TARANPREETS1999

5G-Smart-Diabetes

Recent advances in wireless networking and big data technologies, such as 5G networks, med- ical big data analytics, and the Internet of Things, along with recent developments in wearable computing and artificial intelligence, are enabling the development and implementation of innovative diabetes monitoring systems and applications. Due to the life-long and systematic harm suffered by diabetes patients, it is critical to design effective methods for the diagnosis and treatment of diabetes. Based on our comprehensive investigation,this article classifies those methods into Diabetes 1.0 and Diabetes 2.0, which exhibit deficiencies in terms of networking and intelligence. Thus, our goal is to design a sustainable, cost-effective, and intelligent diabetes diagnosis solution with personalized treatment. In this article, we first propose the 5G-Smart Diabetes system, which combines the state-of-the-art technologies such as wearable 2.0, machine learning, and big data to generate comprehensive sensing and analysis for patients suffering from diabetes. Then we present the data sharing mechanism and personalized data analysis model for 5G-Smart Diabetes. Finally, we build a 5G-Smart Diabetes testbed that includes smart clothing, smartphone, and big data clouds. The experimental results show that our system can effectively provide personalized diagnosis and treatment suggestions to patients.

A-Time-Efficient-Approach-For-Detecting-Errors-In-Big-Sensor-Data-On-Cloud

Big sensor data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity it is difficult to process using on-hand database management tools or traditional data processing applications. Cloud computing provides a promising platform to support the addressing of this challenge as it provides a flexible stack of massive computing, storage, and software services in a scalable manner at low cost. Some techniques have been developed in recent years for processing sensor data on cloud, such as sensor-cloud. However, these techniques do not provide efficient support on fast detection and locating of errors in big sensor data sets. For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach which exploits the full computation potential of cloud platform and the network feature of WSN. Firstly, a set of sensor data error types are classified and defined. Based on that classification, the network feature of a clustered WSN is introduced and analyzed to support fast error detection and location. Specifically, in our proposed approach, the error detection is based on the scale-free network topology and most of detection operations can be conducted in limited temporal or spatial data blocks instead of a whole big data set. Hence the detection and location process can be dramatically accelerated. Furthermore, the detection and location tasks can be distributed to cloud platform to fully exploit the computation power and massive storage. Through the experiment on our cloud computing platform of U-Cloud, it is demonstrated that our proposed approach can significantly reduce the time for error detection and location in big data sets generated by large scale sensor network systems with acceptable error detecting accuracy.

AnagramsDictonary

This is an Anagrams Game in which the user is given with a word and he/she has to find its anagrams. He/she can add one letter of his choice if he wants but the anagram should not contain the original word as substring.

BlackHole_starter

Tittle is "Black-Hole App". In This application to create a hierarchy structure and choosen possible words or numbers out of one Black Hole word.

NewsApp

Restaurant-Application

In This Application to make online food ordering system like Restaurant, Zomato, etc

Sports-website

Sports Website using HTML, CSS and JAVASCRIPT

SwiftUICalculator

TimerApp

TODOListApp

TARANPREETS1999/Sports-website

Sports Website using HTML, CSS and JAVASCRIPT

TARANPREETS1999/5G-Smart-Diabetes

Recent advances in wireless networking and big data technologies, such as 5G networks, med- ical big data analytics, and the Internet of Things, along with recent developments in wearable computing and artificial intelligence, are enabling the development and implementation of innovative diabetes monitoring systems and applications. Due to the life-long and systematic harm suffered by diabetes patients, it is critical to design effective methods for the diagnosis and treatment of diabetes. Based on our comprehensive investigation,this article classifies those methods into Diabetes 1.0 and Diabetes 2.0, which exhibit deficiencies in terms of networking and intelligence. Thus, our goal is to design a sustainable, cost-effective, and intelligent diabetes diagnosis solution with personalized treatment. In this article, we first propose the 5G-Smart Diabetes system, which combines the state-of-the-art technologies such as wearable 2.0, machine learning, and big data to generate comprehensive sensing and analysis for patients suffering from diabetes. Then we present the data sharing mechanism and personalized data analysis model for 5G-Smart Diabetes. Finally, we build a 5G-Smart Diabetes testbed that includes smart clothing, smartphone, and big data clouds. The experimental results show that our system can effectively provide personalized diagnosis and treatment suggestions to patients.

