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Start Over Author "Nishan A" Publisher elsevier b.v.
174 results on '"Nishan A"'

26. Deep Learning based Eye Tracking on Smartphones for Dynamic Visual Stimuli.

Author

Gunawardena, Nishan, Ginige, Jeewani Anupama, Javadi, Bahman, and Lui, Gough

Abstract

Performing human gaze estimation using smartphones is invaluable in human-computer interaction with various potential appli- cations, ranging from user interface enhancements to medical research. We developed three deep learning-based mobile device eye-tracking architectures for dynamic visual stimuli. This includes a combination of Convolutional Neural Networks (CNN) with two different Recurrent Neural Networks (RNN), namely Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Our CNN+LSTM and CNN+GRU models achieved an average Root Mean Square Error of 0.955cm and 1.091cm, respectively. The codes and models are available on https://github.com/NishNilanka/MobileEye.git. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Fractal Dimension and Radiomorphometric analysis of Orthopanoramic radiographs in patients with tobacco and areca nut associated oral mucosal lesions: A pilot in-vivo study in a North Indian cohort.

Author

Santolia, Divya, Dahiya, Swati, Sharma, Sheetal, Khan, Maroof Ahmed, Mohammed, Nishan, Priya, Harsh, Gupta, Srishti R, Bhargava, Stuti, and Gupta, Dr. Shalini R

Abstract

Objective: The aim of this study was to determine the fractal dimension (FD) and radiomorphometric indices (RMIs) in the mandible from orthopantomographic radiographs in patients with oral lesions associated with smokeless/smoking tobacco (SLT/ST) and areca nut habits in a North Indian cohort.Study Design: A prospective, cross-sectional, observational pilot study was conducted of 120 subjects, including controls and 3 study groups of 30 patients each with oral submucous fibrosis, tobacco pouch keratosis, and oral leukoplakia (OL). Two observers calculated FD and the RMIs of mandibular cortical thickness (MCT), panoramic mandibular index (PMI), and mandibular cortical index (MCI).Results: Mean FD was significantly reduced compared to controls with all oral lesions (P < .05) and with all habits in 3 of 4 regions of interest (P < .05). MCT was significantly reduced with OL (P < .005) and in ST users (P < .05). PMI did not differ regarding lesion status or habits. Compared to the controls, MCI C2 type was significantly more common in all oral lesions (P ≤ .005) and all types of habit (P < .005). Inter- and intraobserver agreement was strong to excellent.Conclusions: FD and RMI values were significantly altered compared to controls in oral lesions associated with tobacco and areca nut habits and in the dominant type of habit. [ABSTRACT FROM AUTHOR]

Published
2022
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49. Characterization of the volatile organic compounds present in the headspace of decomposing human remains

Author

Hoffman, Erin M., Curran, Allison M., Dulgerian, Nishan, Stockham, Rex A., and Eckenrode, Brian A.

Subjects
Organic compounds -- Analysis, Human remains (Archaeology) -- Analysis, Extraction (Chemistry) -- Methods, Gas chromatography -- Usage, Mass spectrometry -- Usage, Law
Abstract

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.forsciint.2008.12.022 Byline: Erin M. Hoffman (a), Allison M. Curran (a), Nishan Dulgerian (b), Rex A. Stockham (b), Brian A. Eckenrode (c) Keywords: Human remains; Canines; Volatile organic compounds; Clandestine burials; Solid-phase microextraction; SPME Abstract: Law enforcement agencies frequently use canines trained to detect the odor of human decomposition to aid in determining the location of clandestine burials and human remains deposited or scattered on the surface. However, few studies attempt to identify the specific volatile organic compounds (VOCs) that elicit an appropriate response from victim recovery (VR) canines. Solid-phase microextraction (SPME) was combined with gas chromatography-mass spectrometry (GC-MS) to identify the VOCs released into the headspace associated with 14 separate tissue samples of human remains previously used for VR canine training. The headspace was found to contain various classes of VOCs, including acids, alcohols, aldehydes, halogens, aromatic hydrocarbons, ketones, and sulfides. Analysis of the data indicates that the VOCs associated with human decomposition share similarities across regions of the body and across types of tissue. However, sufficient differences exist to warrant VR canine testing to identify potential mimic odor chemical profiles that can be used as training aids. The resulting data will assist in the identification of the most suitable mixture and relative concentrations of VOCs to appropriately train VR canines. Author Affiliation: (a) Oak Ridge Institute for Science and Education, Oak Ridge, TN, United States (b) Evidence Response Team Unit, FBI Laboratory, Quantico, VA, United States (c) Counterterrorism and Forensic Science Research Unit, FBI Laboratory, Quantico, VA, United States Article History: Received 7 February 2008; Revised 15 December 2008; Accepted 29 December 2008

Published
2009

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