Wydawnictwa Uczelniane / TUL Press

Stały URI zbioruhttp://hdl.handle.net/11652/17

Przeglądaj

Filtry

2016 - 201942020 - 202320

Ustawienia

Tematsearch.filters.applied.operator.equals: search.filters.subject.sztuczna inteligencja

Wyniki wyszukiwania

Teraz wyświetlane 1 - 10 z 24
  • Pozycja
    V konferencja dydaktyczno-naukowa dla lektorów i nauczycieli języków obcych. Lektor High-Tech 2023
    (Wydawnictwo Politechniki Łódzkiej, 2023) Gałaj, Magdalena (Red.); Budzińska, Katarzyna (Red.)
  • Pozycja
    An Investigation on the Use of Deep Generative Model in Urban Land Use Planning
    (Lodz University of Technology Press, 2023) Leung Ming Tze; Lin Minqi; Yu Peiheng
    Land use planning is an important tool to ensure that the need of people can be met and the land resources can be used efficiently. It has even been suggested that land use planning is a key to sustainable development. On the other hand, there has been a recent trend to adopt the idea of deep generative models in the realm of design. Attempts have been made to investigate the feasibility to generate architectural design options by using deep generative models. It would also be of interest to extend this idea and examine how deep generative models could be adopted in urban planning tasks. In the current study, a computational workflow to adopt deep generative model for land use planning has been proposed. The land use in various tertiary planning units (TPUs) in Hong Kong was adopted as the training data. After the training process, hypothetical TPUs was fed into the model to generate options of land use planning for these planning units. Results from the current study should unfold a new dimension in the realm of land use planning, in the sense that the proposed workflow can generate options for planners for further planning development investigation.
  • Pozycja
    Lung Xray Images Analysis for COVID-19 Diagnosis
    (Wydawnictwo Politechniki Łódzkiej, 2023) Kloska, Anna; Tarczewska, Martyna; Giełczyk, Agata; Marciniak, Beata
    Background: The SARS-CoV-2 pandemic began in early 2020. It paralyzed human life all over the world and threatened our security. Thus, proposing some novel and effective approaches to diagnosing COVID-19 infections became paramount. Methods: This article proposes a method for the classification of chest X-ray images based on the transfer learning. We examined also different scenarios of dataset augmentation. Results: The paper reports accuracy=98%, precision=97%, recall=100% and F1-score=98% in the most promising approach. Conclusion: Our research proofs that machine learning can be used in order to support medics in chest X-ray classification and implementing augmentation can lead to improvements in accuracy, precision, recall, and F1-scores.
  • Pozycja
    AMUseBot: Towards Making the Most out of a Task-oriented Dialogue System
    (Wydawnictwo Politechniki Łódzkiej, 2023) Christop, Iwona; Dudzic, Kacper; Krzymiński, Mikołaj
    This paper presents AMUseBot, a task-oriented dialogue system designed to assist the user in completing multi-step tasks. Taking into consideration that the fundamental issues with such systems are poor user ratings and high rates of uncompleted tasks, the main goal of the project is to keep the user focused and provide engaging conversations. We approach these problems by the introduction of dynamic multimodal communication and graph-based task management.
  • Pozycja
    AloneKnight – Enabling Affective Interaction within Mobile Video Games
    (Wydawnictwo Politechniki Łódzkiej, 2023) Jemioło, Paweł; Świder, Krzysztof; Storman, Dawid; Adrian, Weronika T.
    Artificial intelligence is used in various contexts, including video games, where it can enhance the game design and adapt content to players’ emotional states through affective computing. In this paper, we present an example of an affective mobile game and compare participants’ opinions after playing two versions of the game, with and without an affective loop. The game was developed using Unity. In the affective version, physiological data is recorded and analysed to detect emotions based on facial expressions and electrodermal activity, which then affects the game. The study with 11 participants showed positive feedback for the game with affective loop.
  • Pozycja
    Predictive User Interface for Emerging Experiences
    (Wydawnictwo Politechniki Łódzkiej, 2023) Kapusta, Paweł; Duch, Piotr
