Faculty Profile

Dr. Jawwad Ahmed Shamsi

Department Of
Computer Science

About

Teaching Courses

Research Interests

I am interested in devleoping efficient, scalable, secure, and capable systems that can utilize the increasing potential of Big Data and Artificial Intelligence to meet the needs of masses. Utilizing cloud and HPC is important to meet scalability and efficiency. Similarly Deep Learning is significant in understanding the patterns of Big Data. My ultimate goal is to utilize research and development to meet SDGs.

Projects

S.No# Project Title Project Amount (Rs) Year of Completion
1
An AI Based Adaptive Framework for personalized e learning

Project Leader  :  Dr Jawwad Shamsi
Department    :  Computer Science
Funding Agency   :  NCAI


This research aims to propose an AI based personalized e-learning content recommendation framework which can be used to create an adaptive and adaptable learning system that caters to both in school and out of school children, empowering them to carry out self-paced learning and attain primary grade literacy. Adaptiveness will be achieved by assessing the level of each student and providing appropriate content, whereas adaptability will be incorporated through cognitive behavior and personal preferences and capabilities through the use of machine learning and deep learning-based AI approaches. This AI based content recommendation framework will be integrated in MUSE Learning Application from the industrial partner SABAQ in order to incorporate and enhance the decision-making capabilities in the system for the provision of tailored content to each user.

12,915,000

-

2
The efficient communication infrastructure for smart cities

Project Leader  :  Dr Jawwad Shamsi
Department    :  Computer Science
Funding Agency   :  NRPU


The smart city consists of a large number of loT devices, which are being placed for measurements and sensing. The sensed data is aggregated and sent to a cloud which make operational decisions to support and manage city services. In the coming years, it is expected that smart cities will play a major role in meeting growing challenges of population growth and urbanization. As large number of loT devices emit data, an efficient networking framework is needed to support large scale communication. Challenges include high speed switching, protection of cloud against attacks, aggregation of data, and proper validation and verification of messages emitting from loT devices. A robust mechanism is needed to meet these challenges effectively. Our proposed research has three major modules which are being developed for this purpose. The firewall and switching module will be used for efficient processing of large-scale loT messages, wheeras, thecomputaional module will be used for processing loT data and computation of results. The third module, will be responsible for effective validation and verificaiton of loT data. Together the three modules will be responsible for providing efficient network processing for lo Ts. The project is motivated to utilize high computational power of GPUs. Through this end, NVIDIA based research also be utilized in this project.

The project's cruicial objectives are :
  1. To develop an efficient network switching and routing infrastructure in order to support high speed networking for a large number of loT devices in a smart city. The framework must also possess capable firewall for protection of cloud against network attacks.
  2. To develop a middleware for big data processing for a large number of IOT devices in a smart city.
  3. To develop an efficient network for message validation and verifcation for IOT devices.

5,638,448

2021

3
NVIDIA Teaching and Research Center grant

Project Leader  :   Dr Jawwad Shamsi
Department    :   Computer Science
Funding Agency   :   NVIDIA


In 2014, FAST-NUCES was selected as an NVIDIA Teaching and Research Center. The award recognizes FAST-NUCES to develop high performance computing based solutions for various projects such as computer vision, security, and network performance.

2,000,000

2016

4
Block Chain and Health Applications

Project Leader  :   Dr Jawwad Shamsi
Department    :   Computer Science
Funding Agency   :   National Grassroot IGNITE ICT R&D


Privacy is recognized as a basic human right by the United Nations in the Universal Declaration of Human Rights (UDHR) at the 1948 United Nations General Assembly. The aim of Patient-Centric Healthcare system using Blockchain technology is to ensure patient agency with respect to the patient’s personal health information. Consistent with GDPR standards, using Blockchain technology, we can develop a system (with Easy-to-use Interface) in compliance with HIPAA and HL7 standards of personal privacy. Once the data is accessible, epidemics analytics can be performed on the health data to predict patterns of certain diseases and infections prevalent in a particular area. Furthermore, interoperability among various Healthcare Organizations for better quality and availability of data is a concern that is possible using this project.

