The Computer Science Journal (ISSN: 1508-2806; e-ISSN: 2300-7036) is a quarterly (quaterly in organization, but published on a continuous basis) published by the AGH University of Krakow Poland since 1999. We publish original papers concerning theoretical and applied computer science problems. 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. Please note: we don't have any article processing charges, our journal is non-profit. The journal is indexed by Web of Science and SCOPUS. Starting in 2025 we publish on a continuous basis, speeding up the production process. Please mind that the only possible way to submit the paper to our journal is to use the PDF generated using our Overleaf template! The papers must have BIBTEX-processed bibliography and vector-based graphics wherever possible. Other papers will be desk-rejected. We accept use of AI/LLMS only for correction of the texts, the papers containing generated content will be rejected outright. Web of Science Impact Factor = 0.3 Scopus Quartile = 3 <!-- Our journal is indexed in the following services: Google Scholar, CrossRef metadata search, Directory of Open Access Journals, Open Archives Initiative, Digital Libraries Federation, BazTech, Index Copernicus, Ulrich's Periodicals Directory, EBSCOhost Applied Sciences, DBLP, ERIH PLUS, SCOPUS and Emerging Sources Citation Index - part of Clarivate Web of Science. This is an open access journal in accordance with the BOAIdefinition of open access. The content of the journal is freely available according to the Creative Commons License Attribution 4.0 International (CC BY 4.0) SUBMISSION PAGE DIRECT LINK Please note: We do not apply any Article Processing Charges. Our journal is a fully non-profit endeavour. The journal is Open Access (also free of charges). First Author of the accepted paper receives one complimentary hardcopy. We accept PDF or DOC/DOCX manuscripts for review. For final typesetting we strongly prefer Latex/Bibtex. If the authors of the accepted paper are unable to prepare the paper in Latex it will be translated to Latex - for the cost of likely significant delay in publishing. You may use our Overleaf template to prepare your paper. We review Survey papers - but only if the authors cite in their paper 3 recent papers by themselves, devoted to the area of the survey. We do not accept nor review surveys authored by non-experts. The paper submitted to our journal is expected to be 15-20 pages long. You are free to publish the early version of the paper in Arxiv, Research Gate and similar websites - but the paper should be updated with the final version, after the paper is accepted and published. We speed up the publication process by publishing early birds versions of the paper (with DOI). The submitted paper should follow typical guidelines for scientific publications - see for example this Tutorial by Jennifer Widom. We are using IThenticate to prevent (self)plagiarism. You can check our position at Scimago Journal & Country Rank. -->
AGH University of Krakow, Faculty of Computer Science
CAN ARTIFICIAL INTELLIGENCE PREDICT A TSUNAMI?
n this article, we build a model for tsunami simulation based on physics-informed neural networks and the finite difference method. We then check how the numerical results obtained using these two methods differ from each other. Assuming that the finite difference method gives accurate results, we estimate the error resulting from the use of physics-informed neural networks. We compare this estimate with surveys conducted among computer science students in order to assess the level of public trust among specialists in the numerical results obtained using artificial intelligence tools. In particular, we assess how reliable tsunami predictions obtained using physics-informed neural networks are and what the public perception of the reliability of such predictions is.
Copyright (c) 2025 Computer Science https://creativecommons.org/licenses/by/4.0
BIELIK 7B V0.1: POLISH LANGUAGE MODEL - DEVELOPMENT, INSIGHTS, AND EVALUATION
We introduce Bielik 7B v0.1, a 7-billion-parameter generative text model for Polish language processing. Trained on curated Polish corpora, this model addresses key challenges in language model development through innovative techniques. These include Weighted Instruction Cross-Entropy Loss, which balances the learning of different instruction types, and Adaptive Learning Rate, which dynamically adjusts the learning rate based on training progress. To evaluate performance, we created the Open PL LLM Leaderboard and Polish MT-Bench, novel frameworks assessing various NLP tasks and conversational abilities. Bielik 7B v0.1 demonstrates significant improvements, achieving a 9 percentage point increase in average score compared to Mistral-7B-v0.1 on the RAG Reader task. It also excels in the Polish MT-Bench, particularly in Reasoning (6.15/10) and Role-playing (7.83/10) categories. This model represents a substantial advancement in Polish language AI, offering a powerful tool for diverse linguistic applications and setting new benchmarks in the field.
