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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Lygdenov, Sodnom ITMO University Faculty of Software Engineering and Computer Engineering Saint Petersburg Russia

ISBN: (纸本)9798350395044

The subject of research. Analysis of the impact of priority data transmission in high-load multi-channel systems represented as a mass service system with request prioritization under different variations in the distribution of random variables of packet arrival intensity in the network and service time in the channels. Method. The study of the influence of priorities on the efficiency of data transmission in multi-channel systems was carried out using simulation and mathematical statistics methods. Main results. The method of numerical calculation of time parameters based on the simulation model of multi-channel data transmission system with heterogeneous load is proposed. Simulation experiments evaluating the impact of priority data transmission at different coefficients of variation of packet intensity and service time in the channels on the time characteristics of the multi-channel system are conducted. Practical significance. The obtained results can be applied to the design of multi-channel computer systems of high load data transmission, providing the optimal use of priorities to improve the efficiency of data transmission. © 2024 IEEE.

关键词： Random variables

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Efficiency in Multi-Channel Data Transmission Systems with Priority Service Under High Load

Efficiency in Multi-Channel Data Transmission Systems with P...

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2024 International Russian Automation Conference, RusAutoCon 2024

作者： Lygdenov, Sodnom Itmo University Faculty of Software Engineering and Computer Engineering Saint Petersburg Russia

The subject of research. Analysis of the influence of priority service in multi-channel data transmission systems characterized by limited storage capacity and high load, taking into account the non-stationary characteristics of the intensity of packet arrivals and service time. Method. The functional characteristics of the multi-channel system were calculated and analyzed using simulation methods and mathematical statistics. Main results. A simulation model of a multi-channel system with priority service has been developed, designed to evaluate the functional characteristics of the system under high load. Experiments have been conducted to study the impact of priority service on delays and packet losses of heterogeneous traffic. Practical significance. The results obtained can be used in the design of multi-threaded data transmission systems operating under conditions of high heterogeneous load. © 2024 IEEE.

关键词： data transmission networks limited-capacity storage devices multi-channel systems network efficiency priority servicing of packets queuing systems simulation experiments

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Application of VGIS Digital Twin Platform for Enhanced Oilfield Disaster Management and Environmental Protection

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第14期48卷 27-42页

作者： Alabbasi, Awab Aljabery, Maalim A. Bölük, Pınar Sarısaray Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept. Bahcesehir University Istanbul34353 Turkey Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah61004 Iraq Faculty of Engineering and Natural Sciences Software Engineering Dept. Bahçeşehir University Istanbul34353 Turkey

VGIS (Virtual Geographic Information System) Platform is a unified oilfield operations management platform based on MaaS (Management as a Service) that integrates advanced technologies such as AIoT (Artificial Intelligence & Internet of Things), GIS (Geographic Information System), Digital Twin, and AR (Augmented Reality)/ VR (Virtual Reality). The VGIS platform connects, collects, integrates, analyses, and acts on disparate data sources to provide the oilfield with a centralized view to help make informed decisions fast, bring operational visibility across all assets to improve safety and operational efficiency, reduce costs and improve margins, provide comprehensive analytics and decision support, increase business agility, and reduce management complexity. This is a case study of the application of the VGIS platform Majnoon oilfield, Basrah, Iraq. VGIS digital transformation, production process, and KPIs (Key performance indicators) are highly managed taking oilfield management to a higher level globally, in this study, the objective is to address and resolve the issue of high costs associated with using cloud services by utilising a local Data Center (a large group of networked computer servers typically used by organizations for the remote storage, processing, or distribution of large amounts of data). This approach involves directly uploading 3D maps to ensure faster service on the intranet, eliminating the need to pay for cloud *** aims providing a platform for researching and implementing monitoring and controlling processes in a more convenient and remote manner. The vGIS platform has emerged as a crucial instrument in the realm of disaster response and crisis management. This technology enables responders to rapidly evaluate, analyse, and respond to crucial information in real-time. This research examined the application of vGIS technology in disaster response and analyse the potential advantages and obstacles associated with its utilisation. © 2

关键词： Information management

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A Trustworthy Analysis Approach for Chatbots on Health Data: ChatGPT-4 Example 29

A Trustworthy Analysis Approach for Chatbots on Health Data:...

