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Building Secure and Decentralized EHR Systems with Blockchain and Smart Contracts 11th

Building Secure and Decentralized EHR Systems with Blockchai...

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11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Alex David, S. Carmel Mary Belinda, M.J. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9789819717231

this paper proposes a method for managing electronic health records (EHRs) using blockchain technology. the system architecture consists of a decentralized network, smart contracts, and data models. Blockchain enables secure storage and access of EHRs by multiple parties while smart contracts define rules for accessing and updating EHR data. the method involves designing a decentralized database, setting up a blockchain network, developing smart contracts, implementing security and privacy measures, and designing user interfaces. the paper provides equations to model the blockchain network, and smart contracts are defined based on conditions for stakeholders and preconditions for actions. A smart contract determines whether an action is permitted for a stakeholder based on their conditions and action preconditions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

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Content-based Image Retrieval in Cyber-Physical System Modeling of Robots 11th

Content-based Image Retrieval in Cyber-Physical System Model...

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11th international conference on Recent Trends in Computing, ICRTC 2022

作者： Anantha Prabha, P. Subashree, B. Deva Priya, M. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9789811988240

Cyber-Physical System (CPS) is used in industries and automated plants as they have modern administration abilities and are capable of performing real-time processing in a distributed architecture. In mechanical plants, they are expected to perform image recognition and retrieval, and other Machine learning (ML) processes for efficient functioning and screening of assembling units and products. the existing strategies like remote sensing retrieval and the Gaussian mixture model for Image Retrieval (IR) are not suitable for huge datasets. the drawbacks of the existing strategies can be eliminated based on features of data by using Improvised Density Scale-Invariant Feature Transform (ID-SIFT). this approach mainly focuses on geometrical features like color, texture, size and shape-based image matching offering an improved accuracy of 89%. this approach is relatively efficient when compared to existing algorithms used in IR. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical System

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Robotic Process Automation and Audit Quality: A Comprehensive Analysis 11

Robotic Process Automation and Audit Quality: A Comprehensiv...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Solanki, Umesh Mehta, Kamakshi Shukla, Vinod Kumar Tapmi School of Business Department of Engineering and Architecture India Manipal University Jaipur Rajasthan Jaipur India Amity University Dubai United Arab Emirates India

ISBN: (纸本)9798350350357

the rapid evolution of technology has precipitated the widespread adoption of Robotic Process Automation (RPA) across industries, offering the potential to revolutionize traditional audit procedures. In the realm of auditing, RPA stands as a game-changer, promising to automate repetitive tasks, enhance data accuracy, and empower auditors to focus on intricate, judgment-based activities. this paper explores the transformative journey of RPA, tracing its evolution from early applications in the 2000s to its current sophistication. Auditors have recognized its potential to revolutionize traditional methodologies, initiating pilot programs and experiments that demonstrated efficiency gains and error reductions, particularly in routine tasks. the maturation phase from 2019 to 2021 witnessed the integration of RPA into complex audit processes, accompanied by challenges such as data security and ethical considerations. Presently, RPA is seamlessly integrated with advanced technologies like artificial intelligence and machine learning, contributing to significant improvements in operational efficiency and reduced error risks in audit processes. A thorough literature review synthesizes key studies, highlighting RPA's dynamic journey and addressing challenges through a balanced approach. the paper concludes by outlining crucial regulatory considerations in India and globally, emphasizing ethical technology use and the need for ongoing oversight. Future research directions include the integration of RPA with emerging technologies, exploration of ethical implications, continuous monitoring of regulatory landscapes, tracking technological advancements, assessing evolving skill sets for auditors, and studying user experiences for a comprehensive understanding of RPA's transformative impact on auditing. In summary, the paper underscores the necessity for auditors to navigate the delicate balance between embracing RPA innovation and upholding the integrity and quality of audit enga

关键词： Efficiency

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11th international conference on E-Health and Bioengineering, EHB 2023

11th International Conference on E-Health and Bioengineering...

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11th international conference on E-Health and Bioengineering, EHB 2023