PREFACE: 2ND INTERNATIONAL WORKSHOP ON MACHINE LEARNING AND QUANTUM COMPUTING APPLICATIONSIN MEDICINE AND PHYSICS
This is a preface for the special issue including extended versions of the selected papers submitted to 2ND INTERNATIONAL WORKSHOP ON MACHINE LEARNING AND QUANTUM COMPUTING APPLICATIONSIN MEDICINE AND PHYSICS.
FL-MEC: FEDERATED LEARNING FOR NETWORK TRAFFIC CLASSIFICATION ON THE NETWORK EDGE
Nowadays, two technological trends, Federated Learning (FL) and Edge Computing (EC), are becoming more and more important and influential. FL is a distributed machine learning strategy that allows learning on distributed data. It primarily allows performing learning operations close to the user, where we can gather data. This approach lies in the EC domain, where the main goal is to move computation closer to the end user (e.g., from the centralized cloud). In our work, we apply the FL and EC in the context of network flow classification. We achieved an accuracy of 0.957 with the FL model, compared to 0.924 for the best local model. We achieved these results thanks to the federated averaging performed on neural network layers. To verify our approach, we executed all our experiments on a virtualized environment that emulates existing mid-scale EC network infrastructure, including limitations related to resource constraints on edge nodes.
PRELIMINARY STUDY ON ARTIFICIAL INTELLIGENCE METHODS FOR CYBERSECURITY THREAT DETECTION INCOMPUTER NETWORKS BASED ON RAWDATA PACKETS
Most of the intrusion detection methods in computer networks are based on traffic flow characteristics. However, this approach may not fully exploit the potential of deep learning algorithms to directly extract features and patterns from raw packets. Moreover, it impedes real-time monitoring due to the neces- sity of waiting for the processing pipeline to complete and introduces depen- dencies on additional software components. In this paper, we investigate deep learning methodologies capable of de- tecting attacks in real-time directly from raw packet data within network traffic. Our investigation utilizes the CIC IDS-2017 dataset, which includes both benign traffic and prevalent real-world attacks, providing a comprehensive foundation for our research.
CHARACTERISTIC SKY BACKGROUND FEATURES AROUND GALAXY MERGERS
In the context of finding galaxy merger in large-scale surveys, we applied Machine Learning algorithms that, instead of using the images as it is the current standard, made used of flux measurements. Training multiple NNs using a class-balanced dataset of mergers and non-mergers Sloan Digital Sky Survey, we found that the sky background error parameters could provide a validation 92.64 ± 0.15 % accuracy of and a training accuracy of 92.36 ± 0.21 %. Moreover, analysing the NN identifications led us to find that a simple decision diagram using the sky error for two flux filters is enough to get a 91.59 % accuracy. By understanding how the galaxies vary along the diagram, and trying to parametrize the methodology in the deeper images of the Hyper Suprime-Cam, we are currently trying to define and generalize this sky error-based methodology.
DEVELOPING ARTIFICIAL INTELLIGENCE IN THE CLOUD: THE AI INFN PLATFORM
The INFN CSN5-funded project AI INFN (“Artificial Intelligence at INFN”) aims to promote ML and AI adoption within INFN by providing comprehensive support, including state of-the-art hardware and cloud-native solutions within INFN Cloud. This facilitates efficient sharing of hardware accelerators without hindering the institute’s diverse research activities. AI INFN advances from a Virtual-Machine-based model to a flexible Kubernetes-based platform, offering features such as JWT-based authentication, JupyterHub multitenant interface, distributed file system, customizable conda environments, and specialized monitoring and accounting systems. It also enables virtual nodes in the cluster, offloading computing payloads to remote resources through the Virtual Kubelet technology, with InterLink as provider. This setup can manage workflows across various providers and hardware types, which is crucial for scientific use cases that require dedicated infrastructures for different parts of the workload. Results of initial tests to validate its production applicability, emerging case studies and integration scenarios are presented.
