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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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Research Paper

Shopping guide

The main objective of this project is Sales prediction,which is forecasting the future sales of a product.The attributes used as an input is weekly sales and dates of sale.This paper proposes a machine learning model to predict sales sales of a product.The machine learning technique used is linear regression.The successful prediction of sales will maximize the shopkeeper's gains.

Published by: Riya Mate, Aditi Patil, Isha Bansod, Neha Khare, P. V. Khandare

Author: Riya Mate

Paper ID: V5I3-1418

Paper Status: published

Published: May 16, 2019

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Thesis

An experimental study on high performance fibre reinforced concrete

Concrete is the most extensively used construction material in the world. The main ingredient of conventional concrete is Portland cement. However, the conventional concrete is of low compressive strength, split tensile strength, flexural strength. In current research is going to carry out the test on latex modified steel fiber reinforced concrete. According to various research papers, it has been found that steel fiber gives maximum strength as compare to other fiber. Hence, steel fiber(straight) of two different percentage i.e 0.5% and 1% with incorporation of styrene butadiene rubber(SBR) latex polymer in concrete modified with a percentage 10% of M25 grade, two different mix proportion is cast. The comparison between conventional concrete and HPFRC were found out by different test above. The various parameter s like load carrying capacity, ductility character, and stress-strain variation has to be analyzed.

Published by: Vikalp Singh Thakur, Swati Agrawal

Author: Vikalp Singh Thakur

Paper ID: V5I3-1428

Paper Status: published

Published: May 16, 2019

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