Search Results for author: Sajad Mousavi

Found 18 papers, 4 papers with code

A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration

no code implementations18 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.

Sustainability of Data Center Digital Twins with Reinforcement Learning

1 code implementation16 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.

reinforcement-learning Reinforcement Learning (RL)

Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning

no code implementations27 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).

Classification Image Classification +2

Carbon Footprint Reduction for Sustainable Data Centers in Real-Time

no code implementations21 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.

Multi-agent Reinforcement Learning

PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability

no code implementations5 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.


ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning

no code implementations10 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.

Arrhythmia Detection Heartbeat Classification +3

An Uncertainty Estimation Framework for Risk Assessment in Deep Learning-based Atrial Fibrillation Classification

no code implementations30 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.

General Classification

ECG Language Processing (ELP): a New Technique to Analyze ECG Signals

no code implementations13 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.


HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks

no code implementations12 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.

Atrial Fibrillation Detection

An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations

1 code implementation26 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.


Single-modal and Multi-modal False Arrhythmia Alarm Reduction using Attention-based Convolutional and Recurrent Neural Networks

no code implementations25 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.


An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs

no code implementations17 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.

Clustering Specificity

SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

3 code implementations5 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.

EEG Sleep Stage Detection

Learning to predict where to look in interactive environments using deep recurrent q-learning

no code implementations17 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).

Atari Games Q-Learning +2

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