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Mantua J, Symonette SA, Eldringhoff HP, Overman GA, Chaudhury S. Concerns about the future linked with poor sleep quality in US army special operations soldiers withdrawing from Afghanistan. BMJ Mil Health 2024; 170:183-184. [PMID: 35654470 DOI: 10.1136/bmjmilitary-2022-002143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 05/25/2022] [Indexed: 11/03/2022]
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Vyas J, Bhumika, Das D, Chaudhury S. Federated learning based driver recommendation for next generation transportation system. EXPERT SYSTEMS WITH APPLICATIONS 2023; 225:119951. [DOI: 10.1016/j.eswa.2023.119951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Malladi SPK, Mukherjee J, Larabi MC, Chaudhury S. Towards explainable deep visual saliency models. COMPUTER VISION AND IMAGE UNDERSTANDING 2023:103782. [DOI: 10.1016/j.cviu.2023.103782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Bhugra S, Kaushik V, Gupta A, Lall B, Chaudhury S. AnoLeaf: Unsupervised Leaf Disease Segmentation via Structurally Robust Generative Inpainting. 2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV) 2023. [DOI: 10.1109/wacv56688.2023.00635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Ralekar C, Choudhary S, Gandhi TK, Chaudhury S. Development of Character Recognition Model Inspired by Visual Explanations. IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE 2023:1-11. [DOI: 10.1109/tai.2023.3289167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Bhugra S, Mukherjee P, Kaushik V, Jha R, Lall B, Chaudhury S. TARSNet: Topology Aware Root Segmentation Network for plant phenotyping. PROCEEDINGS OF THE THIRTEENTH INDIAN CONFERENCE ON COMPUTER VISION, GRAPHICS AND IMAGE PROCESSING 2022. [DOI: 10.1145/3571600.3571660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Kumar Malladi SP, Mukhopadhyay J, Larabi MC, Chaudhury S. Lighter and Faster Two-Pathway CMRNet for Video Saliency Prediction. 2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) 2022. [DOI: 10.1109/icip46576.2022.9897252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Mittal S, Venugopal VK, Agarwal VK, Malhotra M, Chatha JS, Kapur S, Gupta A, Batra V, Majumdar P, Malhotra A, Thakral K, Chhabra S, Vatsa M, Singh R, Chaudhury S. A novel abnormality annotation database for COVID-19 affected frontal lung X-rays. PLoS One 2022; 17:e0271931. [PMID: 36240175 PMCID: PMC9565456 DOI: 10.1371/journal.pone.0271931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 07/10/2022] [Indexed: 12/23/2022] Open
Abstract
Consistent clinical observations of characteristic findings of COVID-19 pneumonia on chest X-rays have attracted the research community to strive to provide a fast and reliable method for screening suspected patients. Several machine learning algorithms have been proposed to find the abnormalities in the lungs using chest X-rays specific to COVID-19 pneumonia and distinguish them from other etiologies of pneumonia. However, despite the enormous magnitude of the pandemic, there are very few instances of public databases of COVID-19 pneumonia, and to the best of our knowledge, there is no database with annotation of abnormalities on the chest X-rays of COVID-19 affected patients. Annotated databases of X-rays can be of significant value in the design and development of algorithms for disease prediction. Further, explainability analysis for the performance of existing or new deep learning algorithms will be enhanced significantly with access to ground-truth abnormality annotations. The proposed COVID Abnormality Annotation for X-Rays (CAAXR) database is built upon the BIMCV-COVID19+ database which is a large-scale dataset containing COVID-19+ chest X-rays. The primary contribution of this study is the annotation of the abnormalities in over 1700 frontal chest X-rays. Further, we define protocols for semantic segmentation as well as classification for robust evaluation of algorithms. We provide benchmark results on the defined protocols using popular deep learning models such as DenseNet, ResNet, MobileNet, and VGG for classification, and UNet, SegNet, and Mask-RCNN for semantic segmentation. The classwise accuracy, sensitivity, and AUC-ROC scores are reported for the classification models, and the IoU and DICE scores are reported for the segmentation models.
