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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711-720 订阅

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Aerial Image Stitching Using IMU data from a UAV 8

Aerial Image Stitching Using IMU Data from a UAV

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8th international conference on Image, Vision and computing, ICIVC 2023

作者： Iz, Selim Ahmet Unel, Mustafa Sabanci University Faculty of Engineering and Natural Sciences Istanbul Turkey

ISBN: (纸本)9798350335231

Unmanned Aerial Vehicles (UAVs) are widely used for aerial photography and remote sensing applications. One of the main challenges is to stitch together multiple images into a single high-resolution image that covers a large area. Feature-based image stitching algorithms are commonly used but can suffer from errors and ambiguities in feature detection and matching. To address this, several approaches have been proposed, including using bundle adjustment techniques or direct image alignment. In this paper, we present a novel method that uses a combination of IMU data and computer vision techniques for stitching images captured by a UAV. Our method involves several steps such as estimating the displacement and rotation of the UAV between consecutive images, correcting for perspective distortion, and computing a homography matrix. We then use a standard image stitching algorithm to align and blend the images together. Our proposed method leverages the additional information provided by the IMU data, corrects for various sources of distortion, and can be easily integrated into existing UAV workflows. Our experiments demonstrate the effectiveness and robustness of our method, outperforming some of the existing feature-based image stitching algorithms in terms of accuracy and reliability, particularly in challenging scenarios such as large displacements, rotations, and variations in camera pose. © 2023 IEEE.

关键词： Aerial photography

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Prediction of Coagulant Dosage in Waterworks Based on Ensemble Learning 12

Prediction of Coagulant Dosage in Waterworks Based on Ensemb...

引用

12th international conference of Information and Communication Technology, ICTech 2023

作者： Hu, Yingtao Zhang, Yonghong Jian, Duan Li, Jin University of Chinese Academy of Sciences Shenyang Institute of Computing Technology Chinese Academy of Sciences Beijing China Shandong University Laboratory of Big Data and Artificial Intelligence Technology Jinan250100 China Institute of Future Artificial Intelligence Technology and Innovative Applications Beijing100025 China

ISBN: (纸本)9798350332919

There are many processes of water treatment in waterworks, among which coagulation and precipitation plays an important role, which is directly related to the guarantee of water quality. The traditional coagulant dosing technology relies too much on manual labor, has poor stability and high drug consumption. The rapid development of artificial intelligence and machine learning provides a new way to solve the coagulant prediction problem. Combined with the advanced theory of artificial intelligence, a prediction method of coagulant based on multi-model fusion Stacking ensemble learning approach is proposed in this paper. Considering the differences in data observation and training principles of different algorithms, the advantages of each model are fully utilized to build a Stacking ensemble learning model for coagulant prediction with multiple machine learning algorithms embedded, and the base learners of the model contain XGBoost and SVR. data processing goes through steps such as missing value filling, outlier detection, data conversion, and combining with business scenarios for feature construction, feature selection, etc. Finally, the validity of the algorithm was verified using the production data of a water plant. The prediction results show that the coagulant prediction method based on Stacking ensemble learning approach with multi-model fusion has higher prediction accuracy compared with the traditional single-model prediction. © 2023 IEEE.

关键词： Forecasting

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Technical Analysis of Stock Market using data Science and its tools 7

Technical Analysis of Stock Market using Data Science and it...

引用

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

作者： Pagar, Shrawani Jaiswal, Aditya Auti, Atharva Purohit, Vansh Mishra, Vivek Shah and Anchor Kutchhi Engineering College Department of Cybersecurity Mumbai India Shah and Anchor Kutchhi Engineering College Department of Computer Science Mumbai India

ISBN: (纸本)9798350304268

This paper presents a technical analysis approach for the stock market using data science and its associated tools. The objective is to develop a systematic framework that leverages data science techniques to analyze stock market data and make informed investment decisions. The proposed methodology involves data collection, preprocessing, feature engineering, and the application of various machine learning algorithms for predicting stock prices and identifying potential trading opportunities. The study demonstrates the effectiveness of data science in enhancing traditional technical analysis methods and provides insights into the potential benefits of integrating data-driven approaches into stock market analysis. Experimental results on historical stock data validate the proposed approach's accuracy and highlight its potential for improving investment strategies. © 2023 IEEE.

关键词： dataframe demand and supply zone explosive candles jupyter notebook python sql yfinance

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Attribute-wise Sample Generator: A Module for Generalized Long Tail data

Attribute-wise Sample Generator: A Module for Generalized Lo...

