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96 results on '"Kaul, Amit"'

9. Comparative Study of Segmentation Techniques Used for Optic Disc Segmentation

Author

Shukla, Shivesh Madhawa, Kaul, Amit, Nath, Ravinder, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Mayank, editor, Gupta, P.K., editor, Tyagi, Vipin, editor, Flusser, Jan, editor, Ören, Tuncer, editor, and Kashyap, Rekha, editor

Published
2019
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10. Multimodal Biometric Authentication System Using Hand Shape, Palm Print, and Hand Geometry

Author

Jaswal, Gaurav, Kaul, Amit, Nath, Ravinder, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Verma, Nishchal K., editor, and Ghosh, A. K., editor

Published
2019
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12. Multimodal Biometric Authentication System Using Local Hand Features

Author

Jaswal, Gaurav, Kaul, Amit, Nath, Ravinder, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Reddy Edla, Damodar, editor, Lingras, Pawan, editor, and Venkatanareshbabu K., editor

Published
2018
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15. Palmprint and Finger Knuckle Based Person Authentication with Random Forest via Kernel-2DPCA

Author

Jaswal, Gaurav, Kaul, Amit, Nath, Ravinder, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Shankar, B. Uma, editor, Ghosh, Kuntal, editor, Mandal, Deba Prasad, editor, Ray, Shubhra Sankar, editor, Zhang, David, editor, and Pal, Sankar K., editor

Published
2017
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17. Optimized Ensemble Machine Learning Approach for Emotion Detection from Thermal Images.

Author

Katual, Jayaprakash and Kaul, Amit

Subjects
*MACHINE learning, *THERMOGRAPHY, *PARTICLE swarm optimization, *PSYCHOLOGICAL well-being, *EMOTIONS, *DEEP learning, *K-nearest neighbor classification
Abstract

Emotions indicate the feelings of the individual which are linked with personal experiences, moods, and affective states. Detection of emotion can be helpful in many fields like maintaining a patient's psychological well-being, surveillance, driver monitoring, etc. In this paper, an effective machine learning approach has been put forth for emotion detection where an ensemble of three out of five best-performing classifiers has been formed to enhance the classification accuracy. Two deep learning models (AlexNet and ResNet) have been optimally combined with k -nearest neighbor (KNN). The optimal weights for ensemble weighted averaging of classifiers have been computed with aid of particle swarm optimization (PSO) and genetic algorithm (GA) optimization. The developed framework has been tested on two publicly available datasets. An overall accuracy of above 95% has been achieved on the testing set for both datasets. The best performance was obtained by training the classifiers with segmented images and combining them by using the weights obtained through PSO. The results depicted the efficiency of the optimized ensemble machine learning approach for all performance measures used in this study in comparison to the performance of individual classifiers and majority voting fusion. [ABSTRACT FROM AUTHOR]

Published
2024
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26. PPG and fingerprint: robust bimodal biometric system

Author

Walia, Akhil and Kaul, Amit

Abstract

Technological advancements in the field of biometrics have resulted in the development of completely automatic methods for human recognition leading to a better and secure lifestyle. However, even though biometric traits like fingerprints exhibit a high degree of permanence, still certain security issues exist. The main objective of this work is to tackle spoofing attacks by introducing some liveness property in the biometric system. The PPG signal possesses various properties such as inherent liveness, ease of acquisition and low development cost. A robust biometric system, immune to direct attacks, using a combination of PPG and fingerprint has been suggested in this work. Score level fusion has been employed for integration of two modalities in parallel mode by optimising the scores of individual traits using interior point algorithm. This bimodal biometric with PPG and fingerprint as two modalities seems quite practical. A CRR of 100% has been achieved in experiments conducted on 38 healthy subjects.

Published
2023
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27. PPG-based Windkessel model parameter identification via unscented Kalman filtering

Author

Walia, Akhil and Kaul, Amit

Abstract

Modelling of arterial system is helpful in understanding the cardiovascular system and related ailments. Among various methods, Windkessel model is one approach which plays significant role in understanding the working principle of natural arterial system. The Windkessel models describe the hydraulic properties of arterial system. In this paper, PPG-based Windkessel model has been suggested which utilises PPG signal as measurement. State dynamics of proposed model has also been developed. The main contribution of this work lies on the identification of model parameters using extended Kalman filter (EKF) and unscented Kalman filter (UKF). Estimated parameters are compared with nominal values to validate model structures. The comparative analysis has been carried out with the pre-existing method. Execution time taken to simulate the proposed model for modelling a single PPG pulse is approximately one second.

Published
2023
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48. Response of different Bt cotton (Gossypium hirsutum L.) hybrids to canopy modification practices

Author

Kaul, Amit, Deol, J. S., Brar, A. S., Kaul, Amit, Deol, J. S., and Brar, A. S.

Abstract

A field investigation was carried out to characterize the growth and development of Bt cotton hybrids by detopping and use of plant growth retardants during the rainy (kharif) season of 2011 and 2012. The experimental site had loamy sand soils with normal in reaction. The experiment (split plot) involved three Bt cotton hybrids (MRC 7017, MRC 7031 and RCH 314) in main plots and growth regulation treatments (Mepiquat chloride (MC) @ 300 ppm, 2, 3, 5-tri iodo benzoic acid (TIBA) @ 100 ppm and Maelic hydrazide (MH) @ 250 ppm) in sub plots with four replications. Hybrid MRC 7017 produced significantly higher (p<0.01) seed cotton yield which was attributed to the maximum number of sympodial branches plant-1, total number of flowers and picked bolls plant-1. Application of MC @ 300 ppm, TIBA @ 100 ppm and MH @ 250 ppm reduced plant height, leaf area index and total dry matter accu-mulation than control. Detopping treatment significantly (p<0.01) reduced plant height than control but attained more plant height than all the PGRs. MC @ 300 ppm, TIBA @ 100 ppm and MH @ 250 ppm at 80 days after sowing had beneficial effect on seed cotton yield. Detopping done at 80 days after sowing failed to influence the seed cotton yield dur-ing both the years. The results revealed that foliar application of MC @ 300 ppm yielded more seed cotton by improv-ing the setting percentage and therefore, increased number of picked (open) bolls plant-1 without exhibiting any ad-verse effect on quality traits.

Published
2016

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