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Holistic understanding of digital transformation of health care system as a point-of-departure for system requirements specification

Holistic understanding of digital transformation of health c...

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2024 Ongoing Research, Practitioners, Posters, Workshops, and Projects of the International Conference EGOV-CeDEM-ePart, EGOV-CeDEM-ePart-Ongoing 2024

作者： Karunaratne, Thashmee Ayele, Workneh Larsson, Aron Assom, Luigi Züniga, Gisele Dept. of Computer and Systems Sciences Stockholm University Kista 164 40 Sweden School of Industrial Engineering and Management Royal Institute of Technology/KTH Stockholm100 44 Sweden Dept. of Communication Quality Management and Information Systems Mid Sweden University Sundsvall851 70 Sweden Actadig Stockholm Sweden

The challenges regarding harmonised interoperable e-health services are enormous due to the complexity of the domain, comparatively high demands of privacy and security, implications of storing, sharing, and managing health data as well as unclear definitions and standards for patient's electronic health records. Therefore, the advantages of digitalisation are yet to reach the end user-patient, especially in the emergent regions where digital infrastructure is limited. The focus is on the north and northeast regions of Brazil, where a holistic understanding of the requirements for a digital transformation of the health care system is explored through the lens of an expert panel using a Delphi methodology, complemented by a comparison with related innovations in the digital health domain. © 2023 Copyright for this paper by its authors.

关键词： Health care

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Leveraging Deep Learning in Implementing Efficient Healthcare Processes 15

Leveraging Deep Learning in Implementing Efficient Healthcar...

引用

15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Nomula, Varun Kumar Mohammed, Abdul Sajid Neravetla, Anuteja Reddy Dhanasekaran, S. Department of Analytics/Industrial & Systems Engineering Georgia Institue of Technology United States Dept of School of Computer and Information Sciences University of the Cumberlands Kentucky United States Department of Information Technology University of the Cumberlands Kentucky United States Department of Electronics and Communication engineering Sri Eshwar College of engineering Coimbatore641202 India

ISBN: (纸本)9798350370249

deep learning is a quick-developing subfield of machine studying. It has made big improvements in diverse areas consisting of computer imagination and prescient, natural language processing, and self-reliant driving. Recently, it has additionally been used to improve healthcare strategies, and this has caused greater efficient and price-effective remedies for conditions and diseases. For instance, deep getting to know has been efficiently used to classify medical photographs, together with X-rays and CAT scans, and hit upon diseases. It can also be used to expect affected person results and examine large datasets of scientific statistics and different clinical statistics. In addition, deep learning is being used to improve the accuracy and speed of diagnosis, prevent medical events, and recommend remedies. It has additionally been used to broaden personalized medicinal drug models by gaining knowledge of affected persons' clinical records and other health-related statistics. Deep getting to know can, as a result, be used to enhance and automate healthcare strategies, main to higher care, and step forward affected person outcomes. ©2024 IEEE.

关键词： Deep reinforcement learning

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Implementing AI-driven Diagnostic Decision Support systems for Smart Healthcare 15

Implementing AI-driven Diagnostic Decision Support Systems f...

引用

15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Neravetla, Anuteja Reddy Nomula, Varun Kumar Mohammed, Abdul Sajid Dhanasekaran, S. University of the Cumberlands Department of Information Technology KY United States Georgia Institue of Technology Department of Analytics/Industrial & Systems Engineering United States University of the Cumberlands Dept of School of Computer and Information Sciences KY United States Sri Eshwar College of engineering Department of Electronics and Communication engineering Coimbatore641202 India

ISBN: (纸本)9798350370249

Clever healthcare has turned out to be an increasingly more crucial assignment inside the area of healthcare due to its capability to decorate affected person care transport. Using AI-driven diagnostic decision guide systems is one way to permit more green, correct, and regular healthcare decisions. The structures use records from clinical information to growth predictive fashions that may be used to help the analysis of numerous illnesses. With an AI-driven choice guide device, healthcare carriers can successfully select capability diagnoses based on an affected person's medical information and other elements. The system also can offer hints for treatment plans primarily based on the analysis. AI-pushed structures can also provide actual-time remarks to clinicians, assisting them in improving accuracy and decreasing scientific mistakes. Via the use of AI-pushed preference help systems, healthcare carriers can optimize healthcare shipping, major to better affected person results. © 2024 IEEE.

