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检索条件"任意字段=Conference on Visualization and Data Analysis 2008"

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An analysis of International conference Proceedings on Artificial General Intelligence (AGI) from 2008 to 2020: A data-Mining Mapping analysis 8

An Analysis of International Conference Proceedings on Artif...

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8th International conference on ICT & Accessibility (ICTA)

作者： Shehata, Boulus Tlili, Ahmed Huang, Ronghuai Beijing Normal Univ Smart Learning Inst Beijing 100875 Peoples R China

ISBN: (纸本)9781665466417

This paper analyzes the proceedings of 13 international conferences on Artificial General Intelligence AGI, that are part of the lecture notes in artificial intelligence. The motivation for this study is to explore the publication networks and areas where AGI could be useful to Information & Communication Technology ICT in education. SPSS and Microsoft Excel were used to enter, screen, clean and visualize the analysis of the data. Furthermore, all published proceedings were uploaded into Voyant Tools and VOSviewer for data mining and visualization. After obtaining the most frequently used keywords and terms, certain patterns lead to trends in the research on AGI. The analysis leads to discussions and suggestions of possible opportunities where AGI may be integrated with education to assist people with or without a disability. The contribution of the study is in the overall mapping analysis of the conference proceedings, which would inspire further exploration and collaboration with potential scholars, institutions, and countries.

关键词： Artificial General Intelligence International conference Proceedings data Mining analysis

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IPL data analysis and visualization for Team Selection and Profit Strategy 7

IPL Data Analysis and Visualization for Team Selection and P...

引用

7th International conference on Computing Methodologies and Communication, ICCMC 2023

作者： Saranya, G. Swaminathan, Aravind Joel Benjamin, J. Surendran, R. Nelson, Leema Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai campus Chennai India Data Science Middlesex University Dubai United Arab Emirates Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9781665464086

Day by day, the role of data science and machine learning in cricket is increasing due to the large amount of data generated from a single player on a whole line. The field of data science is the intensive study of data to extract insights and knowledge from the data and apply the acquired knowledge and actionable insights. These available data and statistics are used to predict things like the team's first-innings score and the probability of winning the second team, etc. This study works on Indian Premier League (IPL) data analysis 2022 and data visualization for the duration (2008-2020) using Python. This application modules are followed by preprocessing, data analysis, and visualization, and finally create a model that predicts the team's overall score and the probability of winning. When building models, python algorithms such as Numpy is used for S cientific Computing, Pandas used to perform data analysis, and finally Matplotlib and S eaborn to enable data visualization. Finally, this paper helps the trainers and owners of the team during the auction to select an emerging player to win the matches in the entire season and to win the trophy and to get profit to the owners by the profit strategy done in this analysis report. © 2023 IEEE.

关键词： data visualization

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IPL data analysis and visualization Using Microsoft Power BI Tool 14

IPL Data Analysis and Visualization Using Microsoft Power BI...

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14th International conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Jaipurkar, Manasvi Ragit, Neha Tambuskar, Chetana Saraf, Pranay Deepak G H Raisoni College of Engineering Nagpur India

ISBN: (纸本)9798350335095

Cricket represents the most popular activity in India, and it is played there in all of its regions in various formats. Indian regional teams, international competitions all field players in the Indian Premier League (IPL), a national cricket championship. Cricket involves lots of data, analysis, prediction and statistics. Several parameters can be used to predict the outcome of a cricket match. This paper's main topics are the process model, Microsoft Power BI operations like numerous related graphical insights, dashboards, visualizations. To make accurate visualizations, predictions about things like the actual and predicted team's score and the likelihood that the team will win, overall match analysis by currently available data, Using machine learning models and Power BI tools. The research started by using Python libraries used in Power BI. The IPL match data played between 2008 and 2022, then moved on to per-processing, data analysis, and visualization, and finally developed a dashboard and reports that forecasts the teams' overall score and likelihood of winning. It is concerned with the interactive visualization of IPL databases utilizing the various modules of the Microsoft Power BI Tool and the Random Forest algorithm outperforms other algorithms in terms of accuracy, scoring 93.27 %. © 2023 IEEE.

关键词： visualization

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data visualization analysis of Research Hotspots and Trends in Low Carbon Campus Studies 5

Data Visualization Analysis of Research Hotspots and Trends ...

引用

5th International conference on Artificial Intelligence and Computer Engineering

作者： Li, Fei Hohai Univ Sch Marxism Nanjing Jiangsu Peoples R China

ISBN: (纸本)9798331528911;9798331528928

Based on the background of the 'dual-carbon' goal, the construction of low-carbon campuses is still in its infancy. In order to understand the development of low-carbon campus research and study the research hotspots and future development trends of low-carbon campus in domestic and foreign academia, this paper uses CiteSpace software to visualise and analyse the literature in CNKI and WoS databases from 2014-2024, and draws the conclusions that (1) domestic and foreign scholars have continued to pay attention to the issue, and there has been a significant surge in research progress in this field in 2008. (2) Domestic and foreign research collaboration networks have not yet been completed, and teamwork is weak. (3) There is a focus bias in domestic and foreign research hotspots, with domestic research focusing on low-carbon culture, concepts and conceptual interpretations, and foreign research focusing on quantitative research on energy conservation and emission reduction. In the future, the research in this field should further promote the sharing of resources, innovate the mode of cooperation, deepen the integration of disciplines, and establish visualisation standards, so as to provide certain theoretical references and practical help for the construction of low-carbon campuses.

