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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Solomatin, Alexander Federal Research Center 'Computer Science and Control' of Ras Department of Mathematical Methods of Regional Programming Moscow Russia

ISBN: (纸本)9798350375718

Instead of the traditional 'system-environment' pair for objects of the regional economy, it is proposed to consider metasystems - these objects together with all interacting objects of the microenvironment. Each object is considered as a point in the multidimensional space of characteristics. Objects are visualized to reflect their development and interaction with other objects. © 2024 IEEE.

关键词： Strategic planning

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Optimal Control in the Problem With Two Gas Fields 17

Optimal Control in the Problem With Two Gas Fields

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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Skiba, Alexander Federal Research Center 'Computer Science and Control' of Ras Department of Mathematical Methods of Regional Programming Moscow Russia

ISBN: (纸本)9798350375718

We explore a continuous aggregated dynamic model for developing two gas fields. The new borehole commissioning rates are the control parameters. Changes in the average flow rate of producing boreholes and current natural gas production are proportional. It is necessary to solve the problem of maximizing discounted accumulated income for two gas fields. We analyze the optimal control problem with an unrestricted right end and a fixed time. The Pontryagin maximum principle is a main tool for solving the problem. The special optimal control mode is of particular interest. We highlight the critical aspects of all possible optimal controls. © 2024 IEEE.

关键词： Gas industry

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The Experience of Training the Future Teachers to the Implementation of E-learning at School in the Context of teaching Practice

The Experience of Training the Future Teachers to the Implem...

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Technology Enhanced Learning in Higher Education (TELE), International Conference on

作者： Sergey Kasyanov Tatyana Klevetova Svetlana Komissarova Anastasiya Maksimova The Department of Computer Science and Methods of Teaching Computer Science) Volgograd State Socio-Pedagogical University Volgograd Russia

The article deals with the approaches to the definition of online education. Its basic components are described. There is considered the experience of the training of the students in the pedagogical universities to th... 详细信息

ISBN: (数字)9798350353532

ISBN: (纸本)9798350353549

关键词： Training Electronic learning Production Online services

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Verification of Genuineness of Educational Internet Resources Using Machine Learning methods 4

Verification of Genuineness of Educational Internet Resource...

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4th International Conference on Technology Enhanced Learning in Higher Education, TELE 2024

作者： Privalov, Aleksandr N. Smirnov, Vadim A. Institute of Advanced Information Technologies Tula State Lev Tolstoy Pedagogical University Tula Russia Ivanovo State University Department of Mathematics Computer Science and Teaching Methods Shuya Russia

ISBN: (纸本)9798350353532

The analysis of various sources has been carried out, confirming the demand for protective solutions against information threats from fake websites of organizations. The relevance of the research topic is confirmed by the presence of addresses similar in spelling to popular educational Internet resources in the list of domain names of the RU zone registered over the past year. When developing security solutions, it is proposed to use a site verifying the genuineness method based on the use of machine learning algorithms. A review of research by various authors has been conducted in order to determine the most frequently used features for verifying the genuineness of Internet resources in the machine learning models. The authors propose their own verifying the genuineness features that can be used in a situation where data about an Internet resource is extracted from the domain name registry. The proposed features differ from the known ones in that they contain signs of micro-markup on the site and storage of site settings files for search engines on servers. The developed features were tested using a naive Bayesian classifier as a machine learning algorithm. The proposed features can be used in the implementation of a software tool to counteraction threats from fake educational sites. © 2024 IEEE.

关键词： Adversarial machine learning

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Evaluating Perceived Workload, Usability and Usefulness of Artificial Intelligence Systems in Low-Resource Settings: Semi-automated Classification and Detection of Community Acquired Pneumonia 3rd

Evaluating Perceived Workload, Usability and Usefulness of...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Muzumala, Malaizyo G. Zulu, Ernest O. Chibuta, Peter Nyirenda, Mayumbo Phiri, Lighton Department of Computer Science University of Zambia Lusaka Zambia Department of Radiology University Teaching Hospitals Lusaka Zambia Department of Library and Information Science University of Zambia Lusaka Zambia

