2021 Third International Conference on Transdisciplinary AI (TransAI)最新文献_第2页

eRxNet: A Pipeline of Convolutional Neural Networks for Tuberculosis Screening eRxNet:用于结核病筛查的卷积神经网络管道

2021 Third International Conference on Transdisciplinary AI (TransAI) Pub Date : 2021-09-01 DOI: 10.1109/TransAI51903.2021.00017

Terence Griffin, Qilei Chen, Xinzi Sun, Dechun Wang, M. Brunette, Yu Cao, Benyuan Liu

{"title":"eRxNet: A Pipeline of Convolutional Neural Networks for Tuberculosis Screening","authors":"Terence Griffin, Qilei Chen, Xinzi Sun, Dechun Wang, M. Brunette, Yu Cao, Benyuan Liu","doi":"10.1109/TransAI51903.2021.00017","DOIUrl":"https://doi.org/10.1109/TransAI51903.2021.00017","url":null,"abstract":"Tuberculosis (TB) is a contagious disease affecting millions of people annually worldwide. Treatment of this disease and reduction in local epidemics can be improved markedly by increasing the speed and efficiency of screening and diagnosis. eRxNet is a pipeline of convolutional neural networks designed to provide healthcare professionals with detailed and accurate analysis of chest X-rays (CXRs) for TB screening. The pipeline combines whole image classification, object detection (bounding boxes), and instance segmentation (polygonal masks) to provide data analysis at varying levels of detail. In order to construct a high performing system, a comparison of different CNN architectures applied to these tasks is presented. Images from two large TB datasets, UML-Peru and TBX11K, were used for training and evaluation of the models. Combining the two datasets required the development of a preprocessing stage which includes lung segmentation and image enhancement. We show that the resulting four stage pipeline of CNNs, using a combination of DenseNet, Faster R-CNN, and Mask R-CNN, has sufficiently strong performance to be a useful tool for TB screening.","PeriodicalId":426766,"journal":{"name":"2021 Third International Conference on Transdisciplinary AI (TransAI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117307769","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: 1

Audio-Visual Evaluation of Oratory Skills 演讲技巧的视听评价

2021 Third International Conference on Transdisciplinary AI (TransAI) Pub Date : 2021-09-01 DOI: 10.1109/TransAI51903.2021.00026

Tzvi Michelson, Shmuel Peleg

引用次数: 2

A Smart Framework for Automatically Analyzing Electrocardiograms 一种自动分析心电图的智能框架

2021 Third International Conference on Transdisciplinary AI (TransAI) Pub Date : 2021-09-01 DOI: 10.1109/TransAI51903.2021.00019

Fabio Persia, Stefania Costantini, C. Ferri, Lorenzo De Lauretis, D. D’Auria

引用次数: 4

FAIR Ontologies for Transparent and Accountable AI: A Hospital Adverse Incidents Vocabulary Case Study 透明和可问责人工智能的公平本体:医院不良事件词汇案例研究

2021 Third International Conference on Transdisciplinary AI (TransAI) Pub Date : 2021-09-01 DOI: 10.1109/TransAI51903.2021.00024

M. Basereh, A. Caputo, Rob Brennan

引用次数: 4

Passive User Identification and Authentication with Smartphone Sensor Data 基于智能手机传感器数据的被动用户识别和认证

2021 Third International Conference on Transdisciplinary AI (TransAI) Pub Date : 2021-09-01 DOI: 10.1109/TransAI51903.2021.00009

Aaditya Raval, Mohd Anwar

{"title":"Passive User Identification and Authentication with Smartphone Sensor Data","authors":"Aaditya Raval, Mohd Anwar","doi":"10.1109/TransAI51903.2021.00009","DOIUrl":"https://doi.org/10.1109/TransAI51903.2021.00009","url":null,"abstract":"A unique digital identity, user ID, is essential for everyday online activities in the Internet era. These user IDs represent a user in a digital environment using stored credentials on a system called authentication system. It is possible to capture unique patterns of user movements from smartphone sensor data. This paper presents a framework for passive user identification and authentication using onboard sensors of an Android smartphone. Using this framework, we propose a data preprocessing scheme that uses the absolute difference of consecutive repeated measurements of 7 onboard sensors. We developed 5 models for user identification and authentication using various machine learning and deep learning methods. The experimental results and performance assessment conclude that the Sequential Neural Network (SNN) model performs best with 98.24% accuracy for authenticating users (binary classification) and 84.41% accuracy during multi-class classification for user identification. Furthermore, all the models developed for this research, namely MLP, SNN, CNN, SVM, and Bi-LSTM, provide 100% precision during binary classification for passive user authentication. Our models were trained on 556,746 sensor data samples, each having 20 features. These features include the proximity sensor data, light sensor data, triaxial measurements from accelerometers, gravity sensors, gyroscopes, magnetometers, and rotational vector sensors. We study the possible influence of absolute difference samples for user identification and authentication.","PeriodicalId":426766,"journal":{"name":"2021 Third International Conference on Transdisciplinary AI (TransAI)","volume":"142 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114000628","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

