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Lecture Notes in Networks and Systems 2024年 1076 LNNS卷 v-vii页

作者： Biele, Cezary Kacprzyk, Janusz Kopeć, Wieslaw Możaryn, Jakub Owsiński, Jan W. Romanowski, Andrzej Sikorski, Marcin National Information Processing Institute Warszaw Poland Polish Academy of Sciences Systems Research Institute Warsaw Poland Polish-Japanese Academy of Information Technology Warsaw Poland Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland Institute of Applied Computer Science Faculty of Electrical Electronic Computer and Control Engineering Lódż University of Technology LODZ Poland Department of Applied Informatics in Management Faculty of Management and Economics Gdańsk University of Technology Gdańsk Poland

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Situational management of production systems based on a balanced scorecard

Situational management of production systems based on a bala...

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2020 International Conference engineering and Telecommunication, En and T 2020

作者： Khanova, Anna Protalinsky, Oleg Bondareva, Irina Khanova, Yulya Astrakhan State Technical University Dept. Applied Informatics Astrakhan Russia Moscow Power Engineering Institute Dept. of Automated Control Systems of Thermal Processes Moscow Russia Saint-Petersburg Electrotechnical University 'Leti' Dept. of Mathematical Support and Computer Application St. Petersburg Russia

ISBN: (纸本)9781728188294

This paper describes the process of forming and selecting options for managerial decisions, including determining the problem situation from a set of indicators of a balanced scorecard and the presence or absence of similar situations in a set of typical situations. The structure of the management procedure is formalized, which includes the following blocks: preprocessing, state evaluation, managerial decision issuing, decision making. A situational model is built. The procedure for issuing managerial decisions is described, including the correction of a set of managerial decisions in terms of the composition and parameters, classification and parameterization of managerial decisions. This scorecard combines decision-making principles in fuzzy representations (assessing a problem situation) and in structured problem situations (issuing managerial decisions). The risk of not meeting the goals of the balanced scorecard decreased by an average of 76% and averaged 24% after implementing the recommended management decisions on the simulation model of the production systems. © 2020 IEEE.

关键词： Decision making

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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Ethical Considerations for Participatory Health through Social Media: Healthcare Workforce and Policy Maker Perspectives

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Yearbook of Medical informatics 2020年第1期29卷 71-76页

作者： Rivera-Romero, Octavio Konstantinidis, Stathis Denecke, Kerstin Gabarrón, Elia Petersen, Carolyn Househ, Mowafa Merolli, Mark Mayer, Miguel Ángel Computer Engineering School Universidad de Sevilla Seville Spain University of Nottingham Nottingham United Kingdom Bern University of Applied Sciences Bern Switzerland Norwegian Centre of E-Health Research University Hospital North Norway Norway Division of Biomedical Statistics and Informatics Mayo Clinic MN Rochester United States College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Health and Biomedical Informatics Centre Melbourne Medical School University of Melbourne Melbourne Australia Research Programme on Biomedical Informatics Hospital del Mar Medical Research Institute Universitat Pompeu Fabra Barcelona Spain

Objectives: To identify the different ethical issues that should be considered in participatory health through social media from different stakeholder perspectives (i.e., patients/service users, health professionals, health information technology (IT) professionals, and policy makers) in any healthcare context. Methods: We implemented a two-round survey composed of open ended questions in the first round, aggregated into a list of ethical issues rated for importance by participants in the second round, to generate a ranked list of possible ethical issues in participatory health based on healthcare professionals’ and policy makers’ opinions on both their own point of view and their beliefs for other stakeholders’ perspectives. Results: Twenty-six individuals responded in the first round of the survey. Multiple ethical issues were identified for each perspective. Data privacy, data security, and digital literacy were common themes in all perspectives. Thirty-three individuals completed the second round of the survey. Data privacy and data security were ranked among the three most important ethical issues in all perspectives. Quality assurance was the most important issue from the healthcare professionals’ perspective and the second most important issue from the patients’ perspective. Data privacy was the most important consideration for patients/service users. Digital literacy was ranked as the fourth most important issue, except for policy makers’ perspective. Conclusions: Different stakeholders’ opinions fairly agreed that there are common ethical issues that should be considered across the four groups (patients, healthcare professionals, health IT professionals, policy makers) such as data privacy, security, and quality assurance. © 2020 IMIA and Georg Thieme Verlag KG.

