A Smart Feature Reduction Approach to Detect Botnet Attack in IoT

2021 ◽  
pp. 17-23
Author(s):  
Rup Kumar Deka ◽  
Kausthav Pratim Kalita ◽  
Dhruba Kumar Bhattacharyya ◽  
Debojit Boro
Keyword(s):  
Feature Reduction ◽  
Reduction Approach

EMD-DWT based transform domain feature reduction approach for quantitative multi-class classification of breast lesions

Ultrasonics ◽  
2017 ◽  
Vol 80 ◽  
pp. 22-33 ◽  
Author(s):  
Sharmin R. Ara ◽  
Syed Khairul Bashar ◽  
Farzana Alam ◽  
Md. Kamrul Hasan
Keyword(s):  
Feature Reduction ◽  
Breast Lesions ◽  
Transform Domain ◽  
Multi Class Classification ◽  
Reduction Approach

scholarly journals A combinatorial cooperative-tabu search feature reduction approach

2013 ◽  
Author(s):  
E. Ansari ◽  
M.H. Sadreddini ◽  
B. Sadeghi Bigham ◽  
F. Alimardani
Keyword(s):  
Tabu Search ◽  
Feature Reduction ◽  
Search Feature ◽  
Reduction Approach

An improved feature reduction approach based on redundancy techniques for predicting female urinary incontinence

2011 ◽  
Vol 88 (9) ◽  
pp. 1852-1859 ◽  
Author(s):  
Irene Díaz ◽  
Elena Montañés ◽  
Elías F. Combarro ◽  
Monserrat Espuña-Pons
Keyword(s):  
Urinary Incontinence ◽  
Feature Reduction ◽  
Female Urinary Incontinence ◽  
Reduction Approach

scholarly journals KPIs-Based Clustering and Visualization of HPC jobs: a Feature Reduction Approach

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mohamed Soliman Halawa ◽  
Rebeca P. Diaz Redondo ◽  
Ana Fernandez Vilas
Keyword(s):  
Feature Reduction ◽  
Reduction Approach

scholarly journals Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

2017 ◽  
Vol 232 (15) ◽  
pp. 2711-2722 ◽  
Author(s):  
Juan Jose Saucedo-Dorantes ◽  
Miguel Delgado-Prieto ◽  
Roque Alfredo Osornio-Rios ◽  
Rene de Jesus Romero-Troncoso
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Condition Monitoring ◽  
High Performance ◽  
Industrial Applications ◽  
Feature Reduction ◽  
Data Set ◽  
Multi Stage ◽  
Statistical Time ◽  
Reduction Approach

Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The feature selection and feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage feature reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage feature reduction approach involves a feature selection and a feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based features. Second, a feature selection is done by performing an analysis of the Fisher score. Third, a feature extraction is realized by means of the linear discriminant analysis technique. Finally, fourth, the diagnosis of the considered faults is done by means of a fuzzy-based classifier. The effectiveness and performance of the proposed diagnosis methodology are evaluated by considering a complete data set of experimental test, making the proposed methodology suitable to be applied in industrial applications with power transmission systems.

scholarly journals A Deep Learning-Based Framework for Human Activity Recognition in Smart Homes

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Alaeddine Mihoub
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Computational Models ◽  
Ambient Assisted Living ◽  
Smart Homes ◽  
Feature Reduction ◽  
Behavior Modeling ◽  
Learning Techniques ◽  
Selection Approach ◽  
Reduction Approach

Human behavior modeling in smart environments is a growing research area treating several challenges related to ubiquitous computing, pattern recognition, and ambient assisted living. Thanks to recent progress in sensing devices, it is now possible to design computational models able of accurate detection of residents’ activities and daily routines. For this goal, we introduce in this paper a deep learning-based framework for activity recognition in smart homes. This framework proposes a detailed methodology for data preprocessing, feature mining, and deep learning techniques application. The novel framework was designed to ensure a deep exploration of the feature space since three main approaches are tested, namely, the all-features approach, the selection approach, and the reduction approach. Besides, the framework proposes the evaluation and the comparison of several well-chosen deep learning techniques such as autoencoder, recurrent neural networks (RNN), and some of their derivatives models. Concretely, the framework was applied on the “Orange4Home” dataset which represents a recent dataset specially designed for smart homes research. Our main findings show that the best approach for efficient classification is the selection approach. Furthermore, our overall results outperformed baseline models based on random forest classifiers and the principal component analysis technique, especially the results of our RNN-based model for the all-features approach and the results of our autoencoder-based model for the feature reduction approach.

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