TARANPREETS1999/A-Time-Efficient-Approach-For-Detecting-Errors-In-Big-Sensor-Data-On-Cloud

Big sensor data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity it is difficult to process using on-hand database management tools or traditional data processing applications. Cloud computing provides a promising platform to support the addressing of this challenge as it provides a flexible stack of massive computing, storage, and software services in a scalable manner at low cost. Some techniques have been developed in recent years for processing sensor data on cloud, such as sensor-cloud. However, these techniques do not provide efficient support on fast detection and locating of errors in big sensor data sets. For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach which exploits the full computation potential of cloud platform and the network feature of WSN. Firstly, a set of sensor data error types are classified and defined. Based on that classification, the network feature of a clustered WSN is introduced and analyzed to support fast error detection and location. Specifically, in our proposed approach, the error detection is based on the scale-free network topology and most of detection operations can be conducted in limited temporal or spatial data blocks instead of a whole big data set. Hence the detection and location process can be dramatically accelerated. Furthermore, the detection and location tasks can be distributed to cloud platform to fully exploit the computation power and massive storage. Through the experiment on our cloud computing platform of U-Cloud, it is demonstrated that our proposed approach can significantly reduce the time for error detection and location in big data sets generated by large scale sensor network systems with acceptable error detecting accuracy.

TARANPREETS1999/AnagramsDictonary

This is an Anagrams Game in which the user is given with a word and he/she has to find its anagrams. He/she can add one letter of his choice if he wants but the anagram should not contain the original word as substring.

TARANPREETS1999/Data-Structures-Demystified-master

Data Structures in C/C++

TARANPREETS1999/GhostDictonary

This workshop's activity will be a game called Ghost. Ghost is a word game in which players take turns adding individual letters to a growing word fragment, trying not to be the one to complete a valid word. Each fragment must be the beginning of an actual word, and, for our purposes, we will consider 4 to be the minimum word length. The player who completes a word or creates a fragment that is not the prefix of a word loses the round.

TARANPREETS1999/Helping-Foundation-Website

TARANPREETS1999/Library-Managament-Application

In this application make Library Management System used various types of android API's and Plugin's.

TARANPREETS1999/NewsApp

TARANPREETS1999/Ping-Pong

Web Application using html, css & javascript

TARANPREETS1999/Quiz_Contest

Quiz context app made by java language and Android with various types of applications

TARANPREETS1999/Restaurant-Application

In This Application to make online food ordering system like Restaurant, Zomato, etc

TARANPREETS1999/School-Management-System

TARANPREETS1999/Scrane-Dice

Scarne’s Dice is a turn-based dice game where players score points by rolling a die.The winner is the first player that reaches (or exceeds) 100 points.

TARANPREETS1999/SmallCalculator

TARANPREETS1999/StudentManage

In This Application to create Student management System like all details of school and college Students

TARANPREETS1999/SwiftUICalculator

TARANPREETS1999/TimerApp

TARANPREETS1999/TODOListApp

TARANPREETS1999/TouringMusician_starter

This app is called "Touring Musician". It will present the user with a map and let them add gig locations to the map and figure out a path through the various gigs.

TARANPREETS1999/Word-Stack

It is a single-user word game in which the user try to separate out two words of the same length whose letters have been scrambled (but the order of the letters has been preserved).

TARANPREETS1999/WordLadder_starter

This workshop's app will be a game called Word Ladder. The game gives the players two starting words of the same length (gain and fire in this example) and a number of blank spaces in between. The objective of the game is to fill each blank space with a word that differs from the word above and the word below it by a single letter.

TARANPREETS1999/coronavirus-tracker

Spring Boot Java app to track coronavirus confirmed cases across the globe

TARANPREETS1999/Cost-effective-Privacy-Preserving

Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data-intensive applications without infrastructure investment. Along the processing of such applications, a large volume of intermediate data sets will be generated, and often stored to save the cost of re-computing them. However, preserving the privacy of intermediate data sets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate data sets. Encrypting ALL data sets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate data sets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to en/decrypt data sets frequently while performing any operation on them. In this paper, we propose a novel upper bound privacy leakage constraint-based approach to identify which intermediate data sets need to be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy requirements of data holders can still be satisfied. Evaluation results demonstrate that the privacy-preserving cost of intermediate data sets can be significantly reduced with our approach over existing ones where all data sets are encrypted.

TARANPREETS1999/COVID-19

Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE

TARANPREETS1999/FirebaseUI-Android-master

FirebaseUI android master application using Firebase

TARANPREETS1999/get-started-python

A Python application and tutorial that use Flask framework to provide a REST API to receive requests from the UI. The API then persists the data to a Cloudant database.

TARANPREETS1999/Hearti-Health

TARANPREETS1999/linux-content

TARANPREETS1999/TODO-LIST-APP