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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6946 条记录，以下是581-590 订阅

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data Deduplication Using Python 7

Data Deduplication Using Python

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Deshingkar, Harshada Desai, Preeti Deshmane, Shrinidhi Deshmukh, Aarya Desai, Tanuja Desai, Sankhya Ubale, Ganesh Vishwakarma Institute of Technology Desh 666 Upper IndiranagarBibwewadi Maharashtra Pune India Vishwakarma Institute of Technology 666 Upper IndiranagarBibwewadi Maharashtra Pune India

ISBN: (纸本)9798350304268

This paper aims to develop a data deduplication technique for optimizing storage utilization in enterprise environments and for general users. data deduplication is a process that identifies and eliminates duplicate data from storage systems, resulting in reduced requirements and lower costs. The project involves working on duplicate files from the folders present in storage systems and processing them to reduce the redundancy of data. The results of the project will provide valuable insights on usage of data deduplication technique to delete duplicate files. This method can be used by users to optimize their storage utilization and reduce their storage costs. Additionally, the project will contribute to the field of data storage and management by providing a way to reduce the constant storage of duplicate files. © 2023 IEEE.

关键词： data deduplication Inline deduplication Post-processing Deduplication python storage utilization

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Information Fusion for Seveirty Detection using Machine Learning in data Mining Healthcare 2

Information Fusion for Seveirty Detection using Machine Lear...

引用

2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Gupta, Vaishali Bhambu, Pawan Chandigarh Engineering College Department of Computer Science & Engineering Jhanjeri India Vivekananda Global University Department of Computer Science Enginnering Jaipur India

ISBN: (纸本)9798350347456

Improved patient care and less mental strain on healthcare providers are two benefits of using algorithms for machine learning to healthcare data. These algorithms may be used to spot irregularities in vital signs, which might speed up medical assistance or provide light on a disease's progression. While there is a wealth of literature comparing the unsupervised and supervised performances of anomaly detection algorithms on popular public datasets, this same level of conceptual comparison is lacking when it comes to physiological data. Knowing one's heart rate may provide valuable insight on one's health and level of physical activity, making it an underutilised data source. Specifically, we used and compared five machine learning methods, two of which were unsupervised and the other three supervised, to identify outliers in heart rate data. The algorithms were tested using physiological data from human subjects' hearts. Results demonstrated that both outlier factor and regression trees algorithms were effective in detecting heart rate anomalies, with both models successfully generalising from their simulation heart rate data training to real-world heart rate data. In addition, the findings lend credence to the idea that, in the absence of real labelled data, simulated data can be used to configure methodologies to a certain degree of performance, indicating that this kind of training could be particularly useful in the initial rollout of a system with no preexisting data. © 2023 IEEE.

关键词： data mining

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Vulnerability Name Prediction Based on Enhanced Multi-Source Domain Adaptation 22

Vulnerability Name Prediction Based on Enhanced Multi-Source...

引用

IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Xing, Ying Zhao, Mengci Yang, Bin Zhang, Yuwei Li, Wenjin Gu, Jiawei Yuan, Jun Xu, Lexi Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Nanjing Univ Aeronaut & Astronaut Minist Ind & Informat Technol Key Lab Safety Crit Software Dev & Verificat Nanjing 211106 Peoples R China Yunnan Key Lab Software Engn Kunming 650091 Yunnan Peoples R China China Unicom Res Inst Beijing 100048 Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China NSFOCUS Technol Grp Co Ltd Beijing 100089 Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Software products have brought convenience to modern society but also pose significant security risks due to various types of vulnerabilities. Identifying vulnerability names is vital for program repair and software maintenance, but the lack of training data presents a challenge. Big data analytics and machine learning can help overcome this challenge by processing large amounts of data and improving the accuracy of vulnerability name prediction. Considering that the data is often from datasets composed of multiple sources, a feature-based or attention-based multi-source domain adaptation (MSDA) approach is required. In this paper, we propose an MSDA method based on both feature and attention to accomplish the task of predicting vulnerability names, called Multi-Source Domain Adaptation for Vulnerability Name Prediction (MSDA-VNP). First, MSDA-VNP reduces domain divergence by adversarial training and then uses domain-invariant features to obtain feature correlations between individual source and target domains. In combination with the obtained domain correlations, Weighted multi-kernel Maximum Mean Discrepancy (WMK-MMD) is proposed as the attention mechanism. Second, a data augmentation strategy is employed to enhance MSDA-VNP to identify privacy-related vulnerabilities. To evaluate our approach, we conducted experiments on eight Java real-world projects in the Software Assurance Reference dataset (SARD). The experimental results show that the proposed method MSDA-VNP performed efficiently and stably for the 44 types of vulnerabilities involved. The data augmentation strategy has also been proved to be effective as an enhancement for the proposed method MSDA-VNP.

