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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare

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Artificial Intelligence-based Service Chains Scheduling for ...

International Conference on Contemporary computing and Informatics (IC3I)

作者： Mukesh Soni Maher Ali Rusho Haewon Byeon Azzah AlGhamdi Shaikh Abdul Hannan Parth Rameshchandra Dave Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune Marashtra Department of Lockheed Martin Engineering Management University of Colorado Boulder Colorado Department of AI and Software & Medical Big Data Research Centre Inje University Gimhae Republic of Korea Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Faculty of Computing and Information AlBaha University AlBaha Kingdom of Saudi Arabia Department of Computer Engineering L. D. College of Engineering Ahmedabad Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare.

关键词： Schedules Job shop scheduling Service function chaining Hospitals Processor scheduling Reinforcement learning Games Emergency services Network function virtualization Standards

Teaching Information Security Management Using an Incident of Intellectual Property Leakage 31

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Teaching Information Security Management Using an Incident o...

31st Australasian Conference on Information systems, ACIS 2019

作者： Ahmad, Atif Maynard, Sean B. Motahhir, Sameen Alshaikh, Moneer School of Computing and Information Systems The University of Melbourne ParkvilleVIC Australia Center for Digital Humanities Department of Languages and Linguistics Information Technology University Lahore Pakistan Department of Cybersecurity College of Computer Science and Engineering University of Jeddah Saudi Arabia

Case-based learning (CBL) is a powerful pedagogical method of creating dialogue between theory and practice. CBL is particularly suited to executive learning as it instigates critical discussion and draws out relevant experiences. In this paper we used a real-world case to teach Information Security Management to students in Management Information systems. The real-world case is described in a legal indictment (T-mobile USA Inc v. Huawei Device USA Inc. and Huawei Technologies Co. LTD) alleging theft of intellectual property (trade secrets) and breaches of contract concerning confidentiality and disclosure of sensitive information. The incident concerns a mobile phone testing robot (Tappy) developed by T-mobile USA to automate testing of mobile phones prior to launch. T-mobile alleges Huawei stole the technology by copying the robot’s specifications and stealing parts and software to develop its own testing robot. The incident scenario is interesting as it relates to a business asset that has both digital and physical components that has been compromised through an unconventional cyber-physical attack facilitated by insiders. The scenario sparked an interesting debate among students about the scope and definition of security incidents, the role and structure of the security unit, the utility of compliance-based approaches to security, and the inadequate use of threat intelligence in modern security strategies. © 2020 Ahmad, Maynard, Motahhir & Alshaikh.

关键词： Cybersecurity

Logic-based Resilience Computation of Power systems Against Frequency Requirements

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arXiv 2025年

作者： Monir, Negar Sadabadi, Mahdieh S. Soudjani, Sadegh School of Computing Newcastle University Newcastle upon Tyne United Kingdom Department of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom Max Planck Institute for Software Systems Germany

Incorporating renewable energy sources into modern power grids has significantly decreased system inertia, which has raised concerns about power system vulnerability to disturbances and frequency instability. The conventional methods for evaluating transient stability by bounding frequency deviations are often conservative and may not accurately reflect real-world conditions and operational constraints. To address this, we propose a framework for assessing the resilience of power systems against disturbances while adhering to realistic operational frequency constraints. Our approach leverages the Lur’e system representation of power system dynamics and Signal Temporal Logic (STL) to capture the essential frequency response requirements set by grid operators. This enables us to translate frequency constraints into formal robustness semantics. We then formulate an optimization problem to identify the maximum disturbance that the system can withstand without violating these constraints. The resulting optimization is translated into a scenario optimization while addressing the uncertainty in the obtained solution. The proposed methodology has been simulated on the Single Machine Infinite Bus case study and 9-Bus IEEE benchmark system, demonstrating its effectiveness in assessing resilience across various operating conditions and delivering promising results. Copyright © 2025, The Authors. All rights reserved.

关键词： Temporal logic

An Adaptive Decision-Making Approach for Better Selection of a Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

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arXiv 2023年

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to developers and business owners requirements. Therefore, several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using two blockchain platforms selected based on our proposed decision-making map approach for the selection of the top two suitable platforms for healthcare insurance frauds detection application, followed by an evaluation of their performances. Our classification shows that the largest percentage of blockchain platforms could be used for all types of application domains, and the second biggest percentage is to develop financial services only, even though generic platforms can be used, while a small number is for developing in other specific application domains. Our decision-making map revealed that Hyperledger Fabric is the best blockchain platform for detecting healthcare insurance frauds. The performance evaluation of the top two selected platforms indicates that Fabric surpassed Neo in all *** Codes 6804, 68M11, 68U35 © 2023, CC BY-NC-ND.

关键词： Smart contract

Correction to: Fuzzy logic and CPSO-optimized key management for secure communication in decentralized IoT networks: A lightweight solution

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Peer-to-Peer Networking and Applications 2024年第6期17卷 4396-4396页

作者： Vellingiri, J. Vedhavathy, T.R. Senthil Pandi, S. Bala Subramanian, C. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India Department of Computer Science Engineering Kalasalingam Academy of Research and Education Krishnankoil India

A Multiclass Depression Detection in Social Media Based on Sentiment Analysis 17th

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A Multiclass Depression Detection in Social Media Based on S...

