Informatics Publishing Limited

Soman, K. P.

• Innovative Feature Sets for Machine Learning based Telugu Character Recognition

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Authors

J. Jyothi ¹, K. Manjusha ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641 112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 8, No 24 (2015), Pagination: 

Abstract

In this Information age, all sources of information like historic documents, books, manuscripts are digitized and are available all over the world through internet in the form of scanned copies. These scanned images contain valuable information which are available either in colour or black and white for pleasant viewing. Optical Character Recognition (OCR) technology provides facility to search for keywords in these digital copies. In this paper, new method in which building an OCR system for Telugu language script; mainly focussing on the character recognition module. Features extracted through Discrete Wavelet Transform (DWT), Projection Profile (PP) and Singular Value Decomposition (SVD) is evaluated using k-Nearest Neighbour (k-NN) and Support Vector Machine (SVM) classifiers. Most productive results are obtained from the DWT features with SVM classifiers.

Keywords

Discrete Wavelet Transform, K-nearest Neighbour, Optical Character Recognition, Singular Value Decomposition, Support Vector Machine, Telugu Character Recognition.

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• ADMM based Hyperspectral Image Classification Improved by Denoising using Legendre Fenchel Transformation

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Authors

C. Aswathy ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641 112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 8, No 24 (2015), Pagination: 

Abstract

This paper discusses about a sparsity based algorithm used for Hyperspectral Image (HSI) classification where the test pixel vectors are sparsely represented as the linear combination of a few number of training samples from a well-organised dictionary matrix. The sparse vector is obtained using Basis Pursuit (BP) which is a constrained l4 minimization problem. This problem is solved by using a simple and powerful iterative algorithm known as Alternating Direction Method of Multipliers (ADMM) which significantly reduces the computational complexity of the problem and thereby speeds up the convergence. The classification accuracy is considerably improved by including efficient preprocessing techniques to remove the unwanted information (noise) present in Hyperspectral images. This paper uses a fast and reliable denoising technique based on Legendre Fenchel Transformation (LFT) to effectively denoise each band of HSI prior to ADMM based classification (proposed method). A comparison of proposed technique with one of the convex optimization tools namely, CVX is given to exhibit the fast convergence of the former method. The experiment is performed on standard Indian Pines dataset captured using AVIRIS sensor. The potential of the proposed method is illustrated by analyzing the classification indices obtained with and without applying any preprocessing methods. With only 10% training set, an overall accuracy of 96.76% is obtained for the proposed method at a much faster rate compared to computation time taken by CVX solver.

Keywords

Alternating Direction Method of Multipliers, Basis Pursuit, Classification, Hyperspectral Denoising, Legendre Fenchel Transformation

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• Comparative Analysis on Aerial Image Enhancement Techniques

Abstract Views :224  |  PDF Views:0

Authors

S. Sabarinath ¹, K. P. Soman ²

Affiliations
1 Remote Sensing and Wireless Sensor Networks, Centre for Excellence in Computational Engineering and Networking, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore - 641 112, Tamil Nadu, IN
2 Centre for Excellence in Computational Engineering and Networking, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore - 641 112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 8, No 24 (2015), Pagination: 

Abstract

In this paper a comparative analysis on various aerial image enhancement techniques are carried out with that of wavelet based enhancement technique based on certain quality assessments. Paper illustrates wavelet based enhancement algorithm performed in an intelligent manner and the obtained result is compared with other conventional enhancement techniques. From the comparative analysis performed using mean square error, peak signal to noise ratio and structured similarity index measurement the wavelet based enhancement technique is found to give better enhancement result. Since the algorithms proposed are applicable to color images this work can be further extended to other applications like feature extractions of roads and buildings, medical image segmentation and video streaming.

Keywords

Aerial Image, Color Restoration, Enhancement, Histogram Equalization, Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity, Wavelet.

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• Randomized Kernel Approach for Named Entity Recognition in Tamil

Abstract Views :163  |  PDF Views:0

Authors

N. Abinaya ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641 112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 8, No 24 (2015), Pagination: 

Abstract

In this paper, we present a new approach for Named Entity Recognition (NER) in Tamil language using Random Kitchen Sink algorithm. Named Entity recognition is the process of identification of Named Entities (NEs) from the text. It involves the identifying and classifying predefined categories such as person, location, organization etc. A lot of work has been done in the field of Named Entity Recognition for English language and Indian languages using various machine learning approaches. In this work, we implement the NER system for Tamil using Random Kitchen Sink algorithm which is a statistical and supervised approach. The NER system is also implemented using Support Vector Machine (SVM) and Conditional Random Field (CRF). The overall performance of the NER system was evaluated as 86.61% for RKS, 81.62% for SVM and 87.21% for CRF. Additional results have been taken in SVM and CRF by increasing the corpus size and the performance are evaluated as 86.06% and 87.20% respectively.

