Velagapudi Ramakrishna Siddhartha Engineering College

About: Velagapudi Ramakrishna Siddhartha Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 1307 authors who have published 1155 publications receiving 6163 citations.

Malware Detection in Executable Files Using Machine Learning

Abstract: Extrusion of personal or sensitive data by malicious software is causing a great damage to the world. This is a very critical issue for individuals as well as various sectors of the society at large. Hundreds and thousands of cyber attackers’ cross-swords for computer systems by dropping a bombshell of malware with an intention to rift crucial data. That is why, securing this data is an important issue for the researchers. This paper focuses on developing an application which can distinguish a malicious and legitimate file with the help of machine learning algorithms.

EODVGA: An Enhanced ODV Based Genetic Algorithm for Multi-Depot Vehicle Routing Problem

Abstract: Multi-Depot Vehicle Routing Problem (MDVRP) is a familiar combinative optimization problem that simultaneously determines the direction for different vehicles from over one depot to a collection of consumers. Researchers have suggested variety of meta-heuristic and heuristic algorithms to elucidate MDVRP, but none of the existing technique has improved the fitness of the solution at the time of initial population generation. This motivates to propose an enhanced ODV based population initialization for Genetic Algorithm (GA) to solve MDVRP effectively. The Ordered Distance Vector (ODV) based population seeding method is a current and effective population initialization method for Genetic Algorithm to produce an early population with quality, individual diversity and randomness. In the proposed model, the customers are first grouped based on distance to their nearest depots and then routes are scheduled and optimized using enhanced ODV based GA. The experiments are performed based on different types of instances of Cordeau. From the experimental results, it is very clear that the proposed technique outperforms the existing techniques in terms of convergence rate, error rate and convergence diversity.

A Feature Subset Based Decision Fusion Approach for Scene Classification Using Color, Spectral, and Texture Statistics

Abstract: Content Based Image Retrieval (CBIR) deals withthe automatic extraction of images from a database based ona query. For efficient retrieval the digital image CBIR requiressupport of scene classification algorithms. The Cognitive psychology suggests that the basic level classification is efficient withthe global features. However, a detailed classification requires acombination of the global and the local features. In this paper, we propose a decision fusion of the classification results based onlocal and global features. The proposed algorithm is a multi stageapproach, in the stage-1 the algorithm separates the completedatabase into natural and artificial images using spectral features. In the stage-2, the texture and color features are used to furtherclassify the image database into subcategories. The results of theproposed decision fusion algorithm give a 5% better classificationaccuracy than the single best classifier.

Quantitative Analysis of AGV System in FMS Cell Layout

Abstract: Material handling is a specialised activity for a modern manufacturing concern. Automated guided vehicles (AGVs) are invariably used for material handling in flexible manufacturing Systems (FMSs) due to their flexibility. The quantitative analysis of an AGV system is useful for determining the material flow rates, operation times, length of delivery, length of empty move of AGV and the number of AGVs required for a typical FMS cell layout. The efficiency of the material handling system, such as AGV can be improved by reducing the length of empty move. The length of empty move of AGV depends upon despatching and scheduling methods. If these methods of AGVs are not properly planned, the length of empty move of AGV is greater than the length of delivery .This results in increase in material handling time which in turn increases the number of AGVs required in FMS cell. This paper presents a method for optimising the length of empty travel of AGV in a typical FMS cell layout.

Framework for Stress Detection Using Thermal Signature

Abstract: Autonomic nervous system (ANS) activity requires usage of contact sensors with patients' body. Computational psychophysiology based on thermal imaging is suggested as an alternative. It is a non-invasive and non-contact method that can be used for medical applications such as stress detection, human psychology, geriatric medicine, autonomic nervous activity, medical diagnostics and psychophysiology. It is free from pain and radiations. Very few works are reported to identify stress states at individual level. This work presents a framework to detect stress based on heart rate variability (HRV). Methods were proposed for extracting thermal signatures such as cardiac pulse, breath rate, sudomotor response, and stress response from various regions. Psychophysiological disorders are categorized as bradycardia, tachycardia, stress, and no stress. The system enables monitoring of thermal features at four facial areas such as forehead, neck, periorbital, and nose. The proposed system is tested on bench mark datasets and proved with high confidence w.r.t existing works and ground truth values.