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Search results for: lsvm
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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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ARIMA vs LSTM on NASDAQ stock exchange data
PublicationThis study compares the results of two completely different models: statistical one (ARIMA) and deep learning one (LSTM) based on a chosen set of NASDAQ data. Both models are used to predict daily or monthly average prices of chosen companies listed on the NASDAQ stock exchange. Research shows which model performs better in terms of the chosen input data, parameters and number of features. The chosen models were compared using...
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LSTM-based method for LOS/NLOS identification in an indoor environment
PublicationDue to the multipath propagation, harsh indoor environment significantly impacts transmitted signals which may adversely affect the quality of the radiocommunication services, with focus on the real-time ones. This negative effect may be significantly reduced (e.g. resources management and allocation) or compensated (e.g. correction of position estimation in radiolocalisation) by the LOS/NLOS identification algorithm. This paper...
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Improving Effectiveness of SVM Classifier for Large Scale Data
PublicationThe paper presents our approach to SVM implementation in parallel environment. We describe how classification learning and prediction phases were pararellised. We also propose a method for limiting the number of necessary computations during classifier construction. Our method, named one-vs-near, is an extension of typical one-vs-all approach that is used for binary classifiers to work with multiclass problems. We perform experiments...
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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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Balanced Spider Monkey Optimization with Bi-LSTM for Sustainable Air Quality Prediction
PublicationA reliable air quality prediction model is required for pollution control, human health monitoring, and sustainability. The existing air quality prediction models lack efficiency due to overfitting in prediction model and local optima trap in feature selection. This study proposes the Balanced Spider Monkey Optimization (BSMO) technique for effective feature selection to overcome the local optima trap and overfitting problems....
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Using LSTM networks to predict engine condition on large scale data processing framework
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OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems
PublicationCurrently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered...
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Using LSTM networks to predict engine condition on large scale data processing framework
PublicationAs the Internet of Things technology is developing rapidly, companies have an ability to observe the health of engine components and constructed systems through collecting signals from sensors. According to output of IoT sensors, companies can build systems to predict the conditions of components. Practically the components are required to be maintained or replaced before the end of life in performing their assigned task. Predicting...
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Approximation of Fractional Order Dynamic Systems Using Elman, GRU and LSTM Neural Networks
PublicationIn the paper, authors explore the possibility of using the recurrent neural networks (RNN) - Elman, GRU and LSTM - for an approximation of the solution of the fractional-orders differential equations. The RNN network parameters are estimated via optimisation with the second order L-BFGS algorithm. It is done based on data from four systems: simple first and second fractional order LTI systems, a system of fractional-order point...
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Method of selecting the LS-SVM algorithm parameters in gas detection process
PublicationIn this paper we showed the method of resistive gas sensors data processing. The UV irradiation and temperature modulation was applied to improve gas sensors’ selectivity and sensitivity. Noise voltage across the sensor’s terminals (proportional to its resistance fluctuations) was recorded to estimate power spectral density. This function was an input data vector for LS-SVM (least squares – support vector machine) algorithm, which...
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User Authentication by Eye Movement Features Employing SVM and XGBoost Classifiers
PublicationDevices capable of tracking the user’s gaze have become significantly more affordable over the past few years, thus broadening their application, including in-home and office computers and various customer service equipment. Although such devices have comparatively low operating frequencies and limited resolution, they are sufficient to supplement or replace classic input interfaces, such as the keyboard and mouse. The biometric...
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Text Documents Classification with Support Vector Machines
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Real‐Time PPG Signal Conditioning with Long Short‐Term Memory (LSTM) Network for Wearable Devices
PublicationThis paper presents an algorithm for real‐time detection of the heart rate measured on a person’s wrist using a wearable device with a photoplethysmographic (PPG) sensor and accelerometer. The proposed algorithm consists of an appropriately trained LSTM network and the Time‐Domain Heart Rate (TDHR) algorithm for peak detection in the PPG waveform. The Long Short‐Term Memory (LSTM) network uses the signals from the accelerometer...
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Autocovariance based weighting strategy for time series prediction with weighted LS-SVM
PublicationPrzedstawiono metodę konstrukcji algorytmów z funkcją jądra, a także dwa algorytmy uzyskane poprzez użycie różnych funkcji straty. Zaproponowano kowariacyjną strategię ważenia algorytmów z kwadratową funkcją straty do problemu predykcji chaotycznych przebiegów czasowych.
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Ewolucyjny dobór funkcji jądra SVM wspólnego dla zbioru podobnychzadań klasyfikacyjnych
PublicationPraca przedstawia ewolucyjną metodę kształtowania funkcji jądra wmetodzie SVM wspólnego dla zbioru podobnych zadań klasyfikacyjnych(z tej samej dziedziny) z wykorzystaniem aproksymatora neuronowego.Pokazano możliwość wbudowania funkcji ekstrakcji cech do funkcji jądraSVM za pomocą prostego łączenia aproksymatorów standardowej funkcjijądra i ekstraktora. Opisane zostały również teoretyczne podstawy metodywektorów wspierających (SVM).
