Human Resources in Europe. Estimation, Clusterization, Machine Learning and Prediction

Advanced Technology ◽

High Tech ◽

Technology Products ◽

Abstract We estimate the relationships between innovation and human resources in Europe using the European Innovation Scoreboard of the European Commission for 36 countries for the period 2010-2019. We perform Panel Data with Fixed Effects, Random Effects, Pooled OLS, Dynamic Panel and WLS. We found that Human resources is positively associated to “Basic-school entrepreneurial education and training”, “Employment MHT manufacturing KIS services”, “Employment share Manufacturing (SD)”, “Lifelong learning”, “New doctorate graduates”, “R&D expenditure business sector”, “R&D expenditure public sector”, “Tertiary education”. Our results also show that “Human Resources” is negatively associated to “Government procurement of advanced technology products”, “Medium and high-tech product exports”, “SMEs innovating in-house”, “Venture capital”. In adjunct we perform a clusterization with k-Means algorithm and we find the presence of three clusters. Clusterization shows the presence of Central and Northern European countries that has higher levels of Human Resources, while Southern and Eastern Europe has very low degree of Human Resources. Finally, we use seven machine learning algorithms to predict the value of Human Resources in Europe Countries using data in the period 2014-2021 and we show that the linear regression algorithm performs at the highest level.

The SMEs Innovation in Europe

10.21203/rs.3.rs-1087106/v1 ◽

2021 ◽

Author(s):

Alberto Costantiello ◽

Laureti Lucio ◽

Leogrande Angelo

Keyword(s):

Panel Data ◽

Population Density ◽

Fixed Effects ◽

Government Procurement ◽

Advanced Technology ◽

High Tech ◽

Dynamic Panel ◽

Technology Products ◽

Firm Investments ◽

Intellectual Assets

Abstract In this article we investigate the determinants of SMEs Innovation in Europe. We use data from the European Innovation Scoreboard of the European Commission in the period 2000-2019 for 36 countries. Data are analyzed through Panel Data with Fixed Effects, Random Effects, Dynamic Panel at 1 Stage and WLS. Results show that the presence of Innovators is positively associated with “Enterprise births”, “Government Procurement of Advanced Technology Products”, “Firm Investments”, “Intellectual Assets”, “Sales Impacts”, “Share High and Medium High-Tech Manufacturing” and negatively associated to “FDI Net Inflows” and “Population Density”.

The Broadband Penetration in Europe

10.21203/rs.3.rs-1036548/v1 ◽

2021 ◽

Author(s):

Angelo Leogrande ◽

ALBERTO COSTANTIELLO ◽

LUCIO LAURETI

Keyword(s):

Panel Data ◽

Fixed Effects ◽

Polynomial Regression ◽

Government Procurement ◽

Advanced Technology ◽

Technology Products ◽

Firm Investments ◽

Intellectual Assets ◽

Knowledge Intensive

Abstract In this article we estimate the determinants of broadband penetration in Europe. We use data from the European Innovation Scoreboard of the European Commission for 37 countries in the period 2010-2019. We apply Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, OLS and Dynamic Panel. We found that the level of “Broadband Penetration” in Europe is positively associated to “Enterprises Providing ICT Training”, “Innovative Sales Share”, “Intellectual Assets”, “Knowledge-Intensive Service Exports”, “Turnover Share SMEs”, “Innovation Friendly Environment” and negatively associated with “Government procurement of advanced technology products”, “Sales Impact”, “Firm Investments”, “Opportunity-Driven Entrepreneurship”, “Most Cited Publications”, “Rule of Law”. In adjunct we perform a clusterization with k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of three different clusters. Finally, we apply eight machine learning algorithms to predict the level of “Broadband Penetration” in Europe and we find that the Polynomial Regression algorithm is the best predictor and that the level of the variable is expected to increase of 10,4%.