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29th International Conference on Information Technology, IT 2025

作者： Arslanoǧlu, Kübra Karaköse, Mehmet The University of Firat The Faculty of Engineering The Department of Software Engineering Elazig Turkey The University of Firat The Faculty of Engineering The Department of Computer Engineering Elazig Turkey

ISBN: (纸本)9798331517649

Considering the increasing and widespread use of chatbots, it is of great importance to provide methods and tools to address ethical concerns and to make users aware of various aspects of a chatbot, including non-functional features. The Generative Pre-Trained Transformer (GPT) is a state-of-the-art Natural Language Generation (NLG) model, enhanced by the latest version of GPT-4. This study evaluates the trustworthiness of the responses of three different chatbot models, BlenderBot with Hugging Face, ChatGPT-3.5 and ChatGPT-4, to 100 randomly selected medical questions. The accuracy and semantic similarity of the answers were measured by BLEU, ROUGE-1 and Cosine Similarity metrics, and response times were also recorded as an important performance factor. The results showed that GPT-4 exhibited superior performance, thus being able to produce more accurate and contextually reliable responses. However, the significantly longer response time of GPT-4 emerged as a disadvantage that may affect real-time utilisation. These findings provide an important reference for the effective use of GPT-4 in the context of health chatbots. The study contributes to the literature on improving the effectiveness of chatbots in healthcare by drawing attention to the importance of balancing speed, accuracy and trustworthiness, as well as the fact that the answers obtained are drawn with Application Programming Interface (API). © 2025 IEEE.

关键词： Electronic health record

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Application of an Agent-Based Model to Develop Ontological Data Visual Management Tool

Scientific Visualization

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Scientific Visualization 2023年第5期15卷 73-88页

作者： Razdyakonov, D.S. Muromcev, D.I. Faculty of Software Engineering and Computer Technologies ITMO University St. Petersburg Russia

The paper presents an agent-based model of interactive visualization and proposes a method for its application in the development of the ontological data visual management tool. The process of user interaction with the visualization is represented as a state graph, where each node is a separate visualization. The initial state is known, and the final state is formed in the process of user interaction with the ontological data visual management tool. Each intermediate visualization from the state graph is transferred into the multidimensional Euclidean data space formed on the basis of the visualized ontology, which allows to calculate weighting coefficients on the state graph edges and to search the graph using known algorithms. Application of the model allows for a reduction in the labor intensity of executing user scripts due to the reduction of step-by-step visualization creation to the task of searching in the state space. © 2023 National Research Nuclear University. All rights reserved.

关键词： Visualization

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ACOCA-G: scenario-based context query generation for evaluating performance of context management platforms

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Discover Internet of Things 2025年第1期5卷 1-28页

作者： De Silva, Ravindi Zaslavsky, Arkady Loke, Seng W. Jayaraman, Prem Prakash Faculty of Information Technology Deakin University Melbourne Australia Department of Computer Science and Software Engineering Swinburne University Melbourne Australia

Context-awareness is a pivotal trend within the Internet of Things research area, facilitating the near real-time processing and interpretation of relevant sensor data to enhance data processing efficiency. Context Management Platforms (CMPs), as advanced IoT middleware platforms offer promising solutions to IoT applications by offering seamless interoperability between different IoT silos. However, significant gaps still remain in evaluating the performance of CMPs, especially the data retrieval aspect of CMPs. Existing IoT middleware evaluation approaches lack flexible, user-friendly approaches to generate query loads to address aforementioned limitation. This paper introduces ACOCA-G (Adaptive COntext CAching-Generator), a configurable automated framework designed to generate scenario-based context queries to evaluate data retrieval of CMPs. ACOCA-G transforms real-world scenarios into scene descriptions in a specified format, which are then converted into Context-aware Scene Graphs (CSGs). ACOCA-G framework introduces a novel situation reasoning approach that integrates concepts of Context Spaces Theory (CST) and enhanced Situation State Machines (SSM). CSG reasoning identifies situations, facilitating the generation of dynamic and context-specific query loads. The framework produces template-based queries of varied complexities, demonstrating ACOCA-G’s flexibility and adaptability in generating queries closely aligning with real-world query complexities. Empirical evaluations using real-world IoT datasets and scenes of diverse complexities assess ACOCA-G in terms of query completeness, volume, and generation efficiency, providing insights into accuracy, scalability, and effectiveness. As a pioneering solution for systematically evaluating CMP performance, ACOCA-G serves as a robust tool for addressing gaps in CMP data retrieval assessment. © The Author(s) 2025.

关键词： Middleware

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Application of Artificial Intelligence Methods for Generating Mathematical Problems in Primary Education: Analysis of Effectiveness and Prospects for Automated Assessment 7

Application of Artificial Intelligence Methods for Generatin...

