HUNHODRL: Energy efficient resource distribution in a cloud environment using hybrid optimized deep reinforcement model with HunterPlus scheduler.

Abstract

This study aims to enhance the educational security and legitimacy by overcoming the problem of real-time student signature verification. The issue is raised from the growing issue about identity theft and academic fraud in schools, which compromises the validity of tests and other academic evaluations. To overcome these problems, the paper presents a deep learning-based method for signature verification made possible by employing the cutting-edge Convolutional Neural Networks (CNNs). The proposed method utilizes a VGG19 architecture trained and adjusted to handle the unique characteristics of student signatures. Initially, the procedure is pre-processing the image, after the key signature features are extracted. After passing these characteristics across VGG19 network, the signature's authenticity is classified as either unreliable or malicious nodes. The proposed method offers a flexibility and scalability for various educational settings with its capacity to manage both batch and individual processing. The model's efficacy is demonstrated by experiment with accuracy, precision, and recall values, which surpasses the existing techniques. The method ensures dependable performance under circumstances by illustrating resilience to several kinds of noise and distortion. The proposed deep learning model results pay a way for addressing the issue of student signature verification, enhancing the academic institutions' security and legitimacy.