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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...

引用

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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Fractal data Story Model of Fusion Time and Space for Big data 5

Fractal Data Story Model of Fusion Time and Space for Big Da...

引用

5th International Conference on Machine Learning, Big data and Business Intelligence, MLBDBI 2023

作者： Hu, Yunzhu Liu, Xinxiong Luo, Jieyu Du, Wei Huazhong University of Science and Technology Wuhan Hubei China China Construction Institute of Advanced Technology Wuhan Hubei China

ISBN: (纸本)9798350359930

In the era of big data in China, the use of data storytelling method for Big data visualization and Storytelling is an important part of data Science theory to guide practice. On the basis of investigation and analysis of story model, combined with China's national conditions, this paper explores how to integrate space-time dimension and Fractal Theory into the construction process of data stories. It is of great significance to improve the theoretical system of data Storytelling and Chinese practice. Through the analysis of a variety of data story structures proposed in the existing research, it is found that there is a common rules between these data story structure curves and Koch Curves in morphology, Fractal theory and methods are further introduced into the study of data Storytelling, and a Fractal data Story Model of Fusion Time and Space for Big data(FDSM) is *** this paper, the FDSM is applied to the large High-Resolution Displays of Electricity System design project in large power enterprises in China. Practice has proved that using this model to design data stories, on the one hand, is beneficial to convey and present complex big data information and improve data intelligibility;On the other hand, the presentation of data stories through the integration of time and space can fully grasp the rhythm of the story and dispatch the audience's mood, that improve the memorability and experience of the data. The proposal of the model and the method provides a more authentic, scientific and operable Chinese method and path for data Storytelling research. © 2023 ieee.

关键词： data Science

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EFFICIENT APPLICATION OF DRONE WITH SATELLITE data FOR EARLY-STAGE WHEAT DETECTION: FOR PRECISION AGRICULTURE MONITORING

EFFICIENT APPLICATION OF DRONE WITH SATELLITE DATA FOR EARLY...

引用

ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Kukunuri, Anjana N. J. Singh, Dharmendra Indian Inst Technol Roorkee Dept Elect & Commun Engn Roorkee 247667 Uttar Pradesh India

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Early-stage wheat detection is quite useful and important for monitoring national food security and crop management systems. However, it is difficult to differentiate early-stage wheat among the ploughed and bare lands using medium to low-resolution satellite data at a large scale. Further, the estimated wheat sowing area would be erroneous due to misclassifications of one class to the other class. On the other hand, drones provide high-resolution data at the field level. Hence, there is a need to fuse drone and satellite data for precision agriculture information at a large scale. Therefore, in this paper, we have explored the application of machine-learning classifiers for early-stage wheat detection using drone and sentinel 1A SAR data. Further, the area is estimated for each class and is compared with the area estimated from the high-resolution drone image. Both qualitative and quantitative analysis of the obtained classification results is carried out with the help of field survey data.

关键词： Early-stage wheat satellite data drone precision agriculture machine learning

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Implementation of Multi-dimensional Power Big data visualization Platform Based on Python

Implementation of Multi-dimensional Power Big Data Visualiza...

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2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023

作者： Chen, Peiguang Yu, Xuanduo Li, Dongqi Peng, Cong Yuan, Ruihan Han, Shunjie Research Institute of Economics and Technology State Grid Jilin Electric Power Co. Ltd. Jilin China School of Electrical and Electronic Engineering Changchun University of Technology Changchun China

ISBN: (纸本)9798350309003

With the rapid development of social economy, related energy companies and organizations have produced a large number of research results and information data, but due to different standards and different caliber, they have not been effectively integrated and applicated in-depth. Therefore, it is necessary to build a comprehensive energy information pool covering the whole industry and build a multi-dimensional information data display platform. This paper builds a multi-dimensional data display platform based on big data technology by relying on data collection and processing, real-time interaction of front and back end data, and data visualization technology. First of all, Requests, Pandas and other tool libraries in Python are used to achieve data collection, analysis and processing, so as to achieve information search and information intelligent collection. Secondly, the call function in Python is used to introduce the data that has been processed in the data acquisition program file and needs to interact with the front end is into this file, and then the real-time data interaction is realized by using Flask framework and Ajax technology. Finally, using the HTML standard markup language, CSS style set and Echarts gallery technology based on JavaScript web programming language, a multi-dimensional energy data visualization display platform is constructed. © 2023 ieee.

