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Wyniki wyszukiwania dla: VIBRATIONS, MACHINE LEARNING, BUILDINGS
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Study of various machine learning approaches for Sentinel-2 derived bathymetry
PublikacjaIn recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines. While direct, in-situ measurements are performed regularly, they are expensive, time-consuming and impractical to be performed in short time intervals. At the same time, an ever-increasing amount of satellite imaging data becomes available. With these images, it...
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Predicting emotion from color present in images and video excerpts by machine learning
PublikacjaThis work aims at predicting emotion based on the colors present in images and video excerpts using a machine-learning approach. The purpose of this paper is threefold: (a) to develop a machine-learning algorithm that classifies emotions based on the color present in an image, (b) to select the best-performing algorithm from the first phase and apply it to film excerpt emotion analysis based on colors, (c) to design an online survey...
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Robust and Efficient Machine Learning Algorithms for Visual Recognition
PublikacjaIn visual recognition, the task is to identify and localize all objects of interest in the input image. With the ubiquitous presence of visual data in modern days, the role of object recognition algorithms is becoming more significant than ever and ranges from autonomous driving to computer-aided diagnosis in medicine. Current models for visual recognition are dominated by models based on Convolutional Neural Networks (CNNs), which...
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Evaluation of Machine Learning Methods for the Experimental Classification and Clustering of Higher Education Institutions
PublikacjaHigher 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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Integrating Experience-Based Knowledge Representation and Machine Learning for Efficient Virtual Engineering Object Performance
PublikacjaMachine learning and Artificial Intelligence have grown significant attention from industry and academia during the past decade. The key reason behind interest is such technologies capabilities to revolutionize human life since they seamlessly integrate classical networks, networked objects and people to create more efficient environments. In this paper, the Knowledge Representation technique of Set of Experience...
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Optical method supported by machine learning for urinary tract infection detection and urosepsis risk assessment
PublikacjaThe study presents an optical method supported by machine learning for discriminating urinary tract infections from an infection capable of causing urosepsis. The method comprises spectra of spectroscopy measurement of artificial urine samples with bacteria from solid cultures of clinical E. coli strains. To provide a reliable classification of results assistance of 27 algorithms was tested. We proved that is possible to obtain...
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Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
PublikacjaBiochar 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
PublikacjaThis 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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Monitoring of the circular saw vibrations with machine vision system.
PublikacjaPraca przedstawia metodologię wyznaczania drgań obracających się pił tarczowych z wykorzystaniem technik wizyjnych. Na podstawie otrzymanych wyników można wyznaczyc prędkości krytyczne piły oraz podac obszary prędkości zalecanych (najmniejsze wartości drgań poprzecznych piły).
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Noise profiling for speech enhancement employing machine learning models
PublikacjaThis 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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Modeling of wood frame structures with different insulation materials under damaging dynamic loading
PublikacjaWood frame buildings are very popular in regions that are exposed to different dynamic excitations, such as damaging earthquakes. Their seismic resistance is really important in order to prevent structural damages and human losses. This paper presents the results of advanced numerical investigation carried out using the FEM. Based on the models of wall panels, the numerical model of real structure of the wood frame building has...
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Real and Virtual Instruments in Machine Learning – Training and Comparison of Classification Results
PublikacjaThe continuous growth of the computing power of processors, as well as the fact that computational clusters can be created from combined machines, allows for increasing the complexity of algorithms that can be trained. The process, however, requires expanding the basis of the training sets. One of the main obstacles in music classification is the lack of high-quality, real-life recording database for every instrument with a variety...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublikacjaPreplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile...
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Platelet RNA Sequencing Data Through the Lens of Machine Learning
PublikacjaLiquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublikacjaPreplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile strengths...
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DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
PublikacjaWe 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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Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries
PublikacjaCatheter-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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A machine learning approach to classifying New York Heart Association (NYHA) heart failure
PublikacjaAccording 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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COVID-19 severity forecast based on machine learning and complete blood count data
PublikacjaProper triage of COVID-19 patients is a key factor in eective case management, especially with limited and insucient resources. In this paper, we propose a machine-aided diagnostic system to predict how badly a patient with COVID-19 will develop disease. The prognosis of this type is based on the parameters of commonly used complete blood count tests, which makes it possible to obtain data from a wide range of patients.We chose...
