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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] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 05/25/2022] [Indexed: 11/03/2022]
Affiliation(s)
- Janna Mantua
- Operational Research Team, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - S A Symonette
- United States Army Special Operations Command, Ft. Benning, Georgia, USA
| | - H P Eldringhoff
- Operational Research Team, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - G A Overman
- Operational Research Team, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - S Chaudhury
- Operational Research Team, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
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2
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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] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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3
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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] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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4
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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] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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5
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Ralekar C, Choudhary S, Gandhi TK, Chaudhury S. Development of Character Recognition Model Inspired by Visual Explanations. IEEE Trans Artif Intell 2023:1-11. [DOI: 10.1109/tai.2023.3289167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Affiliation(s)
- Chetan Ralekar
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shubham Choudhary
- Harvard University John A Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Tapan Kumar Gandhi
- Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India
| | - Santanu Chaudhury
- Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India
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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] [What about the content of this article? (0)] [Affiliation(s)] [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] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Affiliation(s)
| | - Jayanta Mukhopadhyay
- IIT Kharagpur,Visual Information Processing Lab,Dept. of Computer Science & Engg.,India
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Affiliation(s)
- Surbhi Mittal
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
| | | | | | | | | | | | | | | | - Puspita Majumdar
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
- Department of Computer Science, IIIT Delhi, New Delhi, India
| | - Aakarsh Malhotra
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
- Department of Computer Science, IIIT Delhi, New Delhi, India
| | - Kartik Thakral
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
| | - Saheb Chhabra
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
- Department of Computer Science, IIIT Delhi, New Delhi, India
| | - Mayank Vatsa
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
| | - Richa Singh
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
- * E-mail:
| | - Santanu Chaudhury
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
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Vyas J, Das D, Chaudhury S. DriveBFR: Driver Behavior and Fuel-Efficiency-Based Recommendation System. IEEE Trans Comput Soc Syst 2022; 9:1446-1455. [DOI: 10.1109/tcss.2021.3112076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Affiliation(s)
- Jayant Vyas
- Department of Computer Science and Engineering, IIT Jodhpur, Jodhpur, India
| | - Debasis Das
- Department of Computer Science and Engineering, IIT Jodhpur, Jodhpur, India
| | - Santanu Chaudhury
- Department of Computer Science and Engineering, IIT Jodhpur, Jodhpur, India
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Rout DK, Subudhi BN, Veerakumar T, Chaudhury S, Soraghan J. Multiresolution visual enhancement of hazy underwater scene. Multimed Tools Appl 2022; 81:32907-32936. [DOI: 10.1007/s11042-022-12692-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [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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11
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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 Recognit 2022; 122:108243. [PMID: 34456368 PMCID: PMC8379001 DOI: 10.1016/j.patcog.2021.108243] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Affiliation(s)
| | | | | | | | | | | | | | | | - Ashwin Pudrod
- Ashwini Hospital and Ramakant Heart Care Centre, 431602, India
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Pareek V, Chaudhury S, Singh S. Handling non-stationarity in E-nose design: a review. SR 2022; 42:39-61. [DOI: 10.1108/sr-02-2021-0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [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] [What about the content of this article? (0)] [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] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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15
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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] [What about the content of this article? (0)] [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] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Affiliation(s)
