Institute of Information Technology I-72
Head of the unit:
Piotr Szczepaniak, MSc, PhD, DSc, Full Professor
Potential promotors:
Agnieszka Duraj, MSc, PhD, DSc
Łukasz Chomątek, MSc, PhD
Contact person:
Agnieszka Duraj, MSc, PhD, DSc, tel: (+48 42) 631-27-96 (+48 42) 631-39-54, agnieszka.duraj@p.lodz.pl
Scope of activities:
Intelligent quantitative and qualitative analysis of big data with outlier detection. The general goal of the research is improvement of known methods of data analysis and knowledge extraction, as well as development of new ones. The goal is achieved with the use of classic approaches and methods of the artificial intelligence. Particular attention is paid to evolutionary algorithms, their novel variants, and fuzzy sets applications.
Present activities:
The research is focused on the following issues:
- separation of data for native, foreign, and outlier ones;
- variants of linguistic summarization applied to outlier detection;
- innovative approach to case-based reasoning;
- development of evolutionary algorithms, multi-objective in particular;
- multi-objective approach to outlier detection;
- hierarchical methods;
- outlier detection in data steams;
- innovations in methods based on statistics, distance, and density;
- verification of methods on real-world data;
- extraction and generalization of knowledge; consideration of context.
The definition of an outlier often requires the cumulative application of several different criteria (e.g. low cardinality and distance from dominant "typical" patterns). For this reason, it is natural to develop and use multi-criteria optimization methods, here evolution algorithms.
Future activities:
- Development of effective methods of detecting anomalies in data sets or patterns.
- Development in the field of multi-criteria optimization methods, which consists in:
- Adaptation of optimization algorithms (in this case genetic) to classification tasks (exception - not exception).
- Development of dedicated genetic operators for the exception detection problem.
- Defining the method of selecting the components of the objective functions used in the multi-criteria optimization task.
Publications/patents, awards, projects:
- A. Duraj, P. S. Szczepaniak: Linguistic Summaries Using Interval-Valued Fuzzy Representation of Imprecise Information-An Innovative Tool for Detecting Outliers. International Conference on Computational Science - ICCS, pp.500--513, Springer, 2021.
- A. Duraj, P. S. Szczepaniak: Outlier Detection in Data Streams—A Comparative Study of Selected Methods. 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES, Elsevier, 2021.
- A. Duraj, P. S. Szczepaniak, L. Chomatek: Intelligent Detection of Information Outliers Using Linguistic Summaries with Non-monotonic Quantifiers. Springer Nature Switzerland AG 2020 M.-J. Lesot et al. (Eds.): IPMU 2020, CCIS 1239, pp. 787–799, 2020. https://doi.org/10.1007/978-3-030-50153-2_58. 4. P.S. Szczepaniak, A. Duraj (2018): Case-Based Reasoning – the Search for Similar Solutions and Identification of Outliers. Complexity (ID 9280787; open access)
Keywords:
intelligent data analysis, outlier detection
The portfolio of research groups was created as part of the Programme "STER" – Internationalisation of doctoral schools” as part of the realization of the project “Curriculum for advanced doctoral education & taining – CADET Academy of Lodz University of Technology”.
Institute of Information Technology I-72
Head of the unit:
Piotr Szczepaniak, MSc, PhD, DSc, Full Professor
Potential promotors:
Bartłomiej Stasiak, MSc, PhD, DSc
Arkadiusz Tomczyk, MSc, PhD
Paweł Tarasiuk, MSc
Łukasz Pierścieniewski MSc (PhDstudent)
Contact person:
Arkadiusz Tomczyk, MSc, PhD, tel: (+48 42) 631-27-96 (+48 42) 631-39-54, arkadiusz.tomczyk@p.lodz.pl
Scope of activities:
The team deals with analysis of images and multidimensional data using machine learning, which includes the following tasks: classification, knowledge extraction, object localization and detection, segmentation, object tracking, etc. That type of analysis, in particular in problems with diverse data and structures, is often hard for simple, algorithmic approaches. In such situation applicable are machine learning techniques, which, basing on available knowledge (usually on a set of samples), try to automatically select a proper algorithmic model and its parameters. Currently, the most popular approaches in data analysis are: support vector machines (SVM), classic perceptron (MLP), etc., and in case of images – convolutional (CNN) and recurrent (RNN) neural networks. Team competencies cover also classic methods of image analysis like active contours (AC) and their original generalizations: active hyper-contours (AH) as well as active partitions (AP). Their advantage, which is particularly important, is ability to take into account domain knowledge while processing data. A crucial element of the conducted activity is also analysis of graph data, using extensions of convolutional networks, which has a wide area of application starting from chemical data analysis, through social networks analysis, finishing on classic image analysis.
