2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)最新文献_第2页

Enhancing Model Explainability in Financial Trading Using Training Aid Samples: A CNN-Based Candlestick Pattern Recognition Approach 利用训练辅助样本增强金融交易模型的可解释性:一种基于cnn的烛台模式识别方法

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00021

Yun-Cheng Tsai, Jun-Hao Chen

{"title":"Enhancing Model Explainability in Financial Trading Using Training Aid Samples: A CNN-Based Candlestick Pattern Recognition Approach","authors":"Yun-Cheng Tsai, Jun-Hao Chen","doi":"10.1109/IRI58017.2023.00021","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00021","url":null,"abstract":"Candlestick pattern recognition is a widely adopted technique in financial trading, leveraging visual patterns to analyze price movements. Deep Convolutional Neural Networks (CNNs) have exhibited remarkable accuracy in this domain. However, the increasing demand for transparency and explainability in CNN-based models raises concerns regarding their applicability in trading decision-making. This paper addresses these concerns by presenting a framework that enhances the explainability of CNN-based candlestick pattern recognition models. Our approach introduces an innovative data augmentation method to generate training aid samples, facilitating the model’s learning process within human domains. By incorporating this framework, traders gain valuable insights into the decision-making process, comprehending the rationale behind the model’s predictions. Our proposed approach exposes the inherent “black box” nature of CNN-based models, improving their interpretability and empowering traders to make well-informed decisions based on transparent and understandable insights. This advancement holds significant potential for enhancing decision-making processes in financial trading and fostering trust among traders.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134080996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-Based Detection and Classification of Uveal Melanoma Using Convolutional Neural Networks and SHAP Analysis 基于深度学习的基于卷积神经网络和SHAP分析的葡萄膜黑色素瘤检测与分类

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00044

Esmaeil Shakeri, Emad A. Mohammed, Trafford Crump, E. Weis, C. Shields, Sandor R. Ferenczy, B. Far

{"title":"Deep Learning-Based Detection and Classification of Uveal Melanoma Using Convolutional Neural Networks and SHAP Analysis","authors":"Esmaeil Shakeri, Emad A. Mohammed, Trafford Crump, E. Weis, C. Shields, Sandor R. Ferenczy, B. Far","doi":"10.1109/IRI58017.2023.00044","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00044","url":null,"abstract":"Uveal melanoma (UM) is a severe intraocular cancer in adults aged 50-80, often originating from choroidal nevus, a common intraocular tumour. This transformation can lead to vision loss, metastasis, and even death. Early prediction of UM can reduce the risk of death. In this study, we employed transfer learning techniques and four convolutional neural network (CNN)-based architectures to detect UM and enhance the interpretation of diagnostic results. To accomplish this, we manually gathered 854 RGB fundus images from two distinct datasets, representing the right and left eyes of 854 unique patients (427 lesions and 427 non-lesions). Preprocessing steps, such as image conversion, resizing, and data augmentation, were performed before training and validating the classification results. We utilized InceptionV3, Xception, DenseNet121, and DenseNet169 pre-trained models to improve the generalizability and performance of our results, evaluating each architecture on an external validation set. Addressing the issue of interpretability in deep learning (DL) models to minimize the blackbox problem, we employed the SHapley Additive exPlanations (SHAP) analysis approach to identify regions of an eye image that contribute most to the prediction of choroidal nevus (CN). The performance results of the DL models revealed that DenseNet169 achieved the highest accuracy 89%, and lowest loss value 0.65%, for the binary classification of CN. The SHAP findings demonstrate that this method can serve as a tool for interpreting classification results by providing additional context information about individual sample images and facilitating a more comprehensive evaluation of binary classification in CN.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124683208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating Sarcastic Language Datasets in Various Standards for Sarcasm Detection 整合不同标准的讽刺语言数据集进行讽刺检测

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00022

Shih-Hung Wu, Xie-Sheng Hong

引用次数: 0

Gaze Analytics Dashboard for Distributed Eye Tracking 用于分布式眼动追踪的凝视分析仪表板

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00031

Yasasi Abeysinghe, Bhanuka Mahanama, Gavindya Jayawardena, Mohan Sunkara, V. Ashok, S. Jayarathna

{"title":"Gaze Analytics Dashboard for Distributed Eye Tracking","authors":"Yasasi Abeysinghe, Bhanuka Mahanama, Gavindya Jayawardena, Mohan Sunkara, V. Ashok, S. Jayarathna","doi":"10.1109/IRI58017.2023.00031","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00031","url":null,"abstract":"Understanding the focus and visual scanning behavior of users during a collaborative activity in a distributed environment can be helpful in improving users’ engagement. Eye tracking measures can provide informative cues to understanding human visual search behavior. In this study, we present a distributed eye-tracking system with a gaze analytics dashboard. This system extracts eye movements from multiple participants utilizing common off-the-shelf eye trackers, generates real-time traditional positional gaze measures and advanced gaze measures such as ambient-focal coefficient $mathcal{K}$, and displays them in an interactive dashboard. We evaluate the proposed methodology by developing a gaze analytics dashboard and conducting a pilot study to (1) investigate the relationship between $mathcal{K}$ with collaborative behavior, and (2) compare it against the User Experience Questionnaire (UEQ) benchmark. Our results show that groups that spent more time had more ambient attention, and our dashboard has a higher overall impression compared to the UEQ benchmark.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132725649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

