The prevailing Nation in society is that wealth brings comfort and luxury , so it is a challenging and daunting task to find out which is more effective and accurate method for stock rate prediction so that a buy or sell signal can be generated for given stocks. Predicting stock index with traditional time series analysis has proven to be difficult an Artificial Neural network may be suitable for the task. A Neural Network has the ability to extract useful information from large set of data. This paper presents a review of literature application of Artificial Neural Network for stock market predictions and from this literature found that Artificial Neural Network is very useful for predicting world stock markets.

Background: Research on smartphone and Internet addiction has increased rapidly, indicating its clinical and social significance. This study aimed at exploring the possible relationship between smartphone addiction, self-esteem, and social anxiety. Materials and Methods: A total of 464 young college-going adults participated in this study [male = 175 (37.71%), female = 289 (62.3%)] between the ages of 18 and 28 years old. The study participants were asked to complete a survey having three different questionnaires, namely “Smartphone Addiction Scale,” “Rosenberg’s Self-Esteem Scale,” “and the Interaction Anxiousness Scale.” This study also focuses on gender and age differences regarding smartphone addiction. Karl Pearson’s correlation coefficient and unpaired t-test were used to test the significance of the relationship among study variables. Regression analysis was performed to predict smartphone addiction by using Age, Rosenberg’s Self-Esteem Score, and Interaction Anxiousness Score. Results: The mean smartphone addiction scale (SAS) total score was higher in males as compared with females (P = 0.01). No significant difference was observed in RSE total score and IAS total score among males and females (P > 0.05). Significant correlations were observed among SAS total, RSE total, IAS total, and Age (in years) (P < 0.05). Regression analysis was applied to predict SAS total score by using independent variables such as age, RSE total, and IAS total. The coefficients for age and RSE total score were significant (P < 0.01), and the IAS total was not significant (P > 0.05). Conclusion: Males reported having higher smartphone addiction levels as compared with females. A positive correlation was observed between Social Anxiety and Smartphone addiction. A negative correlation was observed between self-esteem and smartphone addiction, which indicates that the lower the self-esteem, the higher will be the smartphone addiction. Age was negatively correlated with smartphone addiction score and social anxiety score, whereas age was positively correlated with self-esteem.

Background: Overweight and obesity are shown to be independent risk factors for hypertension by several epidemiological studies. A practical, inexpensive and easily performed method for evaluation of body fat is anthropometry. Hence the present study was undertaken to explore association between anthropometric indices and blood pressure and determine efficacy of neck circumference to identify overweight subjects and define NC cutoff levels for overweight and obesity.Methods: Cross sectional, comparative study conducted on apparently healthy medical college students, 150 having parental history of hypertension and 150 without a parental history of hypertension. Height, weight, waist circumference, hip circumference, neck circumference, body mass index, waist-hip ratio, waist- height ratio, and blood pressure were measured. Data was analyzed using SPSS version 20.Results: Prevalence of pre-hypertension is 42.33%. 54.33% pre-hypertensive subjects had family history of hypertension but there is no statistically significant association between family history of hypertension and pre-hypertension. Neck circumference correlated with BMI, WC, W/H ratio (p<0.05) indicating that NC could be a useful screening tool. NC cutoff values determining overweight & obesity in this study is >33.30 cm in females and >37.15 cm in males. 61.76% and 38.98% pre-hypertensive males and females respectively have BMI above 25Kg/m² in comparison to 19.51% normotensive males and 20.88% normotensive females.Conclusions: Study reveals development of hypertension is attributable to overweight and obesity and no statistically significant relationship has been established between family history of hypertension and risk for developing hypertension. NC>37.15cm for males and >33.30 cm for females was the best cut off levels for determining overweight/obese subjects.