An import place occupies sound signal analysis. Although it is a completely different domain, it is possible to apply here artificial intelligence tools, usually used for other data, as well. The results of time-frequency analysis can be processed analogously to image data and machine learning allows to successfully identify and recognize patters, including highlevel ones. Consequently, it is possible, to analyze the content of music recordings in a similar way to humans and automatically specify their semantically important features. It must be emphasized that during its activities the team pays a special attention on possibility of interpretation of automatically created models, which is of huge importance for their subsequent practical application.
Present activities:
Current activities include:
- image content analysis basing on structural representations other than regular grid of pixels,
- graph data analysis
- generalization of such techniques like active contours,
- development and adaptation of currently leading deep learning solutions (convolutional and recurrent neural networks),
- explanation of the processes occurring in trained models and interpretation of their results,
- music recordings analysis, feature extraction and pattern analysis in sound signal using neural networks
- searching for melody patterns in music recordings. Current research involves:
- development of convolutional, including graph, neural networks together with method of their working interpretation,
- structural prediction, semantic segmentation, active contours and their generalizations,
- searching for methods of additional, export knowledge usage, other then set of samples, during data analysis,
- usage and interpretation of convolutional networks in sound signal analysis.
Future activities:
Future activities covers:
- further work on theoretical development of classic and graph convolutional neural networks
- strengthening interpretation abilities of existing and new solutions
- cooperation with external partners in order to apply classic and designed solutions for solving practical problems
Keywords:
multidimensional data analysis, analysis of images and their sequences, graph analysis, pattern recognition, machine learning, artificial intelligence
List of internship proposal in this research team:
research topics:
- Image analysis using graph convolutional neural networks.
- Interpretation of convolutional, including graph, neural networks working.
The portfolio of research groups was created as part of the Programme "STER" – Internationalisation of doctoral schools” as part of the realization of the project “Curriculum for advanced doctoral education & taining – CADET Academy of Lodz University of Technology”.
Institute of Information Technology I-72
Head of the unit:
Adam Wojciechowski, PhD, DSc, TUL Prof.
Potential promotors:
Adam Wojciechowski, PhD, DSc, TUL Prof.
Piotr Napieralski, PhD, DSc, TUL Prof.
Contact person:
Adam Wojciechowski, PhD, DSc, TUL Prof., tel.: 42-631-27-96, adam.wojciechowski@p.lodz.pl
Scope of activities:
Major areas of research include contemporary challenges in computer graphics and computer vision:
- photorealistic rendering and digital image synthesis,
- polygonal mesh processing and analysis,
- point cloud analysis and processing,
- scene processing and analysis based on image data from video cameras and depth sensors,
- affective computing, face image analysis, eye movement tracking,
- computer animation synthesis, also in terms of emotion engines,
- machine learning in testing computer games and graphical interfaces,
- biomechanics of motion, musculoskeletal models, artificial intelligence in animation synthesis and analysis.
Present activities:
Current research in the analysis and processing of laser scan point clouds is concerned with semantic segmentation and classification of indoor and outdoor objects. The research focuses on the efficient application of graph convolutional neural networks for large unstructured datasets. The research finds applications in automotive, geo location, construction and all kinds of inventories.