The Intellectual Assets in Europe

10.21203/rs.3.rs-1051250/v1 ◽

2021 ◽

Author(s):

Costantiello Alberto ◽

Laureti Lucio ◽

Leogrande Angelo

Keyword(s):

Fixed Effects ◽

Government Procurement ◽

Advanced Technology ◽

High Tech ◽

Dynamic Panel ◽

Research Systems ◽

Technology Products ◽

Employment Share ◽

Intellectual Assets ◽

Knowledge Intensive

Abstract In this article we investigate the determinants of the Intellectual Assets in Europe. We use data from the European Innovation Scoreboard of the European Commission in the period 2000-2019 for 36 countries. Data are analyzed using Panel with Fixed Effects, Random Effects, WLS, Pooled OLS, Dynamic Panel at 1 Stage. Results show that the presence of Intellectual Assets in Europe is positively associated with “Enterprise Births”, “Top R&D Spending Enterprises per 10 mln Population”, “Employment Share Manufacturing”, “Share High and Medium high-tech Manufacturing”, “Attractive Research Systems”, “Finance and Support”, “Innovators”, “Sales Impact” and is negatively associated to “Government Procurement of Advanced Technology Products” and “Share Knowledge-Intensive Services”

The Impact of Venture Capital Expenditures on Innovation in Europe

10.21203/rs.3.rs-936328/v1 ◽

2021 ◽

Author(s):

Angelo Leogrande ◽

ALBERTO COSTANTIELLO ◽

LUCIO LAURETI

Keyword(s):

Panel Data ◽

Venture Capital ◽

Fixed Effects ◽

Government Procurement ◽

Advanced Technology ◽

Capital Expenditures ◽

High Tech ◽

Technology Products ◽

The Impact ◽

The Relationship

Abstract We investigate the relationship between “Venture Capital Expenditures” and innovation in Europe. Data are collected from the European Innovation Scoreboard for 36 countries in the period 2010-2019. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, Dynamic Panel. Results show that the level of Venture Capitalist Expenditure is positively associated to “Foreign Doctorate Students” and “Innovation Index” and negatively related to “Government Procurement of Advanced Technology Products”, “Innovators”, “Medium and High-Tech Products Exports”, “Public-Private Co-Publications”. In adjunct, cluster analysis is realized with the algorithm k-Means and the Silhouette coefficient, and we found the presence of four different clusters for the level of “Venture Capital Expenditures”. Finally, we propose a confrontation among 8 different algorithms of machine learning to predict the level of “Venture Capital Expenditures” and we find that the linear regression generates the best results in terms of minimization of MAE, MSE, RMSE.

The Employment in Innovative Enterprises in Europe

10.21203/rs.3.rs-1221158/v1 ◽

2022 ◽

Author(s):

Lucio Laureti ◽

Costantiello Alberto ◽

Marco Maria Matarrese ◽

Angelo Leogrande

Keyword(s):

Machine Learning ◽

Panel Data ◽

Fixed Effects ◽

Value Added ◽

High Tech ◽

Dynamic Panel ◽

Silhouette Coefficient ◽

High Tech Product ◽

And Training

Abstract In this article we evaluate the determinants of the Employment in Innovative Enterprises in Europe. We use data from the European Innovation Scoreboard of the European Commission for 36 countries in the period 2000-2019 with Panel Data with Fixed Effects, Panel Data with Random Effects, Dynamic Panel, WLS and Pooled OLS. We found that the “Employment in Innovative Enterprises in Europe” is positively associated with “Broadband Penetration in Europe”, “Foreign Controlled Enterprises Share of Value Added”, “Innovation Index”, “Medium and High-Tech Product Exports” and negatively associated to “Basic School Entrepreneurial Education and Training”, “International Co-Publications”, and “Marketing or Organizational Innovators”. Secondly, we perform a cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient and we found the presence of four different clusters. Finally, we perform a comparison among eight different machine learning algorithms to predict the level of “Employment in Innovative Enterprises” in Europe and we found that the Linear Regression is the best predictor.

Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

Remote Sensing ◽

10.3390/rs13132433 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2433

Author(s):

Shu Yang ◽

Fengchao Peng ◽

Sibylle von Löwis ◽

Guðrún Nína Petersen ◽

David Christian Finger

Keyword(s):

Machine Learning ◽

Weather Conditions ◽

Dust Storms ◽

Lidar Data ◽

Data Sets ◽

Doppler Lidar ◽

Lidar Measurements ◽

Using Data ◽

Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2020-0069 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Fathima Aliyar Vellameeran ◽

Thomas Brindha

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Prediction Models ◽

Future Research ◽

Disease Prediction ◽

Learning Approaches ◽

Research Papers ◽

Using Data ◽

Intelligent Methods

Abstract Objectives To make a clear literature review on state-of-the-art heart disease prediction models. Methods It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed. Results The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions. Conclusions The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.

