Glaucoma is a disease which affects the eye and causes blindness. It is an ophthalmologist disease characterized by an increase in Intraocular Pressure (IOP). The glaucoma usually affects the optic disc on the retina which increases the cup size. There are various parameters to identify and diagnose glaucoma. The clustering technique is introduced to detect the glaucoma from the optic disc and cup in the retinal fundus images. Fuzzy C Means (FCM) Clustering is used for clustering the data in which the data points are clustered with different membership degree. But it does not fully utilize the spatial information in the image. The Modified Spatial Fuzzy C-Means clustering with spatial rotation has been proposed to detect glaucoma in retinal fundus images. The first and foremost step is preprocessing operation, in which the optic cup and disk of the input image is being rotated. Initially the optic disk is rotated in some angle and the distance between the data points are measured and a cluster is formed based on the centroid. The centroid and data point along with the cluster can be identified in each step then the common set of points is clustered together. This process continues until no more centroid is found. The cluster with more data points that do not match with the original image is considered as the retinal image with glaucoma disease. In future, this algorithm can be extended to larger clinical databases in order to identify the glaucoma at the maximum level.

The study is executed to assess the millennial perception towards the purchase decisions of green products in Chennai city. The study used both primary and secondary data. The secondary data obtained through research articles, magazines, and daily newspapers. The primary data was gathered from the millennial that are purchasing the green products in Chennai city. The sample size of the study is 591. A simple convenience sampling method was used. The study found that the factors i.e., Environmentally Friendly, Environmental Responsibility, Healthy, Natural products, and environmentally protection, and Social Appeal significantly influenced green products’ purchase decisions among the millennial in the study region. The study also originates that there is significant difference among the age group of concerning the purchase decisions of green products certain aspects like Environmentally friendly, and Healthy. The study results also exhibited that the male and female millennial have similar perception on purchase of green products in the study region.

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Context: A high suicide rate is an index of social disorganization. In India, it is the second leading cause of death among 15-29 years age group. Young age, female sex, poor education, unemployment and socio economic deprivation are some of the potential risk factors. Materials and Methods: The aim of the study is to assess the life events that provoked suicide attempt in a tertiary care centre. It was a cross-sectional study conducted among 476 patients with attempted suicide by convenient sampling method. Study was conducted from January 2016 to May 2017.Data was collected using a pre-tested, semi-structured questionnaire. Descriptive and Inferential statistics were used to analyse the data. Results: Mean age of study participants was 30.65+0.75 years. Among 476, 57.78% of them were males and 24.57% of study participants had family history of suicide. There is association between life events that led to attempt suicide and number of previous attempts of suicide. Among various life events, family problems were the most common factor irrespective of number of previous suicide attempts followed by financial crisis, work-related factors and personal factors. Conclusion: Suicide and attempted suicides are becoming globally endemic. Socio-demographic factors and the nature of their living in a society plays a major role in this. Healthy living comes from healthy family and good working environment.

Coins are integral part of our day to day life. We use coins everywhere like grocery store, banks, buses, trains etc. So it becomes a basic need that coins can be sorted and counted automatically. For this it is necessary that coins can be recognized automatically. In this paper we have developed an ANN (Artificial Neural Network) based Automated Coin Recognition System for the recognition of Indian Coins of denomination `1, `2, `5 and `10 with rotation invariance. We have taken images from both sides of coin. So this system is capable of recognizing coins from both sides. Features are extracted from images using techniques of Hough Transformation, Pattern Averaging etc. Then, the extracted features are passed as input to a trained Neural Network. 97.74% recognition rate has been achieved during the experiments i.e. only 2.26% miss recognition, which is quite encouraging.