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MUDRA: A Multi-dimensional Privacy Attainment Framework for Healthcare Blockchain Transactions

IEEE Transactions on Consumer Electronics 2025年

作者： Chhabra, Archana Saha, Rahul Kumar, Gulshan Kim, Tai Hoon Lovely Professional University School of Computer Science and Engineering Punjab India Chonnam National University School of Electrical and Computer Engineering Korea Republic of

The integration of blockchain technology into consumer electronics is transforming healthcare by enhancing data security and streamlining operations in devices like wearable health monitors, smart medical devices, and electronic health records. However, this integration raises significant privacy concerns, especially regarding potential data leaks through automated processes like smart contracts. Traditional Private Information Retrieval (PIR) methods struggle with balancing data accessibility and patient confidentiality. This creates computational inefficiencies and interoperability challenges that hinder the implementation of standardized privacy measures in blockchain. We introduce the MUlti-Dimensional pRivacy Attainment (MUDRA) framework, a novel solution addressing critical privacy challenges in the storage and retrieval of patient and healthcare service provider (HSP) data. MUDRA leverages the innovative Dual Server Ring Private Information Retrieval (DS-RPIR) protocol to securely retrieve Personal Health Information (PHI) without publishing sensitive data to the blockchain, ensuring enhanced privacy and preventing unauthorized access. By integrating the Interplanetary File System (IPFS), MUDRA preserves data confidentiality and supports time-controlled revocation, allowing patients to set time-limited access to their medical records, automatically revoking access after the specified period. The framework introduces a new metric, Privacy Attainment (PA), to quantify transaction privacy and evaluate the private retrieval of sensitive data. When implemented on the Hyperledger Fabric network, MUDRA achieves 3.5% higher throughput (59 TPS), reduced **time complexity O(nlogn), and 8 ms latency-2.25 ms less than existing solutions. These performance improvements, coupled with robust privacy features, position MUDRA as a pioneering solution for addressing privacy concerns in the rapidly evolving field of healthcare consumer electronics. © 2025 IEEE.

关键词： Electronic health record

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End-of-Sentence Token Modeling for Streaming Conformer-Based Korean Children's Speech Recognition Applied to a Social Robot

End-of-Sentence Token Modeling for Streaming Conformer-Based...

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2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023

作者： Jeong, Seunghun Park, Yejin Kim, Hong Kook Ai Graduate School Korea Republic of Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju61005 Korea Republic of

ISBN: (纸本)9798350344318

This paper proposes an end-of-sentence (EOS) token modeling approach for streaming conformer-based Korean children's speech recognition applied to a social robot. The baseline system for speech recognition is a streaming conformer-based speech recognition model with the joint connectionist temporal classification attention-based encoder-decoder architecture, and it is trained with the AIHub Child•Infant Free-Talk dataset. However, it is difficult to determine sentence boundaries in children's speech because the speaking style of children is somewhat different from that of adults. Thus, the proposed approach is to insert EOS tokens into each sentence of a large children's dataset to detect sentence boundaries. Finally, we evaluated the performance of the proposed approach on a dataset collected under scenarios of a voice interaction between children and a social robot. Consequently, the proposed EOS token modeling decreased character error rate by 1.7%, compared to the baseline. © 2023 IEEE.

关键词： Speech recognition

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Deadlock-free Supervisor Design for Robotic Manufacturing Cells With Uncontrollable and Unobservable Events

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IEEE/CAA Journal of Automatica Sinica 2021年第3期8卷 597-605页

作者： Bo Huang MengChu Zhou Cong Wang Abdullah Abusorrah Yusuf Al-Turki School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia

In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net ***,a Petri net for the deadlock control of such systems is *** admissible markings and first-met inadmissible markings(FIMs)are ***,place invariants are designed via an integer linear program(ILP)to survive all admissible markings and prohibit all FIMs,keeping the underlying system from reaching deadlocks,livelocks,bad markings,and the markings that may evolve into them by firing uncontrollable *** also ensures that the obtained deadlock-free supervisor does not observe any unobservable *** addition,the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added *** condition under which the supervisor is maximally permissive in behavior is ***,experimental results with the proposed method and existing ones are given to show its effectiveness.

关键词： Deadlock prevention Petri nets robotic manufacturing cells structure-minimized supervisor supervisory control uncontrollability unobservability

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Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm

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IEEE Journal of Biomedical and Health Informatics 2024年第3期28卷 1173-1184页

作者： Ahmad, Nehal Lai, Kuan-Ting Tanveer, M. Indian Institute of Technology Indore Simrol Department of Mathematics Indore453552 India National Taipei University of Technology Department of International Program of Electrical Engineering and Computer Science Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the 'STrutred Analysis of the REtina (STARE) Database' accessed on Oct. 2020 (https://***/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm. © 2013 IEEE.