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Long Short-Term Memory (LSTM) neural networks in predicting fair price level in the road construction industry
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Klasyfikator SVM w zastosowaniu do synchronizacji sygnału OFDM zniekształconego przez kanał wielodrogowy
PublicationW 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...
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Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm
PublicationThis paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based) nonlinear regression to determine the gas concentration...
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Improved RSS-Based DoA Estimation Accuracy in Low-Profile ESPAR Antenna Using SVM Approach
PublicationIn this paper, we have shown how the overall performance of direction-of-arrival (DoA) estimation using lowprofile electronically steerable parasitic array radiator (ESPAR) antenna, which has been proposed for Internet of Things (IoT) applications, can significantly be improved when support vector machine (SVM) approach is applied. Because the SVM-based DoA estimation method used herein relies solely...
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Improved RSS-Based DoA Estimation Accuracy in Low-Profile ESPAR Antenna Using SVM Approach
PublicationIn this paper, we have shown how the overall performance of direction-of-arrival (DoA) estimation using lowprofile electronically steerable parasitic array radiator (ESPAR) antenna, which has been proposed for Internet of Things (IoT) applications, can significantly be improved when support vector machine (SVM) approach is applied. Because the SVM-based DoA estimation method used herein relies solely on received signal strength...
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WYKORZYSTANIE SIECI NEURONOWYCH I METODY WEKTORÓW NOŚNYCH SVM W PROCESIE ROZPOZNAWANIA AKTYWNOŚCI RUCHOWEJ PACJENTÓW DOTKNIĘTYCH CHOROBĄ PARKINSONA
PublicationChoroba Parkinsona (ang. PD - Parkinson Disease) zaliczana jest do grupy chorób neurodegeneracyjnych. Jest to powoli postępująca choroba zwyrodnieniowa ośrodkowego układu nerwowego. Jej powstawanie związane jest z zaburzeniem produkcji dopaminy przez komórki nerwowe mózgu. Choroba manifestuje się zaburzeniami ruchowymi. Przyczyna występowania tego typu zaburzeń nie została do końca wyjaśniona. Leczenie osób dotkniętych PD oparte...
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Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm
PublicationIntroduction: Intracranial hemorrhage detection in 3D Computed Tomography (CT) brain images has gained more attention in the research community. The major issue to deal with the 3D CT brain images is scarce and hard to obtain the labelled data with better recognition results. Methods: To overcome the aforementioned problem, a new model has been implemented in this research manuscript. After acquiring the images from the Radiological...
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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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Characteristics of La0.8Sr0.2Ga0.8Mg0.2O3-delta -supported micro-tubular solid oxide fuel cells with bi-layer and tri-layer electrolytes
PublicationIn this study, La0.8Sr0.2Ga0.8Mg0.2O3−δ (LSGM)-supported micro tubular solid oxide fuel cells (T-SOFCs) with two different configurations, one containing an LSGM–Ce0.6La0.4O2−δ (LDC) bi-layer electrolyte (Cell A) and one containing an LDC–LSGM–LDC tri-layer electrolyte (Cell B), were fabricated using extrusion and dip-coating. After optimizing the paste formulation for extrusion, the flexural strength of the dense and uniform LSGM...
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Characteristics of La 0.8 Sr 0.2 Ga 0.8 Mg 0.2 O 3-δ -supported micro-tubular solid oxide fuel cells with LaCo 0.4 Ni 0.6-x Cu x O 3-δ cathodes
PublicationIn this study, micro-tubular solid oxide fuel cells (T-SOFCs) with extruded La0.8Sr0.2Ga0.8Mg0.2O3-δ (LSGM) electrolyte as the mechanical support and LaCo0.4Ni0.6O3-δ (LCNO) or LaCo0.4Ni0.4Cu0.2O3-δ (LCNCO) as cathodes were prepared and characterized. Partial substitution of Cu for the Ni-ion positions in the LCNO lattices was found to significantly enhance the densification and accelerate the grain growth. The porosity-corrected...
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A Simple Neural Network for Collision Detection of Collaborative Robots
PublicationDue to the epidemic threat, more and more companies decide to automate their production lines. Given the lack of adequate security or space, in most cases, such companies cannot use classic production robots. The solution to this problem is the use of collaborative robots (cobots). However, the required equipment (force sensors) or alternative methods of detecting a threat to humans are usually quite expensive. The article presents...
