Initiatives

Project School

Project School started this year, as one of the basic pillars of Learning


Objective, milestone:

The ultimate objective of Project School is to provide students with a well-rounded skill set that
includes hands-on experience with end-to-end projects, as well as an understanding of the
integration of multiple technologies into a single frame. This will not only enhance their technical
abilities but also give them a competitive edge in the job market. We believe that Project School
will be a valuable addition to our educational institution and will help our students to be better
prepared for the industry.

Selection process


● Eligibility of Project School is open to all students, with selection based on their performance in a
machine test on programming languages. Seats are limited and will be allotted based on test
scores and availability. Once selected, students are free to form their own teams of 5 members.


Resources allocated for the project:
● To ensure that the projects are completed to the highest standard, students will be given time and
space to discuss and review their work. This review process will be designed through tasks and
reading assignments that guide students through different methods of problem-solving and help
them to finish their projects successfully. The venue for project school will be at the Virtusa
Excellence Centre, where students will have access to the necessary resources and support to
complete their projects.


Finish line:
● It is our aim that every student in project school will complete 4 different projects, gaining a
strong understanding of the latest technologies and industry practices. We are confident that this
initiative will equip our students with the necessary skills and knowledge to secure high-paying
jobs with confidence
Details about Project School:
● Students selection starts from first semester of second year
● Students have to complete one project per semester per student
● Every 10 students per project (2 teams of 5 each)
● Every project has a mentors
● Students have to select their project domain in AI / ML, AWS, Cyber Security, Blockchain, IOT,
Unreal, YOLO v4, Etc.,
● The project review will be done weekly basis only.
● Project School Process Flow

Project School Project Titles:

  1. AutoSurgery: AI driven auto instrument and operation procedure detection for surgeries
  2. Earth Observatory Data Cube for Nizamabad district
  3. Command and Control movements of a drone using Alexa
  4. FabricRealEstate: HyperledgerFabric driven RealEstate Procurement System
  5. SwachCampus – Litter Detection and Reporting System
  6. False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive
    Analytics
  7. Aves (Bird species) Detecting Mobile application
  8. SwachCampus – Litter Detection and Reporting System
  9. Remote Monitoring and Management of Drip Irrigation using Digital Twin Technology:

Details of a few present semester projects

  1. Swatch campus – litter detection and reporting system
  2. Remote Monitoring and Management of Drip Irrigation using Digital Twin Technologies

Aves Bird Species Detection


OBJECTIVE
● To Design a responsive mobile application for Bird species identification.
● To help user to detect the species of birds.
● It also helps the user listen to the sound made by the bird.

TECHNICAL DESCRIPTION
● Bird species is identified by using the inceptionV3 Deep Learning Model.
● The frontend provides users information and predicted results which is developed in
Flutter using Dart.
● Firebase is used as a database.
● The deep learning model is trained using Tensor-Flow library.
● A web viewer is option is used to fetch information and audio picker is used to pick the audio.
DATASET DESCRIPTION
Name: – BIRDS 450 SPECIES
Source: – Kaggle
Link: – https://www.kaggle.com/datasets/gpiosenka/100-bird-species
● This Data set is of 450 bird species. 70,626 training images, 22500 test images (5 images per
species) and 2250 validation images (5 images per species). The data set also include a file
birds.csv. This csv file contains 5 columns. The file paths column contains the relative file
path to an image file. The labels column contains the bird species class name associated with
the image file. The scientific label column contains the Latin scientific name for the image.
The data set column denotes which dataset (train, test or valid) the file path resides in. The
class_id column contains the class index value associated with the image file’s class.

MODEL DESCRIPTION
Model name: – InceptionV3

PLATFORM USED
● Android Studio for Front-end development and Back-end development.
● Kaggle for Dataset
● Google colab and jupyter notebook for model building
SYSTEM CONFIGURATION
● Name: – DESKTOP-SF7E57H
● Processor: – Intel(R) Core (TM) i7-10750H CPU @ 2.60GHz 2.59 GHz
● RAM: – 16.0 GB (15.8 GB usable)
SUMMARY
This bird species identification application is a project that uses machine learning
techniques to identify different bird species along with it’s audio from images. The overall goal
of this project is to create a system that can accurately classify different bird species based on
their visual characteristics.

ePashudhan-an online Portal for Dairy Farmers:


