Search Results for author: Vineeth Vijayaraghavan

Found 7 papers, 3 papers with code

BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using Photoplethysmogram

1 code implementation29 Nov 2021 Rishi Vardhan K, Vedanth S, Poojah G, Abhishek K, Nitish Kumar M, Vineeth Vijayaraghavan

Blood pressure (BP) is one of the most influential bio-markers for cardiovascular diseases and stroke; therefore, it needs to be regularly monitored to diagnose and prevent any advent of medical complications.

Blood pressure estimation Feature Engineering

End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge Devices

1 code implementation23 Nov 2021 Sideshwar J B, Sachin Krishan T, Vishal Nagarajan, Shanthakumar S, Vineeth Vijayaraghavan

The feature engineering employed in this research catered to optimizing the resource-efficient classifier used in the proposed pipeline, which was able to outperform the best performing standard ML model by $10^5\times$ in terms of memory footprint with a mere trade-off of 2% classification accuracy.

Arrhythmia Detection Atrial Fibrillation Detection +1

SOMPS-Net : Attention based social graph framework for early detection of fake health news

no code implementations22 Nov 2021 Prasannakumaran D, Harish Srinivasan, Sowmiya Sree S, Sri Gayathri Devi I, Saikrishnan S, Vineeth Vijayaraghavan

On account of the increased vulnerability of people to such deceptive fake news, a reliable technique to detect misinformation at its early stages is imperative.

Misinformation Nutrition

A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones

no code implementations12 Feb 2021 Sai Vishwanath Venkatesh, Prasanna D. Kumaran, Joish J Bosco, Pravin R. Kumaar, Vineeth Vijayaraghavan

Thus, there is a need besides just detecting malware intrusion in smartphones but to also identify the data that has been stolen to assess, aid in recovery and prevent future attacks.

BIG-bench Machine Learning Malware Detection

End-to-End Deep Learning for Reliable Cardiac Activity Monitoring using Seismocardiograms

no code implementations12 Oct 2020 Prithvi Suresh, Naveen Narayanan, Chakilam Vijay Pranav, Vineeth Vijayaraghavan

Continuous monitoring of cardiac activity is paramount to understanding the functioning of the heart in addition to identifying precursors to conditions such as Atrial Fibrillation.

Electrocardiography (ECG)

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