A NETWORK-BASED COMPUTATIONALPIPELINE TO STUDY THE VARIABILITY OFTRANSCRIPTOME PROFILES FOR HUMANDISEASES
Machine learning applications to high-throughput data in medicine – one of the biggest resource for understanding complex disease – so far have been limited. Here we present a computational approach for assessing the intrinsic variability in the most prominent data type, transcriptomics data for disease cohorts. Our study looks at situations, where multiple datasets for the same disease are available. We leverage concepts of network medicine to assess, how the match between a biological network and a set of differentially expressed genes varies across different networks and experiments. Our results show that different biological networks yield markedly different results. Also, the clustering of diseases depends strongly on the choice of parameters contained in the data analysis and network processing.
AQMLATOR – AN AUTO QUANTUM MACHINE LEARNING E-PLATFORM
A successful Machine Learning (ML) model implementation requires three main components: training dataset, suitable model architecture and training procedure. Given dataset and task, finding an appropriate model might be challenging. AutoML, a branch of ML, focuses on automatic architecture search — a meta method that aims at removing human from ML system design process. The success of ML and the development of quantum computing (QC) in recent years led to a birth of new fascinating field called Quantum Machine Learning (QML) that, amongst others, incorporates quantum computers into ML models. In this paper we present AQMLator, an Auto Quantum Machine Learning platform that aims to automatically propose and train the quantum layers of an ML model with minimal input from the user. This way, data scientists can bypass the entry barrier for QC and use QML. AQMLator uses standard ML libraries, making it easy to introduce into existing ML pipelines.
RECONSTRUCTION OF MUON BUNDLES IN KM3NET DETECTORS USING MACHINE LEARNING METHODS
The KM3NeT Collaboration is installing the ARCA and ORCA neutrino detectors at the bottom of the Mediterranean Sea. The focus of ARCA is neutrino astronomy, while ORCA is optimised for neutrino oscillation studies. Both detectors are already operational in their intermediate states and collect valuable data, including the measurements of the muons produced by cosmic ray interactions in the atmosphere. This work explores the potential of machine learning models for the reconstruction of muon bundles, which are multi-muon events. For this, data collected with intermediate detector configurations of ARCA and ORCA was used in addition to simulated data from the envisaged final configurations of those detectors. Prediction of the total number of muons in a bundle as well as their total energy and even the energy of the primary cosmic ray is presented.
IMPROVING PET SCANNER TIME-OF-FLIGHTRESOLUTION USING ADDITIONAL PROMPT PHOTON
Positronium Imaging requires two classes of events: double-coincidences originated from pair of back-to-back annihilation photons and triple-coincidences comprised with two annihilation photons and one additional prompt photon. The standard reconstruction of the emission position along the line-of-response of triple-coincidence event is the same as in the case of double-coincidence event; an information introduced by the high-energetic prompt photon is ignored. In this study, we propose to extend the reconstruction of position of triple-coincidence event by taking into account the time and position of prompt photon. We incorporate the knowledge about the positronium lifetime distribution and discuss the limitations of the method based on the simulation data. We highlight that the uncertainty of the estimate provided by prompt photon alone is much higher than the standard deviation estimated based on two annihilation photons. We finally demonstrate the extent of resolution improvement that can be obtained when estimated using three photons.
ENHANCING PRIVACY AND ACCURACY IN FEDERATED LEARNING FOR REGRESSION WITH SERVER-SIDE FILTERING TO ADDRESS OUTLIERS
In the contemporary landscape characterized by extensive data proliferation, the amalgamation of information derived from a multitude of devices is imperative for the advanced machine learning models. Nevertheless, the centralization of such data engenders significant apprehensions regarding privacy, particularly when the data is fetched from a heterogeneous array of devices including mobile phones, cameras, sensors, computers, and workstations. Federated Learning proffers a solution to these privacy-related dilemmas by maintaining a decentralized architecture, thereby enabling local devices to preserve their data while concurrently exchanging model parameters. Despite its promise, Federated Learning encounters substantial obstacles concerning data quality, which may arise from inherent biases, the presence of outliers, and the utilization of compromised devices. To mitigate these challenges, we advocate for the implementation of a server-side filtering methodology within Federated Learning, specifically tailored for regression-related problems. Based on this architecture, local devices train the model on their own data sets and then send the learned parameters to a central server. The server is then tasked with the filtration of erroneous contributions, thereby enhancing the overall accuracy of the model. This methodology is substantiated through the application of the Mean Squared Error metric, a widely recognized standard within regression analysis, thereby augmenting both the efficiency and dependability of the learning process while safeguarding user privacy an essential component of Federated Learning.