关键词： Python

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ANOVA Simultaneous Component analysis for the Efficient Exploration of Massive Network Traffic

ANOVA Simultaneous Component Analysis for the Efficient Expl...

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ieee symposium on Network Operations and Management

作者： José Camacho Research Centre for Information and Communication Technologies (CITIC-UGR) University of Granada Spain

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

The notion of network observability has been behind network monitoring and management practices from the early times. In the era of Big data, observability is not only a matter of devising the best data measurement techniques (e.g., the network telemetry framework in RFC9232), but also of properly engineering good practices for data visualization, exploration, and understanding. In this paper, we extend ANOVA Simultaneous Component analysis (ASCA), for the visualization of Big data collected from a network. ASCA is a combination of analysis of Variance (ANOVA) and Principal Component analysis (PCA) employed mainly in the clinical sciences. With this Big data extension of ASCA, we provide insights into the massive and complex data of the Netmob 2023 data Challenge.

关键词： data visualization Telecommunication traffic Complex networks Measurement techniques Big data Telemetry Observability

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Special Session: CAD for Hardware Security - Promising Directions for Automation of Security Assurance 41

Special Session: CAD for Hardware Security - Promising Direc...

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41st ieee VLSI Test symposium (VTS)

作者： Aftabjahani, Sohrab Tehranipoor, Mark Farahmandi, Farimah Ahmed, Bulbul Kastner, Ryan Restuccia, Francesco Meza, Andres Ryan, Kaki Fern, Nicole van Woudenberg, Jasper Velegalati, Rajesh Breunesse, Cees-Bart Sturton, Cynthia Deutschbein, Calvin Intel Corp Data Ctr Platform Engn & Architecture Hillsboro OR 97124 USA Univ Florida Dept Elect & Comp Engn Gainesville FL USA Univ Calif San Diego Dept Comp Sci & Engn San Diego CA USA Riscure Inc San Francisco CA USA Univ N Carolina Dept Comp Sci Chapel Hill NC 27515 USA Willamette Univ Dept Comp & Data Sci Salem OR USA

ISBN: (纸本)9798350346305

Hardware security creates a hardware-based security foundation for secure and reliable operation of systems and applications used in our modern life. The presence of design for security, security assurance, and general security design life cycle practices in product life cycle of many large semiconductor design and manufacturing companies these days indicates that the importance of hardware security has been very well observed in industry. However, the high cost, time, and effort for building security into designs and assuring their security - due to using many manual processes - is still an important obstacle for economy of secure product development. This paper presents several promising directions for automation of design for security and security assurance practices to reduce the overall time and cost of secure product development. First, we present security verification challenges of SoCs, possible vulnerabilities that could be introduced inadvertently by tools mapping a design model in one level of abstraction to its lower level, and our solution to the problem by automatically mapping security properties from one level to its lower level incorporating techniques for extension and expansion of the properties. Then, we discuss the foundation necessary for further automation of formal security analysis of a design by incorporating threat model and common security vulnerabilities into an intermediate representation of a hardware model to be used to automatically determine if there is a chance for direct or indirect flow of information to compromise confidentiality or integrity of security assets. Finally, we discuss a pre-silicon-based framework for practical and time-and-cost effective power-side channel leakage analysis, root-causing the side-channel leakage by using the automatically generated leakage profile of circuit nodes, providing insight to mitigate the side-channel leakage by addressing the high leakage nodes, and assuring the effectiveness of the miti

关键词： CAD for Security Hardware Security Security Verification Security Assurance Property Generation Property Extension Security Property Expansion Security Property Translation Formal Security analysis Power Side-Channel Leakage Power Side-Channel analysis Design for Security

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Visual analysis of Spatio-Temporal Trends in Time-Dependent Ensemble data Sets on the Example of the North Atlantic Oscillation 14

Visual Analysis of Spatio-Temporal Trends in Time-Dependent ...

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14th ieee Pacific visualization symposium (ieee PacificVis)

作者： Vietinghoff, Dominik Heine, Christian Bottinger, Michael Maher, Nicola Jungclaus, Johann Scheuermann, Gerik Univ Leipzig Leipzig Germany Deutsch Klimarechenzentrum Hamburg Germany Max Planck Inst Meteorol Hamburg Germany