Research in the area of facial image analysis focuses on detection and classification of micro-expressions in facial images and efficient tracking of eye movements in vision systems equipped with a regular video camera. A separate thread concerns the processing and classification of incomplete face images, such as those occluded by a mask.
A separate thread of research is the application of artificial neural networks and machine learning methods, mainly unsupervised, to automate testing of computer games and their graphical interfaces.
Research related to photorealistic image synthesis focuses on high-performance rendering of the phenomenon of sunlight scattering in the Earth's atmosphere and teaching neural network models with a limited number of images. The search is for neural network models with broad generalization properties to allow style transfer.
Research in animation synthesis is concerned with creating, biomechanically correct musculoskeletal models and applying machine learning techniques to generate biomechanically correct motion sequences.
Future activities:
Each of the research threads is actively working to improve existing solutions and create new ones.
Publications/patents, awards, projects:
- Walczak, j., Najgebauer, P., Scherer, R., & Wojciechowski, A. (2021, July). CVA-GNN: Convolutional Vicinity Aggregation Graph Neural Network for Point Cloud Classification. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
- Gałaj, T., Pietrusiak, F., Galewski, M., Ledzion, R., & Wojciechowski, A. (2021). Hybrid Integration Method for Sunlight Atmospheric Scattering. IEEE Access, 9, 40681-40694.
- Walczak, J., Poreda, T., & Wojciechowski, A. (2019). Effective planar cluster detection in point clouds using histogram-driven KDlike partition and shifted mahalanobis distance based regression. Remote Sensing, 11(21), 2465.
- Project NCBR - LIDER XI, pt. „Semantic analysis of a 3D point clouds”, 01.12.2020 - 01.12.2023 r., Nr LIDER/25/0092/L11/19/NCBR/2020
Keywords:
machine learning, point clouds, semantic segmentation, classification, affective computing, face analysis, eye tracking, animation, computer games, real-time rendering, graphical interfaces
List of internship proposal in this research team:
The portfolio of research groups was created as part of the Programme "STER" – Internationalisation of doctoral schools” as part of the realization of the project “Curriculum for advanced doctoral education & taining – CADET Academy of Lodz University of Technology”.
Institute of Information Technology I-72
Head of the unit:
Adam Wojciechowski, PhD, DSc, TUL Prof.
Potential promotors:
Adam Wojciechowski, PhD, DSc, TUL Prof.
Agnieszka Wosiak, PhD, DSc, TUL Prof.
Bartłomiej Stasiak, PhD, DSc, TUL Prof.
Piotr Napieralski, PhD, DSc, TUL Prof.
Contact person:
Adam Wojciechowski, PhD, DSc, TUL Prof., tel.: 42-631-27-96, adam.wojciechowski@p.lodz.pl
Scope of activities:
The main areas of research include contemporary challenges in the intelligent analysis of medical, sensory, or statistical data, but also the creation of natural, affective user interfaces and immersive visualization in virtual and augmented reality environments. In particular:
- processing and analysis of biophysiological data, including EEG data using statistical methods, machine learning techniques, or feature selection. Research concerns both the analysis of mental states and brain-computer interfaces,
- processing and analysis of environmental data in the problem of control of HVAC systems and rationalization of energy consumption,
- processing and analysis of sensory data in human-machine interaction,
- affective user interfaces, affective computing, facial image analysis, eye tracking,
- creation of immersive virtual environments and augmented reality environments for computer games and simulations, visualization of architectural heritage, or cognitivemotor rehabilitation.
Present activities:
Research in the field of analysis and processing of EEG signals aims to increase the effectiveness of classification of mental states: valence, arousal, dominance and to create methods for efficient detection of imaginary motor tasks in the context of BCI.
Independent research concerns modelling of environmental phenomena in office buildings, hotels, schools and offices to create methods for effective control of HVAC systems, rationalization of energy consumption, anomaly detection.
Research in the field of virtual environments and augmented reality focuses on the creation of ergonomically correct, stimulation environments, both in the area of computer games and serious games in the tasks of neuro-rehabilitation, cognitive and motor rehabilitation in the elderly and popularization of cultural heritage.
