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共 3354 条记录，以下是41-50 订阅

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A Preliminary Many Objective Approach for Extracting Fuzzy Emerging Patterns 15th

A Preliminary Many Objective Approach for Extracting Fuzzy E...

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15th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2020

作者： Garcia-Vico, Angel Miguel Carmona, Cristobal J. Gonzalez, Pedro del Jesus, Maria Jose Interuniversity Andalusian Institute on Data Science and Computation Intelligence University of Jaén Jaén23071 Spain

ISBN: (纸本)9783030578015

A preliminary many objective algorithm for extracting fuzzy emerging patterns is presented in this contribution. The proposed algorithm employs fuzzy logic together with an evolutionary algorithm. The aim is to expand the complex search space that we have in emerging pattern mining. The experimental study presented in this paper faces this new proposal regarding an ensemble of one of the most used algorithms within supervised descriptive rule discovery. Results presents a set of patterns with a major interpretability and precision for the new proposal which could be interesting for experts in real-world applications. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Evolutionary algorithms

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Overview of Network Security Situation Awareness Based on Big data 6th

Overview of Network Security Situation Awareness Based on Bi...

引用

6th international conference on advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Qian, Weimin Lai, Hongtu Zhu, Quan Chang, Kuo-Chi School of Information Science and Engineering Fujan University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujan University of Technology Fuzhou350118 China

ISBN: (纸本)9783030697167

With the rapid development of Internet technology, network security issues have become more complex and changeable. Situational awareness can dynamically reflect network security’s overall situation and predict the development trend of network security. The application of big data technology creates opportunities for breakthroughs in large-scale network security situational awareness research. This paper introduces the application of big data in network security situational awareness. By evaluating network security and perceiving anomalous events in the network, we can predict the future security situation and block anomalous feedback. Network security situational awareness based on big data can better deal with increasingly complex network security problems. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network security

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Analysis of Patent Assertions using Computational Techniques

Analysis of Patent Assertions using Computational Techniques

引用

Applied Artificial Intelligence and computing (ICAAIC), international conference on

作者： Dipika Sarkar Subir Gupta Pratik Kumar Halder Dept. of C.S.E(AIML) Haldia Institute of Technology Haldia W.B India Dept.ofC.S.E Gargi Memorial Institute of Technology Kolkata W.B India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This research presents a comprehensive analysis of Indian patent assertions from 2016 to 2021, marked by significant technological and economic developments. The study uses advanced computational techniques to address the growing complexity and volume of patent data, which traditional analysis methods fail to manage effectively. The research aims to dissect the vast dataset of Indian patent claims, employing analytical methods including summary statistics, trend analysis, field comparison, correlation analysis, panel data analysis, and ANOVA to unveil nuanced insights into India’s innovation landscape dynamics. The findings reveal a discernible surge in patent filings, underscoring an evolving innovation ecosystem responsive to technological advancements and global trends. The trend analysis elucidates the shifts in technology focus, identifying sectors that exhibit rapid growth or decline in patent activities. Comparative analysis across different technology domains highlights India’s innovative strengths and areas needing attention, aligning with global innovation patterns. Correlation and panel data analyses offer a deeper understanding of the interplay between different sectors and temporal shifts in patent filings, unveiling the underlying factors driving these trends. The ANOVA results confirm the sector-specific dynamism in India’s patent landscape, indicating significant differences in claim numbers across fields. This study provides critical insights for policymakers, investors, and industry stakeholders, guiding strategic decision-making and policy formulation to foster a conducive environment for innovation. The robust methodological approach and comprehensive analysis underscore the study’s contribution to understanding and navigating the complexities of India’s IP ecosystem, offering a valuable resource for shaping future innovation strategies and enhanding India’s global innovation footprint.

关键词： Economics Technological innovation Patents data analysis Correlation Ecosystems Decision making

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Graph Representation Learning: Foundations, Methods, Applications and Systems 21

Graph Representation Learning: Foundations, Methods, Applica...

