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Your search keyword '"Bodade, Rajesh M."' showing total 28 results
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28 results on '"Bodade, Rajesh M."'

2. Comparison of Higher-Order Spectral Features Clubbed with Classification Techniques for the Labeling of Adventitious Sounds in the Breathing Cycle

Author

Dubey, Rupesh, Bodade, Rajesh M., Dubey, Divya, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Kumar, Amit, editor, Senatore, Sabrina, editor, and Gunjan, Vinit Kumar, editor

Published
2022
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3. MILITARY-EYES: A Real-Time Hyper-Tuned and Optimised Object Detection Approach Tailored for Military Dataset

Author

Verma, Rajesh, Vishwakarma, Santosh Kumar, and Bodade, Rajesh M

Abstract

Accurately identifying all objects of interest inside the specified frame of reference is crucial for object identification techniques to allow machine vision to interpret images successfully. Theoretical frameworks from computer vision and deep learning have informed many potential solutions to this problem. However, current approaches frequently fail when faced with objects going through random geometric changes and continually show shortcomings when identifying small, dense objects. This research looks at state-of-the-art object detection methods, compares them, and then suggests a convolutional object detection network that has been tweaked to fix the problems with the existing methods. Comparing our research to existing approaches, we find that they perform better. We accomplish this by training deep convolutional networks to detect geometric transformations and adjusting the networks to handle multi-scaled features. The results achieved after the optimization of the You only look once (YOLO) V5 & V7 are stated in this paper. The experiments demonstrated that YOLO v8 achieved higher accuracy than the hyper-tuned YOLO V5 and V7, with an average Precision (mAP) score of 52.8% on the customized military dataset running at 60 Frames Per Second (FPS). Object detection using deep neural networks (DNNs) poses a significant challenge due to the high computational and power requirements, mainly when deployed on general-purpose platforms such as CPUs and GPUs. However, addressing these limitations becomes crucial for efficient edge computing tasks like object detection, where faster, smaller, and energy-efficient solutions are essential. To overcome these challenges, system-on-chip (SoC) designs emerge as a promising solution.

Published
2024
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9. Related Work

Author

Bodade, Rajesh M., Talbar, Sanjay N., Kacprzyk, Janusz, Series editor, Bodade, Rajesh M., and Talbar, Sanjay N.

Published
2014
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10. Iris Segmentation

Author

Bodade, Rajesh M., Talbar, Sanjay N., Kacprzyk, Janusz, Series editor, Bodade, Rajesh M., and Talbar, Sanjay N.

Published
2014
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15. Analysis of Features based on Wavelet Bi-Spectrum and Power Spectrum for the Detection of Adventitious Lung Sounds.

Author

Dubey, Rupesh, Bodade, Rajesh M., and Dubey, Divya

Subjects
*POWER spectra, *LUNGS, *RANDOM forest algorithms, *SOUNDS
Abstract

Lung sounds contribute essential information about a patient's health. Here, for detecting adventitious sounds of lungs, two sets of eight features each (total 16 features), based on wavelet bi-spectrum (WBS) and power spectrum (WPS), respectively, are proposed. The feature sets are analyzed using five classifiers with one to seven sub-classifier types. A matrix (17X14) of seven evaluation parameters compares the feature sets. Results show that Random Forest, Random Tree, Random committee with WPS and WBS features, LMT with WPS, and Randomizable filter classifier with WBS have shown the best results updating the accuracy obtained in previous researches. [ABSTRACT FROM AUTHOR]

Published
2023
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24. FrontMatter.

Author

Bodade, Rajesh M. and Talbar, Sanjay N.

Published
2014

28. BackMatter.

Author

Bodade, Rajesh M. and Talbar, Sanjay N.

Published
2014

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