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COVID-19 severity forecast based on machine learning and complete blood count data
PublikacjaProper triage of COVID-19 patients is a key factor in eective case management, especially with limited and insucient resources. In this paper, we propose a machine-aided diagnostic system to predict how badly a patient with COVID-19 will develop disease. The prognosis of this type is based on the parameters of commonly used complete blood count tests, which makes it possible to obtain data from a wide range of patients.We chose...
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Modeling lignin extraction with ionic liquids using machine learning approach
PublikacjaLignin, next to cellulose, is the second most common natural biopolymer on Earth, containing a third of the organic carbon in the biosphere. For many years, lignin was perceived as waste when obtaining cellulose and hemicellulose and used as a biofuel for the production of bioenergy. However, recently, lignin has been considered a renewable raw material for the production of chemicals and materials to replace petrochemical resources....
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Explainable machine learning for diffraction patterns
PublikacjaSerial crystallography experiments at X-ray free-electron laser facilities produce massive amounts of data but only a fraction of these data are useful for downstream analysis. Thus, it is essential to differentiate between acceptable and unacceptable data, generally known as ‘hit’ and ‘miss’, respectively. Image classification methods from artificial intelligence, or more specifically convolutional neural networks (CNNs), classify...
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Optical method supported by machine learning for dynamics of C‐reactive protein concentrations changes detection in biological matrix samples
PublikacjaIn this article we present the novel spectroscopy method supported with machine learning for real-time detection of infectious agents in wastewater. In the case of infectious diseases, wastewater monitoring can be used to detect the presence of inflammation biomarkers, such as the proposed C-reactive protein, for monitoring inflammatory conditions and mass screening during epidemics for early detection in communities of concern,...
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Improving platelet‐RNA‐based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification
PublikacjaLiquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community...
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Speed estimation of a car at impact with a W-beam guardrail using numerical simulations and machine learning
PublikacjaThis paper aimed at developing a new method of estimating the impact speed of a passenger car at the moment of a crash into a W-beam road safety barrier. The determination of such a speed based on the accident outcomes is demanding, because often there is no access to full accident data. However, accurate determination of the impact speed is one of the key elements in the reconstruction of road accidents. A machine learning algorithm...
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Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
PublikacjaSince high-quality real data acquired from selected road sections are not always available, a traffic control solution can use data from software traffic simulators working offline. The results show that in contrast to microscopic traffic simulation, the algorithms employing neural networks can work in real-time, so they can be used, among others, to determine the speed displayed on variable message road signs. This paper describes...
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Experimental tuning of AuAg nanoalloy plasmon resonances assisted by machine learning method
PublikacjaPlasmonic nanostructures based on AuAg nanoalloys were fabricated by thermal annealing of metallic films in an argon atmosphere. The nanoalloys were chosen because they can extend the wavelength range in which plasmon resonance occurs and thus allow the design of plasmonic platforms with the desired parameters. The influence of initial fabrication parameters and experimental conditions on the formation of nanostructures was investigated....
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Machine learning approach to packaging compatibility testing in the new product development process
PublikacjaThe paper compares the effectiveness of selected machine learning methods as modelling tools supporting the selection of a packaging type in new product development process. The main goal of the developed model is to reduce the risk of failure in compatibility tests which are preformed to ensure safety, durability, and efficacy of the finished product for the entire period of its shelf life and consumer use. This kind of testing...
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Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers
PublikacjaThis chapter presents the application of new information technology in education for the training of air traffic controllers (ATCs). Machine learning, multi-criteria decision analysis, and text analysis as the methods of artificial intelligence for ATCs training have been described. The authors have made an analysis of the International Civil Aviation Organization documents for modern principles of ATCs education. The prototype...
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Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design
PublikacjaThe design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering...
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Detection of People Swimming in Water Reservoirs with the Use of Multimodal Imaging and Machine Learning
PublikacjaEvery year in many countries, there are fatal unintentional drownings in different water reservoirs like swimming pools, lakes, seas, or oceans. The existing threats of this type require creating a method that could automatically supervise such places to increase the safety of bathers. This work aimed to create methods and prototype solutions for detecting people bathing in water reservoirs using a multimodal imaging system and...
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Solubility of dapsone in deep eutectic solvents: Experimental analysis, molecular insights and machine learning predictions
PublikacjaBackground. Dapsone (DAP) is an anti-inflammatory and antimicrobial active pharmaceutical ingredient used to treat, e.g., AIDS-related diseases. However, low solubility is a feature hampering its efficient use. Objectives. First, deep eutectic solvents...