Copyright (c) 2026 Computer Science https://creativecommons.org/licenses/by/4.0
EXPLAINING ADDITIVE WEIBULL MODEL PARAMETER ESTIMATION WITH XAI: A SHAP AND LIME ANALYSIS
Conventional machine learning models face limitations in conducting time-to-event analyses because of censoring issues. This study introduces a Deep Additive Weibull (DAW) model that utilizes deep learning techniques for the survival analysis of right-censored COVID-19 patient data. Also, we explore several methods for "opening the black box" of the DAW model, including local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP), to enhance model trustworthiness. The DAW model leverages neural networks for survival analysis, specifically to estimate survival probabilities for each patient using an autoencoder-based network. The DAW model achieved a concordance index of 0.9699 for training and 0.92339 for testing. Our findings show that the DAW model effectively captures nonlinearities and complex interactions. We also assessed the impact of specific features on the model's prediction, providing valuable insights. Both SHAP and LIME plots highlight similar features as important, such as pneumonia, diabetes, age and inmsupr, indicating consistent model behavior across different explanation methods. Moreover, we demonstrated that explainable machine learning (ML) can elucidate how models make prediction, which is crucial for increasing trust and adoption of innovative ML techniques in healthcare.
MODIFIED HONEY BEE ALGORITHM WITH RANDOM SELECTION OF VIRTUAL MACHINES FOR DYNAMIC LOAD BALANCING
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, adapting 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 propose a modified Honey Bee Load Balancing algorithm that incorporates the random selection of virtual machines. Utilizing the CloudAnalyst tool for simulation, we compare traditional and proposed Honey Bee Load Balancing algorithms to evaluate overall response time and data center processing time. The proposed algorithm demonstrates superior performance in these parameters compared to the traditional approach.
ENHANCED GNN WITH CAUSAL PROXIMITY VECTORS: BRIDGING CAUSALITY AND PROXIMITY IN GRAPH NEURAL NETWORKS
A knowledge graph is a structured representation of entities and their relationships, often used in biomedical domains to model complex interactions. Graph Neural Networks (GNNs), which utilize these graphs, are effective for predicting interactions missing in the knowledge graph. However, GNN lacks the ability to incorporate causal reasoning, which is crucial in biomedical applications. Additionally, they limit their ability to generalize to unseen data. In oncology, where treatment regimens are intricate and patient responses are highly variable, predicting Adverse Drug Reactions (ADRs) is particularly difficult. Existing models fail to capture the indirect, high-granularity information needed for accurate ADR prediction. To address these challenges, we propose the Causality and Proximity-based Relational Multihead Attention Model (CPRMAM). This model leverages a knowledge graph of ADR-related cancer case studies and introduces a causal proximity vector to prioritize relevant relationships. By employing an inductive GNN approach, CPRMAM generalizes to unseen data, improving ADR prediction.
Special Section - Natural language processing for intelligent modelling
TOWARD RAM FORENSICS SUPPORTEDBY MACHINE-LEARNING METHODS
In this article, we propose an enhancement to the computer forensics technique of using Machine Learning tools to analyse the contents of RAM in order to extract information potentially useful during an investigation. In the specific case presented, the use of the extracted information to generate more optimal dictionaries for dictionary cryptanalysis is considered. Increasing user awareness is making cryptanalysis of passwords increasingly difficult for law enforcement. Long and complex passwords are impossible to crack, even when high-performance computing platforms are available. A sensible method of optimization is to look for hints to use a dictionary that contains text phrases more likely to be used in the specific case under attack. Such a hint could be an analysis of RAM taken from the suspect computer. Machine learning methods can significantly facilitate this task. In this article, we also explore the effectiveness of such an approach and its usefulness in practical applications. We also consider applications of the proposed approach for other purposes, such as OSINT.
