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Search results for: DATASET CONSTRUCTION
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How to Sort Them? A Network for LEGO Bricks Classification
PublicationLEGO bricks are highly popular due to the ability to build almost any type of creation. This is possible thanks to availability of multiple shapes and colors of the bricks. For the smooth build process the bricks need to properly sorted and arranged. In our work we aim at creating an automated LEGO bricks sorter. With over 3700 different LEGO parts bricks classification has to be done with deep neural networks. The question arises...
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Food Classification from Images Using a Neural Network Based Approach with NVIDIA Volta and Pascal GPUs
PublicationIn the paper we investigate the problem of food classification from images, for the Food-101 dataset extended with 31 additional food classes from Polish cuisine. We adopted transfer learning and firstly measured training times for models such as MobileNet, MobileNetV2, ResNet50, ResNet50V2, ResNet101, ResNet101V2, InceptionV3, InceptionResNetV2, Xception, NasNetMobile and DenseNet, for systems with NVIDIA Tesla V100 (Volta) and...
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Characterizing the Scalability of Graph Convolutional Networks on Intel® PIUMA
PublicationLarge-scale Graph Convolutional Network (GCN) inference on traditional CPU/GPU systems is challenging due to a large memory footprint, sparse computational patterns, and irregular memory accesses with poor locality. Intel’s Programmable Integrated Unffied Memory Architecture (PIUMA) is designed to address these challenges for graph analytics. In this paper, a detailed characterization of GCNs is presented using the Open-Graph Benchmark...
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Comparison of Selected Neural Network Models Used for Automatic Liver Tumor Segmentation
PublicationAutomatic and accurate segmentation of liver tumors is crucial for the diagnosis and treatment of hepatocellular carcinoma or metastases. However, the task remains challenging due to imprecise boundaries and significant variations in the shape, size, and location of tumors. The present study focuses on tumor segmentation as a more critical aspect from a medical perspective, compared to liver parenchyma segmentation, which is the...
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Information Extraction from Polish Radiology Reports using Language Models
PublicationRadiology reports are vital elements of directing patient care. They are usually delivered in free text form, which makes them prone to errors, such as omission in reporting radiological findings and using difficult-to-comprehend mental shortcuts. Although structured reporting is the recommended method, its adoption continues to be limited. Radiologists find structured reports too limiting and burdensome. In this paper, we propose...
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Speech Analytics Based on Machine Learning
PublicationIn this chapter, the process of speech data preparation for machine learning is discussed in detail. Examples of speech analytics methods applied to phonemes and allophones are shown. Further, an approach to automatic phoneme recognition involving optimized parametrization and a classifier belonging to machine learning algorithms is discussed. Feature vectors are built on the basis of descriptors coming from the music information...
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Increasing K-Means Clustering Algorithm Effectivity for Using in Source Code Plagiarism Detection
PublicationThe problem of plagiarism is becoming increasingly more significant with the growth of Internet technologies and the availability of information resources. Many tools have been successfully developed to detect plagiarisms in textual documents, but the situation is more complicated in the field of plagiarism of source codes, where the problem is equally serious. At present, there are no complex tools available to detect plagiarism...
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Improving Accuracy of Respiratory Rate Estimation by Restoring High Resolution Features With Transformers and Recursive Convolutional Models
PublicationNon-contact evaluation of vital signs has been becoming increasingly important, especially in light of the COVID- 19 pandemic, which is causing the whole world to examine people’s interactions in public places at a scale never seen before. However, evaluating one’s vital signs can be a relatively complex procedure, which requires both time and physical contact between examiner and examinee. These re- quirements limit the number...
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Information and communication technologies versus diffusion and substitution of financial innovations. The case of exchange-traded funds in Japan and South Korea
PublicationThe substitution between financial innovations, exchange-traded funds (ETFs), and stock index derivatives (i.e. index financial instruments) is one of the relatively understudied topics of the financial sciences. The current study aims to verify empirically the diffusion and substitution of ETFs in the market for index financial instruments. It presents in-depth analysis of the development of index financial instruments traded...
