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Search results for: Deep Learning

  • Precise Identification of Different Cervical Intraepithelial Neoplasia (CIN) Stages, Using Biomedical Engineering Combined with Data Mining and Machine Learning

    Publication

    - Year 2024

    Cervical 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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  • Spiral Search Grasshopper Features Selection with VGG19-ResNet50 for Remote Sensing Object Detection

    Publication
    • A. Stateczny
    • G. Uday Kiran
    • G. Bindu
    • K. Ravi Chythanya
    • K. Ayyappa Swamy

    - Remote Sensing - Year 2022

    Remote sensing object detection plays a major role in satellite imaging and is required in various scenarios such as transportation, forestry, and the ocean. Deep learning techniques provide efficient performance in remote sensing object detection. The existing techniques have the limitations of data imbalance, overfitting, and lower efficiency in detecting small objects. This research proposes the spiral search grasshopper (SSG)...

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  • A Triplet-Learnt Coarse-to-Fine Reranking for Vehicle Re-identification

    Publication

    - Year 2020

    Vehicle re-identification refers to the task of matching the same query vehicle across non-overlapping cameras and diverse viewpoints. Research interest on the field emerged with intelligent transportation systems and the necessity for public security maintenance. Compared to person, vehicle re-identification is more intricate, facing the challenges of lower intra-class and higher inter-class similarities. Motivated by deep...

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  • Architectural Modifications to Enhance Steganalysis with Convolutional Neural Networks

    Publication

    This paper investigates the impact of various modifications introduced to current state-of-the-art Convolutional Neural Network (CNN) architectures specifically designed for the steganalysis of digital images. Usage of deep learning methods has consistently demonstrated improved results in this field over the past few years, primarily due to the development of newer architectures with higher classification accuracy compared to...

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  • Musical Instrument Tagging Using Data Augmentation and Effective Noisy Data Processing

    Developing signal processing methods to extract information automatically has potential in several applications, for example searching for multimedia based on its audio content, making context-aware mobile applications (e.g., tuning apps), or pre-processing for an automatic mixing system. However, the last-mentioned application needs a significant amount of research to reliably recognize real musical instruments in recordings....

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  • Society 4.0: Issues, Challenges, Approaches, and Enabling Technologies

    Publication

    - CYBERNETICS AND SYSTEMS - Year 2024

    This guest edition of Cybernetics and Systems is a broadening continuation of our last year edition titled “Intelligence Augmentation and Amplification: Approaches, Tools, and Case Studies”. This time we cover research perspective extending towards what is known as Society 4.0. Bob de Vit brought the concept of Society 4.0 to life in his book “Society 4.0 – resolving eight key issues to build a citizens society”. From the Systems...

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  • Optymalizacja zasobów chmury obliczeniowej z wykorzystaniem inteligentnych agentów w zdalnym nauczaniu

    Publication

    - Year 2023

    Rozprawa dotyczy optymalizacji zasobów chmury obliczeniowej, w której zastosowano inteligentne agenty w zdalnym nauczaniu. Zagadnienie jest istotne w edukacji, gdzie wykorzystuje się nowoczesne technologie, takie jak Internet Rzeczy, rozszerzoną i wirtualną rzeczywistość oraz deep learning w środowisku chmury obliczeniowej. Zagadnienie jest istotne również w sytuacji, gdy pandemia wymusza stosowanie zdalnego nauczania na dużą skalę...

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  • Poprawa jakości klasyfikacji głębokich sieci neuronowych poprzez optymalizację ich struktury i dwuetapowy proces uczenia

    Publication

    - Year 2024

    W pracy doktorskiej podjęto problem realizacji algorytmów głębokiego uczenia w warunkach deficytu danych uczących. Głównym celem było opracowanie podejścia optymalizującego strukturę sieci neuronowej oraz zastosowanie uczeniu dwuetapowym, w celu uzyskania mniejszych struktur, zachowując przy tym dokładności. Proponowane rozwiązania poddano testom na zadaniu klasyfikacji znamion skórnych na znamiona złośliwe i łagodne. W pierwszym...

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  • Mobilenet-V2 Enhanced Parkinson's Disease Prediction with Hybrid Data Integration

    Publication

    - Year 2024

    This study investigates the role of deep learning models, particularly MobileNet-v2, in Parkinson's Disease (PD) detection through handwriting spiral analysis. Handwriting difficulties often signal early signs of PD, necessitating early detection tools due to potential impacts on patients' work capacities. The study utilizes a three-fold approach, including data augmentation, algorithm development for simulated PD image datasets,...