OBJECTIVE
● To Design a responsive Mobile application for online registration of dairy farmers
producing milk and other livestock products organically
● This portal shall help the dairy farmers to gain more information about the livestock
and their produce, dairy market trends and many more.
TECHNICAL DESCRIPTION
● Full stack project done with MERN stack.
● The frontend provides farmers information and predicted results which is developed in React Native.
● Backend development done in Express.js and Node.js runtime environment.
● MongoDB is used as database.
● Database :- Any credentials used to Register to the project will be saved in MongoDB.
Login
● credentials are verified by checking the credentials present in the database.
PLATFORM USED
● Visual Studio Code for Front-end and Back-end development.
● MongoDB Atlas Cluster used for database storage

SYSTEM CONFIGURATION
● Name:- Lenovo Ideapad 330
● Processor :- Intel Core i3 7020U
● RAM :- 4GB Unified Memor

SUMMARY
The mobile application for online processing and registration of dairy farmers producing milk
and other livestock products organically. So that this portal shall help the dairy farmers to gain
more information about the livestock and their produce,dairy market trends and many more.

False Data Injection attacks:

PROBLEM STATEMENT
IoT is the latest industrial revolution primarily merging automation with advanced
manufacturing. But IoT is vulnerable to cyber attacks like false data injection. In this project I
created a MERN stack web app that helps us to analyze various predictive models using
LTSM, GRU and CNN. The false data is injected using Python/MATLAB.
SCOPE

This application deals with the problem of developing a Predictive Maintenance system
with the use of Machine Learning (SVM, kNN, Random Forests, Logistic Regression using Auto
Encoders) to classify life cycle of a sensor. The proposed system consists of mainly three phases:
the first phase (i.e., pre-processing), the next phase (i.e., model training) and the final phase (i.e.,
classification). The first phase includes calculation of EOL (End of Life) of sensors used and using
that I am calculating LR (Life Ratio). Then, using LR I am creating labels. These are then
classified into three categories:
Good: Good Condition represents 0.
Moderate: Moderate Condition represents 1.
Warning: Warning Condition represents 2.

TECHNOLOGY STACK
The technologies used in this application are:
● Python 3.10.0
● MATLAB
● Django
The modules used in this application are:
● Tensorflow
● scikit-learn
● matplotlib
● pandas
● Numpy
● Keras
● Seaborn
The algorithms used in this application are:
● kNN (k-Nearest Neighbours)
● Logistic Regression
● Random Forests
● Autoencoders

School Of New and Emerging Technologies(SONET)

With the objective of producing industry ready engineering students. NGIT has instituted a comprehensive SONET program for its students. This program will go a long way in bridging the industry-academia gap that all of us are so well aware of. The SONET program is run concurrently with the B.E. course

Courses

  • Biomedical Imaging
  • Blockchain architecture design & use cases
  • Cybersecurity - Ethical Hacking
  • Full Stack Web Development
  • IoT
  • Machine Learning
  • MEAN Stack Web Development
  • Unreal Programming
  • CUDA Programming
  • Kotlin / Android Programming

Customized Workforce Development

Students undergo one of the following CWD program according to the needs of the recruiting company.

  • J2EE Track: Spring, Hibernate and Struts
  • RIA Track: HTML, CSS and Flex
  • Mobile Application Track: JME, iPhone and Android
  • Testing Track: ISTQB Certification, Web Application Testing, Mobile Application Testing, Game Testing
  • Networking Track: Networking Fundamentals, Linux Server Configuration, Windows Server Configuration, Trouble Shooting.

Arjuna Weekly Programming Competition

About

Arjuna is a weekly coding competition, conducted for NGIT students(1st to 3rd years) . These sessions are conducted every Sunday. Winners will get cash prizes per week based on internal assessment followed by a one on one interview

Functions

  • Three coding activities per Arjuna session (Sunday).
  • Solutions of previous session will be discussed in Java every Arjuna session.
  • At the end of the session new activities will be started for next session.
  • Top 2 students(winners) from each Year of NGIT will get weekly cash prizes.
  • Winners will be decided based on fastest submission time.

Nirantar Finishing School

About

Niranthar Finishing School (NFS) is a weekly coding competition, conducted for NGIT students (1st to 3rd years) . These sessions are conducted alternate days Tuesday, Thursday and Saturday (TTS) every week. Winners will get cash prizes per week.