关键词： clinical ethics ethical issues patient participation patient-centered care Social media

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Integrating V2X solutions in intelligent green cities: an AI-driven point exchange system approach

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Neural Computing and Applications 2025年

作者： Talaat, Fatma M. Shaban, Warda M. ZainEldin, Hanaa Badawy, Mahmoud Elhosseini, Mostafa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence and informatics Horus University New Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah42353 Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

With rapid urbanization, smart cities have become essential for enhancing urban management and sustainability by integrating technological, social, and institutional innovations. Among these innovations, vehicle-to-everything (V2X) communications and electric vehicles (EVs) play a critical role in reducing carbon emissions and optimizing urban mobility. To address the existing gaps in holistic V2X integration, this paper presents a novel eco-assistive fog-based traffic management system (EAFTMS) that leverages a four-tier architecture (IoT, fog, cloud, and application) for scalable, real-time traffic optimization. A key innovation of this system is the AI-driven point exchange system (PES), designed to incentivize sustainable behaviors such as reducing unnecessary vehicle usage and promoting green lifestyle choices. Unlike conventional models, the proposed framework incorporates real-time behavioral monitoring, rewards-based sustainability programs, and V2X-enabled dynamic traffic control. Empirical validation demonstrates that EAFTMS outperforms existing models, including support vector regression (SVR), achieving a 30% reduction in latency, a 40% improvement in response time, a 25% increase in traffic flow efficiency, and a 35% reduction in CO2 emissions. These results highlight the framework’s potential to set new standards in intelligent green cities by offering scalable, practical, and environmentally impactful solutions to urban transportation challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Urban transportation

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A monotonic optimization approach for solving strictly quasiconvex multiobjective programming problems

A monotonic optimization approach for solving strictly quasi...

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作者： Thang, Tran Ngoc Solanki, Vijender Kumar Dao, Tuan Anh Thi Ngoc Anh, Nguyen Van Hai, Pham School of Applied Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam Department of Computer Science and Engineering CMR Institute of Technology Hyderabad TS India Department of Information Technology Uppsala University Uppsala Sweden School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Viet Nam

In this article, we use a monotonic optimization approach to propose an outcome-space outer approximation by copolyblocks for solving strictly quasiconvex multiobjective programming problems which include many classes of captivating problems, for example when the criterion functions are nonlinear fractional. After the algorithm is terminated, with any given tolerance, an approximation of the weakly efficient solution set is obtained containing the whole weakly efficient solution set of the problem. The algorithm is proved to be convergent and it is suitable to be implemented in parallel using convex programming tools. Some computational experiments are reported to show the accuracy and efficiency of the algorithm. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Multiobjective optimization

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Corrections to “Statistical Insights Into Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey”

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IEEE Access 2024年 12卷 136435-136435页

作者： Issac Neha Margret K. Rajakumar K. V. Arulalan S. Manikandan Valentina E. Balas School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India A. A. Child Care Clinic Vellore Tamil Nadu India DRDO Bengaluru Karnataka India Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad Romania

Presents corrections to the paper, Corrections to “Statistical Insights Into Machine Learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey”.

关键词： Pregnancy

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A predictive surrogate model for heat transfer of an impinging jet on a concave surface

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International Journal of Heat and Mass Transfer 2025年 251卷

作者： Salavatidezfouli, Sajad Rakhsha, Saeed Sheidani, Armin Stabile, Giovanni Rozza, Gianluigi Trieste Italy Department of Electrical and Computer Engineering - Signal Processing and Machine Learning Aarhus University Aarhus Denmark Department of Mechanical Engineering Semnan University Semnan Iran Department of Applied Physics Eindhoven University of Technology Netherlands Department of Pure and Applied Sciences Informatics and Mathematics Section University of Urbino Carlo Bo Urbino Italy The Biorobotics Institute Sant'Anna School of Advanced Studies Pisa Italy

