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Biomedically Informed ECG Synthesis: Customizing Cardiac Cycle Phases with Diffusion Model

Biomedically Informed ECG Synthesis: Customizing Cardiac Cyc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Yuxin Ma, Jing Wang, Wei Wu, Zhihao Dong, Suyu Luo, Gongning Wang, Kuanquan Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology Faculty of Computing Harbin China

ISBN: (纸本)9798350386226

Cardiovascular diseases are a major global health challenge, with electrocardiography (ECG) being critical for diagnosis and monitoring. As artificial intelligence and automated ECG diagnostic technologies rapidly advance, the demand for large-scale ECG databases continues to grow. Generative ECG has become a mainstream method to enhance database size and diversity. However, existing methods typically generate ECG randomly or focus on limited physiological categories, lacking the ability to synthesize ECG with varying physiological features and cardiac cycles, which is crucial for various practical applications. In response to this need, we propose a novel approach introducing a diffusion model called DIFF-ECG to generate precisely customized ECG that accurately reflect diverse cardiac conditions. Segmentation-based quality assessments confirmed that the synthesized ECG accurately followed the specified cardiac cycle information, with our model significantly outperforming baseline diffusion and GAN-based methods. Therefore, our approach addresses the critical need for generating clinically relevant and customizable ECG, contributing significantly to the field of automated cardiac disease diagnosis. By enabling fine-tuning of cardiac cycle phases, our method significantly expands the application range of generative ECG, potentially improving the diagnostic accuracy for rare diseases and advancing personalized medicine. © 2024 IEEE.

关键词： Electrocardiography

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Enhanced Detection of Eye Coloboma through Global Attention U-Net (GA-UNet) for Accurate Ophthalmic Image Segmentation 4

Enhanced Detection of Eye Coloboma through Global Attention ...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Dhayanithi, A. Bala, K. Bharath Institute of Higher Education and Research School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai600073 India

ISBN: (纸本)9798331508845

Eye coloboma is classified as a congenital eye disease that results in developmental anomalies of the eye that may result in eye diseases that essentially alter visions. Fractional and timely diagnosis still matter significantly in its effective managing and treating. This work presents a solution with a Transformer-Enhanced U-Net model that was developed with Transfer Learning to help augment the market's ability to identify and delineate eye coloboma from medical images. This work further extend the gains made in U-Net, that has achieved fairly well especially in the biomedical image segmentation problem, with the inclusion of transformer modules for the incorporation of the long range dependencies and contextual information. This enhancement help the model to attend more on such small features as coloboma and overall diagnostic accuracy in addition. By using Transfer Learning with a pretrained backbone such as Efficient Net, feature representations from large pre-existing datasets are fed to the system as a solution to the reduction of training time and while the performance of the system drops no further effect is *** approach involves the use of fine-tuning with Transformer-Enhanced U-Net model utilizing a subset of the eye images, which are labelled according to the existence of coloboma. Next, by adopting the self-attention mechanism, the model becomes able to pay attention to the relevant areas in the image, and hence is sensitive to any form of coloboma. In initial experiments, this scheme outperforms the standard convolutional networks for segmenting complex regions with minimal false negative rates. Moreover, by comparing the features extracted at deep layers with ground truth, the effectiveness of the model to realistic clinical usage is established given that the model is robust to changes in imaging conditions. The purpose of this research therefore is to develop an Auto-Generated diagnostic tool that will be more reliable as it assists the

关键词： Image segmentation

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Using Machine Learning to Identify and Categorize Personally Identifiable Information and Payment Card Industry Data in Textual Content 4

Using Machine Learning to Identify and Categorize Personally...

引用

4th International Conference on Advanced Research in computing, ICARC 2024

作者： Arambawela, Milinda Aponso, Achala University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350384864

The advent of the Internet has significantly stream-lined daily tasks through the rapid increase of online services. Everyday activities, such as purchasing goods and scheduling appointments with healthcare professionals, have become more speedy, efficient and user-friendly with the integration of the Internet. The continuous improvement of online services has led to many people moving towards digital activities. As a result, it has heightened the recording of personal and payment transaction data across various storage mediums, including databases and log files. The protection and regulation of this sensitive data are imperative, aligning with the guidelines outlined in GDPR and PCI-DSS compliances. Recognizing exposed personal data poses a considerable challenge. This research introduces a novel approach to identifying payment card industry data (PCI) and personally identifiable information (PII). The research project proposes a machine learning-based text classification model utilizing the Convolutional Neural Network (CNN) model to discern PII and PCI data within a given text. The CNN model has been constructed and compared against Naive Bayes, Gradient Boost, Random Forest, and Support Vector Machine (SVM) models. The CNN model achieved the highest accuracy at 0.96 (96%). Additionally, the F1 scores for each class were significant, with PII scoring 0.94, PCI scoring 0.95, and Normal scoring 0.99. Following the model's construction and training, it was employed with the saved tokenizer's word indexes and label encoders in the developed classification tool. This tool successfully delivered the promised results, identifying exposed PII and PCI data. © 2024 IEEE.

