Evaldas Vaiciukynas

Kaunas University of Technology · Department of Information Systems

Feature set decomposition through cluster-based partitioning is the subject of this study. Approach is applied for the detection of mild laryngeal disorder from acoustic parameters of human voice using random forest (RF) as a base classifier. Observations of sustained phonation (audio recordings of vowel /a/) had clinical diagnosis and severity lev...

This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor...

Educational chatbots are digital tools designed to assist learners in various educational settings. These chatbots use natural language processing (NLP) and machine learning algorithms to simulate human conversation and respond to user queries in a way that facilitates learning. They can be integrated into various educational platforms such as lear...

This study introduces a novel performance-based weighting scheme for ensemble learning using the Shapley value. The weighting uses the reciprocal of binary cross-entropy as a base learner’s performance metric and estimates its Shapley value to measure the overall contribution of a learner to an equally weighted ensemble of various sizes. Two varian...

Purpose The aim of the article is to analyze the effect of cause-related marketing on the attitude–behaviour gap of green consumption in the cosmetics industry. Specifically, the authors examine the relationship between attitude towards the environment, attitude towards green consumption, subjective norms of green consumption, green consumption int...

The implementation of distributed machine learning using blockchain technologies is frequently complicated by computationally intensive calculations and limitations of smart contract languages supported by the blockchain network. In this study, we present a distributed machine learning architecture that supports heterogeneous machine learning model...

System monitoring is crucial to ensure that the system is working correctly. Usually, it encompasses solutions from the simple configuration of static thresholds for hardware/software key performance indicators to employing anomaly detection algorithms on a stream of numerical data. System logs, on the other hand, is another golden source of the sy...

Today, data collection has improved in various areas, and the medical domain is no exception. Auscultation, as an important diagnostic technique for physicians, due to the progress and availability of digital stethoscopes, lends itself well to applications of machine learning. Due to the large number of auscultations performed, the availability of...

Studies that consider all dimensions of sustainable development (well-being of communities, protection of natural environment, and the competitive economy) are scarce. The main objective of this paper is to evaluate relationships between socio-eco-financial inputs and outputs and investigate high-tech companies' dynamic efficiency. Relationships we...

Deep learning applications are attracting considerable interest nowadays and image analysis pipelines are no exception. Benthic studies often rely on the subjective evaluation of video material recorded using underwater drones. The demand for automatic image segmentation and quantitative evaluation arises due to the large volume of video data colle...

Underwater video surveys play a significant role in marine benthic research. Usually, surveys are filmed in transects, which are stitched into 2D mosaic maps for further analysis. Due to the massive amount of video data and time-consuming analysis, the need for automatic image segmentation and quantitative evaluation arises. This paper investigates...

Egzistuoja daugybė mokymosi procesą palengvintančių priemonių: muzika, tam tikros programėlės telefone, programos kompiuteriuose ir kt. Tačiau kai kalba pasisuka apie verslą, ar duomenų analitiką ir ekonominius procesus, sugalvoti įdomų ir naudingą mokymosi metodą nėra taip lengva. Remdamiesi prancūzų mokslininkų patirtimi, kaip verslo duomenų anal...

Model Driven Architecture (MDA) together with Unified Modelling Language (UML) presents a framework which transfers the emphasis of development from source code to the higher level of abstraction i.e., models. In this paper, we demonstrate the application of MDA principles for generating smart contract code executed on a blockchain. Even though blo...

Underwater imagery is widely used for a variety of applications in marine biology and environmental sciences, such as classification and mapping of seabed habitats, marine environment monitoring and impact assessment, biogeographic reconstructions in the context of climate change, etc. This approach is relatively simple and cost-effective, allowing...

Federated learning is a branch of machine learning where a shared model is created in a decentralized and privacy-preserving fashion, but existing approaches using blockchain are limited by tailored models. We consider the possibility to extend a set of supported models by introducing the oracle service and exploring the usability of blockchain-bas...

Amounts of historical data collected increase and business intelligence applicability with automatic forecasting of time series are in high demand. While no single time series modeling method is universal to all types of dynamics, forecasting using an ensemble of several methods is often seen as a compromise. Instead of fixing ensemble diversity an...

Amounts of historical data collected increase together with business intelligence applicability and demands for automatic forecasting of time series. While no single time series modeling method is universal to all types of dynamics, forecasting using ensemble of several methods is often seen as a compromise. Instead of fixing ensemble diversity and...

Blockchain technology and smart contract development currently lacks clarity in its implementation. The complicated architecture of blockchain is an obstacle that developers face during design and implementation of blockchain-based systems. In this paper we propose a method based on Model Driven Architecture, which could be used for defining and sp...

With an increasing amount of data in intelligent transportation systems, methods are needed to automatically extract general representations that accurately predict not only known tasks but also similar tasks that can emerge in the future. Creation of low-dimensional representations can be unsupervised or can exploit various labels in multi-task le...

The main objective of this work is to establish an automated classification system of seabed images. A novel two-stage approach to solving the image region classification task is presented. The first stage is based on information characterizing geometry, colour and texture of the region being analysed. Random forests and support vector machines are...