- Sai Phani Kumar Malladi
- Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Jayanta Mukherjee
- Department of Computer Science and Engineering, IIT Kharagpur, Kharagpur, India
| | | | - Santanu Chaudhury
- Department of Computer Science and Engineering, IIT Jodhpur, Jodhpur, India
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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 Journal 2022. [DOI: 10.2139/ssrn.4178906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [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 Trans Neural Netw Learn Syst 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] [What about the content of this article? (0)] [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] [What about the content of this article? (0)] [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] [What about the content of this article? (0)] [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] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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22
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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] [What about the content of this article? (0)] [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] [What about the content of this article? (0)] [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] [What about the content of this article? (0)] [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] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Mor A, Kumar M, Chaudhury S. Smart City Umbrella Ontology :Context -Driven Framework For Traffic Planning. Forum for Information Retrieval Evaluation 2021. [DOI: 10.1145/3503162.3503170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Affiliation(s)
- Annu Mor
- UIET, Panjab university, chandigarh, India
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Bhugra S, Kaushik V, Mateos IC, Chaudhury S, Lall B. Unsupervised Learning of Affinity for Image Segmentation: An Inpainting based Approach. 2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ) 2021. [DOI: 10.1109/ivcnz54163.2021.9653321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Sharma R, Sharma M, Shukla A, Chaudhury S. Conditional Deep 3D-Convolutional Generative Adversarial Nets for RGB-D Generation. Mathematical Problems in Engineering 2021; 2021:1-8. [DOI: 10.1155/2021/8358314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Abstract
Generation of synthetic data is a challenging task. There are only a few significant works on RGB video generation and no pertinent works on RGB-D data generation. In the present work, we focus our attention on synthesizing RGB-D data which can further be used as dataset for various applications like object tracking, gesture recognition, and action recognition. This paper has put forward a proposal for a novel architecture that uses conditional deep 3D-convolutional generative adversarial networks to synthesize RGB-D data by exploiting 3D spatio-temporal convolutional framework. The proposed architecture can be used to generate virtually unlimited data. In this work, we have presented the architecture to generate RGB-D data conditioned on class labels. In the architecture, two parallel paths were used, one to generate RGB data and the second to synthesize depth map. The output from the two parallel paths is combined to generate RGB-D data. The proposed model is used for video generation at 30 fps (frames per second). The frame referred here is an RGB-D with the spatial resolution of 512 × 512.
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Affiliation(s)
| | - Manoj Sharma
- ECE Department of Bennet University, Greater Noida, India
| | - Ankit Shukla
- ECE Department of Bennet University, Greater Noida, India
| | - Santanu Chaudhury
- Department of Electrical Engineering, IIT Delhi and Director of IIT Jodhpur, New Delhi, India
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Pareek V, Chaudhury S, Singh S. Hybrid 3DCNN-RBM Network for Gas Mixture Concentration Estimation With Sensor Array. IEEE Sensors J 2021; 21:24263-24273. [DOI: 10.1109/jsen.2021.3105414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Prakash J, Saldanha D, Chaudhury S, Chatterjee K, Srivastava K. All, that was not bad in COVID crisis: Pearls of goodness from the furls of furnace. Ind Psychiatry J 2021; 30:S1-S2. [PMID: 34908654 PMCID: PMC8611528 DOI: 10.4103/0972-6748.328779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/05/2021] [Accepted: 09/12/2021] [Indexed: 11/04/2022] Open
Affiliation(s)
- Jyoti Prakash
- Department of Psychiatry, Armed Forces Medical College, Pune, Maharashtra, India
| | - D Saldanha
- Department of Psychiatry, Dr. D. Y. Patil Medical College, Hospital and Research Centre, Dr D Y Patil Vidyapeeth, Pune, Maharashtra, India
| | - S Chaudhury
- Department of Psychiatry, Dr. D. Y. Patil Medical College, Hospital and Research Centre, Dr D Y Patil Vidyapeeth, Pune, Maharashtra, India
| | - K Chatterjee
- Department of Psychiatry, Armed Forces Medical College, Pune, Maharashtra, India
| | - K Srivastava
- Department of Psychiatry, Armed Forces Medical College, Pune, Maharashtra, India
