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6th ieee Eurasian Conference on Educational Innovation, ECEI 2023

作者： Zhang, Henan Li, Hongyan Economic and Management School Jilin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9798350334289

In the era of the digital economy, the deep integration of new technologies and cross-border e-commerce is forcing the redefinition of talent. Especially, data literacy has become an important indicator for measuring the capacity of cross-border e-commerce talents. Therefore, the problem in the traditional teaching mode, "stressing process and neglecting analysis," needs to be solved urgently. To this end, we integrate big data technology into the practical teaching process of cross-border e-commerce by applying Python tools that are suitable for teachers and students with liberal arts backgrounds. Using web crawler technology in teaching design to improve the availability of business data, data analysis technology is used to enhance the scientific nature of management decision-making, and data visualization technology is used to ensure the timeliness of business decision-making. The teaching demonstration results show that the teaching design scheme has strong operability. The application of big data technology is an effective means to help the practical teaching of cross-border e-commerce and realize the organic integration of students' business thinking and data literacy. It also positively promotes the reform of e-commerce teaching in applied universities, interdisciplinary learning, and the integration of arts and sciences, and meets the needs of industry development talents. © 2023 ieee.

关键词： Big data

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Analysis and visualization of Tweets Using data science: A Case Study of Qatar Airways 7

Analysis and Visualization of Tweets Using Data Science: A C...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Tuli, Malvika Mohana, Rajni Kaur, Anupriya Jaypee University of Information Technology Department of Humanities And Social Sciences Solan India Amity University Amity School of Engineering & Technology Mohali India

ISBN: (纸本)9798350371406

data mining has gained significant importance in various fields due to its ability to extract information from large datasets. This is especially true in a period where social media platforms and review websites provide an open arena where airline passengers can express their opinions, airlines deal with large amount of unstructured data which contains important information about the feelings and preferences of customers. With the rising popularity of data science and interdisciplinary research, this paper aims to understand, examine and interpret the main concerns and emotions of people regarding the world's most top-rated airline, Qatar Airways by implementing sentiment analysis and topic modelling on Twitter data for extracting public sentiments and topics of discussion. We created a data set comprising of tweets for Qatar Airways. The approach starts with crawling tweets for a three-week time period, from 01 June 2023 to 21 June 2023, followed by pre-processing and lastly, implementing sentiment analysis and topic modeling. The analysis was carried out using RapidMiner and Orange. After removal of irrelevant tweets and applying sentiment analysis on a data set of 1305 tweets, 626 tweets were categorized into positive, 195 as negative and 484 as neutral sentiments. After analysis, public sentiment is found to be predominantly positive. Positive reviews were focused on good experience in business class, negative reviews were mostly concerned with baggage handling and neutral opinions were towards sponsorship by the airline. The study has combined knowledge extraction with sentiment analysis. visualizations of sentiment count and topic modeling have also been presented. © 2023 ieee.

关键词： Sentiment analysis

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Model of visualization and Analytics for Open data (Case: Election Voters & Kids Disability Category) 1

Model of Visualization and Analytics for Open Data (Case: El...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Wibowo, Ripto Mukti Fakieh, Bahjat Ramzan, Muhammad Sher Alzahrani, Ahmed Saeed Siddiqui, Muazzam Alzahrani, Bander King Abdulaziz University Faculty of Computing and Information Technology Department of Information Systems Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia Indiana University Luddy School of Informatics Computing and Engineering Bloomington United States

ISBN: (纸本)9781665472753

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big data technology. data cleaning or cleansing, Exploratory data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 ieee.

关键词： Open data

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Web Development Using ReactJS 5

Web Development Using ReactJS

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5th ieee International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Keshari, Pankaj Maurya, Priya Kumar, Pankaj Katiyar, Alok Galgotias University School Of Computer Science And Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350330861

In recent years, there have been considerable developments in web development, and ReactJS has become a potent tool for creating effective and engaging web apps. This abstract offers a succinct synopsis of the study's research paper on ReactJS-based web development. [1] The purpose of the study article is to examine ReactJS's main characteristics, benefits, and potential uses in web development. It explores subjects including ReactJS' component-based architecture, virtual DOM, reconciliation method, and advantages of React Hooks. The paper also covers ReactJS's support for server-side rendering and its interoperability with the creation of mobile apps using React Native [14]. The research paper also emphasises the benefits of adopting ReactJS in online development, including better performance with the virtual DOM, reusable code, increased user interface creation, and SEO friendly web applications. As well investigates the strong ReactJS ecosystem and developer support available. The construction of a real-time chat application and a data visualisation dashboard are showcased as case studies in the article to explain the practical applications of ReactJS. Additionally, it contrasts ReactJS with two other well-known web development frameworks, Angular and ***, in order to assist readers, grasp the unique qualities and applications of each framework. © 2023 ieee.

关键词： data visualization

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Point Cloud and Image data Fusion Methods based on the Least Squares Method 8

Point Cloud and Image Data Fusion Methods based on the Least...

