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Kumar, J. Satheesh
- Electromyography based Detection of Neuropathy Disorder using Reduced Cepstral Feature
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1 Department of Computer Applications, Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
1 Department of Computer Applications, Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 8 (2016), Pagination:Abstract
Background/Objectives: Neuropathy is a disorder which will be detected using Electromyography (EMG) signals. A new transformation based wavelet decomposition method is proposed in this work to categorize normal EMG signals from abnormal neuropathy disorder signals. Methods/Statistical Analysis: Transformation technique is applied to convert the signals into frequency map. Wavelet decomposition method decomposes transformed signal into set of various levels of coefficients. Cepstral feature have been applied to extract meaningful properties and Minimum Redundancy Maximum Relevance (MRMR) method has been applied to reduce dimensionality of cepstral features. Findings: The KNN classifier is used to discriminate neuropathy disorder from healthy Electromyography signals. The results shows better classification accuracy using cepstral feature set. Entire signal has been subdivided into 20 and 40 sub segments for better features. Coefficients for five levels have been extracted where 40 sub segment features shows better classification accuracy than 20 sub segments. In some cases, 3rd and 5th level coefficients of 20 sub segments shows better classification. Application/Improvements: This study helps to detect abnormal EMG signal from normal patterns which helps radiologist for better prediction of various disorders based on EMG signals.Keywords
Cepstral Feature, EMG, Hilbert Transform, KNN, MRMR, NeuropathyFull Text
- A Novel Method to Detect Copy-move Tampering in Digital Images
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Authors
Affiliations
1 Department of Computer Applications, Bharathiar University, Coimbatore-641046, Tamil Nadu, IN
1 Department of Computer Applications, Bharathiar University, Coimbatore-641046, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 8 (2016), Pagination:Abstract
Background/Objectives: Copy-move is one of the familiar and crucial image tampering techniques. To develop a method that automatically detect and locate the copy moved region is a primary objective. Methods/Statistical analysis: A novel method that employs Discrete Cosine Transform (DCT) to transform image spatial co-ordinates into frequency co-efficient is introduced. The 2D-DCT is applied to each sub blocks of tampered image and the high frequency co-efficient of each sub-blocks are extracted as a feature vector. These feature vectors are matched and tampered regions are located. Findings: The proposed method effectively detects and locates the tampered region in an image without using any authentication code or signatures. It also detects very small and multiple copy-move region. The efficiency of proposed method is evaluated on several test images. The results indicate that the accuracy of the method is high, number of false matches and dimension of feature vectors are reduced over the existing methods. Application/Improvements: The proposed method can be applied to criminal investigation, journalistic photography, law-enforcement, and medical imaging. The dimension reduction techniques may be implemented to reduce computation complexity and improve accuracy.Keywords
Copy-move, Dimension Reduction, False Matches, Feature Vector, TamperingFull Text
- Computer Assisted QSAR/QSPR Approaches – A Review
Abstract Views :190 |
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Authors
Affiliations
1 Department of Computer Applications, Bharathiar University, Coimbatore – 641046, Tamil Nadu, IN
2 Department of Computer Applications, Bharathiar University, Coimbatore – 641046, Tamil Nadu
1 Department of Computer Applications, Bharathiar University, Coimbatore – 641046, Tamil Nadu, IN
2 Department of Computer Applications, Bharathiar University, Coimbatore – 641046, Tamil Nadu
Source
Indian Journal of Science and Technology, Vol 9, No 8 (2016), Pagination:Abstract
Background/Objectives: Quantitative Structure-Activity Relationship (QSAR) / Quantitative Structure - Property Relationship (QSPR) model is based on changes in molecular structure that would reflect changes in observed biological activity or physico-chemical property. Methods/Statistical analysis: QSAR/QSPR involves chemistry, biology and statistics fields for analysis. It has been widely accepted model for predicting association between molecular structure and its activity. Over the years many algorithms have been proposed and applied in QSAR/QSPR studies. Framework of model involves molecular structure (graph) representation, calculation of molecular descriptors (graph invariants) and multiple linear regression method is applied for analysis. Model has been validated through statistical parameters (R and R2). Findings: Methods involved in model development were reviewed for QSAR/QSPR