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An Effective Online Stream Feature Selection Auxiliary Method for High-Dimensional Unbalanced data 3

An Effective Online Stream Feature Selection Auxiliary Metho...

引用

3rd ieee international conference on computer Communication and Artificial Intelligence, CCAI 2023

作者： Qian, Xingtong Zhou, Yinghua Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing China

ISBN: (纸本)9798350335262

In the area of feature selection from highdimensional data, online streaming feature selection methods have received extensive attention in the past few decades due to their online selection abilities. Existing online stream feature selection methods perform well on many balanced datasets, But the real datasets are usually high-dimensional and unbalanced. For example, in medical examination data, the proportion of the sick people is much smaller than that of the healthy people. In the face of unbalanced data, traditional stream feature selection algorithms confront problems such as few selected features and low classification accuracy. Therefore, how to perform online stream feature selection under high-dimensional and unbalanced conditions is a challenge. In this paper, a general and easy-toimplement auxiliary algorithm is proposed, which can supplement the existing stream feature selection methods and dig out feature subsets effectively. Finally, the experiments are carried out on seven high-dimensional and unbalanced datasets and the results show that the auxiliary method can improve the traditional online stream feature selection methods and enable the classifiers to achieve better classification performance. © 2023 ieee.

关键词： Feature Selection

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A Multi-Objective Hyper-Heuristic Improved Configuration of Svm Based on Particle Swarm Optimization for Big data Cyber Security 3

A Multi-Objective Hyper-Heuristic Improved Configuration of ...

引用

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

作者： Shekhawat, Jyoti Tajpal Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

Big data Cyber security analytics is a growingly significant field of study and application that uses big data techniques and technology to analyses security event data in order to safeguard networks, computers, and data from illegal access. There have been several reports on Big data Cybersecurity Analytic Systems in the literature, but an exhaustive and systematic analysis of the research from an architectural viewpoint is lacking. When In this paper, the SVM design process is organized as a bi-objective optimization problem with accuracy and model complexity as two competing goals. The system presents a novel, problem-independent hyper-heuristic framework for bi-objective optimization. It is novel to create a hyper heuristic for this situation. The hyper-heuristic structure proposed comprises of low-level heuristics and a significant level system. In light of the pursuit execution, the significant level system figures out which low-level heuristic ought to be utilized to create another SVM arrangement. Every one of the low-level heuristics look through the SVM design search space really utilizing an alternate arrangement of rules. To estimate the Pareto set of SVM arrangements, the suggested design adaptively combines the benefits of degradation with Pareto-based strategies for dealing with bi-objective enhancement. Microsoft's anomalous intrusion detection and big data malware classification have been used to gauge the efficiency of the suggested methodology. © 2023 ieee.

关键词： Cybersecurity

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Research on Development of Government data Elements Based on Blockchain 3

Research on Development of Government Data Elements Based on...

引用

3rd international conference on Digital Society and Intelligent Systems, DSInS 2023

作者： Chen, Shenjin Yuan, Junying Zhang, Jianxin School of Electrical and Computer Engineering NanFang College Guangzhou Guangzhou510970 China

ISBN: (纸本)9798350331387

para>Blockchain is distributed, tamper proof, decentralized, and traceable, but it is difficult to implement. The introduction of smart contracts effectively solves this problem. This research uses the combination of privacy computing technology and blockchain technology to create an efficient and compliant solution for the data element market. With the help of smart contract technology based on blockchain, it provides effective technical management tools for all traders of data elements. Privacy computing and blockchain nodes are deployed in each transaction subject. On the premise of protecting the data privacy of each transaction party, the realization of transaction based on data value and the supervision and audit of the whole transaction process is an important security guarantee for the opening and sharing of government data according to the application. It is the most important business innovation application scenario for data elements to use smart contracts and microservices for sharing data authorization through data element development. © 2023 ieee.

关键词： Smart contract

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Water level prediction using long short-Term memory network with residual structure based on ensemble empirical mode decomposition 3

Water level prediction using long short-Term memory network ...