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8th International Conference on Control, Robotics and Cybernetics, CRC 2023

作者： Li, Jiacheng He, Qing Changsha University of Science and Technology School of Electrical and Information Engineering Changsha China

ISBN: (纸本)9798350330588

To enhance sensing capabilities in terms of accuracy and efficiency, the fusion of multiple sensors possessing complementary characteristics is considered. Considering the absence of color information in 3D laser point cloud data and the lack of spatial distance information in images, this study proposes a measurement approach enabling the fusion of LiDAR and camera data. The objective is to address the challenge of integrating image features and point cloud features in the spatial domain, thereby achieving real-Time visualization of 3D coordinate data corresponding to the point cloud by selecting points on the image. Given the sparsity of the point cloud, interpolation is conducted using the least squares method. The accuracy of the least squares method is compared with that of the nearest point method through mean square error analysis, and the comprehensive experimental results indicate that the least squares method outperforms the nearest point method, yielding a significant improvement in accuracy by 10.288%, when compared to the inverse distance weighted, which shows an enhancement of 11.6%. © 2024 ieee.

关键词： Least squares approximations

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Privacy-Preserving Knee Osteoarthritis Classification: A Federated Learning Approach with GradCAM visualization 26

Privacy-Preserving Knee Osteoarthritis Classification: A Fed...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Rifat, Rakib Hossain Chakraborty Shruti, Abanti Kamal, Marufa Rabiul Alam, Md. Golam BRAC University Dept. of Computer Science and Engineering Mohakhali Dhaka1212 Bangladesh

ISBN: (纸本)9798350359015

Knee Osteoarthritis (KOA) poses a significant global health challenge, impacting a substantial population. The conventional detection process involves multiple tests and meticulous examination by experienced physicians, which is time-consuming and susceptible to misclassification due to subtle variations in X-ray images. Additionally, privacy concerns hinder the sharing of sensitive data like X-rays. This study employs Federated Learning with pre-trained architectures (DenseNet-169, Inception-v2, and MobileNet-v2) to classify three KOA severity grades, utilizing two clients to ensure data privacy. The aim is to develop a generalized model for disease classification, improving efficiency while ensuring the confidentiality of patient information. DenseNet-169 excelled with an F1 score of 81% and an accuracy of 82%, while Inception-v2 and MobileNet-v2 performed well with slight F1 score variations. Moreover, the exploration of GradCAM visualization techniques is conducted to improve interpretability, highlighting the capability of this approach to effectively tackle the intricate challenges linked to Knee Osteoarthritis detection. © 2023 ieee.

关键词： DenseNet Federated learning Grad-CAM Knee Osteoarthritis Medical Images MobileNet Resnet XAI

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Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation 13

Active Learning on Neural Networks through Interactive Gener...

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13th ieee Integrated STEM Education Conference, ISEC 2023

作者： Jeong, Dong H. Cho, Jin-Hee Chen, Feng Josang, Audun Ji, Soo-Yeon University of the District of Columbia Department of Computer Science and Information Technology WashingtonDC United States Virginia Tech. Department of Computer Science Falls Church United States University of Texas at Dallas Department of Computer Science Richardson United States University of Oslo Department of Informatics Oslo Norway Bowie State University Department of Computer Science Bowie United States

ISBN: (纸本)9798350300017

Artificial neural networks (ANNs) have been broadly utilized to analyze various data and solve different domain problems. However, neural networks (NNs) have been considered a black box operation for years because their underlying computation and meaning are hidden. Due to this nature, users often face difficulties in interpreting the underlying mechanism of the NNs and the benefits of using them. In this paper, to improve users' karning and understanding of NNs, an interactive learning system is designed to create digit patterns and recognize the m in real time. To help users clearly understand the visual differences of digit patterns (i.e., 0∼9) and their results with an NN, integrating visualization is considered to present all digit patterns in a two-dimensional display space with supporting multiple user interactions. An evaluation with multiple datasets is conducted to determine its usability for active learning In addition, informal user testing is managed during a summer workshop by asking the workshop participants to use the system. © 2023 ieee.

关键词： Neural networks

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Landslide Monitoring Using IoT System with Cloud Platform 10

Landslide Monitoring Using IoT System with Cloud Platform

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10th ieee Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Rawat, Pradeep Singh Soni, Prateek Kumar Goel, Aryan Kaur, Gurmeet Dit University Computer Science & Engg. Department Dehradun India Gla University Computer Engineering & Application Mathura India

ISBN: (纸本)9798350382471

The natural disasters poses the losses in the disaster prone area. Landslides pose great socio-economic threats to regions which geography of the location favors it. The hill states of the India are more prone to the landslides due to the heavy rain fall other environmental challenges. The researchers found that ninety-nine percent of landslides induced due to heavy rain falls. The severity of disasters especially landslides is gradually increasing day by day in scale and frequency, resulting in widespread loss of human lives and infrastructure of the nation. The detection, monitoring and mitigation of the landslides in hill states is the major concern. The mitigation and monitoring of the effects of landslides can be performed using technology integration. This article presented an innovative landslide monitoring system. It is based on the IoT protocols based monitoring system consisting of cloud platform which provides instant alerts of upcoming landslides on the basis of real time data set collected inside the IoT cloud platform. As part of this monitoring system, landslide data is sent directly to the civil protection department and notified to the local population for the awareness and future precautionary safety measures. The results and discussions section shows the real time data visualization of the landslide inducting parameters, including metrological, geophysical and hydrological parameters. © 2023 ieee.