Keywords

Conditional Random Field (CRF), Named Entities (NEs), Named Entity Recognition (NER), Natural Language Processing (NLP), Random Kitchen Sink (RKS), Support Vector Machine (SVM)

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• Sparse Banded Matrix Filter for Image Denoising

Abstract Views :165  |  PDF Views:0

Authors

V. Sowmya ¹, Neethu Mohan ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Amrita Nagar, Coimbatore – 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 8, No 24 (2015), Pagination: 

Abstract

Noise is one of the prime factors which degrade the quality of an image. Hence, image denoising is an essential image enhancement technique in the image processing domain. In this paper, we use low-pass sparse banded filter matrices for image denoising. Sparsity is the key concept in this filter design. We applied the designed low-pass filter both row-wise and column-wise to denoise the image. The proposed method is experimented on standard test images corrupted with different types of noises namely Gaussian, White Gaussian, Salt & Pepper and Speckle with noise level equals to 0.01, 0.05 and 0.1. The effectiveness of the proposed method of denoising is evaluated by the computation of standard quality metric known as Peak Signal-to-Noise Ratio (PSNR). The experimental result analysis shows that the proposed image denoising technique based on sparse banded filter matrices results in significant improvement in PSNR around 2dB to 8dB for different type of noises with noise level equal to 0.1 and is also aided by the visual analysis.

Keywords

Image Denoising, Low-pass Filter, Noise, Sparse Banded Filter

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• Effect of Wind Farms in Crop Production of Kanyakumari District

Abstract Views :229  |  PDF Views:0

Authors

M. Arya ¹, P. Geetha ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidya Peetham, Coimbatore 641 – 112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 8, No 28 (2015), Pagination: 

Abstract

This paper describes how the wind farms effect the crop production of Kanyakumari District. Remote sensing technology along with GIS has been used here for finding the NDVI values. Paddy yield data were also used for finding the effect of wind farms. Along with the NDVI values, we use temperature, humidity and Rainfall data for finding the Crop production rate. Erdas imagine 8.3 along with ArcGis have been used as the software for image and geo-information analysis.

Keywords

ArcGIS, Crop Yield Assessment, Erdas, Kanyakumari, NDVI

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• Improvement in Kernel based Hyperspectral Image Classification Using Legendre Fenchel Denoising

Abstract Views :182  |  PDF Views:0

Authors

R. Reshma ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore- 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 33 (2016), Pagination: 

Abstract

Hyperspectral images have bulk of information which are widely used in the field of remote sensing. One of the main problems faced by these images is noise. This emphasizes the importance of denoising techniques for enhancing the image quality. In this paper, Legendre Fenchel Transformation (LFT) is used for preprocessing the Indian Pines Dataset. LFT reduces the noise of each band of the hyperspectral image without affecting the edge information. Signal to noise ratio is computed which helps to evaluate the performance of denoising. Further, the denoised image is classified using GURLS and LibSVM and the various accuracies are estimated. The experimental analysis shows that the overall and classwise accuracies are more for the preprocessed data classification when compared to the classification without preprocessing. The classification accuracy is improved with denoising of hyperspectral image.

Keywords

Classification, Denoising, GURLS, Hyperspectral Image, Kernel Methods, Legendre Fenchel, LibSVM

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• Least Square based Signal Denoising and Deconvolution using Wavelet Filters

Abstract Views :176  |  PDF Views:0

Authors

R. Praveena ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 33 (2016), Pagination: 

Abstract

Noise, the unwanted information in a signal reduces the quality of signal. Hence to improve the signal quality, denoising is done. The main aim of the proposed method in this paper is to deconvolve and denoise a noisy signal by least square approach using wavelet filters. In this paper, least square approach given by Selesnick is modified by using different wavelet filters in place of second order sparse matrix applied for deconvolution and smoothing. The wavelet filters used in the proposed approach for denoising are Haar, Daubechies, Symlet, Coiflet, Biorthogonal and Reverse biorthogonal. The result of the proposed experiment is validated in terms of Peak Signal to Noise Ratio (PSNR). Analysis of the experiment results notify that proposed denoising based on least square using wavelet filters are comparable to the performances given by deconvolution and smoothing using the existing second order filter.

