scholarly journals Prediction and Spread Visualization of Covid-19 Pandemic Using Machine Learning

2020 ◽  
Author(s):  
Anit N Roy ◽  
Jais Jose ◽  
Aswin Sunil ◽  
Neha Gautam ◽  
Deepa Nathalia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Public Transportation ◽  
Data Science ◽  
Industrial Area ◽  
Accurate Analysis ◽  
Typical Data ◽  
Additive Regression Model ◽  
Dynamic Map ◽  
Additive Regression ◽  
Planning Objective

The sudden pervasive of severe acute respiratory syndrome Covid-19 has been leading the universe into a prominent crisis. It has influenced each zone, for example, industrial area, horticultural zone, Public transportation, economic zone, and so on. So as to see how Covid-19 affected the globe, we conducted an investigation characterizing the effects of the pandemic over the world using Machine Learning (ML) method. Prediction is a typical data science exercise that helps the administration with function planning, objective setting, and anomaly detection. We propose an additive regression model with interpretable parameters that can be naturally balanced by experts with domain intuition about the time series. We focus on global data beginning from 22nd January 2020, till 26th April 2020 and performed dynamic map visualization of Covid-19 expansion globally by date wise and predicting the spread of virus on all countries and continents. The major advantages of this work include accurate analysis of country-wise as well as province/state-wise confirmed cases, recovered cases, deaths, prediction of pandemic viral attack and how far it is expanding globally.

Application of Machine Learning Algorithms for Managing Well Integrity in Gas Lift Wells

2021 ◽  
Author(s):  
Adel Mohamed Salem Ragab ◽  
Mostafa Sa’eed Yakoot ◽  
Omar Mahmoud
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Oil And Gas ◽  
Predictive Analytics ◽  
Early Recognition ◽  
Petroleum Industry ◽  
Machine Learning Algorithms ◽  
Risk Level ◽  
Accurate Analysis ◽  
Well Integrity

Abstract Well integrity (WI) impairments in oil and gas (O&G) wells are one of the most formidable challenges in the petroleum industry. Managing WI for different groups of well services necessitates precise assessment of risk level. When WI classification and risk assessment are performed using traditional methods such as spreadsheets, failures of well barriers will result in complicated and challenging WI management, especially in mature O&G fields. Industrial practices, then, started moving toward likelihood/ severity matrices which turned out later to be misleading in many cases due to possibility of having skewness in failure data. Developing a reliable model for classifying level of WI impairment is becoming more crucial for the industry. Artificial intelligence (AI) includes advanced algorithms that use machine learning (ML) and computing powers efficiently for predictive analytics. The main objective of this work is to develop ML models for the detection of integrity anomalies and early recognition of well failures. Most common ML algorithms in data science include; random forest, logistic regression, quadratic discriminant analysis, and boosting techniques. This model establishment comes after initial data gathering, pre-processing, and feature engineering. These models can iterate different failure scenarios considering all barrier elements that could contribute to the WI envelope. Thousands of WI data arrays can be literally collected and fed into ML models after being processed and structured properly. The new model presented in this paper can detect different WI anomalies and accurate analysis of failures can be achieved. This emphasizes that managing overall risks of WI failures is a robust and practical approach for direct implementation in mature fields. It also, creates additional enhancement for WI management. This perspective will improve efficiency of operations in addition to having the privilege of universality, where it can be applicable for different well groups. The rising wave of digitalization is anticipated to improve field operations, business performance, and production safety.

Analysis of the involvement of single nucleotide polymorphisms of redox-homeostasis genes in the development of isolated and comorbid cardioand cerebrovascular diseases

2020 ◽  
pp. 37-49
Author(s):  
О.Ю. Бушуева
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Redox Homeostasis ◽  
Total Sample ◽  
Cerebrovascular Diseases ◽  
Cerebral Stroke ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Gender And Age ◽  
Additive Regression Model ◽  
Additive Regression

