Vitamin D (VD) deficiency is a very common disease among elderly people. The lack of VD causes various diseases related to skin, eyes and throat. The previous epidemiological studies tried to predict the vitamin B6 and VD levels from the blood samples. Since this is laborious and time-taking, it is very difficult for the homely people to work on it. There is a strong requirement for the noninvasive method as there is a necessity to detect the deficiency at the early stage. Certain crucial parameters that could be used for analysis are based on the intake of anthropogenic parameters along with the commonly known body vitals. These parameters include the body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHR) and body roundness index (BRI). The dataset used for the prediction of VD has been collected from 501 patients in the age of 40–75 years old. The prediction of VD levels in the body has various complications, like the sex, previous health records, inherent health conditions and body pathology. To consolidate all those parameters and to analyse, a robust model is required to associate the parameters which are used to predict the deficit of VD. A binary set of gated recurrent units (GRUs) are used along with the auto-encoders. The feature extraction and selection module in the network are composed of two different patch-based networks which makes the three-stage network robust. Despite these difficulties, the model is robust enough to predict the levels of VD in the body based on the anthropogenic parameters. To support this network, a sub-VitaDNet module is proposed based on the food taken. Through this network, the food taken is continuously observed and the levels of VD are predicted. Hence, the authors believe that the model is robust enough to predict the VD levels in the body.

In recent years, considerable attention has been paid to 3D face data in many face image processing applications. Detailed 3D Face making is developing technology with multiple real-time applications. This work aims to create an exact 3D Face model with facial emotions designed based on the principle of the Face Vertex Land marking and Wulcheir distance. Convolution Neural Network (DCNN) is deployed to extract relevant facial features and those features are used for further analysis. The 3D Face models are constructed efficiently. The proposed model is a concoction of CoarseNet and FineNet through which a 3D coarse face from a bilinear face model with face landmark alignment is created. It is followed by the local corrective field which tends to refine the 3D rough face with consistent photometric constraint. This work follows the various aspects of 3D face modeling techniques: Deep Learning, Epiploic Geometry, and the One-shot learning (DEO) method. The proposed DEO Model has been evaluated using the FER2013 dataset of face images with six basic emotions via performance metrics like accuracy, precision, sensitivity, specificity, and time. The proposed model outperforms other existing methods with promising and state-of-art results. The accuracy obtained through the proposed work shows higher accuracy (more than 90%), which has been demonstrated using real-world models