MULTI-OBJECTIVE-OPTIMIZATION APPROACH FOR OPTIMAL TASK SCHEDUL-ING THROUGH IN DELAY SENSITIVE CLOUD ENVIRONMENT
Optimizing task scheduling in cloud computing is a major challenge that impacts system performance and resource use. Balancing different workloads, given the limits of the system and user needs, is difficult. Poor management of both underused and overburdened states can lead to problems like high energy use and hardware failures. Therefore, distributing tasks among virtual machines (VMs) is crucial in cloud task scheduling. This work introduces a dynamic load balancing algorithm called CHHO (Cuckoo Harris Hawk multi-objective Optimization). CHHO is a new hybrid method that combines Cuckoo Search Optimization (CSO) and Harris Hawk Optimization (HHO). This combination uses the strengths of both algorithms to address the complex issues of cloud task scheduling. Specifically, CHHO uses Cuckoo Search Optimization to widen the search area of Harris Hawk Optimization, aiming to improve factors such as cost, response time, and resource use. The CHHO algorithm is designed to improve system performance by increasing VM throughput, effectively distributing workloads across VMs, and maintaining a balance among task priorities through dynamic adjustments in task waiting times. To test the performance of CHHO, the algorithm is implemented in the CloudSim simulation environment. It is compared with existing load balancing algorithms on various performance measures. Our simulation results clearly show that CHHO performs better than existing algorithms, providing a strong and efficient solution for load-balancing in cloud computing. Introducing CHHO offers a significant advancement in the field, providing a dynamic and adaptable approach that improves cloud task scheduling and enhances the overall efficiency and effectiveness of cloud computing systems.
THE BENEFITS OF TESTING SOFTWARE IN SE RESEARCH: LESSONS LEARNED FROM TWO PhD PROJECTS
Software engineering (SE) research often involves creating software, either as a primary research output (e.g. in design science research) or as a supporting tool to the traditional research process. Ensuring software quality is essential, as it influences both the research process and the credibility of findings. Integrating software testing methods into SE research can streamline efforts by addressing goals of both research and development processes simultaneously. This paper highlights the advantages of incorporating software testing in SE research, particularly for research evaluation. Through qualitative analysis of software artifacts and insights from two PhD projects, we present ten lessons learned. These experiences demonstrate that, when effectively integrated, software testing offers significant benefits for both the research process and its outcomes.
A DEEP LEARNING DRIVEN TEXT CLASSIFICATION APPROACH WITH NAMED ENTITY RECOGNITION
In natural language processing with text data, which forms the basis of the studies in the field of Artificial Intelligence, various studies such as semantics and natural language generation are carried out, especially the solution of classification problems. This study aims to analyze the effect of detected named entities on text classification performance to make the text preprocessing stage more effective. In order to reduce the analysis time and increase the performance, after the classical preprocessing stage, word filtering was performed with Named Entity Recognition according to the thresholds determined in the 5% and 10% ranges. Analysis was performed with various machine learning, deep learning algorithms, Bidirectional Encoder Representations from Transformers (BERT) and the obtained results are discussed in the last part of the study. In the problem of classifying 50,000 news texts, 93% with Support Vector Machine (SVM) algorithm in statistical classification with machine learning, 87% with Long shortterm memory (LSTM), and 83% with BERT success was achieved. In the analyses performed with LSTM and BERT, although the model performances were numerically lower, it was observed that the semantic integrity was stronger in text classification and that the success increased after Named Entity Recognition (NER) filtering in general. Thus, it can be interpreted that the dataset that is passed through the NER filter according to the threshold values positively affects the model's success in terms of time and performance.
DEVELOPING EXPLAINABLEMACHINE LEARNING MODEL USINGAUGMENTED CONCEPT ACTIVATION VECTOR
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 physical concepts understandable by humans, which hinders the ability to provide a meaningful explanation for the decisions made by these models. We propose a method for measuring the correlation between high-level concepts and the decisions made by a machine learning model. 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, which often occur in imbalanced datasets. We have successfully applied the proposed method to fundus images and managed to quantitatively measure the impact of radiomic patterns on the model’s decisions.
OPTCHAIN: AN ADVANCED OPTIMIZATION METHOD FOR ENHANCING IOT DATA SECURITY VIA BLOCKCHAIN
The increased use of IoT devices in various domains generates abundant data traffic. Securing this data during transfer and storage is essential. Blockchain is now a trending technology to provide security to the data. However, it is observed that blockchain performs poorly while managing large volume data. To mitigate this issue, an advanced Optchain method to reduce the data size before submitting it to the blockchain network is introduced in this paper. This method first classifies data as relevant and irrelevant and then applies compression to the appropriate data to reduce size. This reduces storage requirements, improves processing efficiency, ensures faster transactions, and lowers cost. The proposed method is tested for healthcare IoT data. Compared to current systems, the evaluation findings of the suggested solution show a notable reduction in data size. As data is reduced, it is easily managed by the blockchain network. Thus, the proposed Optchain method provides better security to the IoT-generated data.
