Ebooks de informática, página 2 de 225

Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications captures the state of the art in usage of artificial intelligence in different types of recommendation systems and predictive analysis. The book provides guidelines and case studies for application of artificial intelligence in recommendation from expert researchers and practitioners. A detailed analysis of the relevant theoretical and practical aspects, current trends and future directions is presented. The book highlights many use cases for recommendation systems: · Basic application of machine learning and deep learning in recommendation process and the evaluation metrics · Machine learning techniques for text mining and spam email filtering considering the perspective of Industry 4.0 · Tensor factorization in different types of recommendation system · Ranking framework and topic modeling to recommend author specialization based on content. · Movie recommendation systems · Point of interest recommendations · Mobile tourism recommendation systems for visually disabled persons · Automation of fashion retail outlets · Human resource management (employee assessment and interview screening) This reference is essential reading for students, faculty members, researchers and industry professionals seeking insight into the working and design of recommendation systems.

This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with deep learning. The basic concepts of mathematics and the hardware requirements for deep learning implementation, and some of its popular frameworks for medical applications are also covered. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

Advanced Mathematical Applications in Data Science comprehensively explores the crucial role mathematics plays in the field of data science. Each chapter is contributed by scientists, researchers, and academicians. The 13 chapters cover a range of mathematical concepts utilized in data science, enabling readers to understand the intricate connection between mathematics and data analysis. The book covers diverse topics, including, machine learning models, the Kalman filter, data modeling, artificial neural networks, clustering techniques, and more, showcasing the application of advanced mathematical tools for effective data processing and analysis. With a strong emphasis on real-world applications, the book offers a deeper understanding of the foundational principles behind data analysis and its numerous interdisciplinary applications. This reference is an invaluable resource for graduate students, researchers, academicians, and learners pursuing a research career in mathematical computing or completing advanced data science courses. Key Features: Comprehensive coverage of advanced mathematical concepts and techniques in data scienceContributions from established scientists, researchers, and academiciansReal-world case studies and practical applications of mathematical methodsFocus on diverse areas, such as image classification, carbon emission assessment, customer churn prediction, and healthcare data analysisIn-depth exploration of data sciences connection with mathematics, computer science, and artificial intelligenceScholarly references for each chapterSuitable for readers with high school-level mathematical knowledge, making it accessible to a broad audience in academia and industry.

This book is a comprehensive review of technologies and data in healthcare services. It features a compilation of 10 chapters that inform readers about the recent research and developments in this field. Each chapter focuses on a specific aspect of healthcare services, highlighting the potential impact of technology on enhancing practices and outcomes. The main features of the book include 1) referenced contributions from healthcare and data analytics experts, 2) a broad range of topics that cover healthcare services, and 3) demonstration of deep learning techniques for specific diseases. Key topics: - Federated learning in analysis of sensitive healthcare data while preserving privacy and security. - Artificial intelligence for 3-D bone image reconstruction. - Detection of disease severity and creating personalized treatment plans using machine learning and software tools - Case studies for disease detection methods for different disease and conditions, including dementia, asthma, eye diseases - Brain-computer interfaces - Data mining for standardized electronic health records - Data collection, management, and analysis in epidemiological research The book is a resource for learners and professionals in healthcare service training programs and health administration departments. ReadershipLearners and professionals in healthcare service training programs and health administration departments.

