[PDF] Top 20 DESIGN OF ARTIFICIAL NEURAL NETWORK BASED DATA AGGREGATION IN CLUSTERED WSNS
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DESIGN OF ARTIFICIAL NEURAL NETWORK BASED DATA AGGREGATION IN CLUSTERED WSNS
... the design of wireless sensor network is network ...initialization. Network initialization is the process of deploying the sensor nodes in the sensor field of specified ...the network ... See full document
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Design and implementation smart security system based on Artificial Neural Network
... system based on using several security techniques such as (face recognition, motion detection, LPR, ...input data size (image size) side by side trained multiple face situations such as (lighting condition, ... See full document
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An Approach Based On Artificial Neural Network for Data Deduplication
... of data gathered from numerous data sources constitute the foundation of the majority of existing decision support applications and CRM (Customer Relationship ...on data warehouses is vital because ... See full document
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APPROXIMATE, GENERALIZED FIELD DATA BASED MATHEMATICAL MODELING AND ANN SIMULATION OF PVC PIPE MANUFACTURING PROCESS
... observed data from the experimentation is separated into two parts viz. input data or the data of independent pi terms and the output data or the data of dependent pi ...input ... See full document
10
WDARS: A Weighted Data Aggregation Routing Strategy with Minimum Link Cost in Event Driven WSNs
... many design issues, particularly the trade-offs concerning multiple conflicting improvements such as maximizing the route overlapping for efficient data aggregation and minimizing the total link ... See full document
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Data Aggregation Framework for Clustered Sensor Networks Using Multi Layer Perceptron Neural Network
... dropped data packet ratio of the network with the presence of compromised nodes for all the four ...of data are dropped in the presence of 10 compromised nodes and 42% of data are dropped in ... See full document
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Computational Intelligence Based Data Aggregation Technique in Clustered WSN: Prospects and Considerations
... the Network model, Clustering objectives and Clustering attributes ...The network model consists of the architecture and design of the underlying sensor ...in network model. First is the ... See full document
6
Global solar radiation forecasting based on meteorological data using artificial neural network
... back-propagation neural network with Levenberg- Marquardt (LM) training algorithm and Gradient descent back propagation (GD) ...the neural network, three different artificial ... See full document
5
Neural Network Priority Use of BTS for Optimizing Telecommunications in Indonesia
... Artificial Neural Network Model Modeling System for Optimization of BTS At the time of input data (input) we have got, then can be done to designing Artificial Neural ... See full document
5
Detecting central fixation by means of artificial neural networks in a pediatric vision screener using retinal birefringence scanning
... set”). Data from these two controlled groups were used to cre- ate and calibrate the ...The data were organized as an input matrix of 4 rows and Q columns, with Q = 600 (120 measurements with CF and 480 ... See full document
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Title: CLASSIFICATION ON BREAST CANCER USING GENETIC ALGORITHM TRAINED NEURAL NETWORK
... Abstract— Artificial neural networks have been in the position of producing complex dynamics in control applications over the last decade, especially when they are linked to ...for network ... See full document
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Artificial Neural network for Data mining –A study
... direct lines from the input to output nodes) so that the linear parts of a mapping do not need to be approximated by the nonlinear model. Thus the RBFLN is a more complete model of a general nonlinear mapping. Both MLPs ... See full document
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Abstract
... the artificial neural network used for predicting five−year survival: a) training Error Graph, b) Receiver Operating Characteristic (ROC) curves for the training, validating, and testing sets ... See full document
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Design of Artificial Neural Network for Nuclear Reactor
... An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process ...or data classification, through a ... See full document
6
A Study on Effective Algorithm for Medical Decision Making System
... S. Shen et al., (2003) show the neighborhood attraction based on the traditional fuzzy c-means (FCM) clustering algorithm to improve the segmentation performance. In this algorithm, two factors of neighborhood ... See full document
9
Beat classification of an ecg signal using photoplethysmography and neural network
... Falsely issued alarms in intensive care units (ICUs) disrupt patients’ rest, drain hospital resources, and desensitize the hospital staff to potential emergency situations. It has been estimated that 43% of ... See full document
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ADAPTIVE COLOR FILTER ARRAY INTERPOLATION ALGORITHM BASED ON HUE TRANSITION AND EDGE DIRECTION
... the data. Data mining lies at the interface of statistics, database technology, pattern recognition, ma-chine learning, data visualization, and expert ...systems. Data sources can have records ... See full document
7
Comparative Analysis of Classification Techniques in Data Mining Using Different Datasets
... K-nearest neighbour classification is based on learning by an evaluation, that is, by comparing a given test tuple with training tuples that are similar to it. The training tuples are described by n attributes. ... See full document
10
Modeling of Concrete Slump and Compressive Strength using ANN
... Abstract: Artificial Neural Network (ANN) is a subdivision of Artificial Intelligence are extensively used to answer a complex civil engineering ... See full document
7
Approaches in RSA Cryptosystem Using Artificial Neural Network
... the network performance here, the correlation rate value is considered as where the value is near the integer number 1 or -1 the there is a good prediction of the output and where the value is near zero there is ... See full document
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