关键词： Assignment Capability Healthcare Increasingly Primarily

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Resource Allocation as a Market: A Case Study on Multi-Server Multi-Model Federated Learning

Resource Allocation as a Market: A Case Study on Multi-Serve...

引用

Annual Conference on Wireless On Demand Network systems and Services (WONS)

作者： Maria Diamanti Aisha B Rahman Panagiotis Charatsaris Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9783903176713

ISBN: (纸本)9798331522025

6G networks envision a seamless integration of data and AI into their core operations, marking a new era where distributed client devices actively engage in intelligent processes, supported by a diverse ecosystem of AI service providers. This shift necessitates approaching traditional resource allocation problems from a market perspective, transparently reflecting the gains and losses of clients and providers within the ecosystem. In this paper, we use a multi-server, multi-model Federated Learning (FL) network as a case study to propose a market-based resource allocation framework. The framework models the joint problem of client resource allocation and pricing as a Fisher market, where servers compete to attract clients with private data and local computing resources for distributed model training. The joint problem is formulated as a convex program, aiming to maximize the total server utility related to their model’s accuracy, while incorporating clients’ total computing resource constraints and servers’ budget constraints. An analytical solution to the convex program is derived concluding the so-called Market Equilibrium (ME) point, where market clearance is achieved. The effectiveness of the proposed market-based resource allocation framework is validated through comparisons with established resource allocation and sharing schemes from the literature.

关键词： Federated learning Computational modeling Biological system modeling Ecosystems Distributed databases Pricing Data models Resource management Servers Artificial intelligence

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Evaluation of RF-EMF Exposure for sub-6GHz 5G NR Massive MIMO Base Station 6

Evaluation of RF-EMF Exposure for sub-6GHz 5G NR Massive MIM...

引用

6th IEEE International Symposium on Telecommunication Technologies, ISTT 2022

作者： Sali, Aduwati Wali, Sangin Qahtan Osman, Anwar Faizd Universiti Putra Malaysia Systems Engineering Faculty of Engineering WiPNET Research Centre Dept. of Computer and Communication Selangor Malaysia Pat Square Rohde & Schwarz Selangor Malaysia

ISBN: (数字)9781665489423

ISBN: (纸本)9781665489423

The 5G network is intended to accommodate a significant quantity of mobile data traffic and a great number of wireless connections. It improves cost and power consumption;it offers ultra-low latency and ultra-high dependability to enable new services in various sectors. However, the general public is concerned about the possible health dangers linked with 5G equipment's Radio Frequency (RF) radiation, and numerous localities are actively lobbying to prevent 5G implementation. This research measures the maximum exposure emitted by a 5G base station operating on 3.5 GHz. Considering the transmitted power changes over time with data traffic, the analysis is based on a code selective method. Measurements were conducted at two different distances and three different times to investigate the impacts of distance and time on the level of RF-EMF radiation. The maximum radiation from the base station is 11.69V/m, far less than the accepted limit by the ICNIRP standard. © 2022 IEEE.

关键词： Base stations

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Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised Cloud Computing Approach 2

Leveraging Deep Autoencoders for Security in Big Data Framew...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Roy, Sourav Nijaguna, G.S. Shankar, Smitha Sharath Adnan, Myasar Mundher Umaeswari, P. Department of Electronics and Communication Engineering Bangalore India S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India The Islamic University Najaf Iraq R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798350382952

Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. © 2024 IEEE.

关键词： Cryptography

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ML-Inspired Multiple Relay Selection in UAV-Enabled Untrusted Networks

ML-Inspired Multiple Relay Selection in UAV-Enabled Untruste...

引用

IEEE Conference on Wireless communications and Networking

作者： Lefteris Tsipi Emmanouel T. Michailidis Konstantinos Maliatsos Demosthenes Vouyioukas Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece Dept. of Informatics and Computer Engineering University of West Attica Egaleo Greece Dept. of Digital Systems University of Piraeus Piraeus Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, multiple relay selection (MRS) schemes for untrusted unmanned aerial vehicle (UAV)-enabled networks are proposed. In this context, various machine learning (ML) models are employed to improve secrecy performance by optimally selecting multiple aerial relays from the available ones, all of which are regarded as untrusted. Notably, these ML models can cope with the quickly changing and random positioning of the aerial relays, effectively decoupling the intricate coupling relationship between the secrecy rate, channel coefficients, and inter-node distances. The results indicate that the ML-based MRS schemes obtain sufficient accuracy and better decoupling than a respective exhaustive searching (ES) approach, while also maintaining a lower computational complexity.

