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Lakshminarayanan, R.
- Multilevel Modeling in Heterogeneous Wireless Sensor Network for Improved Energy Efficiency
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Authors
Affiliations
1 Department of Computer Science and Engineering, Gnanamani College Technology, IN
1 Department of Computer Science and Engineering, Gnanamani College Technology, IN
Source
ICTACT Journal on Communication Technology, Vol 10, No 1 (2019), Pagination: 1958-1963Abstract
In this paper, two primary and secondary parameters are proposed for a heterogeneous network model (HNM). In such a model, the heterogeneous network describes nodes with a finite energy level based on the parameter value. It evaluates the performance of the proposed HNM protocol and further tests the HNM multilevel protocol. For a finite heterogeneity level MHNM protocol is denoted by MLHNM-n. The secondary parameter determines the total nodes at each level of heterogeneity. The proposed protocol provides two parameters for determining the energy and density of the cluster heads of residual nodes. The proposed protocol will be tested by six level networks. The energy dissipation decreases and results in an increase in network life.Keywords
Network Lifetime, Multi Heterogeneity, Number of Rounds, Clustering.
References
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- Classification of Brain Tumor using Bees Swarm Optimisation
Abstract Views :242 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Gnanamani College of Technology, IN
2 Department of Computer Science and Engineering, Sri Satya Sai University of Technology and Medical Sciences, IN
1 Department of Computer Science and Engineering, Gnanamani College of Technology, IN
2 Department of Computer Science and Engineering, Sri Satya Sai University of Technology and Medical Sciences, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 1 (2019), Pagination: 2025-2030Abstract
Nowadays, processing the medical image is a most significant diagnostic process. Usually RMI is used to detect the presence of and type of tumor. The following process is very complicated in the brain tumor classification. The treatment of medical images, such as image segmentation, image extraction, and image classification, takes various steps. Various types of properties such as intensity, forms and texture-based features are extracted from a segmented MRI image. The feature selection approach is employed to select a small subset of MRI image features that minimize redundancy and maximize target-related pertinence. This article uses the Bees Swarm Optimization (BSO) for the selection and the Neural Network Classifier to classify the type of tumor in present brain MRI images, and then takes online MRI images which contain brain tumor, then a machine-learning model.Keywords
Neural Network, ACO, Feature Extraction, Classification, MRI.References
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