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Search results for: SUPPORT VECTOR MACHINES
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Support Vector Machines in Biomedical and Biometrical Applications
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Text Documents Classification with Support Vector Machines
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Prediction of Overall In Vitro Microsomal Stability of Drug Candidates Based on Molecular Modeling and Support Vector Machines. Case Study of Novel Arylpiperazines Derivatives
PublicationOther than efficacy of interaction with the molecular target, metabolic stability is the primary factor responsible for the failure or success of a compound in the drug development pipeline. The ideal drug candidate should be stable enough to reach its therapeutic site of action. Despite many recent excellent achievements in the field of computational methods supporting drug metabolism studies, a well-recognized procedure to model...
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Neural Networks, Support Vector Machine and Genetic Algorithms for Autonomous Underwater Robot Support
PublicationIn this paper, artificial neural networks, a classification technique called support vector machine and meta-heuristics genetic algorithm have been considered for development in autonomous underwater robots. Artificial neural networks have been used for seabed modelling as well as support vector machine has been applied for the obstacles classification to avoid some collision problems. Moreover, genetic algorithm has been applied...
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Support Vector Machine Applied to Road Traffic Event Classification
PublicationThe aim of this paper is to present results of road traffic event signal recognition. First, several types of systems for road traffic monitoring, including Intelligent Transport System (ITS) are shortly described. Then, assumptions of creating a database of vehicle signals recorded in different weather and road conditions are outlined. Registered signals were edited as single vehicle pass by. Using the Matlab-based application...
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Dangerous sound event recognition using Support Vector Machine classifiers
PublicationA method of recognizing events connected to danger based on their acoustic representation through Support Vector Machine classification is presented. The method proposed is particularly useful in an automatic surveillance system. The set of 28 parameters used in the classifier consists of dedicated parameters and MPEG-7 features. Methods for parameter calculation are presented, as well as a design of SVM model used for classification....
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RSS-Based DoA Estimation for ESPAR Antennas Using Support Vector Machine
PublicationIn this letter, it is shown how direction-of-arrival (DoA) estimation for electronically steerable parasitic array radiator (ESPAR) antennas, which are designed to be integrated within wireless sensor network nodes, can be improved by applying support vector classification approach to received signal strength (RSS) values recorded at an antenna's output port. The proposed method relies on ESPAR antenna's radiation patterns measured...
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Application of Support Vector Machine for Determination of Impact of Traffic-Induced Vibrations on Buildings
PublicationThe aim of the article is to present an algorithm of Support Vector Machine created to forecast the impact of traffic-induced vibrations on residential buildings. The method is designed to classify the object into one of two classes. The classification into the first class means that there is no impact of vibrations on the building, while classification to the second class indicates the possible influence and suggests the execution...
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Fault detection in the marine engine using a support vector data description method
PublicationFast detection and correct diagnosis of any engine condition changes are essential elements of safety andenvironmental protection. Many diagnostic algorithms significantly improve the detection of malfunctions.Studies on diagnostic methods are rarely reported and even less implemented in the marine engine industry.To fill this gap, this paper presents the Support Vector Data Description (SVDD) method as applied to thefault detection...
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A novel hybrid adaptive framework for support vector machine-based reliability analysis: A comparative study
PublicationThis study presents an innovative hybrid Adaptive Support Vector Machine - Monte Carlo Simulation (ASVM-MCS) framework for reliability analysis in complex engineering structures. These structures often involve highly nonlinear implicit functions, making traditional gradient-based first or second order reliability algorithms and Monte Carlo Simulation (MCS) time-consuming. The application of surrogate models has proven effective...
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Sylwester Kaczmarek dr hab. inż.
PeopleSylwester Kaczmarek received his M.Sc in electronics engineering, Ph.D. and D.Sc. in switching and teletraffic science from the Gdansk University of Technology, Gdansk, Poland, in 1972, 1981 and 1994, respectively. His research interests include: IP QoS and GMPLS and SDN networks, switching, QoS routing, teletraffic, multimedia services and quality of services. Currently, his research is focused on developing and applicability...
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Michał Michna dr hab. inż.