Keywords

Least Square, Peak Signal to Noise Ratio (PSNR), Signal Denoising, Wavelet Filters

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• Predicting the Sentimental Reviews in Tamil Movie using Machine Learning Algorithms

Abstract Views :213  |  PDF Views:0

Authors

Shriya Se ¹, R. Vinayakumar ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore Amrita Vishwa Vidyapetham, Amrita University, Coimbatore – 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 45 (2016), Pagination: 

Abstract

Objective: This paper aims at classifying the Tamil movie reviews as positive and negative using supervised machine learning algorithms. Methods/Analysis: A novel machine learning approaches are needed for analyzing the Social media text where the data are increasing exponentially. Here, in this work, Machine learning algorithms such as SVM, Maxent classifier, Decision tree and Naive Bayes are used for classifying Tamil movie reviews into positive and negative. Features are also extracted from TamilSentiwordnet. Findings: The dataset for this work has been prepared. SVM algorithm performs well in classifying the Tamil movie reviews when compared with other machine learning algorithms. Both cross validation and accuracy of the algorithm shows that SVM performs well. Other than SVM, Decision tree perform well in classifying the Tamil reviews. Novelty/Improvement: SVM gives an accuracy of 75.9% for classifying Tamil movie reviews which is a good milestone in the research field of Tamil language.

Keywords

Machine Learning, Maxent Classifier, Sentimental Analysis, Support Vector Machine, Tamil Language, TamilSentiwordnet.

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• Cuisine Prediction based on Ingredients using Tree Boosting Algorithms

Abstract Views :148  |  PDF Views:0

Authors

R. M. Rahul Venkatesh Kumar ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 45 (2016), Pagination: 

Abstract

Objective: This paper aims at predicting the cuisine based on the ingredients using tree boosting algorithm. Methods/ Analysis: Text mining is important tool for data mining in Ecommerce websites. Ecommerce business is growing with significant rate both in Business-to-Business (B2B) and Business to Customer (B2C) categories. The machine learning based models and prediction method are used in real world ecommerce data to increase the revenue and study customer behavior. Many online cooking and recipe sharing websites have ardent to evolution of recipe recommendation system. In this paper, we describe a scalable end to end tree boosting system algorithms to predict cuisine based on the ingredients and also explored different data analysis and explained about the dataset types and their performances. Novelty/ Improvement: An accuracy of about 80% is obtained for cuisine prediction using XG-Boosting algorithm.

Keywords

Data Analysis, Prediction, Random Forest, Text Analytics, XGBoost.

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• A Fast and Efficient Framework for Creating Parallel Corpus

Abstract Views :200  |  PDF Views:0

Authors

B. Premjith ¹, S. Sachin Kumar ¹, R. Shyam ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN),Amrita School of Engineering, Amrita University, Amrita Vishwa Vidyapeetham, Amritanagar, Coimbatore – 641 112, Tamilnadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 45 (2016), Pagination: 

Abstract

Objectives: A framework involving Scansnap SV600 scanner and Google Optical character recognition (OCR) for creating parallel corpus which is a very essential component of Statistical Machine Translation (SMT). Methods and Analysis: Training a language model for a SMT system highly depends on the availability of a parallel corpus. An efficacious approach for collecting parallel sentences is the predominant step in an MT system. However, the creation of a parallel corpus requires extensive knowledge in both languages which is a time consuming process. Due to these limitations, making the documents digital becomes very difficult and which in turn affects the quality of machine translation systems. In this paper, we propose a faster and efficient way of generating English to Indian languages parallel corpus with less human involvement. With the help of a special type of scanner called Scansnap SV600 and Google OCR and a little linguistic knowledge, we can create a parallel corpus for any language pair, provided there should be paper documents with parallel sentences. Findings: It was possible to generate 40 parallel sentences in 1 hour time with this approach. Sophisticated morphological tools were used for changing the morphology of the text generated and thereby increase the size of the corpus. An additional benefit of this is to make ancient scriptures or other manuscripts in digital format which can then be referred by the coming generation to keep up the traditions of a nation or a society. Novelty: Time required for creating parallel corpus is reduced by incorporating Google OCR and book scanner.