引用

2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Yu, Daao Gu, Pengfei School of Big Data University of Science and Technology of China Hefei China

ISBN: (纸本)9781510671881

Existing long-tail classification (LT) methods ignore attribute class imbalance and focus only on addressing class imbalance where the head class has more samples than the tail class. In fact, even if the classes are balanced, the samples in each class can still be long-tailed due to attribute variability. The latter is fundamentally more general and difficult than the former, not only because attributes are implicitly included in most datasets, but also because they are more complex to construct. Therefore, we introduce a new research question, generalized long-tail classification [1] (GLT), to jointly consider these two imbalances. And the ASG model is proposed to deal with the long-tail problem of attributes on the data set. ASG is a generalized class rebalancing method, which aims to balance the classes and attributes of the training data. It uses a feature displacement model to extract feature displacements, and then generates new samples according to the distribution of attributes in each class. Experiments prove that ASG has achieved good results on various datasets. © 2023 SPIE. All rights reserved.

关键词： Classification (of information)

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Construction of Intelligent Health Management Cloud Platform Based on Big data

Construction of Intelligent Health Management Cloud Platform...

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

作者： Ma, Rui Zhou, Fengjiao Chengdu University of Traditional Chinese Medicine Health and Rehabilitation College Chengdu China Affiliated Hospital of Chengdu University of Traditional Chinese Medicine Acupuncture School Chengdu China

ISBN: (纸本)9798350313413

With the advent of the era of big data, more and more people get the information they need and are interested in through various channels in daily life, and the traditional medical model can no longer meet the needs of human health management. In order to solve this problem, this paper builds an intelligent health management cloud platform based on big data, and uses big data and cloud computing technology to provide users with a new health management platform, which can provide health records, health checks, health monitoring and other functional services. The platform carries out health risk assessment from three aspects: information collection, risk calculation and assessment report, and carries out risk assessment on human health. The average abnormal detection rate of volunteers after intervention is about 23.2%. Based on the application of the big data intelligent health management cloud platform, the detection rate of abnormal diseases among volunteers decreased by about 5%. It can be seen that the application of the intelligent health management cloud platform based on big data has significantly reduced the detection rate of abnormal diseases in volunteers. The construction of such a platform can help individuals maintain their health, provide users with a more personalized and humanized service experience, and meet the diverse needs of people at different levels. This advance in smart medicine helps to improve healthcare delivery and outcomes across the board. © 2023 IEEE.

关键词： big data intelligence cloud platform disease abnormalities health management health risk assessment

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Classification of Massive data Sets Using a Revolutionary Grey Wolf Optimization Algorithm and a Deep Learning Model in a Cloud-Based Setting

Classification of Massive Data Sets Using a Revolutionary Gr...

引用

2023 international conference on data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Anandan, P. Manju, A. Reddy, Murthy Ravaleedhar Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science & Technology School of Computing College of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India Hcl Technologies Ltd Tamil Nadu Chennai India

ISBN: (纸本)9798350348910

The field of big data analytics has attracted a considerable amount of attention in the realm of academic research due to the fact that it is incredibly useful in a wide variety of real-time applications. This is the reason why the subject has received so much attention. The relatively recent development of machine learning and deep learning models has resulted in an improvement in performance. This improvement has been brought about as a result of the development of these models. The application of these models to the study of massive datasets has become much simpler as a result of these models, which has led to an improvement in performance outcomes. When considering the complexity of large data and the processing requirements that it imposes, it is beneficial to employ feature selection strategies that make use of metaheuristic optimization algorithms. This is due to the fact that big data is distinguished by the vast quantity of information that it contains. There is a huge advantage in the fact that these algorithms are able to successfully uncover the best potential set of features, which ultimately results in improved classification performance. In this particular piece of literature, a new approach that is known as the GWOA-DBN model is presented as a solution to the problem. In order to construct this model and the benefits that come along with it, the Grey Wolf Optimization algorithm and the optimal deep belief network have been coupled. The objective of the Apache Spark environment is to find a solution to the problem of classifying enormous volumes of data. This is the goal of the environment, which is the ultimate objective of the environment. The GWOA-DBN technique, which involves the construction of a feature selection method, is built on the base of the Grey Wolf Optimization Algorithm (GWOA), which acts as the cornerstone for the methodology. In the process of utilizing this strategy, the goal is to determine which subset of traits is the most ideal

关键词： Classification (of information)

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Research on Intelligent Recommendation Algorithm Based on data Mining in Credit Bank

Research on Intelligent Recommendation Algorithm Based on Da...

