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

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

引用

2025 International conference on visual Analytics and data visualization, ICVADV 2025

作者： Nagalakshmi, Thirumalasetty Sambasivudu, M. Sai, Thota Siva Ratna Dept. of Cse Telangana India

ISBN: (纸本)9798331521394

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. © 2025 IEEE.

关键词： Semantic Segmentation

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Stepwise Approach to Automatically Building an Ensemble of Classifiers on Football data 16th

Stepwise Approach to Automatically Building an Ensemble of C...

引用

16th Asian conference on Intelligent Information and database Systems (ACiiDS)

作者： Glowania, Szymon Univ Econ Katowice Dept Machine Learning 1 Maja 50 PL-40287 Katowice Poland

ISBN: (纸本)9789819759330;9789819759347

The paper delves into the realm of machine learning applications in sports, particularly focusing on the creation of ensembles of classifiers. It introduces a groundbreaking steps approach, utilizing Dynamic Classifier Selection (DCS), to elevate the precision of predicting outcomes in European football leagues. The methodology involves a meticulous exploration of the integration, preparation, and selection of diverse datasets, presenting a stark contrast to traditional classifier techniques. Rigorous experiments were conducted to validate the efficacy of the proposed steps approach, revealing a significant improvement in prediction accuracy compared to conventional methods. The article not only establishes the effectiveness of the steps approach but also hints at promising avenues for future research. These include the exploration of various voting schemes, the automation of ensemble construction, and the investigation of adaptive voting schemes. The overarching goal is to refine and enhance the process of classifier selection in the analysis of sports data. The results of this research pave the way for an automatic approach to building ensembles of classifiers, addressing a notable gap in the existing literature where such methodologies are not explicitly outlined. The primary focus of the research was to develop an automatic approach for creating classifier ensembles, aiming to substantially enhance the accuracy of sports data predictions. The absence of an explicit automatic approach in the current literature presented an opportunity for this study to contribute a novel step approach. The obtained results not only showcase the efficacy of the proposed method in predicting match outcomes accurately but also highlight the versatility of the approach by its applicability to real data from various sectors. This multifaceted contribution positions the steps approach as a valuable asset in the realm of sports data analysis and prediction.

关键词： Ensembles of classifiers Dynamic Classifier Selection Sport result prediction

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Did Somebody Say 'Gest-IT'? A Pilot exploration of Multimodal data Management 10

Did Somebody Say 'Gest-IT'? A Pilot Exploration of Multimoda...

引用

10th Italian conference on Computational Linguistics, CLiC-it 2024

作者： Pannitto, Ludovica Albanesi, Lorenzo Marion, Laura Martines, Federica Maria Caruso, Carmelo Bianchini, Claudia S. Masini, Francesca Mauri, Caterina Alma Mater Studiorum University of Bologna Italy University of Poitiers FoReLLIS Laboratory France

The paper presents a pilot exploration of the construction, management and analysis of a multimodal corpus. Through a three-layer annotation that provides orthographic, prosodic, and gestural transcriptions, the Gest-IT resource allows to investigate the variation of gesture-making patterns in conversations between sighted people and people with visual impairment. After discussing the transcription methods and technical procedures employed in our study, we propose a unified CoNLL-U corpus and indicate our future steps. © 2024 CEUR-WS. All rights reserved.

关键词： Information management

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Exploring data Workers’ Behaviours in data Quality Discovery 44

Exploring Data Workers’ Behaviours in Data Quality Discover...

