RVM-based human action classification through Gabor and Haar feature extraction

Maheswari, S. Uma; Rani, P. Arockia Jansi

doi:10.1504/ijcvr.2016.073758

Cited by 2 publications

(1 citation statement)

References 23 publications

(13 reference statements)

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“…The use of BLE sensors and AI calculations is critical in self-observing diabetes mellitus in medical care. AI is a major influence in the medical industry by giving the ability to express causes to medical experts to investigate, analyze clinical information [58][59][60][61][62][63][64][65]. Additionally, savvy medical services frameworks are giving constant clinical consideration to penniless patients [66][67].…”

Section: Diabeticsmentioning

confidence: 99%

A Literature Review on Prediction of Chronic Diseases using Machine Learning Techniques

Siddegowda

Devi

2022

IJMTS

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Background/ Purpose: Reviewing of the various work and literature in the proposed areas will help in developing a strong foundation of the domain on which the research is planned. The reason forth for the literature review is to become familiar in the health care domain. Since the area selected is the health care domain, the recent literature review is carried out as it is very important. Objective: A strong background on health care domain is developed and a new problem which is not addressed is discussed. The gaps in the research area are identified. A new solution for solving the problem is designed and developed. Design/Methodology: This work has adapted secondary source of data which is mainly journals, articles and review comments. The relevant literature is selected and a detailed study is conducted. This has helped in drafting the problem statement. Findings/Results: The finding and drawbacks of all the recent work are well studied. The reason for the gap is also well studied and the results of each work are also well analyzed. Research Limitations: A detailed study done on the chronic diseases and its impact has helped to open up the importance of studying about comorbid diseases. The limitations of various machine learning algorithms are also studied. Originality/Value: This paper aims at studying the relevant existing literature that includes research journals, conference papers, technical book chapter and few web sources. All the papers selected were relevant to the proposed work and all papers are recent and from well reputed publisher. The papers are cited by many authors. Paper Type: Literature review paper is carried out on scientific papers, especially from well indexed services.

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Section: Diabeticsmentioning

confidence: 99%

A Literature Review on Prediction of Chronic Diseases using Machine Learning Techniques

Siddegowda

Devi

2022

IJMTS

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Human Action Recognition Through Fused Feature Vector and Kernel Discriminant Analysis

Raju¹

2020

JMCMS

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Aimed at the problems of Intensity, Contour and orientation information, a Human Action Recognition (HAR) method based on Fused Feature Vector (FFV) is proposed in this paper. The FFV is constructed based on three different features such as Intensity features, Gradient features, and Orientation features. These three set of features are obtained through three different feature extraction methods based on Gaussian Filter, Gradient Filter and Gabor filter. Further to ensure optimal discriminant subspace, Kernel Discriminant Analysis is employed as a dimensionality reduction technique. Given the FFV of each action image, Support Vector Machine (SVM) is employed for classification. The proposed recognition model is evaluated systematically on the three public datasets such as KTH dataset, Weizmann dataset and the challenging UCF YouTube action dataset. Experimental results prove that our method outperforms the conventional approaches in terms of recognition accuracy.

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RVM-based human action classification through Gabor and Haar feature extraction

Cited by 2 publications

References 23 publications

A Literature Review on Prediction of Chronic Diseases using Machine Learning Techniques

A Literature Review on Prediction of Chronic Diseases using Machine Learning Techniques

Human Action Recognition Through Fused Feature Vector and Kernel Discriminant Analysis

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