Volume 1 Issue 1: March 2022

This paper presents a novel method of feature extraction using Fan beam projection-based data. The Fanbeam projection covers the image completely and hence gathers all the important information. Even though the image quality is distorted, this type of feature extraction method helps to gather all the important information as there is a huge volume of projection data. Also, the use of multiple detectors speeds up the entire process. All the projections of the image together form a sinogram image which is unique for each facial expression image. Hence, the sinogram image is divided into grids and the histogram formation results in a feature vector for each image. The classification of these feature vectors using Radial Basis Function-based Extreme learning Machine (RBF-ELM) results in high classification accuracy for all the datasets.

This research aims to identify the social and psychological origins of needs, which may result in the need to obtain certain gratifications from social networking sites, different patterns of social networking sites usage, or cause social networking sites addiction, and the possible consequences they may have on millennials social capital and attitudes toward social networking sites advertising. The study adopts the Uses and Gratifications theory and employs a quantitative research method. The sample of the study consisted of 385 millennials, aged from 21-37 years old, who all used Facebook, Instagram, and YouTube platforms. Data were analyzed using the Structural Equation Modeling. The findings of the study provide useful insights regarding millennials behavior on social networking sites, as well as their attitudes towards social networking sites advertising. The findings suggest several implications and recommendations for marketers, which can help in increasing the effectiveness of advertisements directed to millennials.

Classification of brain tumor is highly significant in the medical field in real-world to improve the progress of treatments. The seriousness behind the tumors are normally graded based on the size into grade I, grade II, grade III and grade IV. This is where the process of multi-grade brain tumor classification gains attention. Thus, the article focusses on classifying the brain MRI images into four different grades by proposing a novel and a very efficient classification strategy with high accuracy. The acquired images are pre-processed with the help of an Extended Adaptive Wiener Filter (EAWF) and then segmented using the piecewise Fuzzy C- means Clustering (piFCM) technique. Then the most ideal features such as the texture, intensity and shape features that can best explain the growth of tumors are extracted using the Local Binary Pattern (LBP) and the Hybrid Local Directional Pattern with Gabor Filter (HLDP-GF) techniques. After extracting the ideal features, the Manta Ray Foraging Optimization (MRFO) method has been introduced to optimally select the most relevant features. Finally, a Hybrid Deep Neural Network with Adaptive Rain Optimizer Algorithm (HDNN- AROA) is proposed to classify the grades of brain tumors with high accuracy and efficiency. The proposed technique has been compared with the existing state-of-the-art techniques relevant to brain tumor classification in terms of accuracy, precision, recall and dice similarity coefficient to prove the overall efficiency of the system.