Functions

  • Two coding activities per NFS session (TTS), total six activities weekly.
  • Solutions of previous session will be discussed using python programming language in every NFS session.
  • At the end of the session new activities will be started for next day.
  • Top 15 students(winners) from each year of NGIT will get weekly cash prizes.
  • Winners will be decided based on fastest submission time.

Foundation School

Coding is an essential skill that has transformed the way we live and work. Its importance in the job market and for innovation and problem-solving cannot be overstated. As technology advances, it is crucial to keep up with the latest trends and developments in coding.

Foundation School (FDS) focuses on improving and enhancing the coding skills of students of the first and second years. Students will have FDS coding sessions 2 hours / day , thrice a week for the complete semester. These sessions are apart from the regular University classes.

During these sessions students are given challenging coding problems and are encouraged to solve them. Faculty mentors too are available in the class for those students who need help. During these FDS sessions students significantly hone up their coding skills using the Tessellator , an in-house developed Learning Management System

Foundation School lays a solid foundation for the students to subsequently move into Project School & Finishing School.

NPTEL

NGIT is a Local Chapter for NPTEL (Id: LC-2181)

NPTEL has set up a SWAYAM-NPTEL Local Chapter in college which will be under the headship of a faculty member of the college, who would be their Single Point of Contact (SPOC). SPOC is updated about all the latest NPTEL initiatives and given information which he can disseminate among the students. He will identify suitable mentors for various courses, who can ensure that students are active in a course, are submitting their assignments on time and also clarify the doubts they may have.

NGIT conducts regular Professor-led review classes and labs for the NPTEL courses being pursued by the NGIT students , for the entire duration of the course. This will enable the student to understand the subject better and perform well in the final exam.

About NPTEL

The National Programme on Technology Enhanced Learning (NPTEL) was initiated by seven Indian Institutes of Technology (Bombay, Delhi, Kanpur, Kharagpur, Madras, Guwahati and Roorkee) along with the Indian Institute of Science, Bangalore in 2003. Five core disciplines were identified, namely, civil engineering, computer science and engineering, electrical engineering, electronics and communication engineering and mechanical engineering and 235 courses in web/video format were developed in this phase.

The main goal of NPTEL Phase II (2009-14) was to build on the engineering and core science courses launched previously in NPTEL Phase I. An additional 600 web and video courses were created in all major branches of engineering, physical sciences at the undergraduate and postgraduate levels and management courses at the postgraduate level. Several improvements such as indexing of all video and web courses and keyword search were implemented.

Some highlights:

  • Largest online repository in the world of courses in engineering, basic sciences and selected humanities and social sciences subjects
  • Online web portal http://nptel.ac.in – more than 471 million+ views
  • Youtube channel for NPTEL – most subscribed educational channel, 1.5 million+ channel subscribers, 404 million+ views
  • More than 56000 hours of video content
  • Most accessed library of peer-reviewed educational content in the world
  • 52000+ hours of transcribed content; 51000+ hours of subtitled videos

NPTEL MOOCs

NPTEL began offering open online courses in March 2014 along with certificates from the IITs/IISc for those who completed the courses successfully.

It is now possible for ANYONE outside the IIT System to be able to do an online certification course from NPTEL and get a certificate from the IITs. IITs are reaching out and taking education to the homes of people through this initiative.


Certification process

NPTEL began the initiative of offering certification to students for courses in March 2014. The process of certification is as follows.

  1. Subject Matter Experts (SME - faculty from IITs or partner institutes with input from industry) create recorded video content for courses.
  2. The course is uploaded on the portal and opened for enrollments, which is free.
  3. Every week, about 3 hrs of video content is released along with an assignment , which is evaluated and provides the student with a score.
  4. Teaching Assistants (TAs) and the faculty members support the discussion forum – answering questions and clearing doubts.
  5. If someone wants to get a certificate from the IITs/IISc after doing the course, he/she should register for the in-person proctored certification exam that is conducted in 100+ cities across India in collaboration with an exam partner. The certification exam is not free but has a nominal fee of Rs 1000.
  6. Final score=25% assignment score + 75% exam score. The pass criteria for exams is being changed from July 2019. A learner will pass and be certified only if Average assignment score (out of 100) >= 40 AND Final exam score ( out of 100) >= 40. E-verifiable certificates are made available on nptel.ac.in/noc Only e-certificates will be published. Hard copies of certificates will NOT be printed.
  7. These certificates are envisioned for use in credit transfer to universities or for making the student more employable or for enhancing his growth in his current place of work.