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Record Nr. |
UNINA9910797420703321 |
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Titolo |
India migration report 2014 : diaspora and development / / editor, S. Irudaya Rajan |
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Pubbl/distr/stampa |
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New Delhi : , : Routledge, , 2014 |
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ISBN |
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1-315-65634-5 |
1-317-32479-X |
1-317-32478-1 |
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Descrizione fisica |
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1 online resource (337 p.) |
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Collana |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Economic development - India |
India Emigration and immigration |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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"With a foreword by Oommen Chandy"--cover. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Cover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; Tables; Figures and Boxes; Abbreviations; Preface; Foreword; Acknowledgements; 1. Diaspora and Development: Critical Issues; 2. Diaspora and Development: Case Study of the Indo-EU Diaspora; 3. Diaspora and Development: Theoretical Perspectives; 4. Diaspora, Transnationalism and Development; 5. Engaging the Indian Diaspora for Development; 6. Professional Diaspora Networks and Philanthropy in the Healthcare Sector; 7. Return of Diasporas: India's Growth Story vs Global Crisis |
8. Punjabi Diaspora and Educational Development9. Land, Migration and Identity: Changing Punjabi Transnationalism; 10. Diaspora and Remittances; 11. Future Diasporas? International Student Migration from India to the UK; 12. Ethnic Indians in India's Look East Policy; 13. The Indian Diaspora in Oman; 14. Indian Migrant Experiences in Oman and Bahrain; 15. Nitaqat - Second Wave of Saudization: Is it an Answer to the Domestic Discontent?; 16. Kerala Emigration to Saudi Arabia: Prospects under the Nitaqat Law; 17. Migration and Inequality |
18. International Trade in Health Services: An Indian Experience19. Capability Quotient of the North-Eastern Out-Migrants; 20. Living |
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Conditions of Sri Lankan Tamil Refugees in India; About the Editor; Notes on Contributors; Index |
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Sommario/riassunto |
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India Migration Report 2014 is one of the first systematic studies on contribution of diasporas in development, in countries of origin as well as destination. This volume:examines how diasporic human and financial resources can be utilized for economic growth and sustainable development, especially in education and health;offers critical insights on migrant experiences, transnationalism and philanthropic networks, and indigenization and diaspora policies, as well as return of diasporas; andincludes case studies on Indian migrants in the Gulf region - in particular, Bahrain, Oman and Saudi Arab |
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2. |
Record Nr. |
UNINA9910404080403321 |
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Autore |
Kung Hsu-Yang |
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Titolo |
Deep Learning Applications with Practical Measured Results in Electronics Industries |
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Pubbl/distr/stampa |
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MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
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ISBN |
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Descrizione fisica |
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1 online resource (272 p.) |
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Soggetti |
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History of engineering and technology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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This book collects 14 articles from the Special Issue entitled "Deep Learning Applications with Practical Measured Results in Electronics Industries" of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative |
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adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods. |
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