CONSTRUCTION OF A NEW FAMILY OF EFFICIENT IMBEDDED POLYNOMIALS WITH DISTINCT COEFFICIENTS

We propose a new family of multi-purpose imbedded polynomials having distinct coefficients. There exist relationships between various coefficients of the members of the family, which considerably reduce the computational cost of development as well as using any number of members of the family in a particular problem. Every member polynomial of degree n going through (n+1) focuses {(xi, yi): i=0,1….......n} can be constructed very easily from another member having degree (n-1). In this paper, it is shown that the family of polynomials M exists and is efficient, reliable and more accurate as compared to other available techniques. The family has been successfully applied to the problem of interpolation in this paper. Therefore, the family M is also called the Malik‘s Imbedded Interpolating Polynomials (M.I.I.P). The family M gives similar results as compared to Lagrange Interpolation as for as accuracy is concerned but they are more efficient. The proposed polynomials are more efficient, more stable and more reliable as compared to other traditional interpolation methods due to remarkable reduction in mathematical operations. Our approach and the design of the method is different of available methods of prototype interpolation Methods. We have considered the drawbacks of other methods and eliminated from our approach. The superiority of the family is established and reported

A Technique and Architectural Design for Criminal Detection based on Lombroso Theory Using Deep Learning

Crimes and criminal activities are increasing day by day and there are no proper criteria to search, detect, identify, and predict these criminals. Despite various surveillance cameras in different areas still, crimes are at a peak. The police investigation department cannot efficiently detect the criminals in time. However, in many countries for the sake of public and private security, the initiation of security technologies has been employed for criminal identification or recognition with the help of footprint identification, fingerprint identification, facial recognition, or based on other suspicious activity detections through surveillance cameras. However, there are limited automated systems that can identify the criminals precisely and get the accurate or precise similarity between the recorded footage images with the criminals that already are available in the police criminal records. To make the police investigation department more effective, this research work presents the design of an automated criminal detection system for the prediction of criminals. The proposed system can predict criminals or possibilities of being criminal based on Lombrosso's Theory of Criminology about born criminals or the persons who look like criminals. A deep learning-based facial recognition approach was used that can detect or predict any person whether he is criminal, or not and that can also give the possibility of being criminal. For training, the ResNet50 model was used, which is based on CNN and SVM Classifiers for feature extracting from the dataset. Two different labeled based datasets were used, having different criminals and noncriminals images in the database. The proposed system could efficiently help the investigating officers in narrowing down the suspects' pool