Zuhaib Mansoor

I am a Cloud Enthusiast

About me

Currently Graduated with a Master Degree in Electrical and Computer Engineering. I have completed my education with a specialisation in Computer Engineering field with major in Data Mining, Computer Networks and Software Development. I am an emerging Cloud Computing Enthusiast, aiming to expand my boundaries of knowledge in the IT field continuously. I am currently learning and preparing for AWS certificates so that I can dive deeper in the cloud computing sector.

Education

Master's Degree

Master of Engineering in Electrical and Computer Engineering

University of Windsor

Sept 2021 - December 2022

Bachelor's Degree

Bachelor of Engineering with honours in Electronic Engineering

University of Central Lancashire

Sept 2018 - June 2021

Projects

Oct. 2022 - Dec. 2022

Cloud Computing for Connected Autonomous Vehicles using AWS

  • Designed a Cloud Computing prototype system for Connected Autonomous Vehicles.
  • Utilized the open-source vehicle simulation provided on AWS solutions library to simulate vehicle sensor data for the prototype system.
  • Imported the data from the simulator stored in AWS DynamoDB tables into the prototype system using AWS Kinesis.
  • Created different AWS Lambda functions to perform various tasks such as evaluating a driver safety score, data anomaly detection, creating a trip data table in DynamoDB, and providing notifications for the events to the driver using AWS SNS service.
  • Evaluated that Machine learning and AI algorithms can further make this system more effective and intelligent.
  • This project included the use of the following AWS services:AWS Lambda; AWS DynamoDB; AWS Kinesis: Data Firehose, Data Streams and Data Analytics; AWS SNS; AWS S3
June 2022 - July 2022

Complete Case and Incomplete Case k-NN Data Imputation Model

  • In a group of three, we designed an imputation model that uses the k-NN Data Imputation method for the evaluation of missing data using the k-nearest neighbors of the missing element.
  • Programmed two different types of k-NN imputation: Complete Case and Incomplete Case k-NNI.
  • Compared the performance of both the cases for similar data and designed a detailed report on the pros and cons of using each of the imputation methods for different kinds of data.
  • This project included the use of different python libraries such as pandas, NumPy, math, glob, time, etc.

Certificates