17th International Conference on Information Technology: New Generations, ITNG 2020

作者： Mustafa, Raza Ul Ashraf, Noman Ahmed, Fahad Shabbir Ferzund, Javed Shahzad, Basit Gelbukh, Alexander Department of Computer Science COMSATS University Islamabad Islamabad Pakistan IPN - Computing Research Center Mexico City Mexico Yale School of Medicine New HavenCT United States Deptartment of Software Engineering National University of Modern Languages NUML Islamabad Pakistan

ISBN: (纸本)9783030430191

Depression is a common mental health disorder. Despite its high prevalence, the only way of diagnosing depression is through self-reporting. However, 70% of the patients would not consult doctors at an early stage of depression. Meanwhile people increasingly relying on social media for sharing emotions, and daily life activities thus helpful for detecting their mental health. Inspired by these a total of 179 depressive individuals selected from Twitter, who have reported depression and they are on medical treatment. A sample of their recent tweets collected ranges from (200 to 3200) tweets per person. From their tweets, we selected 100 most frequently used words using Term Frequency-Inverse Document Frequency (TF-IDF). Later, we used the 14 psychological attributes in Linguistic Inquiry and Word Count (LIWC) to classify these words into emotions. Moreover, weights were assigned to each word from happy to unhappy after classification by LIWC and trained machine learning classifiers to classify the users into three classes of depression High, Medium, and Low. According to our study, better features selections and their combination will help to improve performance and accuracy of classifiers. © Springer Nature Switzerland AG 2020.

关键词： Sentiment analysis

Innovation, Research and Development in engineering Education. Activities of the Spanish Chapter of the IEEE Education Society: Directive Board of the IEEE Education Society Spanish Chapter 2020-2021

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Innovation, Research and Development in Engineering Educatio...

Technologies Applied to Electronics Teaching (TAEE)

作者： Ó scar Martí nez Bonastre Cristina Ferná ndez Lluí s Vicent Francisco Mur Manuel Castro Gabriel Diaz Jose Carpio Elio Sancristobal Sergio Martin Jose Sá nchez Francisco Javier Arcega Solsona Juan Suardiaz Muro Manuel Caeiro-Rodrí guez Martí n Llamas Inmaculada Plaza Baltasar Ferná ndez Manjó n Pedro Muñ oz-Merino Edmundo Tovar Telmo Zarraonandia Ayo Javier Garcia Zubia José Antonio Delgado Pení n Department of Statistics Mathematics and Computing Science Operations Research Centre Universidad Miguel Hern&#x00E1 ndez Elche Spain Department of Electronic Technology Universidad Carlos III de Madrid Madrid Spain UPF Barcelona School of Management Barcelona Spain Department of Electrical Electronic and Control Engineering UNED Madrid Spain Department of Electrical Engineering Universidad de Zaragoza Zaragoza Spain Department of Automatics Electrical Engineering and Electronic Technology Technical University of Cartagena Cartagena Spain atlanTTic Research Center University of Vigo Vigo Spain Department of Electronics and Communication Universidad de Zaragoza Zaragoza Spain Department of Software Engineering and Artificial Intelligence Complutense University of Madrid Madrid Spain Department of Telematic Engineering Universidad Carlos III de Madrid Madrid Spain Department of Computer Languages and Systems and Software Engineering Universidad Polit&#x00E9 cnica de Madrid Madrid Spain Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Spain Department of Electronic Engineering Universidad de Deusto Bilbao Spain Universitat Polit&#x00E8 cnica de Catalunya Barcelona Spain

ISBN: (数字)9781665421614

ISBN: (纸本)9781665421621

The Technical Committee of the CESEI (Spanish Chapter of the IEEE Education Society) is focused on the Innovation, Research and Development (I+R+D) of the education in engineering, mainly Electric and Computer engineering. As in other interdisciplinary fields, the I+R+D involves several sources of information and different stakeholders, each one with its own focus and purpose. As a result, sometimes it may be complex for practitioners and researchers to identify the more appropriate entities for their interests. This paper shows how the CESEI Technical Committee tries to solve this difficulty by providing updated information regarding the main publications (journals), events (conferences) and awards to the best academic works. The paper focuses special attention on the activities held during 2020–2021.

关键词： Technological innovation Collaboration Stakeholders engineering education Research and development

A Survey on Explainable Artificial Intelligence for Cybersecurity

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arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

Teaching information security management using an incident of intellectual property leakage

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arXiv 2021年

Case-based learning (CBL) is a powerful pedagogical method of creating dialogue between theory and practice. CBL is particularly suited to executive learning as it instigates critical discussion and draws out relevant experiences. In this paper we used a real-world case to teach Information Security Management to students in Management Information systems. The real-world case is described in a legal indictment (T-mobile USA Inc v. Huawei Device USA Inc. and Huawei Technologies Co. LTD) alleging theft of intellectual property (trade secrets) and breaches of contract concerning confidentiality and disclosure of sensitive information. The incident concerns a mobile phone testing robot (Tappy) developed by T-mobile USA to automate testing of mobile phones prior to launch. T-mobile alleges Huawei stole the technology by copying the robot's specifications and stealing parts and software to develop its own testing robot. The incident scenario is interesting as it relates to a business asset that has both digital and physical components that has been compromised through an unconventional cyber-physical attack facilitated by insiders. The scenario sparked an interesting debate among students about the scope and definition of security incidents, the role and structure of the security unit, the utility of compliance-based approaches to security, and the inadequate use of threat intelligence in modern security strategies. Copyright © 2021, The Authors. All rights reserved.

关键词： Intellectual property