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Sameena, S.
- Distributed Load Balancing Algorithm for Wireless Sensor Network
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Authors
Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Tirupati, IN
2 Department of Computer Science and Engineering, Universal College of Engineering and Technology, Vallioor, IN
3 Department of Computer Science, The Madurai Diraviyam Thayumanavar Hindu College, IN
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Tirupati, IN
2 Department of Computer Science and Engineering, Universal College of Engineering and Technology, Vallioor, IN
3 Department of Computer Science, The Madurai Diraviyam Thayumanavar Hindu College, IN
Source
ICTACT Journal on Communication Technology, Vol 9, No 4 (2018), Pagination: 1908-1912Abstract
A Wireless Sensor Network (WSN) comprises of spatially scattered autonomous sensors to screen physical or natural conditions and to amiably go their information through the system to a Base Station. Grouping is a basic assignment in Wireless Sensor Networks for vitality effectiveness and system quality. Grouping through Central Processing Unit in remote sensor systems is outstanding and being used for quite a while. In this paper, we propose a few procedures that balance the vitality utilization of these hubs and guarantee greatest system lifetime by adjusting the activity stack as similarly as could be expected under the circumstances. Directly grouping through dispersed strategies is being produced for conveying with the issues like system lifetime and vitality. In our work, we connected both concentrated and conveyed k-means clustering calculation in system test system. K-means is a model based algorithm that surrogates between two noteworthy advances, passing on perceptions to groups and processing cluster focuses until the point when a ceasing standard is satisfied. Improved results are accomplished and related which demonstrate that conveyed clustering is compelling than brought together grouping.Keywords
Wireless Sensor Network, Clustering, K-Means, Network Stability.
References
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- P.A. Forero, A. Cano and G.B. Giannakis, “Distributed Feature-based Modulation Classification using Wireless Sensor Networks”, Proceedings of IEEE International Conference on Military Communications, pp. 1-5, 2008.
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- Seema Shivapur, Suvarna G. Kanakaraddi and A.K. Chikaraddi, “Load Balancing Techniques in Wireless Sensor Networks: A Comparative Study”, International Journal of Emerging Technology in Computer Science and Electronics, Vol. 14, No. 2, pp. 21-28, 2015.
- Dipak Wajgi and Nileshsingh V. Thakur, “Load Balancing based Approach to Improve Lifetime of Wireless Sensor Network”, International Journal of Wireless and Mobile Networks, Vol. 4, No. 4, pp. 12-17, 2012.
- Roshani Talmale, M. Nirupama Bhat and Nita Thakare, “Analysis of Load Balancing and Fault Tolerant Routing Protocol for Wireless Sensor Network”, Helix, Vol. 8, No. 5, pp. 3946-3949, 2018.
- Cervical Cancer Detection and Classification by using Effectual Integration of Directional Gabor Texture Feature Extraction and Hybrid Kernel Based Support Vector Classification
Abstract Views :197 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Applications, The MDT Hindu College, IN
3 Department of Computer Science and Engineering, Universal College of Engineering and Technology, IN
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Applications, The MDT Hindu College, IN
3 Department of Computer Science and Engineering, Universal College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 3 (2019), Pagination: 1935-1939Abstract
Planning of invigorating representation is a troublesome and testing process because of the unpredictability of the images and absence of models of the life systems that thoroughly catches the reasonable expressions in each structure. Cervical malignant growth is one of the noteworthy reasons for death among different kinds of the diseases in women around the world. Genuine and auspicious determination can keep the life to some dimension. Therefore, we have proposed a computerized dependable framework for the analysis of the cervical malignancy utilizing surface highlights and machine learning calculation in Pap smear images, it is extremely advantageous to anticipate disease, likewise expands the dependability of the determination. Proposed framework is a multi-organize framework for cell nucleus extraction and disease finding. To begin with, clamor expulsion is performed in the preprocessing venture on the Pap smear images. Exterior highlights are separated from these demand free Pap smear images. Next period of the proposed framework is classification that depends on these separated highlights, SVM classification is utilized. Over 94% exactness is accomplished by the classification stage, demonstrated that the proposed calculation precision is great at recognizing the disease in the Pap smear images.Keywords
Cervical Cancer, Feature Extraction, DGTF, Classification, Hybrid Kernel SVM.References
- Hayder K. Fatlawi, “Enhanced Classification Model for Cervical Cancer Dataset based on Cost Sensitive Classifier”, International Journal of Computer Techniques, Vol. 4, No. 4, pp. 1-8, 2017.
