Faculty Publications
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Publications by NITK Faculty
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Item Automatic identification and ranking of emergency AIDS in social media macro community(CEUR-WS, 2017) Gautam, B.; Annappa, B.Online social microblogging platforms including Twiter are increasingly used for aiding relief operations during disaster events. During most of the calamities that can be natural disasters or even armed atacks, non-governmental organizations look for critical information about resources to support effected people. Despite the recent advancement of natural language processing with deep neural networks, retrieval and ranking of short text becomes a challenging task because a lot of conversational and sympathy content merged with the critical information. In this paper, we address the problem of categorical information retrieval and ranking of most relevance information while considering the presence of short-text and multilingual languages that arise during such events. Our proposed model is based on the formation of embedding vector with the help of textual and statistical preprocessing, and finally, entire training 2,100,000 vectors were normalized using feed-forward neural network for need and availability tweets. Another important contribution of this paper lies in novel weighted Ranking Key algorithm based on top five general terms to rank the classified tweets in most relevance with classification. Lastly, we test our model on Nepal Earthquake dataset (contains short text and multilingual language tweets) and achieved 6.81% of mean average precision on 5,250,000 unlabeled embedding vectors of disaster relief tweets.Item 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.Item When and where?: Behavior dominant location forecasting with micro-blog streams(IEEE Computer Society, 2018) Gautam, B.; Annappa, B.; Singh, A.; Agrawal, A.The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized. In this paper, we provide a novel algorithm to exploit the dynamic fluctuations in user's point-of-interest while forecasting the future place of visit with fine granularity. Our proposed algorithm is based on the dynamic formation of collective personality communities using different languages, opinions, geographical and temporal distributions for finding out optimized equivalent content. We performed extensive empirical experiments involving, real-time streams derived from 0.6 million stream tuples of micro-blog comprising 1945 social person fusion with graph algorithm and feed-forward neural network model as a predictive classification model. Lastly, The framework achieves 62.10% mean average precision on 1,20,000 embeddings on unlabeled users and surprisingly 85.92% increment on the state-of-the-art approach. © 2018 IEEE.Item Performance prediction of data streams on high-performance architecture(Springer Berlin Heidelberg, 2019) Gautam, B.; Annappa, A.Worldwide sensor streams are expanding continuously with unbounded velocity in volume, and for this acceleration, there is an adaptation of large stream data processing system from the homogeneous to rack-scale architecture which makes serious concern in the domain of workload optimization, scheduling, and resource management algorithms. Our proposed framework is based on providing architecture independent performance prediction model to enable resource adaptive distributed stream data processing platform. It is comprised of seven pre-defined domain for dynamic data stream metrics including a self-driven model which tries to fit these metrics using ridge regularization regression algorithm. Another significant contribution lies in fully-automated performance prediction model inherited from the state-of-the-art distributed data management system for distributed stream processing systems using Gaussian processes regression that cluster metrics with the help of dimensionality reduction algorithm. We implemented its base on Apache Heron and evaluated with proposed Benchmark Suite comprising of five domain-specific topologies. To assess the proposed methodologies, we forcefully ingest tuple skewness among the benchmarking topologies to set up the ground truth for predictions and found that accuracy of predicting the performance of data streams increased up to 80.62% from 66.36% along with the reduction of error from 37.14 to 16.06%. © 2019, The Author(s).