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EXTREME RAINFALLS AS A CAUSE OF URBAN FLASH FLOODS; A CASE STUDY OF THE ERBIL-KURDISTAN REGION OF IRAQ
PublicationAim of the study The current paper aims to give a detailed evaluation and analysis of some extreme rainfall events that happened in the last decade in terms of spatial and temporal rainfall distribution, intensity rate, and exceedance probability. Moreover, it examines the effects of each analysed aspect on the resulting flash floods in the studied area. Material and methods In their glossary of meteorology, American Meteorology...
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Construction, production and evaluation of the diagnostic utility of a recombinant Toxoplasma gondii chimeric antigen SAG1-SAG2-MAG1S
Open Research DataThe intracellular parasite Toxoplasma gondii has the ability to infect a wide range of warm-blooded animals, including humans. Currently, diagnosis of toxoplasmosis is based mainly on the use of the native antigens in enzyme immunoassay which allow for detection of IgG, IgM and IgA antibody classes. However, in some cases the performed studies give...
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Construction, production and evaluation of the diagnostic utility of a recombinant Toxoplasma gondii chimeric antigen AMA1-MIC1-MIC3
Open Research DataThe intracellular parasite Toxoplasma gondii has the ability to infect a wide range of warm-blooded animals, including humans. Currently, diagnosis of toxoplasmosis is based mainly on the use of the native antigens in enzyme immunoassay which allow for detection of IgG, IgM and IgA antibody classes. However, in some cases the performed studies give...
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Construction, production and evaluation of the diagnostic utility of a recombinant Toxoplasma gondii chimeric antigen MIC3-MAG1-ROP1
Open Research DataThe intracellular parasite Toxoplasma gondii has the ability to infect a wide range of warm-blooded animals, including humans. Currently, diagnosis of toxoplasmosis is based mainly on the use of the native antigens in enzyme immunoassay which allow for detection of IgG, IgM and IgA antibody classes. However, in some cases the performed studies give...
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Construction, production and evaluation of the diagnostic utility of a recombinant Toxoplasma gondii chimeric antigen MIC1-MIC3-ROP1
Open Research DataThe intracellular parasite Toxoplasma gondii has the ability to infect a wide range of warm-blooded animals, including humans. Currently, diagnosis of toxoplasmosis is based mainly on the use of the native antigens in enzyme immunoassay which allow for detection of IgG, IgM and IgA antibody classes. However, in some cases the performed studies give...
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Construction, production and evaluation of the diagnostic utility of a recombinant Toxoplasma gondii chimeric antigen MIC3-MAG1-AMA1
Open Research DataThe intracellular parasite Toxoplasma gondii has the ability to infect a wide range of warm-blooded animals, including humans. Currently, diagnosis of toxoplasmosis is based mainly on the use of the native antigens in enzyme immunoassay which allow for detection of IgG, IgM and IgA antibody classes. However, in some cases the performed studies give...
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Construction, production and evaluation of the diagnostic utility of a recombinant Toxoplasma gondii chimeric antigen AMA1-MIC1-ROP1
Open Research DataThe intracellular parasite Toxoplasma gondii has the ability to infect a wide range of warm-blooded animals, including humans. Currently, diagnosis of toxoplasmosis is based mainly on the use of the native antigens in enzyme immunoassay which allow for detection of IgG, IgM and IgA antibody classes. However, in some cases the performed studies give...
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Construction, production and evaluation of the diagnostic utility of a recombinant Toxoplasma gondii chimeric antigen SAG1-SAG2-MIC1S
Open Research DataThe intracellular parasite Toxoplasma gondii has the ability to infect a wide range of warm-blooded animals, including humans. Currently, diagnosis of toxoplasmosis is based mainly on the use of the native antigens in enzyme immunoassay which allow for detection of IgG, IgM and IgA antibody classes. However, in some cases the performed studies give...
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Identification of intermetallic phases in the structure of austenitic steel with use of Scanning Kelvin Probe Microscopy
Open Research DataDelta ferrite is formed in austenitic steels during the solidification of the alloy and its welds. It can also occur as a stable phase in any temperature range in high-alloy austenitic-ferritic steels. Depending on the amount, it can change into gamma and sigma phases and into ferrite with variable chromium content. The main role of delta ferrite in...
