scholarly journals Hybrid-based Framework for COVID-19 Prediction via Federated Machine Learning Models

2021 ◽  
Author(s):  
Ameni Kallel ◽  
Molka Rekik ◽  
Mahdi Khemakhem
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Streaming Data ◽  
Cloud Environment ◽  
Short Term ◽  
The People ◽  
Cloud Framework ◽  
Iot Devices ◽  
New Framework

<div>The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. Therefore, a smart monitoring system that detects and tracks the suspected COVID-19 infected persons may improve the clinicians decision-making and consequently limit the pandemic spread. This paper entails a new framework integrating the Machine Learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, Lung UltraSound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a Service (federated-MLaaS); (i) the distributed batch-MLaaS, which is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream-MLaaS installed into a hybrid fog/cloud environment for a short-term decision-making. Stream-MLaaS use a shared federated prediction model stored into the cloud; whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream-ML algorithms from the Python’s libraries.</div>

scholarly journals Hybrid-based Framework for COVID-19 Prediction via Federated Machine Learning Models

2021 ◽  
Author(s):  
Ameni Kallel ◽  
Molka Rekik ◽  
Mahdi Khemakhem
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Streaming Data ◽  
Cloud Environment ◽  
Short Term ◽  
The People ◽  
Cloud Framework ◽  
Iot Devices ◽  
New Framework

<div>The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. Therefore, a smart monitoring system that detects and tracks the suspected COVID-19 infected persons may improve the clinicians decision-making and consequently limit the pandemic spread. This paper entails a new framework integrating the Machine Learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, Lung UltraSound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a Service (federated-MLaaS); (i) the distributed batch-MLaaS, which is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream-MLaaS installed into a hybrid fog/cloud environment for a short-term decision-making. Stream-MLaaS use a shared federated prediction model stored into the cloud; whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream-ML algorithms from the Python’s libraries.</div>

Rescuing Democracy in the Age of the Internet

2015 ◽  
Vol 29 (3) ◽  
pp. 331-344 ◽  
Author(s):  
David Runciman
Keyword(s):  
Decision Making ◽  
The Internet ◽  
Short Term ◽  
The People ◽  
History Of ◽  
Entire History ◽  
The Right ◽  
The Masses ◽  
Alternative Regime

Throughout almost the entire history of democracy—from pre-Socratic Greece up to the second half of the twentieth century—its champions faced little difficulty in identifying its enemies. Critics of democracy consistently lined up to attack it on ideological and philosophical grounds. The litany of complaints was familiar: Democracy is an ignorant, unreliable, unstable form of rule; putting power in the hands of the people entrusts decision-making to those who are incapable of making the right decisions, either because of their natural incapacity or because social arrangements have denuded them of their ability to know what they are doing; democratic politicians pander to the masses, and the masses reward them for it; democracies choose short-term gratification over long-term solutions and eventually pay the price. These charges were invariably accompanied by the promise of something better, the assumption being that almost any alternative regime would be an improvement on the inadequacies of democracy.

scholarly journals "Smart Tanks": An Intelligent High-Throughput Intervention Testing Platform in Daphnia

2021 ◽  
Author(s):  
Yongmin Cho ◽  
Rachael A Jonas-Closs ◽  
Lev Y Yampolsky ◽  
Marc W Kirschner ◽  
Leonid Peshkin
Keyword(s):  
Machine Learning ◽  
Chronological Age ◽  
Complex Behavior ◽  
Learning Models ◽  
Short Term ◽  
Health Span ◽  
Response Profile ◽  
Automated Phenotyping ◽  
Machine Learning Models

We present a novel platform for testing the effect of interventions on life- and health-span of a short-lived semi transparent freshwater organism, sensitive to drugs with complex behavior and physiology - the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioural features of both routine motion and response to stimuli are continuously accurately quantified for large homogeneous cohorts via an automated phenotyping pipeline. We build predictive machine learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life-long and instant assays as illustrated using long term dose response profile of metformin and short term assay of such well-studied substances as caffeine and alcohol.

“The Death of Liberal Internationalism?: Donald Trump, Walmart, and the Two Koreas”

2018 ◽  
Vol 25 (2) ◽  
pp. 169-197
Author(s):  
Mitchell B. Lerner
Keyword(s):  
United States ◽  
The United States ◽  
Business Practices ◽  
Short Term ◽  
New Approach ◽  
Liberal Internationalism ◽  
The People ◽  
Modern Business ◽  
Short Time

The election of Donald J. Trump unsettled many areas of U.S. foreign policy, but few more than the nation’s relationship with Korea. This article argues that the Trump administration’s vision for the world represents a stark break from the tradition of liberal internationalism and instead seeks to take the United States down a path that reflects the modern business practices of giant American corporations. A suitable label for this vision, as the following pages will show, is “Walmart unilateralism.” This framework abandons the traditional American policies of nation building and alliances based on shared ideological values. Instead, it embraces a more short-term approach rooted in financial bottom lines, flexible alliances and rivalries, and the ruthless exploitation of power hierarchies. This new approach, this article concludes, may dramatically transform the American relationship with Korea. Walmart unilateralism in Korea almost certainly will have some short-time positive ramifications for the United States, but its larger failure to consider the history and values of the people living on the Korean Peninsula may generate serious long-term problems for the future experience of the United States in the region.

