Informatics Publishing Limited

K. Anandakumar ¹, M. Punithavalli ²

Affiliations
1 Computer Applications Department, Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, IN
2 Computer Science Department, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN

Abstract

The main objective of this project is to illustrate the maximum utilization of available resources for the data mining activities. Mining information and knowledge from huge data sources such as Weather databases, financial data portals or emerging disease information systems has been recognized by industrial companies as an important area with an opportunity of major revenues from applications such as business data warehousing, process control, and personalized on-line customer services over Internet and web. Distributed Data mining is expected to perform partial analysis of data at clients and then to send the outcome as results to the server where it is sometimes required to be aggregated to the global result. The primary issues to be considered for DDM are Scalability, privacy of data and autonomy of data. These issues can be easily handled when we go for intelligent software agents for Distributed Data mining, because of its inherent features of being autonomous, capable of adaptive and deliberative reasoning.

Keywords

Data Mining, Frequent Item Set, Distributed Data Mining.

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K. Anandakumar ¹, C. S. Vijayasri ¹, M. Punithavalli ²

Affiliations
1 Department of Computer Science, Dr. SNS Rajalakshmi College of Arts and Science, IN
2 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, IN

Abstract

In this paper, the recently developed Extreme Learning Machine (ELM) is used for direct multicategory classification problems in the cancer diagnosis area. It uses Microarray gene expression cancer diagnosis for directing multicategory classification problems in the cancer diagnosis area. The common problems faced by iterative learning methods like local minima improper learning rate and over fitting are avoided by ELM. ELM completes the training at a faster rate. We have evaluated the multicategory classification performance of ELM on three benchmark microarray data sets for cancer diagnosis, namely, the Lymphoma data set. The results indicate that ELM produces comparatively better classification accuracies with reduced training time. The implementation complexity of ELM is very less compared to artificial neural networks methods like conventional back-propagation ANN, Linder's SANN and Support vector machine.

Keywords

ELM, ANOVA, Cancer Classification and Gene Expression.

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G. Angeline Prasanna ¹, K. Anandakumar ²

Affiliations
1 Bharathiar University, Coimbatore - 641 046, Tamil Nadu, IN
2 Bannari Amman Institute of Technology, Sathyamangalam - 638 401, Tamil Nadu, IN

Abstract

Objective: The main intention of this research is to provide secured authentication on mobile devices with the use of multimodal based biometric authentication system under unconstrained settings. Methods: Method used in this research is pattern recognition algorithm namely modified unconstrained cohort normalisation (MUCN) is introduced into the score-level fusion process of multi-biometric system. The goal of proposed MUCN is normalizing the unconstraint modalities by correcting the misclassified scores occur in the UCN. Score normalization of multimodal biometric is enhanced and investigated for improving the accuracy performance of the multimodal biometric in an unconstraint setting. Results: The result of presented pattern recognition algorithm performs well in terms of recognition accuracy when compared to existing schemes.From the comparative evaluation on WVU multimodal data set, the proposed MUCN based Score level fusion achieves 89.2 % of overall recognition rate and out-performs existing state-of-art techniques. Conclusion: The present work demonstrates that the result obtained by MUCN can considerably improve the accuracy of fused biometrics. Thus it can be concluded that with respect to the obtained comparison results from the experiment, the proposed method provides highest recognization rate when compare with other conventional methods of biometric recognition system.

Keywords

Joint Sparse Representation, Modified Unconstrained Cohort Normalisation, Multimodal Biometrics, Score-Level-Fusion.

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M. Sangeetha ¹, K. Anandakumar ²

Affiliations
1 Bharathiar University, Coimbatore - 641 046, Tamil Nadu, IN
2 Bannari Amman Institute of Technology, Sathyamangalam - 638401, Tamil Nadu, IN

Abstract

Objective: The main objective of this research is to establish the semantic gap between human-understandable high-level semantics and machine generated low-level features for Automatic image annotation in Annotation based Online Image Retrieval system. The semantic gap reduction is also concentrated where there will present more semantic gap between the human and machine defined entities. Methods/Statistical Analysis: Semantic annotated Markovian Semantic Indexing (SMSI) is used for retrieving the images and automatically annotates the images in the database using hidden Markov model. In contrast to traditional annotation based image retrieval system, retrieves images based on low-level features, the proposed SMSI semantically retrieves the images by searching semantically annotated images in a database for a user query. Each image in a large collection of training samples is then annotated automatically with the a posteriori probability of concepts present in it. At last semantic retrieval of images can be done by measuring semantic similarity of annotated images in the large database by using Natural Language processing tool namely WordNet. In addition to that entity based ontology representation is introduced which tend to map the human defined higher level keywords to the machine specific lower lever features. It is achieved by converting the lower level feature values into the intermediate level features. Findings: The presented SMSI method possess definite theoretical advantages and also to achieve better Precision versus Recall results when compared to Latent Semantic Indexing (LSI) and Markovian Semantic Indexing (MSI), methods in Annotation-Based online Image Retrieval system. The better accuracy is achieved while retrieving the contents based image annotation where the semantic gap is reduced considerably. Application/Improvements: Thus the analysis of presented work is demonstrates semantically related features of images and achieves improved retrieval result when compare with the other state-of-art techniques.

