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Recent Papers

Effect of BG-II Cotton Hybrids and Non Bt Cotton on Weight of Different Instars of Spodoptera Litura (Fab.)

Laboratory evaluation of eleven Bt cotton cultivars expressing both Cry1Ac and Cry2Ab endotoxins (BT-II) and non Bt cotton on weight of first, second, third and fourth instar larvae of Spodoptera litura. Different plant parts were used i.e. leaves, squares and bolls at 60, 75, 90 and 125 days after sowing of the crop for bioassay. First instar larvae fed on leaves and bolls shows hundred per cent mortality. The final weight of the each instar was reduced at seven days after feeding when compared to non Bt cotton. Reduced in weight was more in case of first, second and third instar than fourth instar larvae. Among leaves, squares and bolls reduction in weight of all four instars was more on leaves followed by squares and bolls

Published by: Ramanjali Thirri, T. V. K Singh

Author: Ramanjali Thirri

Paper ID: V3I3-1273

Paper Status: published

Published: May 18, 2017

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Prediction of Movie Success for Real World Movie Data Sets

Predicting the success of a movie before its release has far been a huge point of concern for directors and producers alike, especially after they factor in real world data and the occurrence of unforeseen circumstances. Since these entities invest massively in movies, they need some kind of reassurance that the movie will be successful and reap in massive profit in terms of financial returns and credibility. In this paper, we talk about a prediction engine we have implemented that uses classification and fuzzy logic to categorize a movie as successful or not as well as used our own algorithm to calculate a target variable which is the IMDB score of a movie based on various parameters. Our engine has proved to be successful from the technical unit testing performed on it with the use of multiple iterations of input values.

Published by: Abhisht Joshi, Sanjai Pramod, Geetha Mary .A

Author: Abhisht Joshi

Paper ID: V3I3-1228

Paper Status: published

Published: May 18, 2017

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Performance Comparison of AODV and DYMO Routing Protocols, for Congestion Detection in VANET

Vehicular Ad Hoc Networks (VANET) is a subclass of Mobile ad hoc networks which provides a distinguished approach for Intelligent Transport System (ITS). VANET’s provide communication between vehicles moving on the roads. Congestion detection and control in VANET is essential and necessary for smart ITS in order to avert an accident. Special considerations are required to develop more sophisticated techniques to avoid and detect congestion. The constrained resources of the Wireless Sensor Networks (WSN) must be considered while devising such technique to achieve the maximum throughput. In this paper we have implemented congestion detection in a collision avoidance scenario in two routing protocols, AODV and DYMO, on the basis of sent and received beacons and packets dropped while nodes communicate with each other or the Base Stations. This paper also discusses and compares the advantages/disadvantages of AODV and DYMO based on battery usage, energy used in transmit mode, throughput, message received and sent. The various ways of congestion control are further discussed.

Published by: Rajashree Dutta, Ranjana Thalore

Author: Rajashree Dutta

Paper ID: V3I3-1319

Paper Status: published

Published: May 18, 2017

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Image Transfer Learning For Image Classification of Flowers Using Tensor Flow

Human Being can process images easily. It’s very easy for us to distinguish between various objects that we come across in our daily life. These tasks which are quite rudimentary for the human brain tend to be very hard problems when the computers are involved in solving the problem. The image recognition is easy to us because we have been trained to recognize the objects through their images since infancy. Machine Learning fields over the last few years have made exceptional progress in this field. But training the computer for the same thing requires a lot of time and effort. This is where Transfer Learning comes into play. We will be using transfer learning on the basic TensorFlow library to train our module on Oxford 17 VGG and Oxford 102 VGG flower data sets. We have majorly used Google’s Inception v3 model and applied it on Oxford data set to categorize flowers. This gave an overall accuracy of 94.8%.

Published by: Ujjwal Rangarh, Tanmay Trehan, Shalini .L

Author: Ujjwal Rangarh

Paper ID: V3I3-1221

Paper Status: published

Published: May 17, 2017

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Performance Comparison of AODV and DYMO Routing Protocols for Boundary Detection in 3-D Wireless Sensor Networks

In order to track and detect continuous nature objects in wireless sensor networks, a large number of sensor nodes are involved. These continuous objects like biochemical diffusions, forest fires, oil spills usually spread over larger area. The nodes that sense the phenomena need to communicate with each other for exchanging the information and also send sensing information to sink, possibly by passing through many intermediate nodes. For many geometry based algorithms triangulation serves as the basis for wireless sensor networks. In this paper, we propose a distributed algorithm that produces a Delaunay triangulation for an arbitrary sensor network, for communication between the sensor nodes. The information of occurrence of an event is then passed to a control node. The communication is done both with and without using relay nodes and a comparison is made between the two methods in terms of battery, total unicast messages received, throughput and delay. The two protocols in which we have worked are AODV and DYMO.

Published by: Aditi Pandey, Ranjana Thalore

Author: Aditi Pandey

Paper ID: V3I3-1320

Paper Status: published

Published: May 17, 2017

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Segmentation of Breast Images Using Gaussian Mixture Models

Breast cancer originates when cells grow uncontrollably in the breast resulting into a tumour that can be felt as a lump or observed on x-ray. The tumour is malignant that is cancerous if the cells invade surrounding tissues or spread to distant areas of the body. Breast Cancer can be observed both in women as well as men. Image processing aims at divide one picture into different types of classes or regions, recognition of objects and detecting of edges, etc that is done after the image is segmented. The main aim of this paper is to detect and separate background and foreground by using Gaussians Mixture Model, the parameters of the model and training data are learned by EM-algorithm. Pixel labelling corresponding to each pixel of true image is done by Bayesian rule. This hi labelled image is constructed during of running EM-algorithm.Numerical method of finding maximum a posterior estimation is done by using EM-algorithm and Gaussians mixture model which we called EM-MAP algorithm.

Published by: Miss. Ankita Satyendra Singh, Prof. M. M. Pawar

Author: Miss. Ankita Satyendra Singh

Paper ID: V3I3-1324

Paper Status: published

Published: May 17, 2017

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