A trajectories' guide to the state space - learning missing terms in bifurcating ecological systems

2021 ◽  
Author(s):  
Rahel Vortmeyer-Kley ◽  
Pascal Nieters ◽  
Gordon Pipa
Keyword(s):  
Machine Learning ◽  
Differential Equations ◽  
Algal Blooms ◽  
Ecological Systems ◽  
Short Term ◽  
Specific System ◽  
Worst Case ◽  
System Components ◽  
Full Knowledge

&lt;p&gt;Ecological systems typically can exhibit various states ranging from extinction to coexistence of different species in oscillatory states. The switch from one state to another is called bifurcation. All these behaviours of a specific system are hidden in a set of describing differential equations (DE) depending on different parametrisations. To model such a system as DE requires full knowledge of all possible interactions of the system components. In practise, modellers can end up with terms in the DE that do not fully describe the interactions or in the worst case with missing terms.&lt;/p&gt;&lt;p&gt;The framework of universal differential equations (UDE) for scientific machine learning (SciML) [1] allows to reconstruct the incomplete or missing term from an idea of the DE and a short term timeseries of the system and make long term predictions of the system&amp;#8217;s behaviour. However, the approach in [1] has difficulties to reconstruct the incomplete or missing term in systems with bifurcations. We developed a trajectory-based loss metric for UDE and SciML to tackle the problem and tested it successfully on a system mimicking algal blooms in the ocean.&lt;/p&gt;&lt;p&gt;[1] Rackauckas, Christopher, et al. &quot;Universal differential equations for scientific machine learning.&quot; arXiv preprint arXiv:2001.04385 (2020).&lt;/p&gt;

Long-Term Decision-Making: Biological and Psychological Evidence

2009 ◽  
Vol 9 (3) ◽  
pp. 9-19 ◽  
Author(s):  
Thomas Princen
Keyword(s):  
Decision Making ◽  
Time Horizon ◽  
Cultural Factors ◽  
Structural Factors ◽  
Short Term ◽  
Environmental Decision Making ◽  
Human Decision ◽  
Psychological Evidence ◽  
Widespread Belief

A central conundrum in the need to infuse a long-term perspective into climate policy and other environmental decision-making is the widespread belief that humans are inherently short-term thinkers. An analysis of human decision-making informed by evolved adaptations—biological, psychological and cultural—suggests that humans actually have a long-term thinking capacity. In fact, the human time horizon encompasses both the immediate and the future (near and far term). And yet this very temporal duality makes people susceptible to manipulation; it carries its own politics, a politics of the short term. A “legacy politics” would extend the prevailing time horizon by identifying structural factors that build on evolved biological and cultural factors.

scholarly journals THE SCALE OF THE SPREAD OF COVID-19 IN GEORGIA AND EFFECTIVENESS OF PREVENTIVE MEASURES IMPLIMENTED BY THE GOVERNMENT – WHAT DO MODELS SAY?

2020 ◽  
pp. 49-57
Author(s):  
IURI ANANIASHVILI ◽  
LEVAN GAPRINDASHVILI
Keyword(s):  
Machine Learning ◽  
Preventive Measures ◽  
Herd Immunity ◽  
Optimal Level ◽  
Short Term ◽  
Machine Learning Methods ◽  
Data Limitations ◽  
The Government ◽  
Different Characteristics

. In this article we present forecasts of the spread of COVID-19 virus, obtained by econometric and machine learning methods. Furthermore, by employing modelling method, we estimate effectiveness of preventive measures implemented by the government. Each of the models discussed in this article is modelling different characteristics of the COVID-19 epidemic’s trajectory: peak and end date, number of daily infections over different forecasting horizons, total number of infection cases. All these provide quite clear picture to the interested reader of the future threats posed by COVID-19. In terms of existing models and data, our research indicates that phenomenological models do well in forecasting the trend, duration and total infections of the COVID- 19 epidemic, but make serious mistakes in forecasting the number of daily infections. Machine learning models, deliver more accurate short –term forecast of daily infections, but due to data limitations, they struggle to make long-term forecasts. Compartmental models are the best choice for modelling the measures implemented by the government for preventing the spread of COVID-19 and determining optimal level of restrictions. These models show that until achieving herd immunity (i.e. without any epidemiological or government implemented measures), approximate number of people infected with COVID-19 would be 3 million, but due to preventive measures, expected total number of infections has reduced to several thousand (1555-3189) people. This unequivocally indicates the effectiveness of the preventive measures.

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