ISBN: (纸本)9783031820069

The use of Artificial Intelligence (AI) techniques in radiological workflows is increasingly becoming mainstream. However, the uptake of AI techniques is still low in low-resource settings such as the Global South. This paper presents a study conducted in a setting with low AI uptake, to determine the impact of AI on Radiologists’ workload when interpreting medical images. Two (2) AI models-a classification model and detection model indicating potential areas of interest-were implemented to facilitate the semi-automated interpretation of medical images for Pneumonia. In addition, a Web-based DICOM Viewer was implemented to interface the AI models. To determine the appropriate model configuration, two (2) experts-a Radiologist and Radiology Resident-participated in a focus group discussion aimed at determining how the AI models could facilitate interpretation processes. A comparative controlled experiment was subsequently conducted with 12 Radiology Residents at a large University teaching Hospital, to assess the impact of AI on the workload and its perceived usefulness. NASA Task Load Index (TLX) and Technology Acceptance Model (TAM) 2 questionnaires were employed to measure the workload and usefulness. The results indicate that the perceived workload is significantly less when using the AI solution, with an overall NASA-TLX score of 1.86. Furthermore, the perceived usefulness of the AI solution is demonstrated through the positive responses for all the eight TAM 2 constructs. This study experimentally demonstrates the potential of utilising AI for the semi-automated interpretation of medical images in low-resource settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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Comparative Analysis of COVID-19 Detection methods Based on Neural Network

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computers, Materials & Continua 2023年第7期76卷 1127-1150页

作者： Inès Hilali-Jaghdam Azhari A Elhag Anis Ben Ishak Bushra M.Elamin Elnaim Omer Eltag Mohammed Elhag Feda Muhammed Abuhaimed S.Abdel-Khalek Computer Sciences and Information Technology Programs Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Mathematics and Statistics College of ScienceTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Quantitative Methods Higher Institute of ManagementUniversity of Tunis1073Tunisia Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam Bin Abdulaziz UniversityAl KharjSaudi Arabia Department of Computer Science Faculty of Science and ArtsKing Khalid UniversityAbha61421Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohagEgypt

In 2019,the novel coronavirus disease 2019(COVID-19)ravaged the *** of July 2021,there are about 192 million infected people worldwide and 4.1365 million *** present,the new coronavirus is still spreading and circulating in many places around the world,especially since the emergence of Delta variant strains has increased the risk of the COVID-19 pandemic *** symptoms of COVID-19 are diverse,and most patients have mild symptoms,with fever,dry cough,and fatigue as the main manifestations,and about 15.7%to 32.0%of patients will develop severe *** are screened in hospitals or primary care clinics as the initial step in the therapy for *** transcription-polymerase chain reaction(PCR)tests are still the primary method for making the final diagnosis,in hospitals today,the election protocol is based on medical imaging because it is quick and easy to use,which enables doctors to diagnose illnesses and their effects more *** to this approach,individuals who are thought to have COVID-19 first undergo an X-ray session and then,if further information is required,a CT-scan *** methodology has led to a significant increase in the use of computed tomography scans(CT scans)and X-ray pictures in the clinic as substitute diagnostic methods for identifying *** provide a significant collection of various datasets and methods used to diagnose COVID-19,this paper provides a comparative study of various state-of-the-art *** impact of medical imaging techniques on COVID-19 is also discussed.

关键词： Neural networks frameworks intelligent systems COVID-19 CT

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A NOTE ON THE QUALITATIVE BEHAVIOUR OF AN ODE MODEL RELATED TO ENDOTOXIN TOLERANCE

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第4期86卷 127-130页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

关键词： Differential equations

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Febrile disease modeling and diagnosis system for optimizing medical decisions in resource-scarce settings

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Clinical eHealth 2024年第1期7卷 52-76页

作者： Asuquo, Daniel Attai, Kingsley Obot, Okure Ekpenyong, Moses Akwaowo, Christie Arnold, Kiirya Uzoka, Faith-Michael Department of Computer Science Faculty of Science University of Uyo Nigeria Department of Mathematics & Computer Science Ritman University Nigeria University of Uyo Teaching Hospital Uyo Nigeria Department of Community Medicine University of Uyo Nigeria Kyambogo University Kampala Uganda Department of Mathematics and Computing Mount Royal University Calgary Canada

Febrile diseases are highly prevalent in tropical regions due to elevated humidity and high temperatures. These regions, mainly comprising low- and middle-income countries, often face challenges related to inadequate medical infrastructure and a lack of skilled personnel for accurately diagnosing febrile diseases. Distinguishing one febrile illness from another posed a significant challenge, adding to the complexity of accurate diagnoses. This study developed a multi-symptom multi-disease model to address this challenge, leveraging exploratory data analysis of patient datasets from field studies and the expertise of medical practitioners specializing in tropical diseases. The research investigated the most effective modeling approach for differentiating among 11 febrile illnesses that are prevalent in Nigeria using three intelligent techniques: Extreme Gradient Boost (XGBoost), Fuzzy Cognitive Map (FCM), and Analytic Hierarchy Process (AHP). Comparative analysis demonstrates that AHP surpassed the others, achieving a precision of 84%, recall of 83%, and an F1-score of 84%. Consequently, the AHP technique was integrated into the development of “Febra Diagnostica,” an app aimed at enhancing febrile disease diagnosis in resource-constrained settings. The app was then deployed and utilized in select Nigerian states, offering scalability and empowering frontline health workers in primary health facilities. Febra Diagnostica featured user-friendly interfaces, automated diagnosis and treatment suggestions, streamlined referrals, and provisions for further investigations. Encryption, access control, and multi-factor authentication were some of the security and privacy considerations in the app which gained acceptance from medical experts and adapted to regulatory and ethical policies for smart healthcare systems. © 2023