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  • Efficiency of Artificial Intelligence Methods for Hearing Loss Type Classification: an Evaluation

    Publication
    • M. Kassjański
    • M. Kulawiak
    • T. Przewoźny
    • D. Tretiakow
    • J. Kuryłowicz
    • A. Molisz
    • K. Koźmiński
    • A. Kwaśniewska
    • P. Mierzwińska-Dolny
    • M. Grono

    - Journal of Automation, Mobile Robotics and Intelligent Systems - JAMRIS - Year 2024

    The evaluation of hearing loss is primarily conducted by pure tone audiometry testing, which is often regarded as golden standard for assessing auditory function. If the presence of hearing loss is determined, it is possible to differentiate between three types of hearing loss: sensorineural, conductive, and mixed. This study presents a comprehensive comparison of a variety of AI classification models, performed on 4007 pure tone...

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  • Experience-Oriented Intelligence for Internet of Things

    Publication

    - CYBERNETICS AND SYSTEMS - Year 2017

    The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. The main reason behind this interest is the capabilities of the IoT for seamlessly integrating classical networks and networked objects, and hence allows people to create an intelligent environment based on this powerful integration. However, how to extract useful information from data produced by IoT and facilitate...

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  • Neural network model of ship magnetic signature for different measurement depths

    Publication

    This paper presents the development of a model of a corvette-type ship’s magnetic signature using an artificial neural network (ANN). The capabilities of ANNs to learn complex relationships between the vessel’s characteristics and the magnetic field at different depths are proposed as an alternative to a multi-dipole model. A training dataset, consisting of signatures prepared in finite element method (FEM) environment Simulia...

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  • Sensors and Sensor’s Fusion in Autonomous Vehicles

    Publication

    - SENSORS - Year 2021

    Autonomous vehicle navigation has been at the center of several major developments, both in civilian and defense applications. New technologies such as multisensory data fusion, big data processing, and deep learning are changing the quality of areas of applications, improving the sensors and systems used. New ideas such as 3D radar, 3D sonar, LiDAR, and others are based on autonomous vehicle revolutionary development. The Special...

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  • Music information retrieval—The impact of technology, crowdsourcing, big data, and the cloud in art.

    The exponential growth of computer processing power, cloud data storage, and crowdsourcing model of gathering data bring new possibilities to music information retrieval (mir) field. Mir is no longer music content retrieval only; the area also comprises the discovery of expressing feelings and emotions contained in music, incorporating other than hearing modalities for helping this issue, users’ profiling, merging music with social...

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  • Medical Image Dataset Annotation Service (MIDAS)

    Publication

    - Year 2020

    MIDAS (Medical Image Dataset Annotation Service) is a custom-tailored tool for creating and managing datasets either for deep learning, as well as machine learning or any form of statistical research. The aim of the project is to provide one-fit-all platform for creating medical image datasets that could easily blend in hospital's workflow. In our work, we focus on the importance of medical data anonimization, discussing the...

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  • Computer-assisted pronunciation training—Speech synthesis is almost all you need

    Publication

    - SPEECH COMMUNICATION - Year 2022

    The research community has long studied computer-assisted pronunciation training (CAPT) methods in non-native speech. Researchers focused on studying various model architectures, such as Bayesian networks and deep learning methods, as well as on the analysis of different representations of the speech signal. Despite significant progress in recent years, existing CAPT methods are not able to detect pronunciation errors with high...

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  • Smart Knowledge Engineering for Cognitive Systems: A Brief Overview

    Publication

    - CYBERNETICS AND SYSTEMS - Year 2022

    Cognition in computer sciences refers to the ability of a system to learn at scale, reason with purpose, and naturally interact with humans and other smart systems, such as humans do. To enhance intelligence, as well as to introduce cognitive functions into machines, recent studies have brought humans into the loop, turning the system into a human–AI hybrid. To effectively integrate and manipulate hybrid knowledge, suitable technologies...

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  • Akustyczna analiza parametrów ruchu drogowego z wykorzystaniem informacji o hałasie oraz uczenia maszynowego

    Publication

    - Year 2018

    Celem rozprawy było opracowanie akustycznej metody analizy parametrów ruchu drogowego. Zasada działania akustycznej analizy ruchu drogowego zapewnia pasywną metodę monitorowania natężenia ruchu. W pracy przedstawiono wybrane metody uczenia maszynowego w kontekście analizy dźwięku (ang.Machine Hearing). Przedstawiono metodologię klasyfikacji zdarzeń w ruchu drogowym z wykorzystaniem uczenia maszynowego. Przybliżono podstawowe...

