Zobrazeno 1 - 10 of 78 pro vyhledávání: '"V. Seenivasagam"'
1
Akademický článek
REVIEW OF HEART DISEASE PREDICTION SYSTEM USING DATA MINING AND HYBRID INTELLIGENT TECHNIQUES
Autor: R. Chitra, V. Seenivasagam
Publikováno v: ICTACT Journal on Soft Computing, Vol 3, Iss 4, Pp 605-609 (2013)
The Healthcare industry generally clinical diagnosis is done mostly by doctor’s expertise and experience. Computer Aided Decision Support System plays a major role in medical field. With the growing research on heart disease predicting system, it h
Externí odkaz: https://doaj.org/article/14e6aa6c7c374a71b00b84d35f825906
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2
Akademický článek
Comparative Study on Various Authentication Protocols in Wireless Sensor Networks
Autor: S. Raja Rajeswari, V. Seenivasagam
Publikováno v: The Scientific World Journal, Vol 2016 (2016)
Wireless sensor networks (WSNs) consist of lightweight devices with low cost, low power, and short-ranged wireless communication. The sensors can communicate with each other to form a network. In WSNs, broadcast transmission is widely used along with
Externí odkaz: https://doaj.org/article/f719a6616b1b4cb5bb686198942fe6cd
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4
Automatic segmentation and classification of liver tumor from CT image using feature difference and SVM based classifier-soft computing technique
Autor: R. Manjula Devi, V. Seenivasagam
Publikováno v: Soft Computing. 24:18591-18598
The liver is essential for endurance and to carry out a large number of significant functions, including manufacture of indispensable proteins, and metabolism of fats and carbohydrates. The examination of CT might be employed for planning and managin
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::619b69d2aced968efb18632ac3cfe9ab
https://doi.org/10.1007/s00500-020-05094-1
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5
RETRACTED ARTICLE: Classification of lung cancer stages with machine learning over big data healthcare framework
Autor: V. Seenivasagam, R. Sujitha
Publikováno v: Journal of Ambient Intelligence and Humanized Computing. 12:5639-5649
With the fast pace in collating big data healthcare framework and accurate prediction in detection of lung cancer at early stages, machine learning gives the best of both worlds. In this paper, a streamlining of machine learning algorithms together w
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0de177fb8396d687c94239ff6cc44736
https://doi.org/10.1007/s12652-020-02071-2
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7
Hybrid two-dimensional dual tree—biorthogonal wavelet transform and discrete wavelet transform with fuzzy inference filter for robust remote sensing image compression
Autor: R. Madhumitha, V. Seenivasagam, S. Sudhakar Ilango
Publikováno v: Cluster Computing. 22:13473-13486
Image compression plays a crucial role in digital image processing, it is also very important for efficient transmission and storage of images. In particular, remote sensing makes it possible to collect image data on dangerous or inaccessible areas (
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c725c5393ce834c7e950d06557113131
https://doi.org/10.1007/s10586-018-1982-9
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8
Color image segmentation using feedforward neural networks with FCM
Autor: S. Arumugadevi, V. Seenivasagam
Publikováno v: International Journal of Automation and Computing. 13:491-500
This paper proposes a hybrid technique for color image segmentation. First an input image is converted to the image of CIE L*a*b* color space. The color features "a" and "b" of CIE L*a*b* are then fed into fuzzy C-means (FCM) clustering which is an u
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fe34b0b62353b425922d1e016f0ffd97
https://doi.org/10.1007/s11633-016-0975-5
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10
Extraction with Map-Reduce Framework and Correlation-based Feature Selection in Lung Cancer Towards Big Data
Autor: R Sujitha and V Seenivasagam
Publikováno v: Indian Journal of Science and Technology. 13:805-816q
Background/objectives: To extract nucleus and cytoplasm that intend to optimize features in high-dimensional images such as all types of raw sputum cells. To calculate following features efficiently: Area, Perimeter, Intensity, NC Ratio, and Circular
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ac7537adedaf47fad8dcd7ccb9b543da
https://doi.org/10.17485/ijst/2020/v13i07/149846
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