    This research paper focuses on the use of predictive techniques to improve interaction with user interfaces in emerging experiences such as Virtual Reality, Augmented Reality, Metaverse, and touchless kiosks and dashboards. We propose the concept of intelligent snapping, which uses gaze tracking, head-pose tracking, hand tracking, as well as gesture recognition and hand posture recognition to catch the intent of the person rather than the actual input.
  • Pozycja
    Are Quantified Boolean Formulas Hard for Reason-Able Embeddings?
    (Wydawnictwo Politechniki Łódzkiej, 2023) Potoniec, Jędrzej
    We aim to establish theoretical boundaries for the applicability of reason-able embeddings, a recently proposed method employing a transferable neural reasoner to shape a latent space of knowledge graph embeddings. Since reason-able embeddings rely on the ALC description logic, we construct a dataset of the hardest concepts in ALC by translating quantified boolean formulas (QBF) from QBFLIB, a benchmark for QBF solvers. We experimentally show the dataset is hard for a symbolic reasoner FaCT++, and analyze the results of reasoning with reason-able embeddings, concluding that the dataset is too hard for them.
  • Pozycja
    Increasing Skin Lesions Classification Rates using Convolutional Neural Networks with Invariant Dataset Augmentation and the Three-Point Checklist of Dermoscopy
    (Wydawnictwo Politechniki Łódzkiej, 2023) Milczarski, Piotr; Borowski, Norbert; Beczkowski, Michał
    In the paper, we show how to tackle the problem of lack of the rotation invariance in CNN networks using the authors’ Invariant Dataset Augmentation (IDA) method. The IDA method allows to increase the classification rates taking into account as an example the classification of the skin lesions using a small image set. In order to solve the problem of the lack of rotation invariance, IDA method was used and the dataset was increased in an eightfold and invariant way. In the research, we applied the IDA methods and compared the results of VGG19, XN and Inception-ResNetv2 CNN networks in three skin lesions features classification defined by wellknown dermoscopic criterions e.g. the Three-Point Checklist of Dermoscopy or the Seven-Point Checklist. Due to Invariant Dataset Augmentation, the classification rate parameters like true positive rate by almost 20%, false positive rate as well as the F1 score and Matthews correlation coefficient have been significantly increased opposite to type II error that has significantly decreased. In the paper, the confusion matrix parameters result in: 98-100% accuracy, 98-100% true positive rate, 0.0-2.3% false positive rate, tests F1=0.95 and MCC=0.95. That general approach can provide higher results while using CNN networks in other applications.
  • Pozycja
    Grounded HyperSymbolic Representations Learned through Gradient-Based Optimization
    (Wydawnictwo Politechniki Łódzkiej, 2023) Łuczak, Piotr; Ślot, Krzysztof; Kucharski, Jacek
    Hyperdimensional computing is a novel paradigm, capable of processing complex data structures with simple operations. Its main limitations lie in the conversion of real world data onto hyperdimensional space, which due to lack of a universal translation scheme, oftentimes requires application-specific methods. This work presents a novel method for unsupervised hyperdimensional conversion of arbitrary image data. Additionally, this method is augmented by the ability of creating HyperSymbols, or class prototypes, provided that such class labels are available. The proposed method achieves promising performance on MNIST dataset, both in translating individual samples as well as producing HyperSymbols for downstream classification task.
  • Pozycja
    A Convolutional and Recurrent Neural Network-based Approach for Speech Emotion Recognition
    (Wydawnictwo Politechniki Łódzkiej, 2023) Duch, Piotr; Wiatrowska, Izabela; Kapusta, Paweł
    Speech emotion recognition (SER) is a crucial aspect of humancomputer interaction. In this article, we propose a deep learning approach, using CNN and RNN architectures, for SER using both convolutional and recurrent neural networks. We evaluated the approach on four audio datasets, including CREMA-D, RAVDESS, TESS, and EMOVO. Our experiments tested various feature sets and extraction settings to determine optimal features for SER. Our results demonstrate that the proposed approach achieves high accuracy rates and outperforms state-of-the-art algorithms.