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visual analysis of Heterogenous Air Pollution data 21

Visual Analysis of Heterogenous Air Pollution Data

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4th International conference on Computer Science and Software Engineering, CSSE 2021

作者： Yang, Xudong Peng, Hui Zhang, Qingming School of Computer Science and Technology Southwest University of Science and Technology China

ISBN: (纸本)9781450390675

Some studies have shown that air pollution is one of the important risk factors for human health. In order to find out the transmission mode, space-Time patterns, main pollution causes and potential mode of air pollution, we analyzed the multi-granularity and multi-dimensional data, and designed a visual analysis scheme of air pollution data, which integrated time, space and relevant factors, and used many kinds of visual charts, including map, calendar chart, single axis scatter chart and so on. Based on these, a data-driven visual analysis system is designed and implemented, and a variety of visual analysis models are established. The interactive methods such as filtering, scaling and exploration can assist users to carry out the data analysis tasks such as data overview, key data tracking, comparing of correlation, analytic reasoning, hypothesis testing and potential mode discovery, and realize the analysis of key causes, spatiotemporal evolution and transmission mode of air pollution. The system is simple and efficient, which can provide valuable reference for in-depth analysis of air pollution data and evaluation of air pollution control measures. © 2021 ACM.

关键词： Air pollution

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visual analysis of Tobacco Leaf Recognition Research Based on CiteSpace

Visual Analysis of Tobacco Leaf Recognition Research Based o...

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International conference on Software Technology and Engineering (ICSTE)

作者： Yanting Dai Hua Zhou Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China

ISBN: (数字)9798350378955

ISBN: (纸本)9798350378962

This study utilizes CiteSpace software to conduct a visual analysis of the literature in the field of tobacco leaf recognition technology in recent years, aiming to reveal the research hotspots, development trends, and contributions of key institutions and scholars in this domain. By analyzing the changes in the number of publications, keyword co-occurrence networks, author collaboration patterns, and keyword burst situations, this paper demonstrates the in-depth exploration and innovation in both basic research and practical applications of tobacco leaf recognition technology. Firstly, through the analysis of the annual publication volume, we found that research on tobacco leaf recognition has shown a significant growth trend over the past decade, indicating the increasing importance of this field in both academia and industrial applications. Secondly, the analysis of research institutions and authors reveals the dominant positions of institutions such as the Chinese Academy of Agricultural Sciences, the Chinese Academy of Sciences, and N anjing Agricultural University, as well as the outstanding contributions of scholars like Wang Ying, Wang Lu, and Guo Yongfeng in terms of publication volume and collaboration relationships. Furthermore, the keyword co-occurrence network and cluster analysis show that the research focus of tobacco leaf recognition technology covers multiple keyword groups, including gene expression, transcription factors, plant resistance, and biosynthesis, reflecting researchers' multifaceted efforts in theoretical exploration and practical applications. Finally, the keyword burst analysis reveals the rise and fall of keywords in different periods. For instance, the period from 2014 to 2017 was mainly concentrated on “gene expression” and “transgenic tobacco,” while from 2018 to 2020, emerging research hotspots such as “functional analysis” and “cell death” became prominent. This study provides profound insights and significant references for the

关键词： Industries visualization Technological innovation Collaboration Market research Software Functional analysis Pattern recognition Gene expression Immune system

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Next-Generation Artificial Intelligence Solutions for Comprehensive analysis and Improvement of Heat Dissipation in High-Intensity Boiling Environments

Next-Generation Artificial Intelligence Solutions for Compre...

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2024 OPJU International Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Gadiyar, H. Manoj T. Kumar, S Vinod Raj, Vijilius Helena Yadav, Dinesh Kumar Hasoni, Mortada Kareem Sabi Lakhanpal, Sorabh Canara Engineering College Department of Computer Science and Business System India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology AlTalb Collage of Nursing Dhi Qar64001 Iraq Lovely Professional University Phagwara India