Title Page iii 第三页标题

2021 Third International Conference on Transdisciplinary AI (TransAI) Pub Date : 2021-09-01 DOI: 10.1109/transai51903.2021.00002

引用次数: 0

Classification and Feature Extraction for Hydraulic Structures Data Using Advanced CNN Architectures 基于高级CNN架构的水工结构数据分类与特征提取

2021 Third International Conference on Transdisciplinary AI (TransAI) Pub Date : 2021-09-01 DOI: 10.1109/TransAI51903.2021.00032

Sameerah Talafha, Di Wu, Banafsheh Rekabdar, Ruopu Li, Guangxing Wang

{"title":"Classification and Feature Extraction for Hydraulic Structures Data Using Advanced CNN Architectures","authors":"Sameerah Talafha, Di Wu, Banafsheh Rekabdar, Ruopu Li, Guangxing Wang","doi":"10.1109/TransAI51903.2021.00032","DOIUrl":"https://doi.org/10.1109/TransAI51903.2021.00032","url":null,"abstract":"An efficient feature selection method can significantly boost results in classification problems. Despite ongoing improvement, hand-designed methods often fail to extract features capturing high- and mid-level representations at effective levels. In machine learning (Deep Learning), recent developments have improved upon these hand-designed methods by utilizing automatic extraction of features. Specifically, Convolutional Neural Networks (CNNs) are a highly successful technique for image classification which can automatically extract features, with ongoing learning and classification of these features. The purpose of this study is to detect hydraulic structures (i.e., bridges and culverts) that are important to overland flow modeling and environmental applications. The dataset used in this work is a relatively small dataset derived from 1-m LiDAR-derived Digital Elevation Models (DEMs) and National Agriculture Imagery Program (NAIP) aerial imagery. The classes for our experiment consist of two groups: the ones with a bridge/culvert being present are considered \"True\", and those without a bridge/culvert are considered \"False\". In this paper, we use advanced CNN techniques, including Siamese Neural Networks (SNNs), Capsule Networks (CapsNets), and Graph Convolutional Networks (GCNs), to classify samples with similar topographic and spectral characteristics, an objective which is challenging utilizing traditional machine learning techniques, such as Support Vector Machine (SVM), Gaussian Classifier (GC), and Gaussian Mixture Model (GMM). The advanced CNN-based approaches combined with data pre-processing techniques (e.g., data augmenting) produced superior results. These approaches provide efficient, cost-effective, and innovative solutions to the identification of hydraulic structures.","PeriodicalId":426766,"journal":{"name":"2021 Third International Conference on Transdisciplinary AI (TransAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129285884","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: 1

Dataset Augmentation with Generated Novelties 生成新奇的数据集增强

2021 Third International Conference on Transdisciplinary AI (TransAI) Pub Date : 2021-09-01 DOI: 10.1109/TransAI51903.2021.00015

A. Nesen, K. Solaiman, Bharat K. Bhargava

{"title":"Dataset Augmentation with Generated Novelties","authors":"A. Nesen, K. Solaiman, Bharat K. Bhargava","doi":"10.1109/TransAI51903.2021.00015","DOIUrl":"https://doi.org/10.1109/TransAI51903.2021.00015","url":null,"abstract":"As machine learning models take over an increasingly larger number of domains in our lives, their accuracy, fairness, transparency and adaptability become of greater importance. In the everchanging environments, the resulting ability of the models to perform accurately depends on whether they are able to handle novel, unpredicted and unforeseen instances, examples and classes or any other novel changes in the world of model operation, such as environmental, contextual, distributional changes. The proper handling of novelties sustains the model’s usefulness and adeptness in the long run. The efficiency of response to the encounter of novelties depends on the efforts that were invested at the model training, design and data collection stages. In this work, we propose a variety of approaches and methods which can be incorporated into the novelty generation techniques at the earliest stages of creating the machine learning dataset and the model to assure its robustness and reduce the bias. We revisit distinctions between novelties and anomalies to define a formal novelty generation framework that is domain-agnostic and budget efficient. Then we propose a video-specific use case and evaluate the result of the chosen methods on the video dataset. Our methods aim at making the machine learning solutions adaptable, responsible and show improvement in the accuracy and ability of the models to detect novelties.","PeriodicalId":426766,"journal":{"name":"2021 Third International Conference on Transdisciplinary AI (TransAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124627185","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: 1

Interpreting Keystrokes to Ascertain Human Mood 解释击键以确定人类情绪

2021 Third International Conference on Transdisciplinary AI (TransAI) Pub Date : 2021-09-01 DOI: 10.1109/TransAI51903.2021.00028

Bernard Aldrich, Hilda Goins, Mohd Anwar

引用次数: 1

Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic 基于Shannon熵的理论人工智能识别新冠肺炎病毒株

2021 Third International Conference on Transdisciplinary AI (TransAI) Pub Date : 2021-09-01 DOI: 10.1109/TransAI51903.2021.00016

H. Nieto-Chaupis

引用次数: 2