This book is a comprehensive review of secondary metabolite production from plant tissue culture. The editors have compiled 12 meticulously organized chapters that provide the relevant theoretical and practical frameworks in this subject using empirical research findings. The goal of the book is to explain the rationale behind in vitro production of secondary metabolites from some important medicinal plants. Biotechnological strategies like metabolic engineering and the biosynthesis, transport and modulation of important secondary metabolites are explained along with research studies on specific plants. In addition to the benefits of secondary metabolites, the book also aims to highlight the commercial value of medicinal plants for pharmaceutical and healthcare ventures. Topics covered in this part include: 1. Elicitation Strategies and Metabolic Engineering to boost metabolite production with case studies in metabolic engineering with examples of Scaevola Taccada and Catharanthus Roseus. 2. Stress response investigation and the role of glandular trichomes as bio-cell factories 3. Plant growth regulators and rapid regeneration techniques using swift plantlets regeneration and phytochemical characterization 4. Nutraceuticals, antimicrobials, and genomic applications of in vitro cultures with an example of Hassawi rice and its genomics 5. Sustainable approaches for saving endangered medicinal plants The book caters to a wide readership. It primarily prepares graduate students, researchers, biotechnologists, giving them a grasp of the key methodologies in the secondary metabolite production. It is a secondary reference for support executives, industry professionals, and policymakers at corporate and government levels to understand the importance of plant tissue culture and maximizing its impact in the herbal industry. Readership Graduate students and researchers in plant biotechnology courses; industry professionals and policymakers in the herbal industry.

Basics of Python Programming: A Quick Guide for Beginners is an essential companion to mastering the Python programming language. The book presents information about Python in 12 structured chapters with a strong emphasis on fundamentals and practical information. Starting with basic operators, functions and expressions, contents explain file handling, exception handling and modules. The book concludes with advanced topics such as object oriented programming and machine learning. Key Features: Fundamental Focus: Covers the core concepts of Python programming to build a strong foundation in python programming in an easy-to-understand format.Practical Demonstrations: Learn by doing. This textbook includes hands-on practical demonstrations that reinforce your understanding of Python concepts.IDE Guidance: Includes programming and installation guidance for Python-supporting Integrated Development Environments (IDEs).Explores Python Frameworks: Introduces Python frameworks such as Matplotlib, TensorFlow, PyTorch, Scikit-Learn, and NLTK for complex projects.Python for Machine Learning: Gives a preliminary understanding of Python for machine learning tasks for data science and AI applications. Basics of Python Programming: A Quick Guide for Beginners is the perfect starting point for aspiring students, programmers and tech enthusiasts. Whether youre a student looking to build a solid foundation in Python or an industry professional venturing into machine learning and artificial intelligence, this textbook has you covered. Readership Computer science, engineering and technology students; programming enthusiasts and professionals.

Bioinformatics, and by extension omic sciences the collective disciplines that are dependent on the use of extensive datasets of biological information present a challenge of data management for researchers all over the world. Big data collected as part of research projects and experiments can be complex, with several kinds of variables involved. Coupled with continuously changing bioinformatics and information technology tools, there is a need to bring a multidisciplinary approach into these fields. Advances in Bioinformatics, Biostatistics and Omic Sciences attempts to realize an integrated approach between all omic sciences, exploring innovative bioinformatics and biostatistical methodologies which enable researchers to unveil hidden sides of biological phenomena. This volume presents reviews on the following topics which give a glimpse of recent advances in the field:- New Integrated Mitochondrial DNA Bioinformatics Pipeline to Improve Quality Assessment of Putative Pathogenic Variants from NGS Experiments - Variant Calling on RNA Sequencing Data: State of Art and Future Perspectives - An innovative Gene Prioritization Pipeline for WES analyses - New Integrated Differential Expression Approach for RNA-Seq Data Analysis - Innovations in Data Visualization for Straightforward Interpretation of Nucleic Acid Omics Outcomes This volume serves as a guide for graduate students in bioinformatics as well as researchers planning new projects as a part of their professional and academic activities.

Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments is a 2-part book set which presents discoveries, innovative ideas, concepts, practical solutions, and novel applications of Human-Computer Interaction (HCI) and related disciplines such as artificial intelligence, machine learning, data mining, computer vision, and natural language processing. The book provides readers with information about HCI trends which are shaping the future of smart, interconnected urban and industrial environments. This is the second of the two volumes of the edited books. The chapters of this volume cover topics like ERP usability in educational settings, the role of AI in enhancing HCI functionality, usability of local mobile healthcare apps, analyzing the usage of social media apps and a review of HCI systems for disaster management and systems for tracking traffic safety violations. Contributions are authored by experts and scientists in the field of HCI and its interrelated disciplines from 9 different countries Albania, China, India, Indonesia, Nigeria, Pakistan, Spain, the United Kingdom, and the United States. Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments is an informative reference for scientists, researchers, and developers in both academia and industry who wish to learn, design, implement, and apply these emerging technologies in HCI in different sectors, with the goal of realizing futuristic technology-driven living and functional smart cities and environments.

Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments is a 2-part book set which presents discoveries, innovative ideas, concepts, practical solutions, and novel applications of Human-Computer Interaction (HCI) and related disciplines such as artificial intelligence, machine learning, data mining, computer vision, and natural language processing. The book provides readers with information about HCI trends which are shaping the future of smart, interconnected urban and industrial environments. Contributions are authored by experts and scientists in the field of HCI and its interrelated disciplines from 8 different countries Chile, China, Croatia, India, Iran, Malaysia, Peru, and South Korea. The chapters of this volume present novel and state of the art research works conducted at the intersection of HCI aimed at developing trust, increasing user acceptance, augmenting user performance, and fostering human-technology partnerships. Chapters cover usability testing in digital healthcare systems, user experience testing of handicapped children and assistive technologies for visually impaired users and a gamified user experience design for learning. The volume also presents a review of twitter usability testing among Indian users, along with specific cases of arthritis diagnostic systems, meteorological draught analysis and the role of EUPS in improving GUI design to improve the user experience. Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments is an informative reference for scientists, researchers, and developers in both academia and industry who wish to learn, design, implement, and apply these emerging technologies in HCI in different sectors, with the goal of realizing futuristic technology-driven living and functional smart cities and environments.

How to Design Optimization Algorithms by Applying Natural Behavioral Patterns is a guide book that introduces readers to optimization algorithms based on natural language processing. Readers will learn about the basic concept of optimization, optimization algorithm fundamentals and the methods employed to formulate natural ideas and behaviors into algorithms. Readers will learn how to create their own algorithm from the information provided in the text. The book is a simple reference for students and programming enthusiasts who are interested in learning about optimization and the process of designing algorithms designed to mimic natural phenomena. The information will also benefit engineers who are learning about optimization techniques.

This book is a review of recent artificial intelligence approaches, initiatives and applications in engineering and science fields. It features contributions that highlight the use of techniques such as machine learning, mining engineering, modeling and simulation, and fuzzy logic methods in the fields of communication, networking and information engineering. The collection of chapters should inspire scholars involved in theoretical and applied sciences to contribute to research using computational intelligence principles and methods in their respective research communities. Professionals working on systems engineering, communications, innovative computing systems and adaptive technologies for sustainable growth, will also be able to benefit from the information provided in the book.

Artificial Intelligence, Machine Learning and User Interface Design is a forward-thinking compilation of reviews that explores the intersection of Artificial Intelligence (AI), Machine Learning (ML) and User Interface (UI) design. The book showcases recent advancements, emerging trends and the transformative impact of these technologies on digital experiences and technologies. The editors have compiled 14 multidisciplinary topics contributed by over 40 experts, covering foundational concepts of AI and ML, and progressing through intricate discussions on recent algorithms and models. Case studies and practical applications illuminate theoretical concepts, providing readers with actionable insights. From neural network architectures to intuitive interface prototypes, the book covers the entire spectrum, ensuring a holistic understanding of the interplay between these domains. Use cases of AI and ML highlighted in the book include categorization and management of waste, taste perception of tea, bird species identification, content-based image retrieval, natural language processing, code clone detection, knowledge representation, tourism recommendation systems and solid waste management. Advances in Artificial Intelligence, Machine Learning and User Interface Design aims to inform a diverse readership, including computer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts. ReadershipComputer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts.