ISBN: (纸本)9798350373783

This research delves into the realm of AI-driven heat dissipation, offering a suite of algorithms to address the challenges posed by high-intensity boiling environments. The proposed predictive modeling algorithm, rooted in linear regression, provides a systematic and robust approach through 20 meticulously designed steps. This algorithm, along with neural networks, reinforcement learning, generative adversarial networks (GANs), and genetic algorithms, forms a versatile toolbox for thermal management. Linear regression, through its step-by-step predictive modeling process, ensures accurate heat dissipation predictions. Neural networks, employing backpropagation, showcase adaptability and complexity, excelling in real-time adaptation. Reinforcement learning, specifically Q-Learning, introduces dynamic decision-making strategies, vital for navigating complex thermal scenarios. GANs contribute by generating realistic synthetic data, an invaluable asset for simulating high intensity boiling environments. Genetic algorithms, resembling natural selection, prove versatile and scalable in evolving optimal solutions. The research evaluates these algorithms comprehensively, demonstrating the proposed method's superiority in prediction accuracy, energy efficiency, adaptability, and overall performance. The proposed predictive modeling algorithm stands out as a benchmark, showcasing its potential for practical implementation and optimization in diverse scenarios. This comprehensive exploration sets the stage for innovative solutions to the intricate challenges of thermal management, as continuous adaptation and fine-tuning remain pivotal in this dynamic landscape. © 2024 IEEE.

关键词： Generative adversarial networks

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2022 Global Reliability and Prognostics and Health Management conference, PHM-Yantai 2022

2022 Global Reliability and Prognostics and Health Managemen...

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2022 Global Reliability and Prognostics and Health Management conference, PHM-Yantai 2022

ISBN: (纸本)9781665496315

The proceedings contain 267 papers. The topics discussed include: reliability growth model based on Bayesian parameter inference;adaptive maximum cyclostationarity blind deconvolution and its application on incipient fault diagnosis of bearing;a flexible battery capacity estimation method using a portion of charging curve;application of a method of identifying instantaneous shaft speed from spectrum in aeroengine vibration analysis;a monocular visual inertial odometer based on weighted median pre-integration;improved VMD method based on nonlinear Hoyer for abnormal impact interference;application exploration of digital twin in rail transit health management;a subsystem data based reliability acceptance test plan derivation method;fault feature extraction of rolling-element bearing based on neighborhood rough set;safety design and analysis method of single event effects for civil aircraft;auxiliary detection method of illegal actions in football teaching based on binocular vision;robust motion pose capture method of aerobics based on visual simulation imaging;and application of sparse representation method based on K-SVD-ADMM in anomaly detection of satellite telemetry.

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Understanding Plant Leaf Diseases: A Review, analysis, exploration of Techniques and Technologies

Understanding Plant Leaf Diseases: A Review, Analysis, Explo...

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visual Analytics and data visualization (ICVADV), International conference on

作者： Thirumalasetty Nagalakshmi M Sambasivudu Thota Siva Ratna Sai Dept. of CSE Malla Reddy College of Engineering and Technology (A) Telangana India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Modern agriculture relies on plant leaf disease detection to reduce crop losses and guarantee food security. This study takes a look at the three main types of methods used to identify plant leaf diseases: conventional methods, techniques based on Machine Learning (ML), and methodology based on Deep Learning (DL). Although they provide essential insights, the scalability and precision of traditional methods-which depend on chemical analysis, image processing, and human inspection-are severely lacking. ML techniques, which make use of algorithms such as Random Forests (RF) and Support Vector Machines (SVM), automate feature-based categorization, which improves detection. Automated disease identification with high accuracy has recently been made possible by DL, which uses sophisticated methods like semantic segmentation, object recognition, and transfer learning in conjunction with architectures like Convolutional Neural Networks (CNNs). This study examines all of these approaches in detail, comparing and contrasting their advantages, disadvantages, and possible uses. Emerging developments, such as eXplainable Artificial Intelligence (XAI) and Internet of Things (IoT)-based systems, are also highlighted in the discussion, with an emphasis on how they will influence smart and sustainable agriculture in the future.

关键词： Support vector machines Deep learning Reviews Explainable AI Semantic segmentation Plants (biology) Transfer learning Food security Object recognition Diseases

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Influence of Reservoir Heterogeneity of Thick Bioclastic Limestone Reservoir on Water Injection Development and Its Strategies: A Case Study of Lower Member of M Formation in A Oilfield, Iraq 12th

Influence of Reservoir Heterogeneity of Thick Bioclastic Lim...