关键词： Couplings Accuracy Predictive models Autonomous aerial vehicles Security Ensemble learning Relays Computational complexity Context modeling

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TRUSTCACHE: Trust-based Content Caching in Information-Centric Networks

TRUSTCACHE: Trust-based Content Caching in Information-Centr...

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IEEE Conference on Global communications (GLOBECOM)

作者： Sean Tsikteris Aisha B Rahman Md Sadman Siraj Panagiotis Charatsaris Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The Information-Centric Networking (ICN) paradigm has reshaped the modern network architectures and promises efficient content delivery to the end-users. This paper introduces TRUSTCACHE, a novel framework enabling the content caching within ICNs and focusing on the trust-based ICN selection and optimal cache memory allocation to the Content Providers (CPs). The TRUSTCACHE framework incorporates the Optimistic Q-learning with Upper Confidence Bound reinforcement learning algorithm that enables the CPs to autonomously select ICNs based on their cache memory availability and trust levels. Also, TRUSTCACHE enables the CPs to jointly consider the reliability of the ICNs and their cache memory availability by integrating a novel trust model. Furthermore, TRUSTCACHE leverages the multilateral bargaining principles in order to ensure the optimal cache memory allocation among the CPs, in terms of aligning with their profit margin characteristics. Simulation-based experiments validate TRUSTCACHE’s operational efficiency across diverse CP profit margin profiles and highlight its superiority over alternative models lacking trust-based ICN selection or employing proportional fairness strategies for cache memory allocation.

关键词： Q-learning Cache memory Information-centric networking Focusing Resource management Reliability Global communication Game theory

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TOPMG: Trust-Based Crowdsourcing through Multilateral Bargaining Game Theory

TOPMG: Trust-Based Crowdsourcing through Multilateral Bargai...

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IEEE Conference on Global communications (GLOBECOM)

作者： Panagiotis Charatsaris Adedamola Adesokan Aisha B Rahman Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Crowdsourcing plays a critical role in modern information gathering and task execution, yet it faces challenges regarding the task selection and equitable monetary incentives distribution. In this paper, we introduce the TOPMG framework, which addresses these challenges by enabling the workers to select tasks based on their historically experienced monetary incentives and the platforms’ trustworthiness. Specifically, the TOPMG framework utilizes a reinforcement learning approach based on the principles of Optimistic Q-learning with Upper Confidence Bound (OQ-UCB) algorithm, guiding the platform selection process by considering the workers’ monetary incentives, profit, and the platforms’ trustworthiness. Also, the proposed framework introduces a multilateral bargaining game to allocate the platforms’ monetary incentives to the workers by prioritizing their information contribution, fairness, and the platforms’ reputation. Simulation results demonstrate TOPMG’s operational dynamics, scalability, and efficacy, as well as its superiority over existing methodologies.

关键词： Crowdsourcing Q-learning Social networking (online) Simulation Scalability Heuristic algorithms Games Global communication Game theory Faces

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Synergia: Device-Edge Server Association for ISAC-assisted Mobile Edge Computing systems

Synergia: Device-Edge Server Association for ISAC-assisted M...

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IEEE Conference on Global communications (GLOBECOM)

作者： Panagiotis Charatsaris Arianna Santamaria Penafiel Maria Diamanti Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The efficient operation of the unified Integrated Sensing and communication (ISAC) – Mobile Edge Computing (MEC) systems is important for enhancing data sensing, communication, and computation processes in next-generation wireless systems. Despite prior research focusing on these systems, little attention has been given to optimizing the device-edge server associations. This paper addresses this gap by introducing the novel two-stage device-edge server association Synergia framework. Firstly, representative utility functions capture the characteristics of the devices and MEC servers by jointly considering their sensing, communication, and computation characteristics. Secondly, the Estimated Synergia framework leverages the Matching Theory to rapidly determine an initial device-server matching by disregarding the devices’ externalities, i.e., the matching decisions of other devices. Thirdly, the Accurate Synergia model refines and improves this matching by using the coalition formation games, while considering the devices’ externalities in optimizing the utilities of both the devices and the MEC servers. Extensive numerical evaluations demonstrate the Synergia’s operational efficiency and scalability, outperforming reinforcement learningbased approaches. Also, a real-world application involving car accident detection validates its applicability.

关键词： Wireless communication Multi-access edge computing Scalability Games Pricing Integrated sensing and communication Numerical models Servers Global communication Next generation networking

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