PSO-WESRGAN: A NOVEL DOCUMENT IMAGE SUPER RESOLUTION
One of the major challenges of document images that can hinder readability and the analysis of information is low resolution, typically caused by low-pixel density scanning or excessive compression to save storage space. This results in images lacking fine details, making it difficult to recognize important information. Super-resolution techniques are essential to addressing these issues. These techniques enhance image quality by increasing resolution while maintaining fine details. The PSO-WESRGAN is an innovative method, which combines wavelet processing, deep transfer learning, and particle swarm optimization (PSO). Wavelet processing analyzes image detail at diverse scales and orientations, while transfer-based deep learning advantages pre-trained models on vast image datasets. By integrating PSO, the method’s efficiency is enhanced through optimal exploration of the solution space to identify the best parameters for the super-resolution model. Experimental results demonstrate the effectiveness of this approach and pave the way for future advances in document image resolution.
DETECTION AND FORECASTING OF PARKINSON DISEASE PROGRESSION FROM SPEECH SIGNAL FEATURES USING MULTI-LAYER PERCEPTRON AND LSTM
Accurate diagnosis of Parkinson disease, especially in its early stages, can be a challenging task. The application of machine learning techniques help improve the diagnostic accuracy of Parkinson’s disease progression. In this research work, two well-known feature selection methods (Relief-F and Sequential Forward Selection) were employed to identify the diagnostic features of audio signals of Parkinson disease patients and were used to train Multi-Layer Perceptron (MLP) and recurrent neural network Long Short-Term Memory(LSTM) for detection of disease and prediction of its progression. The MLP accurately detected Parkinson disease stages whereas LSTM successfully predicted Parkinson Stage 2 and 3.
A PARALLEL APPROACH FOR METAHEURISTICS SOLVING THE LABS PROBLEM USING CPU AND GPU
The paper contributes to solving the low autocorrelation binary sequence (LABS) problem that remains an open hard-optimization problem with many applications. The current direction of research is focused on developing algorithms dedicated to parallel architectures such as GPGPU or multi-core CPUs. The paper follows this direction and proposes new heuristics developed from the steepest-descent local search algorithm that extends the notion of a neighborhood of a given sequence. The introduced algorithms utilise the parallel nature of multicore CPUs and provide an effective method of solving the LABS problem. The efficiency levels of SDSL and the new algorithm are presented; to ensure an effective comparison, they were both implemented in the same manner. The comparison shows that exploring the larger neighborhood improves the efficiency of the search method.
GDPKG-LLM: INTEGRATING GENE, DISEASE, AND PHARMACOGENOMICS KNOWLEDGE GRAPHS FOR COGNITIVE NEUROSCIENCE USING LARGE LANGUAGE MODELS
Using the structures of large language models (LLMs) in creating knowledgediagrams to understand more about the relationship between the entities ofcognitive and biological sciences has become a hot point of research. Due to thegreat knowledge behind the curtain and the deep connections of this research,it is not possible to use the traditional approaches of machine learning and deeplearning. In this study,the main goal is to create a comprehensive and integratedknowledge graph(KG) from the combination of three knowledge sources: GeneOntology (GO), Disease Ontology (DO), and PharmKG. Large language models(LLMs) have been used to create this knowledge base. The main purpose ofthis KG is to understand the relationships between genes, diseases and drugs.The pro- posed approach was called GDPKG-LLM. It 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 edgesfrom these three knowledge bases and provide a rich KG. This graph providesmeaningful relationships, making it a valuable resource for future research inpersonalized medicine and neuroscience. The reviewed evaluation criteria showthe superiority of GDPKG-LLM, which strengthens the validity of this model.
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.
