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A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics

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Abstract

This paper presents a state of the art machine learning-based approach for automation of a varied class of Internet of things (IoT) analytics problems targeted on 1-dimensional (1-D) sensor data. As feature recommendation is a major bottleneck for general IoT-based applications, this paper shows how this step can be successfully automated based on a Wide Learning architecture without sacrificing the decision-making accuracy, and thereby reducing the development time and the cost of hiring expensive resources for specific problems. Interpretation of meaningful features is another contribution of this research. Several data sets from different real-world applications are considered to realize the proof-of-concept. Results show that the interpretable feature recommendation techniques are quite effective for the problems at hand in terms of performance and drastic reduction in development time.

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Author information

Authors and Affiliations

  1. TCS Research & Innovation, Tata Consultancy Services, Ecospace, Ecospace, Kolkata, 700160, India

    Snehasis Banerjee & Tanushyam Chattopadhyay

  2. Computer Vision & Pattern Recognition Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700108, India

    Utpal Garain

Authors
  1. Snehasis Banerjee
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  2. Tanushyam Chattopadhyay
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  3. Utpal Garain
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Correspondence to Snehasis Banerjee.

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Recommended by Associate Editor Hong-Nian Yu

Snehasis Banerjee received the M. Eng. degree in software engineering from Jadavpur University, India in 2015. He is a scientist at TCS Research & Innovation. Currently, he is working on signal processing automation for IoT Analytics. He is a secretary, Association for Computing Machinery (ACM) Kolkata Professional Chapter and Treasurer, Computer Society of India (CSI) Kolkata Chapter and recognized as resource person, CSI Region-2 India East. He is India’s representative to ISO Software & Systems Sectional Committee, nominated by Computer Society (CS) of India. He is also the lead of CS Pathsala program (joint initiative of TCS and ACM) to bring computing to all schools in India, sponsored by Google for Kolkata region. He has been part of technical program committee and organizing committee of major events like CSI Convention 2012, 2017, ACM Annual Event 2017, IEEE Tensymp 2019, Tata Innovista Regionals 2017, 2018. He has served as jury in many contests such as Digital Impact Square National Hackathon and ACM Kolkata B.Tech/B.E. Project Contest. He was a winner of TCS BDA Stylus Paper Contest 2019 and awarded Best Paper at Tactics Analytics Symposium 2015.

His research interests include modern aspects of the artificial intelligence field including IoT analytics and cognitive computing.

Tanushyam Chattopadhyay is currently working as a principal scientist at TCS Research & Innovation, Kolkata to deploy and automate analytics on IoT platform. He was awarded with the University Gold medal in master of computer applications (MCA) from Indian Institute of Engineering Science and Technology (II-EST), India in 2002. He started his career as research in Indian Statistical Institute, Kolkata and later on, joined Tata Consultancy Services Limited in 2004. Later he was awarded with the Ph. D. degree from Jadavpur University for the work done at ISI, India in 2012. He had training on speech signal processing from IISC Bangalore, image processing from Indian Institute of Technology (IIT) Kharagpur, and video processing from IIT Delhi. He has 20+ granted patents across the globe. He also authored a book and some book chapters. He has published nearly 60+ papers in peer reviewed journals and conferences. He serves as a member of Board of Governor of several academic institutions. His current research is evolved around developing an analytics solution for TCS built IoT platform namely TCS Connected Universal Platform (TCUP) which involves both research and engineering in different areas of data science.

His research interests include image processing and IoT analytics.

Utpal Garain received the B. Sc. and M. Sc. degrees in computer science and engineering from Jadavpur University, India 1994 and 1997, respectively, and the Ph. D. degree from Indian Statistical Institute, India in 2005. He is a professor in Indian Statistical Institute, India and the coordinator of the Center for Artificial Intelligence and Machine Learning (CAIML). He is one of the associate editors of International Journal on Document Analysis and Recognition (IJDAR). Previously, he served as the Chair for International Association for Pattern Recognition (IAPR) Technical Committee (TC-6) on Computational Forensics for 2013–2017. He has been serving as program committee member for several international conferences including International Conference on Pattern Recognition (ICPR), International Conference on Document Analysis and Recognition (ICDAR), International Conference on International Conference on Frontiers in Handwriting Recognition (ICFHR), etc. Moreover, he has been regularly reviewing papers for several international journals in the field of natural language processing (NLP), vision and image analysis. For his significant contribution in pattern recognition and its applications for language engineering, he received the Young Engineer Award in 2006 from the Indian National Academy of Engineering (INAE), the prestigious Indo-US Research Fellowship (IUSSTF) in the field of Engineering Sciences in 2011 and JSPS Invitational Fellowship for Research in Osaka University, Japan in 2016.

His present research interest is focused on exploring deep learning methods for language, image, video and IoT analytics.

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Banerjee, S., Chattopadhyay, T. & Garain, U. A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics. Int. J. Autom. Comput. 16, 800–811 (2019). https://doi.org/10.1007/s11633-019-1185-8

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