A derivative element is the creation of natural methods of human-computer interaction that ensure effective implementation of immersive tasks. Research in the area of user interfaces, on the other hand, consists mainly in the search for marker and marker less methods for analysing images from video cameras and stereo pairs. Natural communication through voice and facial movement analysis is also not negligible.
Future activities:
Each of the research threads is actively working to improve existing solutions and create new ones.
Publications/patents, awards, projects:
- Wojciechowski, A., Wiśniewska, A., Pyszora, A., Liberacka-Dwojak, M., & Juszczyk, K. (2021). Virtual reality immersive environments for motor and cognitive training of elderly people–a scoping review. Human Technology, 17(2), 145-163.
- Dura, A., Wosiak, A., Stasiak, B., Wojciechowski, A., & Rogowski, J. (2021, June). Reversed Correlation-Based Pairwised EEG Channel Selection in Emotional State Recognition. In International Conference on Computational Science (pp. 528-541).
- Opałka, S., Stasiak, B., Wosiak, A., Dura, A., & Wojciechowski, A. (2021, June). EEG-Based Emotion Recognition– Evaluation Methodology Revisited. In International Conference on Computational Science (pp. 525-539). Springer, Cham.
- Project NCBR pt. „Predictive energy management system EnMS”, 01.07.2020 - 30.06.2023 r., POIR.01.01.01-00-0281/20
- Project NCBR pt. „Grydsen-innovative psychological therapies for seniors using VR technology”, 01.10.2020-30.09.2022 r., POIR.01.01.01-00-0951/19-00
- Project CHISTERA pt.”ReHaB - Towards an ecologically valid symbiosis of BCI and head-mounted VR displays: focus on collaborative post-stroke neurorehabilitation”, Nr: CHIST-ERA-20-BCI-004, 2022-2024
Keywords:
Machine learning, regression, classification, feature selection, EEG, HRV, affective computing, face analysis, micro expressions, eye tracking, VR, AR, gaming, stereoscopy, HVAC, HCI, BCI
List of internship proposal in this research team:
Each research area has an open list of post-doctoral fellowships and post-doc positions, as well as a wide range of graduate degrees that can be completed through projects or collaborations with industry.
The portfolio of research groups was created as part of the Programme "STER" – Internationalisation of doctoral schools” as part of the realization of the project “Curriculum for advanced doctoral education & taining – CADET Academy of Lodz University of Technology”.
Institute of Information Technology I-72
Head of the unit:
Piotr Napieralski
Contact person:
Piotr Napieralski, tel: 42-631-27-96, piotr.napieralski@p.lodz.pl
Scope of activities:
- Stereoscopic visualization and digital image quality,
- Affective user interfaces,
- Psychology of perception
- Computer graphics and animation
- Artificial Intelligence in film image analysis
Present activities:
- Cooperation with S3D laboratory from the L. Schiller National Film School
- Development and development of intelligent algorithms for recognition and analysis of changes of pupil size
- Development of algorithms for recognition of areas of interest in a film image
- Development of algorithms for identification of errors in stereoscopic images
Future activities:
establishing cooperation with other academic centers and business entities
Publications/patents, awards, projects:
- Wojciechowski, A., Napieralski, P., & Lipiński, P. (2021). TEWI 2021 (Technology, Education, Knowledge, Innovation) (A. Wojciechowski, P. Napieralski, & P. Lipiński, eds.). https://doi.org/10.34658/9788366741102
- Zieliński Marcin, Napieralski Piotr, Daszuta Marcin, S. D. (2021). Smart Events in Behavior of Non-player Characters in Computer Games. In P. M. A. S. Maciej Paszynski, Dieter Kranzlmüller, Valeria V. Krzhizhanovskaya, Jack J. Dongarra (Ed.), International Conference on Computational Science ICCS 2021 (pp. 164–177). https://doi.org/10.1007/978-3-030-77977-1_13
- Daszuta, M., Szajerman, D., & Napieralski, P. (2020). New emotional model environment for navigation in a virtual reality. Open Physics, 18(1), 864–870. https://doi.org/10.1515/phys20200199
- Kornacka, M., Kamila, C.-B., Napieralski, P., & Anna, B.-M. (2020). Rumination, mood, and maladaptive eating behaviors in overweight and healthy populations. Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity. https://doi.org/10.1007/s40519-020-00857z
