Z
Zeenat Tariq
Researcher at University of Missouri–Kansas City
Publications - 10
Citations - 132
Zeenat Tariq is an academic researcher from University of Missouri–Kansas City. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 5, co-authored 8 publications receiving 43 citations.
Papers
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Proceedings ArticleDOI
Lung Disease Classification using Deep Convolutional Neural Network
TL;DR: A deep learning model called Lung Disease Classification (LDC), combined with advanced data normalization and data augmentation techniques, for high-performance classification in lung disease diagnosis and guarantees better performance than other previously reported approaches.
Proceedings ArticleDOI
Speech Emotion Detection using IoT based Deep Learning for Health Care
TL;DR: An integrated deep learning model for detecting human emotions using speech signals and its implementation in real-time using the Internet of Things (IoT) based deep learning for the care of older adults in nursing homes is proposed.
Journal ArticleDOI
Feature-Based Fusion Using CNN for Lung and Heart Sound Classification
TL;DR: A fusion of three optimal convolutional neural network models by feeding the image feature vectors transformed from audio features, which confirmed the superiority of the proposed fusion model compared to the state-of-the-art works.
Proceedings ArticleDOI
Audio IoT Analytics for Home Automation Safety
TL;DR: The aim of the paper is to perform audio analytics based on the audio sensor data that is continuously monitoring the home environment automatically through an audio Internet of Things (IoT) system and validated that Convolutional Neural Network shows the best performance compared to other machine learning algorithms.
Proceedings ArticleDOI
IoT based Urban Noise Monitoring in Deep Learning using Historical Reports
TL;DR: A new Internet of things (IoT) solution, called the Urban Noise Monitoring (UNM) system, which can classify real-time environmental audio sound using an embedded system such as Raspberry pi 4 and log the data in the Google Cloud.