Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, have gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by analyzing and evaluating the potential of intelligent processing of clinical visual data at the edge allowing the remote healthcare centers, lacking advanced diagnostic facilities, to benefit from the multi-modal data securely. To this aim, we utilize the emerging concept of clustered federated learning (CFL) for an automatic diagnosis of COVID-19. Such an automated system can help reduce the burden on healthcare systems across the world that has been under a lot of stress since the COVID-19 pandemic emerged in late 2019. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific type of COVID-19 imagery) are trained with central data, and improvements of 16\% and 11\% in overall F1-Scores have been achieved over the multi-modal model trained in the conventional Federated Learning setup on X-ray and Ultrasound datasets, respectively. We also discuss in detail the associated challenges, technologies, tools, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.Comment: preprint versio

Point prevalence survey of antimicrobial use during the COVID-19 pandemic among different hospitals in Pakistan : findings and implications

The COVID-19 pandemic has significantly influenced antimicrobial use in hospitals raising concerns regarding increased antimicrobial resistance (AMR) through their overuse. The objective of this study was to assess patterns of antimicrobial prescribing during the current COVID-19 pandemic among hospitals in Pakistan, including the prevalence of COVID-19. A point prevalence survey (PPS) was performed among 11 different hospitals from November 2020 to January 2021. The study included all hospitalized patients receiving an antibiotic on the day of the PPS. The Global-PPS web-based application was used for data entry and analysis. Out of 1024 hospitalized patients, 662 (64.64%) received antimicrobials. The top three most common indications for antimicrobial use were pneumonia (13.3%), central nervous system infections (10.4%) and gastrointestinal indications (10.4%). Ceftriaxone (26.6%), metronidazole (9.7%) and vancomycin (7.9%) were the top three most commonly prescribed antimicrobials among surveyed patients, with the majority of antibiotics administered empirically (97.9%). Most antimicrobials for surgical prophylaxis were given for more than one day, which is a concern. Overall, a high percentage of antimicrobial use, including broad-spectrums, was seen among the different hospitals in Pakistan during the current COVID-19 pandemic. Multifaceted interventions are needed to enhance rational antimicrobial prescribing including limiting their prescribing post-operatively for surgical prophylaxis

Green - Lean Practices and Reverse Logistics:Evidence from Manufacturing Industry of a Developing Economy

The past literature has seen considerable growth in lean and green concepts in the context of developed countries. However, despite the increasing interdependencies between developing and developed economies, little is known in the context of developing economies. This study investigates the impact of green and lean practices, and reverse logistics on organizational performance in the context of a developing economy. We employ a quantitative research design, where a sample of 170 responses was generated from senior managers of manufacturing firms in Pakistan – a developing economy. The analysis uncovers those green practices have a significant positive nexus with organizational performance in a developing economy like Pakistan. Further, we find that lean practices and reverse logistics serially mediate the relationship between green practices and organizational performance. We thus suggest green practices, lean practices, and reverse logistics as key levers to enhance performance and gain a competitive advantage for the firms in the developing economy like Pakistan. This is among the few studies testing serial mediation of lean practices and reverse logistics between green practices and organizational performance. The findings offer numerous contributions to both theory and practice

Kumaraswamy Half-Logistic Distribution: Properties and Applications

This study elucidates a three-parameter probabilistic model generalized from Kumaraswamy family using half logistic distribution as a baseline model named as Kumaraswamy half logistic distribution. The properties of the observed model are also explored. Further, we explain the behavior of failure rate, cumulative failure rate, and survival rate functions. Monte Carlo simulation study is being conducted to estimate the parameters under ML estimation method. Moreover, two practical applications illustrate the flexibility and better fit of the observed model

SAGBI Bases in <i>G</i>-Algebras

In this article, we develop the theory of SAGBI bases in G-algebras and create a criterion through which we can check if a set of polynomials in a G-algebra is a SAGBI basis or not. Moreover, we will construct an algorithm to compute SAGBI bases from a subset of polynomials contained in a subalgebra of a G-algebra

An active learning method for diabetic retinopathy classification with uncertainty quantification

In recent years, deep learning (DL) techniques have provided state-of-the-art performance in medical imaging. However, good quality (annotated) medical data is in general hard to find due to the usually high cost of medical images, limited availability of expert annotators (e.g., radiologists), and the amount of time required for annotation. In addition, DL is data-hungry and its training requires extensive computational resources. Furthermore, DL being a black-box method lacks transparency on its inner working and lacks fundamental understanding behind decisions made by the model and consequently, this notion enhances the uncertainty on its predictions. To this end, we address these challenges by proposing a hybrid model, which uses a Bayesian convolutional neural network (BCNN) for uncertainty quantification, and an active learning approach for annotating the unlabeled data. The BCNN is used as a feature descriptor and these features are then used for training a model, in an active learning setting. We evaluate the proposed framework for diabetic retinopathy classification problem and demonstrate state-of-the-art performance in terms of different metrics. Graphical abstract: [Figure not available: see fulltext.]. 2022, International Federation for Medical and Biological Engineering.Adeel Razi is affiliated with The Wellcome Centre for Human Neuroimaging supported by core funding from Wellcome (203147/Z/16/Z).Scopu

Effect of Acid Treatment on the Recovery of Valuable Metals from Steel Plant Exhaust

The response of different metals such as Zn, Fe, Pb, Cr and Mn during leaching of Electric Arc Furnace (EAF) dust in acid medium has been investigated. The major proportion of EAF dust constitutes of these metals and their recovery by means of a chemical process is not only economical but also imparts positive impact on the environment. The leaching of metals from dust is achieved using different concentration of sulphuric acid, and the dust samples have been characterized both before and after leaching. Based on the results, several recommendations have been suggested for the optimization of H2SO4 concentration,that lead to the maximum recovery of these metals. Under the optimized conditions, it has been determined that the satisfactory leaching yield of Zn (95%) can be obtained at a concentration of 1M