Sarika Jain – författare

Web Semantics strengthen the description of web resources to exploit them better and make them more meaningful for both humans and machines, thereby contributing to the development of a knowledgeintensive data web. The world is experiencing the movement of concept from data to knowledge and the movement of web from document model to data model. The underlying idea is making the data machine understandable and processable. In the light of these trends, conciliation of Semantic and the Web is of paramount importance for further progress in the area. Web Semantics: Cutting Edge and Future Directions in Healthcare describes the three major components of the study of Semantic Web, namely Representation, Reasoning, and Security with a special focus on the healthcare domain. This book summarizes the trends and current research advances in web semantics, emphasizing the existing tools and techniques, methodologies, and research solutions. It provides easily comprehensible information on Web Semantics including semantics for data and semantics for services. Presents a comprehensive examination of the emerging research in areas of the semantic web, including ontological engineering, semantic annotation, reasoning and intelligent processing, semantic search paradigms, semantic web mining, and semantic sentiment analysis Helps readers understand key concepts in semantic web applications for biomedical engineering and healthcare, including mapping disparate knowledge bases, security issues, multilingual semantic web, and integrating databases with knowledge bases Includes coverage of key application areas of the semantic web, including clinical decision-making, biodiversity science, interactive healthcare, intelligent agent systems, decision support systems, and clinical natural language processing

Leveraging Artificial Intelligence in Global Epidemics provides readers with a detailed technical description of the role Artificial Intelligence plays in various stages of a disease outbreak, using COVID-19 as a case study. In the fight against epidemics, medical staff are on the front line; but behind the lines the battle is fought by researchers, and data scientists. Artificial Intelligence has been helping researchers with computer modeling and simulation for predictions about disease progression, the overall economic situation, tax incomes and population development. In the same manner, AI can prepare researchers for any emergency situation by backing the medical science. Artificial Intelligence plays a key and cutting-edge role in the preparedness for and dealing with the outbreak of global epidemics. It can help researchers analyze global data about known viruses to predict the patterns of the next pandemic and the impacts it will have. Not only prediction, AI plays an increasingly important role in assessing readiness, early detection, identification of patients, generating recommendations, situation awareness and more. It is up to the right input and the innovative ways by humans to leverage what AI can do. As COVID-19 has grabbed the world and its economy today, an analysis of the COVID-19 outbreak and the global responses and analytics will pay a long way in preparing humanity for such future situations. Provides readers with understanding of how Artificial Intelligence can be applied to the prediction, forecasting, detection, and testing of global epidemics, using COVID-19 and other recent epidemics such as Ebola, Corona viruses, Zika, influenza, Dengue, Chikungaya, and malaria as case studies Includes background material regarding readiness for coping with epidemics, including Machine Learning models for prediction of epidemic outbreaks based on existing data Includes technical coverage of key topics such as generating recommendations to combat outbreaks, genome sequencing, AI-assisted testing, AI-assisted contact tracing, situation awareness and combating disinformation, and the role of Artificial Intelligence and Machine Learning in drug discovery, vaccine development, and drug re-purposing

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.

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: Covers various aspects of in-depth solutions of software engineering problems using computational intelligence techniques Discusses the latest evolutionary approaches to preliminary theory of different solve optimization problems under software engineering domain Covers heuristic as well as meta-heuristic algorithms designed to provide better and optimized solutions Illustrates applications including software requirement prioritization, software cost estimation, reliability assessment, software defect prediction, and more Highlights swarm intelligence-based optimization solutions for software testing and reliability problems

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: Covers various aspects of in-depth solutions of software engineering problems using computational intelligence techniques Discusses the latest evolutionary approaches to preliminary theory of different solve optimization problems under software engineering domain Covers heuristic as well as meta-heuristic algorithms designed to provide better and optimized solutions Illustrates applications including software requirement prioritization, software cost estimation, reliability assessment, software defect prediction, and more Highlights swarm intelligence-based optimization solutions for software testing and reliability problems

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.

Neuro-Symbolic AI: Integrating Neural Networks and Symbolic Reasoning explores the convergence of two historically distinct paradigms in artificial intelligence-data-driven neural networks and logic-based symbolic reasoning. This book presents a comprehensive roadmap of this emerging hybrid discipline, offering deep theoretical insights, practical methodologies, and transformative applications across diverse research sectors, including healthcare, finance, engineering, and autonomous systems. It is structured into four parts-Foundational Principles, Hybrid Models and Techniques, Real-World Applications, and Emerging Challenges, bringing together cutting-edge research and expert perspectives to highlight how Neuro-Symbolic AI enhances interpretability, reasoning capabilities, and trust in intelligent systems.While neural networks have achieved remarkable success in perception and pattern recognition tasks, they often lack the reasoning, transparency, and generalizability that symbolic systems excel at. Conversely, symbolic AI lacks the flexibility and scalability of deep learning. This handbook directly addresses these challenges by providing a structured approach to Neuro-symbolic AI, presenting rigorous theoretical foundations, state-of-the-art hybrid techniques (e.g., knowledge graphs, compositionality, category theory), and diverse real-world applications. This book consolidates research insights, methodological innovations, and practical use cases into a single, accessible volume.Presents theoretical advances, hybrid model architectures, and practical applications in a single volumeProvides comprehensive coverage across machine learning, symbolic AI, and cognitive computingIncludes real-world case studies from healthcare, autonomous systems, and scientific discovery that offer readers concrete insights into how Neuro-Symbolic AI is implementedFeatures a dedicated section on emerging challenges and future directions, such as scalability, energy efficiency, security, and AGI potential, giving readers a roadmap for future research, innovation, and responsible deployment of hybrid AI systems

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 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.

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.

This book constitutes refereed proceedings of the 2nd International Semantic Intelligence Conference (ISIC 2022). This book covers a wide range of topics, including semantic web engineering, ontology-based data access, multimodal and multilingual access, machine-to-machine communications and interoperability, knowledge extraction and ontology learning from the web, computational paradigms and computational intelligence, distributed and mobile systems, and many others. This book includes novel contributions and the latest developments from researchers across industry and academia. This book serves as a valuable reference resource for academics and researchers across the globe.

This book constitutes refereed proceedings of the 2nd International Semantic Intelligence Conference (ISIC 2022). This book covers a wide range of topics, including semantic web engineering, ontology-based data access, multimodal and multilingual access, machine-to-machine communications and interoperability, knowledge extraction and ontology learning from the web, computational paradigms and computational intelligence, distributed and mobile systems, and many others. This book includes novel contributions and the latest developments from researchers across industry and academia. This book serves as a valuable reference resource for academics and researchers across the globe.

The topics covered in this volume are data mining, patient electronic health records, healthcare portals, telemedicine, automatic identification and data collector systems, RFID and localization techniques, usability and ubiquity in e-Health, artificial intelligence for healthcare decision-making, etc.

The topics covered in this volume are data mining, patient electronic health records, healthcare portals, telemedicine, automatic identification and data collector systems, RFID and localization techniques, usability and ubiquity in e-Health, artificial intelligence for healthcare decision-making, etc.

The topics covered in this volume are data mining, patient electronic health records, healthcare portals, telemedicine, automatic identification and data collector systems, RFID and localization techniques, usability and ubiquity in e-Health, artificial intelligence for healthcare decision-making, etc.