NUST Institutions Library Catalogue catalog › Details for: Uncovering Natural Clinical Data-Driven Clusters of Irritable Bowel Syndrome (IBS) Endophenotypes Using Machine Learning Techniques/

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Uncovering Natural Clinical Data-Driven Clusters of Irritable Bowel Syndrome (IBS) Endophenotypes Using Machine Learning Techniques/ Ahsan Ijaz,

TextIslamabad : SEECS, NUST, 2023Description: 99 p. ; 30 cmSubject(s): MS-Data ScienceDDC classification: 200 Online resources: Click here to access online

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Item type	Current location	Home library	Shelving location	Call number	Status	Date due	Barcode	Item holds
Soft Copy	Central Library (CL)	Central Library (CL)	Thesis	200 (Browse shelf)	Available		CL-T-6860

Total holds: 0

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Previous	200 Monitoring and Quantification of Parkinson’s Disease Motor Symptoms using Wearable Sensors for Smart Healthcare/	200 Enhancing Intrusion Detection: Leveraging Federated Learning and Hybrid Deep Learning Models/	200 Fog Resource Sharing to Enable Pay Per Use Model/	200 Uncovering Natural Clinical Data-Driven Clusters of Irritable Bowel Syndrome (IBS) Endophenotypes Using Machine Learning Techniques/	200 Personalized Lifestyle Recommendations for Chronic Kidney Disease Patients Based on Individual Test Results/	200 Containerized microservice framework on edge device for health care IOT system/	200 Performance analysis of CR-NOMA enabled Backscatter communications using Deep Reinforcement Learning/	Next

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