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  • Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention

    Publication

    - Year 2021

    This paper describes two novel complementary techniques that improve the detection of lexical stress errors in non-native (L2) English speech: attention-based feature extraction and data augmentation based on Neural Text-To-Speech (TTS). In a classical approach, audio features are usually extracted from fixed regions of speech such as the syllable nucleus. We propose an attention-based deep learning model that automatically de...

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  • IFE: NN-aided Instantaneous Pitch Estimation

    Publication

    Pitch estimation is still an open issue in contemporary signal processing research. Nowadays, growing momentum of machine learning techniques application in the data-driven society allows for tackling this problem from a new perspective. This work leverages such an opportunity to propose a refined Instantaneous Frequency and power based pitch Estimator method called IFE. It incorporates deep neural network based pitch estimation...

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  • Pedestrian detection in low-resolution thermal images

    Over one million people die in car accidents worldwide each year. A solution that will be able to reduce situations in which pedestrian safety is at risk has been sought for a long time. One of the techniques for detecting pedestrians on the road is the use of artificial intelligence in connection with thermal imaging. The purpose of this work was to design a system to assist the safety of people and car intelligence with the use...

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  • Evaluating Accuracy of Respiratory Rate Estimation from Super Resolved Thermal Imagery

    Non-contact estimation of Respiratory Rate (RR) has revolutionized the process of establishing the measurement by surpassing some issues related to attaching sensors to a body, e.g. epidermal stripping, skin disruption and pain. In this study, we perform further experiments with image processing-based RR estimation by using various image enhancement algorithms. Specifically, we employ Super Resolution (SR) Deep Learning (DL) network...

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  • Artificial intelligence for software development — the present and the challenges for the future

    Since the time when first CASE (Computer-Aided Software Engineering) methods and tools were developed, little has been done in the area of automated creation of code. CASE tools support a software engineer in creation the system structure, in defining interfaces and relationships between software modules and, after the code has been written, in performing testing tasks on different levels of detail. Writing code is still the task...

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  • Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices

    Publication
    • A. G. Pereira
    • A. Ojo
    • C. Edward
    • L. Porwol

    - Year 2020

    There are growing opportunities to leverage new technologies and data sources to address global problems related to sustainability, climate change, and biodiversity loss. The emerging discipline of GeoAI resulting from the convergence of AI and Geospatial science (Geo-AI) is enabling the possibility to harness the increasingly available open Earth Observation data collected from different constellations of satellites and sensors...

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  • Exploring Neural Networks for Musical Instrument Identification in Polyphonic Audio

    Publication

    - IEEE INTELLIGENT SYSTEMS - Year 2024

    The purpose of this paper is to introduce neural network-based methods that surpass state-of-the-art (SOTA) models, either by training faster or having simpler architecture, while maintaining comparable effectiveness in musical instrument identification in polyphonic music. Several approaches are presented, including two authors’ proposals, i.e., spiking neural networks (SNN) and a modular deep learning model named FMCNN (Fully...

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  • Detecting Objects of Various Categories in Optical Remote Sensing Imagery Using Neural Networks

    Publication

    - Year 2024

    The effective detection of objects in remote sensing images is of great research importance, so recent years have seen a significant progress in deep learning techniques in this field. However, despite much valuable research being conducted, many challenges still remain. A lot of research projects focus on detecting objects of a single category (class), while correctly detecting objects of different categories is much harder. The...

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  • Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models

    Publication
    • R. Yurt
    • H. Torpi
    • P. Mahouti
    • A. Kizilay
    • S. Kozieł

    - IEEE Access - Year 2023

    This work addresses artificial-intelligence-based buried object characterization using 3-D full-wave electromagnetic simulations of a ground penetrating radar (GPR). The task is to characterize cylindrical shape, perfectly electric conductor (PEC) object buried in various dispersive soil media, and in different positions. The main contributions of this work are (i) development of a fast and accurate data driven surrogate modeling...