关键词： Accelerometer sensor Arduino IDE Arduino uno Cloud Internet of Things Soil moisture Sensor

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Underwriting Knowledge Graph Construction, Maintenance and Its Application on Explainable Exclusion 10

Underwriting Knowledge Graph Construction, Maintenance and I...

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10th ieee International Conference on Behavioural and Social Computing, BESC 2023

作者： Van Nguyen, Khanh Tilocca, Peter Huo, Huan Xu, Guandong University of Technology Sydney School of Computer Science Sydney Australia Zurich Financial Services Life and Investments Sydney Australia

ISBN: (纸本)9798350395884

The current underwriting rule engine (URE) that our industry partner and many other companies have implemented is a rule-based system that streamlined the underwriting manual into a customisable question tree flowchart with question branches leading to exclusion codes as the final result. As the hardcoded process proceeds, the task becomes sub-optimal and loses the customisation aspect for the customers. To address these challenges, we propose constructing an Underwriting Knowledge Graph (UKG) that incorporates an overview of customer risk profiles using explainable exclusion. The UKG leverages interconnected information related to customer attributes, historical data, and exclusion codes to provide valuable insights into risk profiles and potential associations with specific exclusion codes. Our investigation into domain-specific requirements leads to the first-ever UKG, based on real-life underwriting data from an insurance company in Australia. We also present a novel method for UKG maintenance that facilitates multi-label classification. The UKG offers a comprehensive view of the insurance ecosystem, aids risk profile visualization, and enables explainable exclusion classifications. With potential applicability across industries, the UKG promises to improve risk assessment and decision-making for other highly personalized services beyond life insurance. © 2023 ieee.

关键词： Risk assessment

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HCMT: A Novel Hierarchical Cross-Modal Transformer for Recognition of Abnormal Behavior

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ieee ACCESS 2024年 12卷 161296-161311页

作者： Liu, Hai Chuan Khairuddin, Anis Salwa Mohd Chuah, Joon Huang Zhao, Xian Min Wang, Xiao Dan Fang, Li Ming Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia Chongqing Key Lab Publ Big Data Secur Technol Chongqing 401420 Peoples R China Southern Univ Coll Fac Engn & Informat Technol Skudai 81300 Johor Malaysia Chongqing Coll Mobile Commun Chongqing 401520 Peoples R China Chongqing Open Univ Sch Management Chongqing 400052 Peoples R China

Enhancing video recognition systems with advanced abnormal behavior recognition technologies is crucial for school safety and campus security. Traditional methods primarily rely on visual data and often fail to recognize complex behaviors due to intricate backgrounds. Similarly, traditional audio processing techniques struggle to capture transient anomalies, as they have limited capacity to handle complex sounds. This study overcomes these limitations by integrating audio and visual data, addressing the shortcomings of visual-only modalities in recognizing subtle behaviors. This study introduces a novel Hierarchical Cross-Modal Transformer (HCMT), which innovatively combines multiple hierarchical branches of visual and audio. The innovative integration of hierarchical audio and visual modalities in HCMT enables capturing low-level features often overlooked by single late-stage fusion methods, thus learning global features more effectively. The audio branch utilizes the newly developed Audio Temporal Spectrogram Transformer (ATST), which employs a global sparse uniform sampling technique to effectively capture the transient nature of audio-based abnormalities, thereby enhancing behavior recognition robustness. The HCMT model demonstrated a Top-1 accuracy of 79.45% and a Top-5 accuracy of 98.44% on the challenging Campus Abnormal Behavior Recognition Hard (CABRH8) dataset, consisting of eight indistinguishable human abnormal behaviors. The ATST significantly improved Top-1 accuracy by 7.45% over visual benchmarks alone. Furthermore, the HCMT recorded Top-1 and Top-5 accuracies of 84.93% and 97.63% on the CABR50 dataset, outperforming prior models that relied solely on visual data. It underscores the adaptability of the HCMT approach. The model's complexity includes 992 GFLOPs, achieving 28 frames per second (FPS). The model's generalizability was also confirmed on additional datasets, including UCF-101, which achieved advanced outcomes.

关键词： visualization Feature extraction Transformers Accuracy Spatiotemporal phenomena Adaptation models Transient analysis Computational modeling Computational complexity Behavioral sciences Video recognition systems campus abnormal behaviors visual and audio branches hierarchical cross-modal transformer audio temporal spectrogram transformer

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