Filtry
wszystkich: 854
-
Katalog
- Publikacje 689 wyników po odfiltrowaniu
- Czasopisma 36 wyników po odfiltrowaniu
- Konferencje 53 wyników po odfiltrowaniu
- Osoby 37 wyników po odfiltrowaniu
- Projekty 2 wyników po odfiltrowaniu
- Kursy Online 14 wyników po odfiltrowaniu
- Wydarzenia 3 wyników po odfiltrowaniu
- Dane Badawcze 20 wyników po odfiltrowaniu
Wyniki wyszukiwania dla: DEEP NEURAL NETWORKS, EXPLAINABLE ARTIFICIAL INTELLIGENCE, ADVER-SARIAL ATTACKS
-
From Individual to Collective: Intelligence Amplification with Bio-Inspired Decisional DNA and its Extensions
PublikacjaIn nature, deoxyribonucleic acid (DNA) contains the genetic instructions used in the development and functioning of all known living organisms. The idea behind our vision is to develop an artificial system, an architecture that would support discovering, adding, storing, improving and sharing information and knowledge among agents and organizations through experience. We propose a novel Knowledge Representation (KR) approach in...
-
A compact smart sensor based on a neural classifier for objects modeled by Beaunier's model
PublikacjaA new solution of a smart microcontroller sensor based on a simple direct sensor-microcontroller interface for technical objects modeled by two-terminal networks and by the Beaunier’s model of anticorrosion coating is proposed. The tested object is stimulated by a square pulse and its time voltage response is sampled four times by the internal ADC of microcontroller. A neural classifier based on measurement data classifies the...
-
Credibility of Threats to Jam Anonymous Traffic Remapping Attacks in Ad Hoc WLANs
PublikacjaIn ad hoc networks, selfish stations can pursue a better quality of service (QoS) by performing traffic remapping attacks (TRAs), i.e., by falsely assigning their traffic to a higher priority class, which can hurt honest stations’ QoS. To discourage the attackers, honest stations can announce their dissatisfaction with the perceived QoS. If such a threat fails, a costly data frame jamming defense can be launched. We analyze the...
-
Mitigation of Fake Data Content Poisoning Attacks in NDN via Blockchain
PublikacjaAbstract—Information-centric networks struggle with content poisoning attacks (CPAs), especially their stronger form called Fake Data CPA, in which an intruder publisher uploads content signed with stolen credentials. Following an existing graphinfection based approach leveraging the constrained time when stolen credentials are useful, we design a blockchain-based mitigation scheme for Named Data Networking architectures. We postulate...
-
Controlling computer by lip gestures employing neural network
PublikacjaResults of experiments regarding lip gesture recognition with an artificial neural network are discussed. The neural network module forms the core element of a multimodal human-computer interface called LipMouse. This solution allows a user to work on a computer using lip movements and gestures. A user face is detected in a video stream from a standard web camera using a cascade of boosted classifiers working with Haar-like features....
-
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention
PublikacjaThis 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...
-
Emotion Recognition from Physiological Channels Using Graph Neural Network
PublikacjaIn recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work, GraphSleepNet (a GNN for classifying the stages of sleep) was adjusted for emotion recognition and validated for this purpose. The...
-
Solubility Characteristics of Acetaminophen and Phenacetin in Binary Mixtures of Aqueous Organic Solvents: Experimental and Deep Machine Learning Screening of Green Dissolution Media
PublikacjaThe 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...
-
DIAGNOSIS OF MALIGNANT MELANOMA BY NEURAL NETWORK ENSEMBLE-BASED SYSTEM UTILISING HAND-CRAFTED SKIN LESION FEATURES
PublikacjaMalignant melanomas are the most deadly type of skin cancer but detected early have high chances for successful treatment. In the last twenty years, the interest of automated melanoma recognition detection and classification dynamically increased partially because of public datasets appearing with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task...
-
Assessing the attractiveness of human face based on machine learning
PublikacjaThe attractiveness of the face plays an important role in everyday life, especially in the modern world where social media and the Internet surround us. In this study, an attempt to assess the attractiveness of a face by machine learning is shown. Attractiveness is determined by three deep models whose sum of predictions is the final score. Two annotated datasets available in the literature are employed for training and testing...