PeopleMichal Michna received the M.Sc. and Ph.D. degrees in electrical engineering from the Gdansk University of Technology (GUT), Gdansk, Poland, in 1998 and 2005, respectively. Since 2004, he was employed at the Department of Power Electronics and Electrical Machines of the Gdańsk University of Technology (assistant, assistant professor, senior lecturer). In 2010-2015 he was a deputy of head of the Department of Power Electronics and...
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Basic evaluation of limb exercises based on electromyography and classification methods
PublicationSymptoms caused by cerebral palsy or stroke deprive a person partially or even completely of his ability to move. Nowadays we can observe more technologically advanced rehabilitation devices which incorporate biofeedback into the process of rehabilitation of such people. However, there is still a lack of devices that would analyse, assess, and control (independently or with limited support) specialised movement exercises. Here...
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Sztuczne sieci neuronowe oraz metoda wektorów wspierających w bankowych systemach informatycznych
PublicationW artykule zaprezentowano wybrane metod sztucznej inteligencji do zwiększania efektywności bankowych systemów informatycznych. Wykorzystanie metody wektorów wspierających czy sztucznych sieci neuronowych w połączeniu z nowoczesną technologią mikroprocesorową umożliwia znaczący wzrost konkurencyjności banku poprzez dodanie nowych funkcjonalności. W rezultacie możliwe jest także złagodzenie skutków kryzysu finansowego.
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Efficiency comparison of selected endoscopic video analysis algorithms
PublicationIn the paper, selected image analysis algorithms were examined and compared in the task of identifying informative frames, blurry frames, colorectal cancer and healthy tissue on endoscopic videos. In order to standardize the tests, the algorithms were modified by removing from them parts responsible for the classification, and replacing them with Support Vector Machines and Artificial Neural Networks. The tests were performed in...
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Comparison of selected electroencephalographic signal classification methods
PublicationA variety of methods exists for electroencephalographic (EEG) signals classification. In this paper, we briefly review selected methods developed for such a purpose. First, a short description of the EEG signal characteristics is shown. Then, a comparison between the selected EEG signal classification methods, based on the overview of research studies on this topic, is presented. Examples of methods included in the study are: Artificial...
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Two Stage SVM and kNN Text Documents Classifier
PublicationThe paper presents an approach to the large scale text documents classification problem in parallel environments. A two stage classifier is proposed, based on a combination of k-nearest neighbors and support vector machines classification methods. The details of the classifier and the parallelisation of classification, learning and prediction phases are described. The classifier makes use of our method named one-vs-near. It is...
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Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data
PublicationThis paper describes a research study that investigates the use of machine learning algorithms on synthetic data to classify the risk of developing preeclampsia by pregnant women. Synthetic datasets were generated based on parameter distributions from three real patient studies. Four models were compared: XGBoost, Support Vector Machine (SVM), Random Forest, and Explainable Boosting Machines (EBM). The study found that the XGBoost...
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Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods
PublicationOne of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at...
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In uence of Low-Level Features Extracted from Rhythmic and Harmonic Sections on Music Genre Classi cation
PublicationWe present a comprehensive evaluation of the infuence of 'harmonic' and rhythmic sections contained in an audio file on automatic music genre classi cation. The study is performed using the ISMIS database composed of music files, which are represented by vectors of acoustic parameters describing low-level music features. Non-negative Matrix Factorization serves for blind separation of instrument components. Rhythmic components...
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Selecting Features with SVM
PublicationA common problem with feature selection is to establish how many features should be retained at least so that important information is not lost. We describe a method for choosing this number that makes use of Support Vector Machines. The method is based on controlling an angle by which the decision hyperplane is tilt due to feature selection. Experiments were performed on three text datasets generated from a Wikipedia dump. Amount...
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Comparing the Effectiveness of ANNs and SVMs in Forecasting the Impact of Traffic-Induced Vibrations on Building
PublicationTraffic - induced vibrations may cause damage to structural elements and may even lead to structural collapse. The aim of the article is to compare the effectiveness of algorithms in forecasting the impact of vibrations on buildings using the Machine Learning (ML) methods. The paper presents two alternative approaches by using Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). Factors that may affect traffic-induced...
