1.

Record Nr.

UNINA9910449689303321

Titolo

Reaching the poor with health, nutrition, and population services [[electronic resource] ] : what works, what doesn't, and why / / [edited by] Davidson R. Gwatkin, Adam Wagstaff, Abdo Yazbeck

Pubbl/distr/stampa

Washington, DC, : The World Bank, 2005

ISBN

1-280-26416-0

9786610264162

0-8213-5962-2

Descrizione fisica

1 online resource (380 p.)

Altri autori (Persone)

GwatkinDavidson R

WagstaffAdam

YazbeckAbdo

Disciplina

362.1/086/942

Soggetti

Health services accessibility

Health services accessibility - Developing countries

Human services

Human services - Developing countries

Poor - Medical care

Poor - Medical care - Developing countries

Poor - Nutrition

Poor - Nutrition - Developing countries

Electronic books.

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Contents; Foreword; Preface; Acknowledgments; Abbreviations, Acronyms, and Data Notes; PART 1. INTRODUCTION; FIGURES; TABLES; PART 2. AFRICA STUDIES; PART 3. ASIA STUDIES; ANNEX TABLES; PART 4. LATIN AMERICA STUDIES; ANNEX FIGURE; About the Authors; Index

Sommario/riassunto

This volume presents eleven case studies that document how well or poorly health, nutrition, and population programs have reached disadvantaged groups in the countries of Africa, Asia, and Latin America where they were undertaken. The studies were commissioned by the Reaching the Poor Program, undertaken by the Word Bank in



cooperation with the Bill and Melinda Gates Foundation and the Dutch and Swedish governments, in an effort to find better ways of ensuring that health, nutrition, and population programs benefit the neediest. These case studies, reinforced by other material gathered by the

2.

Record Nr.

UNINA9910483082303321

Titolo

The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy [[electronic resource] ] : SPIoT-2020, Volume 2 / / edited by John MacIntyre, Jinghua Zhao, Xiaomeng Ma

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021

ISBN

3-030-62746-2

Edizione

[1st ed. 2021.]

Descrizione fisica

1 online resource (XXXII, 863 p. 222 illus., 128 illus. in color.)

Collana

Advances in Intelligent Systems and Computing, , 2194-5365 ; ; 1283

Disciplina

004.678

Soggetti

Engineering—Data processing

Cooperating objects (Computer systems)

Computational intelligence

Machine learning

Big data

Data Engineering

Cyber-Physical Systems

Computational Intelligence

Machine Learning

Big Data

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Includes index.

Sommario/riassunto

This book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6,



2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.