- Rogalska, A., Rynkiewicz, F., Daszuta, M., Guzek, K., & Napieralski, P. (2019). Blinking Extraction in Eye gaze System for Stereoscopy Movies. Open Physics, 17(1), 512–518. https://doi.org/10.1515/phys-2019-0053
- Napieralski, P., & Rynkiewicz, F. (2019). Modeling Human Pupil Dilation to Decouple the Pupillary Light Reflex. Open Physics, 17(1), 458–467. https://doi.org/10.1515/phys-2019-0047
- Kowalczyk, M., & Napieralski, P. (2019). A structural quality evaluation model for threedimensional simulations. Open Physics, 17(1). https://doi.org/10.1515/phys-2019-0035
Keywords:
stereoscopy, film, animation, affective computing, artificial intelligence, machine learning, 3D
List of internship proposal in this research team:
methods of measurement and evaluation of cinema parameters
The portfolio of research groups was created as part of the Programme "STER" – Internationalisation of doctoral schools” as part of the realization of the project “Curriculum for advanced doctoral education & taining – CADET Academy of Lodz University of Technology”.
Institute of Information Technology I-72
Head of the unit:
Dr hab. inż. Przemysław Ignaciuk
Potential promotors:
Dr hab. inż. Przemysław Ignaciuk
Dr hab. inż. Michał Morawski
Contact person:
Przemysław Ignaciuk, tel.: 42-631-27-96, przemyslaw.ignaciuk@p.lodz.pl
Scope of activities:
The research activities concentrate on the current challenges of Information Technology and Optimization:
- Models and algorithms in networked systems – design and optimization
- Diagnosis and control of industrial processes and time-delay systems
- Analysis and countermeasures of disturbances in distributed architectures
- Quality of Service in data transmission and logistic networks
- Sustainable development of resource management systems
Present activities:
In recent years, one may observe significant progress achieved in the development of applications combining data transmission networks, distributed and automatic control systems, e.g., intelligent transport, or Internet of Things. The undertaken research work encompasses the analysis of existing networked systems and design of new protocols and algorithmic schemes. The objective is to ensure an adequate performance level despite inopportune phenomena obstructing the physical system implementation, e.g., delay, disturbances, or data loss in network transmission.
Both formal – incorporating advanced tools of control and optimization theories – and practical – involving real devices and data transfer networks – activities are undertaken to meet the current challenges in the field. The scope of conducted research encompasses design and deployment of digital control and diagnostic solutions with the emphasis placed on achieving high operational efficiency while retaining robustness to delay, data loss, and equipment faults.
Future activities:
Analysis of complex, multi-channel systems of resource and information exchange, e.g., communication networks governed MPTCP and QUIC protocols, and multi-mode transport systems.
Publications/patents, awards, projects:
Publications:
- P. Ignaciuk, M. Morawski: Discrete-time sliding-mode controllers for MPTCP networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 51, 2021
- M. Morawski, P. Ignaciuk: Choosing a proper control strategy for multipath transmission in Industry 4.0 applications. IEEE Transactions on Industrial Informatics, Vol. 18, 2022
- P. Ignaciuk: Linear-quadratic optimal control of multi-modal distribution systems with imperfect channels. International Journal of Production Research, Vol. 60, 2022
Grants:
- Robust control solutions for multi-channel networked flows, NCN, currently realized research project in the OPUS program, Lodz University of Technology, 2022–2024
- Application of artificial intelligence for optimization of truck transportation solutions, NCBiR, currently realized research project in the framework of Smart Growth Operational Program in cooperation with Inelo company, Lodz University of Technology, 2020–2023
Keywords:
Data transmission networks, Production and logistic systems, Networked control systems, Time-delay systems, Communication protocols, Modelling, Optimization
List of internship proposal in this research team:
Modelling of modern dynamical systems with distributed architectures, design of resource management algorithms, simulation and experimental verification.
