Staff and Student Research

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    Geofog4health: A Fog-Based SDI Framework for Geospatial Health Big Data Analysis
    (Springer Heidelberg, 2018-02-21) Barik, Rabindra Kumar; Dubey, Harishchandra; Mankodiya, Kunal; Sasane, Sapana Ashok; Misra, Chinmaya; Dubey, Harishchandra
    Spatial Data Infrastructure (SDI) is an important framework for sharing geospatial big data using the web. Integration of SDI with cloud computing led to emergence of Cloud-SDI as a tool for transmission, processing and analysis of geospatial data. Fog computing is a paradigm where embedded computers are employed to increase the throughput and reduce latency at the edge of the network. In this study, we developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data. We built prototypes using Intel Edison and Raspberry Pi for studying the comparative performance. We conducted a case study on Malaria vector-borne disease positive maps of Maharastra state in India. The proposed framework had provision of lossless data compression for reduced data transfer. Also, overlay analysis of geospatial data was implemented. In addition, we discussed energy savings, cost analysis and scalability of the proposed framework with respect to efficient data processing. We compared the performance of the proposed framework with the state-of-the-art Cloud-SDI in terms of analysis time. Results and discussions showed the efficacy of the proposed system for enhanced analysis of geo-health big data generated from a variety of sensing frameworks.
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    Speech and Language Processing for Assessing Child–Adult Interaction Based on Diarization and Location
    (Springer New York LLC, 2019-06-05) Hansen, John H. L.; Najafian, Maryam; Lileikyte, Rasa; Irvin, D.; Rous, B.; 0000-0003-1382-9929 (Hansen, JHL); Hansen, John H. L.; Najafian, Maryam; Lileikyte, Rasa
    Understanding and assessing child verbal communication patterns is critical in facilitating effective language development. Typically speaker diarization is performed to explore children’s verbal engagement. Understanding which activity areas stimulate verbal communication can help promote more efficient language development. In this study, we present a two-stage children vocal engagement prediction system that consists of (1) a near to real-time, noise robust system that measures the duration of child-to-adult and child-to-child conversations, and tracks the number of conversational turn-takings, (2) a novel child location tracking strategy, that determines in which activity areas a child spends most/least of their time. A proposed child–adult turn-taking solution relies exclusively on vocal cues observed during the interaction between a child and other children, and/or classroom teachers. By employing a threshold optimized speech activity detection using a linear combination of voicing measures, it is possible to achieve effective speech/non-speech segment detection prior to conversion assessment. This TO-COMBO-SAD reduces classification error rates for adult-child audio by 21.34% and 27.3% compared to a baseline i-Vector and standard Bayesian Information Criterion diarization systems, respectively. In addition, this study presents a unique location tracking system adult-child that helps determine the quantity of child–adult communication in specific activity areas, and which activities stimulate voice communication engagement in a child–adult education space. We observe that our proposed location tracking solution offers unique opportunities to assess speech and language interaction for children, and quantify the location context which would contribute to improve verbal communication. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
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    Hybrid Mist-Cloud Systems for Large Scale Geospatial Big Data Analytics and Processing: Opportunities and Challenges
    (Springer Heidelberg, 2019-01-08) Barik, Rabindra Kumar; Misra, Chinmaya; Lenka, Rakesh K.; Dubey, Harishchandra; Mankodiya, Kunal; Dubey, Harishchandra
    The cloud and fog computing paradigms are developing area for storing, processing, and analysis of geospatial big data. Latest trend is mist computing which boost fog and cloud concepts for computing process where edge devices are used to help increase throughput and reduce latency to support at client edge. The present research article discussed the mist computing emergence for geospatial analysis of data from various geospatial applications. It also created a framework based on mist computing, i.e., MistGIS for analytics in mining domain from geospatial big data. The developed MistGIS platform is used in Tourism Information Infrastructure Management and Faculty Information Retrial System. Tourism Information Infrastructure Management is to assimilate entire geospatial data in context to travel/tourism places constitute of various lakes, mountains, rivers, forests, temples, mosques, churches, monuments, etc. It can aid all the stakeholders or users to acquire sufficient data in subsequent research studies. In this study, it has taken the Temple City of India, Bhubaneswar as the case study. Whereas Faculty Information Retrial System facilitated many functionalities with respect to finding the detail information of faculties according to their research area, contact details, and email ids, etc in all 31 National Institutes of Technology (NITs) in India. The framework is built with the Raspberry Pi microprocessor. The MistGIS platform has been confirmed by prelude analysis which includes cluster and overlay. The outcome show that mist computing assist cloud and fog computing to provide the analysis of geospatial big data.
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    DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering
    (MDPI Multidisciplinary Digital Publishing Institute, 2019-05-22) Sahoo, A. K.; Pradhan, C.; Barik, R. K.; Dubey, Harishchandra; 0000-0003-0476-3884 (Dubey, H); Dubey, Harishchandra
    In today's digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by by analyzing a patient's lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient's health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient's health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches. © 2019 by the author.
