classifier with examples of applications were described. Algorithm that allows the diagnosis and classification of handwritten digits by using these methods with techniques such as Otsu thresholding algorithm and Canny edge detection algorithm was presented. The practical process of learning and classification using mentioned tools was shown. Advantages and disadvantages of the used method were indicated and opportunities for improvement and development of handwriting recognition algorithm by modifying the parameters of the techniques of machine learning and pattern recognition were shown.

8

Classification of animals to determine the migration potential at the construction of new infrastructure

Matuska S., Hudec R., Benco M., Zachariasova M.

Archives of Transport System Telematics

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2013

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Vol. 6, iss. 4

26--30

EN

At the planning and construction of new infrastructures, the information about migration potential of animals in a target area is needed. This information will be used to design of migration corridors for wild animals. To determine the migration potential of animals based on distributed video camera system, new methods for object recognition and classification are developed. In general, an object recognition system consists of three steps, namely, the image feature extraction from the training database, training the classifier and evaluation of query image of object/animal. In this paper, an extraction of local key point by SIFT or SURF descriptors, bags of key points method in combination with SVM classifier and two hybrid key points detection methods are proposed in detail.