The volume is a collection of essays by acclaimed and widely published international scholars of space working within different disciplines, such as social sciences, history, applied sciences and media theory, literary and cultural studies (American, Canadian, French, German, Mexican-American, and Polish). Their contributions substantiate the argument that the debate on space has produced a polyphony of argumentation which resulted in the multiplication and diversification of perspectives and interpretations of the studied concept. The volume captures the present state of the most recent debate on space, exploring the importance of its multifaceted nature evinced by the abundance of research on such related terms as border,boundary, and/or region.

###BOLD#TEXT[Integrating the role of culture is critical in preventing suicide]###Written by leading suicide researchersExplores suicide in different cultural contextsHighlights how to conduct culturally sensitive studiesBuilding on the seminal work of Colucci and Lester (2013), this volume examines the unique perspective of the role culture plays in suicide research and prevention. With the current domination of individual and largely biomedical approaches in the field, these leading social scientists and suicide researchers carefully show how important integrating sociocultural factors is in helping to prevent people from dying by suicide and support those who live with suicidality.The first section addresses the fundamental issues of why "culture" is of vital importance in understanding and preventing suicidal behavior, what the "cultural meaning" of suicide is, and where current research and theory are taking us. It concludes with a thought-provoking perspective on suicide as a staged performance. The second section features a mixed-methods cross-cultural study on the meanings of suicide in Australian, Indian, and Italian cultures. Additional chapters explore a culturally specific form of suicide (i.e., sati in India), suicide among Roma and Irish Travellers, and a community-based suicide prevention strategy for different populations, including migrants and refugees and people from low-and-middle-income Asian countries. The authors conclude with insightful recommendations for conducting culturally sensitive and meaningful studies.This volume is essential reading for anyone involved in suicide research and prevention and more broadly to those interested in the sociocultural and political contexts of mental health.

Data Management and Security in Blockchain Systems offers a comprehensive exploration of how blockchain technology is reshaping the landscape of data management and security. This book addresses key aspects of blockchain-based systems, including data integrity, transparency, and tamper resistance, making it an essential resource for students, researchers, and professionals.Covering topics from blockchain-enabled IoT traffic management to the integration of AI for enhanced security, this book presents solutions to current challenges such as cyberattacks, smart grid security, and scalable network designs. Each chapter is thoughtfully structured to provide readers with a solid understanding of blockchain applications in diverse domains. Perfect for those seeking to understand blockchains potential to secure and manage data in an increasingly interconnected world. Key Features:- Comprehensive overview of data management and security in blockchain networks.- Practical insights into IoT, smart grids, and AI integration.- In-depth analysis of cybersecurity challenges and solutions.

Marvels of Artificial and Computational Intelligence in Life Sciences is a primer for scholars and students who are interested in the applications of artificial intelligence (AI and computational intelligence (CI) in life sciences and other industries. The book consists of 16 chapters (9 of which focus on AI and 7 which showcase the benefits of CI approaches to solve specific problems). Chapters are edited by subject experts who describe the roles and applications of AI and CI in different parts of our lives in a concise and lucid manner. The book covers the following key themes: AI Revolution in Healthcare and Drug Discovery:AIs Impact on Biology and Energy ManagementAI and CI in Physical Sciences and Predictive ModelingComputational Biology The editors have compiled a good blend of topics in applied science and engineering to give readers a clear understanding of the multidisciplinary nature of the two facets of computing. Each chapter includes references for advanced readers. Audience Researchers and industry professionals in the field of electronics and nanotechnology; students taking advanced courses in electronics and technology.

Machine learning is a subfield of artificial intelligence, broadly defined as a machines capability to imitate intelligent human behavior. Like humans, machines become capable of making intelligent decisions by learning from their past experiences. Machine learning is being employed in many applications, including fraud detection and prevention, self-driving cars, recommendation systems, facial recognition technology, and intelligent computing. This book helps beginners learn the art and science of machine learning. It presens real-world examples that leverage the popular Python machine learning ecosystem, The topics covered in this book include machine learning basics: supervised and unsupervised learning, linear regression and logistic regression, Support Vector Machines (SVMs). It also delves into special topics such as neural networks, theory of generalisation, and bias and fairness in machine learning. After reading this book, computer science and engineering students - at college and university levels - will receive a complete understanding of machine learning fundamentals and will be able to implement neural network solutions in information systems, and also extend them to their advantage.

