Concepts And Techniques Of Graph Neural Networks
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Concepts And Techniques Of Graph Neural Networks
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Author : Kumar, Vinod
language : en
Publisher: IGI Global
Release Date : 2023-05-22
Concepts And Techniques Of Graph Neural Networks written by Kumar, Vinod and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-22 with Computers categories.
Recent advancements in graph neural networks have expanded their capacities and expressive power. Furthermore, practical applications have begun to emerge in a variety of fields including recommendation systems, fake news detection, traffic prediction, molecular structure in chemistry, antibacterial discovery physics simulations, and more. As a result, a boom of research at the juncture of graph theory and deep learning has revolutionized many areas of research. However, while graph neural networks have drawn a lot of attention, they still face many challenges when it comes to applying them to other domains, from a conceptual understanding of methodologies to scalability and interpretability in a real system. Concepts and Techniques of Graph Neural Networks provides a stepwise discussion, an exhaustive literature review, detailed analysis and discussion, rigorous experimentation results, and application-oriented approaches that are demonstrated with respect to applications of graph neural networks. The book also develops the understanding of concepts and techniques of graph neural networks and establishes the familiarity of different real applications in various domains for graph neural networks. Covering key topics such as graph data, social networks, deep learning, and graph clustering, this premier reference source is ideal for industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.
Federated Learning And Privacy Preserving In Healthcare Ai
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Author : Lilhore, Umesh Kumar
language : en
Publisher: IGI Global
Release Date : 2024-05-02
Federated Learning And Privacy Preserving In Healthcare Ai written by Lilhore, Umesh Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-02 with Medical categories.
The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI.
Graph Neural Networks Essentials And Use Cases
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Author : Pethuru Raj Chelliah
language : en
Publisher: Springer Nature
Release Date : 2025-07-25
Graph Neural Networks Essentials And Use Cases written by Pethuru Raj Chelliah and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-25 with Computers categories.
This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals—and how they complement advances in generative, agentic, and physical AI—this book is an essential resource for understanding and leveraging their potential. The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation. Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data—complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains. GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged: Graph Convolutional Networks (GCNs): These networks learn from a node’s local neighborhood by aggregating information from adjacent nodes, updating the node’s representation in the process. Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance. Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data. GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.
Applied Assistive Technologies And Informatics For Students With Disabilities
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Author : Rajesh Kaluri
language : en
Publisher: Springer Nature
Release Date : 2024-05-28
Applied Assistive Technologies And Informatics For Students With Disabilities written by Rajesh Kaluri and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-28 with Computers categories.
The book “Applied Assistive Technologies and Informatics for Students with Disabilities” provides a comprehensive guide to assist students with learning disabilities in higher education via modern assistive technologies and informatics. This book will take us on a tour of the various modern assistive technologies, such as artificial intelligence (AI), blockchain, computer vision (CV), text analytics (TA), the metaverse, human-computer interaction (HCI), digital twins (DT), and federated learning (FL), and how they support higher education students with learning disabilities. This book is intended for students with learning disabilities, scientists and researchers, lecturers and teachers, academic and corporate libraries, practitioners, and professionals who are interested in providing inclusive education to students with learning disabilities through the application of modern assistive technologies and informatics. This book is ideal for readers who are new to the subject and knowledgeable about the principles of inclusive education. In addition, it is a fantastic resource for teachers and parents assisting students with learning disabilities. This book can be a powerful tool to educate more students about learning disabilities, which can help eradicate the bullying of these students.
Data Mining Concepts And Techniques
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Author : Mr. Harish Reddy Gantla
language : en
Publisher: Xoffencer International Book Publication House
Release Date : 2024-07-02
Data Mining Concepts And Techniques written by Mr. Harish Reddy Gantla and has been published by Xoffencer International Book Publication House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-02 with Computers categories.