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Vyas J, Das D, Chaudhury S. DriveBFR: Driver Behavior and Fuel-Efficiency-Based Recommendation System. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2022; 9:1446-1455. [DOI: 10.1109/tcss.2021.3112076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Rout DK, Subudhi BN, Veerakumar T, Chaudhury S, Soraghan J. Multiresolution visual enhancement of hazy underwater scene. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:32907-32936. [DOI: 10.1007/s11042-022-12692-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 02/12/2021] [Accepted: 02/21/2022] [Indexed: 07/19/2023]
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Malhotra A, Mittal S, Majumdar P, Chhabra S, Thakral K, Vatsa M, Singh R, Chaudhury S, Pudrod A, Agrawal A. Multi-task driven explainable diagnosis of COVID-19 using chest X-ray images. PATTERN RECOGNITION 2022; 122:108243. [PMID: 34456368 PMCID: PMC8379001 DOI: 10.1016/j.patcog.2021.108243] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 07/06/2021] [Accepted: 08/08/2021] [Indexed: 05/07/2023]
Abstract
With increasing number of COVID-19 cases globally, all the countries are ramping up the testing numbers. While the RT-PCR kits are available in sufficient quantity in several countries, others are facing challenges with limited availability of testing kits and processing centers in remote areas. This has motivated researchers to find alternate methods of testing which are reliable, easily accessible and faster. Chest X-Ray is one of the modalities that is gaining acceptance as a screening modality. Towards this direction, the paper has two primary contributions. Firstly, we present the COVID-19 Multi-Task Network (COMiT-Net) which is an automated end-to-end network for COVID-19 screening. The proposed network not only predicts whether the CXR has COVID-19 features present or not, it also performs semantic segmentation of the regions of interest to make the model explainable. Secondly, with the help of medical professionals, we manually annotate the lung regions and semantic segmentation of COVID19 symptoms in CXRs taken from the ChestXray-14, CheXpert, and a consolidated COVID-19 dataset. These annotations will be released to the research community. Experiments performed with more than 2500 frontal CXR images show that at 90% specificity, the proposed COMiT-Net yields 96.80% sensitivity.
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Pareek V, Chaudhury S, Singh S. Handling non-stationarity in E-nose design: a review. SENSOR REVIEW 2022; 42:39-61. [DOI: 10.1108/sr-02-2021-0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Abstract
Purpose
The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.
Design/methodology/approach
The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.
Findings
The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.
Originality/value
The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.
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Madan S, Diwakar A, Chaudhury S, Gandhi T. Pneumonia Classification Using Few-Shot Learning with Visual Explanations. INTELLIGENT HUMAN COMPUTER INTERACTION 2022:229-241. [DOI: 10.1007/978-3-030-98404-5_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Hayat AA, Chaudhary S, Boby RA, Udai AD, Dutta Roy S, Saha SK, Chaudhury S. Conclusion. VISION BASED IDENTIFICATION AND FORCE CONTROL OF INDUSTRIAL ROBOTS 2022:175-178. [DOI: 10.1007/978-981-16-6990-3_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Hayat AA, Chaudhary S, Boby RA, Udai AD, Dutta Roy S, Saha SK, Chaudhury S. Vision Based Identification and Force Control of Industrial Robots. STUDIES IN SYSTEMS, DECISION AND CONTROL 2022. [DOI: 10.1007/978-981-16-6990-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Malladi SPK, Mukherjee J, Larabi MC, Chaudhury S. EG-SNIK: A Free Viewing Egocentric Gaze Dataset and Its Applications. IEEE ACCESS 2022; 10:129626-129641. [DOI: 10.1109/access.2022.3228484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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mor A, Kumar M, Chaudhury S. Multi-Task Real-Time Heterogeneous Traffic Capacity Analysis in Traffic Videos Using Faster Rcnn and Mld- Sort. SSRN ELECTRONIC JOURNAL 2022. [DOI: 10.2139/ssrn.4178906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Sharma S, Chaudhury S. Block Sparse Variational Bayes Regression Using Matrix Variate Distributions With Application to SSVEP Detection. