Demanding good governance : : lessons from social accountability initiatives in Africa / / Mary McNeil and Carmen Malena, editors

• Record Nr.: UNINA9910970291403321
• Pubbl/distr/stampa: Washington, D.C. : , : World Bank, , c2010
• ISBN: 9786612725630; 9781282725638; 1282725637; 9780821383834; 0821383833
• Edizione: [1st ed.]
• Descrizione fisica: xxv, 236 pages : illustrations, map ; ; 23 cm
• Altri autori (Persone): McNeilMary <1956->; MalenaCarmen
• Disciplina: 320.6096
• Soggetti: Social accounting - Africa; Public administration - Africa
• 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: Cover; Title Page; Copyright; Contents; Foreword; Acknowledgments; About the Editors; About the Contributors; Abbreviations; Map: Social Accountability Initiatives from Seven Selected Countries; Chapter 1: Social Accountability in Africa: An Introduction; Chapter 2: Participatory Budgeting in Fissel, Senegal; Chapter 3: Civic Participation in Policy and Budgetary Processes in Ilala Municipal Council, Tanzania; Chapter 4: Tracking the Ghana District Assemblies Common Fund; Chapter 5: Enhancing Civil Society Capacity for Advocacy and Monitoring: Malawi's Poverty Reduction Strategy Budget; Chapter 6: Gender-Sensitive and Child-Friendly Budgeting in ZimbabweChapter 7: The Nigeria Extractive Industries Transparency Initiative and Publish What You Pay Nigeria; Chapter 8: Citizen Control of Public Action: The Social Watch Network in Benin; Chapter 9: Social Accountability in Africa: An Analysis; Index; Back cover
• Sommario/riassunto: Social accountability refers to the wide range of citizen actions to hold the state to account, as well as actions on the part of government, media, and other actors that promote or facilitate these efforts. Social ; accountability strategies and tools help empower ordinary citizens to exercise their inherent rights to hold governments accountable for the use of public funds and how they exercise authority. This book explains what social accountability means in the African context, distilling some common success factors and lessons that can help other practitioners and innovators in the field. D

Machine Learning for Disease Detection, Prediction, and Diagnosis : Challenges and Opportunities / / edited by Tanupriya Choudhury, Avita Katal

• Record Nr.: UNINA9911009334903321
• Autore: Choudhury Tanupriya
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
• ISBN: 981-9642-41-8
• Edizione: [1st ed. 2025.]
• Descrizione fisica: 1 online resource (480 pages)
• Collana: Medicine Series
• Altri autori (Persone): KatalAvita
• Disciplina: 610.285631
• Soggetti: Medicine, Preventive; Health promotion; Diseases; Diseases - Animal models; Machine learning; Health Promotion and Disease Prevention; Disease Models; Machine Learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Chapter 1 Introduction to machine learning and Image Processing for disease detection -- Chapter 2 Comparative Study of Various Deep Learning Methods for Prediction of Disease -- Chapter 3 Introduction to deep learning for disease prediction -- Chapter 4 A survey of image classification techniques for the prediction of diseases -- Chapter 5 Prediction of disease related to heart by using different techniques: A survey -- Chapter 6 Automated Plant Disease Diagnosis with Machine ; Learning -- Chapter 7 Exploring Disease Prediction Techniques through Data Mining: A Comprehensive Overview -- Chapter 8 Detection of Parkinson’s disease using different machine learning techniques: A comparative analysis -- Chapter 9 Kidney Disease Prediction by Machine Learning Techniques -- Chapter 11 Prediction of Diabetes by using the different machine learning algorithms -- Chapter 12 Investigation of Machine Learning Algorithms in detecting Chronic Kidney Disorder -- Chapter 13 Skin Disease Prediction using machine learning techniques -- Chapter 14 A Comparative Study of Different Machine Learning Techniques for Skin Disease Detection -- Chapter 15 Leveraging MLP-Mixer for Improved Melanoma Diagnosis Using Skin Lesion Images -- Chapter 16 Application of AI to detect Brain Tumors -- Chapter 17 Revolutionizing Brain Tumor Detection: Unleashing the Power of Artificial Intelligence -- Chapter 18 Disease detection and diagnosis using artificial intelligence techniques for sustainable economic growth -- Chapter 19 Developing a COVID-19 Prediction Kit Using Machine Learning -- Chapter 20 Plant Disease Detection: Comprehensive Review of Methods and Techniques.
• Sommario/riassunto: The book “Machine Learning for Disease Detection, Prediction, and Diagnosis” can be a comprehensive guide to the novel concepts, techniques, and frameworks essential for improving the viability of existing machine-learning practices. It provides an in-depth analysis of how these new technologies are helpful to detect, predict and diagnose diseases more accurately. The book covers various topics such as image classification algorithms, supervised learning methods like support vector machines (SVM), deep neural networks (DNNs), convolutional neural networks (CNNs), etc. unsupervised approaches such as clustering algorithms as well as reinforcement learning strategies. This book is an invaluable resource for anyone interested in machine-learning applications related to disease detection or diagnosis. It explains different concepts and provides practical examples of how they can it implements using real-world data sets from medical imaging datasets or public health records databases, among others. Furthermore, it offers insights into recent advances made by researchers which have enabled automated decision-making systems based on AI models with improved accuracy over traditional methods. This text also discusses ways through which current models could improve further by incorporating domain knowledge during the model training phase, thereby increasing their efficacy even further.