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    nepar-ai-algorithm
    NETWORKED PATTERN RECOGNITION (NEPAR) FRAMEWORK
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    On likely to the others we worked with our patented artificial intelligence algorithm
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    NEPAR in-depth explanation

    NEPAR in-depth

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NETWORKED PATTERN RECOGNITION (NEPAR) FRAMEWORK

This project aims to propose a comprehensive new framework, namely Networked Pattern Recognition (NEPAR) Framework for different applications. The NEPAR and five different classification methods (SVM, NB, LR, DT, and kNN) are improved by adding information from the proposed network metrics. Information from observations is extracted by building the network, and feature properties for each observation are used to classify the output. Six different datasets (Australian credit approval, diabetes, breast cancer, abalone, SturPlus fMRI, German credit, as seen below figures) are used to show the framework outperforms other traditional classification. Therefore, we developed new AI assistant for mental disorders by using the NEPAR algorithm.

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Dimensional Reduction

We plot the data in 2D, so we can understand how the algorithm sees it.

Information Retrieval

We can see where the algorithm struggles most, and focus on that.

Transferable Learning

Our algorithm can apply knowledge from similar tasks.

Scalable Infrastructure

So we can run algorithms on hundreds of millions of samples..

Interaction Capture

Our algorithm (the NEPAR) can capture the interactions of patients,

Powerful Classification

We have a powerfull detection system for the understanding of the patients,.

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SELECTED JOURNAL PUBLICATIONS

CONFERENCE PROCEEDİNGS (PEER-REVİEWED)

CONFERENCE ABSTRACTS AND PRESENTATIONS