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Search results for: MACHINE%20LEARNING,%20MUSIC%20ANALYSIS,%20TONALITY
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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublicationDesigning microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective...
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Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
PublicationWetlands 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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Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls
PublicationEfficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and post-processing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced...
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A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection
PublicationComputational cost is an important consideration for memory encoding prediction models that use data from dozens of implanted electrodes. We propose a method to reduce computational expense by selecting a subset of all the electrodes to build the prediction model. The electrodes were selected based on their likelihood of measuring brain activity useful for predicting memory encoding better than chance (in terms of AUC). A logistic...
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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Network lifetime maximization in wireless mesh networks for machine-to-machine communication
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Machine-to-Machine communication and data processing approach in Future Internet applications
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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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Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning
PublicationMethods 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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Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning
PublicationTheophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally...
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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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Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
PublicationControlled ovarian stimulation is tailored to the patient based on clinical parameters but estimating the number of retrieved metaphase II (MII) oocytes is a challenge. Here, we have developed a model that takes advantage of the patient’s genetic and clinical characteristics simultaneously for predicting the stimulation outcome. Sequence variants in reproduction-related genes identified by next-generation sequencing were matched...
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Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series
PublicationThis study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Viet- nam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost...
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Is it too late now to say we’re sorry? Examining anxiety contagion and crisis communication strategies using machine learning
PublicationIn 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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Optical method supported by machine learning for dynamics of C‐reactive protein concentrations changes detection in biological matrix samples
PublicationIn 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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Machine Learning Algorithm-Based Tool and Digital Framework for Substituting Daylight Simulations In Early- Stage Architectural Design Evaluation
PublicationThe aim of this paper is to examine the new method of obtaining the simulation-based results using backpropagation of errors artificial neural networks. The primary motivation to conduct the research was to determine an alternative, more efficient and less timeconsuming method which would serve to achieve the results of daylight simulations. Three daylight metrics: Daylight Factor, Daylight Autonomy and Daylight Glare Probability have...
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MACHINE DYNAMICS PROBLEMS
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Machine Translation
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Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction
PublicationRegarding the unpredictable and complex nature of seismic excitations, there is a need for vulnerability assessment of newly constructed or existing structures. Predicting the seismic limit-state capacity of steel Moment-Resisting Frames (MRFs) can help designers to have a preliminary estimation and improve their views about the seismic performance of the designed structure. This study improved data-driven decision techniques in...
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Determinants of anxiety levels among young males in a threat of experiencing military conflict–Applying a machine-learning algorithm in a psychosociological study
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Tuning Ferulic Acid Solubility in Choline-Chloride- and Betaine-Based Deep Eutectic Solvents: Experimental Determination and Machine Learning Modeling
PublicationDeep eutectic solvents (DES) represent a promising class of green solvents, offering particular utility in the extraction and development of new formulations of natural compounds such as ferulic acid (FA). The experimental phase of the study undertook a systematic investigation of the solubility of FA in DES, comprising choline chloride or betaine as hydrogen bond acceptors and six different polyols as hydrogen bond donors....
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Precise Identification of Different Cervical Intraepithelial Neoplasia (CIN) Stages, Using Biomedical Engineering Combined with Data Mining and Machine Learning
PublicationCervical cancer (CC) is one of the most common female cancers worldwide. It remains a significant global health challenge, particularly affecting women in diverse regions. The pivotal role of human papillomavirus (HPV) infection in cervical carcinogenesis underscores the critical importance of diagnostic strategies targeting both HPV infection and cervical...
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Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects
PublicationMachine 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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Strength testing machine results
Open Research DataThe aim of the research project is to determine the rotational stiffness of the connection between the purlin and the part of the truss top chord. The attached files are referred to the forces and displacements obtained from the strength testing machine (two sensors). The 16 specimens were tested on the experimental set up.
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Multidimensional Scaling Analysis Applied to Music Mood Recognition
PublicationThe paper presents two experiments aimed at categorizing mood associated with music. Two parts of a listening test were designed and carried out with a group of students, most of whom where users of online social music services. The initial experiment was designed to evaluate the extent to which a given label describes the mood of the particular music excerpt. The second subjective test was conducted to collect the similarity data...
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Parametrization and Correlation Analysis Applied to Music Mood Classification .
