COMPUTER SCIENCE (CN-csci)

Permanent URI for this communityhttps://repo.agh.edu.pl/handle/AGH/102745

Adres wydawniczy: Kraków : Wydawnictwa AGH, 2004-
O czasopiśmie: http://csci.agh.edu.pl/
ISSN: 1508-2806 e-ISSN: 2300-7036
DOI: http://dx.doi.org/10.7494/csci

The main areas of interest of the journal are theoretical aspects of computer science, soft computing, HPC, cloud and distributed processing and simulation, multimedia systems and computer graphics, and natural language processing.

New! Aktualny numer: 2025 - Vol. 26 - No. 3

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Now showing 1 - 10 of 547

Access status: Open Access ,
Developing explainable machine-learning model using augmented concept activation vector
(Wydawnictwa AGH, 2025) Hassanpour, Reza; Oztoprak, Kasim; Netten, Niels; Busker, Tony; Bargh, Mortaza S.; Choenni, Sunil; Kizildag, Beyza; Kilinc, Leyla Sena
Machine-learning models use high-dimensional feature spaces to map their inputs to the corresponding class labels; however, these features often do not have a one-to-one correspondence with the physical concepts that are understandable by humans. This hinders the ability to provide meaningful explanations for the decisions that are made by these models. We propose a method for measuring the correlation between high-level concepts and the decisions that are made by machine-learning models. Our method can isolate the impact of a given high-level concept and accurately measure it quantitatively. Additionally, this study aims to determine the prevalence of frequent patterns in machine-learning models that often occur in imbalanced data sets. We successfully applied the proposed method to fundus images and managed to quantitatively measure the impacts of the radiomic patterns on the model’s decisions.
Access status: Open Access ,
Attention-based multiple-representation method for fingerprint-presentation-attack detection
(Wydawnictwa AGH, 2025) Uttam, Atul Kumar; Agrawal, Rohit; Jalal, Anand Singh
Fingerprint biometrics are one of the most common authentication mechanisms; however, such systems are often compromised by presentation attacks that are made by presentation-attack instruments. Most fingerprint-presentationattack- detection approaches show poor performance due to the large variations in the presentation-attack instruments and the limited feature representation of the input fingerprint. Therefore, this article proposes a hybrid model of shallow and deep features with multiple representations of input fingerprints. To obtain these shallow and deep features, we first enhanced the texture of the input fingerprint through a novel median adaptive local binary pattern filter and an existing binarized statistical image feature. After this, the input fingerprint image and two textured enhanced images are concatenated along with the channel dimension for multiple representations. Finally, an extended ResNeXt architecture with channel and spatial attention (EResNeXt) was used for relevant feature extraction and presentation attack detection. EResNeXt was evaluated in the LivDet-2015 and LivDet-2017 data sets, and ACEs (average classification errors) were obtained at 0.94 and 0.49, respectively.
Access status: Open Access ,
Anaphora solvED Ad-DL-BERT model for text summarization with auto encoding using the topic description and several priors (ATDS) approach
(Wydawnictwa AGH, 2025) Upadhyay, Sunil; Soni, Hemant Kumar
Although several models for automatic text summarization exist, there are still limitations – like the anaphora problem that occurs during summarizations. To overcome such limitations, this paper proposes the Added dropout- Deleted Layer norm-Bidirectional Encoder Representations from Transformers (Ad-DL-BERT)-based extractive text summarization (ETS). Primarily, the input document’s sentences are prepared for accurate summarization by preprocessing; then, the unwanted sentences are removed. With the Auto encoding using the Topic Description and Several priors (ATDS) approach, any sentences under the same topic are clustered afterwards. Moreover, keywords for summarization are extracted with an AnaphoraPOS (An-POS) extractor. For removing the redundant sentences, the rankings with Exponential Linear Unit- Generative Adversarial Network (ELU-GAN) and saliency score assignment processes are performed thereafter. Also, assignments for sentences are performed to enhance the coherency, sorting, and cosine-similarity score. Lastly, the Ad-DL-BERT-generated summary and the proposed technique’s performance are evaluated on the document understanding conference (DUC2002) data set. Regarding the clustering time, execution time, recall-oriented understudy for the gisting evaluation (ROUGE-1) scores of recall, F-measure, and precision, the experimental outcomes exhibited the proposed technique’s dominance over the conventional approaches.
Access status: Open Access ,
Optimized lossless audio compression using DCT energy thresholding and machine learning technique
(Wydawnictwa AGH, 2025) Debnath, Asish; Mondal, Uttam Kr.
This paper proposes a novel lossless audio compression technique, utilizing the Discrete Cosine Transform (DCT) coefficient-controlled technique based on energy thresholding, an XOR-based neural network compression model, and a CNN model. Initially, the DCT is applied to the input audio signal to achieve better energy compaction, followed by transforming selected DCT coefficients into a compressed binary stream. Subsequently, this binary stream is passed to two prediction-based optimized models: an XOR model and a CNN model for further compression.The binary stream is divided into two equal pieces, the data and the key. The XOR neural network model processes the data and key to produce an compressed XORed binary stream. Using a proposed CNN architecture, this stream is further compressed with latent space representations to produce compressed audio data. The simulation findings are analyzed using various statistical and robustness measures and compared with existing approaches.
Access status: Open Access ,