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Obtaining a Well-Trained Artificial Intelligence Algorithm from Cross-Validation in Endoscopy
PublicationThe article shortly discusses endoscopic video analysis problems and artificial intelligence algorithms supporting it. The most common method of efficiency testing of these algorithms is to perform intensive cross-validation. This allows for accurately evaluate their performance of generalization. One of the main problems of this procedure is that there is no simple and universal way of obtaining a specific instance of a well-trained...
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Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
PublicationThe aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was...
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Rapid Evaluation of Poultry Meat Shelf Life Using PTR-MS
PublicationThe use of proton transfer reaction mass spectrometry (PTR-MS) for freshness classification of chicken and turkey meat samples was investigated. A number of volatile organic compounds (VOCs) were selected based on the correlation (> 95%) of their concentration during storage at 4 °C over a period of 5 days with the results of the microbial analysis. In order to verify if the selected compounds are not sample-specific, a number...
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Further developments of parameterization methods of audio stream analysis for secuirty purposes
PublicationThe paper presents an automatic sound recognition algorithm intended for application in an audiovisual security monitoring system. A distributed character of security systems does not allow for simultaneous observation of multiple multimedia streams, thus an automatic recognition algorithm must be introduced. In the paper, a module for the parameterization and automatic detection of audio events is described. The spectral analyses...
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Playback detection using machine learning with spectrogram features approach
PublicationThis paper presents 2D image processing approach to playback detection in automatic speaker verification (ASV) systems using spectrograms as speech signal representation. Three feature extraction and classification methods: histograms of oriented gradients (HOG) with support vector machines (SVM), HAAR wavelets with AdaBoost classifier and deep convolutional neural networks (CNN) were compared on different data partitions in respect...
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Multiscalar Model Based Control Systems for AC Machines
PublicationContents of the Chapter: Nonlinear transformations and feedback linearization. Models of the squirrel cage induction machine: Vector model of the squirrel cage induction machine. Multiscalar models of the squirrel cage induction machine.Feedback linearization of multiscalar models of the induction motor.Models of the double fed induction machine: Vector model of the double fed induction machine. Multiscalar model of the...
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Automatic labeling of traffic sound recordings using autoencoder-derived features
PublicationAn approach to detection of events occurring in road traffic using autoencoders is presented. Extensions of existing algorithms of acoustic road events detection employing Mel Frequency Cepstral Coefficients combined with classifiers based on k nearest neighbors, Support Vector Machines, and random forests are used. In our research, the acoustic signal gathered from the microphone placed near the road is split into frames and converted...
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Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning
PublicationThe paper presented the methodology for the construction of a soft sensor used for activated sludge bulking identification. Devising such solutions fits within the current trends and development of a smart system and infrastructure within smart cities. In order to optimize the selection of the data-mining method depending on the data collected within a wastewater treatment plant (WWTP), a number of methods were considered, including:...
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Gas Detection Using Resistive Gas Sensors And Radial Basis Function Neural Networks
PublicationWe present a use of Radial Basis Function (RBF) neural networks and Fluctuation Enhanced Sensing (FES) method in gas detection system utilizing a prototype resistive WO3 gas sensing layer with gold nanoparticles. We investigated accuracy of gas detection for three different preprocessing methods: no preprocessing, Principal Component Analysis (PCA) and wavelet transformation. Low frequency noise voltage observed in resistive gas...
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A comparative analysis of the effectiveness of corporate bankruptcy prediction models based on financial ratios: Evidence from Colombia, 2008 to 2015
PublicationLogit and discriminant analyses have been used for corporate bankruptcy prediction in several studies since the last century. In recent years there have been dozens of studies comparing the several models available, including the ones mentioned above and also probit, artificial neural networks, support vector machines, among others. For the first time for Colombia, this paper presents a comparative analysis of the effectiveness...
