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Search results for: AIR QUALITY, POLLUTANT DETECTION, NITROGEN DIOXIDE, SENSOR CORRECTION, MACHINE LEARNING, ARTIFICIAL NEURAL NETWORKS
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Data augmentation for improving deep learning in image classification problem
PublicationThese days deep learning is the fastest-growing field in the field of Machine Learning (ML) and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this...
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Horizontally-split-drain MAGFET - a highly sensitive magnetic field sensor
PublicationWe propose a novel magnetic field sensitive semiconductor device, viz., Horizontally-Split-Drain Magnetic-Field Sensitive Field-Effect Transistor (HSDMAGFET) which can be used to measure or detect steady or variable magnetic fields. Operating principle of the transistor is based on one of the galvanomagnetic phenomena and a Gradual Channel Detachment Effect (GCDE) and is very similar to that of Popovic and Baltes's SDMAGFET. The...
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Evaluation of Machine Learning Methods for the Experimental Classification and Clustering of Higher Education Institutions
PublicationHigher education institutions have a big impact on the future of skills supplied on the labour market. It means that depending on the changes in labour market, higher education institutions are making changes to fields of study or adding new ones to fulfil the demand on labour market. The significant changes on labour market caused by digital transformation, resulted in new jobs and new skills. Because of the necessity of computer...
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Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor
PublicationNitrous oxide (N2O) is a key parameter for evaluating the greenhouse gas emissions from wastewater treatment plants. In this study, a new method for predicting liquid N2O production during nitrification was developed based on a mechanistic model and machine learning (ML) algorithm. The mechanistic model was first used for simulation of two 15-day experimental trials in a nitrifying sequencing batch reactor. Then, model predictions...
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Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
PublicationBiochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine-learning-based forecasts,...
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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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Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-Learned Features
PublicationNematodes Caenorhabditis elegans (C. elegans) have been used as model organisms in a wide variety of biological studies, especially those intended to obtain a better understanding of aging and age-associated diseases. This paper focuses on automating the analysis of C. elegans imagery to classify the muscle age of nematodes based on the known and well established IICBU dataset. Unlike many modern classification methods, the proposed...
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A framework for detection of selfishness in multihop mobile ad hoc networks
PublicationThe paper discusses the need for a fully-distributed selfishness detection mechanism dedicated for multihop wireless ad hoc networks which nodes may exhibit selfish forwarding behaviour. The main contribution of this paper is an introduction to a novel approach for detecting and coping with the selfish nodes. Paper describes a new framework based on Dempster-Shafer Theory called Dempster-Shafer Theory-based Selfishness Detection...
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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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Modelling relation between oxidation resistance and tribological properties of non-toxic lubricants with the use of artificial neural networks
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Methods of Artificial Intelligence for Prediction and Prevention Crisis Situations in Banking Systems
PublicationIn this paper, a support vector machine has been studied due to prediction of bank crisis. To prevent outcomes of crisis situations, artificial neural networks have been characterized as applied to stock market investments, as well as to test the credibility of the bank's customers. Finally, some numerical experiments have been presented.
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Neural network training with limited precision and asymmetric exponent
PublicationAlong with an extremely increasing number of mobile devices, sensors and other smart utilities, an unprecedented growth of data can be observed in today’s world. In order to address multiple challenges facing the big data domain, machine learning techniques are often leveraged for data analysis, filtering and classification. Wide usage of artificial intelligence with large amounts of data creates growing demand not only for storage...
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Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
PublicationPlain weave composite is a long-lasting type of fabric composite that is stable enough when being handled. Open-hole composites have been widely used in industry, though they have weak structural performance and complex design processes. An extensive number of material/geometry parameters have been utilized for designing these composites, thereby an efficient computational tool is essential for that purpose. Different Machine Learning...
