Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Jain, S.

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    Industrial automation using internet of things
    (IGI Global, 2019) Chandrasekaran, K.; Jain, S.
    This chapter presents a comprehensive view of Industrial Automation using internet of things (IIoT). Advanced Industries are ushering in a new age of physical production backed by the information-based economy. The term Industrie 4.0 refers to the 4th paradigm shift in production, in which intelligent manufacturing technology is interconnected with physical machines. IIoT is basically a convergence of industrial systems with advanced, near-real-time computing and analytics, powered by low cost and low power sensing devices leveraging global internet connectivity. The key benefits of Industrial IoT systems are a) improved operational efficiency and productivity b) reduced maintenance costs c) improved asset utilization, monitoring and maintenance d) development of new business models e) product innovation and f) enhanced safety. Key parameters that impact Industrial Automation are a) Security b) Data Integrity c) Interoperability d) Latency e) Scalability, Reliability, and Availability f) Fault tolerance and Safety, and g) Maintainability, Serviceability, and Programmability. © 2020 by IGI Global. All rights reserved.
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    Industrial Automation Using Internet of Things
    (IGI Global, 2021) Jain, S.; Chandrasekaran, K.
    This chapter presents a comprehensive view of Industrial Automation using internet of things (IIoT). Advanced Industries are ushering in a new age of physical production backed by the information-based economy. The term Industrie 4.0 refers to the 4th paradigm shift in production, in which intelligent manufacturing technology is interconnected with physical machines. IIoT is basically a convergence of industrial systems with advanced, near-real-time computing and analytics, powered by low cost and low power sensing devices leveraging global internet connectivity. The key benefits of Industrial IoT systems are a) improved operational efficiency and productivity b) reduced maintenance costs c) improved asset utilization, monitoring and maintenance d) development of new business models e) product innovation and f) enhanced safety. Key parameters that impact Industrial Automation are a) Security b) Data Integrity c) Interoperability d) Latency e) Scalability, Reliability, and Availability f) Fault tolerance and Safety, and g) Maintainability, Serviceability, and Programmability. © 2022 by IGI Global. All rights reserved.
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    Impact of group norms in eliciting response in a goal driven virtual community
    (UHAMKA PRESS uhamkapress@yahoo.co.id, 2013) Jain, S.; Sinha, T.; Shah, A.; Sharma, C.; Rosé, C.
    With the proliferation of social media into our daily lives, online communities have become an important platform for collaborative learning and education. To connect users with varying knowledge levels and increase the net learning throughput, these communities often follow a question-answer based approach. Understanding what drives attention to help-seeking questions can reduce the amount of questions that go unnoticed or remain unanswered by the community. In this paper we discuss an important feature that affects the activity of the community, namely the community norms. We present a machine learning based trigger-driven feedback model that functions by (i) differentiating between help-seeking questions and follow-up posts - i.e. posts that are part of an ongoing discussion, and (ii) a dynamic intervention scheme to help improve question formulation. Our findings show that adhering to the community norms significantly increases the chance of eliciting a response.
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    Semantic sentiment analysis using context specific grammar
    (Institute of Electrical and Electronics Engineers Inc., 2015) Bhuvan, B.M.; Rao, V.D.; Jain, S.; Ashwin, T.S.; Guddeti, G.
    The increasing number of e-commerce and social networking sites are producing large amount of data pertaining to reviews of a product, restaurant etc. A keen observation reveals that the text data gathered from any social review site are specific to a context and are subjective in nature promoting varied perceptions of sentiments. The novel idea is to define context specific grammar as semantics for a particular domain. Our research aims to develop a scalable model where features obtained from matching semantic patterns are used to predict the sentiment polarity of movie reviews and also provide a sentiment score for each review. The proposed model is intended to be flexible so that it could be applied to any domain by redefining the semantics specific to that domain. There are many other models which give accuracies greater than 80% using various methods. A study suggests that 70% accurate program is as good as humans as they have varied perceptions of sentiment about a movie review as it is a subjective summary of a movie. Our model might give lesser accuracy but it uses a cognitive approach trying to catch these varied perceptions by learning from a combination of positive and negative grammars. Analyzing results from various experiments we find that Logistic Regression with SGD on Apache Spark performs better with accuracy of 64.12% while being highly scalable. High dependency on the grammars is a limitation of the model. Improvements can be done by defining different quality and quantity of grammars. © 2015 IEEE.
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    Investigating the "wisdom of crowds" at scale
    (Association for Computing Machinery, Inc acmhelp@acm.org, 2015) Mysore, A.S.; Yaligar, V.S.; Ibarra, I.A.; Simoiu, C.; Goel, S.; Arvind, R.; Sumanth, C.; Srikantan, A.; Bhargav, H.S.; Pahadia, M.; Dobhal, T.; Ahmed, A.; Shankar, M.; Agarwal, H.; Agarwal, R.; Anirudh-Kondaveeti, S.; Arun-Gokhale, S.; Attri, A.; Chandra, A.; Chilukuri, Y.; Dharmaji, S.; Garg, D.; Gupta, N.; Gupta, P.; Jacob, G.M.; Jain, S.; Joshi, S.; Khajuria, T.; Khillan, S.; Konam, S.; Kumar-Kolla, P.; Loomba, S.; Madan, R.; Maharaja, A.; Mathur, V.; Munshi, B.; Nawazish, M.; Neehar-Kurukunda, V.; Nirmal-Gavarraju, V.; Parashar, S.; Parikh, H.; Paritala, A.; Patil, A.; Phatak, R.; Pradhan, M.; Ravichander, A.; Sangeeth, K.; Sankaranarayanan, S.; Sehgal, V.; Sheshan, A.; Shibiraj, S.; Singh, A.; Singh, A.; Sinha, P.; Soni, P.; Thomas, B.; Tuteja, L.; Varma-Dattada, K.; Venkataraman, S.; Verma, P.; Yelurwar, I.