Распространенные и зачастую сочетающиеся кардио- и цереброваскулярные заболевания (КЦВЗ), включающие артериальную гипертензию (АГ), ишемическую болезнь сердца (ИБС) и мозговой инсульт (МИ), представляют собой основную причину смертности во всем мире. Окислительный стресс имеет множество патологических эффектов на сосудистый гомеостаз и в настоящее время рассматривается как один из общих механизмов развития КЦВЗ. Целью исследования было изучение ассоциации однонуклеотидных полиморфизмов генов редокс-гомеостаза rs2070424 SOD1, rs4880 SOD2, rs769214 CAT, rs713041 GPX4, rs41303970 GCLM, rs17883901 GCLC, rs854560 PON1, rs7493 PON2, rs1695 GSTP1, rs2266782 FMO3 с развитием изолированных и сочетанных форм КЦВЗ. Материалом для исследования послужила выборка неродственных индивидов славянского происхождения, общей численностью 2702 человека. В исследование вошли 1815 пациентов с различными кардио- и цереброваскулярными заболеваниями и их сочетаниями: с изолированной АГ (иАГ), с изолированной ишемической болезнью сердца (иИБС), с сочетанием АГ и ИБС (АГ+ИБС), с мозговым инсультом (МИ) на фоне АГ (АГ+МИ); с коморбидной кардио- и цереброваскулярной патологией (АГ+ИБС+МИ). Из общей выборки здоровых лиц (N=887) были сформированы 5 контрольных групп, соответствующих по полу и возрасту каждой из групп нозологических форм заболеваний. Генотипирование SNP проводили методом ПЦР в режиме реального времени путем дискриминации аллелей с помощью TaqMan-зондов. Для анализа ассоциаций генотипов с развитием заболеваний пользовались лог-аддитивной регрессионной моделью. Все расчеты выполнены относительно минорного аллеля; введены поправки на пол и возраст. SNP rs1695 GSTP1 был связан исключительно с развитием иАГ (OR=1,19, 95%CI=1,01-1,39, р=0,034). SNP rs7493 PON2 был связан с развитием всех исследованных коморбидных кардио- и цереброваскулярных заболеваний: АГ+ИБС (adjOR=1,32, adj95%CI=1,07-1,63, adjp=0,01); АГ+МИ (adjOR=1,79, adj95%CI=1,45-2,21, adjp<0,0001); АГ+ИБС+МИ (adjOR=1,51, adj95%CI=1,09-2,09, adjp=0,01), а также с укорочением протромбинового времени (adjDifference=-0,35; adjp=0,01). SNP rs2266782 FMO3 был связан с фенотипом АГ+МИ (adjOR=1,24, adj95%CI=1,02-1,51, adjp=0,03), а также снижал возраст манифестации МИ (adjDifference=-2,31; adjp=0,03). Таким образом, установлено, что однонуклеотидные полиморфизмы генов редокс-гомеостаза могут представлять важную генетическую компоненту формирования дифференцированности кардио- и цереброваскулярных фенотипов. Common and often comorbid cardio- and cerebrovascular diseases (CCVD), including arterial hypertension (AH), coronary heart disease (CHD), and cerebral stroke (CS), are the leading cause of death worldwide. Oxidative stress has many pathological effects on vascular homeostasis and is currently regarded as one of the common mechanisms for the development of CCVD. The aim of our study was to investigate the association of single nucleotide polymorphisms of the redox-homeostasis genes rs2070424 SOD1, rs4880 SOD2, rs769214 CAT, rs713041 GPX4, rs41303970 GCLM, rs17883901 GCLC, rs854560 PON1, rs7493 PON2, rs1695 GSTP1, rs2266782 FMO3 with the development of isolated and comorbid CCVD. A total 2702 individuals of Slavic origin were included for this study. The patients group included 1815 subjects with various CCVD and their combinations: isolated AH (IAH); isolated IHD (IIHD), combination of AH and IHD (AH+IHD); combination of AH and CS (AH+CS); comorbid cardio- and cerebrovascular pathology (AH+IHD+CS). From the total sample of healthy individuals (N=887), 5 sex- and age-matched control groups were formed. Genotyping was performed using TaqMan-based PCR. To analyze the associations of genotypes with the risk of diseases, a log-additive regression model was used. All calculations were performed relative to the minor allele; corrections for gender and age have been introduced. SNP rs1695 GSTP1 was associated with IAH exclusively (OR=1.19, 95%CI=1.01-1.39, P=0.034). SNP rs7493 PON2 was associated with the development of all studied comorbid CCVD: AH+IHD (adjOR=1.32, adj95%CI=1.07-1.63, adjP=0.01); AH+CS (adjOR=1.79, adj95%CI=1.45-2.21, adjP<0.0001); AH+IHD+CS (adjOR=1.51, adj95%CI=1.09-2.09, adjP=0.01), as well as shortening of prothrombin time (adjDifference=-0.35; adjP=0.01). SNP rs2266782 FMO3 was associated with the development of AH+CS (adjOR=1.24, adj95%CI=1.02-1.51, adjP=0.03), as well as decreased age of manifestation of CS (adjDifference=-2.31; adjP=0.03). Thus, it was found that genes involved in regulation of redox-homeostasis, can represent an important genetic component in the formation of differentiation of cardio- and cerebrovascular phenotypes.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

