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检索条件"任意字段=2023 IEEE Visualization in Data Science, VDS 2023"

共 1363 条记录，以下是1151-1160 订阅

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SMOTE Oversampling and Near Miss Undersampling Based Diabetes Diagnosis from Imbalanced dataset with XAI visualization

SMOTE Oversampling and Near Miss Undersampling Based Diabete...

引用

ieee Symposium on Computers and Communications (ISCC)

作者： Nasim Mahmud Nayan Ashraful Islam Muhammad Usama Islam Eshtiak Ahmed Mohammad Mobarak Hossain Md Zahangir Alam Department of Computer Science and Engineering University of Information Technology and Sciences Bangladesh Center for Computational and Data Sciences Independent University Bangladesh Department of Computer Science and Engineering Independent University Bangladesh School of Computing and Informatics University of Louisiana at Lafayette USA Department of Computer Science and Engineering Daffodil International University Bangladesh

This study investigated the predictive ability of ten different machine learning (ML) models for diabetes using a dataset that was not evenly distributed. Additionally, the study evaluated the effectiveness of two oversampling and undersampling methods, namely the Synthetic Minority Oversampling Technique (SMOTE) and the Near-Miss algorithm. Explainable Artificial Intelligence (XAI) techniques were employed to enhance the interpretability of the model's predictions. The results indicate that the extreme gradient boosting (XGB) model combined with SMOTE oversampling technique exhibited the highest accuracy and an F1-score of 99% and 1.00 respectively. Furthermore, the utilization of XAI methods increased the dependability of the model's decision-making process, rendering it more appropriate for clinical use. These results imply that integrating XAI with ML and oversampling techniques can enhance the early detection and management of diabetes, leading to better diagnosis and intervention.

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Design of Factory Environmental Monitoring System based on ESP32

Design of Factory Environmental Monitoring System based on E...

引用

Frontiers Technology of Information and Computer (ICFTIC), ieee International Conference on

作者： Wenze Song Xuezhen Wang Tianqi Wang Zhixuan Li Zening Zhang Lishan Yang School of Physics Science and Information Engineering Liaocheng University Liaocheng China

With the rapid population of embedded devices, Industrial Internet of Things (IIoT) has provided a promising opportunity to enhance intelligent industrial systems by leveraging the growing ubiquity of wireless sensor and optical devices, which is crucial to ensure the reliable operation of the traditional industrial system and benefit the manufacturing process. In this paper, an efficient factory environmental monitoring system is designed for production data acquisition, which is based on ESP32-S3 and network database, enabling autonomous data collection, analysis and visualization via graph signal processing method, which reduces transmission volume and conserves network bandwidth. The experimental results show that the designed monitoring system with low cost, convenient operation and strong safety, which provides a practical solution to collect and analyze the data obtained by the industrial wireless sector.

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Deep Low Light Image Enhancement Via Multi-Scale Recursive Feature Enhancement and Curve Adjustment

Deep Low Light Image Enhancement Via Multi-Scale Recursive F...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haiyan Jin Dawei Wei Haonan Su Department of Computer Science and Engineering Xi’an University of Technology

Photographs taken in low-illumination environment have a low signal-to-noise ratio and impaired visual quality. Enhancing lowlight images tends to amplify noise. To address this problem, we propose a Multi-Scale Recursive Feature Enhancement (MSRFE) network for low light image enhancement. The MSRFE network consists of several Feature Enhancement (FE) blocks which are applied to enhance the multi-scale image feature and remove the noise recursively in each scale residual map between adjacent scale feature. Then, a deep recursive Curve Adjustment (CA) block is proposed further fine-tunes the output of MSRFE netowrk by learning a non-linear curve which can adjust the image luminance and details. We evaluate the proposed method on both real and synthetic datasets. The results show that our proposed method outperforms other state-of-the-art methods on both visual and objective evaluation indicators.

关键词： visualization Image resolution Speech enhancement Signal processing Feature extraction Iron data mining

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Improving students' cognitive abilities in online environment based on neurofeedback

Improving students' cognitive abilities in online environmen...

