aa2-wqd7001

AA2 Submission

algorithms_course

Algorithms Course by Dr. Mostafa Saad

algorithms_course_udemy

Algorithms course by Dr.Mostafa Saad

arff-datasets

The collection of ARFF datasets of the Connectionist Artificial Intelligence Laboratory (LIAC)

arroyo

Arroyo is a distributed stream processing engine written in Rust

aws-ecommerce-oltp-olap

E-Commerce OLTP and OLAP Infrastructure on AWS

bookstore-aws-lakehouse

codeforces_solutions

Solutions to codeforces problems using Python

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

creative-force-task

customer-service-data-platform

A highly efficient End to End ETL pipeline that enables customer services officers to query realtime data related to customers.

dev-environment-files

dsa

Implementation of Data Structures and Algorithms in Python

fracture-stage-classification

Classification of Fracture Stages in Oil & Gas drilling operations

fracture-treating-pressure

Fracture Treating Pressure Prediction using LSTM

fyp-pfhe-optimization

Energy demand has increased substantially recently, the need of energy efficient equipment in process plants and industries has become a must to stay withing the emission boundaries set by the government. Plate Fin Heat Exchangers (PFHE) are one of the most energy efficient and thermally effective heat exchangers that’s due to their high compactness which makes their surface area density higher than traditional heat exchangers. However, a big issue associated with them is their high cost due to the complexity involved in the manufacturing process. In this project optimization has been conducted to the design of the Plate Fin Heat Exchanger with the objective of minimizing its total annual cost (TAC). However, to ensure that the optimization of the TAC does not come with the cost of lower performance, constraints are added in the objective function to ensure that the pressure drop, flow characteristics of the working fluids and the heat duty of the PFHE are within acceptable boundaries, Optimization of the PFHE will be done using Grey Wolf Optimization Algorithm (GWO), Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO) , The results obtained from these algorithms will be compared with each other and compared with results obtained by other researches who used different optimization Algorithms with the aim of evaluating the best preforming algorithm in optimizing thermal systems generally and PFHE’s specifically.

ibm_coursera

introdatascience

WQD7001 Tutorial 2

it-cert-automation-practice

Google IT Automation with Python Professional Certificate - Practice files

mall-customer-segmentation

nvim

Lunar Vim with 100% lua config

pump-monitoring

Real-time pump monitoring

pvt-correlation-for-crude-oils

PVT Correlation for Crude Oils using Neural Networks

rta_unconventional_o-g_reserviors

rustlings

:crab: Small exercises to get you used to reading and writing Rust code!

slidify-presentation

slidify-presentation

student-performance-shiny-app

Student Performance Shiny App

unkey

Open source API authentication and authorization

web-development

Web Development Course by Angela

weka

Exercise Files for Problem Solving with Machine Learning