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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

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Smart Healthcare systems: IoT and AI for Heart Disease Prediction 5

Smart Healthcare Systems: IoT and AI for Heart Disease Predi...

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5th international conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Vishnu, K. Sebastian, Thomas Binu, P.K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350379945

Fast food consumption and changes in lifestyle are associated with an increase in heart related problems. This study looks at how cloud computing, the Internet of Things and machine learning can be combined for CVD risk prediction and real-time monitoring. The IoT-ML-Cloud paradigm aims to increment the precision of Cardio Vascular Disease prediction by merging these technologies. The system comprises three main modules: Prediction, Data collection and User engagement. Utilizing Arduino and ESP32 modules. An Android application facilitates patient data input and real-time monitoring and predictions using ML models deployed on the Microsoft Azure cloud. Bluetooth technology utilised to facilitates real-time data transfer to the cloud. This paper proposes Bi-LSTM algorithm with an accuracy of 96.55% for the heart disease prediction. Along with highlighting significant discoveries from numerous ML algorithms in the diagnosis of Cardio Vascular Disease, it also highlights the significance of cloud computing and data mining for large-scale medical data analysis. © 2024 ieee.

关键词： Electronic health record

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Quantum Machine Learning in Disease Detection and Prediction: a survey of applications and future possibilities 47

Quantum Machine Learning in Disease Detection and Prediction...

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47th ieee-Computer-Society Annual international conference on Computers, Software, and Applications (COMPSAC)

作者： Upama, Paramita Basak Kolli, Anushka Kolli, Hansika Alam, Subarna Syam, Mohammad Shahriar, Hossain Ahamed, Sheikh Iqbal Marquette Univ Dept Comp Sci Milwaukee WI USA Marquette Univ Ubicomp Lah Milwaukee WI USA Univ Texas Austin Dell Med Ctr Austin TX USA Kennesaw State Univ Dept informat Technol Kennesaw GA USA

ISBN: (纸本)9798350326970

Quantum machine learning (QML) in the field of disease detection and prediction use quantum computing techniques and algorithms to analyze and classify large datasets of medical information, by identifying subtle patterns and predict the occurrence or progression of diseases. It involves applying machine learning techniques to data from biological and medical research, such as-genomic and proteomic data, medical imaging, electronic health records, and clinical trial data, using quantum computing algorithms and architectures to perform these analyses more efficiently and accurately than classical computing methods. This approach has the potential to provide new insights into complex biological systems and facilitate the development of more effective treatments and personalized medicine. In this paper, a systematic review of the use of QML algorithms has been conducted, which focuses on the detection and prediction of diseases among patients. The current essence of the field along with the challenges and limitations of current works have also been discussed. After evaluating the implemented and proposed methods of data analysis, algorithm development, usefulness and efficiency of the system in various disease detection and prediction, a recommendation was made on the open research scopes in this field at the end of the paper.

关键词： quantum machine learning (QML) disease detection disease prediction classical machine learning quantum computing healthcare

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Port distributed Energy Management Considering USVs Charging and Discharging in Polymorphic Network 14

Port Distributed Energy Management Considering USVs Charging...

引用

14th international conference on Information Science and Technology, ICIST 2024

作者： Qu, Qi Teng, Fei Xu, Qi Navigation College Dalian Maritime University Dalian China College of Marine Electrical Engineering Dalian Maritime University Dalian China Research Institute of Intelligent Networks Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350353334

To reduce the environmental impact of port carbon emissions and promote the sustainable development of ports, this paper proposes a port distributed energy management strategy considering the charging and discharging of unmanned surface vehicles (USVs) under the polymorphic network. Firstly, taking into account the trend of continuous automation of ports, data centers are used to meet the ports' growing computing power needs. A data center power consumption calculation model considering data processing delay constraints is proposed. Secondly, taking the port microgrid operating cost, the main grid electricity purchase or sales cost, carbon cost, data center operating cost and USVs charging and discharging cost as objective functions, a port distributed energy management model considering USVs charging and discharging is established. Then, a distributed algorithm based on mixed integer linear programming is proposed to solve the problem. Finally, simulation results demonstrate the effectiveness of the proposed method. © 2024 ieee.

关键词： Zero-carbon

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Real-Time Thyroid Nodule Precise MIoT Leveraging Quad-Node Raspberry Pi Clusters and NVIDIA Jetson Nano for Edge AI 3

Real-Time Thyroid Nodule Precise MIoT Leveraging Quad-Node R...

