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Balakrishnan, S.
- An Efficient and Complete Automatic System for Detecting Lung Module
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Authors
Affiliations
1 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
1 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 11, No 26 (2018), Pagination: 1-5Abstract
Objectives: To make a fully automated algorithm that is based on simple and quick steps, which produces consistent output for the same inputs. Methods/Statistical Analysis: For thorax and lung segmentation, region growing based method is used to segment the region of interest. The missing parts of the lungs are reconstructed using morphological operations. After that, nodules are detected based on the features of the reconstructed image. Artificial Neural Network has been used for classifying the images. Findings: An aggregate of 100 pictures with determination of 512 × 512 pixels with eight bits for every shading channel are caught. 90% affectability was obtained with 0.05 false positives for each picture. Application/Improvements: This framework distinguishes the phase of lung malignancy. The outcomes demonstrate that the tumors are of various measurements. By estimating the measurements of the tumor the lung disease stage can be recognized precisely utilizing the proposed technique. The outcomes indicate great potential for lung growth identification at beginning time.Full Text
References
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- Integrated Anthropometric Approach for Ceaseless Authentication
Abstract Views :163 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
2 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
1 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
2 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 11, No 26 (2018), Pagination: 1-4Abstract
Objectives: To model a novel ceaseless client validation method to authorize the client regardless of their body position before the capturing system. The system ceaselessly validates the client with their various soft anthropometric parameters such as (e.g. wearables and skin) in addition to hard biometrics. Methods/Statistical Analysis: The proposed system mechanically stores in the soft anthropometric parameters each time the client logs in and integrate the anthropometric parametric features along with the conventional face traits for verification thus fusing the combination of hard and soft biometric attributes to attest a client ceaselessly. The methodology comprises of various modes such as initialization, validation and regeneration. Findings: Various samples of facial colour features and user’s cloth colour features are used as soft biometrics in this system for authorization. The experimental results of AR show the extensive improvement over the existing methods. Application/Improvements: This methodology eliminates the challenges faced in face recognition due to different expressions and postures, lighting effects. Thus the key discriminating features are authenticated using hard and soft biometrics thus making it a high secure technology.Full Text
References
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- Solami EA, Boyd C, Clark A, Ahmed I. User-representative feature selection for keystroke dynamics. 5th International Conference on Network and System Security. 2011; p. 229–33. Crossref.
- Sujatha T, Sangeetha T, Balakrishnan S, Susila N. Honey/sugar template based on biometric protection using bloom filter. International Journal of Pure and Applied Mathematics. 2018; 119(12): 1143–55.