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X-ray image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques

computer Systems Science & engineering 2022年第11期43卷 671-682页

作者： Shabana Habib Saleh Alyahya Aizaz Ahmed Muhammad Islam Sheroz Khan Ishrat Khan Muhammad Kamil Department of Information Technology College of ComputerQassim UniversityBuraidah52571Saudi Arabia Department of Electrical Engineering College of Engineering&Information TechnologyOnaizah CollegesOnaizah51911Saudi Arabia Digital Image Processing Lab Islamia CollegePeshawar25120KPKPakistan Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electrical Engineering National University of Computer and Emerging SciencesPeshawarPakistan

In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic *** the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected *** COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain *** paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral *** approach includes preprocessing,feature extraction,and ***,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature ***,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is *** results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic *** is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.

关键词： image pre-processing detection classification x-ray images filter

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Pansharpening Methods Based on a Redundant Contourlet Transform

Pansharpening Methods Based on a Redundant Contourlet Transf...

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2023 International Conference on Earth Observation and Geo-Spatial Information, ICEOGI 2023

作者： Ourabia, Soumeya Baaziz, Nadia Smara, Youcef University Center of Tipaza -Morsli Abdelah-Institut of Sciences Laboratory of Image Processing and Radiation -USTHB Departement of Electronics Tipaza Algeria University of Quebec in Outaouais - Uqo Research Laboratory in Multimedia Information Department of Computer Science and Engineering GatineauQC Canada University of Sciences and Technology Houari Boumediene -USTHB Laboratory of Image Processing and Radiation Faculty of Electrical Engineering Algiers Algeria

ISBN: (纸本)9798350396720

The pansharpening of high-resolution panchromatic (Pan) image and low-resolution multispectral (MS) images represents an important task in the remote sensing field. It allows the joint exploitation of the information derived from both types of images. However, this may cause a distortion of spectral information or a lack in details and structures. The multiresolution analysis using wavelet transforms has proved its efficiency in the pansharpening domain, due to the representation of the image content over different resolution levels. In image fusion applications, we always need more detail information to be incorporated in the pansharpening procedure in order to produce enhanced results. However, the limitation of the wavelet transform to three types of oriented detail coefficients prevents this need from being met. To overcome this limitation, we propose to use the redundant contourlet transform (RCT) which extracts a richer multiscale directional information from the image. For this purpose, two RCT-based pansharpening methods are introduced and suitable data fusion procedures are described. We conducted several experiments on two different datasets acquired respectively by ALSAT-2A and IKONOS satellites. The visual results as well as quantitative results from evaluation metrics demonstrate the performance of the proposed methods. © 2023 IEEE.

关键词： image enhancement

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Emotion Classification Through Speech Data Analysis 23

Emotion Classification Through Speech Data Analysis

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Proceedings of the Winter Simulation Conference

作者： Luzalen Marcos Kristiina Valter Mai Abdolreza Abhari Resilience Engineering Lab Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada Distributed Systems and Multimedia Processing (DSMP) Lab Department of Computer Science Toronto Metropolitan University Toronto Canada

ISBN: (纸本)9798350369663

Good quality healthcare services require effective communication between the patient and the healthcare provider. This work will help improve the areas of healthcare systems automation and optimization by applying Speech Emotion Recognition (SER) in health consultations to prevent miscommunication between patients and healthcare providers. Crowd-Sourced Emotional Multimodal Actors Dataset (CREMA-D) was used to compare the performances of different machine learning models in classifying emotions. Before feeding the raw dataset to the models, exploratory data analysis was done to determine features that should be considered for future analysis. Our results showed that depending on the emotion, there are some syllables in the text that were emphasized or took time to be pronounced by the speaker. After data analysis, the dataset was fed into different models and determined that the Support Vector Machine (SVM) is a machine-learning model for SER.

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Stochastic Recursive Gradient Descent Optimization-based on Foreground Features of Fisher Vector 14

Stochastic Recursive Gradient Descent Optimization-based on ...