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Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening
PublicationBeta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural...
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Independent dynamics of slow, intermediate, and fast intracranial EEG spectral activities during human memory formation
PublicationA wide spectrum of brain rhythms are engaged throughout the human cortex in cognitive functions. How the rhythms of various low and high frequencies are spatiotemporally coordinated across the human brain during memory processing is inconclusive. They can either be coordinated together across a wide range of the frequency spectrum or induced in specific bands. We used a large dataset of human intracranial electroencephalography...
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Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network
PublicationCOVID-19, also known as the Coronavirus disease-2019, is an transferrable disease that spreads rapidly, affecting countless individuals and leading to fatalities in this worldwide pandemic. The precise and swift detection of COVID-19 plays a crucial role in managing the pandemic's dissemination. Additionally, it is necessary to recognize COVID-19 quickly and accurately by investigating chest x-ray images. This paper proposed a...
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Bimodal deep learning model for subjectively enhanced emotion classification in films
PublicationThis research delves into the concept of color grading in film, focusing on how color influences the emotional response of the audience. The study commenced by recalling state-of-the-art works that process audio-video signals and associated emotions by machine learning. Then, assumptions of subjective tests for refining and validating an emotion model for assigning specific emotional labels to selected film excerpts were presented....
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Clothes Detection and Classification Using Convolutional Neural Networks
PublicationIn this paper we describe development of a computer vision system for accurate detection and classification of clothes for e-commerce images. We present a set of experiments on well established architectures of convolutional neural networks, including Residual networks, SqueezeNet and Single Shot MultiBox Detector (SSD). The clothes detection network was trained and tested on DeepFashion dataset, which contains box annotations...
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Optimization algorithm and filtration using the adaptive TIN model at the stage of initial processing of the ALS point cloud
PublicationAirborne laser scanning (ALS) provides survey results in the form of a point cloud. The ALS point cloud is a source of data used primarily for constructing a digital terrain model (DTM). To generate a DTM, the set of ALS observations must be first subjected to the point cloud processing methodology. A standard methodology is composed of the following stages: acquisition of the ALS data, initial processing (including filtration),...
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Application of multivariate statistics in assessment of green analytical chemistry parameters of analytical methodologies
PublicationThe study offers a multivariate statistical analysis of a dataset, including the major metrological, “greenness” and methodological parameters of 43 analytical methodologies applied for aldrin determination (a frequently analyzed organic compound) in water samples. The variables (parameters) chosen were as follows: metrological (LOD, recovery, RSD), describing the “greenness” (amount of the solvent used, amount of waste generated)...
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The Verification of the Usefulness of Electronic Nose Based on Ultra-Fast Gas Chromatography and Four Different Chemometric Methods for Rapid Analysis of Spirit Beverages
PublicationSpirit beverages are a diverse group of foodstuffs. They are very often counterfeited which cause the appearance of low quality products or wrongly labelled products on the market. It is important to find a proper quality control and botanical origin method enabling the same time preliminary check of the composition of investigated samples, which was the main goal of this work. For this purpose, the usefulness of electronic nose...
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Automated Valuation Model based on fuzzy and rough set theory for real estate market with insufficient source data
PublicationObjective monitoring of the real estate value is a requirement to maintain balance, increase security and minimize the risk of a crisis in the financial and economic sector of every country. The valuation of real estate is usually considered from two points of view, i.e. individual valuation and mass appraisal. It is commonly believed that Automated Valuation Models (AVM) should be devoted to mass appraisal, which requires a large...
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Self-Supervised Learning to Increase the Performance of Skin Lesion Classification
PublicationTo successfully train a deep neural network, a large amount of human-labeled data is required. Unfortunately, in many areas, collecting and labeling data is a difficult and tedious task. Several ways have been developed to mitigate the problem associated with the shortage of data, the most common of which is transfer learning. However, in many cases, the use of transfer learning as the only remedy is insufficient. In this study,...
