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Design and implementation of algorithm execution time predictor for remote sensing product workflow 3

Design and implementation of algorithm execution time predic...

引用

3rd international conference on Advanced algorithms and Signal Image processing, AASIP 2023

作者： Zhou, Bing Wu, Qiong Li, Jiaguo Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475001 China School of Computer and Information Engineering Henan University Kaifeng475001 China Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9781510668522

Remote sensing product production is an important task in the field of remote sensing engineering. Compared with the production process of remote sensing products, the implementation process of remote sensing product production is a multi-Task serial and highly concurrent process that integrates a large number of remote sensing digital image processing algorithms and various processes. Remote sensing product workflows transform complex computing tasks into processes that can be described, managed, and automated. In the workflow, algorithms are the smallest execution unit of tasks, and their execution time has a significant impact on the performance and efficiency of the entire workflow. Therefore, accurate prediction of algorithm execution time is crucial for optimizing workflow performance. This paper proposes and designs an algorithm execution time predictor for remote sensing product workflows. Firstly, feature extraction algorithms are used to extract features from algorithm execution logs, and the prediction model suitable for this scenario is determined by analyzing the characteristics of operators in remote sensing product workflows. Then, the model is trained using historical data sets, and the test set results are compared with the actual execution time to verify the accuracy and practicality of the model. Experimental results show that the proposed algorithm execution time predictor outperforms the baseline model in terms of prediction accuracy and time efficiency © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Remote sensing

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Pipeline data calibration for the construction of three-dimensional underground pipeline system

Pipeline data calibration for the construction of three-dime...

引用

2023 international conference on Computer Graphics, Artificial Intelligence, and data processing, ICCAID 2023

作者： Han, Zhiling Zhang, Kuanshuang Wang, Yudan Fei, Tingwei Xu, Yongwei Wu, Zhicheng Beijing Jinghang Institute of Computing and Communication No.1 Yungang West Road Fengtai District Beijing100074 China

ISBN: (纸本)9781510677685

To solve the problem that manual calibration is time-consuming and laborious when some nodes of pipeline data are not marked, this paper proposes an intelligent calibration method of pipeline segment and pipeline point data based on the "node-edge"relationship of the network. Combining with ArcGIS, Python and other tools, it employs the broken line at the cross point and the break point to intelligently generate important unmarked nodes in the original network, which can avoid manual inspection of pipeline segment by segment, diagnose and correct the error data in the original data during marking the starting and ending points of the pipeline. A project case study is employed to verify the efficiency and feasibility of the method, in which the processed pipeline data provides basic data support for the two-dimensional (2D) and three-dimensional(3D) modeling and system construction of the pipeline, improves the accuracy of data processing and the efficiency of the pipeline data calibration. © 2024 SPIE.

关键词： data handling

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Exploratory data Analysis and data Segmentation using K means Clustering 3

Exploratory Data Analysis and Data Segmentation using K mean...

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3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Devaghi, P. Sudha, S. Hindustan Institute of Technology and Science Computer Application Chennai India

ISBN: (纸本)9798350399264

Customer Segmentation plays a vital role in CRM, where we are managing all your organization's relationships and interactions between the customers. It helps to find potential customers and to take proper decisions in digital marketing. Segmenting or grouping the customers based on various attributes like age, gender, interest, and purchase behavior helps e-commerce companies to increase their revenue. One of the major problems today in customer segmentation is the level of noise in the data, which increases the data storage as well as impacts the data mining analysis results. This research focuses on filtering the noise in the data and a comparison study of various clustering algorithms will be done, and results will be analyzed, and a new generic data model will be built that works with any e-commerce platform data. More than doing the customer segmentation in this study we will also analyze how customers responded to the marketing campaigns. © 2023 IEEE.