The portfolio of research groups was created as part of the Programme "STER" – Internationalisation of doctoral schools” as part of the realization of the project “Curriculum for advanced doctoral education & taining – CADET Academy of Lodz University of Technology”.
Institute of Information Technology I-72
Head of the unit:
dr hab. inż. Piotr Lipiński
Potential promotors:
dr hab. inż. Piotr Lipiński
Contact person:
Piotr Lipiński, tel: 42-631.-27-96, piotr.lipinski@p.lodz.pl
Scope of activities:
- Intelligent data fusion algorithms for object localization,
- Time series analysis using adaptive transforms,
- Intelligent algorithms for time series forecasting,
- Digital Watermarking algorithms for images using artificial intelligence, Steganographic algorithms for images.
Present activities:
We are currently working on:
- Real-time Indoor locating system algorithms for noisy measurement data acquired by wireless systems
- Time series analysis and intelligent algorithms development for ecodriving system in havy duty trucks (The National Centre for Research and Development project)
- Algorithms for crypto-currency price forecasting using Deep Neural Networks
- Intelligent anomaly detection algorithms using distrubuted ledger technology for cloud applications (The National Centre for Research and Development project)
- Intelligent algorithms for indoor environment control and Energy consumption reduction (The National Centre for Research and Development project)
Future activities:
Continuation of present activities
Keywords:
Indoor localization, artificial intelligence, data fusion, ecodriving, time series forecasting, crypto forecasting, cluster anomaly detection.
The portfolio of research groups was created as part of the Programme "STER" – Internationalisation of doctoral schools” as part of the realization of the project “Curriculum for advanced doctoral education & taining – CADET Academy of Lodz University of Technology”.
Institute of Information Technology I-72
Head of the unit:
Dariusz Puchala, PhD, DSc
Potential promotors:
Marcin Ostrowski, PhD
Dariusz Puchała, PhD, DSc
Mateusz Smoliński, PhD
Kamil Stokfiszewski, PhD
Contact person:
Dariusz Puchała, PhD, DSc, tel: 48-42-631-27-96, dariusz.puchala@p.lodz.pl
Scope of activities:
The main research areas being investigated by the team include the following topics:
- lossy image compression - development of algorithms and techniques for lossy image compression based on adaptive linear transformations, artificial neural networks with dense structures, as well as convolutional artificial neural networks, by taking into account the overall optimization of the compression process, also in terms of subsequent entropy coding methods;
- computationally effective algorithms for signal processing and analysis - development and implementation of computationally effective signal processing algorithms for sequential and parallel hardware architectures (CPU, GPU, Multi-GPU, FPGA systems), focused on processing and analysis of large datasets (big data) and modelling the time complexity of algorithms for the selected of the listed hardware architectures;
- quantum computations and simulations - answering the question whether quantum parallelism can be used for more effective simulation of quantum physical phenomena, and what phenomena are subject to such simulations.
Present activities:
At the present moment, research is conducted to develop structures and algorithms for training artificial neural networks in the field of lossy compression of natural images, including artificial convolutional neural networks, as well as the structures that allow for automatic training of Karhunen-Loève transform or transformations with the similar energy distribution, in order to implement block quantization and entropy coding.
Another research topic is the development of artificial neural networks with sparse structures inspired by the dataflow diagrams of fast algorithms for calculating linear transformations, which translates into a smaller number of parameters to be trained, as well as the better generalization of results in practical applications.
In addition, the research is conducted on the development of computationally effective mass-parallel algorithms for calculation of one-dimensional and also two-dimensional separable wavelet transforms using graphics cards (GPUs).
Future activities:
Future research will be focused on lossy compression of color images using convolutional neural networks. Another research issue will be the extension of the so far developed mass-parallel algorithms for calculating wavelet transforms to the case of natively two-dimensional wavelet transforms.