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44th International conference on Information Systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

作者： Chen, Tianwa Demartini, Gianluca Indulska, Marta Sadiq, Shazia School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia Business School The University of Queensland Brisbane Australia

ISBN: (纸本)9781713893622

The rise of data re-purposing has resulted in an unprecedented opportunity to create new value from existing data assets. Unlike previous approaches that relied on well-understood data quality requirements, this open-ended setting demands a discovery-oriented approach. Despite a growing market of tools that assist with data exploration and profiling, data quality discovery continues to drain the time of data workers due to its cognitively demanding and contextual nature. Accordingly, we study data quality discovery through purpose-built experimental platforms that mimic typical data exploration platforms, and explore implicit sensemaking behaviours of data workers when required to write code or use available functions, by exploiting eye-tracking and log data. Our analysis of visual scanning and attention patterns reveals various sensemaking loops amongst data workers with different performance levels in different settings. Our findings provide insights for researchers and tool designers to consider how to support different data workers in data quality discovery. Copyright © 2023 Tianwa Chen, Gianluca Demartini, Marta Indulska, Shazia Sadiq.

关键词： Eye tracking

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Fine-Tuning Large Language Models for Automatic Font Skeleton Generation: exploration and analysis 17th

Fine-Tuning Large Language Models for Automatic Font Skelet...

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17th Asian conference on Computer Vision, ACCV 2024

作者： Liu, Yuxuan Fujii, Yasuhisa Zhu, Xinru Nohara, Kayoko School of Environment and Society Institute of Science Tokyo Tokyo Japan Google DeepMind Tokyo Japan

ISBN: (纸本)9789819609079

Despite the pivotal role that font skeletons could play in typeface research and font design, the availability of font skeleton data is sparse and limited. This research explores the possibility of using Large Language Models (LLMs) to generate font skeleton data based on font outline data. Our method represents font skeletons and font outlines as sequences of text tokens derived from SVG commands, and formulates the font skeleton task as a language modeling task predicting the token sequence for a font skeleton given the token sequence of a font outline. As a first attempt, we fine-tuned GPT-3.5 on a dataset of 8,213 Japanese font outlines and corresponding skeletons. Both quantitative and qualitative evaluations show the effectiveness of the approach in terms of rasterized pixel distance, Chamfer distance, and visual analysis. Our proposed method achieved average results of 14.678 for rasterized pixel distance and 1.713 for Chamfer distance, both better than the baseline method (PolyVectorization). In visual analysis, we found better generation results for complex shapes which logograms such as Chinese characters tend to have, than for the simple shapes of syllabograms such as Japanese kana, phonograms such as Latin alphabets, and symbols. Although our fine-tuned model has limitations in generating the skeletons of other font styles, this research establishes a foundation for the automatic generation of font skeletons using LLMs, setting the stage for future work on automatic skeleton generation and the wider application of font skeletons in typography. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Musculoskeletal system

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Exploring data Workers’ Behaviours in data Quality Discovery 34

Exploring Data Workers’ Behaviours in Data Quality Discover...

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34th Australasian conference on Information Systems, ACIS 2023

关键词： Eye tracking

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Beyond the Learning Analytics Dashboard: Alternative Ways to Communicate Student data Insights Combining visualisation, Narrative and Storytelling 22

Beyond the Learning Analytics Dashboard: Alternative Ways to...

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12th Annual International conference on Learning Analytics and Knowledge (LAK)

作者： Nieto, Gloria Milena Fernandez Kitto, Kirsty Shum, Simon Buckingham Martinez-Maldonado, Roberto Univ Technol Sydney Sydney NSW Australia Monash Univ Melbourne Vic Australia

ISBN: (纸本)9781450395731

Learning Analytics (LA) dashboards have become a popular medium for communicating to teachers analytical insights obtained from student data. However, recent research indicates that LA dashboards can be complex to interpret, are often not grounded in educational theory, and frequently provide little or no guidance on how to interpret them. Despite these acknowledged problems, few suggestions have been made as to how we might improve the visual design of LA tools to support richer and alternative ways to communicate student data insights. In this paper, we explore three design alternatives to represent student multimodal data insights by combining data visualisation, narratives and storytelling principles. Based on foundations in data storytelling, three visual-narrative interfaces were designed with teachers: i) visual data slices, ii) a tabular visualisation, and iii) a written report. These were validated as a part of an authentic study where teachers explored activity logs and physiological data from co-located collaborative learning classes in the context of healthcare education. Results suggest that alternatives to LA dashboards can be considered as effective tools to support teachers' reflection, and that LA designers should identify the representation type that best fits teachers' needs.

