praitaliya

cbir_using_ffbpnn

Content-based image retrieval (CBIR) is a technique that helps in searching a user desired information from a huge set of image files and interpret user intentions for the desired information. The retrieval of information is based on features of image like colour, shape, texture, annotation etc. In addition, the system with neural network can learn by itself: it can automatically extract features of images during the retrieval phase and can store them as additional information to improve the performance. We propose the system for querying, including efficient graphical user interfaces and efficient retrieval of images.

cbir_using_surf

Content-Based Image Retrieval (CBIR) is a challenging task which retrieves the similar images from the large database. Most of the CBIR system uses the low-level features such as color, texture and shape to extract the features from the images. In Recent years the Interest points are used to extract the most similar images with different view point and different transformations. In this project the SURF is combined with the color feature to improve the retrieval accuracy. SURF is fast and robust interest points detector/descriptor which is used in many computer vision applications. To improve the performance of the system the SURF is combined with Color Moments since SURF works only on gray scale images. The KD-tree with the Best Bin First (BBF) search algorithm is to index and match the similarity between the features of the images. Finally, Voting Scheme algorithm is used to rank and retrieve the matched images from the database.

image_classification_using_deep_learning

Thousands of images are generated every day, which implies the necessity to classify and access them by an easy and faster way. The main objective of classification is to identify the features occurring in the image. Neural networks (NNs), inspired by biological neural system, are a family of supervised machine learning algorithms that allow machine to learn from training instances as mathematical models. NNs have been widely applied in the fields of classification, optimization, and control theory. This work compares the classification of images using Convolutional Deep Neural Network approaches.

smarttrolley

Smart Trolley is a system which helps in reducing time of people while shopping in the marts. It is an effective approach in which a person only needs to take care of the items he wants to purchase by adding them into the trolley. The trolley with the help of RFID Reader will add and remove the items. It will calculate the total cost of items and will give quick billing resulting into short queue and less time consumption at the payment counter. It also comprises of mobile application attached to it for user`s convenience culminating into simple, easy and straight forward usage.

SubjectiveLogic

In this project we have demonstrated the implementation of a simple decision support system that allows us to generate the opinions about likelihood of particular disease. The parameters of subjective logic are calculated on the basis of EMS dataset and the opinion is generated which is further used on various concepts like subjective logic operators and complex subjective logic expressions. Our architecture allows users to interact with the system in order to provide input for getting the particular opinion. The concept of subjective logic allows to deal with uncertainty thereby providing the accurate predictions based on the data given.

praitaliya/cbir_using_surf

Content-Based Image Retrieval (CBIR) is a challenging task which retrieves the similar images from the large database. Most of the CBIR system uses the low-level features such as color, texture and shape to extract the features from the images. In Recent years the Interest points are used to extract the most similar images with different view point and different transformations. In this project the SURF is combined with the color feature to improve the retrieval accuracy. SURF is fast and robust interest points detector/descriptor which is used in many computer vision applications. To improve the performance of the system the SURF is combined with Color Moments since SURF works only on gray scale images. The KD-tree with the Best Bin First (BBF) search algorithm is to index and match the similarity between the features of the images. Finally, Voting Scheme algorithm is used to rank and retrieve the matched images from the database.

praitaliya/image_classification_using_deep_learning

Thousands of images are generated every day, which implies the necessity to classify and access them by an easy and faster way. The main objective of classification is to identify the features occurring in the image. Neural networks (NNs), inspired by biological neural system, are a family of supervised machine learning algorithms that allow machine to learn from training instances as mathematical models. NNs have been widely applied in the fields of classification, optimization, and control theory. This work compares the classification of images using Convolutional Deep Neural Network approaches.

praitaliya/SubjectiveLogic

In this project we have demonstrated the implementation of a simple decision support system that allows us to generate the opinions about likelihood of particular disease. The parameters of subjective logic are calculated on the basis of EMS dataset and the opinion is generated which is further used on various concepts like subjective logic operators and complex subjective logic expressions. Our architecture allows users to interact with the system in order to provide input for getting the particular opinion. The concept of subjective logic allows to deal with uncertainty thereby providing the accurate predictions based on the data given.

praitaliya/cbir_using_ffbpnn

Content-based image retrieval (CBIR) is a technique that helps in searching a user desired information from a huge set of image files and interpret user intentions for the desired information. The retrieval of information is based on features of image like colour, shape, texture, annotation etc. In addition, the system with neural network can learn by itself: it can automatically extract features of images during the retrieval phase and can store them as additional information to improve the performance. We propose the system for querying, including efficient graphical user interfaces and efficient retrieval of images.

praitaliya/smarttrolley

Smart Trolley is a system which helps in reducing time of people while shopping in the marts. It is an effective approach in which a person only needs to take care of the items he wants to purchase by adding them into the trolley. The trolley with the help of RFID Reader will add and remove the items. It will calculate the total cost of items and will give quick billing resulting into short queue and less time consumption at the payment counter. It also comprises of mobile application attached to it for user`s convenience culminating into simple, easy and straight forward usage.

praitaliya/charts

Helm Charts

praitaliya/client_server_socket_shell

server-client socket functions using C.

praitaliya/django_app

praitaliya/FirebaseBlog

This is a blog application, developed using ember-js as front end and Firebase database.

praitaliya/MDS

praitaliya/MongoDBBlog

This is a blog application, developed using ember-js as front end and backend with Mongo DB Atlas.

praitaliya/SISAPP

Student Information System is the web-based application to organize student data, with having the functionality of student registration, course registration, instructor registration and many more.

praitaliya/test

praitaliya/websearchengine

Created a search engine with functionalities such as counting the frequency of words using Boyer Moore, Brute Force Algorithm, Web Page Ranking using Quicksort Algorithm, spell checking and suggestion using Edit Distance algorithm and Java, HTML to TEXT converter using Jsoup converter