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Using Blockchain for Incentive-Driven FHIR-based Health Record Exchange 24

Using Blockchain for Incentive-Driven FHIR-based Health Reco...

引用

39th Annual ACM Symposium on Applied Computing, SAC 2024

作者： Rosar, Moritz Chuang, Kun-Ta Dept. of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9798400702433

Medical data is highly sensitive data, and patients are particularly concerned about the exposure of their identities [1]. A study found that 64% of participants were agreeable to sharing their health data when anonymized, highlighting the importance of privacy in medical data sharing [5]. © 2024 Copyright held by the owner/author(s).

关键词： Sensitive data

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ZCube: A Zero-Trust, Zero-Knowledge, and Zero-Memory Platform for Privacy and yet Secured Access 6

ZCube: A Zero-Trust, Zero-Knowledge, and Zero-Memory Platfor...

引用

6th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

作者： Quach, Vinh Dantu, Ram Talapuru, Sirisha Zaman, Shakila Pokharel, Apurba University of North Texas Dept. of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350386745

As concerns over privacy and trust escalate among individuals and corporations, traditional memory-based trust systems are increasingly seen as inadequate and undesirable. The rise of microservice architecture complicates this by making data paths harder to track. Given these concerns, the critical question arises: How can we establish trust or verify an entity's credibility without collecting private information or relying on comparisons with pre-existing data, while knowing the exact data path of each request? To address this challenge, we introduce a lightweight, scalable, ZCube platform that employs nested Zero Knowledge Proofs (ZKPs), eliminating reliance on memory or evaluations based on past actions. This platform verifies and maintains trust in real-time without centralizing trust in any specific part of the system. It anticipates intentions and rigorously monitors them, ensuring operational integrity while preserving privacy. Distinctive for its ZKP trustless setup incorporating blockchain, each request begins anew and is rigorously monitored bidirectionally during execution. Our tests on the realistic example of a microservice-based distributed system, OpenTelemetry Demo, confirm that our novel approach - combining proof segmentation with plan adherence - exhibits fast response times of less than 200ms on average for most test cases. This ZCube platform surpasses traditional methods in efficiency and security. Our analysis shows comprehensive security and enhanced performance while effectively maintaining bidirectional trustlessness. © 2024 IEEE.

关键词： Differential privacy

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Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions 40

Vertical Validation: Evaluating Implicit Generative Models f...

引用

40th Conference on Uncertainty in Artificial Intelligence, UAI 2024

作者： Elkady, Mai Bui, Thu Ribeiro, Bruno Inouye, David I. Computer Science Dept. Purdue University West LafayetteIN United States Electrical and Computer Engineering Dept. Purdue University West LafayetteIN United States

There has been a growing excitement that implicit graph generative models could be used to design or discover new molecules for medicine or material design. Because these molecules have not been discovered, they naturally lie in unexplored or scarcely supported regions of the distribution of known molecules. However, prior evaluation methods for implicit graph generative models have focused on validating statistics computed from the thick support (e.g., mean and variance of a graph property). Therefore, there is a mismatch between the goal of generating novel graphs and the evaluation methods. To address this evaluation gap, we design a novel evaluation method called Vertical Validation (VV) that systematically creates thin support regions during the train-test splitting procedure and then reweights generated samples so that they can be compared to the held-out test data. This procedure can be seen as a generalization of the standard train-test procedure except that the splits are dependent on sample features. We demonstrate that our method can be used to perform model selection if performance on thin support regions is the desired goal. As a side benefit, we also show that our approach can better detect overfitting as exemplified by memorization. © 2024 Proceedings of Machine Learning Research. All rights reserved.

关键词： Generative adversarial networks

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Epileptic Seizure Detection using Variational Quantum Classifier

Epileptic Seizure Detection using Variational Quantum Classi...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Meesala, Gopichand Kumar, Lalit Pandey, Manish Khare, Nilay Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350381887

Epilepsy, a neurological disorder characterized by recurrent and unpredictable seizures, demands timely and accurate detection for effective management and patient safety. While traditional machine learning approaches have shown promise in seizure detection, this study explores the potential of quantum computing, specifically utilizing a Variational Quantum Classifier. The Variational Quantum Classifier, grounded in quantum mechanics, aims to enhance computational capabilities for seizure detection. Leveraging quantum computing's intrinsic parallelism and superposition properties, the model focuses on processing complex and high-dimensional Electroencephalogram signals commonly used in epilepsy diagnosis. The model undergoes training on a dataset containing both seizure and non-seizure instances, optimizing parameters for increased accuracy. Performance evaluations utilize metrics such as sensitivity, specificity, and overall classification accuracy. © 2024 IEEE.