Keywords

Google OCR, Machine Translation, Parallel Corpus, Statistical Machine Translation, Scansnap SV600 Scanner.

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• Word Embedding Models for Finding Semantic Relationship between Words in Tamil Language

Abstract Views :196  |  PDF Views:0

Authors

S. G. Ajay ¹, M. Srikanth ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 45 (2016), Pagination: 

Abstract

Objective: Word embedding models were most predominantly used in many of the NLP tasks such as document classification, author identification, story understanding etc. In this paper we make a comparison of two Word embedding models for semantic similarity in Tamil language. Each of those two models has its own way of predicting relationship between words in a corpus. Method/Analysis: The term Word embedding in Natural Language Processing is a representation of words in terms of vectors. Word embedding is used as an unsupervised approach instead of traditional way of feature extraction. Word embedding models uses neural networks to generate numerical representation for the given words. In order to find the best model that captures semantic relationship between words, using a morphologically rich language like Tamil would be great. Tamil language is one of the oldest Dravidian languages and it is known for its morphological richness. In Tamil language it is possible to construct 10,000 words from a single ischolar_main word. Findings: Here we make comparison of Content based Word embedding and Context based Word embedding models respectively. We tried different feature vector sizes for the same word to comment on the accuracy of the models for semantic similarity. Novelty/Improvement: Analysing Word embedding models for morphologically rich language like Tamil helps us to classify the words better based on its semantics.

Keywords

CBOW, Content based Word Embedding, Context based Word Embedding, Morphology, Semantic and Syntactic, Skip Gram.

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• Knowledge based Approach for English-Malayalam Parallel Corpus Generation

Abstract Views :160  |  PDF Views:0

Authors

Sooraj Sudhakaran ¹, Shimil Jose ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 45 (2016), Pagination: 

Abstract

Objective: This paper aims in providing an overview about a part of Natural Language Generation – Parallel sentence generation which involves the generation of the English sentence as well as its Malayalam translated version. Methods/Analysis: A template based sentence generator approach is followed here. A system is proposed which takes input from a manually created bilingual dictionary and fills the slots in the template for parallel sentence generation. Finding: Using the proposed method, we have generated a total of 25,208 parallel sentences. This can be used in bilingual Machine Translation dictionary. Application/Improvement: In the proposed case use only four templates but by increasing the number of templates and by updating the dictionary, we can increase the size of the parallel corpus that can be generated.

Keywords

Bilingual, English-Malayalam, Machine Translation, Parallel sentence, Templates.

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• Regularized Least Square Approach for Remote Sensing Image Denoising using Wavelet Filters

Abstract Views :177  |  PDF Views:0

Authors

M. Swarna ¹, P. Megha ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 45 (2016), Pagination: 

Abstract

Noise in remote sensing images (aerial and satellite) is caused due to various reasons such as atmospheric interference or lack of quality in sensors used to capture them. Removal of noise in an efficient way is a big challenge for researchers. In this paper, one dimensional signal denoising based on weighted regularized least square method is mapped to two dimensional image de noising. Objectives: This paper introduces a novel image denoising technique based on least square weighted regularization. Methods/Statistical Analysis: The proposed technique for image denoising based on Least Square (LS) approach is experimented on five different satellite and aerial images corrupted by gaussian noise with varying noise levels and regularization parameter lambda (λ) for different wavelet filter coefficients such as ‘haar’, ‘symlet’, ‘daubechies’ and coiflet. The effectiveness of the proposed method of image denoising is compared against the existing second order filter [based on LS] and conventional wavelet based image denoising technique based on the standard metric called Peak Signal to Noise Ratio (PSNR). Findings: From the experimental result analysis obtained it is inferred that the wavelet filters outperforms the second order filter and the conventional wavelet based image denoising. The complexity of the mathematics is low in our proposed method for image denoising. Applications/Improvements: The proposed denoising technique can be adopted as a faster pre-processing step in most of the image processing applications.

Keywords

Gaussian Noise, Image Denoising, Least Square, Peak Signal-to-Noise Ratio, Wavelet Filter Coefficients.