-
Data Acquisition in a Manoeuver Auto-negotiation System
PublikacjaTypical approach to collision avoidance systems with artificial intelligence support is that such systems assume a central communication and management point (such as e.g. VTS station), usually located on shore. This approach is, however, not applicable in case of an open water encounter. Thus, recently a new approach towards collision avoidance has been proposed, assuming that all ships in the encounter, either restricted or open...
-
Neural network agents trained by declarative programming tutors
PublikacjaThis 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...
-
Integrated Functional Safety and Cybersecurity Evaluation in a Framework for Business Continuity Management
PublikacjaThis article outlines an integrated functional safety and cybersecurity evaluation approach within a framework for business continuity management (BCM) in energy companies, including those using Industry 4.0 business and technical solutions. In such companies, information and communication technology (ICT), and industrial automation and control system (IACS) play important roles. Using advanced technologies in modern manufacturing...
-
Marek Czachor prof. dr hab.
Osoby -
Optimizing control by robustly feasible model predictive control and application to drinking water distribution systems
PublikacjaThe paper considers optimizing Model Predictive Control (MPC) for nonlinear plants with output constraints under uncertainties. Although the MPC technology can handle the constraints in the model by solving constraint model based optimization task, satisfying the plant output constraints under the model uncertainty still remains a challenge. The paper proposes Robustly Feasible MPC (RFMPC), which achieves feasibility of the outputs...
-
Evolutionary Algorithm for Selecting Dynamic Signatures Partitioning Approach
PublikacjaIn the verification of identity, the aim is to increase effectiveness and reduce involvement of verified users. A good compromise between these issues is ensured by dynamic signature verification. The dynamic signature is represented by signals describing the position of the stylus in time. They can be used to determine the velocity or acceleration signal. Values of these signals can be analyzed, interpreted, selected, and compared....
-
TOWARDS EXPLAINABLE CLASSIFIERS USING THE COUNTERFACTUAL APPROACH - GLOBAL EXPLANATIONS FOR DISCOVERING BIAS IN DATA
PublikacjaThe paper proposes summarized attribution-based post-hoc explanations for the detection and identification of bias in data. A global explanation is proposed, and a step-by-step framework on how to detect and test bias is introduced. Since removing unwanted bias is often a complicated and tremendous task, it is automatically inserted, instead. Then, the bias is evaluated with the proposed counterfactual approach. The obtained results...
-
Surface EMG-based signal acquisition for decoding hand movements
Dane BadawczeBiosignal processing plays a crucial role in modern hand prosthetics. The challenge is to restore functionality of a lost limb based on the signals acquired from the surface of the stump. The number of sensors (emg channels) used for signal acquisition influence the quality of a prosthetic hand. Modern algorithms (including neural networks) can significantly...
-
Performance analysis of an rfid-based 3d indoor positioning system combining scene analysis and neural network methods
PublikacjaThe main purpose of this research is to improve localization accuracy of an active Radio Frequency Identification, RFID tag, in 3D indoor space. The paper presents a new RFID based 3D Indoor Positioning System which shows performance improvement. The proposed positioning system combines two methods: the Scene Analysis technique and Artificial Neural Network. The results of both simulation using Log-Distance Path Loss Model and...
-
International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems
Konferencje -
[Soft Skills] Smart metering - social risk perception and risk governance (2023/2024)
Kursy OnlineThe aim of the course is to broaden the understanding of the risks associated with technology and to present the concept of social risk perception and risk management in the context of smart metering technology. In the current phase of technological development - called the Fourth Industrial Revolution - rapid and profound changes are creating new, particularly destabilizing threats. In the increasingly complex technological systems...
-
Potential and Use of the Googlenet Ann for the Purposes of Inland Water Ships Classification
PublikacjaThis article presents an analysis of the possibilities of using the pre-degraded GoogLeNet artificial neural network to classify inland vessels. Inland water authorities monitor the intensity of the vessels via CCTV. Such classification seems to be an improvement in their statutory tasks. The automatic classification of the inland vessels from video recording is a one of the main objectives of the Automatic Ship Recognition and...
-
Multi-criteria Differential Evolution for Optimization of Virtual Machine Resources in Smart City Cloud
PublikacjaIn a smart city, artificial intelligence tools support citizens and urban services. From the user point of view, smart applications should bring computing to the edge of the cloud, closer to citizens with short latency. However, from the cloud designer point of view, the trade-off between cost, energy and time criteria requires the Pareto solutions. Therefore, the proposed multi-criteria differential evolution can optimize virtual...