关键词： Analytic hierarchy process Febrile diseases Fuzzy cognitive map Intelligent diagnosis and optimization Machine learning

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Unlocking the power of blockchain in education:An overview of innovations and outcomes

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Blockchain(Research and Applications) 2023年第4期4卷 99-117页

作者： Amr El Koshiry Entesar Eliwa Tarek Abd El-Hafeez Mahmoud Y.Shams Department of Curricula and Teaching Methods College of EducationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Faculty of Specific Education Minia UniversityMinia61519Egypt Department of Mathematics and Statistics College of ScienceKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of ScienceMinia UniversityMinia61519Egypt Computer Science Unit Deraya UniversityMinia UniversityMinia61519Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33516Egypt

Blockchain is a revolutionary technology that has the potential to revolutionize various industries,including finance,supply chain management,healthcare,and *** decentralized,secure,and transparent nature makes it ideal for use in industries where trust,security,and efficiency are of paramount *** integration of blockchain technology into the education system has the potential to greatly improve the efficiency,security,and credibility of the educational *** creating secure and transparent platforms for tracking and verifying students’academic achievements,blockchain technology can help to create a more accessible and trustworthy education system,making it easier for students to showcase their skills and knowledge to potential *** the potential benefits of blockchain in education are significant,there are also several challenges that must be addressed in order to fully realize the potential of this technology in the educational *** of the major challenges include adoption,technical knowledge,interoperability,regulation,cost,data privacy and security,scalability,and *** necessary equipment for the implementation of blockchain technology in education is diverse and critical to the success of this innovative *** should carefully consider this equipment when planning their implementation of blockchain technology in education to ensure the efficient and secure transfer of educational data and transactions within the blockchain *** technology has the potential to play a significant role in promoting sustainability education and advancing the sustainability goals of both individuals and *** should consider incorporating blockchain technology into their sustainability education programs,in order to enhance the transparency,verifiability,and efficiency of their sustainability-related *** the use of blockchain technology in education is still i

关键词： Blockchain Education Sustainability Internet of things Machine learning Credential verification

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A Transfer Learning-Based Framework for Enhanced Classification of Perceived Mental Stress Using EEG Spectrograms

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IEEE Access 2025年 13卷 89266-89286页

作者： Khan, Sheharyar Noorani, Sadam Hussain Frnda, Jaroslav Rauf, Usman Arsalan, Aamir Saeed, Sanay Muhammad Umar University of Engineering and Technology Department of Computer Engineering Taxila47050 Pakistan HITEC University Department of Computer Science Taxila47050 Pakistan University of Žilina Faculty of Operation and Economics of Transport and Communications Department of Quantitative Methods and Economic Informatics Žilina01026 Slovakia Fatima Jinnah Women University Department of Software Engineering Rawalpindi46000 Pakistan

Stress is a significant health concern, impacting both physical and mental well-being. Prolonged exposure to stress can lead to numerous physical health issues, including cardiovascular diseases, and a weakened immune system. This study presents a novel methodology for classifying perceived mental stress using electroencephalography (EEG) signals. By utilizing the publicly available Leipzig Study for Mind-Body-Emotion Interactions dataset, we analyze EEG data collected from 53 participants over a 7-minute resting-state duration. Our approach involves transforming EEG signals into spectrograms using the Short-Time Fourier Transform (STFT), resulting in a time-frequency representation of the input signals. We employ transfer learning to fine-tune three pre-trained deep neural networks i.e., ResNet50, EfficientNetB0, and DenseNet121 for classifying stress into two and three levels. Our findings demonstrate that the ResNet50 model achieves superior classification accuracies of 95.80% and 86.02% for two and three-level stress classification, respectively, outperforming existing state-of-the-art methods. This study is the first to utilize STFT-generated spectrograms and transfer learning for perceived stress classification, highlighting the efficacy of deep learning techniques in quantifying perceived mental stress through non-invasive EEG recordings. Our results indicate that the proposed method can significantly enhance the accuracy of stress classification frameworks, offering potential improvements in mental health assessment and intervention strategies. © 2013 IEEE.

关键词： Noninvasive medical procedures

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