Search results for: instance segmentation,synthetic dataset,deep learning
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Mohsan Ali Master of Science in Computer Science
PeopleMohsan Ali is a researcher at the University of the Aegean. He won the Marie-Curie Scholarship in 2021 in the field of open data ecosystem (ODECO) to pursue his PhD degree at the University of the Aegean. Currently, he is working on the technical interoperability of open data in the information systems laboratory; this position is funded by ODECO. His areas of expertise are open data, open data interoperability, data science, natural...
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The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification
PublicationDeveloping of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublicationPreplaced-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
PublicationLiquid 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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Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study
PublicationThis study explores the edaravone solubility space encompassing both neat and binary dissolution media. Efforts were made to reveal the inherent concentration limits of common pure and mixed solvents. For this purpose, the published solubility data of the title drug were scrupulously inspected and cured, which made the dataset consistent and coherent. However, the lack of some important types of solvents in the collection called...
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Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
PublicationMany studies have been performed to put quantifying uncertainties into the seismic risk assessment of reinforced concrete (RC) buildings. This paper provides a risk-assessment support tool for purpose of retrofitting and potential design strategies of RC buildings. Machine Learning (ML) algorithms were developed in Python software by innovative methods of hyperparameter optimization, such as halving search, grid search, random...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublicationPreplaced-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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An automated learning model for twitter sentiment analysis using Ranger AdaBelief optimizer based Bidirectional Long Short Term Memory
PublicationSentiment analysis is an automated approach which is utilized in process of analysing textual data to describe public opinion. The sentiment analysis has major role in creating impact in the day-to-day life of individuals. However, a precise interpretation of text still relies as a major concern in classifying sentiment. So, this research introduced Bidirectional Long Short Term Memory with Ranger AdaBelief Optimizer (Bi-LSTM RAO)...
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Reinforced Secure Gossiping Against DoS Attacks in Post-Disaster Scenarios
PublicationDuring and after a disaster, the perceived quality of communication networks often becomes remarkably degraded with an increased ratio of packet losses due to physical damages of the networking equipment, disturbance to the radio frequency signals, continuous reconfiguration of the routing tables, or sudden spikes of the network traffic, e.g., caused by the increased user activity in a post-disaster period. Several techniques have...
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The electrical properties mesurements of SOFC with Ce0.9Cu0.1O2-s functional layer
Open Research DataThe dataset includes the electrical properties mesurements of SOFC with Ce0.9Cu0.1O2-s layer. Samples were produced using aqueous soft chemistry methods (microemulsion method) and applied in form of a layer onto the anode of the commercial SOFC. The SOFC was working under biogas feeding at 750oC. The layers were sintered at 1100oC.
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The electrical properties mesurements of SOFC with Ce0.9Co0.1O2-s functional layer
Open Research DataThe dataset includes the electrical properties mesurements of SOFC with Ce0.9Co0.1O2-s layer. Samples were produced using aqueous soft chemistry methods (microemulsion method) and applied in form of a layer onto the anode of the commercial SOFC. The SOFC was working under biogas feeding at 750oC. The layers were sintered at 1100oC.
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The electrical properties mesurements of SOFC with Ce0.8Co0.1Cu0.1O2-s functional layer
Open Research DataThe dataset includes the electrical properties mesurements of SOFC with Ce0.8Co0.1Cu0.1O2-s layer. Samples were produced using aqueous soft chemistry methods (microemulsion method) and applied in form of a layer onto the anode of the commercial SOFC. The SOFC was working under biogas feeding at 750oC. The layers were sintered at 1100oC.
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The electrical properties mesurements of SOFC with Ce0.8Co0.15Cu0.05O2-s functional layer
Open Research DataThe dataset includes the electrical properties mesurements of SOFC with Ce0.8Co0.15Cu0.05O2-s layer. Samples were produced using aqueous soft chemistry methods (microemulsion method) and applied in form of a layer onto the anode of the commercial SOFC. The SOFC was working under biogas feeding at 750oC. The layers were sintered at 1100oC.
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Development of thin film SPME sorbents based on deep eutectic solvents and their application for isolation and preconcentration of endocrine-disrupting compounds leaching from diapers to urine
PublicationIn this study, the thin film solid phase microextraction (TF-SPME) based on a novel sorbent composed of deep eutectic solvent (DES) is proposed. Among several synthesized sorbents the most efficient was DES containing trihexyl(tetradecyl)phosphonium chloride and docosanol mixed with polydimethylsiloxane (PDMS) in a molar ratio 1:3:0.25 to achieve a more stable structure. The proposed sorbent was applied on a fiberglass mesh and...
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Predicting sulfanilamide solubility in the binary mixtures using a reference solvent approach
PublicationBackground. Solubility is a fundamental physicochemical property of active pharmaceutical ingredients. The optimization of a dissolution medium aims not only to increase solubility and other aspects are to be included such as environmental impact, toxicity degree, availability, and costs. Obtaining comprehensive...