关键词： K-means clustering

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Privacy protection oriented on data stream computing

Privacy protection oriented on data stream computing

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2022 international conference on Computer Application and Information Security, ICCAIS 2022

作者： He, Renjie Zheng, Xianghan Wang, Gong School of Computer and Big Data Fuzhou University Fujian Fuzhou China The Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fujian Fuzhou China

ISBN: (纸本)9781510663459

data security has always been one of the public concerns, and homomorphic encryption technology, as one of the effective means to ensure data security, has always been a hot topic of academic research. Traditional machine learning is trained in a batch environment. In practice, data owners tend to outsource data and model training tasks to third-party clouds with huge computing resources, but data outsourcing carries the risk of privacy leakage. Therefore, in order to prevent this situation, an effective method is to encrypt the data before the outsourcing, and the third-party cloud trains the machine model of the ciphertext data. This method has special requirements for encryption algorithms which should support computation directly on the ciphertext data. This paper presents a novel data flow computing privacy protection framework based on the homomorphic encryption algorithm CKKS and the flow data processing engine Flink, called SDPPF (Streaming data Privacy Protection on Flink). The proposed framework supports the functions of data stream encryption, decryption and ciphertext computing, and expands the functions of vector point multiplication and array sum operation on the basic simultaneous operation supported by the original CKKS algorithm. This paper also selects a classical machine learning algorithm: KNN algorithm, combined with the homomorphic encryption algorithm CKKS to realize the privacy protection machine learning algorithm. © 2023 SPIE.

关键词： Cryptography

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Analytical Results of Heart Attack Prediction Using data Mining Techniques 1

Analytical Results of Heart Attack Prediction Using Data Min...

引用

1st IEEE international conference on Contemporary computing and Communications, InC4 2023

作者： Abhijith, S. Mishra, Shaleen Prathap, Boppuru Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

In the modern era of living a fast lifestyle, people are not more conscious of their food eating and lifestyle. Due to these reasons, the chances of having a cardiac-related disease have risen drastically. This paper has studied the various supervised and unsupervised machine learning algorithms in comparative methods with best accuracy. Models like classification algorithms, regression algorithms, and clustering algorithms have been used for this paper. This research paper majorly focuses on patients with certain medical attributes that indicate a higher risk of heart disease. The model almost gives a good accuracy for all the regression and classification models when compared to the clustering models. Among all the algorithms, random forest and decision tree gives better accuracy © 2023 IEEE.

关键词： K-means clustering

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Determining Important Features in Multidimensional Health data for Individualized Precision Healthcare

Determining Important Features in Multidimensional Health Da...

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2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Cong, Ruichen Wu, Jianlun Nishimura, Shoji Ogihara, Atsushi Jin, Qun Graduate School of Human Sciences Waseda University Tokorozawa Japan School of Public Health Zhejiang Chinese Medical University Hangzhou China Waseda University Faculty of Human Sciences Tokorozawa Japan

ISBN: (纸本)9798350304602

In recent years, there has been a growing need for individuals' health management by using sensors and wearable devices to record daily activity and monitor health indicators. A large amount of health data needs to be analyzed to investigate the essential impact factors related to individuals' health and help individuals manage their health. In this paper, we investigate the important features influencing personal health from health data obtained from wearable devices and health data based on Traditional Chinese Medicine (TCM). We focus on investigating and selecting more influential health features and then performing machine learning algorithms for modeling. The results show that daily activity consumption is of a greater influence on wearable device data, and the pulse position that represents the kidney is identified as having the most significant impact on TCM health status among all pulse positions. Moreover, we selected the most influential features to perform the regression model and compared them with all the features. The results show that after feature selection, the Mean Squared Error (MSE) is smaller, and the R-square Score (R2) is greater than before. © 2023 IEEE.

关键词： Mean square error

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Application of Artificial Intelligence, Big data and Cloud computing in Optimizing Elevator Safety Performance 2

Application of Artificial Intelligence, Big Data and Cloud C...

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2nd IEEE international conference on Electrical Engineering, Big data and algorithms, EEBDA 2023

作者： Hu, Yunshu Tianjin University of Technology Tianjin China

ISBN: (纸本)9781665462532

With the development of society, the number of elevators is increasing, and optimizing the safety performance of elevators has become an urgent need for social safety management. Artificial intelligence, big data and other emerging technologies are gradually applied to all walks of life. Its application in the elevator industry has also attracted the attention of the industry and academia, but the related research is still in its infancy. Based on artificial intelligence, big data and cloud computing, this paper designs the elevator safety performance optimization scheme from five aspects respectively, reduces the possibility of failure before the accident, responds quickly after the accident, reduces the loss, and then collects the fault information to cooperate with the cloud to further optimize the fault prediction algorithm. These solutions can be cycled to continuously improve the safety performance of the elevator. © 2023 IEEE.

