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5 Latest Research Papers On ML You Must Read In 2019

Over the last few years, India has emerged as among the top countries in Asia to contribute a number of research work in the field of AI, machine learning and Natural Language Processing. In this article, we take a look at the top five recent research paper submission by Indian researchers in

1.Cricket Analytics and Predictor


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Authors: Suyash Mahajan,  Salma Shaikh, Jash Vora, Gunjan Kandhari,  Rutuja Pawar,

Institute: Walchand Institute of Technology, Solapur,

Abstract:  The paper embark on predicting the outcomes of Indian Premier League (IPL) cricket match using a supervised learning approach from a team composition perspective. The study suggests that the relative team strength between the competing teams forms a distinctive feature for predicting the winner. Modeling the team strength boils down to modeling individual player‘s batting and bowling performances, forming the basis of our approach.

Research Methodology: In this paper, two methodologies have been used. MySQL database is used for storing data whereas Java for the GUI. The algorithm used is Clustering Algorithm for prediction. The steps followed are as

  • Begin with a decision on the value of k being the number of clusters.
  • Put any initial partition that classifies the data into k clusters.
  • Take every sample in the sequence; compute its distance from centroid of each of the clusters. If sample is not in the cluster with the closest centroid currently, switch this sample to that cluster and update the centroid of the cluster accepting the new sample and the cluster losing the sample.

2.Real Time Sleep / Drowsiness Detection – Project Report

Author: Roshan Tavhare

Institute: University of Mumbai

Abstract: The main idea behind this project is to develop a nonintrusive system which can detect fatigue of any human and can issue a timely warning. Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state which they often fail to recognize early enough.

Research Methodology: A training set of labeled facial landmarks on an image. These images are manually labeled, specifying specific (x, y) -coordinates of regions surrounding each facial structure.

  1. Priors, more specifically, the probability on distance between pairs of input pixels. The pre-trained facial landmark detector inside the dlib library is used to estimate the location of 68 (x, y)-coordinates that map to facial structures on the face.

3. A Study of Various Text Augmentation Techniques for Relation Classification in Free Text

Authors: Chinmaya Mishra Praveen Kumar and Reddy Kumar Moda,  Syed Saqib Bukhari and Andreas Dengel

Institute: German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany

Abstract: In this paper, the researchers explore various text data augmentation techniques in text space and word embedding space. They studied the effect of various augmented datasets on the efficiency of different deep learning models for relation classification in text.

Research Methodology: The researchers implemented five text data augmentation techniques (Similar word, synonyms, interpolation, extrapolation and random noise method)  and explored the ways in which we could preserve the grammatical and the contextual structures of the sentences while generating new sentences automatically using data augmentation techniques.

  1. Smart Health Monitoring and Management Using Internet of Things, Artificial Intelligence with Cloud Based Processing

Author: Prateek Kaushik

Institute: G D Goenka University, Gurugram

Abstract: This research paper described a personalised smart health monitoring device using wireless sensors and the latest technology.

Research Methodology: Machine learning and Deep Learning techniques are discussed which works as a catalyst to improve the  performance of any health monitor system such supervised machine learning algorithms, unsupervised machine learning algorithms, auto-encoder, convolutional neural network and restricted boltzmann machine.

5.Internet of Things with BIG DATA Analytics -A Survey

Author: A.Pavithra,  C.Anandhakumar and V.Nithin Meenashisundharam

Institute: Sree Saraswathi Thyagaraja College,

Abstract: This article we discuss about Big data on IoT and how it is interrelated to each other along with the necessity of implementing Big data with IoT and its benefits, job market

Research Methodology: Machine learning, Deep Learning, and Artificial Intelligence are key technologies that are used to provide value-added applications along with IoT and big data in addition to being used in a stand-alone mod

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Akshaya Asokan
Akshaya Asokan works as a Technology Journalist at Analytics India Magazine. She has previously worked with IDG Media and The New Indian Express. When not writing, she can be seen either reading or staring at a flower.

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