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33rd IEEE International conference on Computer Communications and Networks (ICCCN)

作者： Makkawy, Saleh J. Alblwi, Abdalrahman H. De Lucia, Michael J. Barner, Kenneth E. Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA Umm Al Qura Univ Dept Comp Sci Mecca Saudi Arabia

ISBN: (纸本)9798350348439;9798350384611

Detecting malicious software applications is crucial in protecting user data and privacy. The increasing popularity of operating systems (OS) such as Android, iOS, and Windows has led to an expansion in the number of malicious software applications targeting these platforms. Several studies have explored various techniques to detect malware threats, including signature-based detection, behavioral analysis, dynamic analysis, and static analysis. However, these conventional methods may fail to detect new variants of malware with unique characteristics. This research paper introduces a unified approach that combines entropy bytecode-to-image visualization with ensemble learning to enhance the identification of malicious patterns. To validate the efficacy of the entropy encoder, the AndroZoo-2011 application data set was used as a training set, comprising 184,474 samples, including 49,150 and 13,324 malicious and benign applications, respectively. The applied method demonstrates improved performance with an accuracy of 95.77%, a precision of 90.60%, and an F1 score of 92.21% compared to other existing encoders, making our approach more applicable to real-world scenarios.

关键词： Android malicious software entropy static analysis dynamic analysis behavior analysis bytecode convolutional neural network

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Tableau-Based data visualization and Regression analysis of Landcover and Climatic Trends

Tableau-Based Data Visualization and Regression Analysis of ...

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2023 IEEE World conference on Communication and Computing, WCONF 2023

作者： Yaswanth, Thottempudi Naga Sri Mohammad, Shaik Imran Suvarna Vani, K. Lokeshwar, Ganta V R Siddhartha Engineering College Department of CSE Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350311204

visualization is a key technology for analyzing and presenting vegetation cover and climatic changes. Interactive visualization has emerged as a popular and effective approach for communicating complex data and insights related to climate change. This project focuses on the interactive visualization of land cover and climate changes using conventional maps and graphs, with a particular focus on the districts of Rajasthan. By utilizing the data provided by the Bhuvan geo platform, this study aims to identify trends in climate and land cover over the past 30 years. The datasets collected for this study include information on climatic aspects such as temperature, Earth surface temperature, humidity, and precipitation for the cities of Jodhpur, Bikaner, and Jaisalmer, as well as land cover data for the major districts in Rajasthan. The land cover includes data on croplands, forests, builtup lands, and water bodies which were collected for the years 2011-12 and 2015-16. The use of Tableau software provides an interactive platform for analyzing and presenting data, making it easier for stakeholders and policymakers to understand the data and make informed decisions. Linear regression is used to establish relationship and predict the climatic changes corresponding to change in years. This study aims to provide an accurate representation of the data, including detailed information about the source, in a way that makes it easy to understand complicated data relationships and data driven insights. Through interactive data visualization, this study aims to identify how land cover and climate trends are changing over time and to identify potential causes for these changes. The final result is the visualized data report which helps to identify the trends in climate and land cover. © 2023 IEEE.

关键词： data visualization

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analysis and visualization of Published Literature on Healthcare Services Research from 2011 to 2022: a Bibliometric and Network analysis 7

Analysis and Visualization of Published Literature on Health...

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7th International conference on Biomedical Engineering and Applications (ICBEA)

作者： Guo, Bo Shukor, Nur Syufiza Ahmad Ishak, Irny Suzila Univ Selangor Dept Comp Fac Commun Visual Art & Comp Bestari Jaya Selangor Malaysia Fuyang Normal Univ Sch Comp & Informat Engn Fuyang Peoples R China

ISBN: (纸本)9798350323238

Healthcare services are one of the major public issues facing the world and have become a research hotspot in recent years. This paper provides a review and quantitative analysis of the literature related to healthcare services to sort out the research trends and hotspots in this field. This paper used bibliometric analysis to search keywords in the Web of Science database in TITLE-ABS-KEY format. After screening, the final 4188 articles from 2011 to 2022 were collated, and co-citation analysis was performed using VOSviewer literature visualization analysis software. The global healthcare services literature has grown steadily year after year. Health Care Sciences & Services (9.5%) and General & Internal Medicine (8.7%) were the most common areas of research, with the United States (N=1016) accounting for the largest share of research. "BMC Health Services Research" is the most active journal (N=149). Healthcare services are one of the current research focuses in the field of healthcare institutions. In the future, further research into assistive technologies and tools for healthcare services, such as the Internet of Things, cloud services, and big data analytics will be investigated. In addition, research on healthcare services focusing on special needs populations, especially the elderly, is a hotspot for future research, broadening the boundaries of research and having practical value.

关键词： healthcare services VOS viewers bibliometric analysis publication

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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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Study on Statistical analysis of Post-Earthquake Road Use Recovery and Its Application

Study on Statistical Analysis of Post-Earthquake Road Use Re...