关键词： dual-carbon Low Carbon Campus visual analysis knowledge map analysis

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Speech Mastery Detection Using Advanced Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) Techniques 4

Speech Mastery Detection Using Advanced Natural Language Pro...

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4th International conference on Artificial Intelligence and Signal Processing

作者： Raut, Pratikhya Anitha, Korikana Sowmya, Sasubilli Vinnu, Kongara Velagapudi Ramakrishna Siddhartha Engn Coll Deeme Dept Elect & Commun Engn Vijayawada Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

In today's globalized world, English communication skills are essential for career advancement and cross-cultural collaboration, enhancing access to information and opportunities. Automatic speech recognition, or ASR, is a separate machine-driven method for transcription and decoding spoken language. An ASR system typically uses a microphone to capture a speaker's audio input, analyze it using a model, algorithm,or pattern, and output the results, which are often text mes-sages (Lai, Karat, Yankelovich, 2008). This paper provides a thorough method for utilizing Python-based tools and modulesto extract and analyze linguistic characteristics from audio data. The suggested approach turns spoken language into text using voice recognition technology, and then it uses natural language processing (NLP) methods to extract different linguisticmetrics. Word count, sentence count, vocabulary size, average sentence length, average word length, sentiment score, speech pace, frequency of pauses, and average length of pauses are some of these measures. The technique also determines the speaker's speaking style. Keywords: audio analysis, linguistic features, natural language processing, pause detection, speech recognition, sentiment analysis, visualization.

关键词： Audio analysis Linguistic Features Natural Language Processing Pause Detection Speech Recognition Sentiment analysis visualization

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Smart Contract data Monitoring and visualization 5

Smart Contract Data Monitoring and Visualization

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5th IEEE International conference on Blockchain and Cryptocurrency (ICBC)

作者： Yap, Seng Kuang Dong, Zhongli Toohey, Mark Lee, Young Choon Zomaya, Albert Y. Aglive Res POB 196 Geelong Vic 3220 Australia Univ Sydney Sch Comp Sci Ctr Distributed & High Performance Comp Camperdown NSW 2006 Australia Macquarie Univ Sch Comp Sydney NSW 2109 Australia Univ Nicosia Nicosia Cyprus

ISBN: (纸本)9798350310191

Blockchain technology has attracted significant industry, academic, and governmental attention since its emerged in 2008. Blockchain use cases are now being explored by traditional, transaction-oriented businesses in the finance, insurance, logistics and healthcare sectors to name a few. This has expanded further with the widespread use of Internet of Things (IoT) devices. Massive amounts of data are generated by IoT devices and are recorded in the blockchain. While blockchain provides many advantages, such as immutability and transparency, its serialized nature makes impossible to read in a single query. Multiple requests are required even for simple tasks, such as displaying an account's transaction history. This further leads to the difficulty i n u nderstanding t he d ata i n the blockchain. In this paper, we address the problem of smart contract visualization in a real-time manner. To this end, we design a visualization dashboard for smart contracts. A visual aid for massive amounts of data helps users understand the blockchain's overall activities, uncover operational risks and provide critical intelligence by visualising unusual activities and connections. Such insights may enable the user to investigate and predict any anomalies or reveal any network vulnerabilities. Cattle farm selected as a use case because the voluminous data can be acquired from IoT sensors on the farm cattle. Our dashboard has been proven to help visualize the life cycle of animals, the distribution of activities and time factor analysis. This visualization can give a user a better perspective of the token functions and results as well as animal management issues.

关键词： Blockchain Ethereum Smart Contract NFT Internet of Things Supply Chain

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Research Hotspot and Frontier analysis of Vehicle Routing Optimization Based on Knowledge Graph 24

Research Hotspot and Frontier Analysis of Vehicle Routing Op...