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:351-365. [PMID: 33048770 DOI: 10.1109/tnnls.2020.3027773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Due to the nonsparse representation, the use of compressed sensing (CS) for physiological signals, such as a multichannel electroencephalogram (EEG), has been a challenge. We present a generalized Bayesian CS framework that is capable of handling representations that arise in the spatiotemporal setting. The proposed model utilizes the standard linear Gaussian observation model associated with the hierarchical modeling of data using the matrix-variate Gaussian scale mixture (GSM). It deploys various random and deterministic parameters to incorporate the knowledge of spatial and temporal correlation present in data. By varying distributions over random parameters, a family of generalized hyperbolic matrix variate distributions is derived. For estimation, we rely on variational Bayes (VB) for random parameters and expectation-maximization (EM) for deterministic parameters. Furthermore, the model is compared with recent developments in matrix-variate distribution-based modeling of data, and we briefly discuss its extension to finite mixtures of skewed distributions. Finally, the framework is applied to the steady-state visual evoked potential (SSVEP)-based EEG benchmark data set, and a comparative study is conducted to show its effectiveness for the frequency detection task. One of the crucial features of the proposed model is that it simultaneously processes multichannel signals with low computational cost and time, making it suitable for real-time systems, especially in a resource-constrained environment.
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Hayat AA, Chaudhary S, Boby RA, Udai AD, Dutta Roy S, Saha SK, Chaudhury S. Uncertainty and Sensitivity Analysis. VISION BASED IDENTIFICATION AND FORCE CONTROL OF INDUSTRIAL ROBOTS 2022:43-73. [DOI: 10.1007/978-981-16-6990-3_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Hayat AA, Chaudhary S, Boby RA, Udai AD, Dutta Roy S, Saha SK, Chaudhury S. Force Control and Assembly. VISION BASED IDENTIFICATION AND FORCE CONTROL OF INDUSTRIAL ROBOTS 2022:115-151. [DOI: 10.1007/978-981-16-6990-3_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Ganguly D, Trivedi A, Kumar B, Patnaik T, Chaudhury S. End-to-End Transformer-Based Architecture for Text Recognition from Document Images. LECTURE NOTES IN ELECTRICAL ENGINEERING 2022:135-146. [DOI: 10.1007/978-981-19-4136-8_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Hayat AA, Chaudhary S, Boby RA, Udai AD, Dutta Roy S, Saha SK, Chaudhury S. Identification. VISION BASED IDENTIFICATION AND FORCE CONTROL OF INDUSTRIAL ROBOTS 2022:75-113. [DOI: 10.1007/978-981-16-6990-3_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Hayat AA, Chaudhary S, Boby RA, Udai AD, Dutta Roy S, Saha SK, Chaudhury S. Introduction. VISION BASED IDENTIFICATION AND FORCE CONTROL OF INDUSTRIAL ROBOTS 2022:1-12. [DOI: 10.1007/978-981-16-6990-3_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Hayat AA, Chaudhary S, Boby RA, Udai AD, Dutta Roy S, Saha SK, Chaudhury S. Integrated Assembly and Performance Evaluation. VISION BASED IDENTIFICATION AND FORCE CONTROL OF INDUSTRIAL ROBOTS 2022:153-174. [DOI: 10.1007/978-981-16-6990-3_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Hayat AA, Chaudhary S, Boby RA, Udai AD, Dutta Roy S, Saha SK, Chaudhury S. Vision System and Calibration. VISION BASED IDENTIFICATION AND FORCE CONTROL OF INDUSTRIAL ROBOTS 2022:13-42. [DOI: 10.1007/978-981-16-6990-3_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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