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    Comparison among ECG Filtering Methods for Non-Linear Noise
    (Institute of Electrical and Electronics Engineers Inc.) Patwary, A. B.; Chowdhury, M. T. I.; Mamun, Nursadul; Mamun, Nursadul
    Electrical functioning of the heart can be translated into a waveform known as Electrocardiography. Perfect diagnosis of heart is very much difficult due to different kinds of noise such as biological signal (EMG), power line interference and Gaussian noise interact during extraction. Several research have been studied in last few decades to develop effective algorithm for eliminating the high frequency noise between any two beats of continuous ECG signal. However, existing metric is capable to eliminate the noise with sacrificing a portion of the ECG rhythm. This study proposes a novel Daubechies wavelet based noise elimination algorithm to remove noises (such as power line interference and random Gaussian noise) from ECG signal. The proposed algorithm presents the best functions of the mother wavelet for eliminating ECG signal noises without scarifying the ECG waveform pattern. Therefore, the propose system maintains the necessary diagnostic information without any distortion of the original ECG signal, maintaining a high SNR and low RMSE with respect to the Haar wavelet and low-pass filter. © 2018 IEEE.
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    Mist Data: Leveraging Mist Computing for Secure and Scalable Architecture for Smart and Connected Health
    (Elsevier B.V., 2018-10-22) Barik, R. K.; Dubey, A. C.; Tripathi, A.; Pratik, T.; Sasane, S.; Lenka, R. K.; Dubey, Harishchandra; Mankodiya, K.; Kumar, V.; Singh A.K.; Jimson J.; Dubey, Harishchandra
    The smart health paradigms employ Internet-connected wearables for tele-monitoring, diagnosis providing inexpensive healthcare solutions. Mist computing reduces latency and increases throughput by processing data near the edge of the network. In the present paper, we proposed a secure mist Computing architecture that is validated on recently released public geospatial health dataset. Results and discussion support the efficacy of proposed architecture for smart geospatial health applications. The present research paper proposed SoA-Mist i.e. a three-tier secure framework for efficient management of geospatial health data with the use of mist devices. It proposed the security aspects in client layer, mist layer, fog layer and cloud layer. It has defined the prototype development by using win-win spiral model with use case and sequence diagram. Overlay analysis has been performed with the developed framework on malaria vector borne disease positive maps of Maharastra state in India from 2011 to 2014 in mobile clients as test case. Finally, It concludes with the comparison analysis of cloud based framework and proposed SoA-Mist framework. © 2018 The Authors. Published by Elsevier B.V.
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    An Unsupervised Visual-Only Voice Activity Detection Approach Using Temporal Orofacial Features
    (International Speech and Communication Association) Tao, Fei; Hansen, John H. L.; Busso, Carlos; Hansen, John H. L.
    Detecting the presence or absence of speech is an important step toward building robust speech-based interfaces. While previous studies have made progress on voice activity detection (VAD), the performance of these systems significantly degrades when subjects employ challenging speech modes that deviate from normal acoustic patterns (e.g., whisper speech), or in noisy/adverse conditions. An appealing approach under these conditions is visual voice activity detection (VVAD), which detects speech using features characterizing the orofacial activity. This study proposes an unsupervised approach that relies only on visual features, and, therefore, is insensitive to vocal style or time-varying background noise. This study proposes an unsupervised approach that relies on visual features. We estimate optical flow variance and geometrical features around lips, extracting the short-time zero crossing rates, short-time variances, and delta features over a small temporal window. These variables are fused using principal component analysis (PCA) to obtain a "combo" feature, which displays a bimodal distributions (speech versus silence). A threshold is automatically determine using the expectation-maximization (EM) algorithm. The approach can be easily transformed into a supervised VVAD, if needed. We evaluate the system in neutral and whisper speech. While speech based VADs generally fail to detect speech activity in whisper speech, given its important acoustic differences, the proposed VVAD achieves near 80% accuracy in both neutral and whisper speech, highlighting the benefits of the system. Copyright
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    Evaluation and Calibration of Short-Term Aging Effects in Speaker Verification
    (International Speech and Communication Association) Kelly, Finnian; Hansen, John H. L.; Kelly, Finnian; Hansen, John H. L.
    A speaker verification evaluation is presented on the Multisession Audio Research Project (MARP) corpus, for which speakers were recorded at regular intervals, in consistent conditions, over a period of three years. It is observed that the performance of an i-vector system with probabilistic linear discriminant analysis (PLDA) modelling decreases progressively, in terms of both discrimination and calibration, as the time intervals between train and test sessions increase. For male speakers, the equal error rate (EER) increases from 2.4% to 4.4% when the interval between sessions grows from several months to three years. An extension to conventional linear score calibration is proposed, whereby short-term aging information is incorporated as an additional factor in the score transformation. This new approach improves discrimination and calibration performance in the presence of increasing time intervals between train and test sessions, compared with score-only calibration. Copyright