引用

8th International conference on Computing and data analysis, ICCDA 2024

作者： Wu, Chenyang Zhang, Zheng Su, Jiang Li, Zhonghao Gao, Qi School of Economics and Management Xidian University Xi’an China State Grid Xi’an China

ISBN: (纸本)9798400710445

Vehicle routing problem is a NP problem. With the continuous in-depth study of VRP model and its solving methods, many branch problems with more constraints have been developed, and their solving methods have become more and more diverse. In order to further review the research status of VRP at home and abroad. This paper employs bibliometric methods and knowledge graph analysis to examine publishing institutions, authors, and research hotspots in vehicle routing optimization studies both domestically and internationally from 2008 to 2023, aiming to provide insights for the field of vehicle routing optimization in China. The main research content includes the use of CiteSpace visualization software to generate the keyword co-occurrence and clustering knowledge map of both domestic and international literature, as well as keyword burst detection maps, so as to interpret the research status in the field. The results show that in the field of vehicle routing optimization, the cooperation relationship between foreign institutions is closer than that of domestic institutions, and research hotspots differ significantly between domestic and foreign studies. Foreign scholars pay more attention to the combination of theory and practice, while domestic scholars focus more on deepening algorithm research. Meanwhile, the research processes at home and abroad show different characteristics. Cold chain logistics and urban transportation have become areas of focus for domestic scholars, who have begun to shift from traditional vehicle route optimization problems to in-depth studies of green transportation. Integrating big data analysis and artificial intelligence technologies into vehicle route optimization is a future research direction. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge graph

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Research on Forest Landscape based on Big data and CiteSpace visualization analysis 2

Research on Forest Landscape based on Big data and CiteSpace...

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2nd International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Lin, Xinya Xu, Han Dong, Jianwen Fujian Agriculture and Forestry University Fuzhou China

ISBN: (纸本)9798350323818

In order to fully grasp the development and research status of forest landscape ecology, this paper analyzed the literatures related to forest landscape from 1982 to 2020 on CNKI and Wanfang data Service platform. Based on Python language environment, this paper uses Scrapy framework to extract literature data, and uses CiteSpace visual analysis software to draw knowledge map. It analyzes and visualizes the amount of articles published, research groups, research hotspots and trends. The results showed that: (1) the development process of forest landscape ecology research was divided into three stages: initial exploration period from 1982 to 2002, rapid development period from 2003 to 2008, and stable development period from 2009 to present. (2) The number of scholars in this field is relatively large and scattered, and the influential core author group and institutional group has not yet been formed;(3) The hot research directions are forest landscape pattern, sustainable development of forest, and forest landscape planning and design. (4) The focus of forest landscape ecology research has gradually shifted from geographic information system to eco-Tourism, urban forest and planning and design, and the research has become specialized and meticulous. © 2023 IEEE.

关键词： Big data

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Analyzing the Research Hotspots and Trends in Crisis Leadership in the Big data Era - - A Bibliometric visualization analysis Based on WoSCC 9

Analyzing the Research Hotspots and Trends in Crisis Leaders...

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9th International conference on Big data and Information Analytics, BigDIA 2023

作者： Luo, Jiali Yang, Qingqing Liu, Guangda National University of Defense Technology College of Systems and Engineering Changsha China

ISBN: (纸本)9798350330076

In order to comprehensively understand the development context and evolution trends of international crisis leadership research in the big data era, this study uses the journal papers retrieved from the Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences Citation Index (SSCI) databases of the Web of Science Core Collection (WoSCC) as the main data source, and with the help of visual analysis tool CiteSpace software, carries out a scientific literature metric analysis on the core authors, research countries, research institutions, publishing journals, theme keywords, and hotspot trends of international crisis leadership research. The series of knowledge maps are drawn. The results show that: The annual number of publications on crisis leadership research has experienced three stages: a slow growth period (2002-2008), a significant growth period (2009-2019), and a rapid growth period (2020-2023). The cumulative publication volume shows a continuous increase trend. The crisis leadership field research literature mainly comes from a few core author groups, and the research of core authors has laid the foundation for crisis leadership. The United States ranks first among the countries conducting research on crisis leadership, followed by England, and China is the most productive country in Asia. © 2023 IEEE.

关键词： Big data

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Extracting Network Structures from Corporate Organization Charts Using Heuristic Image Processing

Extracting Network Structures from Corporate Organization C...

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International conference on data Science, Computation and Security, IDSCS 2023

作者： Sayama, Hiroki Yamanoi, Junichi Binghamton Center of Complex Systems Binghamton University State University of New York BinghamtonNY13902-6000 United States Waseda Innovation Lab Waseda University Tokyo Shinjuku169-8050 Japan

ISBN: (纸本)9789819709748

Organizational structure of corporations has the potential to provide implications for dynamics and performance of corporate operations. However, this subject has remained unexplored because of the lack of readily available organization network datasets. To overcome this gap, we developed a new heuristic image-processing method to extract and reconstruct organization network data from published organization charts. Our method analyzes a PDF file of a corporate organization chart and detects text labels, boxes, connecting lines, and other objects through multiple steps of heuristically implemented image processing. The detected components are reorganized together into Python’s NetworkX Graph object for visualization, validation, and further network analysis. We applied the developed method to the organization charts of all the listed firms in Japan shown in the "Organization Chart/System Diagram Handbook" published by Diamond, Inc., from 2008 to 2011. Out of the 10,008 organization chart PDF files, our method was able to reconstruct 4,606 organization networks (data acquisition success rate: 46%). For each reconstructed organization network, we measured several network diagnostics, which will be used for further statistical analysis to investigate their potential correlations with corporate behavior and performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data acquisition

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