Food Security, poverty reduction and socio-economic well being can be considered as backbone of development.This can be achieved by enhanced education, employment generation, access to infrastructural facilities and increased purchasing power.However, to-date getting a required nutritious diet, clothing and shelter is challenging task and to curtail this MGNREGS was introduced with enormous objectives and policies.Thus, this paper makes an attempt to understand the impact of MGNREGS on food security and employment.This paper is a part of the major research project funded by UGC.The study is based on Primary study conducted in the districts of Bellary, Davangere, Gulbarga, Mandya and Shimoga in Karnataka state through a structured interview schedule comprising of 2500 samples.It is said that the development of a nation depends on the development of rural areas and this strategy can be achieved through MGNREGS.Thus, this paper makes an attempt to realize the intensity of the scheme in addressing food security issues.
In recent era of information technology and big data, a huge volume of data explosion on web makes it difficult to choose any information or item which users really interested about it.To handling such challenges, recommender systems (RS) emerged as helpful tool for recommending such information or item to potentially interested users.Content based recommendation (CBR) and collaborative filtering is two most popular classes of recommendation.Collaborative filtering (CF) approach analyzes user interest while CBR recommends items by object analysis.The main challenges of CF include scalability, synonymy and sparsity which creates obstacle in effective recommendation.In this work, we propose hybrid model of recommender system based on singular value decomposition (SVD), which combines with CF and content based recommendation approach.Experimental evaluation is carried out via prediction accuracy metrics root mean squared error (RMSE) and mean absolute Error (MAE).Empirical results using real dataset demonstrates the effectiveness of proposed approach in comparison with others baseline traditional rating RS.Hybrid recommendation model by combined features shows improved quality and better accuracy in rating prediction, introduced a diversity factors in recommendation.
Introduction:Fatty liver, also referred to as hepatic steatosis, has increased in prevalence during the past 20 years.Due to the fact that fatty liver patients usually have no symptoms when they are diagnosed, this is related to an increase in the prevalence of fatty liver combined with the increasing use of radiologic tests that identify fatty liver.[1] Increased liver fat content (LFC) is linked to factors like obesity, type 2 diabetes, insulin resistance, dietary problems, metabolic and genetic abnormalities, infections, and binge drinking.[2].Hepatic steatosis is typically painless, although it may progress into a more serious liver condition.Regardless of the patient's BMI, type 2 DM increases both the likelihood and the severity of NAFLD.[3].Histologic analysis, ultrasonography, computed tomography, and magnetic resonance imaging can all be used to assess the LFC [4].Histologic analysis regarded to be the most accurate approach for staging and grading fatty liver disease; however, it is an invasive procedure making it less desirable.Although histologic analysis has been shown to be the most reliable