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27th ACM SIGKDD international conference on Knowledge Discovery and data mining (KDD)

作者： Jin, Wei Ma, Yao Wang, Yiqi Liu, Xiaorui Tang, Jiliang Cen, Yukuo Qiu, Jiezhong Tang, Jie Shi, Chuan Ye, Yanfang Zhang, Jiawei Yu, Philip S. Michigan State Univ Comp Sci & Engn E Lansing MI 48824 USA Michigan State Univ Comp Sci & Engn Dept E Lansing MI 48824 USA Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Case Western Reserve Univ Cleveland OH 44106 USA Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA Florida State Univ Tallahassee FL 32306 USA Florida State Univ Comp Sci Tallahassee FL 32306 USA Univ Illinois Chicago IL USA Univ Illinois Comp Sci Chicago IL USA

ISBN: (纸本)9781450383325

Graphs such as social networks and molecular graphs are ubiquitous data structures in the real world. Due to their prevalence, it is of great research importance to extract meaningful patterns from graph structured data so that downstream tasks can be facilitated. Instead of designing hand-engineered features, graph representation learning has emerged to learn representations that can encode the abundant information about the graph. It has achieved tremendous success in various tasks such as node classification, link prediction, and graph classification and has attracted increasing attention in recent years. In this tutorial, we systematically review the foundations, techniques, applications and advances in graph representation learning. We first introduce the foundations on graph theory and graph Fourier analysis. We then cover the key achievements of graph representation learning in recent years. Concretely, we discuss the six topics: 1) network embedding theories and systems;2) foundations of graph neural networks (GNNs);3) CogDL toolkit for GNNs;4) scalable GNNs;5) self-supervised learning in GNNs and 6) heterogeneous graphs and heterogeneous GNNs. Finally, we will introduce the applications of graph representation learning with a focus on recommender systems.

关键词： heterogeneous graphs graph neural networks graph representation learning

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Feature Representation and Organization Method for Public Opinion Big data Based on Association Analysis 2

Feature Representation and Organization Method for Public Op...

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2nd international conference on computing and data Science, CONF-CDS 2021

作者： Wang, Pengju Xue, Huifeng Zhang, Feng School of Automation Northwestern Polytechnical University Shaanxi China School of Information Engineering Yulin University Shaanxi China

In order to improve the data mining level of public opinion big data, by analysing the relationship among semantic web, ontology and RDF (Resource Description Framework), the structure and organization method of public opinion big data association data are separated. Based on the analysis of network public opinion big data organization technology, and then the RDF structure definition of network public opinion knowledge map is given, and the construction method of RDF association analysis map of network public opinion knowledge is realized. Experimental research shows that the proposed method can provide more accurate correlation analysis of public opinion big data, which can effectively realize the analysis and management of public opinion hot events, and provide decision support for the management of network public opinion. © Published under licence by IOP Publishing Ltd.

关键词： data mining

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Rio olympics 2016 on twitter: A descriptive analysis

Rio olympics 2016 on twitter: A descriptive analysis

引用

international conference on Computational Methods and data Engineering, ICMDE 2020

作者： Sharma, Saurabh Gupta, Vishal University Institute of Engineering and Technology Panjab University Chandigarh India

ISBN: (纸本)9789811579066

Social media mining is gaining popularity among researchers as it provides an opportunity to study real-world events, social interactions, network analysis, and most importantly, user behavior. This paper explores the hidden patterns from the content generated on Twitter during the Olympic Games held in the year 2016 in Rio de Janeiro, Brazil. The statistical analysis found that the two features namely retweets and likes have shown a correlation of 95%. The topics of tweets have shown a trend of discussing small events during a day and this pattern is very prominent for all days. The most expressed sentiment during games is turned out to be a positive sentiment. The temporal analysis has shown that users were more active in a specific time of the day, i.e., evening. The user behavior analysis found that new Twitter users were less active as compared to other users. This paper contributes to the studies of social media mining for sports/entertainment events. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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SAR-UNet: Small Attention Residual UNet for Explainable Nowcasting Tasks

SAR-UNet: Small Attention Residual UNet for Explainable Nowc...