- S. Athinarayanan, M.V. Srinath and R. Kavitha, “Computer Aided Diagnosis for Detection and Stage Identification of Cervical Cancer by using Pap Smear Screening Test Images”, ICTACT Journal on Image and Video Processing, Vol. 6, No. 4, pp. 1244-1251, 2016.
- M. Martinez, L.E. Sucar, H.G. Acosta and N. Cruz, “Bayesian Model Combination and Its Application to Cervical Cancer Detection”, Proceedings of Ibero-American Conference on Artificial Intelligence, pp. 622-631, 2006.
- G. Sun, S. Li, Y. Cao and F. Lang, “Cervical Cancer Diagnosis based on Random Forest”, International Journal of Performability Engineering, Vol. 13, No. 4, pp. 446-457, 2017.
- S. Athinarayanan and M.V. Srinath, “Classification of Cervical Cancer Cells in PAP SMEA Screening Test”, ICTACT Journal on Image and Video Processing, Vol. 6, No. 4, pp. 1234-1238, 2016.
- S. Aswathy, M.A. Quereshi, B. Kurian and K. Leelamoni, “Cervical Cancer Screening: Current Knowledge and Practice among Women in a Rural Population of Kerala, India”, Indian Journal of Medical Research, Vol. 136, No. 2, pp. 205-210, 2012.
- M. Anousouya Devi, S. Ravi and J.V.S. Punitha, “Detection of Cervical Cancer using the Image Classification Algorithms”, International Journal of Circuit Theory and Applications, Vol. 9, No. 3, pp. 153-166, 2016.
- G. Peng, B. Koyel and R.S. Joe, “Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis with Fusion-Based Classification”, IEEE Journal of Biomedical and Health Informatics, Vol. 20, No. 6, pp. 1595-1607, 2016.
- K. Thangavel, P.P. Jaganathan and P.O. Easmi, “Data Mining Approach to Cervical Cancer Patients Analysis using Clustering Technique”, Asian Journal of Information Technology, Vol. 5, No. 4, pp. 413-417, 2006.
- S. Athinarayanan and M.V. Srinath, “Robust and Efficient Diagnosis of Cervical Cancer in Pap Smear Images using Texture Features with RBF and Kernel SVM Classification”, ARPN Journal of Engineering and Applied Sciences, Vol. 11, No. 7, pp. 4504-4515, 2016.
- S. Athinarayanan and M.V. Srinath, “Severity Analysis of Cervical Cancer in PAP SMEAR Images by using EEETCM, ERSTCM and CFE Method based Texture Features and Hybrid Kernel Based Support Vector Machine Classifier”, International Journal of Advanced Research, Vol. 4, No. 11, pp. 2751-2764, 2016.
- S. Athinarayanan and M.V. Srinath, “Effective Detection of Cervical Cancer During Pap Smear Screening Test”, Elysium Journal of Engineering Research and Management, Vol. 1, No. 1, pp. 10-14, 2014.
- S. Athinarayanan and M.V. Srinath, “Multi Class Cervical Cancer Classification by using ERSTCM, EMSD and CFE Methods Based Texture Features and Fuzzy Logic Based Hybrid Kernel Support Vector Machine Classifier”, IOSR Journal of Computer Engineering, Vol. 19, No. 1, pp. 23-34, 2017.