-
Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
PublikacjaOne of the main issues associated with steel fiber–reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams. The flexural performance of SFRC beams under bending was...
-
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...
-
Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools
PublikacjaA high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and single-product factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final result (delays, cost overruns). The constant will...
-
Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms
PublikacjaThe study targeted towards drying of cantaloupe slices with various thicknesses in a microwave dryer. The experiments were carried out at three microwave powers of 180, 360, and 540 W and three thicknesses of 2, 4, and 6 mm for cantaloupe drying, and the weight variations were determined. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were exploited to investigate energy and exergy indices of...
-
Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
PublikacjaMany 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...
-
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’...
-
Comparative study on the effectiveness of various types of road traffic intensity detectors
PublikacjaVehicle 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...
-
Minimizing Distribution and Data Loading Overheads in Parallel Training of DNN Acoustic Models with Frequent Parameter Averaging
PublikacjaIn 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...
-
Algorithmic Human Resources Management - Perspectives and Challenges
PublikacjaTheoretical background: Technology – most notably processes of digitalisation, the use of artificial intelligence, machine learning, big data and prevalence of remote work due to pandemic – changes the way organizations manage human resources. One of the increasing trends is the use of so-called “algorithmic management”. It is notably different than previous e-HRM or HRIS (human resources information systems) applications, as it...
-
Selection of Features for Multimodal Vocalic Segments Classification
PublikacjaEnglish speech recognition experiments are presented employing both: audio signal and Facial Motion Capture (FMC) recordings. The principal aim of the study was to evaluate the influence of feature vector dimension reduction for the accuracy of vocalic segments classification employing neural networks. Several parameter reduction strategies were adopted, namely: Extremely Randomized Trees, Principal Component Analysis and Recursive...
-
Zarządzanie zaufaniem w bezprzewodowych sieciach czujników - studium przypadku
PublikacjaW artykule przedstawiono studium przypadku dotyczące zastosowania bezprzewodowej sieci czujników do wspomagania pacjenta z nadwagą w jego środowisku domowym. Przyjęto, że sieć wykorzystuje metodę rozproszonego zarządzania zaufaniem i pokazano, że metoda ta umożliwia wykrycie i izolację czujników realizujących działania sprzeczne z polityką sieci. Ilościowe oszacowanie czasu niezbędnego do wykrycia takich...
-
Vehicle classification based on soft computing algorithms
PublikacjaExperiments and results regarding vehicle type classification are presented. Three classes of vehicles are recognized: sedans, vans and trucks. The system uses a non-calibrated traffic camera, therefore no direct vehicle dimensions are used. Various vehicle descriptors are tested, including those based on vehicle mask only and those based on vehicle images. The latter ones employ Speeded Up Robust Features (SURF) and gradient images...
-
The trajectories of the financial crisis of companies at risk of bankruptcy
PublikacjaThis article concerns the assessment of the trajectory of the collapse of enterprises in Central Europe. The author has developed a model of a Kohonen artificial neural network. This model was used to determine 6 different classes of risk and was allowed to graphically determine the 5- to 10-year trajectory of going bankrupt. The study used data on 140 companies listed on the Warsaw Stock Exchange. This population was divided into...
-
Adversarial attack algorithm for traffic sign recognition
PublikacjaDeep learning suffers from the threat of adversarial attacks, and its defense methods have become a research hotspot. In all applications of deep learning, intelligent driving is an important and promising one, facing serious threat of adversarial attack in the meanwhile. To address the adversarial attack, this paper takes the traffic sign recognition as a typical object, for it is the core function of intelligent driving. Considering...
-
Towards Designing an Innovative Industrial Fan: Developing Regression and Neural Models Based on Remote Mass Measurements
PublikacjaThis article presents the process of the construction and testing a remote, fully autonomous system for measuring the operational parameters of fans. The measurement results obtained made it possible to create and verify mathematical models using linear regression and neural networks. The process was implemented as part of the first stage of an innovative project. The article presents detailed steps of constructing a system to...
-
LSTM-based method for LOS/NLOS identification in an indoor environment
PublikacjaDue to the multipath propagation, harsh indoor environment significantly impacts transmitted signals which may adversely affect the quality of the radiocommunication services, with focus on the real-time ones. This negative effect may be significantly reduced (e.g. resources management and allocation) or compensated (e.g. correction of position estimation in radiolocalisation) by the LOS/NLOS identification algorithm. This paper...
