Informatics Publishing Limited

D. Ashok Kumar ¹, P. Prema ²

Affiliations
1 Department of Computer Science, Government Arts College, Tiruchirappalli, IN
2 Department of Agricultural Economics, Agricultural College and Research Institute, Madurai, IN

Abstract

Apparently weed is a major menace in crop production as it competes with crop for nutrients, moisture, space and light which resulting in poor growth and development of the crop and finally yield. Yield loss accounts for even more than 70% when crops are frown under unweeded condition with severe weed infestation. Weed management is the most significant process in the agricultural applications to improve the crop productivity rate and reduce the herbicide application cost. Existing weed detection techniques does not yield better performance due to the complex background, illumination variation and crop and weed overlapping in the agricultural field image. Hence, there arises a need for the development of effective weed identification technique. To overcome this drawback, this paper proposes a novel Wrapping Curvelet Transformation Based Angular Texture Pattern Extraction Method (WCTATP) for weed identification. In our proposed work, Global Histogram Equalization (GHE) is used improve the quality of the image and Adaptive Median Filter (AMF) is used for filtering the impulse noise from the image. Plant image identification is performed using green pixel extraction and k-means clustering. Wrapping Curvelet transform is applied to the plant image. Feature extraction is performed to extract the angular texture pattern of the plant image. Particle Swarm Optimization (PSO) based Differential Evolution Feature Selection (DEFS) approach is applied to select the optimal features. Then, the selected features are learned and passed through an RVM based classifier to find out the weed. Edge detection and contouring is performed to identify the weed in the plant image. The Fuzzy rule-based approach is applied to detect the low, medium and high levels of the weed patchiness. From the experimental results, it is clearly observed that the accuracy of the proposed approach is higher than the existing Support Vector Machine (SVM) based approaches. The proposed approach achieves better performance in terms of accuracy.

Keywords

Global Histogram Equalization (GHE), Adaptive Median Filter (AMF), Convoluted Gray Level Co-Occurrence Matrix (CGLCM), Wrapping Curvelet Transformation Based Angular Texture Pattern (WCTATP) Extraction Method, Weed Identification.

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D. Ashok Kumar ¹, N. Kannathasan ²

Affiliations
1 Department of Computer Science, Government Arts College, Tiruchirapalli - 620022, Tamil Nadu, IN
2 Department of Computer Science, Kanchi Mamunivar Centre for Post Graduate Studies, Puducherry - 605008, U.T. of Puducherry, IN

Abstract

Background/Objectives: Data Mining has been used to analyze large datasets and establish useful classification and patterns in the datasets. The efficient analysis of data in different format becomes a challenging work. Methods/Statistical Analysis: This work proposed a novel Soil Profile Feature Reduction Model using Principal Component Analysis for data reduction. The proposed model uses the method of k-Means clustering and PC Aapproach for feature reduction which initially applies PCA to acquire reduced uncorrelated attributes showing maximal eigenvalues in the dataset with minimum loss of information. Again proposed model uses k-Means on the PCA reduced dataset to find out discriminative features that will be the most sufficient ones for classification. Findings: The weight by PCA generates attribute weights of the soil profile dataset using a component created by the PCA. The component is mentioned by the component number parameter. The normalize weight parameter is usually set to true to spread the weights between 0 and 1. The attribute weights reflect the relevance of the attributes with respect to the class attribute. The higher weight of an attribute is more relevant, it is considered. This is a combination of clustering approach with feature reduction to get a minimal set attributes relating a suitably high accuracy in describing the original features. The result of clustering is same after reducing the attributes using PCA. The experimental results prove that proposed model is reducing number of initial attributes, reducing computational complexity and improving predictive accuracy in High Dimensional Datasets. Applications/Improvements: The same soil profile feature is implemented by using the other techniques instead of PCA algorithm in future.

Keywords

Clustering, Feature Reduction, k-Means,Principal Component Analysis, Soil Profile

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D. Ashok Kumar ¹, N. Kannathasan ²

Affiliations
1 Department of Computer Science, Government Arts College, Tiruchirapalli - 620022, Tamil Nadu, IN
2 Department of Computer Science, Kanchi Mamunivar Centre for Post Graduate Studies Puducherry – 605008, U.T. of Puducherry, IN

Abstract

Background/Objectives: Fuzzy Logic is derived from Fuzzy Set that deals with reasoning that is accurate rather than precisely based on degrees of membership, and are well-liked tools in the application of prediction, classification and recognition based problems. Methods/Statistical Analysis: This work projected a novel Fuzzy Classification Algorithm to train Fuzzy Inference System to classify and predict soil profile data with soil Total Porosity and proposed a fuzzy-genetic hybrid based clustering algorithm namely GENetic algorithm for SOIL (GENSOIL) profile data. Soil samples were collected from International Soil Reference and Information Centre. Findings: Fuzzy Rule Base has been developed and using in a proposed Fuzzy Classification Algorithm to train Fuzzy Inference System for soil profile data classification and prediction. The proposed algorithm classified the soil samples based on Fuzzy Rule Base using with Fuzzy Membership Function and hybrid with a randomized and optimized Genetic algorithm. A Novel Fuzzy-Genetic Hybrid Classification Algorithm compared with k-Means and Fuzzy C-Means are assessed on the basis of the time complexity of clustering. Applications/Improvements: The advantage of novel Fuzzy-Genetic Hybrid Classification Algorithm is their applicability in different types of related optimization problems with a superior speed of calculation and found solution very close to the best one.