引用

ieee Education Engineering (EDUCON)

作者： Nuraini Jamil Abderrahmane Lakas Abdelkader Nasreddine Belkacem Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Computer and Network Engineering College of Information Technology United Arab Emirates University United Arab Emirates

The brain-computer interface (BCI) and eye-tracking technologies can potentially improve the learning environment in education. Cognitive BCIs can give a deep knowledge of brain functioning, enabling the creation of more effective learning approaches and improving brain-based abilities. This study proposes a neurofeedback strategy based on BCI and eye-tracking to collect factual data (monitoring students' brainwaves and eye movement) and analyze their cognitive capacities during online learning. This study aims to create patterns regarding students' learning behavior based on brain and eye movement responses to learning activities as part of the learning environment. As a result, teachers may adapt to new pedagogical ideas and a more flexible delivery style.

关键词： visualization Distance learning Merging Gaze tracking Position measurement Behavioral sciences Task analysis

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Terminal-ileum Centerline Extraction from Magnetic Resonance Enterography data of Crohn’s Disease Patients*

Terminal-ileum Centerline Extraction from Magnetic Resonance...

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data science and Engineering in Healthcare, Medicine and Biology, ieee EMBS Special Topic Conference on

作者： Rotem Benisty Faten Haj Ali Shinnawi Moshe Porat Anat Illivitzki Moti Freiman Faculty of Electrical & Computer Engineering Technion – Israel Institute of Technology Haifa Israel Department of Radiology Rambam Health Care Campus Haifa Israel Faculty of Biomedical Engineering Technion – Israel Institute of Technology Haifa Israel

Crohn’s disease (CD), a chronic inflammatory bowel disorder, often affects the terminal ileum (TI) and leads to digestive tract inflammation and complications like bowel obstruction. Accurately determining the 3D extent of CD from 2D Magnetic Resonance Enterography (MRE) images requires approximations, as no automated 3D measurement system exists. We developed an intelligent MRE reading application for virtual unfolding and 3D visualization of MRE data. We introduce a semi-automatic algorithm to predict the TI centerline from MRE data to reduce radiologist interaction time. The algorithm involves constructing an orientation classifier and implementing a shortest path algorithm to determine the TI centerline. We evaluated the algorithm’s effectiveness on a database of 123 MRE scans using a k-fold cross-validation experimental setup, comparing the predicted centerline with a radiologist-annotated centerline considered as ground-truth. The results showed good alignment with the ground-truth centerline with minimal interaction *** relevance: This study introduces a semi-automatic algorithm that accurately predicts the terminal ileum centerline from MRE data, streamlining the diagnostic process for Crohn’s disease. By reducing radiologist interaction time in assessing the 3D extent of the disease from 2D images, the algorithm holds promise for improving early diagnosis and treatment strategies for affected patients.

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Visual Explanations: Activation-Based Acute Lymphoblastic Leukemia Cell Classification

Visual Explanations: Activation-Based Acute Lymphoblastic Le...

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ieee International Conference on Development and Learning, ICDL

作者： Muhammad Umair Raza Adil Nawaz Xintong Liu Zhuangzhuang Chen Victor C.M. Leung Jie Chen Jianqiang Li College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

The saliency methods are widely used for generating heatmaps that emphasize the important portions of an input image for deep networks on a specific classification task. Interpretability is crucial for deploying deep neural networks in real-world applications. However, the heatmaps produced by current visual explainable methods may contain or visualize particulars differently. To analyze and compare the visualization of different methods, such as Gradient-based, Activation-based, Perturbation-based, and Region-based methods, we empirically evaluated them on the acute lymphoblastic leukemia (cancer cell) classification task using state-of-the-art convolutional neural networks. We also visualized the essential pathological features (salient parts) that are the reasons for the classification results on the classification of normal versus malignant cell (CNMC) dataset.

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Multi-Agent Reinforcement Learning for Visual Comfort Enhancement of Casual Stereoscopic Photography

Multi-Agent Reinforcement Learning for Visual Comfort Enhanc...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee Conference on

作者： Yuzhong Chen Qijin Shen Yuzhen Niu Wenxi Liu College of Computer and Data Science Fuzhou University China

The goal of casual stereoscopic photography is to allow ordinary users to create a stereoscopic photo using two photos taken casually by a monocular camera. In this poster, we propose a multi -agent re-inforcement learning framework for visual comfort enhancement of casual stereoscopic photography. Each agent calculates a homogra-phy matrix based on the positions of four corners before and after the offsets. Furthermore, we propose a hierarchical stereo transformer based on window attention, which enhances and fuses multiscale correlations between left and right views. Experimental results show that our proposed method achieves superior performance to the state-of-the-art methods.

