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  We provide projects on wireless communication, WEKA, SentiWordNet, SDN, QUALlNet, OPNET, OpenCV, MiniNet, PEERSIM and so on

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  We do research on Wordnet, Grid Computing, Cloud Computing, Computer Networking, etc

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  We provide electrical projects based on power electronics, MATLAB Simulink and SIM Power

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  For Electronics Engineering Students we support technologies like ARM, GSM, GPS, RFID, Robotics, VLSI, NSL, NS3, OMNet++, OPNet, QUALNET, PeerSim

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  For Computer Science Students we support NS2, NS3, MATLAB, Java, OMNeT++, OPNet, CloudSim, Qualnet, Weka, Wordnet, Dot Net, Hadoop,….

WEKA Projects is an acronym for Waikato environment for knowledge analysis. We offer weka academic projects for machine learning application and to extract valuable information from databases. We can develop various number of software application by weka tool. Our main aim of developing weka projects to ensure an innovative technology and to enhance an optiministic solution for data mining issues. We embed machine learning application to analyze computer program and large amount of data based on the concept of IEEE papers. We ensure efficient support to fast and give accurate decision making process. We develop research projects for research scholars and development in machine learning.

WEKA Projects

Features of WEKA Projects:

We adopt following features are:

• It provides how to use Weka workbench.
• Compare and evaluate different data mining techniques.
• For specific problem appropriate approaches are selected.
• Open source and platform independent.
• Ensure efficient performance improvement techniques.
• Flexible service for scripting experiments.

Basics of Weka:

To solve data mining problem we use weka tool. We follow various techniques in MS projects to implement weka projects are given as follows:

• Classifiers.
• Estimators.
• Attribute Selection.
• Filters.
• Clustering.
• Data Preprocessing and Visualization.
• Association Rule.

WEKA Project

Packages in Weka Tool:

We provide various packages for different technique in Weka and described as:

• Classification:

         Java Neural Network Package: Weka plug in implemented by java.

         HMMWEKA: library function contain hidden Markov learning algorithm.

• Collective classifications:

  Provide semi supervised learning algorithm and collective classification algorithm.

  LTBDBC: classification with clustering and dynamic selection algorithm.

• Clustering:

            Clustering composed of following packages.

• FAST OPTIC: random projection in Euclidian distance.
• APCLUSTER: used for bioinformatics and computer vision applications.

• Feature Selection:

           We use packages are:

• RSAR SUBSETEVEL: used to select sub set features in large data set.

• Frequent Pattern Mining:

         XAPRIORI: we use this package to select frequent item in pattern mining algorithm.

• Streaming:

        Snowball Stemmers: we use this to wrap snowball stemmer into weka working model in snowball stemmer algorithm.

• Text Mining:

         NLP: we use NLP package to construct part of speech filter and Penn tree Bank tokenize.

WEKA

Advantage of Weka:

Using a weka tool, we deployed more than 75+ projects with various applications. We provided the Weka advantages are:

• It contains various tools to analyze entire data mining process.
• Ensure number of switching operations to customize the output system.
• It enables sufficient information to generate an accurate output.