引用

2023 IEEE international conference on Image processing and Computer Applications, ICIPCA 2023

作者： Dong, Jing Fan, Wenbo Ding, Lichao Chen, Long Zhan, Yang Tang, Jiaying Army Artillery and Air Defense Forces Academy Vocational Education Centre Shenyang China Army Artillery and Air Defense Forces Academy Teaching and Research Support Centre Shenyang China Army Artillery and Air Defense Forces Academy Teaching and Evaluation Center Shenyang China Army Artillery and Air Defense Forces Academy Office of Academic Affairs Shenyang China

ISBN: (纸本)9798350314670

This article investigates the intelligent recommendation problem in credit banks and designs experiments to validate three algorithms based on association rules, clustering, and classification. In terms of data preprocessing, methods such as data cleaning and feature selection are used. In terms of algorithm implementation, the principles, implementation processes, and pseudocode of the association rule algorithm, clustering algorithm, and classification algorithm are introduced respectively. The results show that all three algorithms can effectively recommend courses suitable for users' learning needs. The research in this article provides new ideas and methods for intelligent recommendation problems in credit banks, and provides some reference for subsequent research. © 2023 IEEE.

关键词： data mining

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Quantum Machine Learning algorithms for Diagnostic Applications: A Review

引用

international Virtual conference on Industry 4.0, IVCI4.0 2021

作者： Pophale, Shruti S. Gadekar, Amit Department of CSE Sandip University Maharashtra Nashik India School of Computer Science and Engineering Sandip University Maharashtra Nashik India

ISBN: (纸本)9789811999888

Big data analytics is a huge information investigation in the utilization of cutting edge scientific procedures against extremely enormous, various in-formational collections that incorporate organized, semi-organized and unstructured information, from various sources, and in various sizes from terabytes to zettabytes. Big data is in many formats such as text, photographs, videos, and so on. Big data goes beyond the storage and processing capability of a traditional computer. Accordingly, distinguishing designs in enormous information is extremely challenging. A classical computing measures a lot of information over a long measure of time. Quantum computing is a new research area where information and data are computed in a short measure of time. This is a survey study that glances at how enormous information and quantum machine learning can be utilized to distinguish information designs for a collection of utilizations. The advantages of quantum guided and unsupervised machine learning are contrasted to those of classical computing. Quantum computing in machine learning algorithms: challenges and techniques are also discussed. Despite the fact that quantum machine learning is a promising area, several real-world applications have been discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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Beyond the Fourth Paradigm - the Rise of AI 19

Beyond the Fourth Paradigm - the Rise of AI

引用

19th IEEE international conference on e-Science, e-Science 2023

作者： Marek, Andreas Rampp, Markus Reuter, Klaus Laure, Erwin Max Planck Computing and Data Facility Garching Germany

ISBN: (纸本)9798350322231

Thanks to the availability of huge amounts of data and improved computational resources, AI methods are gaining importance in scientific workflows, from image recognition and natural language processing to materials science. In many domains the usage of AI is under active investigation and first results show a tremendous potential, suggesting that AI will have significant impact way beyond the currently dominating examples of image and language processing. © 2023 IEEE.

关键词： Image recognition

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Investigation on Multi-objective Optimization of Construction Engineering Based on data Mining Association Rule Algorithm 1

Investigation on Multi-objective Optimization of Constructio...

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1st IEEE international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Gu, Shoupu Binzhou Polytechnic Binzhou256600 China

ISBN: (纸本)9798350323795

In recent years, with the rapid development of computer technology, people have put forward higher requirements for data mining tools and methods. This paper mainly studied multi-objective optimization problems in the field of construction engineering. By establishing models, information such as building structures and buildings was modeled and processed, and influencing factors were analyzed and predicted. After being quantified, the examples were used to illustrate the application effect, and the model was optimized. The test results showed that the data processing efficiency of the multi-objective optimization model for construction engineering based on the data mining association rule algorithm was around 74% to 77% before optimization, and the data processing efficiency after optimization was between 80% to 90%. At the same time, appropriate index parameter values were selected based on the actual situation to maximize the synergy between engineering projects and optimize economic benefits, thereby achieving the goal of improving construction efficiency and quality. © 2023 IEEE.

关键词： Multiobjective optimization

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