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A novel concept for tissue-metal detection and differentiation using an inductive proximity sensor
PublicationIn this paper a novel application of inductive proximity sensors for detection of living tissue by means of measurements of the coil impedance changes at different frequencies is described. The mathematical analyses utilizing Bessel function estimation include detected object size and its distance from a sensor. The main aim of this study is to prove the possibility of distinguishing between metal objects and living tissues. The...
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Process control of air stream deodorization from vapors of VOCs using a gas sensor matrix conducted in the biotrickling filter (BTF)
PublicationThis article presents the validity, advisability and purposefulness of using a gas sensor matrix to monitor air deodorization processes carried out in a peat-perlite-polyurethane foam-packed biotrickling filter. The aim of the conducted research was to control the effectiveness of air stream purification from vapors of hydrophobic compounds, i.e., n-hexane and cyclohexane. The effectiveness of hydrophobic n-hexane and cyclohexane...
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Application of a catalytic combustion sensor (Pellistor) for the monitoring of the explosiveness of a hydrogen-air mixture in the upper explosive limit range
PublicationA new technique is presented for continuous measurements of hydrogen contamination by air in the upper explosive limit range. It is based on the application of a catalytic combustion sensor placed in a cell through which the tested sample passes. The air content is the function of the quantity of formed heat during catalytic combustion of hydrogen inside the sensor. There is the possibility of using the method in industrial installations...
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Evaluation of aspiration problems in L2 English pronunciation employing machine learning
PublicationThe approach proposed in this study includes methods specifically dedicated to the detection of allophonic variation in English. This study aims to find an efficient method for automatic evaluation of aspiration in the case of Polish second-language (L2) English speakers’ pronunciation when whole words are analyzed instead of particular allophones extracted from words. Sample words including aspirated and unaspirated allophones...
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Indoor air quality of everyday use spaces dedicated to specific purposes—a review
PublicationAccording to literature data, some of the main factors which significantly affect the quality of the indoor environment in residential households or apartments are human activities such as cooking, smoking, cleaning, and indoor exercising. The paper presents a literature overview related to air quality in everyday use spaces dedicated to specific purposes which are integral parts of residential buildings, such as kitchens, basements,...
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Evaluation of Ambient Air Odour Quality in Vicinity of Municipal Landfill Using Electronic Nose Technique
PublicationThe paper presents the results of investigation on ambient air quality evaluation with respect to concentration of odorants in a vicinity of municipal landfill. The investigation was carried out with a prototype of electronic nose and a team of panelists utilizing the Nasal Ranger field olfactometers. The prototype was equipped with a set of six semiconductor sensors by FIGARO Co. and one PID-type sensor. Performed measurements...
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Design and Experimental Validation of a Metamaterial-Based Sensor for Microwave Imaging in Breast, Lung, and Brain Cancer Detection
PublicationThis study proposes an innovative geometry of a microstrip sensor for high-resolution microwave imaging (MWI). The main intended application of the sensor is early detection of breast, lung, and brain cancer. The proposed design consists of a microstrip patch antenna fed by a coplanar waveguide with a metamaterial layer-based lens implemented on the back side, and an artificial magnetic conductor (AMC) realized on as a separate...
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Photocatalytic degradation using nitrogen-modified titanium dioxide
PublicationPrzepływowy reaktor z wypełnieniem został zastosowany do przeprowadzenia fotodegradacji 4-chlorofenolu za pomocą światła widzialnego. Fotokatalizator w postaci tlenku tytanu(IV) modyfikowanego azotem, został naniesiony na powierzchnię kulek szklanych. Źródło światła widzialnego stanowiła 8W lampa fluorescencyjna. Powietrze było w sposób ciągły przepuszczane przez zawartość reaktora.
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Electrocatalytic Gas Sensor with Reference Layer
PublicationThis paper presents studies of gas sensors prepared in ceramic technology with Nasicon as a solid electrolyte. Sensors work in the voltammetric mode thus based on the excitation of a sensor with a periodic potential signal while current response is recorded. The main aim is to investigate a Bi8Nb2O17 reference layer influence on sensor properties. Sensors I-V characteristics in different concentration of nitrogen dioxide have been...