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  • Multiclass AdaBoost Classifier Parameter Adaptation for Pattern Recognition

    The article presents the problem of parameter value selection of the multiclass ``one against all'' approach of an AdaBoost algorithm in tasks of object recognition based on two-dimensional graphical images. AdaBoost classifier with Haar features is still used in mobile devices due to the processing speed in contrast to other methods like deep learning or SVM but its main drawback is the need to assembly the results of binary...

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  • Medical Image Segmentation Using Deep Semantic-based Methods: A Review of Techniques, Applications and Emerging Trends

    Publication

    - Information Fusion - Year 2022

    Semantic-based segmentation (Semseg) methods play an essential part in medical imaging analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is classified into an instance, where each class is corresponded by an instance. In particular, the semantic segmentation can be used by many medical experts in the domain of radiology, ophthalmologists, dermatologist, and image-guided radiotherapy. The authors...

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  • Fully Automated AI-powered Contactless Cough Detection based on Pixel Value Dynamics Occurring within Facial Regions

    Publication

    - Year 2021

    Increased 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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  • Integracja bezprzewodowych heterogenicznych sieci IP dla poprawy efektywności transmisji danych na morzu

    Publication

    - Year 2023

    Wraz ze wzrostem istotności środowiska morskiego w naszym codziennym życiu np. w postaci zwiększonego wolumenu transportu realizowanego drogą morską. czy zintensyfikowanych prac dotyczących obserwacji i monitoringu środowiska morskiego, wzrasta również potrzeba opracowania efektywnych systemów komunikacyjnych dedykowanych dla tego środowiska. Heterogeniczne systemy łączności bezprzewodowej integrowane na poziomie warstwy sieciowej...

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  • Toward Robust Pedestrian Detection With Data Augmentation

    Publication

    In this article, the problem of creating a safe pedestrian detection model that can operate in the real world is tackled. While recent advances have led to significantly improved detection accuracy on various benchmarks, existing deep learning models are vulnerable to invisible to the human eye changes in the input image which raises concerns about its safety. A popular and simple technique for improving robustness is using data...

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  • Automated Parking Management for Urban Efficiency: A Comprehensive Approach

    Publication

    - Year 2024

    Effective parking management is essential for ad-dressing the challenges of traffic congestion, city logistics, and air pollution in densely populated urban areas. This paper presents an algorithm designed to optimize parking management within city environments. The proposed system leverages deep learning models to accurately detect and classify street elements and events. Various algorithms, including automatic segmentation of...

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  • Equal Baseline Camera Array—Calibration, Testbed and Applications

    Publication

    - Applied Sciences-Basel - Year 2021

    This paper presents research on 3D scanning by taking advantage of a camera array consisting of up to five adjacent cameras. Such an array makes it possible to make a disparity map with a higher precision than a stereo camera, however it preserves the advantages of a stereo camera such as a possibility to operate in wide range of distances and in highly illuminated areas. In an outdoor environment, the array is a competitive alternative...

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  • Mask Detection and Classification in Thermal Face Images

    Publication

    Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify...

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  • High-quality academic teachers in business school. The case of The University of Gdańsk, Poland

    Publication

    The Bologna process, the increasing number of higher education institutions, the mass education and the demographic problems make the quality of education and quality of the academic teachers a subject of wide public debate and concern. The aim of the paper is to identify the most preferred characteristics of a teacher working at a business school. The research problem was: What should a high-quality business school academic teacher...

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  • Sensors and System for Vehicle Navigation

    Publication

    - SENSORS - Year 2022

    In recent years, vehicle navigation, in particular autonomous navigation, has been at the center of several major developments, both in civilian and defense applications. New technologies, such as multisensory data fusion, big data processing, or deep learning, are changing the quality of areas of applications, improving the sensors and systems used. Recently, the influence of artificial intelligence on sensor data processing and...

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  • OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems

    Publication
    • S. S. Narayana Chintapalli
    • S. Prakash Singh
    • J. Frnda
    • B. P. Divakarachar
    • V. L. Sarraju
    • P. Falkowski-Gilski

    - Heliyon - Year 2024

    Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered...

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  • Segmentation Quality Refinement in Large-Scale Medical Image Dataset with Crowd-Sourced Annotations

    Publication

    Deployment of different techniques of deep learning including Convolutional Neural Networks (CNN) in image classification systems has accomplished outstanding results. However, the advantages and potential impact of such a system can be completely negated if it does not reach a target accuracy. To achieve high classification accuracy with low variance in medical image classification system, there is needed the large size of the...