Keywords

Automatic Image Annotation, Latent Semantic Indexing, Markovian Semantic Indexing, Semantic Annotated Markovian Semantic Indexing.

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S. P. Silambarasan ¹, K. Anandakumar ², R. Venkatalakshmi ³, C. Sasikala ⁴

Affiliations
1 Axis Clinicals Ltd, Hyderabad, Andhra Pradesh, IN
2 Dept. of Pharmaceutical Analysis, Adhiparasakthi College of Pharmacy, Melmaruvathur-603319, Tamilnadu, IN
3 Department of Pharmaceutics, Shri Vishnu College of Pharmacy, Bhimavaram, Andhra Pradesh, IN
4 Department of Pharmacology, Shri Vishnu College of Pharmacy, Bhimavaram, Andhra Pradesh, IN

Abstract

Two new simple, sensitive, rapid, accurate and precise methods, namely UV spectrophotometric and RP-HPLC methods were developed for the estimation of Levosulpiride in bulk and in tablet formulation. In UV spectrophotometric method, Levosulpiride exhibited maximum absorbance at 291.5 nm with apparent molar absorptivity of 2.6031×10³ L mol^-1 cm^-1 in 0.1 M HCl. Beer’s law was obeyed in the concentration range of 10-50 mg/ml. In RP-HPLC method, the elution was done using a mobile phase consisting of methanol and 25 mM phosphate buffer pH 3.5 (pH adjusted with phosphoric acid, 15:85 v/v) on Shimadzu HPLC C₁₈ (4.6×150 mm) column at a flow rate of 0.8 ml/min with UV detection at 293 nm. An external standard calibration method was employed for quantization. The elution time was 5.08 minutes. Beer’s law was found to be obeyed in the concentration range of 4-24 ×g/ml. The results of proposed methods were validated statistically and by recovery studies. The % RSD values for recovery studies were found to be less than 2% for the both methods. Hence the proposed methods were successfully used to determine the drug content in bulk and in tablet formulation.

Keywords

Levosulpiride, UV Spectrophotometry, RP-HPLC, External Standard Calibration.

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K. Anandakumar ¹, K. Varadharajan ¹, R. Subathrai ¹, D. Jothieswari ¹, G. Sivan Seyal ¹

Affiliations
1 Adhiparasakthi College of Pharmacy, Melmaruvathur-603319, Tamil Nadu, IN

Abstract

Two simple, precise and accurate methods were developed for the estimation of Tadalafil in bulk and in formulations. In method A, UV spectra of tadalafil in methanol and water (80:20) exhibits the λmax of 284.5 nm. Method B is based on the formation of bluish green colored complex by the reduction of ferric ions into ferrous ions in presence of potassium ferricyanide as oxidizing agent exhibits the absorption maxima at 828 nm. Beer’s law was obeyed in the concentration range of 5-30 μg/ml and 2-10 μg/ml for method A and B, respectively. The accuracy of the method was determined by recovery studies. The methods were validated as per ICH guidelines. % RSD value was less than 2%; this proved that the methods are having good recovery and reproducibility. The proposed methods are simple, rapid, precise and accurate and hence can be applied for routine quality control analysis of tadalafil in bulk and in tablet dosage forms.

Keywords

Tadalafil, UV-Visible Spectrophotometry, Method Validation, Ferric Chloride, Potassium Ferricyanide.

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K. Anandakumar ¹, G. Kumaraswamy ¹, T. Ayyappan ¹, A. S. K. Sankar ¹, D. Nagavalli ¹

Affiliations
1 Adhiparasakthi College of Pharmacy, Melmaruvathur-603319, Tamil Nadu, IN

Abstract

A simple, selective, linear, precise and accurate RP-HPLC method was developed and validated for rapid assay of Eszopiclone (ESZ) in bulk and in tablet dosage form. Isocratic elution at a flow rate of 1 ml/ min was employed on a Phenomenax Luna C₁₈ column (150×4.6 mm; 5 μ) at ambient temperature. The mobile phase consisted of Acetonitrile: phosphate buffer adjusted to pH 2.5 (25:75% v/v). The UV detection wavelength was 304 nm and 20 μl of sample was injected. The retention time for ESZ was 3.92 min. The method obeys Beer's law in the concentration range of 4-24 μg/ml. The amount of Eszopiclone Present in the formulation was found to be 99.60±1.5389. The % recovery was in the range between 99.25% and 99.86%. The percentage RSD for precision and accuracy of the method was found to be less than 2%. The method was validated as per the standard analytical procedures and statistical parameters. The method was successfully applied for routine analysis of ESZ in bulk and in formulation.

Keywords

Eszopiclone, RP-HPLC, UV Detection and External Standard Calibration Method.

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