ISBN: (纸本)9783031625190

the proceedings contain 168 papers. the special focus in this conference is on E-Health and Bioengineering. the topics include: Cost and Effects of Treatment of Biceps Long Head Tendon Injury;health Technology Assessment: Current State of Access to Medical Device Outcomes;early Stage Health Technology Assessment: Still Unknown Helper of Medical Device Manufacturers;Identifying Hidden Patterns from Health Administrative Claims by Means of "HAC2Vec" Embedding;tracing Real-World Patient Pathway by Harnessing Healthcare Administrative Claims;nation-Wide Benchmarking of Healthcare Indicators: From Inferential Statistics to Dashboard Cards;cost-Effectiveness Analysis of Multiple Sclerosis Treatment Approaches;legal and Procedural Health data Anonymization Framework of the Faculty of Biomedical engineering, Czech Technical University;A Burden of Disease Model for Assessing the Economic and Outcomes Impact of Diseases Associated with HPV Infection;Mean Membrane Potential Estimation for Neural Mass Models in EEG Recordings Using a Linear State Observer;gait Analysis of Electromyographic Spectral Differences in Stroke Survivors and Healthy Controls;cardiac Arrhythmia Identification Using Feature Selection and Rule-Based Classifiers;automatic Detection of High-Quality Fibrillatory Waves Segments from Atrial Fibrillation Electrocardiographic Recordings;murmur Separation and Classification from Heart Sound;Bio-Signal Based Detection of Hyperarousal as Onset of Post-Traumatic Stress Disorder (PTSD) Episodes;heart Rate Variability Machine learning Models to Facilitate Elevated Blood Pressure Detection;improved Hypertension Detection Models Utilizing Pulse Rate Variability and Asymmetry;application of Meta learning in Quality Assessment of Wearable Electrocardiogram Recordings;heart Rate Variability Analysis on Forcecardiography Signals: A Preliminary Study;Accurate ECG-Free Heartbeats Localization in Long-Lasting SCG Recordings;resilience in Adults.

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11th international conference on E-Health and Bioengineering, EHB 2023

11th International Conference on E-Health and Bioengineering...

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11th international conference on E-Health and Bioengineering, EHB 2023

ISBN: (纸本)9783031625220

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A Survey on Smart Hydroponics Farming: An Integration of IoT and AI-Based Efficient Alternative to Land Farming 1

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11th international conference on Frontiers of intelligent Computing: theory and Applications, FICTA 2023

作者： Laddha, Snehal V. Shastrakar, Pratik P. Zade, Sanskruti A. Department of Electronics Engineering Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur440013 India

ISBN: (数字)9789819967063

ISBN: (纸本)9789819967056

Hydroponics-based farming is a sustainable, pesticide-free, and eco-friendly method to produce crops of higher quality and uses fewer resources than traditional methods. In hydroponics systems, temperature, humidity, and water are all important environmental factors that influence plant growth. the system automatically adds the necessary nutrient solution to the water while also collecting data on how much solution is needed based on the solution's electrical conductivity. It ensures that plants receive the nutrients they need to thrive. In hydroponics, artificial intelligence (AI) can be used to analyze environmental conditions and make changes, as well as to analyze plant growth rates and calculate an estimated harvesting time for the plants. AI in farming can detect plant disease and recommend ways to treat it. this paper discusses various techniques based on IoT, AI, and image processing that have been presented by researchers all over the world for the implementation of an automated hydroponics system, as well as subsequent discussions that can help improve this domain. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hydroponics

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Personalized Music Recommendation: A Comparative Study of Machine learning and Deep learning Approaches 11

Personalized Music Recommendation: A Comparative Study of Ma...

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11th IEEE international conference on Advances in Computing and Communications, ICACC 2024

作者： Iqbal, Nafla Mohan, Aiswarya Syjo, Ann Rose Rajagiri School of Engineering and Technology Dept of Artificial Intelligence and Data Science Kakkanad India

ISBN: (纸本)9798350379136

the rapid advancement of music playback technology, particularly within mobile platforms, has fundamentally transformed user engagement with music. Despite significant improvements in music retrieval techniques over the past decade, the evolution of music recommendation systems remains in its early stages. With users having access to extensive online music libraries, the task of selecting preferred tracks from an overwhelming array of options presents a considerable challenge. Individual musical tastes vary widely, and the influx of new users and music necessitates a system that can adapt *** music recommendation systems face the dual challenge of managing vast amounts of data while delivering accurate results, making them resource-intensive. However, the high precision of these models drives their increasing application in commercial settings. this paper proposes a framework aimed at enhancing recommendation accuracy by tailoring suggestions to align more closely with user preferences.A comprehensive analysis of traditional and advanced music recommendation methodologies is presented, focusing on two user-centric approaches: user preference and emotion-based metrics. these methodologies are critically compared, leveraging the Million Song dataset (MSD) for training the recommendation system. Additionally, an Android application has been developed to collect empirical data on user experiences, facilitating a deeper understanding of user interactions and satisfaction with the recommendation framework. this research aims to improve user experience in music discovery by providing more relevant and personalized recommendations. © 2024 IEEE.