关键词： Neural network models

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Unsupervised Domain Adaptation for Entity Blocking Leveraging Large Language Models

Unsupervised Domain Adaptation for Entity Blocking Leveragin...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Wang, Yaoshu Yan, Mengyi Shenzhen Institute of Computing Sciences Fundamental Research Department Shenzhen China Beihang University Computer Science Department Beijing China

ISBN: (纸本)9798350362480

Entity blocking, which aims to find all potentially matched tuple pairs in large-scale data, is an important step for entity resolution. It is non-trivial because it needs to consider both of the effectiveness and efficiency, and the emergence of representation learning has made it possible. Although there exist existing representation learning models for entity blocking, all of them require self-curated training instances in the target domain, which limits their capabilities for unseen data. In this paper, we propose UDAEB, a framework for Unsupervised Domain Adaptation for Entity Blocking that is fine-tuned between the source and target domains using contrastive learning by leveraging the capabilities of LLMs. UDAEB first adopts the adversarial learning strategy to reduce the distribution discrepency between source and target domains as the warmup step. Based on the initially learned representations, UDAEB involves pre-trained LLMs to enrich robust and distinguishable attributes for source and target domains. Furthermore, we propose an iterative step to fine-tune entity blocking model by selecting high-quality training instances with pseudo-labels by leveraging LLMs. Finally we conduct comprehensive experiments to show UDAEB has the superior performance against the state-of-the-art algorithms with aspects of the pair completeness (PC), pair quality (PQ) and the candidate set size ratio (CSSR). © 2024 IEEE.

关键词： Contrastive Learning

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Artificial Intelligence in Agricultural Transformation for Enhancing Food Security through Precision Farming 2

Artificial Intelligence in Agricultural Transformation for E...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Sakpal, Shreya Sudesh Golisano College of Computing and Information Sciences Rochester Institute Of Technology Department of Computer Science RochesterNY14623 United States

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388916

Growing worldwide consumption of food security has laid the foundation to make farming methods more effective and sustainable. Machine learning (ML), which falls under the broader AI framework, has become an innovative enabler for precision farming - we facilitate the continuous analysis of data, the use of this data for modelling Outlooks to support farming activity, and manage resources to improve farming yields. This paper discusses the Implication of AI in precision farming as a concept that enhances crop yield forecast, resource utilisation, and the mitigations of farming hurt on the environment. The proposed AI models also use IoT sensors, remote sensing, and machine learning to decide on the use of water, fertilizers, and pesticides. The simulations reveal quite encouraging gains to the precision of yield accuracies, optimization of resources, and reduction of costs. The investigated AI applications in farming demonstrate a significant capacity to cut the farming manifold and environmental emissions, thereby indicating an ability to support the longer-term health and growth of farming and food security for the globe. However, issues like data quality, the accuracy of models used and declaration or adoption have become issues yet to settle. The study indicates that there is hope for the modern agriculture as being revolutionized by AI and points to the ways of improving the use of AI further. © 2024 IEEE.

关键词： Smart agriculture

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AI Assisted Real Time Response Systems for National Emergency Preparedness and Disaster Management 2

AI Assisted Real Time Response Systems for National Emergenc...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Sakpal, Shreya Sudesh Golisano College of Computing and Information Sciences Rochester Institute Of Technology Department of Computer Science RochesterNY14623 United States

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388916

This paper focuses on a real-time response system supported by AI developed to improve national emergency readiness and disaster response. The proposed system includes decision support for disaster response through real-time data processing with the application of machine learning/deep learning algorithms applied to the data collected from IoT sensors, satellite images and social media commenting. Hence, the proposed system optimizes the resources, the probability of the forecasts and decisions made through the integration of linear programming, probabilistic models, and stochastic optimization. Having positively noted the effect of the system on response time of aggregated queries, the Level 2 priority simulations also revealed that the offered optimization of resource allocation is comprised at 90%, while the deep learning statistical information of disaster prediction conveys an important accuracy of 89%. The Resource management policies ensured effective manner of delivering personal and operational necessities, which flexibly changed depending on the situation. This research also shows the ability of the system to improve Disaster management by increasing efficiency in managing large-scale emergencies. © 2024 IEEE.

