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Sentiment analysis: A Hybrid Approach on Twitter data 2

Sentiment Analysis: A Hybrid Approach on Twitter Data

引用

2nd International conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Maurya, Chandra Gupta Jha, Sudhanshu Kumar Department of Electronics and Communications Faculty of Science University of Allahabad Uttar Pradesh Prayagraj211002 India

In the present scenario, Internet communities, forum and blogging sites play a very crucial role to present opinions, views and the comments on various events. As the reachability of sites are beyond the control of national boundaries, sometimes this leads to the conviction and persuasion of thoughts without considering any legal subsequences, and also influence the belief of others as well, thus finding or identifying the sentiments of public from the social media content is one of the major research issue. analysis of the sentiments of social media data is very difficult to understand as this typically does not exhibit a suspicious pattern in the flow of information like individual's actual opinion on a specific occasion etc. With the advancement of methodology and easy to use, the users of social media sites are now expressing their opinions, sharing their views and experiences through images, text, animation, audio, video etc. Because of the complexity the conventional text based sentiment analysis procedure, a lot of methods have been evolved, however studies suggest mostly more complicated procedure and approaches. Twitter® (or X), is one of the most common platform used widely by individual to express their opinions and sentiments on various events. Twitter sentiment analysis basically deals with the analysis of twitter quotes to find the hidden pattern in the sentiments expressed by the users in past. This paper aims to takes the challenges regarding social media sentiments analysis and developed a hybrid approach (Text and visual sentiment) on twitter data for sentiment analysis by using NLP-based opinion clustering, textual mining, emotion API and some machine learning techniques for visual ontology. Simulation result shows the significance of work. © 2024 Elsevier B.v.. All rights reserved.

关键词： Sentiment analysis

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HoloInset: 3D Biomedical Image data exploration through Augmented Hologram Insets

HoloInset: 3D Biomedical Image Data Exploration through Augm...

引用

IEEE conference on virtual Reality and 3D User Interfaces (IEEE vR)

作者： Choi, JunYoung Jeong, HaeJin Jeong, Won-Ki UNIST Ulsan South Korea Korea Univ Seoul South Korea

ISBN: (纸本)9781665484022

The extended reality (XR) provides realistic depth perception and huge visualization spaces, which can serve as a powerful workspace for 3D data exploration and analysis. However, a direct adaptation of XR to conventional 3D data exploration tasks is less feasible due to several hardware limitations, such as low screen resolution, dizziness, narrow field of view, etc. In this paper, we propose a novel mixed reality visualization scheme, HoloInset, which combines a conventional visual analytics system and a virtual environment to effectively explore 3D biomedical image data. We also demonstrate the usability of the proposed visualization through a real-world analysis case.

关键词： Human-centered computing Human computer interaction (HCI) Interaction paradigms Mixed / augmented reality

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Guidance Source Matters: How Guidance from AI, Expert, or a Group of Analysts Impacts visual data Preparation and analysis 25

Guidance Source Matters: How Guidance from AI, Expert, or a ...

引用

30th International conference on Intelligent User Interfaces, IUI 2025

作者： Narechania, Arpit Endert, Alex Sinha, Atanu R Georgia Institute of Technology AtlantaGA United States Adobe Research Bangalore India

ISBN: (纸本)9798400713064

The progress in generative Artificial Intelligence (AI) has fueled AI-powered tools like co-pilots and assistants to provision better guidance, particularly during data analysis. However, research on guidance has not yet examined the perceived efficacy of the source from which guidance is offered and the impact of this source on the user's perception and usage of guidance. We ask whether users perceive all guidance sources as equal, with particular interest in three sources: (i) "AI,"(ii) "human expert,"and (iii) "a group of human analysts."As a benchmark, we consider a fourth source, (iv) "unattributed guidance,"where guidance is provided without attribution to any source, enabling isolation of and comparison with the effects of source-specific guidance. We design a five-condition between-subjects study, with one condition for each of the four guidance sources and an additional (v) "no-guidance"condition, which serves as a baseline to evaluate the influence of any kind of guidance. We situate our study in a custom data preparation and analysis tool wherein we task users to select relevant attributes from an unfamiliar dataset to inform a business report. Depending on the assigned condition, users can request guidance, which the system then provides in the form of attribute suggestions. To ensure internal validity, we control for the quality of guidance across source-conditions. Through several metrics of usage and perception, we statistically test five preregistered hypotheses and report on additional analysis. We find that the source of guidance matters to users, but not in a manner that matches received wisdom. For instance, users utilize guidance differently at various stages of analysis, including expressing varying levels of regret, despite receiving guidance of similar quality. Notably, users in the AI condition reported both higher post-task benefit and regret. These findings strongly indicate the need to further understand how different guidance sources imp

关键词： visual analytics

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Noisy Agents: Self-supervised exploration by Predicting Auditory Events

Noisy Agents: Self-supervised Exploration by Predicting Audi...

