scholarly journals A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method

Fluids ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 76
Author(s):  
Irfan Bahiuddin ◽  
Setyawan Bekti Wibowo ◽  
M. Syairaji ◽  
Jimmy Trio Putra ◽  
Cahyo Adi Pandito ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Transmission ◽  
Systematic Approach ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Horizontal Distance ◽  
Droplet Generator ◽  
Droplet Model ◽  
Droplet Behavior ◽  
Diameter Reduction

Coronavirus disease 2019 (Covid-19) has been identified as being transmitted among humans with droplets from breath, cough, and sneezes. Understanding the droplets’ behavior can be critical information to avoid disease transmission, especially while designing a device deals with human air respiratory. Although various studies have provided enormous computational fluid simulations, most cases are too specific and quite challenging to combine with other similar studies directly. Therefore, this paper proposes a systematic approach to predict the droplet behavior for coughing cases using machine learning. The approach consists of three models, which are droplet generator, mask model, and free droplet model modeled using feedforward neural network (FFNN). The evaluation has shown that the three FFNNs models’ accuracies are relatively high, with R-values of more than 0.990. The model has successfully predicted the evaporation effect on the diameter reduction and the completely evaporated state, which can be considered unlearned cases for machine learning models. The predicted horizontal distance pattern also agrees with the data in the literature. In summary, the proposed approach has demonstrated the capability to predict the diameter pattern according to the experimental or previous work data at various mask face types.

scholarly journals Machine learning approaches to calibrate individual-based infectious disease models

2021 ◽  
Author(s):  
Theresa Reiker ◽  
Monica Golumbeanu ◽  
Andrew Shattock ◽  
Lydia Burgert ◽  
Thomas A. Smith ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Transmission ◽  
Goodness Of Fit ◽  
Epidemiological Data ◽  
Bayesian Optimization ◽  
Model Complexity ◽  
Learning Approaches ◽  
Dimensional Parameter ◽  
Novel Approach ◽  
Transmission Models

AbstractIndividual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose a novel approach to calibrate disease transmission models via a Bayesian optimization framework employing machine learning emulator functions to guide a global search over a multi-objective landscape. We demonstrate our approach by application to an established individual-based model of malaria, optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Outperforming other calibration methodologies, the new approach quickly reaches an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability.One Sentence SummaryWe propose a novel, fast, machine learning-based approach to calibrate disease transmission models that outperforms other methodologies

scholarly journals Applying of machine learning in the construction of a voice-controlled interface on the example of a music player

2019 ◽  
Vol 13 ◽  
pp. 302-309
Author(s):  
Jakub Basiakowski
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Feedforward Neural Network ◽  
Hidden Layer ◽  
Music Player ◽  
The Impact

The following paper presents the results of research on the impact of machine learning in the construction of a voice-controlled interface. Two different models were used for the analysys: a feedforward neural network containing one hidden layer and a more complicated convolutional neural network. What is more, a comparison of the applied models was presented. This comparison was performed in terms of quality and the course of training.

scholarly journals Machine-learning reveals that Mycobacterium tuberculosis genotypes and anatomic disease site impacts drug resistance and disease transmission among patients with proven extra-pulmonary tuberculosis

2020 ◽  
Author(s):  
Doctor Busizwe Sibandze ◽  
Beki Themba Magazi ◽  
Lesibana Anthony Malinga ◽  
Nontuthuko Excellent Maningi ◽  
Bong Akee Shey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Resistance ◽  
Mycobacterium Tuberculosis ◽  
Disease Transmission ◽  
East Asian ◽  
Extrapulmonary Tuberculosis ◽  
Machine Learning Algorithms ◽  
East African ◽  
Development Of Resistance ◽  
Asian Lineage

