Movie Website

Last Updated on May 3, 2021

About

Project


->Problem statement:Create a simple login form where a user can sign in and then there should be one page where user can rate as well as put comments on the movies (you can make list of any 10 movies). The average rating and comments should be visible besides the movie name.


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Technology Used:


Front end:

CSS3

Bootstrap


Back end:

Nodejs

EJS

Express


Database used:

MongoDB

Mongoose


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Steps required to run the code:


-Make sure you have nodejs installed in your computer if not I have mentioned the process to download it in windows below


-Install nodemon by entering npm install -g nodemon in terminal


-Then install mongodb server on https://www.mongodb.com/try/download/community (If already downloaded ignore this step)


-Clone the project in your computer by git clone https://github.com/abhishekdhar30/Internship_assg02 command on terminal.


-Open the terminal having path set to the directory in which project is present


-Run npm install (this command will install all packages present in our json file)


-Run nodemon app.js command


-If you are unable to install nodemon for some reason you can also use <strong>node app.js</strong> instead of above step


-Open browser and set url as <strong>localhost:3000</strong> and press enter this will open our project


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-> Installation Steps to download nodejs in windows(This step is for those who don't have installed nodejs in their pc)


-Download the Windows installer from the https://nodejs.org/en/ web site. 


-Choose the LTS version that’s shown on the left. 


-Run the installer (the .msi file you downloaded in the previous step.)


-Follow the prompts in the installer (Accept the license agreement, click the NEXT button a bunch of times and accept the default installation settings).


- Restart your computer. You won’t be able to run nodejs until you restart your computer.


- Confirm that Node has been installed successfully on your computer by opening a Hyper terminal and typing in the commands node --version


You should see the version of node you just installed.


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->Logic:


I have created two pages one for login and another for movies.


I have set login page as home page i.e It should be the first page of the project


I have created 10 blocks in movies page for movies in which user can enter ratings and comments and they will be reflect beside to movie name where rating and comments are entered


I have used mongoose mongodb database to store data in database 


The actual work should be like this I have stored the data which user entered in database and when the user click on submit button it will show all data to movies page bcoz I have redirected the page back to movies page 


And I have added one extra feature which is when the user login the page all the previous data in database will be deleted


More Details: Movie website

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Snake Game

Last Updated on May 3, 2021

About

I have made Snake game project using Python language. This is a fun game which you will enjoy during playing. The main objective of this game is to win the game and score more and more points. There are various requirements which are needed to build this Snake game project. These requirements are installation of various packages , modules,etc.

Installation of modules to build Snake game project : -

1. pygame module - It is a cross - platform set of Python modules designed for writing video games. It includes computer graphics and sound libraries designed to be used with the Python programming language.

2. tkinter module - This module is used in Python to build some interesting GUI applications. It uses tk toolkit to create GUI(Graphical User Interface).

So, these modules are used to create GUI applications, to build game -based projects.

Duration of the project - 10 months

My role in Snake game project is of Developer

Skills used - Python language

Modules used - pygame , tkinter

Toolkit used - tk

In this project, there is also a facility which I want to tell you that if you want to play the game again, then you can simply press 'P'.By doing this, you can play the game again and you will surely enjoy the game while playing. It's a fun game , and you can easily score more and more ponts


More Details: Snake game

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Automated Generation Of Videos From News Stories

Last Updated on May 3, 2021

About

Recent advancements in internet, media capturing, and mobile technologies have let fast growing News industries to produce and publish News stories rapidly. In recent days News industry is trying lot to make their news stories attractive and more engaging to their readers. Youngsters these days often do not have much time to go through an entire news article to understand the content yet they want to know all the important elements the article. Recent surveys suggest that Millennials and other similar age group of people prefer news stories as videos over news as text. However manual generation of videos for each news article is considered costly and laborious. Hence there is a requirement for news video generation system that can create interesting, engaging, concise and high-quality news videos from text news stories with little or no human intervention.

This research will develop an end-to-end automated solution for generating videos from news articles. The system will have different NLP based components for automated news content analysis. Detection of key phrases from the news article will be done using NLP based or Deep learning solutions. Named entities in a news story such as person, time, place, brand etc can be automatically detected using NER for highlighting them in videos. Detection of emotions in news text or phrases for automated suggestion of background music or emojis for video production. In addition, famous tweets related to the news covered by the article can be detected and included in the final video. Also images and videos related to news content should be automatically discovered by crawling from internet and can be instantly used as background scenery in the video. This effort will also consider the analysis of the aforementioned steps in a faster manner for real-time video production.

