A Circuit-embedded Reservoir Computer for Smart Noise Reduction of MCG Signals

With the COVID-19 pandemic, it has become necessary to monitor cardiac activities, not only for heart patients but for everyone. However, the traditional way to use heavy machines which are non-portable, intrusive, to check the electrocardiography (ECG) is not possible for everyone. As an alternative, there are sensors that can collect magnetocardiography (MCG) signals by measuring the magnetic field produced by the electrical currents in the heart and can be converted into ECG signals. The sensor for MCG is very sensitive, consume low power, portable, and can be a good alternative to check cardiac activities. But the challenging part of these sensors would be the noise at the low frequencies because the heart also oscillates at the low frequencies. As the relevant signal and noise share the same spectral properties, standard linear filtering techniques are not efficient. In this paper, we propose a physical reservoir computing technique using a circuit that can act as a reservoir and a lightweight machine learning model. The output is modeled to reduce the noise and extract the ECG signals out of the MCG ones.

A Hybrid AI Model for Improving COVID-19 Sentiment Analysis in Social Networks

The recent COVID-19 (novel coronavirus disease) pandemic induced a deep polarization among regional as well as global communities. The sentiments regarding the pandemic and its impact on lifestyle and economy, often expressed via social networks, are regarded as critical metrics for capturing such polarization and formulating appropriate intervention by the relevant authorities. While there exist a myriad of Natural Language Processing (NLP) models for mining social media data, we demonstrate the shortcomings of the individual models in this paper, and explore how to improve the COVID-19 sentiment analysis in social media network data via two hybrid predictive models based on a Long-Short-Term-Memory (LSTM)-based autoencoder and a Convolutional Neural Network (CNN) model coupled with a bi-directional LSTM. Through extensive experiments on the recently acquired Twitter dataset, we compare the COVID-19 sentiments exhibited in the USA and Canada using our proposed hybrid predictive models and demonstrate their superiority over individual Artificial Intelligence (AI) models. © 2022 IEEE.

Optimal Models for Distributing Vaccines in a Pandemic

Distributing vaccines among a massive population is one of the challenging tasks in a pandemic. Therefore, health care organizations need to optimize the assignment of vaccination appointments for people while considering their priorities and preferences. In this paper, we propose two optimal vaccine distribution models as Integer Linear Programming (ILP) models;namely, Priority-based Model (PM) and Priority & Preference-based Model (PPM), to maximize the distribution of vaccines among a given population. In PM, we divide the people among several priority groups and ensure maximum vaccine distribution among the higher priority groups. However, along with the priority groups, PPM also considers a list of preferred vaccine distribution centers and time slots for each person. Thus, this model maximizes vaccine distribution among the higher priority groups by assigning appointments in their desired location and time. We analyzed the performance of our proposed models on a randomly generated dataset. In addition, we also performed a case study for our proposed models on the COVID-19 vaccination dataset from Thunder Bay, Canada. In both experiments, we show that PPM outperforms PM in full-filling people's preferences while maximizing the distribution of vaccines among the higher priority groups.

An Explainable AI Model for Interpretable Lung Disease Classification

In this paper, we develop a framework for lung disease identification from chest X-ray images by differentiating the novel coronavirus disease (COVID-19) or other disease-induced lung opacity samples from normal cases. We perform image processing tasks, segmentation, and train a customized Convolutional Neural Network (CNN) that obtains reasonable performance in terms of classification accuracy. To address the black-box nature of this complex classification model, which emerged as a key barrier to applying such Artificial Intelligence (AI)-based methods for automating medical decisions raising skepticism among clinicians, we address the need to quantitatively interpret the performance of our adopted approach using a Layer-wise Relevance Propagation (LRP)-based method. We also used a pixel flipping-based, robust performance metric to evaluate the explainability of our adopted LRP method and compare its performance with other explainable methods, such as Local Interpretable Model Agnostic Explanation (LIME), Guided Backpropagation (GB), and Deep Taylor Decomposition (DTD). © 2021 IEEE.