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International Journal of Neural Networks
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IEEE TRANSACTIONS ON NEURAL NETWORKS
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Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
PublicationNowadays, Buckling-Restrained Brace Frames (BRBFs) have been used as lateral force-resisting systems for low-, to mid-rise buildings. Residual Interstory Drift (RID) of BRBFs plays a key role in deciding to retrofit buildings after seismic excitation; however, existing formulas have limitations and cannot effectively help civil engineers, e.g., FEMA P-58, which is a conservative estimation method. Therefore, there is a need to...
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DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
PublicationWe present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predict affinities obtained with techniques as different as uPBM, gcPBM and HT-SELEX with an excellent performance, much better than existing algorithms. Due to its nature, the method can...
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Comparative study of neural networks used in modeling and control of dynamic systems
PublicationIn this paper, a diagonal recurrent neural network that contains two recurrent weights in the hidden layer is proposed for the designing of a synchronous generator control system. To demonstrate the superiority of the proposed neural network, a comparative study of performances, with two other neural network (1_DRNN) and the proposed second-order diagonal recurrent neural network (2_DRNN). Moreover, to confirm the superiority...
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Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries
PublicationCatheter-induced dissections (CID) of coronary arteries and/or the aorta are among the most dangerous complications of percutaneous coronary procedures, yet the data on their risk factors are anecdotal. Logistic regression and five more advanced machine learning techniques were applied to determine the most significant predictors of dissection. Model performance comparison and feature importance ranking were evaluated. We identified...
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MACHINE LEARNING SYSTEM FOR AUTOMATED BLOOD SMEAR ANALYSIS
PublicationIn this paper the authors propose a decision support system for automatic blood smear analysis based on microscopic images. The images are pre-processed in order to remove irrelevant elements and to enhance the most important ones - the healthy blood cells (erythrocytes) and the pathologic (echinocytes). The separated blood cells are analyzed in terms of their most important features by the eigenfaces method. The features are the...
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Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage
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Quality Parameters in IMS/NGN Networks
PublicationThe Next Generation Network (NGN) architecture, including elements of the IP Multimedia Subsystem (IMS) concept, is a proposition of a telecommunication network dedicated to the needs of current and future information society. The main goal of NGN is to provide Quality of Service (QoS), for which proper network design is necessary with respect to among others standardized call processing performance parameters, including expected...
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Assessing the time effectiveness of trust management in fully synchronised wireless sensor networks
PublicationThe paper presents the results of the time effectiveness assessment of the distributed WSN Cooperative Trust Management Method - WCT2M in a fully synchronized Wireless Sensor Network (WSN). First we introduce some basic types of synchronization patterns in WSN based on the idea of sleep scheduling. Then we explain how WCT2M works in the network applying the fully synchronized sleep scheduling pattern. Such networks were subjected...
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Distribution of the International Marine Traffic Air Pollutant Emissions in the Port of Split
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Deep learning-based waste detection in natural and urban environments
PublicationWaste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, both for economic and ecological reasons, and the industry demands high efficiency. Current studies towards automatic waste detection are hardly comparable due to the lack of benchmarks and widely accepted standards regarding the used metrics and data. Those problems are addressed in this article by...
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Thermogravimetric analysis data of hydration in air and nitrogen for BaCe0.6Zr0.2Y0.1M0.1O3-δ (M = Fe, Pr, Tb)
Open Research DataThe dataset consists of 6 files of thermogravimetric analysis (TGA) data. The TGA experiments of hydration for BaCe0.6Zr0.2Y0.1Fe0.1O3-δ (BCZYFe), BaCe0.6Zr0.2Y0.1Pr0.1O3-δ (BCZYPr), and BaCe0.6Zr0.2Y0.1Tb0.1O3-δ (BCZYTb) were conducted on Netzsch STA 449.