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• Image Fusion using Variational Mode Decomposition

Abstract Views :156  |  PDF Views:0

Authors

Divya Pankaj ¹, S. Sachin Kumar ¹, Neethu Mohan ¹, K. P. Soman ¹

Affiliations
1 Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore – 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 45 (2016), Pagination: 

Abstract

Background/Objectives: This paper introduced an image fusion algorithm based on Variational Mode Decomposition (VMD). Methods/Statistical Analysis: Image fusion is one of the image enhancement methods which results the image with better quality derived from a set of degraded images. Fused image contains more information than input images and it is efficient for visual perception and computer vision applications. This paper proposed an image fusion technique based on VMD for multi focus images. VMD has been a recently introduced non-recursive decomposition method, which decomposes the image into separate spectral bands called Intrinsic Mode Function (IMF) or modes. The modes are generated with respect to the associated central frequencies and they are band limited. Findings: A fusion rule based on weighing scheme is performed at the decomposition level for increasing the features by decreasing the mutual information. The reconstruction of the IMFs results the final fused image. The performance analysis of the proposed fusion method is experimented using standard objective quality metrics. The efficiency of the proposed method is determined by comparing the method with some state of the art methods. Application/Improvements: The image fusion using VMD is applicable to multi-resolution, multi model multi-sensor images.

Keywords

Fusion Rule, Image Fusion, Image Quality Metrics, 2D-Variational Mode Decomposition, Variational Mode Decomposition.

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• Least Square based Image Denoising using Wavelet Filters

Abstract Views :187  |  PDF Views:0

Authors

P. V. Veena ¹, G. Remmiya Devi ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, Tamil Nadu - 641112, IN

Source

Indian Journal of Science and Technology, Vol 9, No 30 (2016), Pagination: 

Abstract

Background/Objectives: Noise in a digital image, is unwanted information that degrades the quality of an image. The main aim of the proposed method is to denoise a noisy image based on least square approach using wavelet filters. Methods/ Statistical Analysis: One dimensional least square approach proposed by Selesnick is extended to two dimensional image denoising. In our proposed technique of least square problem formulation for image denoising, the matrix constructed using second order filter coefficients is replaced by wavelet filter coefficients. Findings: The method is experimented on standard digital images namely Lena, Cameraman, Barbara, Peppers and House. The images are subjected to different noise types such as Gaussian, Salt and Pepper and Speckle with varying noise level ranging from 0.01db to 0.5db. The wavelet filters used in the proposed approach of denoising are Haar, Daubechies, Symlet, Coiflet, Biorthogonal and Reverse biorthogonal. The outcome of the experiment is evaluated in terms of Peak Signal to Noise Ratio (PSNR). The analysis of the experiment results reveals that performance of the proposed method of least square based image denoising by wavelet filters are comparable to denoising using existing second order sparse matrix. Applications/Improvements: Digital images are often prone to noise; hence, proceeding with further processing of such an image requires denoising. This work can be extended in future to m-band wavelet filters.

Keywords

Image Denoising, Least Square, Peak Signal to Noise Ratio (PSNR), Wavelet Filters.

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• Aerial and Satellite Image Denoising using Least Square Weighted Regularization Method

Abstract Views :174  |  PDF Views:0

Authors

K. Deepa Merlin Dixon ¹, Aleena Ajay ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641 112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 30 (2016), Pagination: 

Abstract

Remotely sensed images are subjected to various types of noises. Noise interrupts the image information; hence noise removal is one of the important pre-processing steps in every image processing applications. Since both noise and edges contain highintensity values, image denoising leads to smoothening of the edges thereby reducing the visual quality of the image. Hence, edge preserved image denoising is an ever-relevant topic. Over decades, several image denoising techniques were developed. Most of the denoising algorithms are very complex and time consuming. Background/Objectives: This paper introduces a novel image denoising technique based on least square weighted regularization. Methods/Statistical Analysis: The onedimensional signal denoising introduced by14 is mapped into two-dimensional image denoising. The proposed method is experimented on a set of colored aerial and satellite images. The column-wise denoising of the image is performed first, followed by row-wise denoising. The performance of the proposed method is evaluated based on the standard quality metric peak signal-to-noise ratio and computational time. Findings: From the experimental results, it is observed that the proposed method outperforms the earlier denoising methods on the basis of time and complexity. Applications/Improvements: The proposed denoising technique can be adopted as a faster pre-processing step in most of the image processing applications.

Keywords

Least Square, Legendre-Fenchel, Peak Signal-to-Noise Ratio, Total Variation, Wavelet.