关键词： Quantum computers

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Sea Erosion Prediction and Monitoring Using IOT and ML 2

Sea Erosion Prediction and Monitoring Using IOT and ML

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Usha Bala, V. Harshavardhan Sai, Y. Slipa Shramila, V. Asritha, M. Bharath, M. Chandu, A. Anil Neerukonda Institute of Technology & Sciences Dept. of Computer Science & Engineering Visakhapatnam India

ISBN: (纸本)9798350366570

One of the major environmental issues that the world is currently facing is sea erosion, which requires an innovative solution for early detection and mitigation. Our paper focuses on the integrated system that combines both IoT and Machine Learning. The four main important environmental factors that are responsible for sea erosion are temperature, wave height, wave speed, and soil moisture. IoT sensors deployed along the prototype design are used to monitor these environmental parameters. Another key component is the machine learning algorithm, which is used analyse the interactions, patterns, and relations among the environmental data. By analysing the patterns among the previous data, the model can predict the likelihood of sea erosion events. This allows for the early detection of erosion events and timely interventions that can minimize the damage or loss caused by sea erosion. The main aim of this project is to help the coastal management authority take smart decisions and proactive steps using both real-time and past data analysis. © 2024 IEEE.

关键词： Soil erosion

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Optimized Parameter Design of Binocular Structured Light System for Precision Reflective Surface CPU in Industrial Assembly Processes

Optimized Parameter Design of Binocular Structured Light Sys...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Yiu, Cheuk Tung Shadow Woo, Kam Tim The Hong Kong University of Science and Technology Dept. of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350350821

This paper constructs a simulated CPU assembly line robot system. The system incorporates binocular structured light vision technology for CPU recognition and localization. The reflective surface of the CPU poses a significant challenge for 3D reconstruction, mainly when dealing with tilted angle situations. This inherent characteristic of the CPU adds complexity to the vision system to capture and reconstruct the object's 3D information accurately. This research aims to optimize the vision system's key parameters, balancing cost-effectiveness and the required precision. By achieving the necessary level of accuracy in picking the CPUs to the desired location, our work aims to enhance robotic assembly operations, offering an optimized parameter configuration approach and supporting the needs of modern industrial processes. © 2024 IEEE.

关键词： Robotic assembly

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Epileptic Seizure Detection Using Quantum Support Vector Classifier

Epileptic Seizure Detection Using Quantum Support Vector Cla...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Meesala, Gopichand Soni, Barkha Pandey, Manish Khare, Nilay Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350381887

Epilepsy is the second most prevalent neurological disorder. It poses significant challenges to both diagnosis and treatment. The information obtained from electroencephalography serves as crucial for understanding the dynamics of epileptic brain activity. It offers a non-invasive means for monitoring and detecting epileptic seizures. This paper explores the use of quantum machine learning algorithms to improve the precision and effectiveness of utilizing EEG data for epileptic seizure detection. Quantum machine learning, leveraging principles from quantum mechanics, holds promise for addressing these challenges by harnessing quantum computational power and exploiting quantum entanglement to process information more efficiently. Quantum features and quantum kernels are employed to capture intricate patterns within the EEG signals, allowing for improved discrimination between normal brain activity and seizure events. Results indicate that the Quantum Support Vector Classifier exhibit a satisfactory classification accuracy compared to traditional techniques despite having constraints with resources. The findings suggest that quantum computing holds great promise in revolutionizing the field of epilepsy diagnosis by providing more robust and accurate tools for distinguishing between epileptic and non-epileptic EEG patterns. © 2024 IEEE.

关键词： Quantum entanglement

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Local Closeness Gravity Model to Identify the Vital Nodes in Complex Networks 16

Local Closeness Gravity Model to Identify the Vital Nodes in...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Nandi, Suman Dutta, Animesh National Institute of Technology Durgapur Dept. of Computer Science and Engineering West Bengal India

