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Research Paper

Effect of strength properties of concrete waste materials (wood powder)

The use of high strength1 concrete is expected to increase as we move into next century in all areas of the world. In recent years there has been a rapid growth of interest in high strength concrete. Concrete is a material used in building construction, consisting of a fine aggregate and a coarse aggregate that is bounded by cement and water with various types of admixtures2 which are available in the market or from the waste materials3. A design mix is specified by the designer principally in terms of strength cement content and water-cement. Since economical parameters and compressive strength are fundamental properties of concrete in two different stages of production, the correlation between costing parameters and compressive strength4 has been used instead of using water-cement ratio versus compressive strength relationship. If we maintain the water-cement ratio and by adding various types of admixtures in concrete we can improve the compressive strength of concrete and also get more strength which will be very economical. In the proposed method, The designer is able to estimate parameters like compressive strength and economical costing at the design stage for a given target strength, in addition to ingredients5 of concrete

Published by: Kashish Arora

Author: Kashish Arora

Paper ID: V4I2-1817

Paper Status: published

Published: April 17, 2018

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Research Paper

Object recognition using CNN

Object recognition is a popular task in computer vision. The method usually requires the presence of a data-set annotated with location information of the objects, which is in the form of bounding boxes around the objects. In this project, we have implemented a method to carry out object recognition in a weakly supervised manner i.e., using partially annotated data-set. The data-set provides the information about what objects are present in the image but not where they are present. We have used a Convolutional Neural Network(CNN) based architecture to perform this task. We also validated by experimenting with different architectures that mere information of presence/ absence of objects in an image (weak labels) does provide their location information for free.

Published by: Jagruti Jadhav, Mehzabeen Attar, Shradha Patil, Saleem Beg

Author: Jagruti Jadhav

Paper ID: V4I2-1827

Paper Status: published

Published: April 17, 2018

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Research Paper

A study on classifiers performance for prediction of diabetic disorder

In recent years, there has been an explosion in the rate of using technology that helps in discovering the diseases. It contributes to treating several complications such as nerve and blood vessel damage, heart problems, and a higher risk of kidney malfunctioning. Data Mining, being the foremost analyzing technique used by researchers that provides effective results in an early diagnosis of diabetes. This research paper focuses on the approaches, namely Decision tree, Naive Bayes and Neural-Fuzzy classifier in predicting disease and their performance is measured using Accuracy evaluation metric. The classification accuracy and response time were compared to the methods using Accuracy and Running Time as Performance criteria. From the study, it is observed that the Decision tree algorithm gives a better accuracy in the overall performance of the classifier among the algorithms under consideration.

Published by: R. Pradeepa, K. Palanivel

Author: R. Pradeepa

Paper ID: V4I2-1887

Paper Status: published

Published: April 17, 2018

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Review Paper

Food recipe application

The proposed project is an Android Application that will provide recipes to the users based on the ingredients available to them. From a list of ingredients, the user has to select the available ingredients and then the application will display a list of recipes which will use the ingredients selected by the user. The user will have filters to select the kind of recipe it wants and then the result can be sorted and filtered according to the user’ needs.

Published by: Hanish Punamiya, Mitesh Jain, Saumil Mavani, Ajay Dhruv

Author: Hanish Punamiya

Paper ID: V4I2-1896

Paper Status: published

Published: April 17, 2018

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Research Paper

Grassberger procaccia algorithm for EEG channel selection

The multi-channel nature of EEG a data poses a big challenge to the development of automatic EEG analysis and classification systems. Due to the “curse of dimensionality” problem, the analysis and classification of several channels may not lead to the desired performance. Accordingly, a number of algorithms have been proposed to identify small ”static” subsets of channels that are capable of differentiating between samples of different classes. However, the identification of small subsets of relevant channels may not always be possible, where for certain applications the smaller the number of channels the less chance that sufficient information is provided. The propose in this project is a dynamic channel selection using GrassbergerProcaccia algorithm that identifies a channel (or a subset of channels) for each time segment of the signal that is relevant to the class of that particular time segment. To achieve this, we embraced the Grassberger–Procaccia algorithm methodology, and particularly the multiple classifier behaviour approaches. Each EEG channel can be chosen to represent a certain unseen time segment of the signal based on the performance, or local accuracy, of its nearest neighbors in the set of training time segments. Results obtained using EEG data from a four-class alertness state classification problem reveal that the proposed approach is capable of achieving competitive performance compared to a traditional static channel selection based method. The algorithm also produced very encouraging results when a method developed by Grassberger Procaccia allows estimation of the dimensional complexity of the state‐space attractor of a time series. Saturation of dimensional‐complexity estimates with increasing values of embedding dimension is considered a strong indication that the time series is governed by deterministic chaos. The present investigation employed the Grassberger‐Procaccia method to estimate EEG dimensional complexity in a multi‐subject, factorial experiment.

Published by: K. Navitha, Shaista Simmeen, K. Likhitha, Jigar Patel

Author: K. Navitha

Paper ID: V4I2-1953

Paper Status: published

Published: April 17, 2018

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Research Paper

Wireless surveillance robot

We propose a cost-effective four-wheel surveillance robot using raspberry pi, micro-controller which employ servomotor, DC motor controlling. Surveillance robot typically consists of a video camera, servomotor, DC motor, raspberry pi, blue tooth module, ultrasonic distance sensor & Laser gun. Designing of the robot in such a way which can be controlled using blue tooth app from the android mobile. We had developed the suitable switches on the app through which we can control the motion of it. Here, Bluetooth act as an interface between the controller and android app. According to the command received from the user, the robot motion can be controlled. Although camera used for the video capturing is wireless too. So, both the video as well as navigation of the camera can happen simultaneously from the same mobile.

Published by: Saurabh Kumar, Nisha Kumari, Pankaj Shukla, Saurabh Verma, Swapnil Srivastava, Mayank Aggarwal

Author: Saurabh Kumar

Paper ID: V4I2-1955

Paper Status: published

Published: April 17, 2018

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