More Details: Automated Generation of Videos from News Stories

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Web App

Last Updated on May 3, 2021

About

In this project I developed a web app using JSP and Html.

I've also used various styling using CSS.

This was a part of my academic project wherein I created a web app like pinterest .

I added a login page using JSP and if the password is incorrect it directs back to login page and if its correct it will direct to the main page where I've splitted the screen into various frameset using html .


In the main frame I've added marquee of html and at the top I've added various links like home page , know about us , show us our interest.

In the home page options it always directs us to the main page if we are at some other page and click at home page. I've used response.sendRedirect of JSP for the directing options to other pages.

In show us our interest I've added various interest options Using JSP using form of JSP which takes input of interest of the visitors.


On the left side of the main frame there are various options like photography , travel , hairstyle etc.

clicking upon them will direct to the page showing various pictures of that interest.


The main page is login.html used for opening the site.

The website runs of Local host .

The server used for the deployment is APACHE-TOMCAT.


The project was done under the guidence of our JAVA professor , through this we also learned various JAVA scriptlet concepts.

More Details: Web app

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Image Processing

Last Updated on May 3, 2021

About

What is image processing ?

The aim of pre-processing is an improvement of the image data that suppresses unwilling distortions or enhances some image features important for further processing, although geometric transformations of images (e.g. rotation, scaling, translation) are classified among pre-processing methods here since similar.

Preprocessing refers to all the transformations on the raw data before it is fed to the machine learning or deep learning algorithm. For instance, training a convolutional neural network on raw images will probably lead to bad classification performances.


convolutional neural network

convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data. ... A neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain.


CNNs are used for image classification and recognition because of its high accuracy. The CNN follows a hierarchical model which works on building a network, like a funnel, and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed.


Problem description:  Case study , We have a dataset has 3 subfolders inside it are single prediction contains only 2 images to test the model and prediction so that we know our CNN model is working , test set with 2000 images (1000 of dogs and 1000 of cats) where we will evaluate our model , training set contains 8000 images 4000 of cats and 4000 of dogs as we are going to train our CNN model on these images of dogs and cats . so basically our CNN model is going to predict whether the image given is of a a cat or a dog. By generating random number on google then choosing the image .  Eg: cat


Prediction for CAT

PREDICTION FOR DOG

More Details: IMAGE PROCESSING

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Artificial Neural Network

Last Updated on May 3, 2021

About

What is ANN?

An artificial neural network (ANN) is the component of artificial intelligence that is meant to simulate the functioning of a human brain. Processing units make up ANNs, which in turn consist of inputs and outputs. An artificial neural network (ANN) is the component of artificial intelligence that is meant to simulate the functioning of a human brain. Processing units make up ANNs, which in turn consist of inputs and outputs.


The input values are processed through all these hidden layers to get the output value just like in human brain.

Neurons are basically building blocks of ANN as the main aim of ANN is to recreate neuron

Dendrites = receiver of signals

Axon is transmitter of signals .

Neurons communicate with one another at junctions called synapses. At a synapse, one neuron sends a message to a target neuron—another cell. Most synapses are chemical; these synapses communicate using chemical messengers. Other synapses are electrical; in these synapses, ions flow directly between cells.

Perceptron is a neural network unit that does contain computations to detect features in data basically artificial neuron.

A cost function is a single value, not a vector, because it rates how good the neural network did as a whole.

Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model.

Problem description: Business problem on real world problem. In the dataset we have 10000 observations of approx 6 months with 14 columns about their information related to bank. RowNumber, CustomerId, Surname, CreditScore, Geography, Gender, Age, Tenure, Balance, NumOfProducts, HasCrCard, IsActiveMember, EstimatedSalary, Exited( dependent variable) whether the person stayed in the bank or left the bank 1 = left , 0 = stayed. So we have to understand the co relation between all the features and exited , now based on this dataset the bank wants to understand why people are preferring/ not preferring their bank as they want maximum customers in their bank . so our trained model will predict whether any new customer will leave the bank or not so that the bank can give some special offers to them so that they stay. We have to train the dataset then deploy the model on future customer by predicting the probability.


TABLE OF CONTENTS:

Importing the libraries

Part 1 - Data Preprocessing

Importing the dataset

Encoding categorical data

Splitting the dataset into the Training set and Test set

Feature Scaling

Part 2 - Building the ANN

Initializing the ANN

Adding the input layer and the first hidden layer

Adding the second hidden layer

Adding the output layer

Part 3 - Training the ANN

Compiling the ANN

Training the ANN on the Training set

Part 4 - Making the predictions and evaluating the model

Predicting the result of a single observation

Predicting the Test set results

Making the Confusion Matrix

 

DATASET