InPosNet: Context Aware DNN for Visual SLAM InPosNet:视觉SLAM的上下文感知深度神经网络

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00012

Anvaya Rai, B. Lall, Astha Zalani, Raghawendra Prakash Singh, Shikha Srivastava

{"title":"InPosNet: Context Aware DNN for Visual SLAM","authors":"Anvaya Rai, B. Lall, Astha Zalani, Raghawendra Prakash Singh, Shikha Srivastava","doi":"10.1109/IRI58017.2023.00012","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00012","url":null,"abstract":"This paper introduces a novel approach to accurately localize a subject in indoor environments by using the scene images captured from the subject’s mobile phone camera. The objective of this work is to present a novel deep neural network (DNN), called InPosNet, that generates a concise representation of an indoor scene while being able to distinguish between their inherent symmetry. It also enables the user in real time distinction between the images of the same location but captured from different orientations, thereby enabling the user to detect the orientation along with position. A localization accuracy of less than 1 meter from ground truth is achieved and enumerated through the experimental results. The novel DNN presented in the work is motivated by MobileNetv3-Small [2], followed by PCA based feature space transformation. PCA helps in feature space dimensionality reduction and projection of query images onto an optimally dense subspace of the original latent feature space. The goal is to present a vision based system that will have the ability to be used for indoor positioning, without any need for additional infrastructure or external hardware.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121404077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Copyright 版权

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/iri58017.2023.00003

引用次数: 0

Predicting Power Outage During Extreme Weather Events with EAGLE-I and NWS Datasets 利用EAGLE-I和NWS数据集预测极端天气事件中的停电情况

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00042

Sangkeun Lee, J. Choi, Gs Jung, Anika Tabassum, Nils Stenvig, S. Chinthavali

引用次数: 0

A Hybrid Deep Learning Architecture for Misinformation Detection on Social Media 一种用于社交媒体错误信息检测的混合深度学习架构

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00040

Amani Alzahrani, Tahani Baabdullah, Aeman Almotairi, D. Rawat

{"title":"A Hybrid Deep Learning Architecture for Misinformation Detection on Social Media","authors":"Amani Alzahrani, Tahani Baabdullah, Aeman Almotairi, D. Rawat","doi":"10.1109/IRI58017.2023.00040","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00040","url":null,"abstract":"Social media has grown to become a popular source of news and information for users around the world. However, the strength of fast dissemination of information to users in diverse places also exposes social media platforms, including Twitter, to the spread of misinformation, such as rumors or false information. Automated classification of such news on social media is a challenging task. In this study, we propose a hybrid deep learning model that utilizes a Features-Based model (FB) extracted from two levels: tweet level and user level, combined with pre-trained text embedding models such as Global Vectors for word representation (GloVe) and Universal Sentence Encoders (USE). The models were evaluated on a real-world dataset containing a collection of Twitter rumors and non-rumors. The experimental evaluation results reveal that our hybrid deep-learning model achieves higher accuracy in detecting rumors compared to the baseline learners and previous methods. Further, a hybrid model that combined a features-based model and text embedding model led to improve performance compared to use a single model.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125292250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large-Scale Analysis of Wikipedia’s Link Structure and its Applications in Learning Path Construction 维基百科链接结构的大规模分析及其在学习路径构建中的应用

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00051

Yiding Song, Chun Hei Leung

{"title":"Large-Scale Analysis of Wikipedia’s Link Structure and its Applications in Learning Path Construction","authors":"Yiding Song, Chun Hei Leung","doi":"10.1109/IRI58017.2023.00051","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00051","url":null,"abstract":"As the largest encyclopedia in history, Wikipedia represents an unprecedented unification of the world’s knowledge. Its internal links are an invaluable resource for understanding the relationships between concepts and information organization on the Web. However, such link structures are not thoroughly examined and barely visualized. In this paper, we take a graph-theoretic approach to investigate the link structure of English Wikipedia, providing an up-to-date snapshot of its knowledge organization, including degree distributions, strongly connected components, and disconnected subgraphs. To the best of our knowledge, we also perform the first k-core visualization over all of Wikipedia. Our results suggest Wikipedia is highly connected, with 90.05% of articles reachable from one another. Inbound links are found to be a better measure of an article’s importance than outbound links and demonstrate a more centralized mode of connection. Based on our observations, we propose a novel, end-to-end framework for automatically constructing learning paths, using Wikipedia links to recursively shortlist and rank prerequisite concepts for understanding new topics.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134266729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accelerating the Search for Stable Full Heusler Compounds through Machine Learning

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00034

Bhavya Mehta, V. Kharche, Sandeep S. Udmale

引用次数: 0