关键词： Contrastive learning

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Querying Healthcare data in Knowledge-Based Systems 11th

Querying Healthcare Data in Knowledge-Based Systems

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11th international conference on Big data Analytics in Astronomy, Science, and engineering (BDA)

作者： Soni, Kanika Sachdeva, Shelly Minj, Anupama Natl Inst Technol Delhi Delhi India

ISBN: (纸本)9783031585012;9783031585029

In the ever-evolving healthcare landscape, integrating knowledge-based systems into data querying processes is becoming imperative. the existing challenges in querying healthcare data lie in the complexity of extracting meaningful insights from vast and heterogeneous datasets. EHRs store different forms of data, and query systems' scalability and performance, especially considering the increasing volume of EHR data, are the main challenges faced. To overcome these challenges, the paper proposes a system with a user-friendly graphical interface for creating Archetype Query Language (AQL) queries in openEHR systems. It consists of three components: User Interface, which allows the user to specify query parameters, modify EHRs paths, filter data, and customize query results;Query builder, which creates the AQL query based on input from the User Interface and Repository of Documents where the compositions are stored and the query result obtained from this component is sent back to User Interface. It stands out with its innovative approach, systematically extracting openEHR schemas and simplifying the creation of complex AQL queries. the system's effectiveness and user satisfaction make learning, using, and developing queries for graph-driven healthcare data knowledge easy. the system enhances the overall functionality and usability of the query builder within the system. It offers a pathway to improved clinical decision-making and patient care outcomes.

关键词： Knowledge-Based System OpenEHR Knowledge Graph Querying Healthcare

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Analysis on Detecting Cardiac Arrhythmia Using Advanced Technologies 8th

Analysis on Detecting Cardiac Arrhythmia Using Advanced Tech...

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8th international conference on Information System Design and intelligent Applications, ISDIA 2024

作者： Radhika, N. Sujatha, D. School of Computer Science & Engineering MRUH Telangana Hyderabad India CSE-AI&ML Department MRCET Telangana Hyderabad India Department of Computational Intelligence MRCET Telangana Hyderabad India

ISBN: (纸本)9789819786657

Essential features of the various cardiac diseases that affect the human heart are provided by the electrocardiogram (ECG). A significant portion of the diagnosis of heart illness comes from the categorization of arrhythmias. An arrhythmia is any alteration to the regular electrical impulse sequence. Conventional techniques for analyzing digital signals, machine learning, and the offshoots, including deep learning approaches are widely used for analyzing and classification of ECG signals and more importantly, for the timely diagnosis of cardiac disorders and arrhythmias. An extensive review of the literature regarding ECG analysis is presented in this document. Also address the computational barriers of machine learning techniques to wearable devices. the study started with a search for pertinent papers and then the gathered data was examined. Examining the assessed ECG features and the machine learning technique for identifying arrhythmias was the second step. the analysis shows that even though the MIT-BIH database requires a large amount of preprocessing work, it chose a sizable proportion of studies. We examine the substantial corpus of literature on pre-recorded ECG data, signal collection, ECG signal processing, and noise reduction, identifying ECG spectrographic states through function technology, classification of ECG signals, and relative perspectives among the examined modules. We reviewed the ultra-modern machine learning strategies for automated arrhythmia detection. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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DUDE-IDS: A Framework for Efficiently Detecting Network-Related Drone Cyberattacks 11

DUDE-IDS: A Framework for Efficiently Detecting Network-Rela...

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11th international conference on Internet of things: Systems, Management and Security, IOTSMS 2024

作者： Tufekci, Burak Quach, Vinh Tunc, Cihan Dantu, Ram The University of North Texas Department of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350366501

Drones have become essential to various industries due to their capabilities in inspections, transportation, mapping, and delivery. As the drone market expands rapidly, so does the need for robust security measures to safeguard against cyberattacks. this paper introduces DUDE-IDS, a novel intrusion detection system (IDS) specifically designed for drones. Firstly, we introduce a time interval approach for generating labeled data. then, we propose new features that can capture time-related patterns in drone network data. this approach enriches the representation of network flows, providing a better understanding of the dynamics of cyberattacks over time. DUDE-IDS takes advantage of knowledge derived from the time interval approach and the augmented feature set, which leads to improved detection capabilities. It utilizes supervised machine learning to analyze network traffic and detect anomalies, achieving high accuracy (95.84%), precision (96.18%), recall (95.84%), and F1 scores (95.62%). Experimental results also demonstrate the efficiency of DUDE-IDS in real-time analysis, utilizing minimal memory resources. through a series of experiments, the paper showcases the capability of DUDE-IDS to successfully detect and differentiate between various attack scenarios such as TCP SYN flood, Man-in-the-Middle (MITM), TCP reset, UDP flood, and deauthentication. © 2024 IEEE.

关键词： Drones

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