CA : A Cancer Journal for Clinicians

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

https://www.healio.com/psychiatry/journals/psycann/1976-12-6-12/{806b8887-c02e-40c8-a228-57f1cdb2bdba}/the-stress-of-life.pdf

The stress of life

Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges

Fuzzy group decision-making for facility location selection

Linear Discriminant Analysis (LDA) is a very common
technique for dimensionality reduction problems as a preprocessing
step for machine learning and pattern classification
applications. At the same time, it is usually used as a
black box, but (sometimes) not well understood. The aim of
this paper is to build a solid intuition for what is LDA, and
how LDA works, thus enabling readers of all levels be able
to get a better understanding of the LDA and to know how to
apply this technique in different applications. The paper first
gave the basic definitions and steps of how LDA technique
works supported with visual explanations of these steps.
Moreover, the two methods of computing the LDA space, i.e.
class-dependent and class-independent methods, were explained
in details. Then, in a step-by-step approach, two numerical
examples are demonstrated to show how the LDA
space can be calculated in case of the class-dependent and
class-independent methods. Furthermore, two of the most
common LDA problems (i.e. Small Sample Size (SSS) and
non-linearity problems) were highlighted and illustrated, and
state-of-the-art solutions to these problems were investigated and explained. Finally, a number of experiments was conducted
with different datasets to (1) investigate the effect of
the eigenvectors that used in the LDA space on the robustness
of the extracted feature for the classification accuracy,
and (2) to show when the SSS problem occurs and how it can
be addressed.

/pdf/linear-discriminant-analysis-a-detailed-tutorial-46ghcxadb9.pdf

Linear discriminant analysis: A detailed tutorial

Trust-based secure clustering in WSN-based intelligent transportation systems

Biometric cattle identification approach based on Weber's Local Descriptor and AdaBoost classifier

An improved moth flame optimization algorithm based on rough sets for tomato diseases detection

The literature contains many proposed solutions for automatic sign language recognition. However, the ArSL (Arabic Sign Language), unlike ASL (American Sign Language), did not take much attention from the research community. In this paper, we propose a new system which does not require a deaf wear inconvenient devices like gloves to simplify the process of hand recognition. The system is based on gesture extracted from 2D images. The Scale Invariant Features Transform (SIFT) technique is used to achieve this task as it extracts invariant features which are robust to rotation and occlusion. Also, the Linear Discriminant Analysis (LDA) technique is used to solve dimensionality problem of the extracted feature vectors and to increase the separability between classes, thus increasing the accuracy of the introduced system. The classifiers, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and minimum distance will be used to identify the Arabic sign characters. Experiments are conducted to check the performance of the proposed system and it showed that the accuracy of the obtained results is around 99%. Also, the experiments proved that the proposed system is robust against any rotation and they achieved an identification rate near to 99%. Moreover, the evaluation shown that the system is comparable to the related work.

Tarek Gaber

Papers

Linear discriminant analysis: A detailed tutorial

Trust-based secure clustering in WSN-based intelligent transportation systems

Biometric cattle identification approach based on Weber's Local Descriptor and AdaBoost classifier

An improved moth flame optimization algorithm based on rough sets for tomato diseases detection

SIFT-Based Arabic Sign Language Recognition System