Pixel classification in land scape images has been found to be challenging. The problem becomes more challenging in forest images due to the similar spectral features of pixels situated close to each other. Geographically weighted variables have been employed to classify the two different species namely Cryptomeria japonica (Japanese Cedar or Sugi) and Chamaecyparisobtusa (Japanese Cypress or Hinoki) and one mixed forest class. Previous attempts have shown reasonable improvement in this task using Genetic Algorithm supported Neural Network over other traditional approaches. Motivated by this, a NSGA-II supported Neural Network (NN-NSGA-II) classifier is proposed. The proposed model has been compared with GA-NN (ANN trained with Genetic Algorithm with a single objective function) classifiers in terms of confusion matrix based performance metrics such as accuracy, precision, recall and F-Measure. Experimental results have indicated that the proposed NN-NSGA-II model is superior to the GA-NN model to a greater extent. © 2017 IEEE.

SOUMYA SEN

GENERAL

S CHATTERJEE

S BANERJEE

K G MAZUMDAR

S BOSE

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Non-dominated sorting genetic algorithm — II supported neural network in classifying forest types

2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2017

Image segmentation is one of the major research domains in several applications including retinal blood vessel segmentation, which is an active research area. Vasculature structure analysis is an interesting and effective method for disease detection and analysis. In this work, a gradient-based blood vessel segmentation technique is proposed to assist retinal image analysis and to extract the retinal vessels. Edge detection is considered one of the major steps in the present work to characterize the boundaries. Itis used to reduce the unusual information and to preserve the necessary structural information. Various filters are constructed to gradient computation and edge detection. In the current work, a new method along with a new filter (kernel) has been proposed to detect edges efficiently. The results are compared with some well-known kernels. The proposed approach achieved Pratt Score 99.1536 value, which outperformed the corresponding values obtained by the other standard edge detectors, namely Sobel, Prewitt, Canny, and Robert's. © 2017 IEEE.

2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, UPCON 2017

Gradient approximation in retinal blood vessel segmentation

Predicting soil moisture quantity could directly help the people engaged in sustainable agriculture and associated socio-economic structures. Recently researchers have engaged traditional and machine learning based models to predict soil moisture quantity. In the current study a modified Flower Pollination Algorithm (MFPA) has been employed to train Artificial Neural Network (ANN) to predict soil moisture quantity. The proposed method is compared with well known PSO (Particle Swarm optimization) supported ANN and Cuckoo Search (CS) supported ANN along with MLP-FFN classifier. The stability of the proposed model in presence of varying weather conditions has been established by performing a stability analysis using data level perturbation. Experimental results have indicated that NN-MFPA achieved an average RMSE of 0.0019 and outperformed other models. The ingenuity of the proposed model is further established by performing Wilcoxon rank test with 5% level of significance. © 2018 Elsevier Inc.

Sustainable Computing: Informatics and Systems

Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications

This work describes an method for biomedical image enhancement using modified Cuckoo Search Algorithm with some Morphological Operation. In recent years, various digital image processing techniques are developed. Computer Vision, machine interfaces, manufacturing industry, data compression for storage, vehicle tracking and many more are some of the domains of digital image processing application. In most of the cases, digital biomedical images contains various types of noise, artifacts etc. and are not useful for direct applications. Before using it in any process, the input image has to be gone through some preprocessing stages; such preprocessing is generally called as image enhancement. In this work, a new technique has been proposed to enhance biomedical images using modified cuckoo search algorithm and morphological operation. Presence of noise and other unwanted objects generates distortion in an image and it will affect the ultimate result of the process. In case of biomedical images, accuracy of the results is very important. It may also decrease the discernibility of many features inside the images. It can affect the classification accuracy. In this work, this issue has been targeted and improved by obtaining better contrast value after converting the color image into grayscale image. The basic property of the cuckoo search algorithm is that the amplitudes of its components are capable to objectively describe the contribution of the gray levels to the formation of image information for the best contrast value of a digital image. The proposed method modified the conventional cuckoo search method by employing the McCulloch's method for levy flight generation. After computing the best contrast value, morphological operation has been applied. In morphological operation based phase, the intensity parameters are tuned for quality enhancement. Experimental results illustrate the effectiveness of this work. © 2017 IEEE.

Journal	Data powered by Typeset2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2017
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open Access	No