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Application of fiber optic sensors using Machine Learning algorithms for temperature measurement of lithium-ion batteries
PublikacjaOptical fiber sensorsusing low-coherence interferometry require processing ofthe output spectrum or interferogramto quickly and accurately determine the instantaneous value of the measured quantity, such as temperature.Methods based on machine learning are a good candidate for this application. The application of four such methods in an optical fiber temperature sensoris demonstrated.Using aZnO-coated...
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Is it too late now to say we’re sorry? Examining anxiety contagion and crisis communication strategies using machine learning
PublikacjaIn this paper, we explore the role of perceived emotions and crisis communication strategies via organizational computer-mediated communication in predicting public anxiety, the default crisis emotion. We use a machine-learning approach to detect and predict anxiety scores in organizational crisis announcements on social media and the public’s responses to these posts. We also control for emotional and language tones in organizational...
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Shaking table experimental study on pounding between adjacent structures founded on different soil types
PublikacjaThe aim of this study is to extensively investigate the effect of the soil type on the response of colliding structures based on shaking table experimental tests. Two single-storey models of steel buildings with different dynamic parameters were considered in this study. Three pounding scenarios were taken into account by applying different seismic gaps (0.5 cm, 1 cm and 1.5 cm as well as the no pounding case). First, the effect...
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Experimental and numerical study on deformed steel columns subjected to impact load during earthquakes
PublikacjaThe soft-storey failure is one of the most typical types of damage observed in buildings during earthquakes. The failure of an upper soft storey of a structure results in large impact load acting on the lower floors and if the resistance of the structural members of the lower storeys is not sufficient it may further lead to progressive collapse of the whole building. The aim of the present paper is to investigate the dynamic behaviour...
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Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects
PublikacjaMachine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects Hammed A. Mojeed & Rafal Szlapczynski Conference paper First Online: 14 September 2023 161 Accesses Part of the Lecture Notes in Computer Science book series (LNAI,volume 14125) Abstract Software development project requires proper planning to mitigate risk and...
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Machine-Learning-Based Global Optimization of Microwave Passives with Variable-Fidelity EM Models and Response Features
PublikacjaMaximizing 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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Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning
PublikacjaMethods Results Discussion References Abbreviations Copyright Abstract Background: The spread of false medical information on the web is rapidly accelerating. Establishing the credibility of web-based medical information has become a pressing necessity. Machine learning offers a solution that, when properly deployed, can be an effective tool in fighting medical misinformation on the web. Objective: The aim of this study is to...
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Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches
PublikacjaExamining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass...
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Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing
PublikacjaCirculating tumor cells (CTCs) are tumor cells that separate from the solid tumor and enter the bloodstream, which can cause metastasis. Detection and enumeration of CTCs show promising potential as a predictor for prognosis in cancer patients. Furthermore, single-cells sequencing is a technique that provides genetic information from individual cells and allows to classify them precisely and reliably. Sequencing data typically...
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Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance
PublikacjaIdentification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable...
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Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers
PublikacjaIn recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for...
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Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection
PublikacjaDue to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for detecting malware...
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Analytical model of torsional vibrations of typical sawing machine main drive system
PublikacjaPrzedstawiono model fizyczny i matematyczny drgań skrętnych napędu głównego typowej pilarki tarczowej. Model tworzą elementy sztywne SES połączone między sobą za pośrednictwem elementów sprężysto - tłumiących EST w układzie szeregowym. W modelu matematycznym uwzględniono: właściwości dynamiczne silnika napędowego, wymiary piły tarczowej, cechy materiału obrabianego (wymiary, rodzaj drewna, wilgotność drewna) oraz właściwości dynamiczne...
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Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
PublikacjaTreatment of municipal wastewater to meet the stringent effluent quality standards is an energy-intensive process and the main contributor to the costs of wastewater treatment plants (WWTPs). Analysis and prediction of energy consumption (EC) are essential in designing and operating sustainable energy-saving WWTPs. In this study, the effect of wastewater, hydraulic, and climate-based parameters on the daily consumption of EC by...
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Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
PublikacjaWetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral...
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MACHINE LEARNING–BASED ANALYSIS OF ENGLISH LATERAL ALLOPHONES
PublikacjaAutomatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and selforganizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’...
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MACHINE LEARNING SYSTEM FOR AUTOMATED BLOOD SMEAR ANALYSIS
PublikacjaIn 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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Data Reduction Algorithm for Machine Learning and Data Mining
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