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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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System for monitoring road slippery based on CCTV cameras and convolutional neural networks
PublicationThe slipperiness of the surface is essential for road safety. The growing number of CCTV cameras opens the possibility of using them to automatically detect the slippery surface and inform road users about it. This paper presents a system of developed intelligent road signs, including a detector based on convolutional neural networks (CNNs) and the transferlearning method employed to the processing of images acquired with video...
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Simulation of influence of the air gap asymmetryon voltage waveforms of a synchronous machine
PublicationResults of simulation of a synchronous generator with the air gap asymmetry characterised by eccentricity are presented in the paper. The Lagrange's energy method has been used in derivation of the model equations. Analysis of influence of the air gap asymmetry on characteristics of self and mutual inductances of windings, as well as analysis of induced voltage waveforms as a function of the air gap asymmetry have been performed....
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Artificial neural networks as a tool for selecting the parameters of prototypical under sleeper pads produced from recycled rubber granulate
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Artificial Neural Network for Multiprocessor Tasks Scheduling
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Approximation task decomposition for artificial neural network.
PublicationW pracy przedstawiono wpływ dekompozycji zadania na czasochłonność projektowania oraz dokładność i szybkość obliczeń sztucznej sieci neuronowej wykorzystanej do rozwiązania rzeczywistego problemu technicznego, którego matematyczny model był znany. Celem obliczeń prowadzonych przez sieć neuronową było określenie wartości współczynnika przepływu m na podstawie znajomości wartości: przewodności dźwiękowej C i średnicy przewodu d (a...
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Modelling of wastewater treatment plant for monitoring and control purposes by state - space wavelet networks
PublicationMost of industrial processes are nonlinear, not stationary, and dynamical with at least few different time scales in their internal dynamics and hardly measured states. A biological wastewater treatment plant falls into this category. The paper considers modelling such processes for monitorning and control purposes by using State - Space Wavelet Neural Networks (SSWN). The modelling method is illustrated based on bioreactors of...
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The Potential of Improving Air Quality by Urban Mobility Management: Policy Guidelines and a Case Study
PublicationThere is a growing recognition among planners and policy makers that proper transformation of urban mobility systems is crucial to the reduction of air pollution emission. The main objective of the work was to review the current urban mobility strategies in the city of Gdańsk, Poland in terms of their potential for the improvement of urban air quality. Firstly, general policy guidelines for mobility systems were formulated based...
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Detection of Cystic Fibrosis Symptoms Based on X-Ray Images Using Machine Learning- Pilot Study
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Aerosol and Air Quality Research
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A machine learning approach to classifying New York Heart Association (NYHA) heart failure
PublicationAccording to the European Society of Cardiology, globally the number of patients with heart failure nearly doubled from 33.5 million in 1990 to 64.3 million in 2017, and is further projected to increase dramatically in this decade, still remaining a leading cause of morbidity and mortality. One of the most frequently applied heart failure classification systems that physicians use is the New York Heart Association (NYHA) Functional...
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How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Image
PublicationThis study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation...
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Machine-Learning-Based Global Optimization of Microwave Passives with Variable-Fidelity EM Models and Response Features
PublicationMaximizing microwave passive component performance demands precise parameter tuning, particularly as modern circuits grow increasingly intricate. Yet, achieving this often requires a comprehensive approach due to their complex geometries and miniaturized structures. However, the computational burden of optimizing these components via full-wave electromagnetic (EM) simulations is substantial. EM analysis remains crucial for circuit...
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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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Design and Analysis of Artificial Neural Network (ANN) Models for Achieving Self-Sustainability in Sanitation
PublicationThe present study investigates the potential of using fecal ash as an adsorbent and demonstrates a self-sustaining, optimized approach for urea recovery from wastewater streams. Fecal ash was prepared by heating synthetic feces to 500 °C and then processing it as an adsorbent for urea adsorption from synthetic urine. Since this adsorption approach based on fecal ash is a promising alternative for wastewater treatment, it increases...