Publications/patents, awards, projects:
Exemplary publications:
- D. Puchala, K. Stokfiszewski, K. Wieloch, Execution Time Prediction Model for Parallel GPU Realizations of Discrete Transforms Computation Algorithms, Bulletin of the Polish Academy of Sciences-Technical Sciences, 2022.
- M. Ostrowski, Simulation of the Schrödinger particle non-elastic scattering with emission of photon in the quantum register, Bulletin of the Polish Academy of Sciences-Technical Sciences, 2020.
- M. Smoliński, Impact of Storage Space Configuration on Transaction Processing Performance for Relational Database in PostgreSQL, Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety Springer, 2018.
Keywords:
signal and image processing, mass-parallel computing, quantum computations
List of internship proposal in this research team:
- development and implementation of 2D mass-parallel algorithms for wavelet transformations,
- research on artificial neural networks with sparse structures.
The portfolio of research groups was created as part of the Programme "STER" – Internationalisation of doctoral schools” as part of the realization of the project “Curriculum for advanced doctoral education & taining – CADET Academy of Lodz University of Technology”.
Institute of Information Technology I-72
Head of the unit:
prof. Adam Niewiadomski, PhD. DSc.
Potential promotors:
prof. Adam Niewiadomski, PhD. DSc.
Contact person:
Adam Niewiadomski, tel: 42-631-27-96, Adam.Niewiadomski@p.lodz.pl
Scope of activities:
- Fuzzy, expert, prediction and decision-making systems
- Databases mining, data mining
- Representing uncertain, incomplete and linguistic information
- Outliers detection and recognition in datasets
- "Soft" computing, soft computing, computing with words (CWW)
- Fuzzy logic systems in applications
- Type-2 fuzzy logic, higher order fuzzy sets
- Web Intelligence, BigData
- Evolutionary computational methods
Present activities:
- Detection of exceptions with fuzzy rules in non-relational databases
- Representation of information using fuzzy logic methods in graph databases
- Detection and recognition of exceptions using linguistic quantification methods
- Fuzzy systems managing the selective catalytic reduction process
- Higher order fuzzy sets and their applications in data analysis
- Multi-subject linguistic summaries of databases
- Hierarchical fuzzy logic systems
Future activities:
establishing cooperation with other academic centers and business entities
Publications/patents, awards, projects:
- Niewiadomski A., Kacprowicz M., Bartczak M.: Outliers Detection In Graph-Represented Databases Using Fuzzy Rules. Pacific Asia Conference on Information Systems, PACIS 2021, 12-14 July, 2021, Dubai, Arabia Saudyjska, ISBN 978-1-7336325-7-7, wykaz konf. punkt. (140pkt.)
- Niewiadomski A., Kacprowicz M.: Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction For Air Pollution Prevention. Journal of Artificial Intelligence and Soft Computing Research, Volume 11, Issue 2, 2021, Sciendo, ISSN 2083-2567, Doi: 10.2478/jaiscr-2021-0006, pp. 85-97, open access, JCR
- Niewiadomski A., Duraj A., Bartczak M.: Outliers Recognition Via Linguistic Aggregation of Graph Databases. Applied Sciences, 2021, Tom 11(16), 7434, MDPI, ISSN: 2076-3417, Doi: 10.3390/app11167434, pp. 1-13, open access, JCR
- Niewiadomski A., Duraj A.: Detecting and Recognizing Outliers in Datasets via Linguistic Information and Type-2 Fuzzy Logic. International Journal of Fuzzy Systems, , nr , str. 878– 889. 2020 r. (70 pkt.)
- Niewiadomski, A., Zbiory rozmyte typu 2. Zastosowania w reprezentowaniu informacji. W serii „Problemy współczesnej informatyki”, pod redakcją L. Rutkowskiego, Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2019.