引用

IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Gan, Chuang Chen, Xiaoyu Isola, Phillip Torralba, Antonio Tenenbaum, Joshua B. MIT Cambridge MA 02139 USA MIT IBM Watson AI Lab Cambridge MA 02142 USA Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Humans integrate multiple sensory modalities (e.g., visual and audio) to build a causal understanding of the physical world. In this work, we propose a novel type of intrinsic motivation for Reinforcement Learning (RL) that encourages the agent to understand the causal effect of its actions through auditory event prediction. First, we allow the agent to collect a small amount of acoustic data and use K-means to discover underlying auditory event clusters. We then train a neural network to predict the auditory events and use the prediction errors as intrinsic rewards to guide RL exploration. We first conduct proof-of-concept experiments using a set of Atari games for an in-depth analysis of our module. We then apply our model to embodied audio-visual exploration using the Habitat simulator and active exploration with a rolling robot using the ThreeDWorld (TDW) simulator. Experimental results demonstrate the advantages of using audio signals over vision-based models as intrinsic rewards to guide RL explorations.

关键词： visualization Neural networks Reinforcement learning Games Robot sensing systems Acoustics Noise measurement

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Web based Real Time Bowel Cancer Stage Suggestion using CNN 2

Web based Real Time Bowel Cancer Stage Suggestion using CNN

引用

2nd International conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Kandasamy, v Aravind, M. Ganesh, Siva P. Raman, Thulasi G. Panimalar Engn Coll Dept Informat Technol Chennai Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

Cancer that develops in the colon or rectum of the large intestine is called bowel cancer (CRC), although it goes by other names as well. The early detection of bowel cancer relies largely on the microscopic inspection of entire tissues samples. Due to its inherent subjectivity, the conventional visual examination is both laborious and wildly inaccurate. Using a deep learning method based on Convolutional Neural Networks (CNN) to distinguish between tumor tissue and healthy tissues, we provide a web-based flask application that may predict the stage of bowel cancer. 4550 image data of seven different tissue types in total are collected and analyzed using pandas and sklearn. A CNN model is trained after preprocessing and cleaning of data, the accuracy of the model is calculated and analyzed using Confusion Matrix. The model is deployed to a production environment using Flask as a Web Application. Though the information may not replace the advice of a doctor, the Application may serve as an intermediate between traditional and automated analysis of tissue for fast prediction of the stage of the cancer by Doctors.

关键词： Deep learning Bowel Cancer Flask Pattern Recognition Convolutional Neural Networks (CNN) data analytics Web Application Pandas Sklearn Python

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verseMeet - A virtual Meeting Application 10

VerseMeet - A Virtual Meeting Application

引用

10th International conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sarumathi, S. Gokul, M. Gokulavelan, M. Shimar Roshan, R.S. K.S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

virtual Reality (vR) has emerged as a potent technological tool for elevating remote collaboration and immersive communication experiences. This paper introduces a sophisticated vR meeting and collaboration application, harnessing the capabilities of vR headsets to craft dynamic and interactive virtual meeting environments. The application seamlessly integrates a diverse array of features, including a collaborative sketch board fostering visual interaction, an embedded web browser for streamlined online exploration, and the ability to import 3D models for real-time visualization. Additionally, strategic plans are in place to implement an avant-garde 3D visualization feature aimed at enhancing data analytics capabilities within the platform. The application offers users the functionality to record and review their sessions. Noteworthy is the adaptive nature of our vR meeting app, tailoring its environment based on the number of users, thereby promoting a distinctive collaborative experience. Developed utilizing vRTK, Unity 3D, and the Photon Engine, this application functions as a standalone system, obviating the necessity for a PC-to-headset intermediary. This paper delves comprehensively into the design, implementation, and development of our vR meeting application, shedding light on its unique capabilities and its potential to revolutionize remote collaboration and data analysis. © 2024 IEEE.

关键词： virtual environments

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Research on Key Technologies for Enhancing Image analysis Capability of Power Transmission and Transformation Equipment Based on Large visual Models 10

Research on Key Technologies for Enhancing Image Analysis Ca...

引用

IEEE 10th International conference on Edge Computing and Scalable Cloud (IEEE EdgeCom) / 11th IEEE International conference on Cyber Security and Cloud Computing (IEEE CSCloud)

作者： Zhang, Wangjun Chen, Yu Wu, Xiong Li, Fan Yang, Hongshan State Grid Shanghai Elect Power Co Shanghai Peoples R China ShanghaiCo Ltd Transwarp Technol Shanghai Peoples R China

ISBN: (纸本)9798350377149;9798350377132

This study significantly enhances the accuracy and efficiency of image analysis for power transmission and transformation equipment by introducing large model technology, bringing innovation to the field of intelligent inspection for power systems. Through an in-depth exploration of key aspects such as data preparation, model selection and training, and image analysis processing, this research employed a pre-trained large model based on the Transformer architecture. It was meticulously fine-tuned and trained according to the characteristics of power equipment images. By preprocessing and augmenting a vast collection of power transmission and distribution equipment images under various conditions, the model successfully extracted features, achieving high-precision defect identification and classification. The model was evaluated using cross-validation and an independent test set method, confirming its superiority over traditional image processing and existing intelligent inspection systems through metrics such as accuracy, recall, and F1 score. Experimental results have shown that this research method significantly outperforms traditional techniques and existing technologies on key performance indicators. Despite significant accomplishments, there remains room for enhancing the model's ability to generalize and adapt to intricate environments. Upcoming efforts will delve into streamlined model training techniques and refine the model's architecture to handle more intricate power image scenarios. Furthermore, the fusion of large model technology with other cutting-edge technologies will be explored to enable more astute, adaptable, and real-time scrutiny of power equipment. This study showcases the vast potential and practical worth of large model technology in power image analysis and intelligent inspection, poised to propel the intelligent evolution of the power sector.