引用

3rd international conference on Electronic Information Engineering and computer, EIECT 2023

作者： Zhang, Zhichao Liang, Xuechun Xie, Lingnan Nanjing Tech University College of Electrical Engineering and Control Science Nanjing China Bank of Nanjing Credit Approval Department Nanjing China

ISBN: (纸本)9798350357707

To solve the problem about low accuracy of water level prediction caused by its nonlinearity and high noise, a water level prediction model based on EEMD-RESNET-LSTM is proposed. First, EEMD (Ensemble Empirical Modal Decomposition) is used for data noise reduction. Then, the RESNET-LSTM model is employed to predict the multiple components and residual term obtained after EEMD decomposition. Finally, the individual prediction results are aggregated to obtain the actual water level predictions. Compared to the three models, LSTM, RESNET-LSTM and EEMD-LSTM, the performance of this model is improved. Its RMSE is 0.127 m, MAE is 0.102 m, and R2 reaches 94.5% on water level prediction of Hongze Lake. © 2023 ieee.

关键词： Empirical mode decomposition

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Power Load Forecasting Model Based on LSTM and Prophet algorithm 3

Power Load Forecasting Model Based on LSTM and Prophet algor...

引用

3rd international conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Zhang, Zhiling Wang, Xu Wang, Jing Yuan, Xueyu College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China Institute of Economics and Finance Polessky State University Pinsk Brest Belarus School of Information Engineering Shaoguan University Shaoguan China

ISBN: (纸本)9798350331578

Power system load forecasting in different periods is of great practical significance to improve the economic and social benefits of the power system, while the complex and variable meteorological conditions, social events and many other uncertain factors may have effects on the load of power system, which makes the application of traditional load forecasting models have certain limitations. At the same time, with the diversification of the load structure of power system, the effect of model application reduces gradually, so the problem of power system load forecasting needs to be further studied. In this paper, the combination method based on LSTM neural network and Prophet time series forecasting model is applied in order to forecast the power load with their respective model advantages for the fluctuation pattern of power load time series in different time periods. The proposed model has a certain reference value for improving the forecasting accuracy of power load and achieving accurate power deployment. © 2023 ieee.

关键词： Long short-term memory

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Research on Mechanism of Rural Professional Internet application Based on Regression Model 3

Research on Mechanism of Rural Professional Internet Applica...

引用

3rd ieee international conference on Electronic Communications, Internet of Things and Big data, ICEIB 2023

作者： Miao, Jinming Lin, Hongzhen School of Management Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350333862

The way of Internet intervention in rural entrepreneurship and its development are discussed to establish a model based on the analysis of the data in the 2017 China General Social Survey (CGSS2017). Using the model, the influence of Internet intervention on the entrepreneurial behavior of rural residents is analyzed. Through four steps of regression analysis, heterogeneity analysis, mechanism analysis, and robustness test, the empirical study is carried out. The result shows that Internet intervention has a positive and significant impact on rural residents' entrepreneurial behavior. Therefore, it is important to vigorously promote the popularization and application of the Internet in rural areas for the rapid development of entrepreneurial behavior in rural areas. At the same time, attention must be paid to the essence and discard the drags in the process of its development. Corresponding support and supervision need to be given to maintain the healthy and rapid development of the entrepreneurial behavior of rural residents. © 2023 ieee.

关键词： Rural areas

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An Algorithm for Extracting Image Features Using a Random Deep Neural Network 3

An Algorithm for Extracting Image Features Using a Random De...

引用

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

作者： Dharmadhikari, Sonali Prasad Rabi, J. Karetla, Girija Rani Joshi, Kapil Shivakumar, K. Tiwari, Mohit Institute of Management and Entrepreneurship Development Pune India Mangalam College of Engineering Department of Mechanical Engineering Kerala Kottayam India We Stern Sydney University School of Computer Data and Mathematical Sciences Australia Uttaranchal Institute of Technology Uttaranchal University India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering A-4 Rohtak Road Psachim Vihar Delhi India

ISBN: (纸本)9798350399264

In this paper, a random deep convolutional neural network-based technique for extracting image features is devised and put into practice. The image features are also richer following the image preprocessing procedure described in this article. Additionally, more consistent feature points are recognized, duplicate local features are deleted, and this prevents the feature matching from encountering too many unmatched pairs. Since the self-encoder can successfully rebuild the training data set, data that is close to the training set, and the data itself, this work offers some original ideas for constructing the model. The study's findings have demonstrated the successful outcomes. © 2023 ieee.