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Emotion Recognition from Physiological Channels Using Graph Neural Network
PublicationIn recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work, GraphSleepNet (a GNN for classifying the stages of sleep) was adjusted for emotion recognition and validated for this purpose. The...
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Material characterisation of biaxial glass-fibre non-crimp fabrics as a function of ply orientation, stitch pattern, stitch length and stitch tension
PublicationDue to their high density-specific stiffnesses and strength, fibre reinforced plastic (FRP) composites are particularly interesting for mobility and transport applications. Warp-knitted non-crimp fabrics (NCF) are one possible way to produce such FRP composites. They are advantageous because of their low production costs and the ability to tailor the properties of the textile to the reinforcement and drape requirements of the application....
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Noise profiling for speech enhancement employing machine learning models
PublicationThis paper aims to propose a noise profiling method that can be performed in near real-time based on machine learning (ML). To address challenges related to noise profiling effectively, we start with a critical review of the literature background. Then, we outline the experiment performed consisting of two parts. The first part concerns the noise recognition model built upon several baseline classifiers and noise signal features...
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Machine learning applied to acoustic-based road traffic monitoring
PublicationThe motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...
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An Automated Method for Biometric Handwritten Signature Authentication Employing Neural Networks
PublicationHandwriting biometrics applications in e-Security and e-Health are addressed in the course of the conducted research. An automated graphomotor analysis method for the dynamic electronic representation of the handwritten signature authentication was researched. The developed algorithms are based on dynamic analysis of electronically handwritten signatures employing neural networks. The signatures were acquired with the use of the...
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Selection of an artificial pre-training neural network for the classification of inland vessels based on their images
PublicationArtificial neural networks (ANN) are the most commonly used algorithms for image classification problems. An image classifier takes an image or video as input and classifies it into one of the possible categories that it was trained to identify. They are applied in various areas such as security, defense, healthcare, biology, forensics, communication, etc. There is no need to create one’s own ANN because there are several pre-trained...
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Machine learning applied to acoustic-based road traffic monitoring
PublicationThe motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...
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Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks
PublicationAge prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried out using high-resolution X-ray scans of parts of the body, such as images of the hands or images of the chest. In this...
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Enabling Deeper Linguistic-based Text Analytics – Construct Development for the Criticality of Negative Service Experience
PublicationSignificant progress has been made in linguistic-based text analytics particularly with the increasing availability of data and deep learning computational models for more accurate opinion analysis and domain-specific entity recognition. In understanding customer service experience from texts, analysis of sentiments associated with different stages of the service lifecycle is a useful starting point. However, when richer insights...
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Hey student, are you sharing your knowledge? A cluster typology of knowledge sharing behaviours among students
PublicationKnowledge Sharing (KS) is crucial for all organisations to better face current and future challenges. It is justifiable to assume that after graduation, students will have to face the coming challenges at societal and business levels, and that they will need the adequate KS skills to do so. Though the importance of KS is established, the understanding of how students pass on their knowledge is still fragmented and underdeveloped....
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Applying the Lombard Effect to Speech-in-Noise Communication
PublicationThis study explored how the Lombard effect, a natural or artificial increase in speech loudness in noisy environments, can improve speech-in-noise communication. This study consisted of several experiments that measured the impact of different types of noise on synthesizing the Lombard effect. The main steps were as follows: first, a dataset of speech samples with and without the Lombard effect was collected in a controlled setting;...
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Construction, production and evaluation of the diagnostic utility of a recombinant Toxoplasma gondii chimeric antigen MIC1-MAG1-MIC3
Open Research DataThe intracellular parasite Toxoplasma gondii has the ability to infect a wide range of warm-blooded animals, including humans. Currently, diagnosis of toxoplasmosis is based mainly on the use of the native antigens in enzyme immunoassay which allow for detection of IgG, IgM and IgA antibody classes. However, in some cases the performed studies give...