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Explainable machine learning for diffraction patterns
PublicationSerial 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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The short-term flicker severity level measured in the industrial power system supplying the rolling mill motors
Open Research DataThe dataset presents a short-term flicker severity level measured on the bus bars of the main switchgear of the industrial power network for the supply of rolling mills. The data were obtained during an experiment whose purpose was to determine a level of short-term and long-term flicker caused by voltage fluctuations. In the virtual application of...
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The application of magnetic fluids in sealing nodes designed for operation in difficult conditions and in machines used in sea environment
PublicationPresented in article MF seals are being researched for sea technology purposes due to their excellent tightness and low resistance of motion. These features are most valuable for ring propellers and ship's main propeller shaft. There are more reasons why technologists payf significant attention for various MFseals applications in their difficult operating conditions. For instance, an advanced pumping systems are designed with contactless...
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The application of magnetic fluids in sealing nodes designed for operation in difficult conditions and in machines used in sea environment
PublicationPresented in article MF seals are being researched for sea technology purposes due to their excellenttightness and low resistance of motion. These features are most valuable for ring propellers and ship'smain propeller shaft. There are more reasons why technologists payf significant attention for various MFseals applications in their difficult operating conditions. For instance, an advanced pumping systemsare designed with contactless...
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On the Role of Polarimetric Decomposition and Speckle Filtering Methods for C-Band SAR Wetland Classification Purposes
PublicationPrevious wetlands studies have thoroughly verified the usefulness of data from synthetic aperture radar (SAR) sensors in various acquisition modes. However, the effect of the processing parameters in wetland classification remains poorly explored. In this study, we investigated the influence of speckle filters and decomposition methods with different combinations of filter and decomposition windows sizes on classification accuracy....
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Narzędzia i metody obliczeń z użyciem MATLABa
EventsDn. 19.03.2020 w godz. 10.00–13.15 na Politechnice Gdańskiej odbędzie się seminarium w języku angielskim poświęcone wykorzystaniu MATLABa w badaniach naukowych i dydaktyce.
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CMGNet: Context-aware middle-layer guidance network for salient object detection
PublicationSalient object detection (SOD) is a critical task in computer vision that involves accurately identifying and segmenting visually significant objects in an image. To address the challenges of gridding issues and feature...
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Comparative study on the effectiveness of various types of road traffic intensity detectors
PublicationVehicle detection and speed measurements are crucial tasks in traffic monitoring systems. In this work, we focus on several types of electronic sensors, operating on different physical principles in order to compare their effectiveness in real traffic conditions. Commercial solutions are based on road tubes, microwave sensors, LiDARs, and video cameras. Distributed traffic monitoring systems require a high number of monitoring...
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Feature Weighted Attention-Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images
PublicationIn remote sensing images, change detection (CD) is required in many applications, such as: resource management, urban expansion research, land management, and disaster assessment. Various deep learning-based methods were applied to satellite image analysis for change detection, yet many of them have limitations, including the overfitting problem. This research proposes the Feature Weighted Attention (FWA) in Bidirectional Long...
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Fully Automated AI-powered Contactless Cough Detection based on Pixel Value Dynamics Occurring within Facial Regions
PublicationIncreased interest in non-contact evaluation of the health state has led to higher expectations for delivering automated and reliable solutions that can be conveniently used during daily activities. Although some solutions for cough detection exist, they suffer from a series of limitations. Some of them rely on gesture or body pose recognition, which might not be possible in cases of occlusions, closer camera distances or impediments...
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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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Towards Scalable Simulation of Federated Learning
PublicationFederated learning (FL) allows to train models on decentralized data while maintaining data privacy, which unlocks the availability of large and diverse datasets for many practical applications. The ongoing development of aggregation algorithms, distribution architectures and software implementations aims for enabling federated setups employing thousands of distributed devices, selected from millions. Since the availability of...
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Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection
PublicationDue 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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A Perspective on Missing Aspects in Ongoing Purification Research towards Melissa officinalis
PublicationMelissa officinalis L. is a medicinal plant used worldwide for ethno-medical purposes. Today, it is grown everywhere; while it is known to originate from Southern Europe, it is now found around the world, from North America to New Zealand. The biological properties of this medicinal plant are mainly related to its high content of phytochemical (bioactive) compounds, such as flavonoids, polyphenolic compounds, aldehydes, glycosides...
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The High Quality Business School Academic Teacher of the 21st Century – Polish Students’ Perspective
PublicationThe literature shows that the success and competence of future managers depend on the quality of their academic teachers. Moreover high quality study requires high quality lecturing/teaching that creates an environment in which deep learning outcomes are made possible for students. The aim was to identify the characteristics of the academic teacher working at business schools, according to the expectations of Polish students...
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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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Impact of AlphaFold on structure prediction of protein complexes: The CASP15‐CAPRI experiment
PublicationWe present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody–antigen complexes, and 7 large assemblies. On average 70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups...
