no code implementations • 18 Apr 2024 • Avisek Naug, Antonio Guillen, Ricardo Luna Gutierrez, Vineet Gundecha, Sahand Ghorbanpour, Sajad Mousavi, Ashwin Ramesh Babu, Soumyendu Sarkar
There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers.
1 code implementation • 16 Apr 2024 • Soumyendu Sarkar, Avisek Naug, Antonio Guillen, Ricardo Luna, Vineet Gundecha, Ashwin Ramesh Babu, Sajad Mousavi
To tackle this, we've developed DCRL-Green, a multi-agent RL environment that empowers the ML community to design data centers and research, develop, and refine RL controllers for carbon footprint reduction in DCs.
no code implementations • 27 Mar 2024 • Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Vineet Gundecha, Avisek Naug, Sahand Ghorbanpour
We present a generic Reinforcement Learning (RL) framework optimized for crafting adversarial attacks on different model types spanning from ECG signal analysis (1D), image classification (2D), and video classification (3D).
no code implementations • 21 Mar 2024 • Soumyendu Sarkar, Avisek Naug, Ricardo Luna, Antonio Guillen, Vineet Gundecha, Sahand Ghorbanpour, Sajad Mousavi, Dejan Markovikj, Ashwin Ramesh Babu
As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide.
no code implementations • 28 Oct 2023 • Sajad Mousavi, Ricardo Luna Gutiérrez, Desik Rengarajan, Vineet Gundecha, Ashwin Ramesh Babu, Avisek Naug, Antonio Guillen, Soumyendu Sarkar
We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination.
no code implementations • 28 Oct 2023 • Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Zachariah Carmichael, Vineet Gundecha, Sahand Ghorbanpour, Ricardo Luna, Gutierrez Antonio Guillen, Avisek Naug
We present a novel framework for generating adversarial benchmarks to evaluate the robustness of image classification models.
no code implementations • 5 Oct 2023 • Avisek Naug, Antonio Guillen, Ricardo Luna Gutiérrez, Vineet Gundecha, Dejan Markovikj, Lekhapriya Dheeraj Kashyap, Lorenz Krause, Sahand Ghorbanpour, Sajad Mousavi, Ashwin Ramesh Babu, Soumyendu Sarkar
The increasing global emphasis on sustainability and reducing carbon emissions is pushing governments and corporations to rethink their approach to data center design and operation.
no code implementations • 15 Jun 2023 • Alireza Shamsoshoara, Fatemeh Lotfi, Sajad Mousavi, Fatemeh Afghah, Ismail Guvenc
The performance of this method is compared to learning from a demonstration technique called behavioral cloning (BC) using a supervised learning approach.
no code implementations • 10 Jun 2023 • Seokmin Choi, Sajad Mousavi, Phillip Si, Haben G. Yhdego, Fatemeh Khadem, Fatemeh Afghah
In the medical field, current ECG signal analysis approaches rely on supervised deep neural networks trained for specific tasks that require substantial amounts of labeled data.
no code implementations • 30 Oct 2020 • James Belen, Sajad Mousavi, Alireza Shamsoshoara, Fatemeh Afghah
The uncertainty is estimated by conducting multiple passes of the input through the network to build a distribution; the mean of the standard deviations is reported as the network's uncertainty.
no code implementations • 13 Jun 2020 • Sajad Mousavi, Fatemeh Afghah, Fatemeh Khadem, U. Rajendra Acharya
For this reason, the ECG signal is a sequence of heartbeats similar to sentences in natural languages) and each heartbeat is composed of a set of waves (similar to words in a sentence) of different morphologies.
no code implementations • 12 Feb 2020 • Sajad Mousavi, Fatemeh Afghah, U. Rajendra Acharya
The cardiologist level performance in detecting this arrhythmia is often achieved by deep learning-based methods, however, they suffer from the lack of interpretability.
1 code implementation • 26 Nov 2019 • Alireza Shamsoshoara, Fatemeh Afghah, Abolfazl Razi, Sajad Mousavi, Jonathan Ashdown, Kurt Turk
This paper studies the problem of spectrum shortage in an unmanned aerial vehicle (UAV) network during critical missions such as wildfire monitoring, search and rescue, and disaster monitoring.
no code implementations • 25 Sep 2019 • Sajad Mousavi, Atiyeh Fotoohinasab, Fatemeh Afghah
This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multimodal biosignals.
no code implementations • 17 Apr 2019 • Behzad Ghazanfari, Fatemeh Afghah, Kayvan Najarian, Sajad Mousavi, Jonathan Gryak, James Todd
In this paper, we propose a novel set of high-level features based on unsupervised feature learning technique in order to effectively capture the characteristics of different arrhythmia in electrocardiogram (ECG) signal and differentiate them from irregularity in signals due to different sources of signal disturbances.
3 code implementations • 5 Mar 2019 • Sajad Mousavi, Fatemeh Afghah, U. Rajendra Acharya
Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders.
3 code implementations • arXiv:1812.07421 2018 • Sajad Mousavi, Fatemeh Afghah
Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmia.
Ranked #1 on Arrhythmia Detection on MIT-BIH AR
no code implementations • 17 Dec 2016 • Sajad Mousavi, Michael Schukat, Enda Howley, Ali Borji, Nasser Mozayani
Bottom-Up (BU) saliency models do not perform well in complex interactive environments where humans are actively engaged in tasks (e. g., sandwich making and playing the video games).