Design and Implementation of a New Model for Privacy Preserving Classification of Data Streams

2018 ◽  
pp. 454-462
Author(s):  
Aradhana Nyati ◽  
Shashi Kant Dargar ◽  
Sandeep Sharda
Keyword(s):  
Data Streams ◽  
Privacy Preserving ◽  
New Model ◽  
Design And Implementation

scholarly journals Efficient Model for Privacy Preserving Classification Of Data Streams

2021 ◽  
Vol 12 (2) ◽  
pp. 1475-1481
Author(s):  
P Rajendra Prasad, Et. al.
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Privacy Preserving ◽  
Data Sets ◽  
Information Revelation ◽  
Privacy Preserving Data Mining ◽  
Inaccurate Data ◽  
Exact Data ◽  
The Web

Privacy preserving data mining has become progressively mainstream since it permits sharing of privacy delicate data for examination purposes .So individuals have gotten progressively reluctant to share their data, regularly bringing about people either declining to share their data or giving inaccurate data. As of late, privacy preserving data mining has been concentrated broadly, on account of the wide multiplication of delicate data on the web. Data Mining manages programmed extraction of already obscure examples from a lot of data sets. These data sets ordinarily contain touchy individual data or basic business data, which thusly get presented to different gatherings during Data Mining exercises. This makes hindrance in Data Mining measure. Answer for this issue is given by Privacy preserving in data mining (PPDM). PPDM is a specific arrangement of Data Mining exercises where procedures are developed to secure privacy of the data, so the information revelation cycle can be completed without obstruction. The target of PPDM is to shield delicate data from spilling in the mining cycle alongside exact Data Mining results. The objective of this paper is to introduce the survey on different privacy preserving strategies which are useful in mining huge measure of data with sensible productivity and security.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

Classification of high-dimensional evolving data streams via a resource-efficient online ensemble

2017 ◽  
Vol 31 (5) ◽  
pp. 1242-1265 ◽  
Author(s):  
Tingting Zhai ◽  
Yang Gao ◽  
Hao Wang ◽  
Longbing Cao
Keyword(s):  
Data Streams ◽  
High Dimensional ◽  
Evolving Data

A Big Data Application for Energy Consumption Management of Commercial Buildings

2021 ◽  
Vol 2 (1) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Data Streams ◽  
Design And Implementation ◽  
Available Energy ◽  
Cost Of Energy ◽  
Data Application ◽  
Consumption Data ◽  
Big Data Application ◽  
The Cost

The demand for energy is increasing rapidly and, after a few years, it may surpass the available energy, which may lead the energy providers to increase the cost of energy consumption to compensate the cost for the production. This paper provides design and implementation details of a prototype big data application developed to help large buildings to automatically manage their energy consumption by setting energy consumption targets, collecting periodic energy consumption data, storing the data streams, displaying the energy consumption graphically in real-time, analyzing the consumption patterns, and generating energy consumption graphs and reports. The application is connected to Mongo NoSQL backend database to handle the large and continuously changing data. This big data energy consumption management system is expected to help the users in managing energy consumption by analyzing the patterns to see if it is within or above the desired consumption targets and displaying the data graphically.

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