Online MCQ Distributed Quiz Portal

Project Guide : Dr. Himangshu Sarma, Assis. Professor Dept. of CSE, IIITSri City

• Added the feature that Admin can stop any user at any time during the eaxm period.
• Added Timer support for each individual question.
• Added control to "Enable" and "Disable" the quiz on the Admin panel by Global Clock.
• Added control to navigate among all the questions of quiz (during the quiz) and finish the quiz whenever the user wants.
• Added control so that user can start the quiz at any time and continue the quiz even if some error or session timeout occurs.
• Added control to store the answers to question and show a detailed analysis of the quiz results.

Keywords: Python3, Django, Postgresql, psycopg2, Distributed Server, UX / UI Design

Compiler Design Tool In C++

Project Guide : Dr. Himangshu Sarma, Assis. Professor Dept. of CSE, IIITSri City

• Added the feature that lexical analysis phage of compiler in which just need to provide the path of input file(file can be C/C++, JAVA, PYTHON, etc...) and this tool will be tokenized each in line of given input file.
• Added the feature of removable of left recursion for the given input grammar.
• Added the feature of removable of left factoring for the given input grammar.
• Added the feature of string validation of Recursive Descent parser for the following fixed grammar...

 1.) E--> E + T

 2.) E--> T

 3.) T--> T * F

 4.) T--> F

 5.) F--> (E)

 6.) F--> a

 The string will be something like a+a$, a+a*a$, a+(a+a*a) and so on...  

• Added the feature of calculation of first for the given input grammar.
• Added the feature of calculation of follow for the given input grammar.
• Added the feature of calculation of First And Follow simultaneously for the given input grammar.
• Added the feature of string validation of SLR parser for the following fixed grammar...

 1.) E--> a E a

 2.) E--> b E b

 3.) E--> c

 The string wiil be something like c, aca,aabbcbbaa, bacab and so on...

Keywords : C++, Qt Creater, PyQt5, GUI, UI / UX Design, PyQt IDE,

Compiler Design Tool In Java

Project Guide : Dr. Himangshu Sarma, Assis. Professor Dept. of CSE, IIITSri City

• Added the feature that lexical analysis phage of compiler in which just need to provide the path of input file(file can be C/C++, JAVA, PYTHON, etc...) and this tool will be tokenized each in line of given input file.
• Added the feature of removable of left recursion for the given input grammar.
• Added the feature of removable of left factoring for the given input grammar.
• Added the feature of string validation of Recursive Descent parser for the following fixed grammar...

 1.) E--> E + T

 2.) E--> T

 3.) T--> T * F

 4.) T--> F

 5.) F--> (E)

 6.) F--> a

 The string will be something like a+a$, a+a*a$, a+(a+a*a) and so on...  

• Added the feature of calculation of first for the given input grammar.
• Added the feature of calculation of follow for the given input grammar.
• Added the feature of calculation of First And Follow simultaneously for the given input grammar.
• Added the feature of Canonical collection of LR(0) items and Tabel of SLR parser for the given grammar.
• Added the feature of string validation of SLR parser for the following fixed grammar...

 1.) E--> a E a

 2.) E--> b E b

 3.) E--> c

 The string wiil be something like c, aca,aabbcbbaa, bacab and so on...

Keywords : JAVA, OpenJDK, Eclipse IDE, JAVA Swing, WindowBuilder, Swing-Designer

Audio Signal Comparison

Project Guide : Dr. Himangshu Sarma, Assis. Professor Dept. of CSE, IIITSri City

• Took two same speech audio signals(apple-like sound) and remove noise by using the Thresholding method.
• Normalized both signals and create frame for both signals
• Applied various filters to remove remain noise and calculate Mel-frequency cepstral coefficients(MFCCs) of both signals.
• Got 38% similarity by Cosine Similarity method.

Keywords : Audio Signal, Speech Processing, ASR, MFCC, GMM, Filter, Python3, Cosine Similarity, Auto Correlation, Cross Correlation

Rice crop Insects and Weed Detection and Classification using Faster R-CNN

Project Guide : Dr. Shrivishal Tripathi, Assis. Professor Dept. of ECE, IIITNaya Rainpur

• As the increase in the world population the demand of the rice is also increases.
• In order to increase the growth of rice in the rice crop it is necessary to detect the weed and insects into the rice crop to minimize the growth of weed and insects so that the growth of the rice can be increased.
• In which i have taken 15 different types of rice crop insects species images and different phases of weed images to train the model.
• The proposed method facilitates the extraction of weed and insects into the rice crop field by RGB images, using Deep Learning(DL) concept Faster region-base convolutional neural networks(Faster R-CNNs).
• It is implemented using PYTHON3 with the help of Tensorflow API.

Keywords : Computer vision, Deep learning, Weed detection, Insects detection, Rice detection, Convolutional neural networks, Region based convolutional neural networks

Social Media Android Application

Project Guide : Dr. Himangshu Sarma, Assis. Professor Dept. of CSE, IIITSri City

• This App is an example of chat application which is basically based on public chatting (message is broadcast to all connected users).
• It is made of two application first, Client application which runs on user’s device and second, Server application which runs on any device on the network.
• The purpose was to....
• Making reading news simple.
• Making messaging system most private
• Generate interest in people to use this app
• Minimize workload of managing resources
• It is implemented using Android Studio, JAVA, XML & Firebase Server side database.

Keywords : Android Operating System(OS), Firebase, News, Software Development Kit(SDK), Open handset Alliance(OHA),

Classification & Analysis of Unstructured Data

Project Guide : Dr. Navanath Saharia, Head. TnP & Assis. Professor Dept. of CSE, IIITManipur

• It's a tool/system which reads the data from files available in multiple SORS (System of Records) and suggest what type of record it is, How old it is, when it was accessed by user last. Extract file metadata, etc.
• it is Metadata-based classification using MongoDB
• It has done with the help of Exiftool
• It is implemented using PYTHON3, PyQt5 & MongoDB

Keywords : Windows, NTFS, Fat32, MongoDB, Exiftool, Matadata, PyQt5