Data Mining - Concepts and Techniques.

A R T I C L E I N F O A B S T R A C T Article history: Received: 15 December, 2020 Accepted: 31 January, 2021 Online: 25 February, 2021 Wireless sensor networks (WSNs) are having large demands in enormous applications for the decade. The main issue in WSNs is estimating the exact location of unknown nodes. All applications are dependent on the location information of unknown nodes in WSNs. Location information of mobile anchor node is used to estimate the location of unknown nodes. A new approach is implemented in this paper for the localization of unknown nodes using Artificial Neural Networks. Specifically, a neural feed network is used for the indoor position process. Also several neural network configuration sets have been tested, which includes Bayesian regularisation (BR), Levenberg-Marquardt (LM), resilient back propagation (RP), Scaled Conjugate Gradient (SCG) and Degree Descent (SCG),etc. At the end results are simulated using MATLAB and Mean Square Error is calculated and compared with other existing approaches. The proposed approach is energy efficient and uses only a two-way message to obtain inputs for the localization. Even the cost is minimized as in the proposed system only one mobile anchor node is used.

/pdf/artificial-neural-network-approach-using-mobile-agent-for-5cxa26ywsp.pdf

Artificial Neural Network Approach using Mobile Agent for Localization in Wireless Sensor Networks

Wireless sensor networks (WSN) usage in numerous applications that increased need has risen to explore more efficient and effective manner. Localization is an important issue that can bring efficiency to WSNs. Localization depicts to calculation of the actual location of unknown nodes in network. This paper focuses on the comparison and analysis of Multi-Layer Perceptron Neural Networks (MLPNN) and Radial Basis Function Neural Networks (RBFNN) for the development of localization framework in WSNs. Using Received Signal Strength Indicator (RSSI) determination of the location a static sensor node on 100 100 m 2 grids from three anchor nodes with fixed positions. The simulation results effectually signify that MLPNN has better localization accuracy as compared to RBFNN.

Analysis of Localization Using ANN Models in Wireless Sensor Networks

The accuracy of localization is a significant criterion to evaluate the practical utility of localization algorithm in wireless sensor networks (WSNs) In localization algorithms, one of the main methods to improve localization accuracy is to increase the number of anchor nodes The number of anchor nodes is always limited because of the hardware constraint, such as cost, energy consumption and so on In this paper, we propose a novel localization algorithm in which the sensor nodes get localized with the help of virtual anchor nodes In this scheme VAN gets localized with the help of minimum number of anchor nodes The objectives to improve the localization accuracy maximize the localization ratio and minimize the cost which incur to deployment of more number of anchor nodes and proposed algorithm is simulated using MAT Lab to evaluate its performance

Localization of Sensor Nodes Using Flooding in Wireless Sensor Networks

The accuracy of localization is a significant criterion to evaluate the practical utility of localization algorithm in wireless sensor networks (WSN). In mostly localization algorithms, one of the main methods to improve localization accuracy is to increase the number of anchor nodes. But the number of anchor nodes is always limited because of the hardware restrict, such as cost, energy consumption and so on. In this paper, we propose a novel which uses forwarding a query message in flooding technique for localization using anchor nodes and once a node localized it acts as virtual anchor node and it helps to localize remaining sensor nodes. It is scheme to increase and upgrade the virtual anchor nodes, while the real number of physical anchors is the same as before.

Localization of Sensor Nodes using Flooding in Wireless Sensor Networks

Diabetes patients are increasing in number so it is necessary to predict , treat and diagnose the disease. Data Mining can help to provide knowledge about this disease. The knowledge extracted using Data Mining can help in treating and preventing the disease. Artificial Neural Network (ANN) can be used to create an classifier from the data. The neural network is trained using backpropagation algorithm The knowledge stored in the neural network is used to predict the disease. The knowledge stored in neural network is extracted using Pos-Neg sensitivity method. The knowledge extracted is in form of sensitivity analysis to analyze the disease and in turn help in treating the disease.

Data Mining using Artificial Neural Network POS_NEG Rules

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Varsha M Patil

Author Tools

Chat about Author

Papers

Localization of Sensor Nodes using Flooding in Wireless Sensor Networks

Data Mining using Artificial Neural Network POS_NEG Rules