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2022 IEEE International conference on Big data, Big data 2022

作者： Wu, Jieling Saito, Mitsugu Graduate School of Engineering Iwate University Design and Media Technology Morioka Japan

ISBN: (纸本)9781665480451

Efficient road recovery is essential for people's wellbeing and economic recovery. The three prefectures on the Pacific side of the Tohoku region (Fukushima, Miyagi, and Iwate), are relatively similar to the three prefectures (Shizuoka, Aichi, and Mie) in the Tokai region in terms of the topographical environment. The purpose of this study is to simulate the recovery of local roads in the Tokai region after the Nankai Trough earthquake, which is expected to occur in the near future based on data from the 2011 Tohoku Earthquake. We identified regional characteristics and other factors influencing road recovery from previous cluster analysis and GIS visualization of driving data from the Tohoku region in the six months following the 2011 earthquake. However, we found that the cluster parameters were too broad, so in this study, we re-clustered the coastal and inland areas and applied stepwise discriminant analysis to construct a road recovery prediction model that can help guide the recovery of municipal roads in the Tokai region after the expected Nankai Trough earthquake. © 2022 IEEE.

关键词： Earthquakes

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Bibliometric analysis of Published Literature on Mobile Healthcare in the Past One Decade 5th

Bibliometric Analysis of Published Literature on Mobile Heal...

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5th International conference on Communication, Device and Networking, ICCDN 2021

作者： Saha, Saibal Kumar Saha, Sangita Jha, Ajeya Department of Management Studies Sikkim Manipal Institute of Technology Sikkim Manipal University Gangtok India Department of Management Studies MIT University Shillong India

ISBN: (纸本)9789811920035

The study aims to highlight the bibliometric analysis of published literature on mobile healthcare in the past one decade. The data consist of literature indexed in Scopus database from 2011 to 2020. The keywords used for the search were m-healthcare, m-healthcare, mobile healthcare, mobile healthcare. Microsoft Excel version 2016 and VOS Viewer have been used for data analysis. Bibliometric analysis was done for co-authorship based on countries, co-occurrence of all keywords using network visualization and overlay visualization. The co-occurrence analysis of all keywords resulted in formation of four clusters. The most impactful keywords were human, health care, m-healthcare, telemedicine and m-health. The keywords cryptography, network security, cloud computing, Internet of Things, big data, wearable sensors and mobile application are being used more often in the recent years. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： mHealth

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Trends and Issues in MOOC Learning Analytics Empirical Research: A Systematic Literature Review (2011-2021)

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EDUCATION AND INFORMATION TECHNOLOGIES 2022年第7期27卷 10135-10160页

作者： Zhu, Meina Sari, Annisa R. Lee, Mimi Miyoung Wayne State Univ Learning Design & Technol Educ Bldg5425 Gullen Mall Detroit MI 48202 USA Yogyakarta State Univ Dept Accounting Educ Yogyakarta Indonesia Indiana Univ Instruct Syst Technol Bloomington IN USA Univ Houston Dept Curriculum & Instruct Houston TX USA

Learning analytics (LA) is a growing research trend and has recently been used in research and practices in massive open online courses (MOOCs). This systematic review of 166 articles from 2011-2021 synthesizes the trends and critical issues of LA in MOOCs. The eight-step process proposed by Okoli and Schabram was used to guide this systematic review in analyzing publication outlets, research purposes and methods, stakeholders, and researchers' geographic locations and subjects. The review showed that MOOC LA research is primarily published in top-tier journals, such as the Journal of Learning Analytics, Journal of Computer Assisted Learning, and Computers & Education, as well as conference proceedings. The review also revealed that LA in MOOCs was used more frequently for the purpose of research than for practice (i.e., learning and teaching). Approximately 60% of the reviewed studies adopted learners' log data and achievement data as the primary data sources. Statistics, machine learning, content analysis, social network analysis, text analysis, and data visualization were the top six specific data analysis techniques used in the MOOC LA studies. Regarding collaboration, more than half of the reviewed studies involved interdisciplinary collaborations, and approximately one-third involved international collaborations. We suggested future studies on MOOC LA interventions to improve learning and teaching practices, and the active interdisciplinary collaboration to increase the rigor of the studies and the dissemination of the knowledge. More detailed discussion and implications for research and practice are presented. This research provides insights on future research and practices regarding LA use in MOOCs.

关键词： MOOCs Learning analytics Systematic review analysis techniques

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Exploring Chronic Disease Trends among Adults in the USA: A Statistical analysis with Visual Insights 32

Exploring Chronic Disease Trends among Adults in the USA: A ...

