Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions

This book discusses the application of data systems and data-driven infrastructure in existing industrial systems in order to optimize workflow, utilize hidden potential, and make existing systems free from vulnerabilities. The book discusses application of data in the health sector, public transportation, the financial institutions, and in battling natural disasters, among others. Topics include real-time applications in the current big data perspective; improving security in IoT devices; data backup techniques for systems; artificial intelligence-based outlier prediction; machine learning in OpenFlow Network; and application of deep learning in blockchain enabled applications. This book is intended for a variety of readers from professional industries, organizations, and students.

Introduction. - One World One Identification Smart System. - Tiered Architectural View of IoT: Structural Analysis and Concerns. - Rethinking Real-Time Applications in the current big data perspective. - Accuracy of Machine Learning Algorithms in predicting Retinal Disease. - A Cloud Trustworthiness Assessment Algorithm. - Improving Security in IoT devices. - Data backup Techniques for Healthcare Systems. - Role of Distributed Mutual Exclusion Algorithm. - Anomaly detection system in health care system enabled in blockchain technology framework. - Artificial intelligence-based Outlier Prediction . - Machine Learning in OpenFlow Network. - Defense Machinery Against Sybil Attacks over Wireless Ad-hoc Network on IoT. - Decentralized application development for health care using smart contracts. - Role of IoT in Healthcare. - Improved Breast Cancer Classification Using K-Means & Logistic Regression. - Sentimental Analysis. - IoT and Smart city. - Blockchain technology-based applications. - Anomaly detectionin cryptocurrencies framework. - Application of deep learning in blockchain enabled applications. - Conclusion.