Data mining is the process of discovering patterns, correlations, and anomalies within large datasets to predict outcomes. Utilizing a variety of techniques drawn from statistics, machine learning, and database systems, data mining aims to transform raw data into useful information. Key concepts include classification, clustering, regression, association rule learning, and anomaly detection. These techniques enable businesses and researchers to make data-driven decisions, uncover hidden trends, and gain a competitive edge. Classification is a predictive modeling technique where a model is trained to categorize data into predefined classes. It's widely used in applications such as spam detection, medical diagnosis, and credit scoring. Clustering, on the other hand, is an unsupervised learning technique that groups data points into clusters based on their similarities. This approach is useful in market segmentation, image processing, and social network analysis. Regression analysis predicts a continuous outcome variable based on one or more predictor variables. It's essential in fields such as finance for forecasting stock prices or in marketing for predicting sales trends. Association rule learning identifies interesting relationships between variables in large databases, often used in market basket analysis to find products frequently bought together. Anomaly detection involves identifying rare items, events, or observations that differ significantly from the majority of the data. This is crucial in fraud detection, network security, and fault detection in industrial systems. The success of data mining projects relies on the quality of the data, the appropriateness of the mining algorithms used, and the proper interpretation of the results. The process of data mining typically involves several steps: data cleaning, data integration, data selection, data transformation, pattern discovery, and knowledge presentation. Each step is critical in ensuring that the final insights are accurate and actionable. As data mining continues to evolve, it is increasingly becoming integral to big data analytics, driving advancements in various domains such as healthcare, finance, marketing, and beyond.
Applying Machine Learning Techniques To Bioinformatics Few Shot And Zero Shot Methods
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Author : Lilhore, Umesh Kumar
language : en
Publisher: IGI Global
Release Date : 2024-03-22
Applying Machine Learning Techniques To Bioinformatics Few Shot And Zero Shot Methods written by Lilhore, Umesh Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-22 with Computers categories.
Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists’ ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics. This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences.
Industrial Quantum Computing
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Author : Umesh Kumar Lilhore
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-12-30
Industrial Quantum Computing written by Umesh Kumar Lilhore and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-30 with Computers categories.
"Industrial quantum computing" (IQC) covers the applications of quantum computing innovations in general industry and industry 4.0. This book presents the application of quantum computations to the financial sector, medical services, the logistics industry, and the manufacturing industry.
Hands On Graph Neural Networks Using Python
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Author : Maxime Labonne
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-04-14
Hands On Graph Neural Networks Using Python written by Maxime Labonne and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-14 with Computers categories.
Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps Purchase of the print or Kindle book includes a free PDF eBook Key Features Implement -of-the-art graph neural architectures in Python Create your own graph datasets from tabular data Build powerful traffic forecasting, recommender systems, and anomaly detection applications Book DescriptionGraph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery. Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you’ll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps. By the end of this book, you’ll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.What you will learn Understand the fundamental concepts of graph neural networks Implement graph neural networks using Python and PyTorch Geometric Classify nodes, graphs, and edges using millions of samples Predict and generate realistic graph topologies Combine heterogeneous sources to improve performance Forecast future events using topological information Apply graph neural networks to solve real-world problems Who this book is for This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you’re new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.
Integrating Neurocomputing With Artificial Intelligence
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Author : Abhishek Kumar
language : en
Publisher: John Wiley & Sons
Release Date : 2025-06-09
Integrating Neurocomputing With Artificial Intelligence written by Abhishek Kumar and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-09 with Technology & Engineering categories.
Integrating Neurocomputing with Artificial Intelligence provides unparalleled insights into the cutting-edge convergence of neuroscience and computing, enriched with real-world case studies and expert analyses that harness the transformative potential of neurocomputing in various disciplines. Integrating Neurocomputing with Artificial Intelligence is a comprehensive volume that delves into the forefront of the neurocomputing landscape, offering a rich tapestry of insights and cutting-edge innovations. This volume unfolds as a carefully curated collection of research, showcasing multidimensional perspectives on the intersection of neuroscience and computing. Readers can expect a deep exploration of fundamental theories, methodologies, and breakthrough applications that span the spectrum of neurocomputing. Throughout the book, readers will find a wealth of case studies and real-world examples that exemplify how neurocomputing is being harnessed to address complex challenges across different disciplines. Experts and researchers in the field contribute their expertise, presenting in-depth analyses, empirical findings, and forward-looking projections. Integrating Neurocomputing with Artificial Intelligence serves as a gateway to this fascinating domain, offering a comprehensive exploration of neurocomputing’s foundations, contemporary developments, ethical considerations, and future trajectories. It embodies a collective endeavor to drive progress and unlock the potential of neurocomputing, setting the stage for a future where artificial intelligence is not merely artificial, but profoundly inspired by the elegance and efficiency of the human brain.
Graph Learning Techniques
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Author : Baoling Shan
language : en
Publisher: CRC Press
Release Date : 2025-02-26
Graph Learning Techniques written by Baoling Shan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-26 with Computers categories.
This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning. This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.