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Research Paper

Comprehensive Training and Placement department

Android-based training and placement automation for campus drive is a System, which automates training and placement activities and provides opportunities to the students, who are eligible according to the company criteria and makes easy the process of managing information regarding students and companies automatically. This system focuses on the automation of the training and placement cell and profile matching. Collecting the resumes, providing notifications about various job openings to the students according to the eligibility and company criteria, managing and inviting the companies for the campus recruitment, classifying the data from the resume submitted by students and creating the recruitment metrics, Observing and controlling the progress of the selection process and communicating with different eligible candidates. This system provides modules like user interface for student and user interface for an administrator. Provides various functionalities like managing student resume, Company Profiles, Job Postings, Authentication, and activation of student profiles, listing out the students as per company’s criteria, provides the list of a shortlisted student with resume to the company .This system reduces the human efforts and maintaining large amount of data efficiently.

Published by: Rishika Sinha, Ranjana C. A., Swathi V., Kumar J., Vijaylaxmi Mekali

Author: Rishika Sinha

Paper ID: V5I3-1429

Paper Status: published

Published: May 17, 2019

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Research Paper

Comparison research on FIR filter with RRC filter using a reconfigurable constant multiplier

This paper proposes a capable constant multiplier architecture with the help of an architecture called Binary Common Sub-Expression (BCSE)algorithm. As multiplication using coefficients or constants plays a necessary role in digital signal processing(DSP). Firstly, a single constant multiplier switching between some constants are converted to an n-bit constant multiplier in which any of the bit can be generated at the output according to the input bit x using the BCSE algorithm. Then an RRC filter is designed using the n-bit multiplier and finally, the performance of the RRC filter is compared with that of FIR filter.

Published by: Arya P. Kumar, Mercy Mathew

Author: Arya P. Kumar

Paper ID: V5I3-1423

Paper Status: published

Published: May 17, 2019

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Research Paper

uDCLUST: A novel algorithm for clustering unstructured data

Data that has been arranged and systematized into an organized and formatted repository, usually a database, so that its elements and essential features and can be made directly accessible for more powerful and adequate processing and analysis is known as Structured Data. Un-structured data is data that doesn’t fit accurately in a traditional database and has no identifiable internal structure and a predefined data model. We cannot perform different operations like update, insert and delete on un-structured data. Clustering is a process of unsupervised learning and is the most common method for mathematical and demographic data analysis. It is the main task of preliminary data mining, and an ordinary technique for statistical data analysis, mathematical data analysis, demographic data analysis, used in many fields, including ML (Machine Learning), recognition of patterns, analysis of images, retrieval of information, bioinformatics, compression of data and computer graphics. Available clustering algorithms have the difficulty to determine the number of clusters in a dataset and also are difficult to cluster outliers even that have common groups. A final related drawback arises from the shape of the data cluster where it is difficult and complex to cluster non-spherical and overlapping datasets. In this framework, we intended and designed an algorithm called uDCLUST (Un-structured Data Clustering), which identifies an appropriate number of clusters in unstructured data as well as cluster outliers easily with non-spherical and overlapping datasets.

Published by: Aamir Ahmad Khandy, Dr. Rohit Miri

Author: Aamir Ahmad Khandy

Paper ID: V5I3-1378

Paper Status: rejected

Submitted: May 17, 2019

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Research Paper

Impact of training and development on employee development aspect of job satisfaction at Amara Raja Batteries Pvt Ltd.

Training and development became vital for organizations in the dynamic business environment with cut-throat competition. Many organizations are willing to invest in it. Job Satisfaction is necessary for employees to give higher productivity which also benefits organizations. It further reduces the turnover ratio and helps retain the skillful workforce. This study aims to find the association between training and development and employee development aspect of job satisfaction. Training satisfaction was divided into four variables such as Satisfaction with Training Session, Training Content Satisfaction, Trainer Satisfaction and Transfer of Learning. High to a moderately significant positive relationship is found between Employee development aspect of job satisfaction and Training satisfaction variables. The organization must concentrate on variables with the least positive relationship to ensure the highest satisfaction for the employees.

Published by: Jahnavi, Susan Chirayath

Author: Jahnavi

Paper ID: V5I3-1208

Paper Status: published

Published: May 17, 2019

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Research Paper

Implementation of wireless farming using IoT

As we know there are many issues surrounding our agriculture sector today lack of proper technology has caused a decline in production in the recent years. As in other countries, we see that there are many technological advancements that have helped in the increase in Production. IoT is one of the technologies that can make a very large impact on the agriculture sector. IoT stands for Internet of things it means that things will be connected to the internet and communicate with each other. In our system we have designed a system that can monitor parameters like temperature, humidity, Gas levels, Light detection, etc. all these parameters will be monitored locally, our system will be connected to the internet via a Wi-Fi module. All the data that has been collected by the system then will be uploaded to the server where it will be displayed using graphs and will be available for analysis.

Published by: Shradha Padmakar Rachcha, Atul Shrivastava

Author: Shradha Padmakar Rachcha

Paper ID: V5I3-1328

Paper Status: published

Published: May 16, 2019

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Research Paper

Optimized short text embedding for bilingual similarity using Probase and BabelNet

Most existing methodologies for text classification represent text as vectors of words, to be specific "bag-of-words." This content portrayal results in a high dimensionality of feature space and much of the time experiences surface jumbling. When it comes to short texts, these become even more serious because of their shortness and sparsity and with the bilingual similarity of text it gets more difficult. This paper proposes an approach to deal with both sparsity and computational complexity of bilingual similarity of short text. English short text is mapped with Probase and Hindi short text is mapped with BabelNet a knowledge base with coverage of words and concepts for 248 languages. A semantic network is created to manipulate the word to word and concept to concept correlation. Unlike the earlier approaches of embedding, words and concepts from both English and Hindi short texts are treated separately to yield word embedding (Word2Vec) and concept embedding (Concept2Vec) respectively. The similarity between bilingual short texts is computed using the skip-gram based word embedding and concept embedding. When evaluated with Pilot and STSS 131 short text benchmark datasets, the proposed optimized bilingual short text embedding gives better similarity score

Published by: Natasha J., Vijayarani J.

Author: Natasha J.

Paper ID: V5I3-1327

Paper Status: published

Published: May 16, 2019

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