Scientific Validation of Bronchodilator, Anti-Histamine and Anti-Oxidant Activities of Siddha Herbo-Mineral Formulation “Nagarasingadhi Chooranam” in In-Vivo and In-Vitro Models.

Siddha system is one of the ancient traditional systems of medicine of our country compiled by the Siddhars. The term “Siddha” is derived from the word “Siddhi” that is Attainment of Perfection or Achievement of Heavenly bliss (1). Among all the system of medicine practised all over the world the Siddha system is undoubtedly the ancient,transcending many centuries.The traditional Siddha System of Medicine is not only used to cure, but also to prevent the onset of disease. It is the first and foremost system of medicine to emphasise health as the perfect state of physical, psychological, social and spiritual wellbeing component of human being, The trial drug Nagarasingadhi chooranam was selected from the classical Siddha literature, “Anuboga Vaidhiya Navaneedham (Part 8)” for the evaluation of safety and efficacy of the drug in Swasakasam (Bronchial asthma), The trial drug was identified and authenticated by the botanist and experts of Gunapadam. Since the trial drug, Nagarasingadhi chooranam purification processes and the drug was prepared according to the classical methods. The purification process of this drug possible to eliminates their toxins and increases its efficacy and the grinding process of this drug helps to change the particle size of the drug for its better bio availability

Consumer Purchasing Decision towards Skin Care Products

The global trend of using skin care products is growing at accelerator rate. As a result of which number of skin care products are emerging as consumer options. The purpose of this study is to analyse the factors influencing consumers decision to purchase skin care products. The study is about the purchasing pattern of people in and around Coimbatore city. A self- designed questionnaire has been designed to collect the information from the respondent. Around 120 samples have been collected for this research. For identifying the purchasing behaviour of the consumer, the respondent was asked to rank the variables based on the Likert scale. The influence of social media on consumer behaviour is also analysed. The statistical analysis that has been done is regression. The insights gained will help the skin care marketers to develop the better growth strategy to sustain the market. This study provides the better understanding of how different variables influence purchasing behaviour

Unsupervised texture classification using vector quantization and deterministic relaxation neural network

This paper describes the use of a neural network architecture for classifying textured images in an unsupervised manner using image-specific constraints. The texture features are extracted by using two-dimensional (2-D) Gabor filters arranged as a set of wavelet bases. The classification model comprises feature quantization, partition, and competition processes. The feature quantization process uses a vector quantizer to quantize the features into codevectors, where the probability of grouping the vectors is modeled as Gibbs distribution. A set of label constraints for each pixel in the image are provided by the partition and competition processes. An energy function corresponding to the a posteriori probability is derived from these processes, and a neural network is used to represent this energy function. The state of the network and the codevectors of the vector quantizer are iteratively adjusted using a deterministic relaxation procedure until a stable state is reached. The final equilibrium state of the vector quantizer gives a classification of the textured image. A cluster validity measure based on modified Hubert index is used to determine the optimal number of texture classes in the image

A combined neural network approach for texture classification

Abstract--In this article, we present a two-stage neural network structure that combines the characteristics of self-organizing map (SOM) and multilayer perceptron (MLP) for the problem of texture classification. The texture features are extracted using a multichannel approach. The channels comprise of a set of Gabor filters having different sizes, orientations, and frequencies to constitute N-dimensional feature vectors. SOM acts as a clustering mechanism to map these N-dimensional feature vectors onto its M-dimensional output space, where in our experiments, the value of M was taken as two. This, in turn, forms the feature space from which the features are fed into an MLP for training and subsequent classification. It is shown that the disadvantage of using Gabor filters in texture analysis, namely, the higher dimensionality of the Gaborian feature space, is overcome by the reduction in the dimensionality of the feature space achieved by SOM. This results in a significant reduction in the learning time of MLP and hence the overall classification time. It is found that this mechanism increases the interclass distance (average distance among the vectors of different classes) and at the same time decreases the intraclass distance (average distance among the vectors of the same class) in the feature space, thereby reducing the complexity of classification. Experiments were performed on images containing tiles of natural textures as well as image data from remote sensing

Robust Defense Scheme Against Selective Drop Attack in Wireless Ad Hoc Networks

© 2013 IEEE. Performance and security are two critical functions of wireless ad-hoc networks (WANETs). Network security ensures the integrity, availability, and performance of WANETs. It helps to prevent critical service interruptions and increases economic productivity by keeping networks functioning properly. Since there is no centralized network management in WANETs, these networks are susceptible to packet drop attacks. In selective drop attack, the neighboring nodes are not loyal in forwarding the messages to the next node. It is critical to identify the illegitimate node, which overloads the host node and isolating them from the network is also a complicated task. In this paper, we present a resistive to selective drop attack (RSDA) scheme to provide effective security against selective drop attack. A lightweight RSDA protocol is proposed for detecting malicious nodes in the network under a particular drop attack. The RSDA protocol can be integrated with the many existing routing protocols for WANETs such as AODV and DSR. It accomplishes reliability in routing by disabling the link with the highest weight and authenticate the nodes using the elliptic curve digital signature algorithm. In the proposed methodology, the packet drop rate, jitter, and routing overhead at a different pause time are reduced to 9%, 0.11%, and 45%, respectively. The packet drop rate at varying mobility speed in the presence of one gray hole and two gray hole nodes are obtained as 13% and 14% in RSDA scheme