Data Science and Interdisciplinary Research: Recent Trends and Applications is a compelling edited volume that offers a comprehensive exploration of the latest advancements in data science and interdisciplinary research. Through a collection of 10 insightful chapters, this book showcases diverse models of machine learning, communications, signal processing, and data analysis, illustrating their relevance in various fields. Key Themes: Advanced Rainfall Prediction: Presents a machine learning model designed to tackle the challenging task of predicting rainfall across multiple countries, showcasing its potential to enhance weather forecasting.Efficient Cloud Data Clustering: Explains a novel computational approach for clustering large-scale cloud data, addressing the scalability of cloud computing and data analysis.Secure In-Vehicle Communication: Explores the critical topic of secure communication in in-vehicle networks, emphasizing message authentication and data integrity.Smart Irrigation 4.0: Details a decision model designed for smart irrigation, integrating agricultural sensor data reliability analysis to optimize water usage in precision agriculture.Smart Electricity Monitoring: Highlights machine learning-based smart electricity monitoring and fault detection systems, contributing to the development of smart cities.Enhanced Learning Environments: Investigates the effectiveness of mobile learning in higher education, shedding light on the role of technology in shaping modern learning environments.Coastal Socio-Economy Study: Presents a case study on the socio-economic conditions of coastal fishing communities, offering insights into the livelihoods and challenges they face.Signal Noise Removal: Shows filtering techniques for removing noise from ECG signals, enhancing the accuracy of medical data analysis and diagnosis.Deep Learning in Biomedical Research: Explores deep learning techniques for biomedical research, particularly in the realm of gene identification using Next Generation Sequencing (NGS) data.Medical Diagnosis through Machine Learning: Concludes with a chapter on breast cancer detection using machine learning concepts, demonstrating the potential of AI-driven diagnostics.

Video data analytics is rapidly evolving and transforming the way we live in urban environments. Video Data Analytics for Smart City Applications: Methods and Trends, data science experts present a comprehensive review of the latest advances and trends in video analytics technologies and their extensive applications in smart city planning and engineering. The book covers a wide range of topics including object recognition, action recognition, violence detection, and tracking, exploring deep learning approaches and other techniques for video data analytics. It also discusses the key enabling technologies for smart cities and homes and the scope and application of smart agriculture in smart cities. Moreover, the book addresses the challenges and security issues in terahertz band for wireless communication and the empirical impact of AI and IoT on performance management. One contribution also provides a review of the progress in achieving the Jal Jeevan Mission Goals for institutional capacity building in the Indian State of Chhattisgarh. For researchers, computer scientists, data analytics professionals, smart city planners and engineers, this book provides detailed references for further reading and demonstrates how technologies are serving their use-cases in the smart city. The book highlights the advances and trends in video analytics technologies and extensively addresses key themes, making it an essential resource for anyone looking to gain a comprehensive understanding of video data analytics for smart city applications.

Pragmatic Internet of Everything (IOE) has emerged as a powerful paradigm for representing and solving complex problems. This reference demonstrates how to coordinate behaviour among a collection of semi-autonomous problem-solving agents: how they can coordinate their knowledge, goals and plans to act together, to solve joint problems, or to make individually or globally rational decisions in the face of uncertainty and multiple, conflicting perspectives. The book presents a collection of articles surveying several major recent developments in Pragmatic Internet of Everything (IOE). The book focuses on issues and challenges that arise in building IOE systems for smart cities in real-world settings. It also presents solutions to the issues faced by system architects. The synthesis of recent thinking, both theoretical and applied, on major IOE problems makes this essential reading for anyone involved in the design and planning of IOT systems for smart cities. Key Features - Summarizes available literature and practical ventures with references - Merges different perspectives on IoT technology thereby giving a 360-degree perspective to the reader - Gives some tips for implementation of practical ventures in this space - Includes an analysis of information gathered from citizens of smart cities