⚡ CSI SFIT Contribution Archive

CSI SFIT Contribution Archives
A repository of all the links of technical articles, research papers & projects of our CSI SFIT Team members over the years.

Table of Contents

1. Research Papers
2. Medium Articles
3. Projects

1. Research Papers

List of all the research papers published by our CSI SFIT Team members over the years:

1. Automated Identification of Disaster News for Crisis Management using Machine Learning and Natural Language Processing
   Jul 4, 2020 | Institute of Electrical and Electronics Engineers (IEEE)
   
   We are living in unprecedented times and anyone in this world could be impacted by natural disasters in some way or the other. Life is unpredictable and what is to come is unforeseeable. Nobody knows what the very next moment will hold, maybe it could be a disastrous one too. The past cannot be changed but it can act constructively towards the betterment of the current situation, ‘Precaution is better than cure’. To be above this uncertain dilemma of life and death situations, ‘Automated Identification of Disaster News for Crisis Management is proposed using Machine Learning and Natural Language Processing’. A software solution that can help disaster management websites to dynamically show the disaster relevant news which can be shared to other social media handles through their sites.
   

Authors from CSI SFIT:

Delicia Fernandes Chairperson'20

Jayashree Domala Chairperson'19

Manmohan Dogra Joint Tech Head'19

Vinit Masrani Treasurer'19

2. A Comparative Study on Solar Power Forecasting using Ensemble Learning
   June 15, 2020 publication | Institute of Electrical and Electronics Engineers (IEEE)
   
   Intensifying requests for energy is preceding towards renewable solar energy integration with nonrenewable energy resources. Unlike other nonrenewable energy resources, solar energy is recurrent. Effectual utilization of spontaneously available energy accurate solar power forecasting is essential. This study aims to predict solar power through deep neural networks (DNNs) and various machine learning (ML) techniques on a solar dataset, namely linear regression, support vector regression, random forest, etc. The dataset that is used contains solar power energy extracted every five minutes. Moreover, a comparative study is carried out between DNNs and ML techniques, which helps in crafting suitable decisions to select appropriate forecasting and prediction techniques.

Vinit Masrani Treasurer'19

Arbaz Tech Head'18

3. Suspect Face Generation
   April 3, 2020 | Institute of Electrical and Electronics Engineers (IEEE)
   
   Currently sketch artists are employed by the police to draw sketches of suspects based on the description given by an eye-witness. These sketches can sometimes be inaccurate due to incorrect drawings of the artist or the incorrect description given by the witness. Generative Adversarial Network (GAN) is a way of training a Neural Network to output images which belong to a specific class. This network is trained by using an adversarial process which pits the generator against the discriminator in a minimax game. Traditional GANs are unable to generate high-resolution images hence, StyleGAN is used to resolve this issue. The generated images may still need to be altered to get a close match so TL-GAN is used to alter the generated image by altering the latent-space input of the StyleGAN. TL-GAN offers users the ability to finely tune one or multiple features of the face holistically. The main objective of the proposed work is to develop a Suspect Face Generation System as the sketches made by sketch artists are only 13 out of 160 times (approx. 8%) accurate. This system will help the society in reduction of misidentification of crime suspects and considerably reduce the crime rate.

Harsh Jalan Chairperson'18

Canute Corda Creative Executive'18

2. Medium Articles

List of all the Medium Articles published by our CSI SFIT Team members over the years:

1. The ideal workflow for your Machine Learning Project by Kaif Kohari
   In this blog, @Kaif10 have summarized all the things one should be aware of while working on a Machine Learning project, from data cleaning and analysis to choosing the right model and hyperparameter tuning.

2. The Large Hadron Collider with Machine Learning by Kaif Kohari
   In this blog, @Kaif10 have writes about the Introduction to the Large Hadron Collider, Basic Particle Physics, Working of Large Hadron Collider, Higgs Boson, Machine Learning, Results and Conclusion.

3. Video-Game Sales Analysis with Python by Kaif Kohari
   In this blog, @Kaif10 works with a cool dataset which contains data about Video Games sales across various regions in the world from 2015.

4. Conditional statements in Python by Jayashree Domala
   A guide to understanding if, else, elif statements in python.

5. Python comparison operators by Jayashree Domala
   Guide to understand the working of comparison operators in Python.

6. Python I/O commands for text files by Jayashree Domala
   An informative guide to perform I/O basics with text files.

7. Booleans in Python by Jayashree Domala
   A guide to understanding booleans in Python.

8. Sets in Python by Jayashree Domala
   A guide to knowing more about set data type in Python.

9. Tuples in python by Jayashree Domala
   A guide to understanding tuples in Python.

10. Understanding python dictionaries by Jayashree Domala
    A guide to learn about dictionaries in Python.

11. Understanding lists in Python by Jayashree Domala
    A guide to learn about dictionaries in Python.

12. Geometry of linear equations for machine learning by Jayashree Domala
    A guide to help you understand linear algebra to get started with machine learning.

13. Understanding Python strings by Jayashree Domala
    A guide to understand the python strings and string formatting.

14. Understanding python number data type in detai by Jayashree Domalal
    A guide to help you understand the Python number data type.

15. How to set up python in a Windows system? by Jayashree Domala
    A step by step guide for python installation.

16. Understanding Artificial Intelligence and its subsets by Jayashree Domala
    A guide to know about AI subsets and how to get started with it.

17. Lyrics Generator using LSTM on TF 2.0 by Manmohan Dogra
    In this article, you will learn the essentials of RNN’s LSTM model, basic about NLP, and how to utilize them to make a Lyrics Generator on feeding few words as input by utilizing the Tensorflow 2.0 framework.

18. How to visualize data using Power BI? by Jenny Dcruz
    Use Microsoft Power BI to gain business intelligent insights.

19. Excel, Tableau, Power BI… What should you use? by Jenny Dcruz
    Find out what you need for data analysis or visualization.

20. Artificial Intelligence in Web Development by Jenny Dcruz
    Learn how to take customer engagement to a new level.

21. Fundamentals of NumPy by Jenny Dcruz
    Over the course of this article, you will learn the various features and functions of the Python library, NumPy.

22. Pandas for data analysis by Jenny Dcruz
    

23. Tableau visualizations by Jenny Dcruz
    

24. How to get financial data using Python? by Jayashree Domala
    

Kaif Kohari Joint Tech Head'20

Jayashree Domala Chairperson'19

Manmohan Dogra Joint Tech Head'19

Jenny Dcruz Joint PR Head'19

3. Projects

List of all the projects done by our CSI SFIT Team members over the years:
To be added.

How to Contribute:

1. Clone repo and create a new branch: $ git checkout https://github.com/CSI-SFIT/CSI-SFIT-Contribution-Archive.git -b name_for_new_branch.
2. Make changes and test.
3. Submit Pull Request with comprehensive description of changes.

Acknowledgements

CSI SFIT Team 2020 - 2021 :

• Chairperson : @Delicia Fernandes