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Amit Singh

Researcher at National Institute of Technology, Patna

Publications -  773
Citations -  18812

Amit Singh is an academic researcher from National Institute of Technology, Patna. The author has contributed to research in topics: Digital watermarking & Medicine. The author has an hindex of 57, co-authored 640 publications receiving 13795 citations. Previous affiliations of Amit Singh include Ithaca College & Center for Infectious Disease Research and Policy.

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MicroRNA-34a Encapsulated in Hyaluronic Acid Nanoparticles Induces Epigenetic Changes with Altered Mitochondrial Bioenergetics and Apoptosis in Non-Small-Cell Lung Cancer Cells

TL;DR: A novel therapeutic approach in form of altered mitochondrial bioenergetics and redox status of cancer cells with underlying changes in epigenetic status of mtDNA that can subsequently results in induction of cancer cell apoptosis is provided.
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Fine-Grained Visual Computing Based on Deep Learning

TL;DR: The constructed fine- grained image categorization model has higher accuracy in image recognition categorization, shorter training time, and significantly better performance in similar feature effects, which provides an experimental reference for the visual computing of fine-grained images in the future.
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Impact of environmental factors on human semen quality and male fertility: a narrative review

TL;DR: In this article , the authors focused on some of these environmental factors that affect semen quality and hence, can cause male infertility, including air pollution, use of pesticides and harmful chemicals, exposure to excessive heat and can lead to decreased male fertility.
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NSCT domain–based secure multiple‐watermarking technique through lightweight encryption for medical images

TL;DR: This paper discusses a lightweight encryption–based secure digital watermarking technique for medical applications that uses redundant discrete wavelet transform and singular value decomposition along with nonsubsampled contourlet transform (NSCT) to improve robustness and imperceptibility.
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SeizSClas: An Efficient and Secure Internet-of-Things-Based EEG Classifier

TL;DR: Wang et al. as discussed by the authors proposed a secure privacy-preserving technique for brain signal classification by transforming a brain signal into an image and then applying transfer learning to solve the classification problem.