    In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon's Mechanical Turk.
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    Evaluation of Machine Learning Frameworks on Bank Marketing and Higgs Datasets
    (Institute of Electrical and Electronics Engineers Inc., 2015) Bhuvan, B.M.; Jain, S.; Rao, V.D.; Patil, N.; Raghavendra, G.S.
    Big data is an emerging field with different datasets of various sizes are being analyzed for potential applications. In parallel, many frameworks are being introduced where these datasets can be fed into machine learning algorithms. Though some experiments have been done to compare different machine learning algorithms on different data, these experiments have not been tested out on different platforms. Our research aims to compare two selected machine learning algorithms on data sets of different sizes deployed on different platforms like Weka, Scikit-Learn and Apache Spark. They are evaluated based on Training time, Accuracy and Root mean squared error. This comparison helps us to decide what platform is best suited to work while applying computationally expensive selected machine learning algorithms on a particular size of data. Experiments suggested that Scikit-Learn would be optimal on data which can fit into memory. While working with huge, data Apache Spark would be optimal as it performs parallel computations by distributing the data over a cluster. Hence this study concludes that spark platform which has growing support for parallel implementation of machine learning algorithms could be optimal to analyze big data. © 2015 IEEE.
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    Layer based 3D clipping
    (Institute of Electrical and Electronics Engineers Inc., 2016) Kedia, Y.; Hendre, A.; Jain, S.; Afroz, F.; Koolagudi, S.G.
    In this paper, we propose an unconventional layer based clipping algorithm for 3D regions. In computer graphics, clipping is used to select the required part of a graphical object, cut it out from the object and display it separately. The proposed algorithm is not based on any other algorithm generally used for clipping in computer graphics and has a much better time efficiency than the other clipping algorithms available. The 3D space i.e. a cuboid is clipped w.r.t. a rectangular clipping window. The novelty of the algorithm is that 2D regions are being clipped down to the dimensions of the intersection region and then varied along the depth(z-axis) to get the volume of intersection. The algorithm has been implemented for both unrotated and rotated cuboids. The proposed algorithm can have massive applications in any field that requires layer-wise imaging of 3D spaces such as 3D printing, medical imaging, modelling, etc. given the simplicity of its implementation. © 2015 IEEE.
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    Detection and analysis model for grammatical facial expressions in sign language
    (Institute of Electrical and Electronics Engineers Inc., 2016) Bhuvan, M.S.; Rao, D.V.; Jain, S.; Ashwin, T.S.; Guddeti, G.R.; Kulgod, S.P.
    The proposed research explores a relatively new area of expression detection through facial points in a sign language to enhance the computer interaction with the deaf and hard of hearing. The research mainly focuses on facial points collected from Kinect as basis for expression detection as opposed to numerous gesture based studies on sign language. This helps in deploying the applications in smart phones as it is feasible to capture facial point easily rather than hand gestures. Exhaustive experimentation is carried out with ten different machine learning algorithms for detecting nine different types of expression modeled as different binary classification problem for each expression. This is done for user dependent model and user independent model scenarios. The optimal classifier for each expression is found to outperform the current state-of-the-art techniques and has ROC area greater than 0.95 for each expression. It is found that user independent model's performance is comparable to user dependent model, hence is suggested as it is easy and efficient to deploy in practical applications. Finally, the importance of each facial point in detecting each type of expression has been mined, which can be instrumental for future research and for various application using facial points as basis for decision making. © 2016 IEEE.
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    Saliency prediction for visual regions of interest with applications in advertising
    (Springer Verlag service@springer.de, 2017) Jain, S.; Kamath S․, S.S.
    Human visual fixations play a vital role in a plethora of genres, ranging from advertising design to human-computer interaction. Considering saliency in images thus brings significant merits to Computer Vision tasks dealing with human perception. Several classification models have been developed to incorporate various feature levels and estimate free eye-gazes. However, for real-time applications (Here, real-time applications refer to those that are time, and often resource-constrained, requiring speedy results. It does not imply on-line data analysis), the deep convolution neural networks are either difficult to deploy, given current hardware limitations or the proposed classifiers cannot effectively combine image semantics with low-level attributes. In this paper, we propose a novel neural network approach to predict human fixations, specifically aimed at advertisements. Such analysis significantly impacts the brand value and assists in audience measurement. A dataset containing 400 print ads across 21 successful brands was used to successfully evaluate the effectiveness of advertisements and their associated fixations, based on the proposed saliency prediction model. © Springer International Publishing AG 2017.
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    Profile Matching of Online Social Network with Aadhaar Unique Identification Number
    (Institute of Electrical and Electronics Engineers Inc., 2017) Gautam, B.; Jain, V.; Jain, S.; Annappa, B.
    Matching user's profile over multiple Online Social Network (OSN) brings many new insights. Considering the fact of user's disambiguation over different OSN, Existing user profile matching are highly computationally expensive. In this paper, we propose a novel e-identity Architecture using Infrastructure as a service (IaaS) of Cloud to reduce the computational cost of the profile matching algorithm. Profile equivalent through proposed architecture are based on the public attributes available in overall social network and lastly, profiles are mapped to the Aadhaar Unique Identification Number (UID). © 2016 IEEE.