ICTACT Journal on Soft Computing

K. Saminathan ¹, T. Chakravarthy ², M. Chithra Devi ³

Affiliations
1 Department of Computer Science and Engineering, Ponnaiyah Ramajayam Institute of Science and Technology University, IN
2 Department of Computer Science, A. Veeriya Vandayar Memorial Sri Pushpam College, IN
3 Department of Software Engineering, Periyar Maniammai University, IN

Abstract

Ensuring security biometrically is essential in most of the authentication and identification scenario. Recognition based on iris patterns is a thrust area of research cause to provide reliable, simple and rapid identification system. Machine learning classification algorithm of support vector machine [SVM] is applied in this work for personal identification. The profuse as well as unique patterns of iris are acquired and stored in the form of matrix template which contains 4800 elements for each iris. The row vectors of 2400 elements are passed as inputs to SVM classifier. The SVM generates separate classes for each user and performs matching based on the template's unique spectral features of iris. The experimental results of this proposed work illustrate a better performance of 98.5% compared to the existing methods such as hamming distance, local binary pattern and various kernels of SVM. The popular CASIA (Chinese Academy of Sciences - Institute of Automation) iris database with fifty users' eye image samples are experimented to prove, that the least Square method of Quadratic kernel based SVM is comparatively better with minimal true rejection rate.

Keywords

Iris Preprocessing, Iris Template, Quadratic Kernel, Support Vector Machine, Hamming, Local Binary Pattern.

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B. Sowmiya ¹, K. Saminathan ¹, M.Chithra Devi ²

Affiliations
1 PG and Research Department of Computer Science, A.V.V.M. Sri Pushpam College, Affiliated to Bharathidasan University, India., IN
2 Department of Computer Science, Queens College of Arts and Science for Women, Affiliated to Bharathidasan University, India., IN

Abstract

Paddy is a major food crop serving more than half the population of people in the world. It is inevitable to improve the quantity and quality of food crop with the growing population. Different factors including soil fertility, water availability, erratic climate variations, diseases, and pests, have an impact on paddy crop yield. It is crucial to identify the root cause for the reduction in yield of paddy. Early disease diagnosis prevents the plants from getting worst through its consecutive stage. The concern with manually diagnosing plant leaf diseases with the naked eye is that the results can be less accurate and even unreliable. Automatic disease diagnosis eliminates the need for experts and provides accurate results. This paper will assist the farmers to identify the leaf diseases automatically with the aid of Convolutional Neural Networks. This research includes paddy leaf disease categories: bacterial blight, blast, tungro, brown spot and healthy leaves. The dataset contains 800 images, 160 images from each of the five categories. Images are resized to 256 * 256 pixels and normalized. The network architecture created with convolutional, maxpooling, flatten and dense layers. The Dataset is divided into training and validation set in 70:30 ratios and model is trained with 20 epochs of batch size 16. The novelty of the study is the implementation of extended Huber loss function for minimizing the loss. Furthermore, it is cross compared with existing loss functions. The Proposed model has achieved 96.63% training accuracy and 86.61% validation accuracy with 5 classes. Performance of model is evaluated with confusion matrix with precision, recall, F1-score and support as parameters.

Keywords

Paddy Disease Detection, Preprocessing, Classification, Huber Loss, Convolutional Neural Network.

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