关键词： Blood vessels

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A Comprehensive Analysis of Cloud Computing in Wireless Communications 2

A Comprehensive Analysis of Cloud Computing in Wireless Comm...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Bansal, Anurag Sharma, Surbhi Shukla, Ajay Narayan Badhoutiya, Arti Research and Technology 13D Research & Strategy India Abes Engineering College Department of Computer Applications UP Ghaziabad India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun India Gla University Department of Electrical Engineering UP Mathura India

ISBN: (纸本)9798350371055

In the present-day digital landscape, cloud computing has grown to be a ubiquitous generation that offers numerous advantages to numerous businesses. Its software in wi-fi communications has had a mainly revolutionary effect, ultimately converting how data is transferred, considered, and saved. This looks at a thorough examination of how cloud computing has affected wireless communications, including its records, most important ideas, and programs. The primary phase of the object appears to be how cloud computing has changed over the years, moving from conventional on-premise computing to its current country, which includes a spread of service offerings and deployment methodologies. Subsequently, it explores the fundamental thoughts of cloud computing, which have virtualization, scalability, and on-demand provisioning, and talks about how these apply to wireless communications. The ability of cloud computing to grow network availability and dependability is one of its primary benefits for wi-fi communications. Using cloud-based computing and garage sources, network operators may additionally lower downtime and increase uptime for their services. They can also better manage unforeseen surges in community visitors and provide a higher user revel. The look additionally addresses how cloud computing helps the short rollout of the latest services and packages within the wireless communications industry. © 2024 IEEE.

关键词： Cloud Computing Data Storage Mobile Technology Network Security Wireless Communications

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment 14

Image based and Point Cloud based Methods for 3D View Recons...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vasikarla, Shantaram Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati India Indian Institute of Technology Dept. of Electrical Engineering Kanpur India California State University Dept. of Computer Science NorthridgeCA United States

ISBN: (纸本)9798350360134

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more. © 2024 IEEE.

关键词： Network coding

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Adaptive Video Streaming: An AI-Driven Approach Leveraging Cloud and Edge Computing

Adaptive Video Streaming: An AI-Driven Approach Leveraging C...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Darwich, Mahmoud Khalil, Kasem Ismail, Yasser Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi University Department of Electrical and Computer Engineering MS38677 United States Southern University and AandM College Electrical Engineering Department Baton RougeLA70807 United States University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA70504 United States

ISBN: (纸本)9798350322347

Video streaming efficiency remains a major challenge, with increasing demands for high-resolution content and minimal buffering times. We propose a novel solution to enhance user experiences. Our approach combines adaptive streaming, AI algorithms predicting network conditions, and edge computing to optimize task allocation. These technologies aim to reduce latency, lower network traffic, and facilitate seamless transitions in video quality. Our results improve the streaming services and enable real-time video applications in remote or mobile environments, presenting substantial implications for the future of video streaming. © 2023 IEEE.

关键词： Video streaming

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Information Constrained Optimal Transport: From Talagrand, to Marton, to Cover

Information Constrained Optimal Transport: From Talagrand, t...

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作者： Bai, Yikun Wu, Xiugang Ozgur, Ayfer Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States

The optimal transport problem studies how to transport one measure to another in the most cost-effective way and has wide range of applications from economics to machine learning. In this paper, we introduce and study an information constrained variation of this problem. Our study yields a strengthening and generalization of Talagrand's celebrated transportation cost inequality. Following Marton's approach, we show that the new transportation cost inequality can be used to recover old and new concentration of measure results. Finally, we provide an application of this new inequality to network information theory. We show that it can be used to recover almost immediately a recent solution to a long-standing open problem posed by Cover regarding the capacity of the relay channel. © 1963-2012 IEEE.

关键词： Machine learning

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer science 2024年第5期5卷 549页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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Deep Learning Based Open Set Recognition: A Survey

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Jisuanji Xuebao/Chinese Journal of computers 2025年第4期48卷 828-863页

作者： Zhang, Qin Liu, Zi-Qi Zhang, Xiao-Lin Zhang, Peng Liu, Han Chen, Xiao-Jun College of Computer Science and Software Engineering ShenZhen University Guangdong ShenZhen518060 China College of Electrical Engineering and Automation Shandong University of Science and Technology Shandong Qingdao266590 China College of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao266590 China

In recent years, machine learning research has continuously made breakthroughs, which has led to the maturity and implementation of a large number of intelligent systems. However, the current machine learning paradigm overly relies on complete prior knowledge, and its application scenarios are limited to static closed specialized systems. The emergence of new categories is one of the main challenges in the research of dynamic open environments, to which the conventional closed-world machine learning methods can not handle it well. In order to break the excessive dependence of existing machine learning on complete category information, the research on open set recognition problem has become a new trend, which extends the traditional closed-world classification methods to open-world applications. Open set recognition problem requires the classification models classify the learned known classes correctly, and effectively identify unknown class samples that appear in the testing phase simultaneously, to avoid the large number of misclassifications. This article systematically surveys the research on open set recognition in recent years, focusing on deep learning based open set recognition methods. It introduces the classic models by sorting out them into six categories based on the main ideas they employ, and horizontally compares various research achievements, and give the comparison details of their performances. Related paper and code links are collected and available online. © 2025 science Press. All rights reserved.

关键词： Deep learning

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