scholarly journals EON OF IMPLEMENTING A MULTIFACETED CLOUD BASED OCR IN APPLE’S COMPASSIONATE APP STORE MILIEU

2016 ◽  
pp. 235-239
Author(s):  
C. Infant Louis Richards ◽  
T. Yuva ◽  
J.SYLVESTER BRITTO
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Character Recognition ◽  
Large Scale ◽  
Scale Up ◽  
App Store ◽  
Large Scale Data ◽  
One Machine ◽  
Scale Data ◽  
Cursive Scripts

Cloud Architectures discourse key hitches surrounding large-scale data dispensation. In customary data processing it is grim to get as many machines as an application needs. Second, it is difficult to get the machines when one needs them. Third, it is difficult to dispense and harmonize a large-scale job on different machines, run processes on them, and provision another machine to recover if one machine fails. Fourth, it is difficult to auto scale up and down based on dynamic workloads. Fifth, it is difficult to get rid of all those machines when the job is done. Cloud Architectures solve such difficulties.Optical character recognition of cursive scripts present a number of thought-provokingsnags in both segmentation and recognition processes and this entices many researches in the arena of contraption learning. This paper presents the best approach based on a mishmash of OCR and Cloud Computing to handle with the Apple’s prerequisite, to make it available in the app store to design a splendid OCR for outdoor portable documents. The enactment results on a comprehensive database show a high notch of accuracy which meets the requirements of viable use.

Study of Map-Reduce over Hadoop Based Cloud Computing Environment

2014 ◽  
Vol 509 ◽  
pp. 175-181
Author(s):  
Wu Min Pan ◽  
Li Bai Ha
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Large Scale ◽  
Programming Model ◽  
Sql Server ◽  
Map Reduce ◽  
Data Set ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Scale Data

Popularity for the term Cloud-Computing has been increasing in recent years. In addition to the SQL technique, Map-Reduce, a programming model that realizes implementing large-scale data processing, has been a hot topic that is widely discussed through many studies. Many real-world tasks such as data processing for search engines can be parallel-implemented through a simple interface with two functions called Map and Reduce. We focus on comparing the performance of the Hadoop implementation of Map-Reduce with SQL Server through simulations. Hadoop can complete the same query faster than SQL Server. On the other hand, some concerned factors are also tested to see whether they would affect the performance for Hadoop or not. In fact more machines included for data processing can make Hadoop achieve a better performance, especially for a large-scale data set.

Advanced monitoring techniques for a large‐scale data‐processing network

2008 ◽  
Vol 25 (5) ◽  
pp. 287-300 ◽  
Author(s):  
B. Martin ◽  
A. Al‐Shabibi ◽  
S.M. Batraneanu ◽  
Ciobotaru ◽  
G.L. Darlea ◽  
...  
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Monitoring Techniques ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Processing Network ◽  
Scale Data

BestPeer++: A Peer-to-Peer Based Large-Scale Data Processing Platform

2014 ◽  
Vol 26 (6) ◽  
pp. 1316-1331 ◽  
Author(s):  
Gang Chen ◽  
Tianlei Hu ◽  
Dawei Jiang ◽  
Peng Lu ◽  
Kian-Lee Tan ◽  
...  
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Peer To Peer ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Processing Platform ◽  
Scale Data

scholarly journals Medimate : Ailment Diffusion Control System with Real Time Large Scale Data Processing

2018 ◽  
Vol 7 (2.31) ◽  
pp. 240
Author(s):  
S Sujeetha ◽  
Veneesa Ja ◽  
K Vinitha ◽  
R Suvedha
Keyword(s):  
Control System ◽  
Data Processing ◽  
Real Time ◽  
Large Scale ◽  
Diffusion Control ◽  
Qr Code ◽  
Healthcare Applications ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Scale Data

In the existing scenario, a patient has to go to the hospital to take necessary tests, consult a doctor and buy prescribed medicines or use specified healthcare applications. Hence time is wasted at hospitals and in medical shops. In the case of healthcare applications, face to face interaction with the doctor is not available. The downside of the existing scenario can be improved by the Medimate: Ailment diffusion control system with real time large scale data processing. The purpose of medimate is to establish a Tele Conference Medical System that can be used in remote areas. The medimate is configured for better diagnosis and medical treatment for the rural people. The system is installed with Heart Beat Sensor, Temperature Sensor, Ultrasonic Sensor and Load Cell to monitor the patient’s health parameters. The voice instructions are updated for easier access.  The application for enabling video and voice communication with the doctor through Camera and Headphone is installed at both the ends. The doctor examines the patient and prescribes themedicines. The medical dispenser delivers medicine to the patient as per the prescription. The QR code will be generated for each prescription by medimate and that QR code can be used forthe repeated medical conditions in the future. Medical details are updated in the server periodically.  

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