Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/545907
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dc.coverage.spatialHuman activity recognition based on IOT using capsule netwoks and extreme gated recurrnet neural network
dc.date.accessioned2024-02-19T06:45:41Z-
dc.date.available2024-02-19T06:45:41Z-
dc.identifier.urihttp://hdl.handle.net/10603/545907-
dc.description.abstractHuman Activity Recognition (HAR) systems have made newlinesubstantial strides in recent years with the help of interconnected sensing newlinedevices and intelligent technologies like Artificial Intelligence (AI), Internet newlineof Things (IoT), and sensors. Sensor-equipped wearable IoT devices are newlineessential for observing and identifying human body movements. Human newlineactivity recognition (HAR) is the process of identifying an activity newlineperformed by one individual or a group of people using spatial and temporal newlineinformation. Applications for computer vision are numerous and require a newlineHAR solution. These include smart home support, medical and healthcare newlineservice monitoring software, and security cameras. newlineRecently, IoT is merged with Machine Learning (ML) and Deep newlineLearning (DL) algorithms to identify human body activities automatically. newlineEarlier approaches showed that the deployment of HAR systems with the newlinehelp of conventional Machine Learning techniques such as Support Vector newlineMachines (SVM), Artificial Neural Networks (ANN), Random Forest (RF), newlineConvolutional Neural Networks (CNN), Recurrent neural Networks (RNN), newlineand Long Short-Term Memory (LSTM). newlineDL algorithms are thought to be more appropriate than ML newlinealgorithms for the purpose of identifying human activities because the HAR newlinesystem comprises of both simple and complicated actions. Motion sensors, newlinesuch as accelerometers and gyroscopes, can be coupled with newlinemicrocontrollers to capture all inputs and send them to the cloud networks newlineusing IoT transceivers in order to monitor human activities effectively. newline newline
dc.format.extentxvi,133p.
dc.languageEnglish
dc.relationp.110-132
dc.rightsuniversity
dc.titleHuman activity recognition based on IOT using capsule netwoks and extreme gated recurrnet neural network
dc.title.alternative
dc.creator.researcherArokiaraj, S
dc.subject.keywordArtificial Intelligence
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordEngineering and Technology
dc.subject.keywordHuman Activity Recognition
dc.subject.keywordinterconnected sensing
dc.description.note
dc.contributor.guideViswanathan, N
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2023
dc.date.awarded2023
dc.format.dimensions21cm.
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File25.89 kBAdobe PDFView/Open
02_prelim pages.pdf1.13 MBAdobe PDFView/Open
03_contents.pdf30.06 kBAdobe PDFView/Open
04_abstracts.pdf11.68 kBAdobe PDFView/Open
05_chapter1.pdf202.12 kBAdobe PDFView/Open
06_chapter2.pdf703.9 kBAdobe PDFView/Open
07_chapter3.pdf1.45 MBAdobe PDFView/Open
08_chapter4.pdf3 MBAdobe PDFView/Open
09_chapter5.pdf1.08 MBAdobe PDFView/Open
10_chapter6.pdf2.66 MBAdobe PDFView/Open
11_chapter7.pdf846.57 kBAdobe PDFView/Open
12_chapter8.pdf16.17 kBAdobe PDFView/Open
13_annexures.pdf237.32 kBAdobe PDFView/Open
80_recommendation.pdf107.28 kBAdobe PDFView/Open


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