关键词： vulnerability name prediction multi-source domain adaptation data augmentation adversarial training attention mechanism

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Big data-Driven Portfolio Simplification: Leveraging Self-Labeled Clustering to Enhance Decision-Making 10

Big Data-Driven Portfolio Simplification: Leveraging Self-La...

引用

16th IEEE/ACM international conference on Utility and Cloud computing (UCC) / 10th IEEE/ACM international conference on Big data computing, Applications and Technologies (BDCAT)

作者： Zhang, Minjuan Wu, Chase Q. Hou, Aiqin New Jersey Inst Technol Dept Data Sci Newark NJ 07006 USA Northwest Univ Sch Informat Sci & Technol Xian 710127 Shaanxi Peoples R China

ISBN: (纸本)9798400704734

In the evolving landscape of business analytical practice, big data stands as a pivotal force, steering organizational strategies, particularly in portfolio management across end-to-end businesses. With the surge in data's volume, variety, veracity and velocity, there is a pressing need for sophisticated computational methods to demystify intricate business portfolios, thereby facilitating astute decision-making. Traditional portfolio analysis techniques, although foundational, grapple with the challenges posed by expansive, multifaceted data and volatile market dynamics. To counter these challenges, our research pioneers an innovative approach, harnessing the power of clustering algorithms to refine and consolidate business portfolios. We employ big data techniques to analyze and categorize extensive portfolio datasets, unearthing inherent groupings and patterns. Leveraging clustering algorithms, we categorize business entities by similarity, yielding a streamlined and lucid portfolio blueprint. Our approach not only enhances the clarity of vast business portfolios but also strengthens strategic decision-making capabilities, propelling organizational nimbleness and market competitiveness. Through comparative analyses, our solution showcases significant advantages in portfolio simplification and decision-making efficacy over conventional techniques.

关键词： Decision making

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FEDMBP: MULTI-BRANCH PROTOTYPE FEDERATED LEARNING ON HETEROGENEOUS data 30

FEDMBP: MULTI-BRANCH PROTOTYPE FEDERATED LEARNING ON HETEROG...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Gao, Tianrun Liu, Xiaohong Yang, Yuning Wang, Guangyu Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China UCL UCL Canc Inst London England

ISBN: (纸本)9781728198354

Federated learning (FL) enables collaborative model training across clients while preserving data privacy. However, FL faces the challenge of data heterogeneity, leading to biased local models that deviate from the global model during optimization. And existing FL algorithms like Federated Averaging (FedAvg) suffer from this issue. To address this problem, we propose a novel approach called multi-branch prototype federated learning (FedMBP). FedMBP creates auxiliary branches within each local model to integrate different levels of local and global prototypes, thus preventing local model drift by aligning local prototypes with global ones. We also introduce mixed cross-entropy on the auxiliary branches to effectively transfer global prototype knowledge to local models. We conduct experiments on three publicly available datasets, including natural and medical image domains. Our experiments demonstrate that FedMBP outperforms existing FL algorithms, achieving superior model performance.

关键词： Federated learning data heterogeneity Prototype Regularization Medical image

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Wearable Smart Health Monitoring Transmitter and Portable Receiver Systems for Enhanced Wellness Insights 2

Wearable Smart Health Monitoring Transmitter and Portable Re...

引用

2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Sivaprasad, T.C. Surendhar, R. Heiner, A.J. Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9798350340235

This study presents the development and evaluation of a smart wearable belt designed for comprehensive health monitoring and fall detection. The wearable integrates advanced sensors, including vibration and heartbeat, to give users real-time insights into their well-being. The vibration sensor, meticulously calibrated for precise fall detection, exhibited high sensitivity and responsiveness, distinguishing between daily activities and potential accidents. Simultaneously, the heartbeat sensor demonstrated accurate and consistent heart rate monitoring, contributing to proactive cardiovascular health management. The seamless integration of these sensors, supported by MQTT connectivity and secure data transmission, ensures a user-friendly experience. Cloud integration enhances data processing and analytics, empowering users with a comprehensive health dashboard. Results indicate the reliability of the fall detection system and the accuracy of heart rate monitoring. This intelligent wearable belt represents a promising advancement in health technology, offering a holistic solution for users seeking real-time health insights and proactive fall prevention. Ongoing research focuses on refining algorithms and incorporating user feedback for continuous improvement and broader applications in personalizedhealth care. © 2023 IEEE.

关键词： data analytics Patient monitoring Smart belt Wearable Sensor

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Research on Transmission line alternatives selection based on cross-system data fusion 8

Research on Transmission line alternatives selection based o...