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The Influence of Selecting Regions from Endoscopic Video Frames on The Efficiency of Large Bowel Disease Recognition Algorithms
PublicationThe article presents our research in the field of the automatic diagnosis of large intestine diseases on endoscopic video. It focuses on the methods of selecting regions of interest from endoscopic video frames for further analysis by specialized disease recognition algorithms. Four methods of selecting regions of interest have been discussed: a. trivial, b. with the deletion of characteristic, endoscope specific additions to the...
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Towards Rational Biosurfactant Design—Predicting Solubilization in Rhamnolipid Solutions
PublicationThe efficiency of micellar solubilization is dictated inter alia by the properties of the solubilizate, the type of surfactant, and environmental conditions of the process. We, therefore, hypothesized that using the descriptors of the aforementioned features we can predict the solubilization efficiency, expressed as molar solubilization ratio (MSR). In other words, we aimed at creating a model to find the optimal surfactant and...
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Efficiency of Artificial Intelligence Methods for Hearing Loss Type Classification: an Evaluation
PublicationThe evaluation of hearing loss is primarily conducted by pure tone audiometry testing, which is often regarded as golden standard for assessing auditory function. If the presence of hearing loss is determined, it is possible to differentiate between three types of hearing loss: sensorineural, conductive, and mixed. This study presents a comprehensive comparison of a variety of AI classification models, performed on 4007 pure tone...
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Feedback Control of Multiphase Induction Machines with Backstepping Technique
PublicationThe paper presents the control possibility of five phase induction machines. In the proposed solution the machine model vector form is not transformed to the (dq)-coordinate system, that is connected to rotor flux vector, but utilizes the stationary system ( αβ ). Moreover, the nonlinear model linearization is based on demonstrated nonlinear variables transformation for i-orthogonal ( αβ )(n) planes. By introducing the backstepping...
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Application of autoencoder to traffic noise analysis
PublicationThe aim of an autoencoder neural network is to transform the input data into a lower-dimensional code and then to reconstruct the output from this code representation. Applications of autoencoders to classifying sound events in the road traffic have not been found in the literature. The presented research aims to determine whether such an unsupervised learning method may be used for deploying classification algorithms applied to...
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Prognozowanie wpływu drgań komunikacyjnych na budynki mieszkalne za pomocą sztucznych sieci neuronowych i maszyn wektorów wspierających
PublicationDrgania komunikacyjne mogą stanowić duże obciążenie eksploatacyjne budynku, powodując zarysowania i spękania tynków, odpadanie wypraw, zarysowania konstrukcji, pękanie elementów konstrukcji lub nawet zawalenie się budynku. Pomiary drgań na rzeczywistych konstrukcjach są pracochłonne i kosztowne, a co ważne nie w każdym przypadku są one uzasadnione. Celem pracy jest analiza autorskiego algorytmu, dzięki któremu z dużym prawdopodobieństwem...
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Comparative Analysis of Text Representation Methods Using Classification
PublicationIn our work, we review and empirically evaluate five different raw methods of text representation that allow automatic processing of Wikipedia articles. The main contribution of the article—evaluation of approaches to text representation for machine learning tasks—indicates that the text representation is fundamental for achieving good categorization results. The analysis of the representation methods creates a baseline that cannot...
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Social media for e-learning of citizens in smart city
PublicationThe rapid development of social media can be applied for citizens’ e-learning in a smart city. Big cities have to cope with several open issues like a growing population or a traffic congestion. Especially, a home and public space is supposed to be used in more efficient way. Sustainable homes and buildings can be planned with using some modern techniques. Even currently, there is a huge problem with a lack of key resources like...
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Detecting type of hearing loss with different AI classification methods: a performance review
PublicationHearing is one of the most crucial senses for all humans. It allows people to hear and connect with the environment, the people they can meet and the knowledge they need to live their lives to the fullest. Hearing loss can have a detrimental impact on a person's quality of life in a variety of ways, ranging from fewer educational and job opportunities due to impaired communication to social withdrawal in severe situations. Early...
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Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures
PublicationDapsone is an effective antibacterial drug used to treat a variety of conditions. However, the aqueous solubility of this drug is limited, as is its permeability. This study expands the available solubility data pool for dapsone by measuring its solubility in several pure organic solvents: N-methyl-2-pyrrolidone (CAS: 872-50-4), dimethyl sulfoxide (CAS: 67-68-5), 4-formylmorpholine (CAS: 4394-85-8), tetraethylene pentamine (CAS:...