ISBN: (纸本)9781665439312

A driving factor of the winter weather in Western Europe is the North Atlantic Oscillation (NAO), manifested by fluctuations in the difference of sea level pressure between the Icelandic Low and the Azores High. Different methods have been developed that describe the strength of this oscillation, but they rely on certain assumptions, e.g., fixed positions of these two pressure systems. It is possible that climate chance affects the mean location of both the Low and the High and thus the validity of these descriptive methods. This study is the first to visually analyze large ensemble climate change simulations (the MPI Grand Ensemble) to robustly assess shifts of the drivers of the NAO phenomenon using the uncertain northern hemispheric surface pressure fields. For this, we use a sliding window approach and compute empirical orthogonal functions (EOFs) for each window and ensemble member, then compare the uncertainty of local extrema in the results as well as their temporal evolution across different CO2 scenarios. We find systematic northeastward shifts in the location of the pressure systems that correlate with the simulated warming. Applying visualization techniques for this analysis was not straightforward;we reflect and give some lessons learned for the field of visualization.

关键词： Artificial intelligence Computer vision Computer vision problemsObject detection Human-centered computing visualization visualization systems and tools visualization toolkits Computing methodologies

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Wind and Solar Power Resource Availability Assessment Using Market Monitoring data 21

Wind and Solar Power Resource Availability Assessment Using ...

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21st ieee International symposium on Intelligent Systems and Informatics, SISY 2023

作者： Gerse, Agnes Fleiner, Rita Dineva, Adrienn Centre for Energy Research Eötvös Loránd Research Network Environmental Physics Department Budapest Hungary Óbuda University John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350343366

The central European electricity market transparency platform, officially launched on 5 January 2015, provides access to a large amount of power system data, including aggregated power generation time series per type of generation. These high-resolution, simultaneous time series available for most European countries offer valuable input for the resource assessment of the variable renewable energy sources in the context of security of supply. However, past reviews identified several issues regarding data quality. Therefore, data quality considerations and the application of suitable data-cleaning techniques are essential prerequisites for the further use and proper interpretation of data. Also, impacts resulting from the continuous development of the power system, such as capacity additions, withdrawals, changes in the geographic distribution of the generation fleet, and the evolution of the technology, need to be considered. Relying on the time series of 2015-2022 as input for wind and solar power resource availability and variability assessments, we analyzed several data quality dimensions. Highlighting the limitations, we demonstrated how the datasets could be used for high-level resource assessments in power system analysis at regional or national level. © 2023 ieee.

关键词： Solar energy

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New data Network Architecture: From Reactive Post-collecting to Intelligent Proactive Pre-sensing

New Data Network Architecture: From Reactive Post-collecting...

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2023 ieee International symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Li, Pengyu Xing, Yanxia Telecom Research Institute 6G Research Center China Beijing China

ISBN: (纸本)9798350321524

With the emergence of scenarios that consume large amounts of data such as AI/ML (artificial intelligence and machine learning) and ISAC (Integrated sensing and communication) technologies, how to efficiently utilize data while protecting data privacy and security, reducing communication overhead, and improving data utilization efficiency have become one of the major research interests for 6G network technologies. Current mobile communication networks only use external data service nodes, and the sensing of data is based on consumer subscription, which cannot meet the data service requirements of 6G networks. We propose a data service network architecture for mobile communication networks that supports intelligent collaboration of distributed data service nodes;transforms reactive subscription mode into proactive sensing mode with the assistance of AI/ML analysis;enhances data sensing, processing and storage mechanisms through the flexible use of data rules and knowledge, thus effectively improving the overall data service capability;further, with this architecture, possible solutions are discussed in detail with emphasis on data sensing difficulties. © 2023 ieee.

关键词： Network architecture

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What does Inter-Cluster Job Submission and Execution Behavior Reveal to Us?

What does Inter-Cluster Job Submission and Execution Behavio...

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ieee International Conference on Cluster Computing (CLUSTER)

作者： Patel, Tirthak Tiwari, Devesh Kettimuthu, Raj Allcock, William Rich, Paul Liu, Zhengchun Northeastern Univ Boston MA 02115 USA Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA

ISBN: (数字)9781665498562

ISBN: (纸本)9781665498562

Modern High Performing Computing (HPC) facilities have multiple computing clusters that serve different purposes. These include large-scale computing clusters and smaller data visualization and analysis clusters, which are meant to shift the load of data analytics jobs from the large-scale systems. We perform the first in-depth characterization of cross-cluster behavior of users and jobs and provide an analysis of three inter-related systems at the Argonne Leadership Computing Facility (ALCF). Our analysis reveals interesting trends related to the resource utilization and predictability of user and job behavior across different clusters.

关键词： High-Performance Computing Supercomputing large-Scale Systems Cross-Cluster analysis

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