关键词： Large Model Power Equipment Image analysis Transformer Architecture

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Enhancing visual Question Answering for Arabic Language Using LLava and Reinforcement Learning 6

Enhancing Visual Question Answering for Arabic Language Usin...

引用

6th International conference on AI in Computational Linguistics, ACLing 2024

作者： Elmaghraby, Asmaa Maged, Samaa Essawey, Mohamed Elfaramawy, Rawan Negm, Esraa Khoriba, Ghada Nile University Giza Egypt

visual Question Answering (vQA) systems have achieved remarkable advancements by combining text-based question answering with image analysis. This integration has resulted in the creating of machines that can comprehend and address questions related to visual content. Despite these technological developments, a notable lack of vQA solutions specifically designed for the Arabic language remains. This gap persists even with the significant progress made in deep learning techniques and the development of Large Language models (LLMs). Our research introduces ArabicQuest, an innovative chatbot designed specifically for Arabic-speaking users. ArabicQuest utilizes the LLava Large Language Model, a dedicated translation model, and reinforcement learning from human feedback (RLHF) to effectively integrate Arabic text with visual data. Through Telegram's application API, ArabicQuest offers a seamless user interface to ask image-based questions. The proposed pipeline continuously improves its accuracy and relevance by incorporating user feedback, achieving an accuracy of 86%. ArabicQuest is trained and evaluated on various datasets, including visual Genome, RSvQA, and ChartsQA, to ensure robust performance. © 2024 The Authors. Published by Elsevier B.v.

关键词： Question answering

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Comparative analysis of various Feature Extraction Methods on IoT 2023 5

Comparative Analysis of Various Feature Extraction Methods o...

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5th International conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： vergin Raja Sarobin, M. Ranjith, J. Ashwath, D. vinithi, K. Smiti Khushi, v. School of Computer Science and Engineering VIT Chennai India

The Internet of Things (IoT) has emerged as a transformative technology with applications spanning diverse domains, from healthcare to smart cities. Efficient utilization of IoT data is essential for extracting meaningful insights and supporting decision-making processes. This research paper focuses on the critical task of feature extraction from IoT data, with a particular emphasis on the IoT 2023 dataset—a comprehensive and widely-used benchmark dataset in the IoT domain. In this study, exploration and comparison of various feature extraction methods are undertaken to enhance understanding of IoT 2023 dataset characteristics and its potential applications. Traditional statistical techniques, as well as machine learning-based approaches for feature extraction, are investigated. Evaluation of these methods is based on their ability to capture relevant information, reduce dimensionality, and enhance the performance of downstream IoT analytics tasks. Extensive experiments are conducted, and a comprehensive performance analysis is presented to guide the selection of the most suitable feature extraction approach for specific IoT applications. The outcomes of this research contribute to ongoing efforts to harness the potential of IoT data by offering a comprehensive understanding of the impact of feature extraction methods on data quality and predictive accuracy. This aids in paving the way for more informed and effective IoT solutions in 2023 and beyond. © 2024 The Authors. Published by Elsevier B.v.

关键词： Internet of things

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visual Belief Elicitation Reduces the Incidence of False Discovery 23

Visual Belief Elicitation Reduces the Incidence of False Dis...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Koonchanok, Ratanond Tawde, Gauri Yatindra Narayanasamy, Gokul Ragunandhan Walimbe, Shalmali Reda, Khairi Indiana Univ Purdue Univ Indiana PA 46202 USA

ISBN: (纸本)9781450394215

visualization supports exploratory data analysis (EDA), but EDA frequently presents spurious charts, which can mislead people into drawing unwarranted conclusions. We investigate interventions to prevent false discovery from visualized data. We evaluate whether eliciting analyst beliefs helps guard against the over-interpretation of noisy visualizations. In two experiments, we exposed participants to both spurious and 'true' scatterplots, and assessed their ability to infer data-generating models that underlie those samples. Participants who underwent prior belief elicitation made 21% more correct inferences along with 12% fewer false discoveries. This benefit was observed across a variety of sample characteristics, suggesting broad utility to the intervention. However, additional interventions to highlight counterevidence and sample uncertainty did not provide significant advantage. Our findings suggest that lightweight, belief-driven interactions can yield a reliable, if moderate, reduction in false discovery. This work also suggests future directions to improve visual inference and reduce bias. The data and materials for this paper are available at https://***/52u6v/

关键词： False discovery belief elicitation graphical inference

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