- Duraj, A., Niewiadomski, A., Szczepaniak, P. S., Detection of outlier information by the use of linguistic summaries based on classic and interval-valued fuzzy sets, International Journal of Intelligent Systems, Vol. 34, Nr 3, ss. 415-438, 2019.
- Niewiadomski, A., Kacprowicz, M., Higher Order Fuzzy Logic in Controlling Selective Catalytic Reduction Systems, Bulletin of The Polish Academy of Sciences, Technical Sciences, Vol. 62, Nr 4, ss.743-750, 2014.
- Niewiadomski, A., On Finity, Countability, Cardinalities, And Cylindric Extensions of Type-2 Fuzzy Sets in Linguistic Summarization of Databases, IEEE Transactions on Fuzzy Systems, Vol. 18, Nr 3, ss. 532-545, 2010.
Keywords:
Artificial intelligence, fuzzy logic systems, expert systems, outlier detection ndrecognition, data analysis and representation, nonrelational databases.
List of internship proposal in this research team:
- Outlier recognition in non-relational databases
- Representing information in graph datasets
The portfolio of research groups was created as part of the Programme "STER" – Internationalisation of doctoral schools” as part of the realization of the project “Curriculum for advanced doctoral education & taining – CADET Academy of Lodz University of Technology”.
Institute of Information Technology I-72
Head of the unit:
Aneta Poniszewska-Marańda, PhD, Eng, DSc, TUL Prof.
Potential promotors:
Aneta Poniszewska-Marańda, PhD, Eng, DSc, TUL Prof.
Contact person:
Aneta Poniszewska-Marańda, PhD, Eng, DSc, TUL Prof., aneta.poniszewska-maranda@p.lodz.pl
Scope of activities:
The scope of research is the development of methods and tools for the creation of intelligent IT solutions, applicable in everyday life, supported by the concepts of a smart city, intelligent society and Internet of Things, using advanced software engineering. By combining the achievements of modern software engineering and artificial intelligence, we can achieve higher quality, performance and usefulness of created information systems in practical applications, while providing security and protection of data stored and transmitted within local and distributed information systems, Internet and mobile applications and data processed in the cloud and Internet of Things systems. The detailed tasks include the development of methods in: software engineering, optimization, metaheuristics, security of data and systems, analysis, processing and extraction of information, the application of machine learning methods, artificial intelligence in planning supplies.
Present activities:
Scientific research conducted in the team includes the following issues:
- Methodologies of software development, with particular emphasis on the analysis and design of information systems.
- Research and quality analysis in the process of software development.
- Application of metaheuristics to solve the problem of supply planning.
- Ensuring the security and protection of data within local and distributed systems, internet and mobile applications as well as data processed in cloud and IoT systems.
- Natural language text processing and extraction of information and associated intelligent management of data, resource and information.
- Construction of models and algorithms for efficient intelligent systems and knowledge base systems with the use of selected machine learning algorithms.
- Research on the use of blockchain in administration, management and eelections.
- Application of blockchain to solve important social and public problems, including ensuring the integrity of data transmission through the network.
- Security methods in VANET vehicle networks (Vehicular Ad-Hoc Network) with the use of Internet of Things concept.
- Intelligent system of automation and analysis of security procedures.
Future activities:
Conducting development works in connection with the economy, transport, public administration and health protection through the development of methods and tools for creating intelligent IT solutions that are applicable in everyday life, supported by the concepts of a smart city, intelligent society and IoT.
Application areas: economy, transport, public administration, health protection, medicine.
Publications/patents, awards, projects:
publons.com/researcher/1487582/aneta-poniszewska-maranda/
scopus.com/authid/detail.uri?authorId=8717200400 orcid.org/0000-0001-7596-0813
Keywords:
Software Engineering, Security, Blockchain, IoT, Machine Learning, Data Analytics, Industry 4.0
List of internship proposal in this research team:
Development of algorithms and tools in the area of research
The portfolio of research groups was created as part of the Programme "STER" – Internationalisation of doctoral schools” as part of the realization of the project “Curriculum for advanced doctoral education & taining – CADET Academy of Lodz University of Technology”.