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Construction, production and evaluation of the diagnostic utility of a recombinant Toxoplasma gondii chimeric antigen MIC1-MAG1-ROP1
Open Research DataThe intracellular parasite Toxoplasma gondii has the ability to infect a wide range of warm-blooded animals, including humans. Currently, diagnosis of toxoplasmosis is based mainly on the use of the native antigens in enzyme immunoassay which allow for detection of IgG, IgM and IgA antibody classes. However, in some cases the performed studies give...
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Construction, production and evaluation of the diagnostic utility of a recombinant Toxoplasma gondii chimeric antigen MIC1-MAG1-AMA1
Open Research DataThe intracellular parasite Toxoplasma gondii has the ability to infect a wide range of warm-blooded animals, including humans. Currently, diagnosis of toxoplasmosis is based mainly on the use of the native antigens in enzyme immunoassay which allow for detection of IgG, IgM and IgA antibody classes. However, in some cases the performed studies give...
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Video traffic data - Interchanges Lodz Polnoc (A1 - A2), Poland
Open Research DataThe data includes video traffic data registered with 13 video cameras at exit and entry lanes of the Lodz Polnoc interchange within A2 motorway in Poland (interchange of motorway A2 and A1), located in the Lodz Agglomeration.The data covers the two days: 29-30.09.2017 (motorway A2).
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Medical Image Dataset Annotation Service (MIDAS)
PublicationMIDAS (Medical Image Dataset Annotation Service) is a custom-tailored tool for creating and managing datasets either for deep learning, as well as machine learning or any form of statistical research. The aim of the project is to provide one-fit-all platform for creating medical image datasets that could easily blend in hospital's workflow. In our work, we focus on the importance of medical data anonimization, discussing the...
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Urban scene semantic segmentation using the U-Net model
PublicationVision-based semantic segmentation of complex urban street scenes is a very important function during autonomous driving (AD), which will become an important technology in industrialized countries in the near future. Today, advanced driver assistance systems (ADAS) improve traffic safety thanks to the application of solutions that enable detecting objects, recognising road signs, segmenting the road, etc. The basis for these functionalities...
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Going all in or spreading your bet: a configurational perspective on open innovation interaction channels in production sectors
PublicationUsing different interaction channels within open innovation partnerships holds the potential to enhance the chance of success in production sectors. However, our comprehension of how open innovation partnerships are affected by varying combinations of interaction channels, and how this corelates with their level of open innovation output, remains limited. There are discrepancies in the current literature regarding the individual...
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Towards application of uncertainty quantification procedure combined with experimental procedure for assessment of the accuracy of the DEM approach dedicated for granular flow modeling
PublicationThere is a high demand for accurate and fast numerical models for dense granular flows found in many industrial applications. Nevertheless, before numerical model can be used its need to be always validated against experimental data. During the validation, it is important to consider how the measurement data sets, as well as the numerical models, are affected by errors and uncertainties. In this study, the uncertainty quantification...
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Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data
PublicationThis paper examines the usefulness of logit regression in forecasting the consumer bankruptcy of households using an imbalanced dataset. The research on consumer bankruptcy prediction is of paramount importance as it aims to build statistical models that can identify consumers in a difficult financial situation that may lead to consumer bankruptcy. In the face of the current global pandemic crisis, the future of household finances...
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A comprehensive review of open data platforms, prevalent technologies, and functionalities
PublicationOpen data can play a crucial role in different sectors of the world,such as government, science, research, technology, culture, andfinance. There are several necessary measures that every organiza-tion needs to consider before opening data. There are three majorsteps to opening the data: (1) Preparation stage, and (2) launchingthe open data initiative (3) In this case, the feedback mechanismstudy such as expand and sustain stage,...
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Minimizing Distribution and Data Loading Overheads in Parallel Training of DNN Acoustic Models with Frequent Parameter Averaging
PublicationIn the paper we investigate the performance of parallel deep neural network training with parameter averaging for acoustic modeling in Kaldi, a popular automatic speech recognition toolkit. We describe experiments based on training a recurrent neural network with 4 layers of 800 LSTM hidden states on a 100-hour corpora of annotated Polish speech data. We propose a MPI-based modification of the training program which minimizes the...