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• Classification of Remotely Sensed Algal Blooms along the Coast of India using Support Vector Machines and Regularized Least Squares

Abstract Views :144  |  PDF Views:0

Authors

M. Jocelyn Babu ¹, P. Geetha ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking Amrita School of Engineering, Amrita Vishwa Vidyapeetham Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 30 (2016), Pagination: 

Abstract

Background/Objectives: The recent times have observed an inflation in frequency of occurrence of Algal Blooms (ABs). In this work, seven most commonly occurring species (i.e., Trichodesmium erythraeum, Noctiluca scintillans/miliaris, Cocholodinium ploykrikoides, Chattonella marina, Karenia mikimotoi and Protoperidinium species) that have contributed to major ABs along the coastline of India in the years 2002 to 2015 are classified. Methods/Statistical Analysis: Processing the data procured by MODIS Aqua sensor, classification of seven species of algae is performed based only on the feature of Remote Sensing Reflectance (R_rs). In contrast to the existing algorithms like band-ratio and interpretation of water discoloration, classification of blooms is based on Support Vector Machine (SVM) and Regularized Least Squares (RLS) algorithms. Findings: Classification is executed using LIBSVM and GURLS library for fast and efficient performance. The classification accuracies achieved using both the classifiers are comparable; the overall accuracy using SVM classifier is 88.37%, whereas that obtained with RLS classifier is 89.98%. Applications/Improvements: These results reveal that the above mentioned algorithms are capable of effectively detecting these ABs which is of immense interest in fisheries and healthcare industries. The algorithms can be further trained with bloom parameters based on in-situ datasets from additional occurrences.

Keywords

Algal Bloom, MODIS Aqua Data, Rrs, RLS, SVM.

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• Multivariate Statistical Technique for the Assessment of Ground Water Quality in Coonoor Taluk, Nilgiri District, Tamilnadu, India

Abstract Views :194  |  PDF Views:0

Authors

M. Kaviarasan ¹, P. Geetha ¹, K. P. Soman ¹

Affiliations
1 Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 8, No 36 (2015), Pagination: 

Abstract

Ground water is one of the most important resources which play a key role in sustainable development. Over the last few decade, the industrial development and population growth has increased the water utilization which creates stress on both water and land resources. In such scenario, assessment of water quality is essential for proper management and utilization. This paper presents the usage of statistical method like Principal Component Analysis and Pearson Correlation coefficient analysis for analysing temporal variations of the ground water. From Coonoor Taluk of Nilgiri district, various samples were collected to analyse physico-chemical factors. The quality of the ground water and the compositions are to be determined by Water Quality Index (WQI) calculation method. A comparison of each parameter with that of standard permissible limit as recommended by WHO.

Keywords

Ground Water, Principal Component Analysis, Water Quality Index, WHO

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• Real Time Vehicular Data Analytics Utilising Bigdata Platforms and Cost Effective ECU Networks

Abstract Views :129  |  PDF Views:0

Authors

Yedu C. Nair ¹, P. V. Neethu ¹, Vijay Krishna Menon ¹, K. P. Soman ¹

Affiliations
1 Center for Excellence in Computational Engineering and Networking, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 30 (2016), Pagination: 

Abstract

Background/Objectives: This paper is aimed at performing real time bigdata analytics on vehicular data collected from a network of ECUs (Electronic Control Unit) in cooperated into the different automobiles. Methods/Statistical Analysis: The analytics has been performed by building a software model that is capable of handling the vehicular data in real time. Bigdata platforms like hadoop, Apache Storm, Apache Spark(real time streaming), Kafka are utilised here. Automotive sensor data from different Electronic Control Units are collected into a central data server and this data is pushed to kafka, from which the real time streaming models pulls the data and perform analysis. Findings: Automotive industry has undergone a drastic revolutionised innovation in the past decade in all of its respective segments. The industry had started utilizing the computational and mathematical aspects from top to bottom in its design strategies to achieve greater reliability on its products out on roads. Latest advancements in this field is the fully autonomous car. Today an automotive is a collection of innumerable sensors and microcontrollers which are under the command of the master ECU. A network of ECUs connected across the globe is a source tap of bigdata. Leveraging the new sources of bigdata by automotive giants boost vehicle performance, enhance loco driver experience, accelerated product designs. Statistical Projections reveal that automotive industry is likely to be the 2nd largest generator of data by mid of 2016. The contribution of this paper to the automotive industry is the real time vehicle monitoring utilizing Bigdata platforms. This can contribute to better customer-industry relations. Applications/Improvements:The model developed in this paper can contribute a lot to the automobile industry as it facilitates real time monitoring of the vehicles. This can improve customer-industry relation.