Challenges and Opportunities for Data Science and Machine Learning in IoT Systems - A Timely Debate: Part 2

2020 ◽  
pp. 2-6
Author(s):  
Sumi Helal ◽  
Flavia C. Delicato ◽  
Cintia B. Margi ◽  
Satyajayant Misra ◽  
Markus Endler
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Challenges And Opportunities

scholarly journals SAT0587 MACHINE-LEARNING DERIVED ALGORITHMS FOR OUTCOMES PREDICTION IN RHEUMATIC DISEASES: APPLICATION TO RADIOGRAPHIC PROGRESSION IN EARLY AXIAL SPONDYLOARTHRITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1252.2-1253
Author(s):  
R. Garofoli ◽  
M. Resche-Rigon ◽  
M. Dougados ◽  
D. Van der Heijde ◽  
C. Roux ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radiographic Progression ◽  
Generalized Additive Models ◽  
Regression Trees ◽  
Information Criterion ◽  
Additive Models ◽  
Super Learner ◽  
Additive Regression ◽  
Selection Operator ◽  
Lasso Method

Background:Axial spondyloarthritis (axSpA) is a chronic rheumatic disease that encompasses various clinical presentations: inflammatory chronic back pain, peripheral manifestations and extra-articular manifestations. The current nomenclature divides axSpA in radiographic (in the presence of radiographic sacroiliitis) and non-radiographic (in the absence of radiographic sacroiliitis, with or without MRI sacroiliitis. Given that the functional burden of the disease appears to be greater in patients with radiographic forms, it seems crucial to be able to predict which patients will be more likely to develop structural damage over time. Predictive factors for radiographic progression in axSpA have been identified through use of traditional statistical models like logistic regression. However, these models present some limitations. In order to overcome these limitations and to improve the predictive performance, machine learning (ML) methods have been developed.Objectives:To compare ML models to traditional models to predict radiographic progression in patients with early axSpA.Methods:Study design: prospective French multicentric cohort study (DESIR cohort) with 5years of follow-up. Patients: all patients included in the cohort, i.e. 708 patients with inflammatory back pain for >3 months but <3 years, highly suggestive of axSpA. Data on the first 5 years of follow-up was used. Statistical analyses: radiographic progression was defined as progression either at the spine (increase of at least 1 point per 2 years of mSASSS scores) or at the sacroiliac joint (worsening of at least one grade of the mNY score between 2 visits). Traditional modelling: we first performed a bivariate analysis between our outcome (radiographic progression) and explanatory variables at baseline to select the variables to be included in our models and then built a logistic regression model (M1). Variable selection for traditional models was performed with 2 different methods: stepwise selection based on Akaike Information Criterion (stepAIC) method (M2), and the Least Absolute Shrinkage and Selection Operator (LASSO) method (M3). We also performed sensitivity analysis on all patients with manual backward method (M4) after multiple imputation of missing data. Machine learning modelling: using the “SuperLearner” package on R, we modelled radiographic progression with stepAIC, LASSO, random forest, Discrete Bayesian Additive Regression Trees Samplers (DBARTS), Generalized Additive Models (GAM), multivariate adaptive polynomial spline regression (polymars), Recursive Partitioning And Regression Trees (RPART) and Super Learner. Finally, the accuracy of traditional and ML models was compared based on their 10-foldcross-validated AUC (cv-AUC).Results:10-fold cv-AUC for traditional models were 0.79 and 0.78 for M2 and M3, respectively. The 3 best models in the ML algorithm were the GAM, the DBARTS and the Super Learner models, with 10-fold cv-AUC of: 0.77, 0.76 and 0.74, respectively (Table 1).Table 1.Comparison of 