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7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Salavatovich, Nazipov Rustam Yurievna, Avksentieva Elena ITMO University Faculty of Software Engineering and Computer System Saint Petersburg Russia ITMO University Faculty of Software Engineering and Computer System Saint-Petersburg Russia

ISBN: (纸本)9798350354973

The present study aims to evaluate the effectiveness of using modern neural network models (ChatGPT 4o, Claude 3.5 Sonnet, GigaChat 3.0, YandexGPT 3) for generating mathematical problems for elementary school students. The research conducts a comparative analysis of the quality of generated problems, assesses the possibility of their direct use without expert review, and develops recommendations for automating the quality control process. The methodology includes the generation of various types of mathematical problems, their expert evaluation according to developed criteria, and statistical analysis of the results. The study identifies the strengths and weaknesses of each model, determines the types of problems in which the models demonstrate the best results, and suggests directions for further improvement of AI systems in the educational context. © 2024 IEEE.

关键词： Neural network models

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An Improved Farmland Fertility Algorithm with Hyper-Heuristic Approach for Solving Travelling Salesman Problem

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computer Modeling in engineering & Sciences 2023年第6期135卷 1981-2006页

作者： Farhad Soleimanian Gharehchopogh Benyamin Abdollahzadeh Bahman Arasteh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmiaIran Department of Software Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

Travelling Salesman Problem(TSP)is a discrete hybrid optimization problem considered *** aims to discover the shortest Hamilton route that visits each city precisely once and then returns to the starting point,making it the shortest route *** paper employed a Farmland Fertility Algorithm(FFA)inspired by agricultural land fertility and a hyper-heuristic technique based on the Modified Choice Function(MCF).The neighborhood search operator can use this strategy to automatically select the best heuristic method formaking the best ***-Kernighan(LK)local search has been incorporated to increase the efficiency and performance of this suggested approach.71 TSPLIB datasets have been compared with different algorithms to prove the proposed algorithm’s performance and *** results indicated that the proposed algorithm outperforms comparable methods of average mean computation time,average percentage deviation(PDav),and tour length.

关键词： Travelling salesman problem optimization farmland fertility optimization algorithm Lin-Kernighan

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Fingerprint Fraud Explainability Using Grad-Cam for Forensic Procedures 23

Fingerprint Fraud Explainability Using Grad-Cam for Forensic...

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23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Manai, Elyes Mejri, Mohamed Fattahi, Jaouhar Department of Computer & Software Engineering Faculty of Science and Engineering Laval University Quebec Canada

ISBN: (纸本)9781643685380

This paper investigates the application of GradCAM, an explainable AI (XAI) technique, to enhance the transparency and precision of fingerprint authentication systems in forensics, particularly in detecting fingerprint mutilation-a common method used to evade biometric security measures. Employing the SOCOfing dataset, which contains both unaltered and synthetically altered fingerprint images, we apply GradCAM to visualize and understand the decision-making process of a convolutional neural network (CNN) model trained to recognize and classify these alterations. Our study not only demonstrates the model's effectiveness in identifying different types of fingerprint modifications but also identifies areas where the model's performance can be enhanced. Through detailed visual analysis, we uncover the model's focus points and assess its reliability across various alteration types and difficulty levels. The insights gained underline the potential of XAI in improving the robustness and reliability of biometric verification systems, paving the way for more secure and equitable AI applications in high-stakes environments. © 2024 IOS Press. All rights reserved.

关键词： Convolutional neural networks

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An Overview for Trustworthy and Explainable Artificial Intelligence in Healthcare 28

An Overview for Trustworthy and Explainable Artificial Intel...

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2024 28th International Conference on Information Technology, IT 2024

作者： Arslanoglu, Kubra Karakose, Mehmet The University of Firat The Faculty of Engineering Department of Software Engineering Elazig Turkey The University of Firat The Faculty of Engineering Department of Computer Engineering Elazig Turkey

ISBN: (纸本)9798350369618

Recently, the increased use of artificial intelligence in healthcare has significantly changed the developments in the field of medicine. Medical centres have adopted AI applications and used it in many applications to predict disease diagnosis and reduce health risks in a predetermined way. In addition to Artificial Intelligence (AI) techniques for processing data and understanding the results of this data, Explainable Artificial Intelligence (XAI) techniques have also gained an important place in the healthcare sector. In this study, reliable and explainable artificial intelligence studies in the field of healthcare were investigated and the blockchain framework, one of the latest technologies in the field of reliability, was examined. Many researchers have used blockchain technology in the healthcare industry to exchange information between laboratories, hospitals, pharmacies, and doctors and to protect patient data. In our study, firstly, the studies whose keywords were XAI and Trustworthy Artificial Intelligence were examined, and then, among these studies, priority was given to current articles using Blockchain technology. Combining the existing methods and results of previous studies and organizing these studies, our study presented a general framework obtained from the reviewed articles. Obtaining this framework from current studies will be beneficial for future studies of both academics and scientists. © 2024 IEEE.

关键词： Blockchain

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