Keywords

ECU, Hadoop, Kafka, Spark, Storm.

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• Climatic Impacts and Reliability of Large Scale Wind Farms in Tamil Nadu

Abstract Views :189  |  PDF Views:0

Authors

M. KaviNandhini ¹, P. Geetha ¹, K. P. Soman ¹

Affiliations
1 Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 6 (2016), Pagination: 

Abstract

Objective: The main objective of this paper describes how the large scale windfarms affect the climate of south west monsoon region. Methods/Analysis: Method used for analysing climatic parameters of before and after installation of wind farms is Gaussian mixture model. ArcGIS and QGIS software is used for image and geo-information analysis. Data from the commercial wind turbine of south west monsoon region like temperature, relative humidity, precipitation, wind speed is used to find the climatic variation. Findings: Large scale wind farms significantly affect the various climatic parameters. These impacts depends on the static stability, increase or decrease in the climatic parameters. Conclusion/ Application: Improvements can be made by taking the ground temperature measured by satellite image and identify the warming effect of night and day time warming effect of large scale wind farm area of southwest monsoon regions.

Keywords

Gaussian Mixture Model, Humidity, Precipitation, Temperature, Wind Farm, Wind Speed

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• Training Tree Adjoining Grammars with Huge Text Corpus Using Spark Map Reduce

Abstract Views :160  |  PDF Views:2

Authors

Vijay Krishna Menon ¹, S. Rajendran ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, IN

Source

ICTACT Journal on Soft Computing, Vol 5, No 4 (2015), Pagination: 1021-1026

Abstract

Tree adjoining grammars (TAGs) are mildly context sensitive formalisms used mainly in modelling natural languages. Usage and research on these psycho linguistic formalisms have been erratic in the past decade, due to its demanding construction and difficulty to parse. However, they represent promising future for formalism based NLP in multilingual scenarios. In this paper we demonstrate basic synchronous Tree adjoining grammar for English-Tamil language pair that can be used readily for machine translation. We have also developed a multithreaded chart parser that gives ambiguous deep structures and a par dependency structure known as TAG derivation. Furthermore we then focus on a model for training this TAG for each language using a large corpus of text through a map reduce frequency count model in spark and estimation of various probabilistic parameters for the grammar trees thereafter; these parameters can be used to perform statistical parsing on the trained grammar.

Keywords

TAGs, Spark, Probabilistic Grammar, RDDs, Parsing.

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• Performance Enhancement of Minimum Volume based Hyper Spectral Unmixing Algorithms by Variational Mode Decomposition

Abstract Views :158  |  PDF Views:0

Authors

Parvathy G. Mol ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 8, No 24 (2015), Pagination: 

Abstract

Hyper spectral unmixing of data has become an indispensable technique in remote sensing zone. Spectral Unmixing is defined as the source separation of a mixed pixel. The fundamental sources are termed as endmembers and percentage of the source content is known as abundances. This paper demonstrates the effect of Variational Mode Decomposition (VMD) on hyper spectral unmixing algorithms based on geometrical minimum volume approaches. The proposed method is experimented on standard hyper spectral dataset namely, cuprite. The effectiveness of the proposed method is subjected to evaluation, based on the standard quality metric namely, Root Mean Square Error (RMSE). The experimental result analysis shows that, the proposed technique enhance the performance of hyper spectral unmixing algorithms based on the geometrical minimum volume based approaches.

Keywords

Endmember Signature, Hyperspectral Imaging (HI), Hyperspectral Unmixing (HU), Variational Mode Decomposition (VMD).

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• Simulated and Self-Sustained Classification of Twitter Data based on its Sentiment

Abstract Views :148  |  PDF Views:0

Authors

S. N. Vinithra ¹, S. J. Arun Selvan ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 8, No 24 (2015), Pagination: 

Abstract

We present a methodology for naturally grouping the estimation of Twitter messages. Miniaturized scale websites are a testing new wellspring of data for information mining methods. The aim of this paper is to focus the careful feeling of the information from the microblogging site Twitter. Tweets regularly likewise contain URLs to different sites. Tweets additionally contain a certain measure of OOV (Out-Of-Vocabulary) words, for example, Hash tags, a labeling framework for points permitting Tweets in a comparative vein of discussion to be found. Other OOV words incorporate notice which is a system to direct a Tweet to one or more users. The KH coder tool gives a conventional precision result where the content is POS labeled and MySQL is utilized for putting away points of interest as a part of the database. The R tool is utilized to view the factual examination of information. Further, machine learning calculation has likewise been performed. A preprocessing and highlight choice system in blend with a Maximum Entropy, Naive Bayes and Decision Tree classifiers has been exhibited and sensible results has been delivered. Accuracy of the machine adapting methods for sentiment has been thought about and statistical representation of the classes has been depicted through KH Coder.