-
Case Study NEB Atlas / part II - Autodesk Forma analysis / Garnizon district in Gdansk, Poland
Dane BadawczeThe data present the results of the work on the analysis of modern settlements. The goal of this part of the research was to analyze housing estates already in place in various European cities. Analyses were performed in real time using artificial intelligence, and responses were searched for sun hours, daylight potential, noise, wind, and microclimate....
-
Case Study NEB Atlas / part II - Autodesk Forma analysis / Seestadt Aspern, Vienna, Austria
Dane BadawczeThe data present the results of the work on the analysis of modern settlements. The goal of this part of the research was to analyze housing estates already in place in various European cities. Analyses were performed in real time using artificial intelligence, and responses were searched for sun hours, daylight potential, noise, wind, and microclimate....
-
Case Study NEB Atlas / part II - Autodesk Forma analysis / Västra Hamnen, Malmö, Sweden.
Dane BadawczeThe data present the results of the work on the analysis of modern settlements. The goal of this part of the research was to analyze housing estates already in place in various European cities. Analyses were performed in real time using artificial intelligence, and responses were searched for sun hours, daylight potential, noise, wind, and microclimate....
-
Case Study NEB Atlas / part II - Autodesk Forma analysis / Hammarby-Sjöstad, Stockholm, Sweden.
Dane BadawczeThe data present the results of the work on the analysis of modern settlements. The goal of this part of the research was to analyze housing estates already in place in various European cities. Analyses were performed in real time using artificial intelligence, and responses were searched for sun hours, daylight potential, noise, wind, and microclimate....
-
Case Study NEB Atlas / part II - Autodesk Forma analysis / Pilestredet Park, Oslo, Norway.
Dane BadawczeThe data present the results of the work on the analysis of modern settlements. The goal of this part of the research was to analyze housing estates already in place in various European cities. Analyses were performed in real time using artificial intelligence, and responses were searched for sun hours, daylight potential, noise, wind, and microclimate....
-
Case Study NEB Atlas / part II - Autodesk Forma analysis / King’s Cross, London, UK.
Dane BadawczeThe data present the results of the work on the analysis of modern settlements. The goal of this part of the research was to analyze housing estates already in place in various European cities. Analyses were performed in real time using artificial intelligence, and responses were searched for sun hours, daylight potential, noise, wind, and microclimate....
-
Case Study NEB Atlas / part II - Autodesk Forma analysis / Oceanhamnen, Helsingborg, Sweden
Dane BadawczeThe data present the results of the work on the analysis of modern settlements. The goal of this part of the research was to analyze housing estates already in place in various European cities. Analyses were performed in real time using artificial intelligence, and responses were searched for sun hours, daylight potential, noise, wind, and microclimate....
-
Case Study NEB Atlas / part II - Autodesk Forma analysis / La Courrouze district in Rennes, France
Dane BadawczeThe data present the results of the work on the analysis of modern settlements. The goal of this part of the research was to analyze housing estates already in place in various European cities. Analyses were performed in real time using artificial intelligence, and responses were searched for sun hours, daylight potential, noise, wind, and microclimate....
-
(eng)aging! 2021 Technologie wspierające aktywność i niezależność seniorów w państwach V4
ProjektyProjekt realizowany w Katedra Inteligentnych Systemów Interaktywnych zgodnie z porozumieniem 22020301/1 z dnia 2021-01-08
-
Problems of modelling toxic compounds emitted by a marine internal combustion engine in unsteady states
PublikacjaContemporary engine tests are performed based on the theory of experiment. The available versions of programmes used for analysing experimental data make frequent use of the multiple regression model, which enables examining effects and interactions between input model parameters and a single output variable. The use of multi-equation models provides more freedom in analysing the measured results, as those models enable simultaneous...
-
I, Robot: between angel and evil
PublikacjaThe boosting of most digital innovations within recent technology progress by artificial intelligence (AI) constitutes a growing topic of interest. Besides its technical aspects, increasing research activity may be observed in the domain of security challenges, and therefore of responsibility related to the controlled or hypothetically uncontrolled or autonomous emergence of AI solutions. Consequently, responsibility and ethics...