引用

12th International Field exploration and Development conference, IFEDC 2022

作者： Yu, Yi-Chang Shen, Bo-Heng Liu, Bo-Zun Zhao, Ya-Xin Wang, Jiu-Zhou Li, Jin-Tao Chen, Jia-Qing Cao, Ming-Kui China National Oil and Gas Exploration and Development Company Ltd Beijing China Petro China Research Institute of Petroleum Exploration and Development Beijing China

ISBN: (纸本)9789819919635

The thick bio clastic limestone oilfield in the Middle East is mainly porous reservoir, and water injection is an important development strategy. In order to deepen the understanding of the influence of the reservoir heterogeneityon water injection development and improve the effect of water injection development, taking the lower member of M Formation in A oilfield in Iraq as an example, the bioclastic limestone reservoir heterogeneity, its effect on water injection development and strategies are studied by comprehensively using the data of core, welllogging, casting thin section, analysis and production performance. In the study area, the distribution of reservoir porosity and permeability is scattered, with the average porosity of 20.27% and the average permeability of 29.6 × 10−3 μm2,the study area include type I to type VI reservoirs. Type I and ii reservoirs aremainly located in the rudist shoals on the margin of platform inMB2-1 andMB2-2layers. The average porosity of the interlayers is 9.83%, and the average permeabilityis 0.71×10−3 μm2, with low seepage capacity, interlayers mainly located in MC1-3, MC1-1, MB2-2 and MB2-1 layers. The bottom water injection effect is poor at the continuous area of interlayers, thus, the reservoir pressure deficit is large, resulting in inconsistent reservoir pressure and production characteristics in different layers, the lower member of M Formation can be divided into two reservoirs. Type I and Type ii reservoirs are main contributors to productivity and also control the dominant direction of injection water. However, Type iiI and Type V reservoirs with rich reserves have low development degree, and there servoir productivity has not been fully released. In the development process, it is necessary to utilize the continuously developed interlayers, insist on separated water injection development units, and take optimization and modification based on reservoir types. In addition, the high efficiency water injection developmen

关键词： Limestone

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MuSe 2022 Challenge: Multimodal Humour, Emotional Reactions, and Stress 22

MuSe 2022 Challenge: Multimodal Humour, Emotional Reactions,...

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30th ACM International conference on Multimedia (MM)

作者： Amiriparian, Shahin Christ, Lukas Koenig, Andreas Messner, Eva-Maria Cowen, Alan Cambria, Erik Schuller, Bjorn W. Univ Augsburg Augsburg Germany Univ Passau Passau Germany Univ Ulm Ulm Germany Hume AI New York NY USA Nanyang Technol Univ Singapore Singapore Imperial Coll London London England

ISBN: (纸本)9781450392037

The 3rd Multimodal Sentiment analysis Challenge (MuSe) focuses on multimodal affective computing. The workshop is held in conjunction with ACM Multimedia'22. Three datasets are provided as part of the challenge: (i) the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset which contains humour-tagged audio-visual data of German football coaches, (ii) the Hume-Reaction dataset, which contains annotations on how people respond to emotional stimuli in terms of seven different emotional expression intensities, and (iii) the Ulm-Trier Social Stress Test (Ulm-TSST) dataset, which consists of audio-visual recordings labelled with continuous emotion values of individuals in stressful circumstances. Based on these datasets three affective computing challenges are defined: 1) Humor Detection Sub-Challenge (MuSe-Humor), for spontaneous humour recognition, 2) Emotional Reactions Sub-Challenge (MuSe-Reaction), for prediction of seven fine-grained 'in-the-wild' emotions, and 3) Emotional Stress Sub-Challenge (MuSe-Stress), for continuous prediction of stressed emotion values. In this summary, we describe the motivation behind the challenge, participation and its conditions, as well as the outcomes. The complete MuSe'22 workshop proceedings are available at: https://***/doi/proceedings/10.1145/3551876.