引用

3rd international conference on computing, Communication, Perception and Quantum Technology, CCPQT 2024

作者： Liu, Xian-Xian Tan, Lijian Yang, Jie Department of Computer and Information Science University of Macau China College of Intelligent Manufacturing Chongqing Industry and Trade Polytechnic Chongqing China College of Artificial Intelligence Chongqing Industry and Trade Polytechnic Chongqing China

ISBN: (纸本)9798331528386

This study introduces the thyroid nodule segmentation grid search based local patch learning (GS-LPL) network as an effective IoT solution for real-time, precise thyroid cancer segmentation. Utilizing the Turing PI and NVIDIA Jetson Nano, we developed a quadruple node distributed computing cluster for real-time cancer segmentation through parallel and distributed computing. GS-LPL features a lightweight neural network, optimized for mini-supercomputers, enabling efficient model training and deployment. The NVIDIA Jetson Nano supports real-time high-definition video processing, ideal for low-powered intelligent video analytics like Network Video Recorders (NVR). Our model is also deployable on mobile devices with limited resources. Experimental results validate our model’s fast, efficient, and accurate performance in thyroid nodule cancer segmentation. © 2024 ieee.

关键词： Cluster computing

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Private Continuous Survival Analysis with distributed Multi-Site Data

Private Continuous Survival Analysis with Distributed Multi-...

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2023 ieee international conference on Big Data, BigData 2023

作者： Bonomi, Luca Lionts, Marilyn Fan, Liyue Vanderbilt University Medical Center Dept. Biomedical Informatics NashvilleTN United States Vanderbilt University Dept. Computer Science NashvilleTN United States University of North Carolina College of Computing and Informatics CharlotteNC United States

ISBN: (纸本)9798350324457

Effective disease surveillance systems require large-scale epidemiological data to improve health outcomes and quality of care for the general population. As data may be limited within a single site, multi-site data (e.g., from a number of local/regional health systems) need to be considered. Leveraging distributed data across multiple sites for epidemiological analysis poses significant challenges. Due to the sensitive nature of epidemiological data, it is imperative to design distributed solutions that provide strong privacy protections. Current privacy solutions often assume a central site, which is responsible for aggregating the distributed data and applying privacy protection before sharing the results (e.g., aggregation via secure primitives and differential privacy for sharing aggregate results). However, identifying such a central site may be difficult in practice and relying on a central site may introduce potential vulnerabilities (e.g., single point of failure). Furthermore, to support clinical interventions and inform policy decisions in a timely manner, epidemiological analysis need to reflect dynamic changes in the data. Yet, existing distributed privacy-protecting approaches were largely designed for static data (e.g., one-time data sharing) and cannot fulfill dynamic data requirements. In this work, we propose a privacy-protecting approach that supports the sharing of dynamic epidemiological analysis and provides strong privacy protection in a decentralized manner. We apply our solution in continuous survival analysis using the Kaplan-Meier estimation model while providing differential privacy protection. Our evaluations on a real dataset containing COVID-19 cases show that our method provides highly usable results. © 2023 ieee.

关键词： Data Privacy distributed Data Survival Analysis

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A Multi-Module Spatial Semantic Network with Gradient Boosting for Efficient Crop Classification

A Multi-Module Spatial Semantic Network with Gradient Boosti...

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2024 international conference on distributed systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Husain, Saif O. Meghana, A. Soujanya, Rajireddy Sunil, G. Reddy Muvva, Vijaya Bhaskar The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India University of Technology and Applied Sciences -Shinas College of Computing and Information Sciences Department of Information Technology Shinas Oman

ISBN: (纸本)9798350375442

For the past few years, accurate crop classification is critical for efficient agricultural management and monitoring. While traditional classification techniques have leveraged spectral indices, they often struggle to fully capture the complex and diverse multispectral features present in agricultural landscapes. This challenge is particularly evident when differentiating crops with similar spectral signatures, leading to misclassification and reduced accuracy in traditional models. To address this problem, a hybrid approach that combines a deep learning model namely Multi-module Spatial Semantic Network (MSSNet) with the Gradient Boosting Classifier (GBC) is proposed. The dataset utilized in this research is sentinel-2, where the median filter is used to remove noise at the preprocessing stage. Then, MSSNet architecture is applied in feature extraction where pre-trained residual network (ResNet50), embedded receptive field module (RFB) and skip connection are incorporated to extract multi-spectral and spatial features. Finally, the extracted features are fed into a GBC, which is suitable for the robust performance in pixel mapping. This MSSNet+GBC combination leverages the strengths and offers improved accuracy in distinguishing between crop types and conditions. The experimental results demonstrate that MSSNet+GBC outperforms conventional methods such as 3D-Convolutional Neural Network (CNN) with higher accuracy of 98.67% using a sentinel-2 dataset for precise crop classification. © 2024 ieee.

关键词： Median filters

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Automated Mapping of Task-Based Programs onto distributed and Heterogeneous Machines 23

Automated Mapping of Task-Based Programs onto Distributed an...

