Comparision of Different Classifiers for Prediction of Breast Cancer

The cell formed in the  breast are known as breast cancer. It occurs mainly in women and it may occur rarely in men also. It is considered as the most common ailment that can lead to large number of death in females every year. In spite of the factuality that cancer is treatable and can be relieve if treated at its early stages; many patients are screened for cancer only at a very late stage. Data mining technique such as classifications provides an efficient technique to classify data, where these methods are commonly used for diagnostic decision making. The Machine learning techniques propound various methods such as statistical and probabilistic methods which allow system to learn from past experiences to distinguish and identify patterns from a standard dataset. The research work presents a review of machine learning techniques which can be used in breast cancer disease detection by applying algorithms on breast cancer Wisconsin data set.  Algorithms such as Navies Bayes, Random Forest, Support Vector Machine, Adaboost and Decision Trees were used. The result outcome shows that Random Forest performs better than other techniques

Nutrition Deficiency Prediction using Machine Learning Techniques

Despite the fact that many developing nations have experienced economic progress, Nutrition- deficiency remains a pervasive problem in the society, with millions of impoverished people's diets lacking in essential macro and micronutrients essential for optimal human health. Lack of awareness of food consumed daily causes Nutrition deficiency among general population, data from multiple health records are used for research and prediction. It investigates the importance of a well-balanced diet for our daily life. The Healthy Food Diversity Index (HFDI) is a supplement to the popular Household Dietary Diversity Score (HDDS). It's a tool for determining the diversity of household food. The HDDS has been established as a reliable source of information, but it has several limitations as a measure of dietary diversity that is linked to nutritional quality.  In this paper, various machine learning techniques such as Random Forest classifier (RF), Support-Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Logistic Regression (LR) are used  to predict Nutrition-Deficiency using house hold risk factors and they compared their Accuracy, Sensitivity and Specificity. The predictions were also compared to the anthropometric classifications used by the National school feeding program to prove the efficiency of the proposed approach

In-vitro propagation, organic farming and comparative phytochemical evaluation of Curculigo Orchioides Gaertn. (Musali)

Medicinal plants belong to the oldest known health care products that have been used by mankind all over the world in the form of folklore medicine, traditional medicines or ethnic medicines. But due to the over dependence of herbal drug industry on the plant population, wild source became depleted. Several drugs became endangered while some others had been in the verge of extinction. Musali (Curculigo orchioides) is such a plant which is facing a threat of extinction. These plants can be produced by different methods. The main 2 methods for the propagation of plants will come under the following headings viz; Conventional way of propagation and Non-conventional way of propagation (In Vitro propagation). If a small change in climate and temperature is affecting the chemical composition of plants, then obviously there may be chances for change in the same while propagating it through different techniques. In this study the preliminary analytical profile of the three samples were done in order to know whether the organic and In Vitro variety can be replaced with the wild. As Musali (Curculigo orchioides Gaertn.) is a drug used in several formulations and Musali Khadiradi Kashayam is one among the important one used for the reproductive problems. So the formulation also needs to be analysed in order to know whether the variation in cultivation techniques will affect the physicochemical and chromatographic parameters

FORMULATION AND EVALUATION OF EXTENDED RELEASE PELLETS OF PIOGLITAZONE HYDROCHLORIDE USING NATURAL AND SYNTHETIC POLYMERS BY FLUIDIZED BED COATING TECHNIQUE

Objective: The objective of the current work was to develop Pioglitazone hydrochloride (HCl) pellets coated with natural polymer extracted from peas gum and also to compare the drug release profile with coatings containing semi-synthetic and synthetic polymers. Methods: Fluidized bed coating technique was used to develop pellets. A 22 factorial design was employed to study the effect of independent variables (inlet air temperature and spray rate), on dependent variables (percentage entrapment efficiency, percentage friability, and average particle size). Optimization was done by fitting experimental data to the software program. Obtained pellets were subjected to different evaluation parameters which are critical in the development of the dosage form. An in vitro lag phase study was carried out for all batches in simulated gastric fluid (0.1N HCl) for 5 h and in vitro drug release study was carried out for optimized batch (E-2 and P-3) in simulated intestinal fluid (pH 7.4 phosphate buffer). Results: The optimized batches E-2 and P-3 showed satisfactory percentage entrapment efficiency of 92.66±1.52, percentage friability of 0.57±0.03, and average particle size of 1424±16 μm. All batches maintained lag phase for 5 h in 0.1N HCl. An optimized batch of two different sizes exhibited a burst release within 30 min in a simulated intestinal fluid with no significant difference in release rate constant (*p>0.05) and followed first-order kinetics. Conclusion: Thus, Pioglitazone HCl pulsatile pellets were successfully developed for treating diabetes mellitus by fluidized bed coating technique employing factorial design