The aim of this study is a transparent tool for analysis of voice (sustained phonation /a/) and query data capable of providing support in screening for laryngeal disorders. In this work, screening is concerned with identification of potentially pathological cases by classifying subject’s data into ’healthy’ and ’pathological’ classes as well as vi...

This study investigates signals from sustained phonation and text-dependent speech modalities for Parkinson’s disease screening. Phonation corresponds to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. Signals were recorded through two channels simultaneously, namely, acoustic cardioid (AC) and...

The aim of this study is the analysis of voice and speech recordings for the task of Parkinson’s disease detection. Voice modality corresponds to sustained phonation /a/ and speech modality to a short sentence in Lithuanian language. Diverse information from recordings is extracted by 22 well-known audio feature sets. Random forest is used as a lea...

In this study we examine how the projected climate change driven decrease in the Baltic Sea salinity can impact the growth, cell size and shape of the recently invaded dinoflagellate Prorocentrum cordatum. In laboratory treatments we mimicked salinity conditions at the edge of the mesohaline south-eastern Baltic and oligohaline-to-limnic Curonian L...

Automatic detection, recognition and geometric characterization of bacteriophages in electron microscopy images was the main objective of this work. A novel technique, combining phase congruency-based image enhancement, Hough transform-, Radon transform- and open active contours with free boundary conditions-based object detection was developed to...

The objective of this study is to evaluate the reliability of acoustic voice parameters obtained using smart phone (SP) microphones and investigate the utility of use of SP voice recordings for voice screening. Voice samples of sustained vowel/a/obtained from 118 subjects (34 normal and 84 pathological voices) were recorded simultaneously through t...

Comprehensive evaluation of results obtained using acoustic and contact microphones in screening for laryngeal disorders through analysis of sustained phonation is the main objective of this study. Aiming to obtain a versatile characterization of voice samples recorded using microphones of both types, 14 different sets of features are extracted and...

Topic of the research is exploration and fusion of non-invasive measurements for an accurate detection of pathological larynx. Measurements for human subject encompass results of a specific survey and information extracted by openSMILE toolkit from several audio recordings of sustained phonation (vowel /a/). Clinical diagnosis, assigned by medical s...

The main objective of this paper is detection, recognition, and abundance estimation of objects representing the Prorocentrum minimum (Pavillard) Schiller (P. minimum) species in phytoplankton images. The species is known to cause harmful blooms in many estuarine and coastal environments. The proposed technique for solving the task exploits images...

2D discrete cosine transform (DCT2D) is known as less common and less effective in the field of speaker recognition techniques than vector quantization (VQ). However, some studies demonstrate that DCT2D with Hamming distance can be sufficiently accurate as well as faster method. Here we compare these techniques using various audio features (MFCC, B...

The aim of this study was to compare acoustic and throat microphones in the voice pathology detection task. Recordings of sustained phonation /a/ were used in the study. Each recording was characterized by a rather large set of diverse features, 1051 features in total. Classification into two classes, namely normal and pathological, was performed u...

Focus of research in Active contour models (ACM) area is mainly on development of various energy functions based on physical intuition. In this work, instead of designing a new energy function, we generate a multitude of contour candidates using various values of ACM parameters, assess their quality, and select the most suitable one for an object a...

Automated contour detection for objects represent-ing the Prorocentrum minimum (P. minimum) species in phy-toplankton images is the core goal of this study. The species is known to cause harmful blooms in many estuarine and coastal environments. Active contour model (ACM)-based image segmentation is the approach adopted here as a potential solution...

Novel prototype-based framework for image segmentation is introduced and successfully applied for cell segmentation in microscopy imagery. This study is concerned with precise contour detection for objects representing the Prorocentrum minimum species in phytoplankton images. The framework requires a single object with the ground truth contour as a...

In this paper identification of laryngeal disorders using cepstral parameters of human voice is researched. Mel-frequency cepstral coefficients (MFCCs), extracted from audio recordings of patient’s voice, are further approximated, using various strategies (sampling, averaging, and clustering by Gaussian mixture model). The effectiveness of similari...

Detection and recognition of objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the main objective of the article. The species is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects in images, stochastic o...

Reliable, automatic and objective detector of pathological voice disorders from speech signals is a long sought-for tool, by voice clinicians as well as by general practitioners. Such a detector can also be used for low-cost and noninvasive mass-screening, diagnosis and early detection of voice pathology for professionals using voice as an essentia...

In this paper identification of laryngeal disorders using cepstral parameters of human voice is investigated. Mel-frequency cepstral coefficients (MFCC), extracted from audio recordings, are further approximated, using 3 strategies: sampling, averaging, and estimation. SVM and LS-SVM categorize preprocessed data into normal, nodular, and diffuse cl...

This paper is concerned with kernel-based techniques for automated categorization of laryngeal colour image sequences obtained by video laryngostro-boscopy. Features used to characterize a laryngeal image are given by the kernel principal components computed using the N-vector of the 3-D colour histogram. The least squares support vector machine (L...

This paper is concerned with kernel-based techniques for categorizing laryngeal disorders based on information extracted from sequences of laryngeal colour images. The features used to characterize a laryngeal image are given by the kernel principal components computed using the N-vector of the 3-D colour histogram. The least squares support vector...