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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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IJARAI Vol. 3 Issue 7 (2014)

Open Access | | 3 papers

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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1

A More Intelligent Literature Search

Author 1: Michael G King Author 2: Alison Van Bree

Although the topic of study relates to an environmental/health issue, it is the methodology described which serves to showcase an embryonic form of a new “more intelligent” protocol of search algorithm. Through the implementation of this algorithm, an extensive automated literature base yielded a single credible solution to a previously… Read full abstract & cite →

automated literature search database search algorithm Craniosynostosis fibroblast growth factor receptor
Abstract doi.org/10.14569/IJARAI.2014.030701 PDF
2

Classifications of Motor Imagery Tasks in Brain Computer Interface Using Linear Discriminant Analysis

Author 1: Roxana Aldea Author 2: Monica Fira

In this paper, we address a method for motor imagery feature extraction for brain computer interface (BCI). The wavelet coefficients were used to extract the features from the motor imagery EEG and the linear discriminant analysis was utilized to classify the pattern of left or right hand imagery movement and… Read full abstract & cite →

Brain computer interface motor imagery wavelet linear discriminant analysis
Abstract doi.org/10.14569/IJARAI.2014.030702 PDF
3

Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

Author 1: Chandra Prasetyo Utomo Author 2: Aan Kardiana Author 3: Rika Yuliwulandari

Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural… Read full abstract & cite →

breast cancer artificial neural networks extreme learning machine medical decision support systems
Abstract doi.org/10.14569/IJARAI.2014.030703 PDF
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