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Pareek V, Chaudhury S, Singh S. Online Pattern Recognition of Time-series Gas Sensor Data with Adaptive 2D-CNN Ensemble. 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) 2021. [DOI: 10.1109/idaacs53288.2021.9660930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Pareek V, Chaudhury S, Singh S. Gas Discrimination & Quantification using Sensor Array with 3D Convolution Regression Dual Network. 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) 2021. [DOI: 10.1109/idaacs53288.2021.9660938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Malladi SPK, Mukhopadhyay J, Larabi C, Chaudhury S. Lighter and Faster Cross-Concatenated Multi-Scale Residual Block Based Network for Visual Saliency Prediction. 2021 IEEE International Conference on Image Processing (ICIP) 2021. [DOI: 10.1109/icip42928.2021.9506710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Affiliation(s)
| | - Jayanta Mukhopadhyay
- IIT Kharagpur,Visual Information Processing Lab,Dept. of Computer Science & Engg.,India
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Pal S, Maity S, Balachandran S, Chaudhury S. "In-vitro Effects of Chlorpyrifos and Monocrotophos on the Activity of Acetylcholinesterase (AChE) in Different Tissues of Apple Snail Pila globosa (Swainson, 1822)". NEPT 2021. [DOI: 10.46488/nept.2021.v20i03.037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The impact of two organophosphorus insecticides [Chlorpyrifos (CPF) and Monocrotophos (MCP)] on non-target wild natural gastropod, Pila globosa (apple snail) from the paddy fields was studied. The activity of acetylcholinesterase (AChE) was monitored on foot-muscle and hepatopancreas tissues of control and exposed snails. In the foot- muscle AChE inhibition progressed and reached 54.19% and 63.13% of the control, whereas, the AChE inhibition in the hepatopancreas reached 46.96% and 53.67% over control after 48 hours of exposure to 1.5 mL.L-1 and 2.5 mL.L-1 CPF respectively. After 48 hours of MCP exposure at 1.5 mL.L-1 and 2.5 mL.L-1 separately, the AChE inhibition of foot muscle was 49.07% and 57.59% respectively while in hepatopancreas it was 44.65% and 48.84% respectively. Our results show more inhibition of AChE activities on the foot-muscle than hepatopancreas in a concentration and time-dependent manner with greater severity by CPF in comparison to MCP. AChE inhibition increased with the increasing exposure time.
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Pareek V, Prajesh R, Chaudhury S, Singh S. Smart Gas Sensing using Single MOS Gas Sensor with Adaptive Gradient Boosting. 2021 Joint 10th International Conference on Informatics, Electronics & Vision (ICIEV) and 2021 5th International Conference on Imaging, Vision & Pattern Recognition (icIVPR) 2021. [DOI: 10.1109/icievicivpr52578.2021.9564180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Madan S, Chaudhury S, Gandhi TK. Automated detection of COVID-19 on a small dataset of chest CT images using metric learning. 2021 International Joint Conference on Neural Networks (IJCNN) 2021. [DOI: 10.1109/ijcnn52387.2021.9533831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Rituraj, Tiwari A, Chaudhury S, Singh S, Saurav S. Video Classification using SlowFast Network via Fuzzy rule. 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2021. [DOI: 10.1109/fuzz45933.2021.9494542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Pandey CK, Singh A, Chaudhury S. A simulation-based analysis of effect of interface trap charges on dc and analog/HF performances of dielectric pocket SOI-Tunnel FET. Microelectronics Reliability 2021; 122:114166. [DOI: 10.1016/j.microrel.2021.114166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Singh P, Chaudhury S, Panigrahi BK. Hybrid MPSO-CNN: Multi-level Particle Swarm optimized hyperparameters of Convolutional Neural Network. Swarm and Evolutionary Computation 2021; 63:100863. [DOI: 10.1016/j.swevo.2021.100863] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Pahal N, Lall B, Chaudhury S. An Ontology Representation Language for Multimedia Event Applications. JWE 2021. [DOI: 10.13052/jwe1540-9589.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Abstract
This paper presents formalization of a new Multimedia Web Ontology Language (E-MOWL) to handle events with media depictions. The temporal, spatial and entity aspects that are implicitly linked to an event are represented through this language to model the context of events. The already existing Multimedia Web Ontology Language (MOWL) can be leveraged for perceptual modelling of a domain, where the concepts manifest into media patterns in the multimedia document and helps in semantic processing of the contents. The language E-MOWL provides a rich method for representing knowledge corresponding to a specific domain wherein the context specifies the intended meaning of each element of the domain of discourse; an element in different context may correspond to different functional role. The context information associated with an event ties the audiovisual data with event related aspects. All these aspects when considered altogether provide the evidence and contribute towards recognizing an event from multimedia documents. The language also enables reasoning with the uncertainty associated with the events and is organized in the form of Bayesian Network (BN). The media items that are semantically relevant can be assimilated together on the basis of their association with events. We have demonstrated the efficacy of our approach by utilizing an ontology for the entertainment category in news domain to offer an application \textit{news aggregation} and event-based book recommendations.