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32nd Irish conference on Artificial Intelligence and Cognitive Science, AICS 2024

作者： Ngo, Vuong M. Sood, Geetika Donohue, Fionnuala Kearney, Patricia Pallin, Jennifer Roantree, Mark Insight School of Computing Dublin City University Ireland Ho Chi Minh City Open University Ho Chi Minh City Viet Nam National Health Intelligence Unit Health Service Executive Ireland School of Public Health University College Cork Ireland

Chronic diseases are some of the most widespread and expensive health conditions in the United States. This study evaluates the prevalence of chronic diseases in the U.S. from 2011 to 2021, using data from the U.S. CDC, along with visualization and descriptive statistical analysis techniques. Despite increased awareness and advancements in prevention, diagnostics, and treatment, the prevalence of certain chronic diseases, such as obesity, diabetes, and asthma, continues to rise each year. Other conditions, such as kidney disease and heavy alcohol consumption, have shown little to no improvement. Notably, the prevalence of these diseases in each state is not directly correlated with population size but is instead influenced by various factors, including living conditions, unhealthy lifestyle habits, socioeconomic status, and access to healthcare services. For instance, Mississippi has an average obesity rate of 37.6% and an average diabetes rate of 12.8%, both of which are partially influenced by an average tobacco use rate of 23%. In comparison, the U.S. averages for obesity, diabetes, and tobacco use are 30.4%, 9.5%, and 18.3%, respectively. © 2024 Copyright for this paper by its authors.

关键词： Lung cancer

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Tableau-Based data visualization and Regression analysis of Landcover and Climatic Trends

Tableau-Based Data Visualization and Regression Analysis of ...

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Communication & Computing (WCONF), World conference on

作者： Thottempudi Naga Sri Yaswanth Shaik Imran Mohammad K Suvarna Vani Ganta Lokeshwar Department of CSE V R Siddhartha Engineering College Vijayawada Andhra Pradesh

visualization is a key technology for analyzing and presenting vegetation cover and climatic changes. Interactive visualization has emerged as a popular and effective approach for communicating complex data and insights related to climate change. This project focuses on the interactive visualization of land cover and climate changes using conventional maps and graphs, with a particular focus on the districts of Rajasthan. By utilizing the data provided by the Bhuvan geǒ platform, this study aims to identify trends in climate and land cover over the past 30 years. The datasets collected for this study include information on climatic aspects such as temperature, Earth surface temperature, humidity, and precipitation for the cities of Jodhpur, Bikaner, and Jaisalmer, as well as land cover data for the major districts in Rajasthan. The land cover includes data on croplands, forests, builtup lands, and water bodies which were collected for the years 2011-12 and 2015-16. The use of Tableau software provides an interactive platform for analyzing and presenting data, making it easier for stakeholders and policymakers to understand the data and make informed decisions. Linear regression is used to establish relationship and predict the climatic changes corresponding to change in years. This study aims to provide an accurate representation of the data, including detailed information about the source, in a way that makes it easy to understand complicated data relationships and datǎ driven insights. Through interactive data visualization, this study aims to identify how land cover and climate trends are changing over time and to identify potential causes for these changes. The final result is the visualized data report which helps to identify the trends in climate and land cover.

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The Current Situation and Future Trend of Big data: visualization analysis of literature Based on Citespace 22

The Current Situation and Future Trend of Big Data: Visualiz...

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5th International conference on Big data and Education, ICBDE 2022

作者： Zeng, Xianfeng Chang, Jing Lai, Yiqiang Huang, Changjiang School of Computer Science South China Business College of Guangdong University of Foreign Studies Guangzhou Guangdong510545 China College of Computer Science Guangzhou College of Applied Science and Technology Guangzhou Guangdong510030 China

ISBN: (纸本)9781450395793

More and more attentions have been paid to the value of big data, and the amount of research literature on big data is also increasing exponentially. In this paper, CiteSpace citation analysis software is used as an analysis tool and big data related literature in the Web of Science core database during 2011- 2021 is used as an analysis object. The major task is to create the knowledge map of big data with multi-dimension, build knowledge graphs of big data from several perspectives and develop visual analyses of the literature. From the sight of time and space. Differences between the numbers and the research contents of literature on big data published by different countries, regions, and institutions have been analyzed and compared;By co-cited literature, the classical papers and core literature in the field of big data have been detected, and the development context of big data domain have been clarified;Cluster analysis has been conducted on hot keywords to find the research hotspots of big data domain in recent years. Burst words detection algorithm has been used to track the research frontiers and development trends in the field of big data. The results show that China has surpassed the developed countries in Europe and the United States in the number of literature on big data. However, there is still a lack of core technology literature. The development of big data and AI will be in the same direction and depend on each other;The improvement of deep learning algorithms based of big data analysis is going to become a frontier research topic in a long-term;The integration of big data and all walks of life should to be deepened, and researchers need to find the breakthrough points for the application of big data based on the characteristics of the industry. © 2022 ACM.

关键词： Big data

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