Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent

Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. .In this papers the different problems that are defined in problem formulation. All these problems are resolved in future. Furthermore the paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

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Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

Archives of Metallurgy and Materials ◽

10.2478/v10172-012-0091-7 ◽

2012 ◽

Vol 57 (3) ◽

pp. 829-835 ◽

Cited By ~ 1

Author(s):

Z. Głowacz ◽

J. Kozik

Keyword(s):

Genetic Algorithm ◽

Feature Selection ◽

Mahalanobis Distance ◽

Distance Measure ◽

Synchronous Motor ◽

Medical Diagnostics ◽

Motor Current ◽

Feature Spaces ◽

Multidimensional Feature Spaces ◽

Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

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Research and implementation of Chinese text feature selection algorithm based on χ2statistics

Computational Intelligence and Industrial Engineering ◽

10.2495/ciie140191 ◽

2014 ◽

Author(s):

Weijiang Wu ◽

Shengkai Wen ◽

Dongmei Xia ◽

Guohe Li

Keyword(s):

Feature Selection ◽

Chinese Text ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Text Feature

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Evaluation of Potent Isoquinoline-Based Thiosemicarbazone Antiproliferatives Against Solid Tumor Models

10.26434/chemrxiv.7877987.v1 ◽

2019 ◽

Author(s):

Daniel Sun ◽

Soumya Poddar ◽

Roy D. Pan ◽

Juno Van Valkenburgh ◽

Ethan Rosser ◽

...

Keyword(s):

Solid Tumor ◽

Small Cell Lung Carcinoma ◽

Single Agent ◽

Resistance Mechanisms ◽

Tumor Models ◽

Cell Lung Carcinoma ◽

Adaptive Resistance ◽

Cancer Models ◽

Nanomolar Range

The lead compound, an ⍺-N-heterocyclic carboxaldehyde thiosemicarbazone HCT-13, was highly potent against a panel of pancreatic, small cell lung carcinoma, and prostate cancer models, with IC90 values in the low-to-mid nanomolar range. We show that the cytotoxicity of HCT-13 is copper-dependent, that it acts as a copper ionophore, induces production of reactive oxygen species (ROS), and promotes mitochondrial dysfunction and S-phase arrest. Lastly, DNA damage response/replication stress response (DDR/RSR) pathways, specifically Ataxia-Telangiectasia Mutated (ATM) and Rad3-related protein kinase (ATR), were identified as actionable adaptive resistance mechanisms following HCT-13 treatment. Taken together, HCT-13 is potent against solid tumor models and warrants in vivo evaluation against aggressive tumor models, either as a single agent or as part of a combination therapy.

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