NUST Institutions Library Catalogue Search for 'au:"Asif Ali"'

Routing Protocols in Mobile Ad Hoc Network (MANETs)

By Asif Ali Jaffri. Islamabad, NUST School of Electrical Engineering & Computer Science, 2003 . ix, 96 p. : , Supervisor : Mr. Nadeem Ahmad. 27 cm..

Place Hold on Routing Protocols in Mobile Ad Hoc Network (MANETs)

Inter Bank Electronic Transactions

By Asif Ali Jaffri. Islamabad, NUST School of Electrical Engineering & Computer Science, 2000 . iii, 28 p. : , Supervisors : Major M Saeed, Dr. S M H Zaidi. 27 cm..

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Design of Natural Gas Fired room air Conditioning System

By Asif Ali. Risalpur NUST . 74p , BE Project Reports (G-61 A)

Place Hold on Design of Natural Gas Fired room air Conditioning System

President of Pakistan Asif Ali Zardari Messages : September 2008 to December 2009

By Asif Ali Zardari. Islamabad DGOFP 2009 . 200p. , PB

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Kachichian /

By Alvi, Asif Ali. Lahore : Almurad publications, 2007 . 136 p. ;

Place Hold on Kachichian /

Numerical Study of Aerodynamic Performance of Unibump over the Surface of AirFoil /

By Ali, Asif. Islamabad: PNEC NUST, 2016 . 67 p. : 30cm..

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Hospital Management System /

By Asif Ali, Syed . Islamabad : SEECS - NUST, 2002 . ix, 71 p. : ill. ; 30cm..

Place Hold on Hospital Management System /

Flash Flood Susceptibility modeling : A study of swat drainage basin, Northern Pakistan

By Asif Ali . Risalpur, MCE, 2019 . 129p. , DM

Place Hold on Flash Flood Susceptibility modeling : A study of swat drainage basin, Northern Pakistan

PR-SOF-1793-Fruit Maturity Device Using Machine Vision

By M. Ubaid Asif Ali Hassan Khalid Jalal Majid. ISLAMABAD NUST COLLEGE OF EME 2019

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Economics for Business Managers

By Ashfaq, Muhammad. Syed Asif Ali Naqvi and Sultan Ali Adil. Faisalabad University of Agriculture, 2022 . 227p 9789697705085

Place Hold on Economics for Business Managers

Deep Neural Networks for Ventilator Pressure Prediction /

By Asif, Ali Raza . . 55p. , Invasive mechanical ventilation is a common medical treatment required for applications spanning anesthesia, neonatal intensive care, and life support for the current COVID-19 pandemic. Despite its importance, the core technology of medical ventilation has remained largely unchanged for years. It is common for a clinician to continuously monitor and adjust the ventilator for a patient manually thus increasing workload. With machine learning (ML) taking center stage in healthcare recently, the question has been asked whether ML based control methods can be developed to replace manual intervention. The main challenge however remains the robustness, safety, and the high cost of development. In addition, the controller must be able to adapt reliably and quickly across varying clinical conditions and requirements not observable directly to the clinicians. Proportional-Integral-Derivative (PID) controllers have been to go to method of choice because of its limited parameters size, fewer samples for tuning, and its ability to generalize over the dynamic lung conditions. Current ventilator or patient simulators are trained by an ensemble of multiple models each simulating the parameters of a single lung. However, human lungs and respectively their parameters or attributes are dynamic and form a continuous space therefore, based on the physiological differences in patient lungs a parametric approach is better suited to improve generalization. This work centers around the possibility of developing an ML-based method which can improve performance simultaneously across a wide range of lung parameters based upon the ISO standard for performance of ventilatory support equipment (ISO 80601-2-80:2018). The results are compared against previously published data and closely match the expected outcome. This is a significant improvement towards a more robust alternative to PID tuning and more importantly cost-effective mechanical ventilators. 30cm.

Place Hold on Deep Neural Networks for Ventilator Pressure Prediction /