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international Joint conference on Neural Networks (IJCNN)

作者： Mathieu Renault Siamak Mehrkanoon Department of Advanced Computing Sciences Maastricht University Maastricht Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands

The accuracy and explainability of data-driven now-casting models are of great importance in many socio-economic sectors reliant on weather-dependent decision making. This paper proposes a novel architecture called Small Attention Residual UNet (SAR-UNet) for precipitation and cloud cover nowcasting. Here, SmaAt-UNet is used as a core model and is further equipped with residual connections, parallel to the depthwise separable convolutions. The proposed SAR-UNet model is evaluated on two datasets, i.e., Dutch precipitation maps ranging from 2016 to 2019 and French cloud cover binary images from 2017 to 2018. The obtained results show that SAR-UNet outperforms other examined models in precipitation nowcasting from 30 to 180 minutes in the future as well as cloud cover nowcasting in the next 90 minutes. Furthermore, we provide additional insights on the nowcasts made by our proposed model using Grad-CAM, a visual explanation technique, which is employed on different levels of the encoder and decoder paths of the SAR-UNet model and produces heatmaps highlighting the critical regions in the input image as well as intermediate representations to the precipitation. The heatmaps generated by Grad-CAM reveal the interactions between the residual connections and the depthwise separable convolutions inside of the multiple depthwise separable blocks placed throughout the network architecture.

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NAS Honeypot Technology Based on Attack Chain 10th

NAS Honeypot Technology Based on Attack Chain

引用

10th international conference on Computer Engineering and Networks, CENet 2020

作者： Liu, Bing Shu, Hui Kang, Fei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9789811584619

With the wide application of network attached storage (NAS), the security problem is becoming more and more serious, together with the increasingly fierce attacks against NAS devices of different manufacturers. In order to better capture various types of attacks against NAS devices and detect security threats in time, this paper proposes a solution named NAS honeypot, mining the potential security threats through modeling and analyzing the NAS threats. Then a NAS honeypot based on device virtualization technology and depth monitoring and attack induction was designed on the basis of the construction NAS attack chain. Experimental results show that the NAS honeypot can effectively capture, record and analyze network attacks against multi-types NAS devices with a strong guiding effect in mastering popular attack method of NAS devices and alleviating the security threats of NAS. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Security systems

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A Novel Pre-processing Method for Enhancing Classification Over Sensor data Streams Using Subspace Probability Detection 1

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16th international conference on Hybrid Artificial Intelligent Systems, HAIS 2021

作者： Zhong, Yan Li, Tengyue Fong, Simon Li, Xuqi Tallón-Ballesteros, Antonio J. Mohammed, Sabah University of Macau Taipa China University of Edinburgh Edinburgh United Kingdom University of Huelva Huelva Spain Lakehead University Thunder Bay Canada

ISBN: (数字)9783030862718

ISBN: (纸本)9783030862701

The rapid development of the Internet of Things has led to the widespread use of sensors in everyday life. Large amounts of data through sensing devices are collected. The data quantity is massive, but most of the data are repetitive and noisy. When traditional classification algorithms are used for classifying sensor data, the performance of the model is often poor because the classification granularity is too small. In order to better data mine the knowledge from the Internet of Things data which is a kind of big data, a new classification model based on subspace probability detection is proposed. This model can be well integrated with traditional data mining algorithms, and the performance on sensor data mining is greatly improved. © 2021, Springer Nature Switzerland AG.

关键词： data Analytics

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L1 norm SVD-based ranking scheme: A novel method in big data mining 3rd

L1 norm SVD-based ranking scheme: A novel method in big data...

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3rd international conference on Big-data and Cloud computing, ICBDCC 2019

作者： Aedula, Rahul Madhukumar, Yashasvi Saha, Snehanshu Mathur, Archana Bora, Kakoli Agrawal, Surbhi PES Institute of Technology Bangalore South Campus Bengaluru India Indian Statistical Institute 8th Mile Mysore Road Bengaluru India

ISBN: (纸本)9789811552847

Scientometrics deals with analyzing and quantifying works in science, technology, and innovation. It is a study that focuses on quality rather than quantity. The journals are evaluated against several different metrics such as the impact of the journals, scientific citation, SJR, SNIP indicators as well as the indicators used in policy and management context. The practice of using journal metrics for evaluation involves handling a large volume of data to derive useful patterns and conclusions. These metrics play an important role in the measurement and evaluation of research performance. Due to the fact that most metrics are being manipulated and abused, it becomes essential to judge and evaluate a journal by using a single metric or a reduced set of significant metrics. We propose l1-norm singular value decomposition (l1-SVD) to efficiently solve this problem. We evaluate our method to study the emergence of a new journal, Astronomy and computing, by comparing it with 46,000 journals chosen from the fields of computing, informatics, astronomy, and astrophysics. © Springer Nature Singapore Pte Ltd 2021.

关键词： Singular value decomposition

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