关键词： Big data

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A Space-Saving Based MLCS Algorithm

A Space-Saving Based MLCS Algorithm

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作者： Jiang, Botian Wang, Chunyang Fu, Yuanyuan Liu, Hai-Lin Guo, Ping Wang, Yuping School of Computer Science and Technology Xidian University Xi'an China Airborne Avionics Aviation Industry Corporation of China Shanghai China School of Mathematics and Statistics Guangdong University of Technology Guangzhou China International Academic Center of Complex Systems Beijing Normal University Beijing China

ISBN: (纸本)9798350304602

In this paper, we propose a branch and bound space saving method for finding all multiple longest common subsequences (MLCS). First, a more reasonable lower bound estimation strategy is designed. To construct a local directed acyclic graph (DAG graph) for the sequences, the size and distribution of the matching point coordinates are both considered when retaining nodes in each layer, so that the real common subsequence obtained is as long as possible. Then, a simpler upper bound estimation strategy is devised. A new concept 'boundary point' is defined, and a new upper bound calculation formula is derived by using the properties of the boundary point. So the upper bound value of the length of the branch of the current node can be quickly estimated, and the node is added to the graph only when this upper bound value is not less than the lower bound value. In addition, a storage-saving graph model is designed, which only retains the nodes with in-degree 0 at any time, and each node stores the string set corresponding to all paths from the source point to the node itself. The comparison experiments with 3 state-of-the-art algorithms on the standard biological data set indicate that the proposed algorithm is more effective and efficient. © 2023 IEEE.

关键词： data mining

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Design of Precise Marketing Model for Energy use Services Aimed at Increasing data Revenue

Design of Precise Marketing Model for Energy use Services Ai...

引用

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

作者： Jiang, Jianfeng Huang, Ruili Zhu, Wenjun Feng, Jiajun State Grid Shanghai Municipal Electric Power Company Marketing Service Center Shanghai China

ISBN: (纸本)9798350313413

As energy demand grows and competition in the energy industry intensifies, energy suppliers are becoming increasingly important to increase data revenue. With the rapid development of internet technology, the speed of data growth has approached saturation. How to maintain stability and continuously explore customer value in increasingly fierce competition has become an important issue for enterprises. Therefore, it is necessary to establish a complete, effective, reasonable, and efficient energy use service system and design precise marketing models. This article mainly applied the methods of comparative analysis and algorithm experiments to analyze the digital methods of precision marketing for energy services. The experimental results showed that the precision marketing algorithm successfully applied outbound marketing to 181 people. © 2023 IEEE.

关键词： data revenue increase energy use services model design precision marketing

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data-driven online learning recommendation system

Data-driven online learning recommendation system

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2023 international conference on Computer Application and Information Security, ICCAIS 2023

作者： Liu, Yuxiang Wang, Han Liu, Minghao School of Computer and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China School of Electronic Engineering and Automation Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510675032

The purpose of this study is to improve the performance and efficiency of online learning recommendation systems. The performance and efficiency of recommendation systems depend on multiple factors, including distributed computing, data storage, collaborative filtering algorithms, and optimization of recommendation accuracy. To address these issues, this study built a distributed computing system and HDFS data storage on the Spark platform, and implemented multiple recommendation algorithms to improve the performance and efficiency of the recommendation system. Through analysis of the Mooper dataset, this study conducted feature modeling and proposed a learning resource recommendation system based on Java, Scala, and web technologies, which can provide users with a better learning resource recommendation experience. In addition, this study also developed an anomaly learning behavior detection algorithm, which can detect anomalies in user behavior. Experimental results show that the accuracy and recall rate of the recommendation system have been improved, and the anomaly detection algorithm has high accuracy and robustness, thereby improving the reliability and stability of the recommendation system. In summary, the innovation of this study lies in proposing a recommendation system based on distributed computing and collaborative filtering algorithm, and making new progress in anomaly learning behavior detection. This study provides a new method for the field of recommendation systems, which can provide better solutions for the performance and efficiency of recommendation systems. © 2024 SPIE.

关键词： Anomaly detection

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