The scope of 9th International Conference Confluence 2019 covers the broad spectrum of Influential areas in the field of Information Technology and Computer Science The major topics include, but not limited to Ad hoc and Sensor Networks Artificial Intelligence Autonomic Computing Big Data Business CloudsCloud Computing Architectures Cloud Computing Consulting Methods Cloud Security, Privacy and Compliance Challenges Content Management Data Mining & Ontology Grid Computing, Image Processing, Intelligent Information System Interaction of Mobile Computing, mCommerce and Clouds Natural Language Processing, Network Architectures and Protocols Network Security & Cryptography Pattern Recognition Quantum Computing Remote Sensing & GIS Service Oriented Architecture and Cloud Computing Soft Computing Software Engineering Software Security & Risk Management Ubiquitous Computing Virtual and Overlay Networks Web Mining Wireless Communication and any other Relevant Topics Field

The book presents a collection of peer-reviewed articles from the International Conference on Innovations in Cyber Physical Systems (ICICPS 2020). The conference provided opportunities for the presentation of new research results and discussion about them. It was also an opportunity to generation of new ideas in all CPS aspects, including theory, tools, applications, systems, test-beds and field deployments. The range of topics explored is wide, and covers security, control, optimization, machine learning, game theory, mechanism design, mobile and cloud computing, model-based design, verification, data mining/analytics, signal processing, and human-in-the-loop shared or supervisory control. This book will be useful to researchers, students, industrialist, developers, and practitioners alike.

Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. Examine fraud patterns in historical data Utilize labeled, unlabeled, and networked data Detect fraud before the damage cascades Reduce losses, increase recovery, and tighten security The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.

This book presents select peer-reviewed proceedings of the International Conference on Frontiers in Smart Systems Technologies (ICFSST 2019). It focuses on latest research and cutting-edge technologies in smart systems and intelligent autonomous systems with advanced functionality. Comprising topics related to diverse aspects of smart technologies such as high security, reliability, miniaturization, energy consumption, and intelligent data processing, the book contains contributions from academics as well as industry. Given the range of the topics covered, this book will prove useful for students, researchers, and professionals alike.

This book comprises select proceedings of the international conference ETAEERE 2020, and covers latest research in the areas of electronics, communication and computing. The book includes different approaches and techniques for specific applications using particle swarm optimization, Otsu’s function and harmony search optimization algorithm, DNA-NAND gate, triple gate SOI MOSFET, micro-Raman and FTIR analysis, high-k dielectric gate oxide, spectrum sensing in cognitive radio, microstrip antenna, GPR with conducting surfaces, energy efficient packet routing, iBGP route reflectors, circularly polarized antenna, double fork shaped patch radiator, implementation of Doppler radar at 24 GHz, iris image classification using SVM, digital image forgery detection, secure communication, spoken dialog system, and DFT-DCT spreading strategies. Given the range of topics covered, this book can be useful for both students and researchers working in electronics and communication.

This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.