Advances in Data Mining and Database Management - Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics ◽

A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics

10.4018/978-1-7998-3053-5.ch021 ◽

2021 ◽

pp. 416-428

Author(s):

R. Suganya ◽

Rajaram S. ◽

Kameswari M.

Keyword(s):

Machine Learning ◽

Literature Review ◽

Data Science ◽

Learning Algorithms ◽

Research Literature ◽

Thyroid Disorder ◽

Classification Models ◽

Indian Women ◽

Currently, thyroid disorders are more common and widespread among women worldwide. In India, seven out of ten women are suffering from thyroid problems. Various research literature studies predict that about 35% of Indian women are examined with prevalent goiter. It is very necessary to take preventive measures at its early stages, otherwise it causes infertility problem among women. The recent review discusses various analytics models that are used to handle different types of thyroid problems in women. This chapter is planned to analyze and compare different classification models, both machine learning algorithms and deep leaning algorithms, to classify different thyroid problems. Literature from both machine learning and deep learning algorithms is considered. This literature review on thyroid problems will help to analyze the reason and characteristics of thyroid disorder. The dataset used to build and to validate the algorithms was provided by UCI machine learning repository.

A Daily Covid-19 Cases Prediction System using Data Mining and Machine Learning Algorithm

10.5121/csit.2021.112320 ◽

2021 ◽

Author(s):

Yiqi Jack Gao ◽

Yu Sun

Keyword(s):

Machine Learning ◽

Random Forest ◽

Hospital Admissions ◽

Polynomial Regression ◽

Learning Algorithm ◽

Learning Algorithms ◽

Policy Makers ◽

Diverse Range ◽

The start of 2020 marked the beginning of the deadly COVID-19 pandemic caused by the novel SARS-COV-2 from Wuhan, China. As of the time of writing, the virus had infected over 150 million people worldwide and resulted in more than 3.5 million global deaths. Accurate future predictions made through machine learning algorithms can be very useful as a guide for hospitals and policy makers to make adequate preparations and enact effective policies to combat the pandemic. This paper carries out a two pronged approach to analyzing COVID-19. First, the model utilizes the feature significance of random forest regressor to select eight of the most significant predictors (date, new tests, weekly hospital admissions, population density, total tests, total deaths, location, and total cases) for predicting daily increases of Covid-19 cases, highlighting potential target areas in order to achieve efficient pandemic responses. Then it utilizes machine learning algorithms such as linear regression, polynomial regression, and random forest regression to make accurate predictions of daily COVID-19 cases using a combination of this diverse range of predictors and proved to be competent at generating predictions with reasonable accuracy.

Knn based Crop and Fertilizer Prediction

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d7436.049420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1453-1456

Keyword(s):

Machine Learning ◽

Data Mining ◽

Crop Yield ◽

Accurate Result ◽

Yield Prediction ◽

Prediction Problem ◽

Environmental Statistics ◽

Classification And Regression ◽

India has always been active in agriculture, in fact even in this age of industrialization agriculture and agriculturebased industries continue to be a main source of income for a large percentage of the population. Machine learning and data mining have become, in the present day, are very important mediums when it comes to research in the crop yielding domain. Many a times we come across news on the paper about farmers committing suicide because of crop failures and increase in loans. In preventing such situations, crop yield prediction software can play a very important role. This research is an attempt in proposing a method to predict the success of crop for a particular area by using data on amounts and ratios of different components of soil like nitrogen, potassium, phosphorus and environmental statistics on temperature and weather. Various machine learning algorithms are used to get an accurate result. KNN is used for classification and regression prediction problem. It also attempts in providing a precise output on what fertilizers can be used to better the yield. Through this, therefore, farmers will also be able to predict their profits and final revenues.