PublicationThe paper presents a study on music mood categorization. First, a review of music mood models is presented. Then, the preparation of a set of music excerpts to be used in the experiments and music parametrization is described. Next, some listening tasks performed to obtain mood descriptors are introduced. Finally,the correlation between mood descriptors and features extracted from parameters is discussed. The paper concludes with...
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Michał Grochowski dr hab. inż.
PeopleProfessor and a Head of the Department of Intelligent Control and Decision Support Systems at Gdansk University of Technology (GUT). He is also a Member of the Board of the Digital Technologies Center of GUT. He received his M.Sc. degree in Control Engineering in 2000 from the Electrical and Control Engineering Faculty at the GUT. In 2004 he received a Ph.D. degree in Automatic Control and Robotics from this...
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Analysis of surface roughness of chemically impregnated Scots pine processed using frame-sawing machine
PublicationThe objective of this work was to evaluate the effect of the impregnation process of pine wood (Pinus sylvestris L.) on roughness parameters of the surface processed on a frame sawing. The samples weredried and impregnated using a commercial procedure by a local company. The touch method withthe use of measuring stylus (pin) was employed to determine of surface roughness of the samplesconsidering parameters, namely, arithmetical...
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A novel hybrid adaptive framework for support vector machine-based reliability analysis: A comparative study
PublicationThis study presents an innovative hybrid Adaptive Support Vector Machine - Monte Carlo Simulation (ASVM-MCS) framework for reliability analysis in complex engineering structures. These structures often involve highly nonlinear implicit functions, making traditional gradient-based first or second order reliability algorithms and Monte Carlo Simulation (MCS) time-consuming. The application of surrogate models has proven effective...
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Numerical analysis of chip removing system operation in circular sawing machine using CFD software
PublicationPaper presents the analysis of the results of numerical simulations of the air flow process of wood chips removing system in the circular sawing machine. The attention is focused on the upper cover and bottom shelter of the chip removing system. Within the framework of the work a systematic numerical modeling of the air flow distribution in the cover and shelter during operation of the selected rotational speed of saw blade with...
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Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction
PublicationNowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To provide an accurate yet reliable prediction model, 32 improved Machine Learning (ML) algorithms were considered using the Python software to investigate the best method for estimating Maximum...
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Solubility Characteristics of Acetaminophen and Phenacetin in Binary Mixtures of Aqueous Organic Solvents: Experimental and Deep Machine Learning Screening of Green Dissolution Media
PublicationThe solubility of active pharmaceutical ingredients is a mandatory physicochemical characteristic in pharmaceutical practice. However, the number of potential solvents and their mixtures prevents direct measurements of all possible combinations for finding environmentally friendly, operational and cost-effective solubilizers. That is why support from theoretical screening seems to be valuable. Here, a collection of acetaminophen...
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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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Statistical Data Pre-Processing and Time Series Incorporation for High-Efficacy Calibration of Low-Cost NO2 Sensor Using Machine Learning
PublicationAir pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given...
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GIS for remote sensing, analysis and visualisation of marine pollution and other marine ecosystem components
PublicationW artykule przedstawiono koncepcję oraz prototyp morskiego systemu GIS do zdalnego monitorowania w czasie rzeczywistym zanieczyszczeń środowiska morskiego. System pozwala na gromadzenie i integrowanie danych z różnego rodzaju sensorów, takich jak echosondy jednowiązkowe, sonary boczne i echosondy wielowiązkowe.
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Iterative learning approach to active noise control of highly autocorrelated signals with applications to machinery noise
PublicationThis paper discusses the design and application of iterative learning control (ILC) and repetitive control (RC) for high modal density systems. Typical examples of these systems are structural and acoustical systems considered in active structural acoustic control (ASAC) and active noise control (ANC) applications. The application of traditional ILC and RC design techniques, which are based on a parametric system model, on systems...
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Dynamic Analysis of an Enhanced Multi-Frequency Inertial Exciter for Industrial Vibrating Machines
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Cogging torque analysis based on energy approach in surface-mounted PM machines
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Employing Subjective Tests and Deep Learning for Discovering the Relationship between Personality Types and Preferred Music Genres
PublicationThe purpose of this research is two-fold: (a) to explore the relationship between the listeners’ personality trait, i.e., extraverts and introverts and their preferred music genres, and (b) to predict the personality trait of potential listeners on the basis of a musical excerpt by employing several classification algorithms. We assume that this may help match songs according to the listener’s personality in social music networks....