Keywords

Data Mining, Decision Tree, Maximum Entropy, Microblogging, Naive Bayes, Oov, Sentiment Classification.

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• Multispectral and Panchromatic Image Fusion using Empirical Wavelet Transform

Abstract Views :149  |  PDF Views:0

Authors

S. Moushmi ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidhyapeetham, Coimbatore – 641112, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 8, No 24 (2015), Pagination: 

Abstract

Pan sharpening is the process of fusion of panchromatic and multispectral image to obtain an output image of high spatial and spectral resolution. It is very important for various remote sensing applications such as image segmentation studies, image classification, temporal change detection etc. The present work demonstrates the application of Empirical Wavelet Transform for the fusion of panchromatic image and multispectral image by simple average fusion rule. The Proposed method is experimented on panchromatic and multispectral images captured by high resolution earth observation satellites such as GeoEye-1, QuickBird, WorldView-2 and World View-3. The effectiveness of our proposed method is evaluated by visual perception and quantitative assessment measures. The experimental analysis shows that the proposed method performs comparable to the existing fusion algorithms such as Multi-resolution Singular Value Decomposition and Discrete Wavelet Transform.

Keywords

Discrete Wavelet Transform, Empirical Wavelet Transform, Image Fusion, Multi-resolution Singular Value Decomposition, Pan Sharpening, Quality Metrics.

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• GURLS vs LIBSVM: Performance Comparison of Kernel Methods for Hyperspectral Image Classification

Abstract Views :138  |  PDF Views:0

Authors

Nikhila Haridas ¹, V. Sowmya ¹, K. P. Soman ²

Affiliations
1 Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641001, Tamil Nadu, IN
2 Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641001, IN

Source

Indian Journal of Science and Technology, Vol 8, No 24 (2015), Pagination: 

Abstract

Kernel based methods have emerged as one of the most promising techniques for Hyper Spectral Image classification and has attracted extensive research efforts in recent years. This paper introduces a new kernel based framework for Hyper Spectral Image (HSI) classification using Grand Unified Regularized Least Squares (GURLS) library. The proposed work compares the performance of different kernel methods available in GURLS package with the library for Support Vector Machines namely, LIBSVM. The assessment is based on HSI classification accuracy measures and computation time. The experiment is performed on two standard Hyper Spectral datasets namely, Salinas A and Indian Pines subset captured by AVIRIS (Airborne Visible Infrared Imaging Spectrometer) sensor. From the analysis, it is observed that GURLS library is competitive to LIBSVM in terms of its prediction accuracy whereas computation time seems to favor LIBSVM. The major advantage of GURLS toolbox over LIBSVM is its simplicity, ease of use, automatic parameter selection and fast training and tuning of multi-class classifier. Moreover, GURLS package is provided with an implementation of Random Kitchen Sink algorithm, which can easily handle high dimensional Hyper Spectral Images at much lower computational cost than LIBSVM.

Keywords

Classification, GURLS, Hyper Spectral Image, Kernel Methods, LIBSVM

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• Novel Regression-Gis based Approach for the Analysis of Spread of Dengue in Palakkad

Abstract Views :135  |  PDF Views:0

Authors

S. Parvathy ¹, P. Geetha ¹, K. P. Soman ¹

Affiliations
1 Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641001, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 8, No 24 (2015), Pagination: 

Abstract

In this paper we try to analyze what is the relationship between number of dengue cases reported with that of the different environmental factors like temperature, rainfall etc. For doing this, we make use of regression analysis along with GIS. This method also helps in predicting the future course of the disease spread as we will be in a position to identify the dependence on different factors. We got a Coefficient of determination R2 value of 0.9218, implying that our method is efficient for accurately identifying the relationship between the different parameters and number of cases reported.

Keywords

Coefficient of Determination, Dengue, GIS, Regression Analysis, Vector Borne Diseases

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