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Advanced fixing of lost migrated data in micro Array using advanced Regression

 6,500.00  1,499.00

  • The main objective of this project is to find the missing data during data transfer.
  • Bi cluster bayesian principal with advanced regression techniques Method has been used with Multidimensional array model
  • Data Sets are provided inside
  • The Bi cluster Bayesian principal with an advanced regression algorithm will compare the existing spatial database with the normal database from the input database.
  • Data processed for cancer disease missing data and Disease prediction.

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Abstract: Advanced fixing of lost migrated data in micro Array using advanced Regression

Fixing missing data using regression is the most complicated problem in data mining. Most of the hospitals are using client-server technology for transferring the data inside the hospital. In case of transferring one huge database to another location or in case of data migration i.e., Migrating database from access to SQL or Migrating data from SQL to Oracle data loss may occur. sometimes the values from the table or the database may disappear known as data missing.

 

To find out the missing values, a prediction algorithm may be used to fill the data. Prediction should be more accurate. So we are implementing a multidimensional array model with Bi cluster Bayesian principal which is an advanced regression algorithm. From the data set, an operational database will be created for cancer patients and another database for normal patients.

 

This database will be unique and different types of sample data are available. The Bi cluster Bayesian principal with an advanced regression algorithm will compare the existing spatial database with the normal database. So the result will be obtained from the dataset itself. Whether the patient is affected by cancer or not, and also their infection range percentage along with the missing values in the database during the time of data migration.

 

Now-a- days, people are using Data Mining techniques to retrieve data from their databases. It is because the volume of their databases is becoming larger and larger every day. Generally, query tools are used to retrieve the data from the database. But, if the database is larger, then it is difficult to retrieve data using query tools. Using data mining techniques, relevant information can be extracted effectively. It is applied to specific records or historical data in the database and retrieves some interesting or hidden information from the database.

 

Usually, query tools are used to retrieve the data from the databases. The user can use SQL (Structured Query Language) if one knows exactly what data they are looking for. Data mining tools such as IBM’s Intelligent Miner is used when the user knows vaguely what they are looking for and whether the data is new or any hidden information.

Synopsis:

The Dataset is available in excel format with some missing values for certain patients. The reasons for Data Missing may be data migration, data modification and so on.

The input data set will be considered as a multidimensional data array to improve accuracy. We are following the rules of Bayesian principal component analysis (BPCA), which is a well-known method for estimating the microarray missing value. But its performance is not satisfactory on the datasets with strong local similarity structure.

BPCA obtains the lowest normalized root-mean-square error of 82.14% on all missing rates. The proposed method achieves 95.79 % accuracy.

This is a new terminology used in the IT field to find some interesting information from a large database. It is a high-level application technique used to present and analyse data for decision-makers.

This project is defined in many ways. Some of the definitions are

  • It refers to finding relevant and useful information from databases.
  • This code deals with pattern identification and hidden information from a large database.
  • It is also known as Knowledge Discovery in Databases (KDD) which is defined as the nontrivial extraction of implicit, previously unknown and potentially useful information from the data.

Keywords:

Data mining, Data engineering, fixing missing data using regression , Lost data, data prediction, cancer data prediction, Disease prediction, Prediction algorithm, online final year CSE project, IT projects.

Helpline:

Visual Studio – 2012, ASP.NET, C#, SQL Server, Java Script.

Visual Studio download Link: https://visualstudio.microsoft.com/vs/older-downloads/

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Language

ASP.NET – Front end, C# – Coding Language, DOT NET 2012, SQL Server 2010

Contains

Full Documentation, Full Source Code, Read me file, Video Demo

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