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14th International Conference on Digital image processing, ICDIP 2022

作者： Kamaleldin, Mohamed Gamal M. Abu-Bakar, Syed A.R. Sheikh, Usman Ullah Electronics and Communications Engineering Department Arab Academy for Science and Technology Cairo11736 Egypt Computer Vision Video and Image Processing Research Lab School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81300 Malaysia

ISBN: (纸本)9781510657564

Human action recognition has been one of the hot topics in computer vision both from the handcrafted and deep learning approaches. In the handcrafted approach, the extracted features are encoded for reducing the size of these features. Amonsgt the state-of-the-art approaches is to encode these visual features using the Gaussian mixture model. However, the size of the codebook is an issue in terms of the computation complexity, especially for large-scale data as it requires encoding using a large codebook. In this paper, we introduced the use of different optimizers to reduce the codebook size while boosting its accuracy. To illustrate the performance, first we use the improved dense trajectories (IDT) to extract the handcrafted features. This is followed with encoding the descriptor using Fisher kernel-based codebook using the Gaussian mixture model. Next, the support vector machine is used to classify the categories. We then use and compare five different Stochastic gradient descent optimization techniques to modify the number of Gaussian components. In this manner we are able to select the discriminative foreground features (as represented by the final number of Gaussian components), and omit the background features. Finally, to show the performance improvement of the proposed method, we implement this technique to two datasets UCF101 and HMDB51. © 2022 SPIE.

关键词： Support vector machines

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Emotion Classification Through Speech Data Analysis

Emotion Classification Through Speech Data Analysis

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Simulation Winter Conference

作者： Luzalen Marcos Kristiina Valter Mai Abdolreza Abhari Resilience Engineering Lab Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Ontario Canada Department of Computer Science Distributed Systems and Multimedia Processing (DSMP) Lab Toronto Metropolitan University Ontario Canada

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Fast Low-parameter Video Activity Localization in Collaborative Learning Environments

arXiv

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arXiv 2024年

作者： Jatla, Venkatesh Teeparthi, Sravani Egala, Ugesh Pattichis, Sylvia Celedón Pattichis, Marios S. Image and Video Processing and Communications Lab Dept. of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM87131 United States Department of Curriculum and Instruction The University of Texas at Austin AustinTX78712-1293 United States

Research on video activity detection has primarily focused on identifying well-defined human activities in short video segments. The majority of the research on video activity recognition is focused on the development of large parameter systems that require training on large video datasets. This paper develops a low-parameter, modular system with rapid inferencing capabilities that can be trained entirely on limited datasets without requiring transfer learning from large-parameter systems. The system can accurately detect and associate specific activities with the students who perform the activities in real-life classroom videos. Additionally, the paper develops an interactive web-based application to visualize human activity maps over long real-life classroom videos. Long-term video activity detection in real-life classroom videos present unique challenges, such as the need to detect multiple simultaneous activities, rapid transitions between activities, long-term occlusions, durations exceeding 15 minutes, and numerous individuals performing similar activities in the background. Moreover, subtle hand movements further complicate the need to differentiate between actual typing and writing activities as opposed to unrelated hand movements. The system processes the input videos using fast activity initializations and current methods for object detection to determine the location and the the person performing the activities. These regions are then processed through an optimal low-parameter dyadic 3D-CNN classifier to identify the activity. The proposed system processes 1 hour of video in 15 minutes for typing and 50 minutes for writing activities. The system uses several methods to optimize the inference pipeline. For each activity, the system determines an optimal low-parameter 3D CNN architecture selected from a family of low-parameter architectures. The input video is broken into smaller video regions that are transcoded at an optimized frame rate. For inference, a

关键词： Object detection

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Long-term Human Participation Assessment In Collaborative Learning Environments Using Dynamic Scene Analysis

arXiv

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arXiv 2024年

作者： Shi, Wenjing Tran, Phuong Celedón-Pattichis, Sylvia Pattichis, Marios S. Image and Video Processing and Communications Lab Dept. of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM87131 United States Department of Curriculum and Instruction The University of Texas at Austin AustinTX78712-1293 United States

