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

共 6952 条记录，以下是101-110 订阅

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Predicting Highway Accident Severity in Brazil: Environmental Factors and Vehicle Features 20

Predicting Highway Accident Severity in Brazil: Environmenta...

引用

20th Annual international conference on Distributed computing in Smart Sensor Systems and the Internet of Things (DCOSS-IoT)

作者： Scholz, Julio C. W. Pinheiro, Yan P. Silva, Thiago H. Univ Tecnol Fed Parana Informat Curitiba Parana Brazil

ISBN: (纸本)9798350369458;9798350369441

This work supports the problematization of the high rate of traffic accidents due to the increased use of automobiles for utility or transport in Brazil. Every year, the lives of approximately 1.3 million people are interrupted due to traffic accidents worldwide. In this regard, this study proposes the use of data mining as an approach to analyze and explore datasets of traffic accidents that occurred on Brazilian highways between the years 2017 and 2022, as provided by the Federal Highway Police. Additionally, vehicle price data were included, allowing for a more comprehensive analysis that also considers the financial value of the vehicles. The goal is to assess the predictive capability of classification models regarding the severity of accidents, focusing on vehicle characteristics and environmental factors. By applying classification algorithms and machine learning explainability techniques, we acquired relevant knowledge regarding the studied data, contributing to understanding and preventing accidents. As a result, the attributes related to vehicle characteristics had a more positive impact on the predictive capability of the models when compared to the attributes describing the environment and other variables.

关键词： road accidents data mining classification explicability Shapley values

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Brain tumor classification model of ResNet-50 network based on different data enhancement algorithms 8

Brain tumor classification model of ResNet-50 network based ...

引用

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

作者： Zou, Menghan Ma, Mingze Wang, Anan Li, Yujie Toe, Teoh Teik Harbin Institute of Technology Weihai School of Computer Science and Technology WeiHai China Harbin Institute of Technology Weihai Welding Technology and Engineering WeiHai China Harbin Institute of Technology Weihai Marine Information Engineering WeiHai China Harbin Institute of Technology Weihai Information Management and Information System WeiHai China Nanyang Technological University Ntu Business Ai Lab Singapore

ISBN: (纸本)9798350302455

The accuracy and stability of brain tumor MRI image classification is significant for the healthcare system, but the traditional models have the defects of difficulty in handling complex features and unstable classification. In this paper, we propose a novel brain tumor classification model based on residual neural network, and use three different data enhancement algorithms: geometric transformation, mixup, and SamplePairing to process a dataset containing four different types of brain tumor images, and finally evaluate the classification effect of our proposed model. Among them, the best results were achieved by using geometric transformations for data enhancement. © 2023 IEEE.

关键词： Image classification

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Study of time series algorithms for real-time weather prediction for a Gamma-ray Experiment 30

Study of time series algorithms for real-time weather predic...

引用

30th international conference on High Performance computing, data, and Analytics (HiPC)

作者： Sarkar, Debangana Chouhan, Nilesh Yadav, Kuldeep Homi Bhabha Natl Inst Bhabha Atom Res Ctr Mumbai Maharashtra India Bhabha Atom Res Ctr Astrophys Sci Div Mumbai Maharashtra India

ISBN: (纸本)9798350383782;9798350383799

Weather parameters and weather forecasting play a crucial role in scientific experiments for the safe operation of the system. Weather prediction involves high-performance computing, which enables building of weather models. Major Atmospheric Cherenkov Experiment (MACE) is a 21m ground-based, very high-energy gamma-ray telescope installed at Hanle, India. Accurate and reliable short-term weather prediction plays a crucial role in ensuring the safe and efficient functioning of the telescope. In the first part of the paper, we describe the multithreaded design of the real-time weather data capture application for the telescope. In the second part, we compare the performance of time series algorithms for weather prediction and the possibility of hybrid models for predicting non-stationary weather factors.

关键词： Gamma Ray Experiment Weather data Analysis Time Series Algorithm Computation

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data Center KPI Prediction based on Contrastive Learning and Frequency Domain Enhancement

Data Center KPI Prediction based on Contrastive Learning and...