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  • An automated learning model for twitter sentiment analysis using Ranger AdaBelief optimizer based Bidirectional Long Short Term Memory

    Publication

    - EXPERT SYSTEMS - Year 2024

    Sentiment 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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  • Enabling Deeper Linguistic-based Text Analytics – Construct Development for the Criticality of Negative Service Experience

    Publication

    - IEEE Access - Year 2019

    Significant progress has been made in linguistic-based text analytics particularly with the increasing availability of data and deep learning computational models for more accurate opinion analysis and domain-specific entity recognition. In understanding customer service experience from texts, analysis of sentiments associated with different stages of the service lifecycle is a useful starting point. However, when richer insights...

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  • Long Distance Vital Signs Monitoring with Person Identification for Smart Home Solutions

    Publication

    - Year 2018

    Abstract— Imaging photoplethysmography has already been proved to be successful in short distance (below 1m). However, most of the real-life use cases of measuring vital signs require the system to work at longer distances, to be both more reliable and convenient for the user. The possible scenarios that system designers must have in mind include monitoring of the vital signs of residents in nursing homes, disabled people, who...

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  • Speaker Recognition Using Convolutional Neural Network with Minimal Training Data for Smart Home Solutions

    Publication

    - Year 2018

    With the technology advancements in smart home sector, voice control and automation are key components that can make a real difference in people's lives. The voice recognition technology market continues to involve rapidly as almost all smart home devices are providing speaker recognition capability today. However, most of them provide cloud-based solutions or use very deep Neural Networks for speaker recognition task, which are...

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  • Neural network agents trained by declarative programming tutors

    Publication

    This paper presents an experimental study on the development of a neural network-based agent, trained using data generated using declarative programming. The focus of the study is the application of various agents to solve the classic logic task – The Wumpus World. The paper evaluates the effectiveness of neural-based agents across different map configurations, offering a comparative analysis to underline the strengths and limitations...

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  • Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network

    Publication

    - Scalable Computing: Practice and Experience - Year 2024

    COVID-19, also known as the Coronavirus disease-2019, is an transferrable disease that spreads rapidly, affecting countless individuals and leading to fatalities in this worldwide pandemic. The precise and swift detection of COVID-19 plays a crucial role in managing the pandemic's dissemination. Additionally, it is necessary to recognize COVID-19 quickly and accurately by investigating chest x-ray images. This paper proposed a...

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  • Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

    Age prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried out using high-resolution X-ray scans of parts of the body, such as images of the hands or images of the chest. In this...

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  • Autoencoder application for anomaly detection in power consumption of lighting systems

    Publication

    - IEEE Access - Year 2023

    Detecting energy consumption anomalies is a popular topic of industrial research, but there is a noticeable lack of research reported in the literature on energy consumption anomalies for road lighting systems. However, there is a need for such research because the lighting system, a key element of the Smart City concept, creates new monitoring opportunities and challenges. This paper examines algorithms based on the deep learning...

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  • Multiplicative Long Short-Term Memory with Improved Mayfly Optimization for LULC Classification

    Publication
    • A. Stateczny
    • S. M. Bolugallu
    • P. B. Divakarachari
    • K. Ganesan
    • J. R. Muthu

    - Remote Sensing - Year 2022

    Land Use and Land Cover (LULC) monitoring is crucial for global transformation, sustainable land control, urban planning, urban growth prediction, and the establishment of climate regulations for long-term development. Remote sensing images have become increasingly important in many environmental planning and land use surveys in recent times. LULC is evaluated in this research using the Sat 4, Sat 6, and Eurosat datasets. Various...

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  • Smartphones as tools for equitable food quality assessment

    Background: The ubiquity of smartphones equipped with an array of sophisticated sensors, ample processing power, network connectivity and a convenient interface makes them a promising tool for non-invasive, portable food quality assessment. Combined with the recent developments in the areas of IoT, deep learning algorithms and cloud computing, they present an opportunity for advancing wide-spread, equitable and sustainable food...

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  • Minimizing Distribution and Data Loading Overheads in Parallel Training of DNN Acoustic Models with Frequent Parameter Averaging

    Publication

    In the paper we investigate the performance of parallel deep neural network training with parameter averaging for acoustic modeling in Kaldi, a popular automatic speech recognition toolkit. We describe experiments based on training a recurrent neural network with 4 layers of 800 LSTM hidden states on a 100-hour corpora of annotated Polish speech data. We propose a MPI-based modification of the training program which minimizes the...

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