Batik Lasem images classification using voting feature intervals 5 and statistical features selection approach

2016 ◽  
Author(s):  
Teny Handhayani
Keyword(s):  
Features Selection ◽  
Statistical Features ◽  
Selection Approach

scholarly journals A Systematic Algorithm for Data Cluster Using Map-Reduce Approach

2019 ◽  
pp. 564-569
Author(s):  
Kechika. S ◽  
Sapthika. B ◽  
Keerthana. B ◽  
Abinaya. S ◽  
Abdulfaiz. A
Keyword(s):  
Feature Selection ◽  
Map Reduce ◽  
Features Selection ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Main Concept ◽  
Selection Approach ◽  
Clustering Data ◽  
Data Objects ◽  
Time Required

We have been studying the problem clustering data objects as we have implemented a new algorithm called algorithm of clustering data using map reduce approach. In cluster, main part is feature selection which involves in recognition of set of features of a subset, since feature selection is considered as a important process. They also produces the approximate and according requests with the original set of features used in this type of approach. The main concept beyond this paper is to give the outcome of the clustering features. This paper which also gives the knowledge about cluster and it's own process. To processing of large datasets the nature of clustering where some more concepts are more helpful and important in a clustering process. In a clustering methodology where more concepts are very useful. The feature selection algorithm which affects, the entire process of clustering is the map-reduce concept. since, feature selection or extraction which is also used in map-reduce approach. The most desirable component is time complexity where efficiency concerns in this criterion. Here time required to find the effective features, where features of quality subsets is equal to effectiveness. The complexity to find based on this criteria based map-reduce features selection approach, which is proposed and evaluated in this paper.

scholarly journals An Evaluation of Entropy Measures for Microphone Identification

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1235
Author(s):  
Gianmarco Baldini ◽  
Irene Amerini
Keyword(s):  
A Priori ◽  
Approximate Entropy ◽  
Identification Accuracy ◽  
Permutation Entropy ◽  
Features Selection ◽  
Statistical Features ◽  
Audio Signals ◽  
Trade Off ◽  
Entropy Measures ◽  
Physical Features

Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori. Usually there is a trade-off between the identification accuracy and the time requested to perform the classification. Various approaches have been used in literature to deal with it, ranging from the application of handcrafted statistical features to the recent application of deep learning techniques. This paper evaluates the application of different entropy measures (Shannon Entropy, Permutation Entropy, Dispersion Entropy, Approximate Entropy, Sample Entropy, and Fuzzy Entropy) and their suitability for microphone classification. The analysis is validated against an experimental dataset of built-in microphones of 34 mobile phones, stimulated by three different audio signals. The findings show that selected entropy measures can provide a very high identification accuracy in comparison to other statistical features and that they can be robust against the presence of noise. This paper performs an extensive analysis based on filter features selection methods to identify the most discriminating entropy measures and the related hyper-parameters (e.g., embedding dimension). Results on the trade-off between accuracy and classification time are also presented.

License number plate recognition system using entropy-based features selection approach with SVM

2018 ◽  
Vol 12 (2) ◽  
pp. 200-209 ◽  
Author(s):  
Muhammad Attique Khan ◽  
Muhammad Sharif ◽  
Muhammad Younus Javed ◽  
Tallha Akram ◽  
Mussarat Yasmin ◽  
...  
Keyword(s):  
Recognition System ◽  
Features Selection ◽  
Selection Approach

Genetic Algorithm-based Feature Selection Approach for Enhancing the Effectiveness of Similarity Searching in Ligand-based Virtual Screening

2020 ◽  
Vol 15 (5) ◽  
pp. 431-444
Author(s):  
Fouaz Berrhail ◽  
Hacene Belhadef
Keyword(s):  
Genetic Algorithm ◽  
Virtual Screening ◽  
Similarity Measures ◽  
Similarity Searching ◽  
Features Selection ◽  
Screening Process ◽  
Selection Approach ◽  
Feature Selection Approach ◽  
The Individual ◽  
Screening Approaches

Background: In the last years, similarity searching has gained wide popularity as a method for performing Ligand-Based Virtual Screening (LBVS). This screening technique functions by making a comparison of the target compound’s features with that of each compound in the database of compounds. It is well known that none of the individual similarity measures could provide the best performances each time pertaining to an active compound structure, representing all types of activity classes. In the literature, we find several techniques and strategies that have been proposed to improve the overall effectiveness of ligand-based virtual screening approaches. Objective: In this work, our main objective is to propose a features selection approach based on genetic algorithm (FSGASS) to improve similarity searching pertaining to ligand-based virtual screening. Methods: Our contribution allows us to identify the most important and relevant characteristics of chemical compounds and to minimize their number in their representations. This will allow the reduction of features space, the elimination of redundancy, the reduction of training execution time, and the increase of the performance of the screening process. Results: The obtained results demonstrate superiority in the performance compared with these obtained with Tanimoto coefficient, which is considered as the most widely coefficient to quantify the similarity in the domain of LBVS. Conclusion: Our results show that significant improvements can be obtained by using molecular similarity research methods at the basis of features selection.

A framework for offline signature verification system: Best features selection approach

2020 ◽  
Vol 139 ◽  
pp. 50-59 ◽  
Author(s):  
Muhammad Sharif ◽  
Muhammad Attique Khan ◽  
Muhammad Faisal ◽  
Mussarat Yasmin ◽  
Steven Lawrence Fernandes
Keyword(s):  
Signature Verification ◽  
Features Selection ◽  
Verification System ◽  
Offline Signature Verification ◽  
Selection Approach
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