10-fold cross-validated AUC between best traditional and machine learning models.Best modelsCross-validated AUCTraditional models M2 (step AIC method)0.79 M3 (LASSO method)0.78Machine learning approach SL Discrete Bayesian Additive Regression Trees Samplers (DBARTS)0.76 SL Generalized Additive Models (GAM)0.77 Super Learner0.74AUC: Area Under the Curve; AIC: Akaike Information Criterion; LASSO: Least Absolute Shrinkage and Selection Operator; SL: SuperLearner. N = 295.Conclusion:Traditional models predicted better radiographic progression than ML models in this early axSpA population. Further ML algorithms image-based or with other artificial intelligence methods (e.g. deep learning) might perform better than traditional models in this setting.Acknowledgments:Thanks to the French National Society of Rheumatology and the DESIR cohort.Disclosure of Interests:Romain Garofoli: None declared, Matthieu resche-rigon: None declared, Maxime Dougados Grant/research support from: AbbVie, Eli Lilly, Merck, Novartis, Pfizer and UCB Pharma, Consultant of: AbbVie, Eli Lilly, Merck, Novartis, Pfizer and UCB Pharma, Speakers bureau: AbbVie, Eli Lilly, Merck, Novartis, Pfizer and UCB Pharma, Désirée van der Heijde Consultant of: AbbVie, Amgen, Astellas, AstraZeneca, BMS, Boehringer Ingelheim, Celgene, Cyxone, Daiichi, Eisai, Eli-Lilly, Galapagos, Gilead Sciences, Inc., Glaxo-Smith-Kline, Janssen, Merck, Novartis, Pfizer, Regeneron, Roche, Sanofi, Takeda, UCB Pharma; Director of Imaging Rheumatology BV, Christian Roux: None declared, Anna Moltó Grant/research support from: Pfizer, UCB, Consultant of: Abbvie, BMS, MSD, Novartis, Pfizer, UCB

scholarly journals Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

2021 ◽  
Vol 18 (9) ◽  
pp. 4499
Author(s):  
Anil Babu Payedimarri ◽  
Diego Concina ◽  
Luigi Portinale ◽  
Massimo Canonico ◽  
Deborah Seys ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Public Transportation ◽  
Prediction Models ◽  
Positive Impact ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Commercial Activities

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

Data science skills: Building partnership for efficient school curriculum delivery in Africa

2020 ◽  
Vol 36 ◽  
pp. 49-62
Author(s):  
Nureni Olawale Adeboye ◽  
Peter Osuolale Popoola ◽  
Oluwatobi Nurudeen Ogunnusi
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Open Data ◽  
School Curriculum ◽  
Global Perspective ◽  
Special Focus ◽  
African Countries ◽  
Tertiary Institutions ◽  
Data Description ◽  
Curriculum Delivery

Data science is a concept to unify statistics, data analysis, machine learning and their related methods in order to analyze actual phenomena with data to provide better understanding. This article focused its investigation on acquisition of data science skills in building partnership for efficient school curriculum delivery in Africa, especially in the area of teaching statistics courses at the beginners’ level in tertiary institutions. Illustrations were made using Big data of selected 18 African countries sourced from United Nations Educational, Scientific and Cultural Organization (UNESCO) with special focus on some macro-economic variables that drives economic policy. Data description techniques were adopted in the analysis of the sourced open data with the aid of R analytics software for data science, as improvement on the traditional methods of data description for learning and thus open a new charter of education curriculum delivery in African schools. Though, the collaboration is not without its own challenges, its prospects in creating self-driven learning culture among students of tertiary institutions has greatly enhanced the quality of teaching, advancing students skills in machine learning, improved understanding of the role of data in global perspective and being able to critique claims based on data.

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