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Muthu Subash Kavitha

dblp:214/3598 · also Muthusubash Kavitha · DBLP profile ↗
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10ranked-venue papers
1as first author
6since 2021 · last 2022
0000-0002-1676-561XORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 9 · 6 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Video understanding and tracking · 75% Deep learning architectures and training · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › convolutional neural network › convolutional neural network architecture
3d convolutional neural network		 0.612022
Weakly-Supervised Action Localization, and Action Recognition Using Global-Local Attention of 3D CNN · Int. J. Comput. Vis. 2022
Computer vision › Video understanding and tracking
action detection		 0.612022
Weakly-Supervised Action Localization, and Action Recognition Using Global-Local Attention of 3D CNN · Int. J. Comput. Vis. 2022
Computer vision › Video understanding and tracking
action recognition		 0.612022
Weakly-Supervised Action Localization, and Action Recognition Using Global-Local Attention of 3D CNN · Int. J. Comput. Vis. 2022
Computer vision › Video understanding and tracking › action detection › temporal action localization
weakly-supervised temporal action localization		 0.612022
Weakly-Supervised Action Localization, and Action Recognition Using Global-Local Attention of 3D CNN · Int. J. Comput. Vis. 2022

Methods — techniques the papers use, named apart from their topics

global-local attention · 0.63d convolutional neural network · 0.6
YearPublicationVenuePosition
2022 Weakly-Supervised Action Localization, and Action Recognition Using Global-Local Attention of 3D CNN
Novanto Yudistira, Muthu Subash Kavitha, Takio Kurita 0001
 Int. J. Comput. Vis.2
2022 Correction to: Extensive framework based on novel convolutional and variational autoencoder based on maximization of mutual information for anomaly detection
Qien Yu, Muthu Subash Kavitha, Takio Kurita 0001
 Neural Comput. Appl.2
2021 Mixture of experts with convolutional and variational autoencoders for anomaly detection
Qien Yu, Muthu Subash Kavitha, Takio Kurita 0001
 Appl. Intell.2
2021 Autoencoder framework based on orthogonal projection constraints improves anomalies detection
Qien Yu, Muthu Subash Kavitha, Takio Kurita 0001
 Neurocomputing2
2021 Extensive framework based on novel convolutional and variational autoencoder based on maximization of mutual information for anomaly detection
Qien Yu, Muthu Subash Kavitha, Takio Kurita 0001
 Neural Comput. Appl.2
2021 Regularizer based on Euler characteristic for retinal blood vessel segmentation
Lukman Hakim, Muthu Subash Kavitha, Novanto Yudistira, Takio Kurita 0001
 Pattern Recognit. Lett.2
2019 Hilbert Vector Convolutional Neural Network: 2D Neural Network on 1D Data
Nasrulloh R. B. S. Loka, Muthu Subash Kavitha, Takio Kurita 0001
 ICANN (1)2
2019 U-Net with Graph Based Smoothing Regularizer for Small Vessel Segmentation on Fundus Image
Lukman Hakim, Novanto Yudistira, Muthu Subash Kavitha, Takio Kurita 0001
 ICONIP (5)3
2019 Learning with Incomplete Labels for Multi-label Image Annotation Using CNN and Restricted Boltzmann Machines
Jonathan Mojoo, Muthu Subash Kavitha, Jun'ichi Miyao, Takio Kurita 0001
 ICONIP (2)3
2012 Automatic assessment of mandibular bone using support vector machine for the diagnosis of osteoporosis
abstract
This study aimed to realize and compare the effectiveness of cortical and trabecular bone measures in a computer-aided system based on support vector machine (SVM) for identifying postmenopausal women with low BMD or osteoporosis. Dental panoramic radiographs of 69 postmenopausal women, as well as bone mineral density (BMD) assessments at the lumbar spine and the femoral neck were used. Average width and variance of continuous measurements of mandibular cortical bone and distribution of average length and angle (direction) of trabecular bone segments on panoramic radiographs were measured by computer-aided systems and used as inputs. The accuracy of the cortical measures using average width and variance was 87% and of the trabecular measures using average length and angle was 65% with RBF kernel-SVM method for diagnosing women with low BMD at the lumbar spine. Likewise, the accuracy of combined cortical and trabecular bone measures was 88%. Our results suggest that the cortical bone measurement is more appropriate than the trabecular bone measurement for triage screening of osteoporosis.
Muthu Subash Kavitha, Takio Kurita 0001, Akira Asano, Akira Taguchi
 SMC1