Amazon Clone

Last Updated on May 3, 2021

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I created Amazon clone with help of React JS which is on process.

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Human Computer Interaction Using Iris,Head And Eye Detection

Last Updated on May 3, 2021

About

HCI stands for the human computer interaction which means the interaction between the humans and the computer.

We need to improve it because then only it would improve the user interaction and usability. A richer design would encourage users and a poor design would keep the users at bay.

We also need to design for different categories of people having different age,color,gender etc. We need to make them accessible to older people.

It is our moral responsibility to make it accessible to disabled people.

So this project tracks our head ,eye and iris to detect the eye movement by using the viola Jones algorithm.But this algorithm does not work with our masks on as it calculated the facial features to calculate the distance.

It uses the eucledian distance to calculate the distance between the previous frame and the next frame and actually plots a graph.

It also uses the formula theta equals tan inverse of b/a to calculate the deviation.

Here we are using ANN algorithm because ANN can work with incomplete data. Here we are using constructive or generative neural networks which means it starts capturing our individual images at the beginning to create our individual patterns and track the eye.

Here we actually build the neural network and train it to predict

Finally we convert it to mouse direction and clicks and double clicks on icons and the virtual keyboard.

As a contributing or moral individuals it is our duty to make devices compatible with all age groups and differently abled persons.

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Emotional Analysis Based Content Recommendation System

Last Updated on May 3, 2021

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As the saying goes, “We are what we see”; the content we see may have an adverse effect on our behavior sometimes. Especially in a country like India, where numerous films and TV series are highly prominent, there are great chances of watching explicit or disturbing content randomly. This may have adverse effects on behavior of people, especially children. And we also know “Prevention is better than cure”. Preventing inappropriate content from going online can be more effective than banning them after release.

To achieve this, we aim to create a content filtering and recommendation system that either recommends a film or TV series or alerts a user with a warning message saying it’s not recommended to watch. Netflix or any other Over-the-top (OTT) platforms perform a filtering process before they buy digital rights for any content. This is where our tool comes handy. It detects absurd or hard emotion inducing content with the help of human emotions. Through this project we aim to create a content detector based on human emotion recognition. We will project scenes to test audience and capture their live emotions.

Then we use “Facebook Deep Face”, a pre-defined CNN based face recognition and facial emotion analysis model to identify faces and analyze their emotions. We use “Deep Learning” methods to recognize facial expressions and then make use of Circumplex Model proposed by James Russell to classify emotions based on arousal and valence values. Based on majority emotion that is projected by audience we would either recommend or not recommend the content for going on-air. This system prevents inappropriate content from going on-air

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Book Recommendation System

Last Updated on May 3, 2021

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Book Recommendation System

Book recommendation is created and deployed in this approach of work, which helps in recommending books. Recommendation achieved by the users feedbacks and rating, this is the online which analyse the ratings, comments and reviews of user, negative positive nature of comments using opinion mining. User searching for the interested book will be displayed in top list and also can read feedback given by people about the book or any searched items. Whenever user search for any book from the large data available, he gets confused from the number of displayed item, which one to choose. In that case recommendation helps and displays on the interested items. This is the trustworthy approach, which is used in this project where selection is based on the dataset.

Clustering

This project used clustering as the central idea. A clustering approach is used. Clustering is based on similarity where similar elements are kept in a single group. Likewise similar element, the irrelevant elements are also reside in a group, which is another group, based on similarity value or maximum size of cluster. The clustering approach which is used in our work is K-mean clustering for grouping of similar users. It is the unsupervised and simplest learning algorithm, which simplifies mining work by grouping similar elements forming cluster. This is done using a parameter called K-centroids. Distance between each element is calculated for checking the similarity and forming a single cluster to reside the similar elements, after comparing with K-centroid parameter.

In this project, 6 clusters were made.

The project is made with 2 separate datsets in .csv format taken from Kaggle.

  1. Books dataset
  2. Ratings

This project is GUI based. The output page has 2 options:

  1. Rate books
  2. Recommend books

The user can chose either according to themselves.

Rate books

In this option, the user can rate books.

Recommend books

In this option the books are recommended to the user, according to their previous readings.

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Human Computer Interaction Using Iris,Head And Eye Detection

Last Updated on May 3, 2021

About

HCI stands for the human computer interaction which means the interaction between the humans and the computer.

We need to improve it because then only it would improve the user interaction and usability. A richer design would encourage users and a poor design would keep the users at bay.

We also need to design for different categories of people having different age,color,gender etc. We need to make them accessible to older people.

It is our moral responsibility to make it accessible to disabled people.

So this project tracks our head ,eye and iris to detect the eye movement by using the viola Jones algorithm.But this algorithm does not work with our masks on as it calculated the facial features to calculate the distance.

It uses the eucledian distance to calculate the distance between the previous frame and the next frame and actually plots a graph.

It also uses the formula theta equals tan inverse of b/a to calculate the deviation.

Here we are using ANN algorithm because ANN can work with incomplete data. Here we are using constructive or generative neural networks which means it starts capturing our individual images at the beginning to create our individual patterns and track the eye.

Here we actually build the neural network and train it to predict

Finally we convert it to mouse direction and clicks and double clicks on icons and the virtual keyboard.

As a contributing or moral individuals it is our duty to make devices compatible with all age groups and differently abled persons.

More Details: Human Computer Interaction using iris,head and eye detection

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Identifying Water Sources For Smallholder Farmers With Agri

Last Updated on May 3, 2021

About

CIAT and The Zamorano Pan-American Agricultural School, in coordination with the United States Agency for International Development (USAID)/Honduras, began in March the validation and dissemination process of the geographic information system (GIS) tool AGRI (Water for Irrigation, by its Spanish acronym).

What is AGRI?

AGRI was developed in ArcGIS 10.1® for western Honduras with the aim of providing support for decision making in identifying suitable water sources for small drip irrigation systems. These systems cover areas of up to 10 hectares and are part of the U.S. government initiative Feed the Future in six departments of western Honduras (Santa Bárbara, Copán, Ocotepeque, Lempira, Intibucá, and La Paz).

AGRI identifies surface-water sources and sites suitable for rainwater harvesting for agriculture. In addition, AGRI maps the best routes for installing water pipes between the first parcel of the irrigation system and the identified water source. The tool is complemented by deforestation analyses of upstream areas, as an indicator of watershed conservation status.

How was AGRI developed?

Developing this tool required the implementation of a complex framework of spatial analysis that included correcting the terrain Digital Elevation Model (DEM), using weather information derived from remote sensors, hydrological analysis such as estimation of runoff and water balance, and modeling the path with lower costs or fewer difficulties in installing pipes across the landscape. Additionally, it was necessary to do digital soil mapping for some variables.

What does AGRI offer to its users?

AGRI was developed based on the following needs identified by USAID-Honduras in relation to the implementation of small irrigation systems in the country:

  1. To find the closest water source that permits transportation of the water by gravity to parcels.
  2. To search for “permanent and sufficient” water sources to establish water outlets.
  3. To find suitable sites for building reservoirs for the harvest of runoff water.
  4. To take into account the protection of water sources for human consumption and other protected zones and avoid possible conflicts on water use.
  5. The tool needs to be easy to use for technicians and agronomists.
  6. The tool should use information that is readily available in the country.

This application was developed at the request of USAID-Honduras and it responds to the implementation needs of its programs. This implementation was led by the Decision and Policy Analysis (DAPA) area of CIAT with the participation of the soil area, which contributed with the digital soil mapping for the project. Likewise, Zamorano University supported the field validation and the analysis of the legal context related to water use, which serves as a basis for the application of this tool.

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