A novel extraction algorithm of video fingerprint based on sparse coding

Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach

BioMed Research International ◽

10.1155/2019/2436924 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Xingjian Chen ◽

Xuejiao Hu ◽

Wenxin Yi ◽

Xiang Zou ◽

Wei Xue

Keyword(s):

Subcellular Localization ◽

Sparse Coding ◽

Sequence Data ◽

Support Vector ◽

Data Sets ◽

Protein Subcellular Localization ◽

Jackknife Test ◽

Apoptosis Protein ◽

Extraction Algorithm ◽

Protein Subcellular Localization Prediction

The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming that they cannot catch up with the growth rate of sequence data anymore. In order to improve the prediction accuracy of apoptosis protein subcellular localization, we proposed a sparse coding method combined with traditional feature extraction algorithm to complete the sparse representation of apoptosis protein sequences, using multilayer pooling based on different sizes of dictionaries to integrate the processed features, as well as oversampling approach to decrease the influences caused by unbalanced data sets. Then the extracted features were input to a support vector machine to predict the subcellular localization of the apoptosis protein. The experiment results obtained by Jackknife test on two benchmark data sets indicate that our method can significantly improve the accuracy of the apoptosis protein subcellular localization prediction.

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An Efficient Maximum Power Extraction Algorithm for Wind Energy Conversion System Using Model Predictive Control

International Journal on Energy Conversion (IRECON) ◽

10.15866/irecon.v7i3.17403 ◽

2019 ◽

Vol 7 (3) ◽

pp. 93

Author(s):

Apoorva Srivastava ◽

Ram Sharan Bajpai

Keyword(s):

Model Predictive Control ◽

Wind Energy ◽

Energy Conversion ◽

Predictive Control ◽

Wind Energy Conversion System ◽

Power Extraction ◽

Wind Energy Conversion ◽

Extraction Algorithm ◽

Conversion System ◽

Energy Conversion System

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GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v5i2.10347 ◽

2020 ◽

Vol 5 (2) ◽

pp. 609

Author(s):

Segun Aina ◽

Kofoworola V. Sholesi ◽

Aderonke R. Lawal ◽

Samuel D. Okegbile ◽

Adeniran I. Oluwaranti

Keyword(s):

Support Vector Machine ◽

Gesture Recognition ◽

Recognition Rate ◽

Recognition Task ◽

Recognition System ◽

Human Interaction ◽

Support Vector ◽

System A ◽

Extraction Algorithm ◽

Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

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