Manuscripts

Recent Papers

Research Paper

Forensic Criminology

This research focuses on the history development of forensic science and also shed light on the fusion of science and law that is how forensic science has brought in the administration of justice. This research aims to point out the flaws in the laws with reference to forensic evidence.

Published by: Barani Manikantan

Author: Barani Manikantan

Paper ID: V5I2-1317

Paper Status: published

Published: March 25, 2019

Full Details
Case Study

Fire monitoring system using RF module

The objective of this project is to design and monitor system for the fire alerts in the surrounding environment using Flame sensor. It is transmitted wirelessly using an RF module. The output is displayed in LCD and Lab-VIEW GUI.

Published by: Vedashree J., Abinaya S., Ganesh Babu C., Muchenedi Hari Kishor

Author: Vedashree J.

Paper ID: V5I2-1268

Paper Status: published

Published: March 25, 2019

Full Details
Survey Report

Twitter sentimental analysis

This paper presents the effectiveness of linguistic features to identify the sentiment of Twitter messages using the apache storm framework. We calculate the effectiveness of present lexical resources and features that capture information about the informal and creative language used in microblogging. In the past few years, there has been a huge growth in the use of microblogging platforms such as Twitter. Influenced by intensification, companies, and media organizations are increasingly seeking ways to excavate Twitter information about what people think and feel about their products and services. Here we download Twitter messages for a particular hashtag and carry out sentiment analysis i.e. to find a positive, negative or neutral sense of that tweet using apache storm framework. Each hashtag may have 1000 of comments and new comments are added every minute, in order to handle so many live tweets we are using apache storm framework.

Published by: Mandar Menjoge, Vedant Bhawalkar, Mazhar Sayyad, Jainam Gosaliya

Author: Mandar Menjoge

Paper ID: V5I2-1396

Paper Status: published

Published: March 25, 2019

Full Details
Case Study

Anomaly detection in code base

Any product when under development, goes through numerous changes before finally being released to the customer. While these changes are being done, it adds new features, modifies existing ones. How do we know if a product is in good shape to be released? Yes, we test the product, run the existing unit, functional, performance tests etc. What if the number of tests is in 10000s. How do we analyse each test result? Is there an automated way to detect the overall health of the product using the results of regression tests? Anomaly Detection using machine learning algorithms gives us a way to find out the overall health of the product. Using Anomaly Detection, we can quickly find out about the code base and if new changes should be allowed in before the existing code base is stabilized. It helps to determine, how far the existing code base is away from being released to the customer. It can help the code base to be almost always stable irrespective of the number of code changes that are merged into it.

Published by: Sudipto Nandan

Author: Sudipto Nandan

Paper ID: V5I2-1418

Paper Status: published

Published: March 25, 2019

Full Details
Research Paper

Bus tracking system using Android and web application

Transportation becomes very difficult in cities like Mumbai. The public transports, especially BUSES are developing around the globe. Such public transports reduce the usage of personal vehicles so reducing fuel consumption and mollifying traffic congestion. The problem with BUSES is that the commuters DO NOT know the exact temporal arrangement of the arrival of BUSES at their stops. This leads to looking ahead to BUSES for 30-35 minutes because the commuters are not aware of what time exactly the BUS is to arrive. The approximate arrival time of BUSES is known but there could also be a delay in arrival thanks to traffic. Seeing that people started avoiding public transports and started victimization non-public vehicles, many applications were developed; but these applications were unable to mitigate the problems. Some applications provided only the arrival time and point in time of BUSES at their supply and destination. Some of them provided time-tables, but even they weren't correct as they failed to contemplate the delay thanks to unpredictable factors like – traffic, harsh weather situation, etc. The timetables were not timely updated leading to waiting for BUSES.

Published by: Mayur Kharat, Manoj Mishra, Sanket Panchal, Pooja Walve, Suyog Kadge

Author: Mayur Kharat

Paper ID: V5I2-1434

Paper Status: published

Published: March 25, 2019

Full Details
Research Paper

Maximize lifetime of Wireless Sensor Network by optimal sink deployment and sensor to sink routing

Wireless sensor networks typically contain hundreds of sensors. The sensors collect data and relay it to sinks through single hop or multiple hop paths. Sink deployment significantly influences the performance of a network. Since the energy capacity of each sensor is limited, optimizing sink deployment and sensor-to-sink routing is crucial. In this paper, this problem is modeled as a mixed integer optimization problem. Then, a novel layer-based diffusion particle swarm optimization method is proposed to solve this large-scale optimization problem. In particular, two sensor-to-sink binding algorithms are combined as inner layer optimization to evaluate the fitness values of the solutions. Particle swarm optimization constitutes currently one of the most important routing algorithms. Its popularity has stimulated the emergence of various variants of swarm-inspired techniques, based in part on the concept of pairwise communication of numerous swarm members solving the optimization problem in hand. PSO is a population-based optimization method. PSO consists of a swarm of particles which move towards an optimal solution of the problem. Particle uses their position and velocities (P Best & G Best) to find an optimal route. This paper show better result of Maximizing lifetime of wireless sensor networking by using PSO routing Fully Informed Particle Swarm Optimization (FIPSO) and Diffusion PSO Performance of those algorithms is also evaluated over a set of benchmark instances Compared to existing methods that the sinks are selected from candidate positions, our method can achieve better performance since they can be placed freely within a geometrical plane. Several numerical examples are used to validate and demonstrate the performance of our method. The reported numerical results show that our method is superior to those existing. Furthermore, our method has good scalability which can be used to deploy a large-scale sensor network.

Published by: Manpreet Singh

Author: Manpreet Singh

Paper ID: V5I2-1470

Paper Status: published

Published: March 25, 2019

Full Details