Sachi Nandan Mohanty

Suneeta Satpathy

Pabitra Kumar Tripathy

The Art of Algorithm Design is a complementary perception of all books on algorithm design and is a roadmap for all levels of learners as well as professionals dealing with algorithmic problems. Further, the book provides a comprehensive introduction to algorithms and covers them in considerable depth, yet makes their design and analysis accessible to all levels of readers. All algorithms are described and designed with a "pseudo-code" to be readable by anyone with little knowledge of programming. This book comprises of a comprehensive set of problems and their solutions against each algorithm to demonstrate its executional assessment and complexity, with an objective to: Understand the introductory concepts and design principles of algorithms and their complexities Demonstrate the programming implementations of all the algorithms using C-Language Be an excellent handbook on algorithms with self-explanatory chapters enriched with problems and solutions While other books may also cover some of the same topics, this book is designed to be both versatile and complete as it traverses through step-by-step concepts and methods for analyzing each algorithmic complexity with pseudo-code examples. Moreover, the book provides an enjoyable primer to the field of algorithms. This book is designed for undergraduates and postgraduates studying algorithm design. Sachi Nandan Mohanty is an Associate Professor in the Department of Computer Engineering, College of Engineering Pune, India, with 11 years of teaching and research experience in Algorithm Design, Computer Graphics, and Machine Learning. Pabitra Kumar Tripathy is the Head of the Department of Computer Science & Engineering, Kalam Institute of Technology, Berhampur, India, with 15 years of teaching experience in Programming Languages, Algorithms, and Theory of Computation. Suneeta Satpathy is an Associate Professor in the Department of Computer Science at Sri Sri University, Cuttack, Odisha, India, with 13 years of teaching experience in Computer Programming, Problem-Solving Techniques, and Decision Mining.

The Art of Algorithm Design

Jyotir Moy Chatterjee

Sirisha Potluri

Monika Mangla

Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.

Machine Learning Approach for Cloud Data Analytics in IoT

Shailendra K. Saxena

The book examines the role of artificial intelligence during the COVID-19 pandemic, including its application in i) early warnings and alerts, ii) tracking and prediction, iii) data dashboards, iv) diagnosis and prognosis, v) treatments, and cures, and vi) social control. It explores the use of artificial intelligence in the context of population screening and assessing infection risks, and presents mathematical models for epidemic prediction of COVID-19. Furthermore, the book discusses artificial intelligence-mediated diagnosis, and how machine learning can help in the development of drugs to treat the disease. Lastly, it analyzes various artificial intelligence-based models to improve the critical care of COVID-19 patients.

Applications of Artificial Intelligence in COVID-19

Xiaobo Zhang

Tanupriya Choudhury

Rabinarayan Satpathy

Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.

Suneeta Satpathy

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