ANAPHORA SOLVED AD-DL-BERT MODEL FOR TEXT SUMMARIZATION WITH AUTO ENCODING USING THE TOPIC DESCRIPTION AND SEVERAL PRIORS (ATDS) APPROACH
Owing to the large amount of digital text content in articles, novels stories, and so on, Automatic Text Summarization (ATS) is becoming a significant task. Abstractive or extractive summaries of single or multi documents have been generated by various researchers. Although several models were generated, there are still limitations like the anaphora problem that occurred during the summarization. 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 pre-processing; then, the unwanted sentences are removed. Afterward, with the Auto encoding using the Topic Description and Several priors (ATDS) approach, the sentences under the same topic are clustered. Moreover, keywords for summarization are extracted with an Anaphora-POS (An-POS) extractor. Thereafter, for removing the redundant sentences, the ranking with Exponential Linear Unit-Generative Adversarial Network (ELU-GAN) and saliency score assignment processes are performed. 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) dataset. Regarding clustering time, execution time, Recall-Oriented Understudy for Gisting Evaluation (ROUGE-1) scores of recall, F-measure, and precision, the experimental outcomes exhibited the proposed techniques’ dominance over the conventional approaches.
HYBRID FRAMEWORK FOR SENTIMENT ANALYSIS OF PATIENT REVIEWS USING LEXICON BASED BIO-BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS
Sentiment analysis identify and categorize the emotions expressed in reviews and written content posted on websites, utilizing text analysis technologies. Prior research has illustrated how analyzing sentiments in pharmaceutical reviews can offer valuable insights to help organizations and medical professionals assess the safety of medications post-market release. These insights protect patients and bolster their trust in healthcare providers. Currently, frameworks in the healthcare sector use either lexical techniques or machine learning models to analyze opinions. Machine learning-based approaches necessitate labeled data, while syntax-based methods are more specific to domains and have broader applications. To enhance results, this study integrates a hybrid approach that merges lexical strategies with deep learning and machine learning models. Reviews are annotated using two comprehensive emotion lexicons, SenticNet and Text Blob. Feature engineering techniques like TF and TF-IDF are utilized to extract crucial features. Lastly, classification tasks are performed using machine learning models and deep learning models tailored to biological literature. Performance metrics are utilized to evaluate the effectiveness of this combined methodology. Experimental results demonstrate that hybridization of lexicon and transformer based medical learning model produces superior outcomes compared to using each method independently. Additionally, Text Blob exhibits impressive performance, achieving 97% accuracy with hybrid of LSTM and CNN. and the another is medical transformer model is Bio Bert model on a drug review dataset, and 95% accuracy with Term Frequency, and the logistic regression model. TextBlob also attains 94% accuracy when paired with Term Frequency and LSTM model , and 97% accuracy when combined with the Bio Bert transformer based Model on a dataset sourced from tweets.
INTRUSION DETECTION USING FEDERATED LEARNING WITH NEURAL NETWORKS
The amount of information shared amongst different devices and the variety of novel methods of network crimes have exponentially increased in recent years because of the widespread use of the internet. Quick identification of all types of attacks would not be possible with conventional methods including firewalls, which focused on data filtering. Dealing with the timely recognition of these types of assaults is very successful for intrusion detection systems (IDS) grounded on ML algorithms. They can efficiently manage the enormous amount of data in order to identify any harmful behaviour. Every network activity is searched for any possibly dangerous activity using IDS based on machine learning. The main objective of the planned effort is to provide analytical analyses of such current intrusion detection systems. Furthermore, examined in this work are the useful data sets and several techniques already in use to develop an effective IDS using single, hybrid, and ensemble machine learning algorithms. The approaches in the literature have then been investigated under several criteria in line to provide a clear road and direction for the next projects that will be successful. Nowadays, companies of all kinds include an intrusion detection system (IDS), which inhibits cybercrime to protect the network, resources, and private data. Many strategies have been suggested and implemented up till now to prevent uncivil behaviour. Since machine learning (ML) approaches are successful, the proposed approach applied several ML models for the intrusion detection system. The CIC IOT 2023 Dataset is the one applied in this paper. Tested were several techniques including random forest, XG Boost, logistic regression, MLP model, and RNN. Following fine-tuning, the federated learning model using neural networks had the best accuracy—99.84%.
OPTIMIZED LOSSLESS AUDIO COMPRESSION USING DCT ENERGY THRESHOLDING AND MACHINE LEARNING TECHNIQUE
In this paper, a novel lossless audio compression technique has been proposed, 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 first processed by the neural network model for XOR operation, and the resulting output is then fed into a CNN model to reduce data dimensionality and generate compressed audio data. The simulation findings are analyzed using various statistical and robustness measures and compared with existing approaches.