This paper aims to comprehensively investigate the efficacy of model order reduction and deep learning techniques in predicting heat transfer of pulsatile impinging jets on a concave surface. We introduce two predictive approaches: one employing a Fast Fourier Transform-augmented Artificial Neural Network for predicting the area-averaged Nusselt number under constant-frequency jet scenarios, and another comparing the performance of LSTM and Transformer neural networks for random-frequency jets. Results indicate that the Transformer significantly outperforms the LSTM, achieving higher accuracy and robustness in predicting Nusselt numbers — covering up to 50% of the cycle with precision, whereas the LSTM covers only 20% with greater error margins. Additionally, the integration of Proper Orthogonal Decomposition with Transformer networks yields a novel strategy for predicting local Nusselt number distributions. This method significantly reduces computational complexity while maintaining high accuracy, with a maximum prediction error of 5%. These findings demonstrate the efficacy of advanced deep learning techniques for temporal prediction of the Nusselt number on complex surfaces, suggesting further applicability in broader fluid dynamics and heat transfer analyses. © 2025 Elsevier Ltd

关键词： Nusselt number

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Automatic Software-Based Self Test Generation for Embedded Processors

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IFAC-PapersOnLine 2018年第6期51卷 125-130页

作者： Hudec, Ján Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava84216 Slovakia

The paper deals with automatic software-based test generation for processors. Processors are basic blocks of current complex systems on chip and embedded systems. Processors testing can be extended by functional tests or using various application programs. Such types of tests are serving as additional tests to structural testing or as tests used in verification. Functional tests (programs) are generated over an instruction set architecture and processor model description. A metric for quality evaluation of the software-based tests is obviously provided by code coverage of a processor model. A new functional test generation method is based on VHDL model of processors and genetic algorithms with using various evolutionary strategies. The contribution to the SBST methods based on GAs using the latest defined ES was identified. Functionality and effectiveness of the developed methods were evaluated in the implemented system AGenMIX over two types of RISC processor. © 2018

关键词： Software testing Application programs Embedded systems Genetic algorithms Integrated circuit testing Quality control Reduced instruction set computing System on chip Code coverage Evolutionary strategies Fitness functions Functional testing Processor Software based self test

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Surveying Cyber Threat Intelligence and Collaboration: A Concise Analysis of Current Landscape and Trends

Surveying Cyber Threat Intelligence and Collaboration: A Con...

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IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Panagiotis Radoglou-Grammatikis Elisavet Kioseoglou Dimitrios Asimopoulos Miltiadis Siavvas Ioannis Nanos Thomas Lagkas Vasileios Argyriou Konstantinos E. Psannis Sotirios Goudos Panagiotis Sarigiannidis Department of Electrical and Computer Engineering Campus ZEP Kozani University of Western Macedonia Kozani Greece William Gladstone 31 K3Y Ltd Sofia Bulgaria MetaMind Innovations P.C. Kila Kozani Greece Centre for Research and Technology Hellas/Information Technologies Institute Thessaloniki Greece Sidroco Holdings Ltd Nicosia Cyprus Department of Computer Science Kavala Campus International Hellenic University Kavala Greece Department of Networks and Digital Media Kingston University London Surrey UK Department of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece School of Physics Aristotle University of Thessaloniki Thessaloniki Greece

The evolution of cyberattacks has been significantly impacted by the rise of Artificial Intelligence (AI). In particular, AI-driven attacks leverage Machine Learning (ML) and Deep Learning (DL) methods to automate tasks like identifying vulnerabilities, crafting convincing phishing emails, and evading conventional security measures. These cyberattacks can adapt in real time, making them more elusive and challenging to detect. Furthermore, AI has enabled the development of AI-powered malware that can learn and evolve, making it even more dangerous. As AI continues to evolve, both attackers and defenders are engaged in a relentless arms race, with cybersecurity professionals striving to harness AI for threat detection and response while cybercriminals seek to exploit AI’s capabilities for their malicious purposes. This ongoing battle underscores the need for proactive and adaptive cybersecurity strategies to mitigate the evolving threats posed by AI-driven cyberattacks. Based on the aforementioned remarks, it is evident that efficient and adaptable countermeasures are necessary. In this paper, we focus our attention on Cyber Threat Intelligence (CTI) mechanisms. CTI is the process of collecting, analysing, and sharing information about potential cybersecurity threats to help organisations proactively defend against cyberattacks. In particular, after providing an overview of the CTI use cases, a brief analysis of existing solutions follows, highlighting the current trends and directions for future work in this research field.

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