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Book part
Publication date: 27 May 2021

Nolwenn Bühler

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When Reproduction Meets Ageing
Type: Book
ISBN: 978-1-83909-747-8

Book part
Publication date: 30 September 2020

Hera Khan, Ayush Srivastav and Amit Kumar Mishra

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a…

Abstract

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a comprehensive overview pertaining to the background and history of the classification algorithms. This will be followed by an extensive discussion regarding various techniques of classification algorithm in machine learning (ML) hence concluding with their relevant applications in data analysis in medical science and health care. To begin with, the initials of this chapter will deal with the basic fundamentals required for a profound understanding of the classification techniques in ML which will comprise of the underlying differences between Unsupervised and Supervised Learning followed by the basic terminologies of classification and its history. Further, it will include the types of classification algorithms ranging from linear classifiers like Logistic Regression, Naïve Bayes to Nearest Neighbour, Support Vector Machine, Tree-based Classifiers, and Neural Networks, and their respective mathematics. Ensemble algorithms such as Majority Voting, Boosting, Bagging, Stacking will also be discussed at great length along with their relevant applications. Furthermore, this chapter will also incorporate comprehensive elucidation regarding the areas of application of such classification algorithms in the field of biomedicine and health care and their contribution to decision-making systems and predictive analysis. To conclude, this chapter will devote highly in the field of research and development as it will provide a thorough insight to the classification algorithms and their relevant applications used in the cases of the healthcare development sector.

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Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

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Book part
Publication date: 9 May 2023

Josephine Wolff

The COVID-19 pandemic accelerated many organisations’ reliance on the Internet, with schools, companies, and government agencies, among other institutions, all shifting their…

Abstract

The COVID-19 pandemic accelerated many organisations’ reliance on the Internet, with schools, companies, and government agencies, among other institutions, all shifting their operations online in a relatively short period of time. This sudden shift created opportunities for cybercriminals, as did the burden on the healthcare sector, the rush for unemployment benefits, and the desire for up-to-date information about outbreaks and health precautions. This chapter examines the trends in cybercrime during the COVID-19 pandemic that were spurred by these changes, as well as the mitigation measures implemented by government and private industry stakeholders to help protect critical infrastructure and online operations. It argues that the nature of cybercrime and the most common forms of criminal activity did not change substantially during the pandemic, but rather that criminals’ focus on whom to target and how to exploit evolving current events enabled them to wield existing criminal tools to greater effect, and potentially also at greater scale, though reliable statistics on the frequency and size of cybercrimes are scarce. Finally, the chapter considers how these trends in cybercrime forced private organisations and governments to undertake new cybersecurity measures and programs and whether the long-term effects of the pandemic might actually include stronger cybersecurity if these efforts continue past the end of the public health crisis.

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Beyond the Pandemic? Exploring the Impact of COVID-19 on Telecommunications and the Internet
Type: Book
ISBN: 978-1-80262-050-4

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Book part
Publication date: 12 March 2001

Stephen H. Schneider and Kristin Kuntz-Duriseti

One of the principal tools in analyzing climate change control policies is integrated assessment modeling. While indispensable for asking logical “what if” questions, such as the…

Abstract

One of the principal tools in analyzing climate change control policies is integrated assessment modeling. While indispensable for asking logical “what if” questions, such as the cost-effectiveness of alternative policies or the economic efficiency of carbon taxes versus R&D subsidies, integrated assessment models (IAMs) can only produce “answers” that are as good as their underlying assumptions and structural fidelity to a very complex multi-component system. However, due to the complexity of the models, the assumptions underlying the models are often obscured. It is especially important to identify how IAMs treat uncertainty and the value-laden assumptions underlying the analysis.In particular, IAMs have difficulty adequately addressing the issue of uncertainty inherent to the study of climate change, its impacts, and appropriate policy responses. In this chapter, we discuss how uncertainty about climate damages influences the conclusions from IAMs and the policy implications. Specifically, estimating climate damages using information from extreme events, contemporary spatial climate analogs and subjective probability assessments, transients, “imaginable” surprises, adaptation, market distortions and technological change are given as examples of problematic areas that IA modelers need to explicitly address and make transparent of IAMs are to enlighten more than they conceal.

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The Long-Term Economics of Climate Change: Beyond a Doubling of Greenhouse Gas Concentrations
Type: Book
ISBN: 978-0-76230-305-2

Book part
Publication date: 4 December 2012

Noralene Uy and Rajib Shaw

Ecosystem management has emerged as a preferred, and often, mandated approach to managing ecological systems. However, this comes with much debate because of the complexity in its…

Abstract

Ecosystem management has emerged as a preferred, and often, mandated approach to managing ecological systems. However, this comes with much debate because of the complexity in its applicability and feasibility due to political, economic, social, cultural, and ecological factors. This chapter outlines the evolving nature of Ecosystem-based Adaptation (EbA), and provides an insight on the future direction.

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Ecosystem-Based Adaptation
Type: Book
ISBN: 978-1-78052-691-1

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