Keywords

Classification, Fuzzy C-Means, Fuzzy Logic, Fuzzy Rule Base, Genetic, K-Means, Soil Profile

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D. Ashok Kumar ¹, S. Dhandapani ¹

Affiliations
1 Department of Computer Science and Applications, Government Arts College Tiruchirapalli - 620 022, Tamil Nadu, IN

Abstract

Background: Handwritten signature is a person's unique identity. Signature verification is an economical biometric method with online and offline schemes. This paper deals with the offline verification of signatures found in bank checks. Method: Extracting feature is the most vital part of a signature verification process. An efficient feature extraction method, Concentric Circles Masking Method, is used to extract robust, scale invariant and rotation invariant features. The extracted feature values are normalized and fed to a feedforward back propagation neural network for classification of the signatures into genuine or forged ones. The feature's performance is measured with various training functions of the neural network. The system modeled is tested with the well-known CEDAR database. Findings: Experimental Analysis shows that the features extracted by this method prove to be efficient. The scanned signature is covered by concentric circles and the pixel distribution ratio in each circle is calculated and used for verification purpose. Since a circle is used, the extracted features are scale and rotation invariant which makes the feature robust. The neural network's training, validation and testing ratio are varied and the performance of various training functions is studied. It is inferred that conjugate gradient back propagation with Fletcher-Reeves updates (traincgf) training function has the maximum average accuracy of 97.89% for the CCMM features.

Keywords

Feature Extraction, Signature Verification, Training Functions Comparison.

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D. Ashok Kumar ¹, P. Prema ²

Affiliations
1 Government Arts College, Trichy, Tamil Nadu, IN
2 Agricultural College and Research Institute, Madurai-625104, Tamil Nadu, IN

Abstract

Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture classification based on wavelet transform has been analysed. The efficient texture feature extraction methods are developed for weed discrimination. Three group feature vector can be constructed by the mean and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade, Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with WECSPE texture feature obtaining high degree of success rate in classification.

Keywords

Pre-Processing, Wavelets, Texture Features, Neural Network.

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Syed Azhar Nizami ¹, D. Ashok Kumar ², S. C. Marihal ²

Affiliations
1 Department of Pharma. Chemistry, Dr HLT College of Pharmacy, Kengal, Channapatna, Karnataka, IN
2 Department of Pharmaceutical Chemistry, Dr HLT College of Pharmacy, Channapatna, Karnataka- 571502, IN

Abstract

The present study deals with the determination of Ferrum Phophoricum and Ferrum metal in some Homeopathic formulations. The method is based on Fe³⁺ reduce to Fe²⁺ with hydroxyl ammonium chloride which react with the 1-10 phenanthroline in the pH range 3-5 to form an orange-red colour complex which shows the maximum absorbance at 518 nm. Beer's law is obeyed in the concentration range of 0.5-3mg/ml. Results of the analysis were validated statistically and by recovery studies. The Percentage label claim and Percentage recoveries estimated were close to 100% with low value of standard deviation and Percentage coefficient of variation.

Keywords

Ferrum Phosohoricum, Spectrophotometry, Fe,sup>2+ 1-10 Phenanthroline Complex.

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D. Ashok Kumar ¹, S. Murugan ²

Affiliations
1 Department of Computer Science, Government Arts College, Tiruchirappalli – 620022, Tamil Nadu, IN
2 Department of Computer Science, Alagappa Government Arts College, Karaikudi – 630003, Tamil Nadu, IN

Abstract

Objectives: To present a novel Fuzzy Time Series Neural Network (FTSNN) with Tracking Signal (TS) approach for forecasting the closing index of the stock market. Methods/Statistical Analysis: A novel approach strives to adjust the number of hidden neurons of a Multi-Layer Feed Forward Neural Network (MLFFNN) model. It uses the Tracking Signal (TS) and rejects all models which result in values outside the interval of [-4, 4]. Findings: The effectiveness of the proposed approach is verified with one step ahead of Bombay Stock Exchange (BSE100) closing stock index of Indian stock market and Taiwan Stock Exchange Stock Index (TAIEX). This novel approach reduces the over-fitting problem, reduces the neural network structure and improves forecasting accuracy. In addition, the presented approach has been tested on standard NN3 (Neural Network 3) forecasting competition time series dataset and this approach out performs the various models tested with the NN3 forecasting competition. Applications/Improvements: The proposed approach can be applied to different types of neural network for forecasting closing stock index/price of stock market data.

Keywords

Forecasting, Fuzzy Time Series Data, Neural Network, Stock Index, Tracking Signal.

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