关键词： Multimodal Sentiment analysis Affective Computing Humor Detection Emotion Recognition Multimodal Fusion Challenge Summary Paper

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Collaboration Spotting X - A visual Network exploration Tool 8

Collaboration Spotting X - A Visual Network Exploration Tool

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8th International conference on Social Network analysis, Management and Security (SNAMS)

作者： Bobic, Aleksandar Le Goff, Jean-Marie Guetl, Christian CERN IPT Dept Geneva Switzerland Graz Univ Technol CoDiS Lab ISDS Graz Austria

ISBN: (纸本)9781665494953

Due to many technological advancements, the amount of connected data drastically increased in the last decade. The analysis of this data and the insights it generates show great potential for supporting decision making processes in various industries and aspects of our lives. Multiple visual analytics solutions have been proposed to gain further insights into such data and gain explainable results. However, the majority of existing solutions are either closed sourced, not available or no longer developed. To mitigate the issues above and based on findings from expert interviews conducted using an existing tool, this paper introduces Collaboration Spotting X, a new network-based interactive visual analytics and information retrieval tool prototype. This prototype enables users to explore connected network datasets such as social network data and bibliometric data using multiple visual cues and interactions. Furthermore, to gain an insight into how this prototype is perceived by users and identify further improvements, a preliminary study with a class of 37 computer science graduate students is described. The study findings show that the students perceive Collaboration Spotting X as a useful tool that helps them complete tasks through visualisation and interaction. Additionally, multiple aspects were identified that might have caused users to experience in addition to positive emotions also some negative emotions during usage. These aspects might have also contributed to a lower usability score. Finally, multiple improvement directions have been identified, which will be implemented in future developments.

关键词： visual Analytics visual Network Analytics Network visualisation visual Analytics Tool visual Network Analytics Tool Social Network visualisation Information Retrieval

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Faces in the Fog: A Deep Dive into Face De-Identification Techniques and Their Comparative analysis 3

Faces in the Fog: A Deep Dive into Face De-Identification Te...

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3rd IEEE International conference on Device Intelligence, Computing and Communication Technologies, DICCT 2025

作者： Goswami, Shaonti Paul, Sudipty Konica Jawlia, Sanyam Goel, Anurag Delhi Technological University Department of Cse New Delhi India

ISBN: (纸本)9798331543358

Face de-identification have drawn a lot of attention due to growing privacy concerns and the growing requirement for anonymization in visual data. This review suggests a systematic exploration of current face de-identification methods, emphasizing three main deep learning-based methods: diffusion models, latent Variable based transformations, and GAN-based models. The in-depth analysis of this literature provides a comparison of how well each strategies protect privacy while retaining data utility by focusing on the advantages and disadvantages of them. Our analysis delves deeper into the benefits and limitations of these models, focusing on important issues such as adversarial robustness, systematic biases, and trade-offs between realistic image generation and privacy preservation. This comprehensive overview synthesizes data from several studies, identifies important gaps in existing approaches and provides guidance to researchers and industry practitioners directing to more reliable and ethical de-identification frameworks. © 2025 IEEE.

关键词： Deep learning

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TweetVis: a visual approach to explore Twitter conversations over time

TweetVis: a visual approach to explore Twitter conversations...

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Colombian Caribbean conference (C3), IEEE

作者： José Llumiquinga Erick Cuenca Arnaud Sallaberry Yachay Tech University Urcuquí Ecuador LIRMM University of Montpellier CNRS AMIS Paul-Valéry University of Montpellier Montpellier France

Information visualization is a field of visual data analysis that focuses on working with abstract information. It also incorporates interactions that make it easier for users to assimilate and understand the information presented quickly. This work focuses on developing TweetVis, a visualization tool that follows the fundamentals of Twitter data analysis. In particular, TweetVis makes it possible to explore Twitter conversations over time, as these conversations exert significant influence in the social and political spheres, underlining the importance of their analysis and exploration. To access Twitter conversations, use is made of the official Twitter API. Once the data is collected, it goes through a processing process that turns it into a graph, where the nodes represent the tweets, and the links denote the interactions between them. TweetVis allows users to explore conversations generated from a tweet interactively over time. Finally, this tool’s effectiveness is tested in a real case, analyzing the tweet generated by a public figure.

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