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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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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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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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QVis: A visual Analytics Tool for Exploring Noise and Errors in Quantum Computing Systems

QVis: A Visual Analytics Tool for Exploring Noise and Errors...

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Quantum Computing and Engineering (QCE), IEEE International conference on

作者： Chad A. Steed Junghoon Chae Samudra Dasgupta Travis S. Humble Oak Ridge National Laboratory Oak Ridge USA

We present the preliminary design and results of QVis, a visual analytics tool for exploring quantum device performance data. QV is helps uncover temporal and multivariate variations in noise properties of quantum devices. We describe the implementations of these methods as well as applications to the analysis of a 127-qubit data set derived from the IBM washington processor over a 16-month period. Both human-interactive and semi-automated analytic methods are included to address requirements for visual exploration, thresholding, and clustering techniques. Our application of QVis to real-world scenarios demonstrates the ability to reveal noteworthy patterns in the behavior of the critical characterization metrics.

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Vision mamba mender 24

Vision mamba mender

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Proceedings of the 38th International conference on Neural Information Processing Systems

作者： Jiacong Hu Anda Cao Zunlei Feng Shengxuming Zhang Yi Wang Lingxiang Jia Mingli Song College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security School of Software Technology Zhejiang University College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

Mamba, a state-space model with selective mechanisms and hardware-aware architecture, has demonstrated outstanding performance in long sequence modeling tasks, particularly garnering widespread exploration and application in the field of computer vision. While existing works have mixed opinions of its application in visual tasks, the exploration of its internal workings and the optimization of its performance remain urgent and worthy research questions given its status as a novel model. Existing optimizations of the Mamba model, especially when applied in the visual domain, have primarily relied on predefined methods such as improving scanning mechanisms or integrating other architectures, often requiring strong priors and extensive trial and error. In contrast to these approaches, this paper proposes the Vision Mamba Mender, a systematic approach for understanding the workings of Mamba, identifying flaws within, and subsequently optimizing model performance. Specifically, we present methods for predictive correlation analysis of Mamba's hidden states from both internal and external perspectives, along with corresponding definitions of correlation scores, aimed at understanding the workings of Mamba in visual recognition tasks and identifying flaws therein. Additionally, tailored repair methods are proposed for identified external and internal state flaws to eliminate them and optimize model performance. Extensive experiments validate the efficacy of the proposed methods on prevalent Mamba architectures, significantly enhancing Mamba's performance. For more information, please visit https://***/.

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AirLens: Multi-Level visual exploration of Air Quality Evolution in Urban Agglomerations

AirLens: Multi-Level Visual Exploration of Air Quality Evolu...

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24th Eurographics/IEEE VGTC conference on visualization (EuroVis)

作者： Qu, Dezhan Lv, Cheng Lin, Yiming Zhang, Huijie Wang, Rong Northeast Normal Univ Sch Informat Sci & Technol Changchun Peoples R China Jilin Univ Key Lab Intelligent Informat Proc Changchun Peoples R China

The precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi-run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi-level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.

关键词： Feedback

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visualization Tools for Machine Learning Pipelines: A Review

Visualization Tools for Machine Learning Pipelines: A Review

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Pacific (formerly Asia-Pacific APVIS) visualization Symposium

作者： Valentina Golendukhina Michael Felderer Lisa Sonnleithner University of Innsbruck Innsbruck Austria German Aerospace Center (DLR) Institute for Software Technology University of Cologne Cologne Germany CDL VaSiCS LIT CPS Lab Johannes Kepler University Linz Linz Austria

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

The increasing complexity and adoption of machine learning (ML) pipelines has led to a rising demand for effective visualization tools. This paper presents a comprehensive review of existing tools for visualizing data flow in machine learning (ML) pipelines. We highlight the tools’ purposes, integration methods, and visualization techniques. We collected and analyzed 22 open-source tools and concepts, analyzing their features and classifying them based on their primary purpose. Our analysis revealed five main purposes of visualization tools: exploration, explanation, visual development, comparison, monitoring. We provide an analysis of their integration methods, from standalone visual interfaces to code-level libraries, as well as a review of various visualization techniques, including Directed Acyclic Graphs (DAGs), pipeline matrices, and annotated visualizations. Our findings highlight the importance of visualization in enhancing the interpretability and efficiency of ML workflows. Moreover, the paper provides key limitations and challenges in current visualization methods to promote future research directions enhancing the usability and functionality of ML pipeline visualization tools.

关键词： visualization Directed acyclic graph Reviews Pipelines data visualization Machine learning visual systems Complexity theory Usability Monitoring

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