关键词： Photography visualization Three-dimensional displays Correlation Fuses Stereo image processing Virtual reality

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Arousal effects on Fitness-to-Drive assessment: algorithms and experiments

Arousal effects on Fitness-to-Drive assessment: algorithms a...

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Consumer Communications and Networking Conference, CCNC ieee

作者： Manuel Andruccioli Maria Mengozzi Roberta Presta Silvia Mirri Roberto Girau Dept. of Computer Science and Engineering University of Bologna Bologna Italy Centro Scienza Nuova Università Degli Studi Suor Orsola Benincasa

Several elements can affect the drivers' behaviour while they are performing driving activities. Ranging from visual to cognitive distractions, emotions and other drivers' conditions (that could emerge from biometric data, such as temperature, heartbeat, pressure, etc.) can play a significant role, performing as a factor that can increase drivers' response time. This could be crucial in avoiding dangerous situations and in deciding and performing actions that could influence the happening of car accidents. This paper introduces the concept of the “Fitness-to-Drive” index and aims to evaluate how the arousal effects can influence the drivers' status. The paper presents some experimental evaluations we have conducted on a driver simulator, discussing the obtained results.

关键词： Temperature sensors Temperature measurement visualization Temperature distribution Heart beat Distance measurement Indexes

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Inpainting of Remote Sensing Sea Surface Temperature image with Multi-scale Physical Constraints

Inpainting of Remote Sensing Sea Surface Temperature image w...

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ieee International Conference on Multimedia and Expo (ICME)

作者： Qichen Wei Zijie Zuo Jie Nie Jiahao Du Yaning Diao Min Ye Xinyue Liang College of Information Science and Engineering Ocean University of China Qingdao China

Sea Surface Temperature (SST) is a significant environmental factor indicating marine revolutions, which is popularly applied in the meteorological forecasting and fishing industry. Due to the limited sensing ability and occlusion caused by clouds or ice, it is difficult to obtain complete SST data. Compared to traditional interpolation-based methods which refill missed data only referred to current SST data, inpainting-based methods have been carried out with the advantage of using historical SST images to train Generative adversarial Networks (GAN) by terms of considering SST data reconstruction task as an image inpainting task. However, different from common inpainting tasks constrained by semantics, the SST image is a scientific data visualization image without semantics but physical constraints. To address this problem, this paper proposes a multi-scale inpainting GAN-based neural networks to guarantee the physical constraint and realize reasonable SST image reconstruction. The proposed framework mainly contains two modules including the Average Estimation Module (AEM) to realize a global constraint so as not to generate excessive deviation, and the Multi-scale Anomaly Decouple Module (MSADM) to preserve data specificity of current SST image from well-designed multi-scale and decoupled perspectives. Finally, a post-fusion module concatenates the "average" and "specificity" features together to accomplish our multi-scale physical constraints SST image inpainting task. Sufficient experiments have been carried out to verify the effectiveness and physical consistency compared with prior SOTA methods applied to the public AVHRR Pathfinder SST dataset.

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Predictive Precision: LSTM-Based Analytics for Real-time Stock Market visualization

Predictive Precision: LSTM-Based Analytics for Real-time Sto...

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ieee International Conference on Big data

作者： Vijayalakshmi Saravanan Acharya Pramod Laxman Poudel Praveen Paramasivam Department of Computer Science University of South Dakota Vermillion USA

ISBN: (数字)9798350324457

ISBN: (纸本)9798350324464

Artificial intelligence (AI) is widely implemented in finance for stock price prediction using deep learning techniques. However, stock data’s high volatility and signal-to-noise ratio can make it difficult for neural networks (NN) to make accurate predictions. Nevertheless, even 70% accuracy can provide a good return value, leading to the increasing adoption of AI. In this study, we utilized long short-term memory (LSTM), a time series forecasting method, to predict the future stock market prices of Twitter (TWTR) and Facebook (FB) on NYSE and evaluated the performance of the model. Additionally, sentiment analysis, which involves understanding information from sentiment rich sources such as news and social media, plays a crucial role in predicting changes in stock market prices. With the abundance of data in modern times, manual data examination is no longer practical, necessitating AI and data mining techniques. The impact of emotion classification on stock market prices was also investigated in this study. The deep learning method involved using an LSTM neural network to learn the features instead of relying on human feature engineering. The NLP model used in this study combines LSTM, an intriguing branch of machine learning that uses past data to predict future values, with real-time visualization for sentiment analysis.

关键词： Training Sentiment analysis Social networking (online) Predictive models Market research Real-time systems Stock markets

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