A Multinomial Logistic Regression Approach for Arrhythmia Detection

2017 ◽  
Vol 8 (4) ◽  
pp. 17-33 ◽  
Author(s):  
Omar Behadada ◽  
Marcello Trovati ◽  
Georgios Kontonatsios ◽  
Yannis Korkontzelos
Keyword(s):  
Logistic Regression ◽  
Big Data ◽  
Multinomial Logistic Regression ◽  
Vascular Diseases ◽  
Arrhythmia Detection ◽  
Medical Sector ◽  
The World ◽  
Regression Approach ◽  
Insight Into ◽  
Tools And Methods

Cardiovascular diseases are the leading causes on mortality in the world. Consequently, tools and methods providing useful and applicable insights into their assessment play a crucial role in the prediction and managements of specific heart conditions. In this article, we introduce a method based on multi-class Logistic Regression as a classifier to provide a powerful and accurate insight into cardiac arrhythmia, which is one of the predictors of serious vascular diseases. As suggested by our evaluation, this provides a robust, scalable, and accurate system, which can successfully tackle the challenges posed by the utilisation of big data in the medical sector.

Updating the 1:50,000 Dutch soil map using legacy soil data: A multinomial logistic regression approach

Geoderma ◽  
2009 ◽  
Vol 151 (3-4) ◽  
pp. 311-326 ◽  
Author(s):  
Bas Kempen ◽  
Dick J. Brus ◽  
Gerard B.M. Heuvelink ◽  
Jetse J. Stoorvogel
Keyword(s):  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Soil Map ◽  
Regression Approach

scholarly journals Impact of Healthcare Predictions with Big Data Analytics and Cognitive Computing Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 4757-4762
Keyword(s):  
Big Data ◽  
Information Society ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Extensive Study ◽  
Comprehensive Analysis ◽  
Cognitive Computing ◽  
The World ◽  
Complex Relationships ◽  
Insight Into

The world has transformed into an information society that exceedingly depends on data. Since information frameworks create large measures of records each day, consistently, it appears the world is achieving the level of data overload. Big data is used to process the enormous volumes of data into revealing shrouded designs, complex relationships, and other helpful information. This work has done a comprehensive analysis of enormous information investigation in medicinal services. A brief insight into the importance of cognitive computing in healthcare has been presented. The extensive study concludes that the Cognitive computing has more impact on healthcare predictions than the big data analytics.

scholarly journals Religion, contact and ambivalent attitudes towards the rights of gays and lesbians in Barbados

2019 ◽  
Author(s):  
Mahalia Jackman
Keyword(s):  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Law Reform ◽  
Gay And Lesbian ◽  
Same Sex ◽  
Caribbean Island ◽  
Interpersonal Contact ◽  
The World ◽  
Close Relationship ◽  
English Speaking

Over 70 countries in the world currently carry anti-gay laws, among which is Barbados, a small English-speaking Caribbean island. This study evaluates whether heterosexuals in Barbados are consistent or ambivalent in their attitudes toward anti-gay law reform and the extent to which competing messages from interpersonal contact and religion affect ambivalence. The analysis revealed that a majority of heterosexuals hold ambivalent attitudes about gay and lesbian rights. Moreover, results from a multinomial logistic regression imply that Barbadians whose views on sexuality were theologically based were less likely to support restrictions on same-sex intimacy when they have a close relationship with a gay man or lesbian. However, this decline in support for the laws brought about by meaningful contact did not translate to support for gay and lesbian rights among the religiously inclined. Rather, it manifested itself as a state of attitudinal ambivalence.

Big Data With IoT for Smart Farming

2021 ◽  
pp. 99-114
Author(s):  
Supriya M. S. ◽  
Meenaxy Roy
Keyword(s):  
Big Data ◽  
New Technologies ◽  
Conventional Farming ◽  
Smart Farming ◽  
The World ◽  
Rapid Pace ◽  
Smart Agriculture ◽  
Iot Devices ◽  
Insight Into ◽  
Growing Population

Smart farming may also be called digital farming. The world is changing and digitizing at a quick rate. So all the work from agriculture to the stock market will become more productive and faster. Speed and efficiency play a key role in coping with the rapid pace of life and growing population. Smart agriculture has removed many of the problems faced by farmers during the conventional farming process. Several technologies are useful in this field, which make them work comfortably. Productivity in all areas of this sector can be increased with the aid of new technologies such as IoT and big data. Data can be accessed and analyzed from any part of the world with the help of IoT devices. The chapter offers insight into technology, such as big data and IoT, its applications in smart farming, as well as future innovations and opportunities.

scholarly journals An Investigation of Factors Affecting the Willingness to Invest in Renewables among Environmental Students: A Logistic Regression Approach

2019 ◽  
Vol 11 (18) ◽  
pp. 5012 ◽  
Author(s):  
Evangelia Karasmanaki ◽  
Spyridon Galatsidas ◽  
Georgios Tsantopoulos
Keyword(s):  
Logistic Regression ◽  
Renewable Energy ◽  
Logistic Regression Model ◽  
Renewable Energy Sources ◽  
Clean Energy ◽  
Small Scale ◽  
Factors Affecting ◽  
The World ◽  
Regression Approach ◽  
Significant Factors

Renewable energy sources (RES) have gained increased popularity across the world mainly due to their ability to contribute to environmental protection through the generation of infinite ‘clean’ energy. To achieve a greater diffusion of renewables, however, small-scale investments implemented by individuals are critically important. In contrast to citizens whose attitudes have been consistently explored by research, there is little evidence on the attitudes towards investments among environmental students who will occupy positions of responsibility and play key roles in the environmental sector in the future. Hence, the purpose of the present study is to identify the most important factors that affect environmental students’ willingness to invest in renewable energy (RE) by developing a logistic regression model. According to our analysis, the participants in their majority expressed their willingness to invest, while environmental values, the low risk and profitability of renewable investments, as well as preferences for certain energy types were significant factors determining this willingness. However, willingness to invest was irrespective of the current taxation and subsidies, suggesting that significant improvements are required in these areas. The present study could be particularly useful for policymakers since the necessary steps to create favorable investment environments in Greece and elsewhere are highlighted.

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