关键词： Deep neural networks

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Forecasting of Cardiovascular Disease Using Machine Learning 3

Forecasting of Cardiovascular Disease Using Machine Learning

引用

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

作者： Azarudeen, A. Hemavathy, P. Jabbar, Abeer Mohsen Abed, Sarah Jassim Fhadil, Fay Ali, Aqeel Hindustan Institute of Technology and Science Department of Computer Application Chennai India Hindustan Institute of Technology and Science Department of It Chennai India National University of Science and Technology Collage of Engineering Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad Iraq

ISBN: (纸本)9798350399264

heart disease or cardiovascular disease includes several circumstances that affect the heart and is one among major reason of putting to demise globally. Here, we employ Machine Learning to predict cardiovascular disease using a data set containing 14 components. computers are taught to learn knowledge on their own using machine learning, a form of artificial intelligence. Machine learning in healthcare is increasingly being used and helps victims and clinicians in a variety of ways. The automated identification and diagnosis system's performance is comparable to that of a skilled radiologist. This article presents multiple heart disease-related characteristics, and includes models constructed using supervised learning techniques such as the Random Forest and K-Nearest Neighbor algorithms. It also focuses on which patients, given certain medical characteristics, are more likely to suffer cardiovascular-disease. © 2023 ieee.

关键词： Forecasting

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Imbalanced Word Counting Using MapReduce in Serverless Platform 3

Imbalanced Word Counting Using MapReduce in Serverless Platf...

引用

3rd ieee international conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Chen, Xiaoyue Li, Xinyue Zhou, Qingyuan Renmin University of China School of Statistics Beijing100872 China Xidian University School of Computer Science and Technology Xi'an710126 China Nanjing University School of Computer Science and Technology Nanjing210023 China

ISBN: (纸本)9798350398410

MapReduce is one of the most widely used programming models for analyzing large-scale datasets. In recent years, serverless computing, especially Function-as-a-Service (FaaS), has received a lot of attention due to its advantages of scalability, low cost, and compatibility. In the serverless environment provided by the AliCloud platform, this paper investigates the different performances of MapReduce when processing massive files under different resource configurations, and proposes a solution to the data skewing problem of MapReduce. The results show that given the same other configurations, CPU cores and WORKERS have a significant impact on function response time: the larger the CPU core in the function configuration, the shorter the function response time, and the high concurrency of WORKERS can also significantly reduce the function response time. In addition, the size of the memory configuration has a negligible effect on the response time of the function. In addition, an optimization scheme is further proposed for the common data skew problem in MapReduce and explore its effect. This scheme can greatly improve the overall task execution efficiency when the data is seriously unevenly distributed. © 2023 ieee.

关键词： MapReduce

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data-driven visual analytics of Human Mobility data and green cover using Image Processing for Smart Cities 3

Data-driven visual analytics of Human Mobility data and gree...

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3rd ieee international conference on Intelligent Technologies, CONIT 2023

作者： Ramanathan, Sivasubramanian Syed, Kulsoom Chavan, Tejaswini B. Tech Computer Science & Business Systems SVKMs NMIMS Navi Mumbai Mumbai India School of Technology Management & Engineering SVKMs NMIMS Navi Mumbai Mumbai India

ISBN: (纸本)9798350338607

This research paper presents an approach to data-driven visual analytics of human mobility data using Kernel Density Estimation visualized through heatmaps, highlighting the need for exploration of forecasting methods and intuitive visualizations using the ARIMA model. A specific geographic area is chosen for the demonstration of the scope of the proposed system. The system is a web application developed using Streamlit, an open-source python framework. To effectively implement the smart city concept, it is crucial to integrate diverse IoT systems, data sources, data streams, and analytical tools into a unified and seamless platform that facilitates the collection, analysis and presentation of information related to urban systems and subsystems. We propose taking into consideration several attributes when analyzing human mobility patterns, such as vehicle/traffic density, modes of transport, transport data, demographics, latitude, and longitude. Additionally, the system utilizes image processing as an efficient method for calculating urban green cover using morphological operations that are computationally cheaper and easy to use as compared to traditional surveys that are time and resource intensive. This information is used to develop smart plans to sustain or increase green cover in select areas, leading to the creation of sustainable and green smart cities. © 2023 ieee.

关键词： Smart city

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