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The Use of Ultra-Fast Gas Chromatography for Fingerprinting-Based Classification of Zweigelt and Rondo Wines with Regard to Grape Variety and Type of Malolactic Fermentation Combined with Greenness and Practicality Assessment
PublicationIn food authentication, it is important to compare different analytical procedures and select the best method. The aim of this study was to determine the fingerprints of Zweigelt and Rondo wines through headspace analysis using ultra-fast gas chromatography (ultra-fast GC) and to compare the effectiveness of this approach at classifying wines based on grape variety and type of malolactic fermentation (MLF) as well as its greenness...
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Algorytmy przetwarzania widm Ramana w procesie detekcji substancji chemicznych
PublicationRozprawa przedstawia szczegółowo algorytmy, jakie są stosowane podczas przetwarzania widm Ramana, rejestrowanych przenośnym spektrometrem o skończonej rozdzielczości. Pracę podzielono na osiem rozdziałów. W pierwszym określono cel i tezy pracy. Rozdział drugi opisuje podstawowe pojęcia dotyczące zjawiska Ramana oraz zasady budowy urządzeń do pomiarów widm Ramana. W rozdziale trzecim scharakteryzowano błędy występujące podczas pomiarów...
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Sensorless Control of Polyphase Induction Machines
PublicationThe basics of transformations of polyphase systems into orthogonal systems are explained. Vector models of induction machines in orthogonal planes are analysed and multiscalar models for rotor flux and main flux together with stator current are presented. A speed observer based on an extended model of the induction machine for selected variables is applied in the control system for the induction machine. On the basis of the model...
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An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants
PublicationThis study presents an advanced algorithm for selecting machine learning (ML) models for nitrous oxide (N2O) emission prediction in wastewater treatment plants (WWTPs) employing the activated sludge process. The examined ML models comprised multivariate adaptive regression spline (MARS), support vector machines (SVM), and extreme gradient boosting (XGboost). The study explores the concept that involves new criteria to select the...
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Novel 2-(2-alkylthiobenzenesulfonyl)-3-(phenylprop-2-ynylideneamino)guanidine derivatives as potent anticancer agents – Synthesis, molecular structure, QSAR studies and metabolic stability
PublicationA series of new 2-(2-alkylthiobenzenesulfonyl)-3-(phenylprop-2-ynylideneamino)guanidine derivatives have been synthesized and evaluated in vitro by MTT assays for their antiproliferative activity against cell lines of colon cancer HCT-116, cervical cancer HeLa and breast cancer MCF-7. The obtained results indicated that these compounds display prominent cytotoxic effect. The best anticancer properties have been observed for derivatives...
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Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation
PublicationTo date, significant progress has been made in the field of railway anomaly detection using technologies such as real-time data analytics, the Internet of Things, and machine learning. As technology continues to evolve, the ability to detect and respond to anomalies in railway systems is once again in the spotlight. However, railway anomaly detection faces challenges related to the vast infrastructure, dynamic conditions, aging...
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Sensorless control system of induction machine supplied by voltage source inverter with output filter
PublicationThe paper focuses on sensorless control of the induction machines supplied by inverter with the output filters. “The novel” idea of the speed observer which is based on the backstepping approach is shown. The standard structure of the exponential observer is extended by the integrators and additional Z vector. The simulation and experimental results validate the proposed solution.
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Verification of the Parameterization Methods in the Context of Automatic Recognition of Sounds Related to Danger
PublicationW artykule opisano aplikację, która automatycznie wykrywa zdarzenia dźwiękowe takie jak: rozbita szyba, wystrzał, wybuch i krzyk. Opisany system składa się z bloku parametryzacji i klasyfikatora. W artykule dokonano porównania parametrów dedykowanych dla tego zastosowania oraz standardowych deskryptorów MPEG-7. Porównano też dwa klasyfikatory: Jeden oparty o Percetron (sieci neuronowe) i drugi oparty o Maszynę wektorów wspierających....