关键词： multimodal data qualitative analysis visual learning analytics

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Interactive Fixation-to-AOI Mapping for Mobile Eye Tracking data based on Few-Shot Image Classification 28

Interactive Fixation-to-AOI Mapping for Mobile Eye Tracking ...

引用

28th Annual conference on Intelligent User Interfaces (IUI)

作者： Barz, Michael Bhatti, Omair Shahzad Alam, Hasan Md Tusfiqur Duy Minh Ho Nguyen Sonntag, Daniel German Res Ctr Artificial Intelligence DFKI Saarbrucken Germany Carl von Ossietzky Univ Oldenburg Oldenburg Germany Univ Stuttgart Stuttgart Germany Max Planck Res Sch Intelligent Syst Stuttgart Germany

ISBN: (纸本)9798400701078

Mobile eye tracking is an important tool in psychology and human-centred interaction design for understanding how people process visual scenes and user interfaces. However, analysing recordings from mobile eye trackers, which typically include an egocentric video of the scene and a gaze signal, is a time-consuming and largely manual process. To address this challenge, we propose a web-based annotation tool that leverages few-shot image classification and interactive machine learning (IML) to accelerate the annotation process. The tool allows users to efficiently map fixations to areas of interest (AOI) in a video-editing-style interface. It includes an IML component that generates suggestions and learns from user feedback using a few-shot image classification model initialised with a small number of images per AOI. Our goal is to improve the efficiency and accuracy of fixation-to-AOI mapping in mobile eye tracking.

关键词： eye tracking interactive machine learning area of interest mobile eye tracking visual attention eye tracking data analysis fixation to AOI mapping

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Modeling analysis of carbon sequestration in visual forests 3

Modeling analysis of carbon sequestration in visual forests

引用

3rd International conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Liu, Chunyan Han, Yao Gao, Shan Harbin Cambridge University Harbin150069 China

Forests are the largest pool of carbon in terrestrial ecosystems and are important for climate change mitigation. By analyzing the carbon sequestration process of forest ecosystem, the carbon sequestration amount of forest can be modeled as a nonlinear function, and the mechanism of forest carbon sequestration can be discussed on this basis. The research first introduced the relevant theories of forest carbon sequestration, followed by the preparation of the visualization system construction based on the research area and data sources, and then the construction of the visualization system, and finally carried out the simulation verification. The results show that the system is effective and accurate. © 2023 The Authors. Published by Elsevier B.V.

关键词： Carbon

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Fragmented visual Attention in Web Browsing: Weibull analysis of Item Visit Times 1

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45th European conference on Information Retrieval (ECIR)

作者： Putkonen, Aini Nioche, Aurelien Laine, Markku Kuuramo, Crista Oulasvirta, Antti Aalto Univ Dept Informat & Commun Engn Espoo Finland Univ Glasgow Sch Comp Sci Glasgow Scotland Univ Helsinki Dept Psychol & Logoped Helsinki Finland

ISBN: (数字)9783031282386

ISBN: (纸本)9783031282379;9783031282386

Users often browse the web in an exploratory way, inspecting what they find interesting without a specific goal. However, the temporal dynamics of visual attention during such sessions, emerging when users gaze from one item to another, are not well understood. In this paper, we examine how people distribute visual attention among content items when browsing news. Distribution of visual attention is studied in a controlled experiment, wherein eye-tracking data and web logs are collected for 18 participants exploring newsfeeds in a single- and multi-column layout. Behavior is modeled using Weibull analysis of item (article) visit times, which describes these visits via quantities like durations and frequencies of switching focused item. Bayesian inference is used to quantify uncertainty. The results suggest that visual attention in browsing is fragmented, and affected by the number, properties and composition of the items visible on the viewport. We connect these findings to previous work explaining information-seeking behavior through cost-benefit judgments.

关键词： Eye-tracking Web browsing visual attention Newsfeeds

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