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2023 international conference for High Performance computing, Networking, Storage and Analysis, SC 2023

作者： Teixeira, Thiago S. F. X. Henzinger, Alexandra Yadav, Rohan Aiken, Alex Stanford University Stanford United States MIT Boston United States

ISBN: (纸本)9798400701092

In a parallel and distributed application, a mapping is a selection of a processor for each computation or task and memories for the data collections that each task accesses. Finding high-performance mappings is challenging, particularly on heterogeneous hardware with multiple choices for processors and memories. We show that fast mappings are sensitive to the machine, application, and input. Porting to a new machine, modifying the application, or using a different input size may necessitate re-tuning the mapping to maintain the best possible *** present AutoMap, a system that automatically tunes the mapping to the hardware used and finds fast mappings without user intervention or code modification. In contrast, hand-written mappings often require days of experimentation. AutoMap utilizes a novel constrained coordinate-wise descent search algorithm that balances the trade-off between running computations quickly and minimizing data movement. AutoMap discovers mappings up to 2.41× faster than custom, hand-written mappers. © 2023 ACM.

关键词： Mapping

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Addressing the Cold Start Challenge through the Implementation of Content-based Filtering and Hybrid Filtering for Movie Recommendation 15

Addressing the Cold Start Challenge through the Implementati...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kolli, Mahi Mukil, L.D. Kartthikeyan, N. Pranavkrishnan, M. Sampath, Nalini Nair, Priyanka C. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

Recommender systems are of great significance for the difficulty in increasing speed as well as the amount of online information of the users to sort out the relevant content as they offer personalized suggestions. Content-based filtering and collaborative filtering are the two very common types of recommender algorithms, but they are still facing some issues, e.g. sparsity and cold-start problems. To cope with these limitations and increase recommendation accuracy, this study proposes a new form of recommendation system that combines collaborative filtering and content-based filtering to form a hybrid recommender system. The system proposed integrates the advantages of both methods and, therefore, offers more precise and individualized recommendations. As an illustration, the hybrid method shows superiority compared to the traditional methods, which proves that deep learning might be the key to advancing recommendation systems. © 2024 ieee.

关键词： Collaborative filtering

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Standardization Aspects of Caching and Coded Delivery in MEC Integrated 5G System 16

Standardization Aspects of Caching and Coded Delivery in MEC...

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16th international conference on COMmunication systems and NETworkS, COMSNETS 2024

作者： Chhangte, Lalhruaizela Patel, Ashish Singh National Institute of Technology Mizoram Department of Cse Aizawl India

ISBN: (纸本)9798350383119

Caching and coded delivery of content at the wireless edge have significantly enhanced content delivery performance. However, an attempt to incorporate it in various edge computing platform standards is lacking. This paper explores the standardization aspects of a caching and coded delivery system in a Multi-access Edge computing (MEC) enabled 5G system. In this paper, coded delivery is proposed as a MEC service that can be utilized by various applications such as caching applications in the MEC platform. The coded delivery service utilizes the Multicast Broadcast Service (MBS) of the 5G system, employing the Point-To-Multipoint (PTM) delivery method for efficient packet delivery to User equipment (UEs). The proposed system accommodates content caching at end-user devices and MEC storage while efficiently transmitting coded multicast to improve the performance of applications such as video streaming, AR/VR for 5G and beyond. © 2024 ieee.

关键词： 5G and beyond Caching Coded Delivery Multi-access Edge computing Service

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Enhancing Spam Email Detection with a Soft Voting Ensemble of Optimized Machine Learning

Enhancing Spam Email Detection with a Soft Voting Ensemble o...

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2024 ieee international conference on computing, Applications and systems, COMPAS 2024

作者： Tazwar, Asif Daiyan, Md Mahir Jiabul Hoque, Md Saifuddin, Mohammed Khaliluzzaman, Md. International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh Fse International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh Fse International Islamic University Chittagong Dept. of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

Spam email detection is crucial for cybersecurity, as it protects user privacy and reduces security risks. The persistent presence of spammers necessitates continuous improvements in spam filtering measures. To address this challenge, this study employs Grid Search Optimizer to fine-tune the parameters of four distinct classifiers: Support Vector Machine (SVM), Random Forest, Naive Bayes, and XGBoost. Soft Voting serves as the final classifier, enhancing overall accuracy by integrating results from optimized classifiers to detect and classify spam emails across the Spam Mails Dataset and Enron1 Dataset. The experimental study compared the proposed model with relevant works and found that hyperparameter tuning and Soft Voting significantly enhanced its performance compared to existing approaches. The proposed ensemble model outperformed individual classifiers, achieving accuracies of 99.32% and 99.12%, respectively. Also, the ensemble model demonstrated an AUC of 1.00 on both datasets, indicating its effectiveness in distinguishing between spam and ham emails. This approach surpasses prior studies in accuracy, generalization, and robustness by innovatively combining Grid Search and Soft Voting. The constructed model exhibits high effectiveness and efficiency in the detection of spam emails. © 2024 ieee.

关键词： Decision trees

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