70,000

2020

5
Smart Exam Surveillance System

Project Leader  :   Dr. Jawwad A. Shamsi
Department    :   Computer Science
Funding Agency   :  National Grassroot IGNITE ICT R&D


Smart Exam Surveillance System is not just a project it’s a solution to a problem in our society. The problem of using unfair means in an examination. This is basically an automation to the work of an invigilator in an examination hall but with better accuracy. Exam surveillance system involves 3 major task including Cheating detection in real time once any malicious activity is detected the next job of the system is to identify the individual that has been caught doing that malicious activity in the examination hall. And lastly the system would automatically interpret the entire scene and alert the management regarding the activity with recorded video streams through a web portal as evidence of the act. As much as the technology is growing providing better tools and technology for high accuracy there still always exists a possibility of an error hence in order to tackle this possibility the exam surveillance system includes a review System so that students get the right to defend themself. To address the major challenge of the project of Human Action Recognition at real time YOLOV3 is being used furthermore We have used pre tested FaceNet Algorithm for face recognition, whereas for web portal Flask framework is used.

70,000

2020

6
Patient Centric Health Care using Block Chain with Epidemic Analysis

Project Leader  :   Dr. Jawwad A. Shamsi
Department    :   Computer Science
Funding Agency   :  National Grassroot IGNITE ICT R&D


Privacy is recognized as a basic human right by the United Nations in the Universal Declaration of Human Rights (UDHR) at the 1948 United Nations General Assembly. The aim of Patient-Centric Healthcare system using Blockchain technology is to ensure patient agency with respect to the patient’s personal health information. Consistent with GDPR standards, using Blockchain technology, we can develop a system (with Easy-to-use Interface) in compliance with HIPAA and HL7 standards of personal privacy. Once the data is accessible, epidemics analytics can be performed on the health data to predict patterns of certain diseases and infections prevalent in a particular area. Furthermore, interoperability among various Healthcare Organizations for better quality and availability of data is a concern that is possible using this project.

70,000

2020

7
Home Automation using hololens

Project Leader  :   Dr. Jawwad A. Shamsi
Department    :   Computer Science
Funding Agency   :  National Grassroot IGNITE ICT R&D


The project is based on the basic concept of Home Automation implemented using emerging technology HoloLens. Normal home appliances, such as televisions, refrigerators, fans, lights, and many others have a physical mechanical switch to turn them on/off. However, our motivation is to replace those switches with augmented 3D switches. HoloLens has unique gesture-based I/O features that will help in interacting with the physical home appliances. HoloLens was connected with the Raspberry Pi via Internet, using Azure IoT Hub. Azure IoT Hub is a medium between both devices to exchange messages. Two Universal Windows Platform (UWP) applications were developed; one was developed for HoloLens and the other for Raspberry Pi. The Raspberry Pi is the brain which will make simple home appliances smart. We connected the bulb with the Raspberry Pi via a 4-Channel Relay and then sent a turn on/off signal to the Raspberry Pi by tapping on the virtual/augmented button visible beside an electronic device. Whenever we tapped on the button, the UWP application running on the HoloLens forwarded and stored the corresponding command at the Microsoft Azure IoT Hub and then the command sent to the connected devices at IoT Hub. Raspberry Pi supplied voltage on the basis of command turn on/off (min is 0 and max is 5). If the command received is “ON”, the bulb was successfully turned on and if the command received is “OFF”, bulb was successfully turned off.

70,000

2019

8
Rapid Detection of Crime Using Deep Learning on Fog Nodes

Project Leader  :   Dr. Jawwad A. Shamsi
Department    :   Computer Science
Funding Agency   :  National Grassroot IGNITE ICT R&D


Due to the rapid urbanization, cities are converting into smart cities. Safety is an essential requirement in a smart city and the performance of the currently available surveillance systems is not real time. To solve the above mentioned problem, we propose a rapid crime detection surveillance system using fog nodes that can detect crime and take appropriate actions in real time. We propose an architecture based on fog nodes that will distribute the data constantly coming from video streams and provide storage, reduced latency, and improved processing. Although fog nodes speed up the processing, we still need a fast object detection algorithm and face recognition system that can detect criminal artifacts and criminals respectively in real time. Therefore, the robust and the most accurate object detector available to date is selected. Experiments of retraining YOLO were performed on different dataset sizes and achieved an mAP of 0.85 on Openimage test and validation set combined with Coco person dataset. We also transferred data between the fog nodes using infini band for our proposed architecture and it provides a significant reduction in transfer time between fog nodes.

70,000

2019

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