Abstract Background: There is a general dearth of information on extrapulmonary tuberculosis (EPTB). Here, we investigated Mycobacterium tuberculosis (Mtb) drug resistance and transmission patterns in EPTB patients treated in the Tshwane metropolitan area, in South Africa.Methods: Consecutive Mtb culture-positive non-pulmonary samples from unique EPTB patients underwent mycobacterial genotyping and were assigned to phylogenetic lineages and transmission clusters based on spoligotypes. MTBDRplus assay was used to search mutations for isoniazid and rifampin resistance. Machine learning algorithms were used to identify clinically meaningful patterns in data. We computed odds ratio (OR), attributable risk (AR) and corresponding 95% confidence intervals (CI). Results: Of the 70 isolates examined, the largest cluster comprised 25 (36%) Mtb strains that belonged to the East Asian lineage. East Asian lineage was significantly more likely to occur within chains of transmission when compared to the Euro-American and East-African Indian lineages: OR= 10.11 (95% CI: 1.56-116). Lymphadenitis, meningitis and cutaneous TB, were significantly more likely to be associated with drug resistance: OR=12.69 (95% CI: 1.82-141.60) and AR = 0.25 (95% CI: 0.06-0.43) when compared with other EPTB sites, which suggests that poor rifampin penetration might be a contributing factor.Conclusions: The majority of Mtb strains circulating in the Tshwane metropolis belongs to East Asian, Euro-American and East-African Indian lineages. Each of these are likely to be clustered, suggesting on-going EPTB transmission. Since 25% of the drug resistance was attributable to sanctuary EPTB sites notorious for poor rifampin penetration, we hypothesize that poor anti-tuberculosis drug dosing might have a role in the development of resistance.

A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer

2015 ◽  
Vol 11 (12) ◽  
pp. 3362-3377 ◽  
Author(s):  
Vinay Randhawa ◽  
Anil Kumar Singh ◽  
Vishal Acharya
Keyword(s):  
Machine Learning ◽  
Oral Cancer ◽  
High Throughput ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Systematic Approach ◽  
Molecular Descriptor ◽  
Classification Model

Network-based and cheminformatics approaches identify novel lead molecules forCXCR4, a key gene prioritized in oral cancer.

scholarly journals A Systematic Approach for Including Machine Learning in Multi-agent Systems

2005 ◽  
pp. 198-211 ◽  
Author(s):  
José A. R. P. Sardinha ◽  
Alessandro Garcia ◽  
Carlos J. P. Lucena ◽  
Ruy L. Milidiú
Keyword(s):  
Machine Learning ◽  
Systematic Approach ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

A LEARNING-BASED SOFTWARE ENGINEERING ENVIRONMENT FOR REUSING DESIGN KNOWLEDGE

1991 ◽  
Vol 01 (04) ◽  
pp. 351-371 ◽  
Author(s):  
SIDNEY C. BAILIN ◽  
ROBERT H. GATTIS ◽  
WALT TRUSZKOWSKI
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Research Program ◽  
Systematic Approach ◽  
Design Knowledge ◽  
Environment Design ◽  
Knowledge Capture ◽  
Knowledge Based ◽  
Engineering Environment ◽  
Distinguishing Features

As part of the NASA/Goddard Code 522.3 research program in software engineering, a Knowledge-Based Software Engineering Environment (KBSEE) is being developed. The KBSEE will support a comprehensive artifact-reuse capability and will incorporate knowledge-based concepts such as machine learning and design knowledge capture. The distinguishing features of this work are that it is a systematic approach to the reuse of knowledge, not just of products, and it implements learning as an explicitly supported function in a software engineering environment. Each of these objectives is currently being pursued in a distinct prototype environment: design knowledge capture and knowledge reuse in KAPTUR (Knowledge Acquisition for Preservation of Tradeoffs and Underlying Rationales), and learning in LEARN (Learning Enhanced Automation of Reuse Engineering). Despite their prototype realization in different environments, the integration of these approaches into an overall KBSEE is a key goal of our work.

A Penalty-Function Approach for Pruning Feedforward Neural Networks

1997 ◽  
Vol 9 (1) ◽  
pp. 185-204 ◽  
Author(s):  
Rudy Setiono
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Penalty Function ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Function Approach ◽  
Penalty Function Approach

This article proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms. The first term is to discourage the use of unnecessary connections, and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network are also given. The effectiveness of this penalty function is tested on three well-known problems: the contiguity problem, the parity problems, and the monks problems. The resulting pruned networks obtained for many of these problems have fewer connections than previously reported in the literature.

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