GDPKG-LLM: integrating gene, disease, and pharmacogenomics knowledge graphs for cognitive neuroscience using large language models
(Wydawnictwa AGH, 2025) Sarabadani, Ali; Fard, Kheirolah Rahsepar; Dalvand, Hamid
Using the structures of large language models (LLMs) in creating knowledge graphs to understand more about the relationship between the entities of cognitive and biological sciences has become a hot research topic. Due to the great knowledge behind the curtain and the deep connections of this research, it is not possible to use the traditional approaches of machine learning and deep learning. In this study,the main goal is to create a comprehensive and integrated knowledge graph(KG) from the combination of three knowledge sources: Gene Ontology (GO), Disease Ontology (DO), and PharmKG. Large Language Models (LLMs) have been used to create this knowledge base. The main purpose of this KG is to understand the relationships between genes, diseases, and drugs. The proposed approach, GDPKG-LLM, has several key steps, including entity matching, similarity analysis, graph alignment, and using GPT-4. GDPKG-LLM was able to extract more than 16,800 nodes and 838,000 edges from these three knowledge bases and provide a rich KG. This graph provides meaningful relationships, making it a valuable resource for future research in personalized medicine and neuroscience. The reviewed evaluation criteria show the superiority of GDPKG-LLM, which strengthens the validity of this model.
Access status: Open Access ,
Modified honey bee algorithm with random selection of virtual machines for dynamic load balancing
(Wydawnictwa AGH, 2025) Sharmah, Daisy; Bora, Kanak Chandra; Khakhlari, Junumoni
Cloud workloads can overwhelm load balancers, leading to inefficiencies and performance issues. To address these challenges, the honey bee load-balancing algorithm is highly effective in enhancing cloud-resource allocation. Inspired by the foraging behavior of honey bees, this algorithm offers a dynamic approach to resource distribution that adapts to changing workloads in real time. This paper delves into the key features and advantages of honey bee load-balancing, focusing on its dynamic resource allocation, overall response time, and data center processing time. Through a comparative study of existing methodologies, we proposed a modified honey bee load-balancing algorithm that incorporated the random selections of virtual machines. Utilizing the CloudAnalyst tool for simulation, we compares traditional and proposed honey bee load-balancing algorithms to evaluate overall response times and data center processing times using user bases, data centers, virtual machine load balancers, time, service broker policies, and regions. The proposed algorithm demonstrates superior performance in these parameters as compared to the traditional approach when using the same metric values for both algorithms.
Computer Science
2025 - Vol. 26 - No. 3
Access status: Open Access ,
Detection and forecasting of Parkinson disease progression from speech signal features using multi-layer perceptron and LSTM
(Wydawnictwa AGH, 2025) Majid, Ali; Hina, Shakir; Asia, Samreen; Sohaib, Ahmed
Accurate diagnosis of Parkinson′s disease, especially in its early stages, can be a challenging task. The application of machine learning (ML) techniques has helped improve the diagnostic accuracy of Parkinson′s disease (PD) detection but integration of diagnostic features in ML models for the prediction of disease progression has remained an unexplored research avenue. In this research work, Long Short Term Memory (LSTM) was trained using diagnostic features on Parkinson patients speech signals, to predict the disease progression while a Multilayer Perceptron (MLP) was trained on the same diagnostic features to detect PD. Diagnostic features were selected using two well known feature selection methods named Relief F and Sequential Forward Selection method. The integration of feature selection methods in LSTM model has resulted in PD progression forecast with an accuracy of 88.7%. Furthermore, with the application of input diagnostic features on MLP, PD stage was accurately detected with an accuracy of 98.63%, precision of 97.64% and recall of 98.8% showing model robustness and efficiency for its potential application in health care.
Access status: Open Access ,
Information – modern theories
(Wydawnictwa AGH, 2025) Krzanowski, Roman
This review deviates from the usual approach to the topic of information by not focusing on Shannon’s Theory of Communication (TOC) and the related or derived concepts. In addition, we do not talk at length about information in relation to knowledge, data, communication, information processing, or similar concepts. Instead, we endeavor to reappraise our understanding of information without favoring any specific perspective. We know a lot about information, and the various conceptualizations of information presented in this paper are proof of this. Nevertheless, we also show that some lingering unresolved questions remain about the nature of information. To somewhat stem the appearance of further new concepts of information, we consider two perspectives, namely ontological and epistemic, and posit that we can potentially reduce all information variants to just these concepts. We then look at two general theories of information: the General Definition of Information (GDI) and the General Theory of Information (GTI), arguing that the GTI appears to be the better of these two options because it is more fundamental and comprehensive with deep metaphysical roots. Finally, we review some recent studies about information’s physical nature, such as for information and mass, meaningful physical information, and the persistence of information. This review, like all reviews, is selective and synthetic, but the extensive reference list provides the necessary resources to explore the discussed ideas in greater detail, as well as study the recent works on the nature of information.
Computer Science
2025 - Vol. 26 - No. 2

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