关键词： Stochastic programming

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Robust Reinforcement Learning with General Utility 38

Robust Reinforcement Learning with General Utility

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Ziyi Wen, Yan Hu, Zhengmian Huang, Heng Department of Computer Science Institute of Health Computing University of Maryland College Park College ParkMD20742 United States

Reinforcement Learning (RL) problem with general utility is a powerful decision making framework that covers standard RL with cumulative cost, exploration problems, and demonstration learning. Existing works on RL with general utility do not consider the robustness under environmental perturbation, which is important to adapt RL system in the real-world environment that differs from the training environment. To train a robust policy, we propose a robust RL framework with general utility, which subsumes many existing RL frameworks including RL, robust RL, RL with general utility, constrained RL, robust constrained RL, pure exploration, robust entropy regularized RL, etc. Then we focus on popular convex utility functions, with which our proposed learning framework is a challenging nonconvex-nonconcave minimax optimization problem, and design a two-phase stochastic policy gradient type algorithm and obtain its sample complexity result for gradient convergence. Furthermore, for convex utility on a widely used polyhedral ambiguity set, we design an algorithm and obtain its convergence rate to a global optimal solution. © 2024 Neural information processing systems foundation. All rights reserved.

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Parkinson's Disease Severity Assessment: A Comparative Study & Interpretability Analysis 5

Parkinson's Disease Severity Assessment: A Comparative Study...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Tiwari, Utkarsh Jahanve, P. Reddy Karna, Siwani Anand, M. Pati, Peeta Basa Prakash, Bhanu K.N. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Bioinformatics Institute A*STAR Singapore

ISBN: (纸本)9798350361155

Parkinson's disease (PD) is a progressive neurological disorder that mainly affects people over the age of sixty. Currently, there is no such cure for PD. However, Treatments are available to help relieve the symptoms related to PD. A rating tool called MDS-UPDRS III is used to gauge the severity and progression and stages of disease severity are measured by the Hoehn and Yahr (NHY) staging system. This study performs a comparative analysis of the effectiveness of various machine learning models such as AdaBoost, XGBoost, Gradient Boosting Classifier (GBC), Decision Trees (DT), K-Nearest Neighbor (KNN), Logistic regression (LR), and Gaussian Naïve Bayes (GNB) on a dataset containing a variety of PD-related features obtained from 2861 persons. Local Interpretable Model-agnostic Explanations (LIME) is employed to perform the analysis of the strength of each feature toward the outcome prediction for various classification tasks. It is observed that the AdaBoost classifier delivers superior performance of 93.2% accuracy and 84.2% F1-Score for the dataset. © 2024 IEEE.

关键词： Adaptive boosting

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Mageswaran, J. Uma Indumathi, G. Kalpana, A.V. Elangovan, G. Lalitha, S.D. Nalini, S. Sohanda, Mayank Kumar Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur DistTamil Nadu India Srm Institute Of Science And Technology Ramapuram India Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India R.M.K Engineering College Department Of Computer Science And Engineering India Srm Institute Of Science And Technolog Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350352689

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus. © 2024 IEEE.

关键词： Buses

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Review of DDoS Attack Detection in Big Data with Cloud using Machine Learning 2

Review of DDoS Attack Detection in Big Data with Cloud using...

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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Radhika, P. Kamalakkannan, S. Kavitha, P. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798350340235

Cloud computing (CC) is a massive breakthrough in Information Technology (IT) that provides end users to access flexible andvirtualizedsources at affordable infrastructure cost and management. One of the most significant technologies in the big-data era is the CC Data Centres (DCs). The Distributed Denial of Service (DDoS) attacks are one of the most serious issues when it comes to the privacy of DC. DDoS attacks using Transmission Control Protocol (TCP) traffic are taken into consideration, which are becoming more prevalent but challenging to identify. The DDoS attack is the focus of this study, along with the technique used to prevent it and lessen the vulnerability of the big data server side. The system entails the delivery of packets in the form of DDoS attacks to cloud-based websites and even addresses the real-time prediction of software layer DDoS attacks using various Machine Learning (ML) and Deep Learning (DL) techniques. As a result, it stands apart among numerous hosts. Additionally, the objective of this paper was to offer a succinct introduction to attack detection approaches for early researchers working on cloud-based big data applications. As a result, these approaches are categorised according to how they function, their strengths and shortcomings are reviewed, and finally, several research papers that used each method are examined. © 2023 IEEE.

关键词： Big Data Cloud computing Deep learning Distributed Denial of Service attacks Machine Learning

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