Sarika Jain – författare

Computational Intelligence Techniques and Their Applications to Software Engineering Problems focuses on computational intelligence approaches as applicable in varied areas of software engineering such as software requirement prioritization, cost estimation, reliability assessment, defect prediction, maintainability and quality prediction, size estimation, vulnerability prediction, test case selection and prioritization, and much more. The concepts of expert systems, case-based reasoning, fuzzy logic, genetic algorithms, swarm computing, and rough sets are introduced with their applications in software engineering. The field of knowledge discovery is explored using neural networks and data mining techniques by determining the underlying and hidden patterns in software data sets. Aimed at graduate students and researchers in computer science engineering, software engineering, information technology, this book:

Computational Intelligence Techniques and Their Applications to Software Engineering Problems focuses on computational intelligence approaches as applicable in varied areas of software engineering such as software requirement prioritization, cost estimation, reliability assessment, defect prediction, maintainability and quality prediction, size estimation, vulnerability prediction, test case selection and prioritization, and much more. The concepts of expert systems, case-based reasoning, fuzzy logic, genetic algorithms, swarm computing, and rough sets are introduced with their applications in software engineering. The field of knowledge discovery is explored using neural networks and data mining techniques by determining the underlying and hidden patterns in software data sets. Aimed at graduate students and researchers in computer science engineering, software engineering, information technology, this book:

This book is an attempt to establish in the readers the importance of creating interoperable data stores and writing rules for handling this data. It also covers extracts from a few project dissertations and a research funded project that the author had supervised.• Describes the power of ontologies for better data management• Provides an overview of knowledge engineering including ontology engineering, tools and techniques• Provides sample development procedures for creating two domain ontologies.• Depicts the utility of ontological representation in situation awareness• Demonstrates recommendation engine for unconventional emergencies using a hybrid reasoning approach.• The text explains how to make better utilization of resources when emergency strikesGraduates and undergraduates doing courses in artificial intelligence, semantic web and knowledge engineering will find this book beneficial.

This book is an attempt to establish in the readers the importance of creating interoperable data stores and writing rules for handling this data. It also covers extracts from a few project dissertations and a research funded project that the author had supervised.• Describes the power of ontologies for better data management• Provides an overview of knowledge engineering including ontology engineering, tools and techniques• Provides sample development procedures for creating two domain ontologies.• Depicts the utility of ontological representation in situation awareness• Demonstrates recommendation engine for unconventional emergencies using a hybrid reasoning approach.• The text explains how to make better utilization of resources when emergency strikesGraduates and undergraduates doing courses in artificial intelligence, semantic web and knowledge engineering will find this book beneficial.

This book brings together a blend of different areas of machine learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms.

Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions describes the predictive analysis and forecasting techniques in different emerging and classical areas using the approaches of ML and AI. It discusses the application of ML and AI in medical diagnostic systems and deals with the security prevention aspects of ML and how it can be used to tackle various emerging security issues. This book also focuses on NLP and understanding the techniques, obstacles, and possible solutions.

This is a valuable reference resource for researchers and postgraduate students in healthcare systems engineering, computer science, cyber-security, information technology, and applied mathematics.

This book brings together a blend of different areas of machine learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms.

Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions describes the predictive analysis and forecasting techniques in different emerging and classical areas using the approaches of ML and AI. It discusses the application of ML and AI in medical diagnostic systems and deals with the security prevention aspects of ML and how it can be used to tackle various emerging security issues. This book also focuses on NLP and understanding the techniques, obstacles, and possible solutions.

This is a valuable reference resource for researchers and postgraduate students in healthcare systems engineering, computer science, cyber-security, information technology, and applied mathematics.

This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior.  It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior.  Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising.

This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.

This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior.  It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior.  Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising.

This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.

This book provides an overview of the emerging smart connected world, and discusses the roles and the usage of underlying semantic computing and Internet-of-Things (IoT) technologies.

The book comprises ten chapters overall, grouped in two parts. Part I “Smart Connected World: Overview and Technologies” consists of seven chapters and provides a holistic overview of the smart connected world and its supporting tools and technologies. Part II “Applications and Case Studies” consists of three chapters that describe applications and case studies in manufacturing, smart cities, health, and more. Each chapter is self-contained and can be read independently; taken together, readers get a bigger picture of the technological and application landscape of the smart connected world.

This book is of interest for researchers, lecturers, and practitioners in Semantic Web, IoT and related fields. It can serve as a reference for instructors and students takingcourses in hybrid computing getting abreast of cutting edge and future directions of a connected ecosystem. It will also benefit industry professionals like software engineers or data scientists, by providing a synergy between Web technologies and applications.