On COVID-19 Prediction Using Asynchronous Federated Learning-Based Agile Radiograph Screening Booths

To combat the novel coronavirus (COVID-19) spread, the adoption of technologies including the Internet of Things (IoT) and deep learning is on the rise. However, the seamless integration of IoT devices and deep learning models for radiograph detection to identify the presence of glass opacities and other features in the lung is yet to be envisioned. Moreover, the privacy issue of the collected radiograph data and other health data of the patients has also arisen much concern. To address these challenges, in this paper, we envision a federated learning model for COVID-19 prediction from radiograph images acquired by an X-ray device within a mobile and deployable screening resource booth node (RBN). Our envisioned model permits the privacy-preservation of the acquired radiograph by performing localized learning. We further customize the proposed federated learning model by asynchronously updating the shallow and deep model parameters so that precious communication bandwidth can be spared. Based on a real dataset, the effectiveness of our envisioned approach is demonstrated and compared with baseline methods.

LiHEA: Migrating EEG Analytics to Ultra-Edge IoT Devices With Logic-in-Headbands

Traditional cloud computing of raw Electroencephalogram (EEG) data, particularly for continuous monitoring use-cases, consumes precious network resources and contributes to delay. Motivated by the paradigm shift of edge computing and Internet of Things (IoT) for continuous monitoring, we focus on this paper on the first step to carry out EEG edge analytics at the last frontier (i.e., the ultra-edge) of our considered cyber-physical system for ensuring users' convenience and privacy. To overcome challenges due to computational and energy resource constraints of IoT devices (e.g., EEG headbands/headsets), in this paper, we envision a smart, lightweight model, referred to as Logic-in-Headbands based Edge Analytics (LiHEA), which can be seamlessly incorporated with the consumer-grade EEG headsets to reduce delay and bandwidth consumption. By systematically investigating various traditional machine and deep learning models, we identify and select the best model for our envisioned LiHEA. We consider a use-case for detecting confusion, representing levels of distraction, during online course delivery which has become pervasive during the novel coronavirus (COVID-19) pandemic. We apply a unique feature selection technique to find out which features are triggered with confusion where delta waves, attention, and theta waves were announced as the three most important features. Among various traditional machine and deep learning models, our customized random forest model demonstrated the highest accuracy of 90%. Since the dataset size might have impacted the performance of deep learning-based approaches, we further apply the deep convolutional generative adversarial network (DCGAN) to generate synthetic traces with representative samples of the original EEG data, and thereby enhance the variation in the data. While the performances of the deep learning models significantly increase after the data augmentation, they still cannot outperform the random forest model. Furthermore, computational complexity analysis is performed for the three best-performing algorithms, and random forest emerges as the most viable model for our envisioned LiHEA.

UV-CDS: An Energy-Efficient Scheduling of UAVs for Premises Sterilization

Despite the severity of the second wave of the novel coronavirus disease (COVID-19) and the recent hope for vaccine roll-outs, many public and private institutions are forced to resume their activities subject to ensuring an adequate sterilization of their premises. The existing off-the-shelf drones for such environment sanitization have limited flight-time and payload-carrying capacity. In this paper, we address this challenge by formulating an optimization problem to minimize the energy consumed by drones equipped with ultraviolet-C band (UV-C) panels. To solve this computationally hard problem, we propose several heuristics, such as a randomized path selection algorithm whose solution is further improved with a genetic algorithm-based UV-C drone-based sterilization (UV-CDS) scheduling technique. We consider educational institutions, confronting increasing infections, as an important use-case for the problem. Due to the energy constraint of the drones, the number of drones required for sterilization of the campus is smartly altered for various campus scenarios. The respective energy-efficient paths in the proposed heuristics and our envisioned UV-CDS are estimated for the drones. The performance is evaluated through extensive computer-based simulations which clearly demonstrates the effectiveness of UV-CDS in terms of sub-optimal performance and much faster execution time in contrast with the other methods. IEEE