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Using Long-Short term Memory networks with Genetic Algorithm to predict engine condition
PublicationPredictive maintenance (PdM) is a type of approach for maintenance processes, allowing maintenance actions to be managed depending on the machine's current condition. Maintenance is therefore carried out before failures occur. The approach doesn’t only help avoid abrupt failures but also helps lower maintenance cost and provides possibilities to manufacturers to manage maintenance budgets in a more efficient way. A new deep neural...
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An automated learning model for twitter sentiment analysis using Ranger AdaBelief optimizer based Bidirectional Long Short Term Memory
PublicationSentiment analysis is an automated approach which is utilized in process of analysing textual data to describe public opinion. The sentiment analysis has major role in creating impact in the day-to-day life of individuals. However, a precise interpretation of text still relies as a major concern in classifying sentiment. So, this research introduced Bidirectional Long Short Term Memory with Ranger AdaBelief Optimizer (Bi-LSTM RAO)...
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Space-vector pulsewidth modulation for a seven-level cascaded H-bridge inverter with the control of DC-link voltages
PublicationThe control strategy of DC-link voltages for a seven-level Cascaded H-Bridge inverter is proposed in this paper. The DC-link voltage balancing is accomplished by appropriate selection of H-Bridges and control of their duty cycles in Space-Vector Modulation (SVM) algorithm. The proposed SVM method allows to maintain the same voltage level on all inverter capacitors. Regardless of the balancing function, the...
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Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
PublicationHypertensive retinopathy (HR) is a retinal disorder, linked to high blood pressure. The incidence of HR-eye illness is directly related to the severity and duration of hypertension. It is critical to identify and analyze HR at an early stage to avoid blindness. There are presently only a few computer-aided systems (CADx) designed to recognize HR. Instead, those systems concentrated on collecting features from many retinopathy-related...
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Space vector modulation in multilevel inverters of the servo drives of the trajectory measurements telescopes
PublicationUsing the MatLab/Simulink mathematical model of a three-phase three-level voltage inverter, the influence of the space-vector modulation (SVM) algorithm on the pulsations of the current (torque) of an AC motor in the range of low rotation speeds is considered. It is shown that the SVM of the second kind does not provide a pulsations level comparable to the pulsations of a sinusoidal pulse-width modulation (SPWM), both in the static...
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Using deep learning to increase accuracy of gaze controlled prosthetic arm
PublicationThis paper presents how neural networks can be utilized to improve the accuracy of reach and grab functionality of hybrid prosthetic arm with eye tracing interface. The LSTM based Autoencoder was introduced to overcome the problem of lack of accuracy of the gaze tracking modality in this hybrid interface. The gaze based interaction strongly depends on the eye tracking hardware. In this paper it was presented how the overall the...
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Efficiency of gas detection algorithms using fluctuation enhanced sensing
PublicationEfficiency of various gas detection algorithms by applying fluctuation enhanced sensing method was discussed. We have analyzed resistance noise observed in resistive WO3- nanowires gas sensing layers. Power spectral densities of the recorded noise were used as the input data vectors for two algorithms: the principal component analysis (PCA) and the support vector machine (SVM). The data were used to determine gas concentration...
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Investigation methods of ionic conductivity measurements in polycrystalline MIEC (mixed ionic-electronic conductor) based on perovskite ceramics
PublicationPaper about the measurements of ionic conductivity in LSM and strontium titanate ceramics.
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Predicting seismic response of SMRFs founded on different soil types using machine learning techniques
PublicationPredicting the Maximum Interstory Drift Ratio (M-IDR) of Steel Moment-Resisting Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of SMRFs. This study aims to explore supervised Machine Learning (ML) algorithms to build a surrogate prediction model for SMRFs to reduce the need for complex modeling. For this purpose, twenty well-known ML algorithms implemented in Python software are trained...
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Effects of La0.8Sr0.2MnO3 and Ag electrodes on bismuth-oxide-based low-temperature solid electrolyte oxygen generators
PublicationIn this study, La0.8Sr0.2MnO3 (LSM) was used as the ceramic electrode in a (Bi1.50Y0.50)0.98Zr0.04O3+δ (BYO)-based solid electrolyte oxygen generator (SEOG) and its performance was compared with that of a previously studied high-fire Ag electrode. Among La0.6Sr0.4Co0.2Fe0.8O3, LaNi0.6Fe0.4O3, Cu1.4Mn1.6O4, and LSM materials, only LSM materials did not trigger any chemical reaction or interdiffusion with BYO at temperatures up to...
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Representation of hypertext documents based on terms, Links and text compressibility
PublicationOpisano metody reprezentacji dokumentów tekstowych oparte na słowach, wzajemnych powiązaniach i metodach kompresji. Dokonano ich oceny w oparciu o klasyfikator SVM.