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Tripathi A, Srivastava S, Lall B, Chaudhury S. Using Scene Graphs for Detecting Visual Relationships. 2020 25th International Conference on Pattern Recognition (ICPR) 2021. [DOI: 10.1109/icpr48806.2021.9412337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Ralekar C, Gandhi TK, Chaudhury S. Collaborative Human Machine Attention Module for Character Recognition. 2020 25th International Conference on Pattern Recognition (ICPR) 2021. [DOI: 10.1109/icpr48806.2021.9413229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Bhugra S, Garg K, Chaudhury S, Lall B. A Hierarchical Framework for Leaf Instance Segmentation: Application to Plant Phenotyping. 2020 25th International Conference on Pattern Recognition (ICPR) 2021. [DOI: 10.1109/icpr48806.2021.9411981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [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.. [DOI: 10.1101/2021.01.07.21249323] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Abstract
AbstractPurposeTo advance the usage of CXRs as a viable solution for efficient COVID-19 diagnostics by providing large-scale annotations of the abnormalities in frontal CXRs in BIMCV-COVID19+ database, and to provide a robust evaluation mechanism to facilitate its usage.Materials and MethodsWe provide the abnormality annotations in frontal CXRs by creating bounding boxes. The frontal CXRs are a part of the existing BIMCV-COVID19+ database. We also define four different protocols for robust evaluation of semantic segmentation and classification algorithms. Finally, we benchmark the defined protocols and report the results using popular deep learning models as a part of this study.ResultsFor semantic segmentation, Mask-RCNN performs the best among all the models with a DICE score of 0.43 ± 0.01. For classification, we observe that MobileNetv2 yields the best results for 2-class and 3-class classification. We also observe that deep models report a lower performance for classifying other classes apart from the COVID class.ConclusionBy making the annotated data and protocols available to the scientific community, we aim to advance the usage of CXRs as a viable solution for efficient COVID-19 diagnostics. This large-scale data will be useful for ML algorithms and can be used for learning radiological patterns observed in COVID-19 patients. Further, the protocols will facilitate ML practitioners for unified large-scale evaluation of their algorithms.Data Availability StatementThe data associated with this work is available here : Radiologists’ Annotations on COVID-19+ X-rays https://osf.io/b35xu/ via @OSFramework andhttp://covbase4all.igib.res.in/.
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Chaudhury S, Nanda N, Tyagi B. Green-Field Versus Merger and Acquisition: Role of FDI in Economic Growth of South Asia. Trade, Investment and Economic Growth 2021:157-167. [DOI: 10.1007/978-981-33-6973-3_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Gupta S, Mukherjee P, Chaudhury S, Lall B, Sanisetty H. DFTNet: Deep Fish Tracker With Attention Mechanism in Unconstrained Marine Environments. IEEE Trans Instrum Meas 2021; 70:1-13. [DOI: 10.1109/tim.2021.3109731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Madan S, Gandhi T, Chaudhury S. Bone Age Assessment for Lower Age Groups Using Triplet Network in Small Dataset of Hand X-Rays. Intelligent Human Computer Interaction 2021:142-153. [DOI: 10.1007/978-3-030-68449-5_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Sinha H, Kumar S, Chaudhury S. A Variational Training Perspective to GANs for Hyperspectral Image Generation. Advances in Intelligent Systems and Computing 2021:417-429. [DOI: 10.1007/978-981-16-2709-5_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Mohan R, Chaudhury S, Lall B. Temporal Causal Modelling on Large Volume Enterprise Data. IEEE Trans Big Data 2021:1-1. [DOI: 10.1109/tbdata.2021.3053879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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