ISBN: (纸本)9798350383119

In complex network analysis, identifying the viral nodes is a major concern of the research domain by which any kind of information or infection is controlled throughout the entire network. Several algorithms have been developed over the past few years to identify the viral nodes (influential spreaders) considering many properties of the network. Among them, some authors proposed gravity-based centrality to identify the vital nodes based on the law of gravity with certain limitations. The major limitation of existing gravity-based methods is the mass of the object (i.e. node) is considered as the degree or kshell index only, which does not always signify the spreading ability of the nodes. To address this research challenge, we propose an innovative Local Closeness Gravity method (named LCG) to measure the influential ability of individual nodes, facilitating the identification of the vital nodes in the network. To minimize the computational complexity of Closeness centrality, at first, we measure the local Closeness centrality of individual nodes considering all the nodes residing in the truncation radius. Thereafter we introduce a new parameter 'information sharing ability' based on connectivity strength to measure the distance between the nodes. Finally, the influential ability of each node is measured based on the gravity model considering the local closeness centrality, kshell index, and the distance. The efficiency of LCG is compared with the existing baseline centrality methods by using the Susceptible-Infectious-Recovered (SIR) simulator. The correlation between the LCG method and the baseline centrality methods with the SIR method is compared by Kendalls' tau method considering various infection probabilities and various percentages of seed nodes respectively. The ranking uniqueness of the LCG method and the baseline centrality methods are also measured by the monotonicity metrics. Through the obtained results and various analyses, it becomes evident that t

关键词： Complex Networks Information sharing ability Local Closeness centrality SIR epidemic simulator Vital nodes

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Revolutionizing Pandemic Healthcare: Mask Detection and Patient Face Recognition 14

Revolutionizing Pandemic Healthcare: Mask Detection and Pati...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Saraswat, Shipra Singh, Sofia Middha, Parth Thirwani, Parth Rohilla, Harsh Amity University Dept. of Computer Science & Engineering Noida India Amity University Dept. of Artificial Intelligence Noida India

ISBN: (纸本)9798350344837

During the COVID-19 pandemic, Delhi, India, faced a pressing issue where approximately 1,500 COVID-19-positive patients went missing. In public health emergencies, such as pandemics, natural disasters, or other calamities, hospitals, and medical centres experience a sudden influx of patients, and hospital management faces difficulties in keeping track of patients, especially when they need to be moved between facilities or when new temporary healthcare facilities are set up. As a consequence of these challenges, there can be an increase in missing person cases. Patients may be inadvertently separated from their families. The human face is a unique biometric system that can determine the age, gender, mood, of an individual, and even identity for verification purposes. Harbouring the power of deep learning and artificial intelligence, one of the most important applications of computer vision is Patient Identification. In this study, we have proposed a state-of-the-art patient face detection model using a twofold model that uses MTCNN short for 'multi-task cascaded convolution neural network' for face detection and alignment purposes with a FaceNet Convolutional Neural Network (CNN) a renowned face embedding algorithm finally with KNN algorithm as a classifier to get an accuracy of 97.1%. Also, to ensure public safety during the pandemic we have constituted a Resnet34 model for mask detection trained on the Face Mask Detection dataset with an accuracy of 97%. This study not only addresses the immediate challenges of patient identification and safety during crises but also carries implications for broader healthcare applications. The proposed models offer promising avenues for enhancing patient care and security. © 2024 IEEE.

关键词： Face recognition

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Enhancing Exercise Efficacy: A Predictive EMG Analysis for Dumbbell Press Workouts 6

Enhancing Exercise Efficacy: A Predictive EMG Analysis for D...

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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Pattanashetty, Pranav T. Chaurasia, Shreya Upadhyaya, Sujatha R. Visweswara, Ramesh Debur Pes University Dept. of Computer Science and Engineering Bengaluru India Ramaiah Memorial Hospital Dept. of Physiotherapy Bengaluru India

ISBN: (纸本)9798350389692

The current paper describes a preliminary model that would help improve the dumbbell press exercise through the use of electromyography (EMG). The model recognizes the optimal and marginal repetitions in sets based on the analysis of the EMG data from the pectoral and deltoid muscles. The model provides features like load, rest, and technique hints for set modifications that will improve set quality and minimize non-optimal repetitions in order to provide safer and more efficient training. The findings of this study can be used to give customized training advice by presenting immediate feedback according to individual EMG patterns. The data used in this study is self-reported and obtained from two participants who have previous experience exercising, so they are aware of the movements done. Additionally, a positive feedback system was established, a formula for dynamic adjustment of the exercise plan, taking into account the fitness and experience of the participants was introduced, which improved the effectiveness of the exercises. The proposed paradigm helps to optimize training outcomes in strength and conditioning programs regarding the maximization of exercise performance and the minimization of the injury risk. The study entails three series of exercise tasks at varying angles with participants completing multiple sets of exercises and EMG data collected. A three-layers Artificial Neural Network, Random Forest Classifier, Logistic Regression and Long Short-Term Memory (LSTM) models were used, and it was observed that the models were highly accurate in the classification of exercise quality and the feedback generated could help in improving performance. © 2024 IEEE.

关键词： Electromyography

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