引用

8th international conference on Intelligent computing and Signal processing, ICSP 2023

作者： He, Bing Xu, Xiaoming Mao, Jiaying State Grid Shanghai Extra High Voltage Company Shanghai China

ISBN: (纸本)9798350302455

The safety of transmission lines is measured from several different perspectives. The data of different video surveillance, UAV monitoring, Blockchain social inspection, and internal and external network manual inspection of the transmission line are all connected to the display screen of the transmission line monitoring center. Then the health level of the transmission line is evaluated based on the different perspectives of data in real time. The evaluation needs a series of standard systems to evaluate comprehensively, the data of a single system is not accurate enough. In this paper, the fuzzy multi-attribute decision-making method is used to evaluate the optimal selection of data for each subsystem, and the optimal alternative is selected during the transmission line comprehensive, The method of mathematics can evaluate the health level of transmission lines more accurately, the results hope to provide some proposals for the operation of transmission lines to develop transmission line maintenance policies. © 2023 IEEE.

关键词： Electric lines

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Real-Time Object Detection to Identify Adults and Children Using YOLO algorithms

Real-Time Object Detection to Identify Adults and Children U...

引用

international conference on Computational Science and Computational Intelligence (CSCI)

作者： Abdulghani, Abdulghani M. Abdulghani, Mokhles M. Walters, Wilbur L. Abed, Khalid H. Jackson State Univ Jackson MS 39217 USA

ISBN: (纸本)9798350361513;9798350372304

Remotely piloted vehicles such as drones or mobile robots are equipped with a light Graphics processing Unit (GPU) to perform image processing tasks with limited complexity level. Thus, such systems essentially require light-weight algorithms to perform complex tasks such as object detection. For this purpose, the YOLO algorithms come in handy with different structures that support light, small, medium, and large devices like YOLO-tiny and YOLO-small. The aim of this research is to use a lightweight version of YOLO algorithms with high accuracy to detect and recognize three objects, namely child, female, and male, in real-time. To achieve this, we have used the YOLOv6-small and YOLOv7-tiny algorithms to detect the three objects. We have also included data augmentation techniques to enhance the detection accuracy and evaluation metrics (precision, recall, and MAP@.5). The results have been promising with the YOLOY6-small, augmented model showing a +6% increase in MAP@.5 far the child object and a +4% increase for the female object. On the other hand, we have managed to reduce wrong detection in some cases using the YOLOv7-tiny algorithm.

关键词： Artificial Intelligence Object Detection YOLOv7-tiny YOLOv6-small data Augmentation Convolutional Neural Network Real-time detection

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Poster: Exploring Synthetic data Generation for Anomaly Detection in the 5G NWDAF Architecture 43

Poster: Exploring Synthetic Data Generation for Anomaly Dete...

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43rd IEEE international conference on Distributed computing Systems (ICDCS)

作者： Ock, Jiwon No, Hyeon Kim, Seongmin Sungshin Womens Univ Seoul South Korea

ISBN: (纸本)9798350339864

The transition of paradigm from non-standalone to standalone mode in 5G network creates an opportunity to utilize innovative machine learning and AI-based technology for network traffic analysis. In particular, NWDAF plays a key role in leveraging AI-based models to optimize and enhance the 5G core network functions, including anomaly detection and load balancing. However, it is challenging to ensure the sufficient performance of prediction algorithms under dynamic conditions in NWDAF without guaranteeing the quality and quantity of training data, but only a select group has access to the 5G dataset. To overcome this issue, this paper proposes an approach to generate high-quality synthetic 5G NWDAF data using CTGAN, a specialized generative model that creates synthetic output based on tabular input data. We provide preliminary results of leveraging CTGAN to generate 5G synthetic data and evaluate the synthesizing quality for anomaly detection.

关键词： 5G Network NWDAF synthetic data CTGAN anomaly detection

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A High Performance computing Platform for Big Biological data Analysis 9

A High Performance Computing Platform for Big Biological Dat...

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9th international conference on Applied System Innovation, ICASI 2023

作者： Yeh, Chang-Wei Huang, Chieh-Wei Yang, Chia-Lee Wang, Yu-Tai National Center for High-performance Computing National Applied Research Laboratories Hsinchu Taiwan

ISBN: (纸本)9798350398380

With the rapid progress of high-throughput sequencing projects, biological data is growing exponentially, creating a need for efficient and scalable algorithms and high-performance computing (HPC) platforms for big biological data analysis. However, traditional HPC platforms are inadequate for meeting the demand for rapid data analysis tasks in bioinformatics research. Analyzing biological sequence data is challenging due to its complexity and long computing time. This poses challenges for researchers to gain a deep understanding of biological functions. Thus, this research presents a large-scale biological data analysis platform enabled by an HPC infrastructure for genomic sequencing data and protein structure analysis. This study lists the characteristics of big biological data and traditional computing platforms, provides a reconfigurable HPC platform for biological data analysis applications. A practical case of HPC platforms for big biological data analytics in Taiwan is also presented. By using a reconfigurable HPC platform, the computational bottleneck can be removed, and data analysis can be accelerated. The platform enables researchers to gain profound insights into the deepest biological functions by addressing the challenges of big biological data analysis. © 2023 IEEE.

关键词： Big data

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