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algorithms, computing and data processing (acdp), international conference on

作者： Kun Xue Xiangyang Feng Ming Zhu Qinglan Luo Xia Shang College of Computer Science and Technology Donghua University Shanghai China Board of Directors Moule Network Technology Co. Ltd Shanghai China Product Department 2 Moule Network Technology Co. Ltd Shanghai China

As the scale and complexity of data centers continue to increase, intelligent operation and maintenance (O&M) has become one of the important technologies to ensure high performance and availability. Among them, Key Performance Indicator (KPI) data reflects the health status of data center systems and plays a key role in intelligent O&M. Accurate KPI time series prediction is essential for detecting KPI anomalies and improving the reliability of data center systems. However, traditional time series prediction models have low accuracy when dealing with KPI data. Therefore, this paper proposes a time series prediction model based on contrastive learning and frequency domain attention mechanism to more fully capture the temporal features of KPI data, improve prediction accuracy and reliability. The experimental results show that the proposed model exhibits strong competitiveness in KPI time series prediction.

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Anomaly Detection and Categorization for a data Quality Management Framework in Financial Regulatory Reporting

Anomaly Detection and Categorization for a Data Quality Mana...

引用

Enterprise Design, Operations, and computing (EDOC) Forum / IDAMS Workshop / SoEA4EE Workshop / IRESEARCH Workshop / MIDas4CS Workshop

作者： Tafech, Aya Univ New South Wales Sydney NSW 2052 Australia

ISBN: (纸本)9783031547119;9783031547126

Financial institutions are subject to stringent regulatory reporting requirements tomanage operational risk in international financialmarkets. Producing accurate and timely reports has raised challenges in current data processes of big data heterogeneity, system interoperability and enterprise-wide management. data quality management is a key concern, with current approaches being timeconsuming, expensive, and risky. This research proposes to design, develop, and evaluate a Financial Reporting data Quality Framework that allows non-IT data consumers to contextualize data observations. The framework will use anomaly algorithms to detect and categorize observations as genuine business activities or data quality issues. To ensure sustainability and ongoing relevance, the framework will also embed an update mechanism.

关键词： Big data data Quality Financial Regulatory Reporting Anomaly detection algorithms Machine Learning

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Fuzzy Image Restoration Algorithm Based on Machine Vision Technology

Fuzzy Image Restoration Algorithm Based on Machine Vision Te...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Shi, Minhong Shanxi Engineering Vocational College Taiyuan China

ISBN: (纸本)9798350313413

Fuzzy image refers to the problems of image blur, noise, and distortion caused by many situations during the acquisition, transmission and processing. Restoration of fuzzy images is an important problem in the field of image processing and computer vision. This study tells about a kind of fuzzy image recovery algorithm with machine vision technology, and realizes the automatic restoration of fuzzy image by using the L-R algorithm. Specifically, We used experimental comparison to compare the advantages and disadvantages of L-R algorithm and traditional algorithm when dealing with fuzzy images, and apply the method to image restoration in actual scenes and compare its parameters. Experimental data shows that the parameters of fuzzy image restoration based on L-R algorithm are 0.0521, 0.2705, and 0.1654 on MSE, RMSE, and PSNR, which are better than traditional inverse deconvolution algorithms. It proves that the fuzzy image recovery algorithm based on machine vision technology has higher recovery effect and better robustness. The algorithm can adjust parameters adaptively, and is suitable for fuzzy image restoration of different types and degrees. © 2023 IEEE.

关键词： algorithm restoration data acquisition image recognition machine vision

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Multi-source Analysis of AI Vulnerabilities: Methodology and algorithms of data Collection 12

Multi-source Analysis of AI Vulnerabilities: Methodology and...