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Lead-free bismuth-based perovskites coupled with g–C3N4: A machine learning based novel approach for visible light induced degradation of pollutants
PublicationThe use of metal halide perovskites in photocatalytic processes has been attempted because of their unique optical properties. In this work, for the first time, Pb-free Bi-based perovskites of the Cs3Bi2X9 type (X = Cl, Br, I, Cl/Br, Cl/I, Br/I) were synthesized and subjected to comprehensive morphological, structural, and surface analyses, and photocatalytic properties in the phenol degradation reaction were examined. Furthermore,...
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Comparative study on total nitrogen prediction in wastewater treatment 1 plant and effect of various feature selection methods on machine learning algorithms performance
PublicationWastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods are used in the pre-processing section for enhancing the model performance. This study aims to evaluate the effect of seven different FS methods (filter, wrapper, and embedded methods) on enhancing the prediction accuracy for total nitrogen (TN) in the WWTP...
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Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen
PublicationDeep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental...
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Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen
PublicationDeep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental...
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Exploration of the Solubility Hyperspace of Selected Active Pharmaceutical Ingredients in Choline- and Betaine-Based Deep Eutectic Solvents: Machine Learning Modeling and Experimental Validation
PublicationDeep eutectic solvents (DESs) are popular green media used for various industrial, pharmaceutical, and biomedical applications. However, the possible compositions of eutectic systems are so numerous that it is impossible to study all of them experimentally. To remedy this limitation, the solubility landscape of selected active pharmaceutical ingredients (APIs) in choline chloride- and betaine-based deep eutectic solvents was...
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Rediscovering Automatic Detection of Stuttering and Its Subclasses through Machine Learning—The Impact of Changing Deep Model Architecture and Amount of Data in the Training Set
PublicationThis work deals with automatically detecting stuttering and its subclasses. An effective classification of stuttering along with its subclasses could find wide application in determining the severity of stuttering by speech therapists, preliminary patient diagnosis, and enabling communication with the previously mentioned voice assistants. The first part of this work provides an overview of examples of classical and deep learning...
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Interpretable deep learning approach for classification of breast cancer - a comparative analysis of multiple instance learning models
PublicationBreast cancer is the most frequent female cancer. Its early diagnosis increases the chances of a complete cure for the patient. Suitably designed deep learning algorithms can be an excellent tool for quick screening analysis and support radiologists and oncologists in diagnosing breast cancer.The design of a deep learning-based system for automated breast cancer diagnosis is not easy due to the lack of annotated data, especially...
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USEFULNESS OF SEMI-MARKOV PROCESSES AS MODELS OF THE OPERATION PROCESSES FOR MARINE MAIN ENGINES AND OTHER MACHINES OF SHIP POWER PLANTS
PublicationThe paper describes the properties of semi-Markov processes and the opportunities and benefits from their use as models of the operation processes for marine combustion engines and other machines of ship power plants. The emphasis is put on the importance of the theory of semi-Markov processes for development of the theory of marine combustion engines and other machines of ship power plants, as well as for development of the operational...
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Simulations CAE of wood pellet machine
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Experimental verification of MWO bearing machine
PublicationPrzedstawiono wyniki weryfikacji doświadczalnej nowego stanowiska przeznaczonego do badań wytrzymałości zmęczeniowej warstwy powierzchniowej łożysk ślizgowych. Badano dwu- i trójwarstwowe cienkościenne panwie ślizgowe. Warstwa nośna wykonana była ze stopu CuPb30. W wariancie trójwarstwowym występowała powłoka ze stopu PbSnCu. Przedstawiono przykłady zaobserwowanych pęknięć zmęczeniowych. Maszyna MWO okazała się w pełni przydatna...
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Double Fed Induction Machine Drives
PublicationContents of the Chapter 22:Machine model.Properties of the DFM.Steady state machine operation.Control rules and decoupled control.Decoupling based on MM machine model.Decoupling based on vector model.Decoupling based on rotor current equation.Overall control system.Control system based on MM model.Control system based on vector model.Estimation of variables.Calculation of the angle between stator and rotor.Remarks about digital...