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Towards automatic classification of Wikipedia content
PublicationArtykuł opisuje podejście do automatycznej klasyfikacji artykułów w Wikipedii. Przeanalizowane zostały reprezentacje tekstu bazujące na treści dokumentu i wzajemnych powiązaniach. Przedstawiono rezultaty zastosowania klasyfikatora SVM.
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Examining Classifiers Applied to Static Hand Gesture Recognition in Novel Sound Mixing System
PublicationThe main objective of the chapter is to present the methodology and results of examining various classifiers (Nearest Neighbor-like algorithm with non-nested generalization (NNge), Naive Bayes, C4.5 (J48), Random Tree, Random Forests, Artificial Neural Networks (Multilayer Perceptron), Support Vector Machine (SVM) used for static gesture recognition. A problem of effective gesture recognition is outlined in the context of the system...
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Classification of Music Genres Based on Music Separation into Harmonic and Drum Components . Klasyfikacja gatunków muzycznych wykorzystująca separację instrumentów muzycznych
PublicationThis article presents a study on music genre classification based on music separation into harmonic and drum components. For this purpose, audio signal separation is executed to extend the overall vector of parameters by new descriptors extracted from harmonic and/or drum music content. The study is performed using the ISMIS database of music files represented by vectors of parameters containing music features. The Support Vector...
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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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Optimized Space Vector Modulation strategy for five phase voltage source inverter with third harmonic injection
PublicationThis paper presents a simple and an effective SVM algorithm for five-phase Voltage-Source Inverters with the possibility to control independently the voltage vectors for fundamental and auxiliary orthogonal subspaces. The essential benefit is that output voltage is generated using only four active voltage vectors with limited numbers of switching. In the proposed solution, four active vectors are arbitrary chosen, independent of...
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High-performance anode-supported solid oxide fuel cells with co-fired Sm0.2Ce0.8O2-δ/La0.8Sr0.2Ga0.8Mg0.2O3−δ/Sm0.2Ce0.8O2-δ sandwiched electrolyte
PublicationIn this study, intermediate-temperature solid oxide fuel cells (IT-SOFCs) with a nine-layer structure are constructed via a simple method based on the cost-effective tape casting-screen printing-co-firing process with the structure composed of a NiO-based four-layer anode, a Sm0.2Ce0·8O2-δ(SDC)/La0·8Sr0.2Ga0.8Mg0·2O3−δ (LSGM)/SDC tri-layer electrolyte, and an La0·6Sr0·4Co0·2Fe0·8O3-δ (LSCF)-based bi-layer cathode. The resultant...
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Autoencoder application for anomaly detection in power consumption of lighting systems
PublicationDetecting energy consumption anomalies is a popular topic of industrial research, but there is a noticeable lack of research reported in the literature on energy consumption anomalies for road lighting systems. However, there is a need for such research because the lighting system, a key element of the Smart City concept, creates new monitoring opportunities and challenges. This paper examines algorithms based on the deep learning...
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Wikipedia Articles Representation with Matrix'u
PublicationIn the article we evaluate different text representation methods used for a task of Wikipedia articles categorization. We present the Matrix’u application used for creating computational datasets ofWikipedia articles. The representations have been evaluated with SVM classifiers used for reconstruction human made categories.
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Underground Water Level Prediction in Remote Sensing Images Using Improved Hydro Index Value with Ensemble Classifier
PublicationThe economic sustainability of aquifers across the world relies on accurate and rapid estimates of groundwater storage changes, but this becomes difficult due to the absence of insitu groundwater surveys in most areas. By closing the water balance, hydrologic remote sensing measures offer a possible method for quantifying changes in groundwater storage. However, it is uncertain to what extent remote sensing data can provide an...
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Multicomponent ionic liquid CMC prediction
PublicationWe created a model to predict CMC of ILs based on 704 experimental values published in 43 publications since 2000. Our model was able to predict CMC of variety of ILs in binary or ternary system in a presence of salt or alcohol. The molecular volume of IL (Vm), solvent-accessible surface (Sˆ), solvation enthalpy (DsolvGN), concentration of salt (Cs) or alcohol (Ca) and their molecular volumes (Vms and Vma, respectively) were chosen...
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Text categorization with semantic commonsense knowledge: First results
PublicationDo przetwarzania tekstów typowo wykorzystuje się reprezentacjeBOW. Podejście takie nie daje jednak dobrych rezultatów w sytuacjigdy podobne dokumenty nie współdzielą ze sobą słów.W artykule zaprezentowano podejście do konstrukcji funkcjijądra dla klasyfikatorów SVM opartego na zewnętrznej bazie wiedzyo pojęciach językowych.
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Efficiency of linear and non-linear classifiers for gas identification from electrocatalytic gas sensor
PublicationElectrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such...