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ARIMA vs LSTM on NASDAQ stock exchange data
PublicationThis study compares the results of two completely different models: statistical one (ARIMA) and deep learning one (LSTM) based on a chosen set of NASDAQ data. Both models are used to predict daily or monthly average prices of chosen companies listed on the NASDAQ stock exchange. Research shows which model performs better in terms of the chosen input data, parameters and number of features. The chosen models were compared using...
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Selection of material for infiltration of non-stoichiometric Sr0.95Ti0.3Fe0.7O3-δ
Open Research DataThe porous electrodes used for the infiltration were prepared from A-site non-stoichiometric Sr0.95Ti0.3Fe0.7O3-δ synthesized by the conventional solid state reaction method. Screen printed electrodes (0.5 cm2) on CGO-20 substrate, were fired at 800 °C for 2 hours in an air atmosphere. Infiltration of the scaffolds was performed by a water-based precursor...
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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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Machine learning-based seismic response and performance assessment of reinforced concrete buildings
PublicationComplexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data...
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Predicting seismic response of SMRFs founded on different soil types using machine learning techniques
PublicationPredicting the Maximum Interstory Drift Ratio (M-IDR) of Steel Moment-Resisting Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of SMRFs. This study aims to explore supervised Machine Learning (ML) algorithms to build a surrogate prediction model for SMRFs to reduce the need for complex modeling. For this purpose, twenty well-known ML algorithms implemented in Python software are trained...
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How personality traits, sports anxiety, and general imagery could influence the physiological response measured by SCL to imagined situations in sports?
Open Research DataThe data were collected to understand how individual differences in personality (e.g. neuroticism), general imagery, and situational sports anxiety are linked to arousal measuring with skin conductance level (SCL) in situational imagery (as scripted for sport-related scenes). Thirty persons participated in the study, aged between 14 and 42 years, with...
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CTD Gdańsk Deep_2001_2005
Open Research DataDataset includes measurements of conductivity (mS cm-1), temperature (°C), sound speed (m s-1) and salinity (PSU) made with the probe Falmouth Scientific Inc. The research was carried out in 2001 (at the turn of May and June), 2003 (beginning of May), 2005 (end of April) and in 2002 and 2004 (at the turn of September and October). In 2002, only temperature...
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Computationally Efficient Surrogate-Assisted Design of Pyramidal-Shaped 3D Reflectarray Antennas
PublicationReflectarrays (RAs) have been attracting considerable interest in the recent years due to their appealing features, in particular, a possibility of realizing pencil-beam radiation patterns, as in the phased arrays, but without the necessity of incorporating the feeding networks. These characteristics make them attractive solutions, among others, for satellite communications or mobile radar antennas. Notwithstanding, available microstrip...
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Hydrodynamic reanalysis of currents in the Baltic Sea using the PM3D model
Open Research DataThe dataset contains the results of numerical modeling of currents in the Baltic Sea since 1998. A long-term reanalysis was performed using a three-dimensional hydrodynamic model PM3D (Kowalewski and Kowalewska-Kalkowska, 2017), a new version of the M3D model (Kowalewski, 1997).
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Sampling-based novel heterogeneous multi-layer stacking ensemble method for telecom customer churn prediction
PublicationIn recent times, customer churn has become one of the most significant issues in business-oriented sectors with telecommunication being no exception. Maintaining current customers is particularly valuable due to the high degree of rivalry among telecommunication companies and the costs of acquiring new ones. The early prediction of churned customers may help telecommunication companies to identify the causes of churn and design...
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Ocean mixed layer dynamics: high-resolution simulations of wind, wave and convective effects
Open Research DataThis dataset contains results of high-resolution numerical simulations of the ocean mixed layer (OML) forced by wind, waves and cooling from the atmosphere, i.e., under strongly turbulent, convective conditions. The goal is to provide detailed, three-dimensional information about OML circulation, turbulent kinetic energy, and temperature and salinity...
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Buried Object Characterization by Data-Driven Surrogates and Regression-Enabled Hyperbolic Signature Extraction
PublicationThis work addresses artificial-intelligence-based buried object characterization using FDTD-based electromagnetic simulation toolbox of a Ground Penetrating Radar (GPR) to generate B-scan data. In data collection, FDTD-based simulation tool, gprMax is used. The task is to estimate geophysical parameters of a cylindrical shape object of various radii, buried at different positions in the dry soil medium simultaneously and independently...
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Long-term hindcast simulation of currents in the Baltic Sea
Open Research DataThe dataset contains the results of numerical modelling of currents over a period of 50 years (1958-2007) in the Baltic Sea . A long-term hindcast simulation was performed using a three-dimensional hydrodynamic model PM3D (Kowalewski and Kowalewska-Kalkowska, 2017), a new version of the M3D model (Kowalewski, 1997). The hydrodynamic model was coupled...