studies. Multiple Linear Regression is one of the best methods for developing QSAR/QSPR model. This work focuses on developing QSPR model for predicting boiling point of alkyl benzene molecules using Multiple Linear Regression method. Wiener index, Harary Index, Hyper Wiener Index, Hyper Harary Index, and Randic index are calculated for analysis. The model has been validated by calculating R and R2 value. Various models were developed based on different combinations of descriptors to analysis which contribute best in predicting boiling point. Best fit model has been identified by developing model with different combinations of descriptors and rank them based on highest R and R2 value. Model with highest value has been taken for prediction of boiling point as best fit model as n (number of molecules) =14, R= 0.9934 and R2=0.9968. Applications/Improvements: Review on methods involved in prediction analysis has enlightened that model with reduced molecular descriptor subset and outlier detection method shows better performance by improving quality of the dataset Main application of QSAR/QSPR analysis is in drug discovery process. As it has reduced the time taken for lead identification and optimization in drug discovery process.Keywords
Descriptor, Descriptor Selection, Mathematical Model, Multiple Linear Regression, QSAR, QSPRFull Text
- An Attribute Weighted Fuzzy Clustering Algorithm for Mixed Crime Data
Abstract Views :146 |
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Authors
Affiliations
1 Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
2 School of Computer Science and Engineering, Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
1 Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
2 School of Computer Science and Engineering, Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 8 (2016), Pagination:Abstract
Background/Objectives: The main objective is to design and develop a clustering algorithm for finding similar sub sets from crime data. This paper focuses a method for developing an algorithm and modify the existing technique in three ways, such as i) new attribute weightage scheme instead of IGR, ii) suitability to mixed data and iii) using FCM-based clustering instead of k-means. Methods/Statistical analysis: Generally, the effectiveness of clustering algorithm is completely based on distance matching that finds the similarity between data records and centroid. Giving equal importance for all the attributes is not much effective in clustering process. Instead, attribute weightage could be included in distance matching. A weight vector is generated based on mutual information. The method for attribute weightage is common for both numerical and categorical data. Finally, the grouping of similar sub sets is done based on FCM-based clustering procedure in which the distance matching is carried out based on the attribute weights. Findings: The experimental analysis has done using crime and hepatitis datasets where the performance of the proposed clustering algorithm has been analyzed. Results show that proposed FCM method has good accuracy than the AK-mode. Application/Improvements: Proposed method plays an important role in crime domain for better prediction. Type II fuzzy can also be used for better closeness analysis.Keywords
FCM Clustering, Crime Data, Overlapping Interval, Non-overlapping Interval, Numerical Data, Categorical DataFull Text
- Semantic data model for knowledge representation and dissemination of cultural heritage site, Poompuhar
Abstract Views :154 |
PDF Views:77
Authors
Affiliations
1 Department of Computer Applications, Bharathiar University, Coimbatore 641 046, India, IN
1 Department of Computer Applications, Bharathiar University, Coimbatore 641 046, India, IN
Source
Current Science, Vol 123, No 10 (2022), Pagination: 1237-1245Abstract
Among the ancient cities and ports of Tamil Nadu, India, Poompuhar is a historical and coastal port that emerged with the increasing maritime trade of the early Chola kingdom. The ancient trade town and the busy port of Poompuhar symbolize the Tamil culture and civilization up to 200 ce. The city was destroyed and washed away by big shore waves during ad 500. The submerged parts and scattered destruction remains have been identified in onshore and offshore excavations around the coastal lines of the Bay of Bengal in Tamil Nadu. Information on the port city can be found in various sources, such as archaeological evidence, historical references, coastal erosion data and Sangam Tamil literature. Here, a methodology is presented for a semantic representation of Poompuhar port city, integrating heterogeneous data to create a knowledge base by mapping and associating related entities. The knowledge base has been created using CIDOC CRM to represent Poompuhar events digitally. The experimental results of the ontology are verified exploring the submergence of Poompuhar use cases for onshore and offshore excavations through a knowledge graphKeywords
Archaeological explorations, cultural heritage, knowledge graph, ontology, semantic data.Full Text
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