FABRICATION AND CHARACTERIZATION OF FAST DISSOLVING FILMS OF ECLIPTA PROSTRATE LEAVES EXTRACT TO TREAT MOUTH ULCERS

Objective: This research focused on the design of fast dissolving herbal film of Eclipta Prostrate leaves extract for mouth ulcers. Methods: The extract of Eclipta Prostrata leaves was formulated as films by solvent casting method using various polymers viz., HPMC E5, HPMC E15, sodium alginate and PVA. The films were designed by using propylene glycol as a plasticizer, SSG as super disintegrate and honey as a sweetener. Furthermore, the films were evaluated for thickness, folding endurance, weight variation, % elongation, surface pH, % moisture uptake, % moisture loss, disintegration and in vitro drug release study. Results: The revealed that all the films were good in appearance and had a smooth texture. Out of all ten formulations, F3 and F5 disintegrated rapidly with a disintegration time of 27 and 32 seconds. The drug release studies revealed that all the formulations had a good release profile, but the F3 formulation showed rapid release i.e. 83.57% in 4 min. The stability studies revealed that the formulations F3 and F5 were found good with non-tackiness, easily separable and disintegrated at 29 and 33 sec respectively with no appearance and drug release. Conclusion: The research revealed that Eclipta prostrate leaves extract can be formulated into oral films for the treatment of mouth ulcers with improved bioavailability and expected patient compliance

A Study on Impact of Television Advertising towards the Selection of Branded Women's Apparel with Special Reference to Consumer at Tiruchirappalli City

A Study on the Impact of Television Advertising towards the Selection of Branded Women’s Apparel With special reference to a consumer at Tiruchirappalli City. Television Advertisement assumes a noteworthy job in present-day life. It shapes the states of mind of the general public and the individual and definitely impacts customer behavior. The customer needs to fight with a gigantic measure of data and have the capacity to settle on a decision, reach determinations and settle on critical choices. This examination plans to set up whether the Trichy City, Tamilnadu, customers affects their purchasing choices because of the impact of Apparel retailer’s TV advertisements. An advertisement is the paid type of unoriginal introduction of thoughts, products, and ventures by distinguished support. The primary goal of promoting is to advise, influence and remind the focused on shoppers with respect to the item. Consistently we go over an assortment of advertisements. When we read a daily paper or a magazine or tune in to the radio or stare at the television or stroll on a street or travel by a transport or a prepare or we see a film or go anyplace else, we run over a type of an advertisement. These advertisements fill in as the main thrust for our buy choices.The main objective of this paper is to study on the Impact of Television Advertising towards the Selection of Branded Women’s Apparel reference to a consumer at Tiruchirappalli City. A descriptive study was done on primary data collected from 125 respondents on basis of judgmental sampling. 125 respondents were given questionnaire and 110 were found to be fully usable for analysis. A questionnaire was used to collect primary data. Likert five-point scaling was given to customers for evaluating their impact of Demographical factors on Apparel retail store selection. IBM SPSS Statistic version 20.0 was used for this analysis and the following tools were administered 1) Reliability Test 2) Factor Analysis and 3) Multiple Regression 4) Chi-square goodness of fit test. The reliability test was made and the obtained coefficient alpha value (Cronbach’s alpha) was 0. 0.937, and hence the data had satisfactory reliability. Factor analysis and Multiple Regression was used to find the Impact of Television Advertising towards the Selection of Branded Women’s Apparel. In the Chi-square test, we are assessing how well the sample data fits the population proportions specified by the hypothesis