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12th IEEE international conference on Intelligent data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Neretin, Oleksii Kharchenko, Vyacheslav Fesenko, Herman National Aerospace University 'KhAI' Kharkiv61070 Ukraine

ISBN: (纸本)9798350358056

The initial stage of assessing the cybersecurity of artificial intelligence (AI) systems and tools for cyber-physical systems is the collecting, processing and integrating of vulnerability data from relevant and reliable information sources using Big data tools. The article provides a classification of sources of information about vulnerabilities of AI systems. The possibilities of automatic scanning of these sources using software scanners are analyzed. The analysis methodology and data structures are proposed, and the necessary set of data for further storing and analyzing is selected. algorithms for direct collection of vulnerability data from various sources are developed. The directions of further research on the development of algorithms and software tools for aggregation, filtering, processing and integration of multisource data on vulnerabilities of AI systems are substantiated. © 2023 IEEE.

关键词： AI systems vulnerabilities assessing the cybersecurity of AI Big data data collecting data integrating data processing

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Demand Work Order Dispatching Method Based on Power Big data and RNN

Demand Work Order Dispatching Method Based on Power Big Data...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Yin, Qing Inner Mongolia Power Hohhot City China

ISBN: (纸本)9798350313413

This paper aimed to explore a method of demanding work order distribution based on power Big data (BD) and Recurrent Neural Network (RNN), so as to improve the efficiency and accuracy of work order distribution. This research would adopt the method of combining BD of electric power and RNN algorithm to establish a demand work order assignment model. According to the experimental results, the model proposed in this article performed better on various indicators. The performance of the RNN model in terms of recall rate was 0.84, which was 27% and 13% higher than the Lasso model and CART model, respectively. In terms of precise values, the performance of this model was 23% and 8% higher than other models. This study provided new ideas and methods for work order processing in power enterprises, and provided strong support for their management and operation. © 2023 IEEE.

关键词： automatic dispatch demand work order allocation model long short term memory network power big data recurrent neural network

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Comparative Study of Map Reduce Task Scheduling Optimization Techniques

Comparative Study of Map Reduce Task Scheduling Optimization...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Kumar, Vishal Kushwaha, Sumit Chandigarh University Computer Applications Department Gharuan Mohali India

ISBN: (纸本)9798350313413

As data processing capabilities improved, the flow of data increased significantly, necessitating the use of distributed systems and a new programming framework, MapReduce, to manage this massive amount of information. Any job requiring a large amount of data must be divided into subtasks for parallel execution. In order for all subtasks to be executed concurrently need to be scheduled parallelly on available set of virtual machines. This is not an easy task;it requires consideration of a number of critical parameters such as make-span, Execution cost, risk of execution, energy consumption and so on. The optimization of these parameters is critical for effective scheduling. The most effective hybrid scheduling techniques have been developed and implemented on a single platform with a diverse set of virtual machines. On a single platform, it compares all well-known and popular scheduling techniques. All experimental values for various parameters of interest for all best-ever scheduling techniques will be recorded for analysis, and results representing parameter-wise best-available scheduling techniques will be presented. This comparative study aids in the direction of future research and serves as a guide for progress from the current state of the research. © 2023 IEEE.

关键词： data processing MapReduce framework Optimization Scheduling Parameters Virtual Machines

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Classification of Blood Cell data using Deep Learning Approach

Classification of Blood Cell Data using Deep Learning Approa...

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2023 IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2023

作者： Yepuri, Sravya Marandi, Ashapurna CSE PES University Bengaluru India CSIR-4th Paradigm Institute Bengaluru India

ISBN: (纸本)9781665462631

Machine Learning is an established branch of artificial intelligence that comprises of algorithms and mathematical relationships and is rapidly being used to clinical research. Machine Learning enables computers to be programmed without explicit information and to learn from it. The outcomes of using these technologies in medical data processing have been amazing, with great effectiveness in detecting illnesses. According to studies, Machine Learning techniques significantly improve complex medical decision-making processes in medical image processing by extracting and then evaluating the features of these pictures. An urgent demand for more sophisticated data analysis approaches has arisen as the number of medical diagnosis tools rose and a significant amount of high-quality data was produced. Traditional methodologies are incapable of examining such vast amounts of data. One of the critical diseases, Leukemia can be found in the blood cell data image of patients by applying machine learning algorithms. Here in this paper, the blood cell images available in the open literature are analyzed using the different algorithm networks for the refinement of images to identify the diseases like leukemia. © 2023 IEEE.

关键词： Diseases

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