The paper develops datasets and methods to assess student participation in real-life collaborative learning environments. In collaborative learning environments, students are organized into small groups where they are free to interact within their group. Thus, students can move around freely causing issues with strong pose variation, move out and re-enter the camera scene, or face away from the camera. We formulate the problem of assessing student participation into two subproblems: (i) student group detection against strong background interference from other groups, and (ii) dynamic participant tracking within the group. A massive independent testing dataset of 12,518,250 student label instances, of total duration of 21 hours and 22 minutes of real-life videos, is used for evaluating the performance of our proposed method for student group detection. The proposed method of using multiple image representations is shown to perform equally or better than YOLO on all video instances. Over the entire dataset, the proposed method achieved an F1 score of 0.85 compared to 0.80 for YOLO. Following student group detection, the paper presents the development of a dynamic participant tracking system for assessing student group participation through long video sessions. The proposed dynamic participant tracking system is shown to perform exceptionally well, missing a student in just one out of 35 testing videos. In comparison, a state-of-the-art method fails to track students in 14 out of the 35 testing videos. The proposed method achieves 82.3% accuracy on an independent set of long, real-life collaborative videos. © 2024, CC BY.

关键词： Students

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Pansharpening Methods Based on a Redundant Contourlet Transform

Pansharpening Methods Based on a Redundant Contourlet Transf...

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Earth Observation and Geo-Spatial Information (ICEOGI), International Conference on

作者： Soumeya Ourabia Nadia Baaziz Youcef Smara Departement of Electronics University Center of Tipaza -Morsli Abdelah-Institut of Sciences Laboratory of Image Processing and Radiation -USTHB Tipaza Algeria Department of Computer Science and Engineering Research Laboratory in Multimedia Information University of Quebec in Outaouais - UQO Gatineau Québec Canada Laboratory of Image Processing and Radiation Faculty of electrical engineering University of Sciences and Technology Houari Boumediene -USTHB Algiers Algeria

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Talking Detection in Collaborative Learning Environments 19th

Talking Detection in Collaborative Learning Environments

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19th International Conference on computer Analysis of images and Patterns, CAIP 2021

作者： Shi, Wenjing Pattichis, Marios S. Celedón-Pattichis, Sylvia LópezLeiva, Carlos Image and Video Processing and Communications Lab Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States Departtment of Language Literacy and Sociocultural Studies University of New Mexico AlbuquerqueNM United States

ISBN: (纸本)9783030891305

We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small projection images are then easily classified using a simple majority vote of standard classifiers. For talking detection, our proposed approach is shown to significantly outperform single activity systems. We have an overall accuracy of 59% compared to 42% for Temporal Segment Network (TSN) and 45% for Convolutional 3D (C3D). In addition, our method is able to detect multiple talking instances from multiple speakers, while also detecting the speakers themselves. © 2021, Springer Nature Switzerland AG.

关键词： computer aided instruction

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Adversarial attacks and defenses in physiological computing:a systematic review

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National Science Open 2023年第1期2卷 62-90页

作者： Dongrui Wu Jiaxin Xu Weili Fang Yi Zhang Liuqing Yang Xiaodong Xu Hanbin Luo Xiang Yu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China School of Civil and Hydraulic Engineering Huazhong University of Science and TechnologyWuhan 430074China College of Public Administration Huazhong University of Science and TechnologyWuhan 430074China Electrical Engineering and Computer Science Department University of MichiganAnn Arbor MI 48109USA School of Management and Sino-European Institute for Intellectual Property Huazhong University of Science and TechnologyWuhan 430074China

Physiological computing uses human physiological data as system inputs in real *** includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based *** computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even ***,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to *** study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense *** hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.

关键词： physiological computing brain-computer interfaces health informatics biometrics machine learning adversarial attack

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