scholarly journals Using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in 2020

BMJ Open ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. e049844
Author(s):  
Banafsheh Sadeghi ◽  
Rex C Y Cheung ◽  
Meagan Hanbury
Keyword(s):  
Machine Learning ◽  
United Arab Emirates ◽  
Hierarchical Clustering ◽  
Clustering Analysis ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Hierarchical Clustering Analysis ◽  
African Countries ◽  
The Usa ◽  
And Performance

ObjectiveTo rank and score 180 countries according to COVID-19 cases and fatality in 2020 and compare the results to existing pandemic vulnerability prediction models and results generated by standard epidemiological scoring techniques.SettingOne hundred and eighty countries’ patients with COVID-19 and fatality data representing the healthcare system preparedness and performance in combating the pandemic in 2020.DesignUsing the retrospective daily COVID-19 data in 2020 broken into 24 half-month periods, we applied unsupervised machine learning techniques, in particular, hierarchical clustering analysis to cluster countries into five groups within each period according to their cumulative COVID-19 fatality per day over the year and cumulative COVID-19 cases per million population per day over the half-month period. We used the average of the period scores to assign countries’ final scores for each measure.Primary outcomeThe primary outcomes are the COVID-19 cases and fatality grades in 2020.ResultsThe United Arab Emirates and the USA with F in COVID-19 cases, achieved A or B in the fatality scores. Belgium and Sweden ranked F in both scores. Although no African country ranked F for COVID-19 cases, several African countries such as Gambia and Liberia had F for fatality scores. More developing countries ranked D and F in fatality than in COVID-19 case rankings. The classic epidemiological measures such as averages and rates have a relatively good correlation with our methodology, but past predictions failed to forecast the COVID-19 countries’ preparedness.ConclusionCOVID-19 fatality can be a good proxy for countries’ resources and system’s resilience in managing the pandemic. These findings suggest that countries’ economic and sociopolitical factors may behave in a more complex way as were believed. To explore these complex epidemiological associations, models can benefit enormously by taking advantage of methods developed in computer science and machine learning.

scholarly journals Classification and Prediction of Software Incidents Using Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Sikandar Ali ◽  
Muhammad Adeel ◽  
Sumaira Johar ◽  
Muhammad Zeeshan ◽  
Samad Baseer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Operational Environment ◽  
Target Class ◽  
Software Failure ◽  
Log Files ◽  
And Performance

An incident, in the perception of information technology, is an event that is not part of a normal process and disrupts operational procedure. This research work particularly focuses on software failure incidents. In any operational environment, software failure can put the quality and performance of services at risk. Many efforts are made to overcome this incident of software failure and to restore normal service as soon as possible. The main contribution of this study is software failure incidents classification and prediction using machine learning. In this study, an active learning approach is used to selectively label those data which is considered to be more informative to build models. Firstly, the sample with the highest randomness (entropy) is selected for labeling. Secondly, to classify the labeled observation into either failure or no failure classes, a binary classifier is used that predicts the target class label as failure or not. For classification, Support Vector Machine is used as a main classifier to classify the data. We derived our prediction models from the failure log files collected from the ECLIPSE software repository.

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

scholarly journals Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction

2021 ◽  
Vol 11 (13) ◽  
pp. 6030
Author(s):  
Daljeet Singh ◽  
Antonella B. Francavilla ◽  
Simona Mancini ◽  
Claudio Guarnaccia
Keyword(s):  
Machine Learning ◽  
Linear Models ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Volume Percentage

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

scholarly journals The visual categorization of production automotive seats on descriptors of comfort

Work ◽  
2021 ◽  
Vol 68 (s1) ◽  
pp. S69-S85
Author(s):  
Tugra Erol ◽  
Cyriel Diels ◽  
James Shippen ◽  
Dale Richards
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Analysis ◽  
The Other ◽  
Visual Design ◽  
Hierarchical Clustering Analysis ◽  
Card Sorting ◽  
Design Attributes ◽  
Comfort Perception ◽  
Automotive Production

BACKGROUND: The role of appearance of automotive seats on perceived comfort and comfort expectancy has been acknowledged in previous research but it has not been investigated in depth. OBJECTIVE: To identify the effects of the appearance of production automotive seats, based on the hypothesis that visual design differentiations are affective in creating comfort expectations. The significance of the descriptors Sporty, Luxurious and Comfortable and the associated visual design attributes was of interest. METHOD: Images from 38 automotive production seats were used in an image-based card sorting app (qCard) with a total of 24 participants. Participants were asked to categorize the different seat designs varying from 1: least, to 9: most for all three descriptors.The resulting data was analyzed using hierarchical clustering analysis. RESULTS: The results indicated that the perceived Sporty, Luxurious and Comfortable were descriptor items that significantly differentiated seats with certain design attributes. It was found that for the Sporty perception the integrated headrest design and angular shapes were key. On the other hand, the Comfort perception was characterised by seating with a separate headrest and rounded seat back/cushion shapes. CONCLUSIONS: For seat design processes, the method enables a practical way to identify elements conveying Sporty, Comfortable and Luxurious perception.

scholarly journals Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jongsung Kim ◽  
Donghyun Kim ◽  
Younghye Bae ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Prediction Model ◽  
Prediction Models ◽  
Meteorological Data ◽  
Heavy Rain ◽  
Machine Learning Techniques ◽  
Damage Prediction ◽  
Explanatory Variables ◽  
The Republic

Prediction models of heavy rain damage using machine learning based on big data were developed for the Seoul Capital Area in the Republic of Korea. We used data on the occurrence of heavy rain damage from 1994 to 2015 as dependent variables and weather big data as explanatory variables. The model was developed by applying machine learning techniques such as decision trees, bagging, random forests, and boosting. As a result of evaluating the prediction performance of each model, the AUC value of the boosting model using meteorological data from the past 1 to 4 days was the highest at 95.87% and was selected as the final model. By using the prediction model developed in this study to predict the occurrence of heavy rain damage for each administrative region, we can greatly reduce the damage through proactive disaster management.

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