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Deep Learning for Natural Language Processing
Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natur...
Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond
This book is the fifth volume in the series of Collected Papers on Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond. This volume specifically delves into the concept of Various SuperHyperConcepts, building on the foundational advancements introduced in previous volumes. The series aims to explore the ongoing evolution of uncertain combinatorics through innovative methodologies such as graphization, hyperization, and uncertainization. These approaches integrate and extend core concepts from fuzzy, neutrosophic, soft, and rough set theories, providing robust frameworks to model and analyze the inherent comp...
New Trends in Intelligent Software Methodologies, Tools and Techniques
Knowledge-based systems, fully integrated with software, have become essential enablers for both science and commerce. But current software methodologies, tools and techniques are not robust or reliable enough for the demands of a constantly changing and evolving market, and many promising approaches have proved to be no more than case-oriented methods that are not fully automated. This book presents the proceedings of the 17th international conference on New Trends in Intelligent Software Methodology, Tools and Techniques (SoMeT18) held in Granada, Spain, 26-28 September 2018. The SoMeT conferences provide a forum for the exchange of ideas and experience, foster new directions in software d...
Gephi Cookbook
If you want to learn network analysis and visualization along with graph concepts from scratch, then this book is for you. This is ideal for those of you with little or no understanding of Gephi and this domain, but will also be beneficial for those interested in expanding their knowledge and experience.
Proceedings of International Conference on Communication and Computational Technologies
This book gathers selected papers presented at 3rd International Conference on Communication and Computational Technologies (ICCCT 2021), jointly organized in virtual format by Rajasthan Institute of Engineering and Technology, Jaipur and Rajasthan Technical University Kota in association with Soft Computing Research Society, during 27–28 February 2021. The volume is a collection of state-of-the-art research work in the cutting-edge technologies related to communication and intelligent systems. The topics covered are algorithms and applications of intelligent systems, informatics and applications, and communication and control systems.
Superhypergraph Neural Networks and Plithogenic Graph Neural Networks: Theoretical Foundations
Hypergraphs extend traditional graphs by allowing edges to connect multiple nodes, while superhypergraphs further generalize this concept to represent even more complex relationships. Neural networks, inspired by biological systems, are widely used for tasks such as pattern recognition, data classification, and prediction. Graph Neural Networks (GNNs), a well-established framework, have recently been extended to Hypergraph Neural Networks (HGNNs), with their properties and applications being actively studied. The Plithogenic Graph framework enhances graph representations by integrating multi-valued attributes, as well as membership and contradiction functions, enabling the detailed modeling ...
Trends in Biomathematics: Chaos and Control in Epidemics, Ecosystems, and Cells
This volume gathers together selected, peer-reviewed papers presented at the BIOMAT 2020 International Symposium, which was virtually held on November 1-6, 2020, with an organization staff based in Rio de Janeiro, Brazil. Topics covered in this volume include infection modeling, with an emphasis on different aspects of the COVID-19 and novel Coronavirus spread; a description of the effectiveness of quarantine measures via dynamic analysis of SLIR model; hemodynamic simulations in time-dependent domains; an optimal control model for the Ebola disease; and the co-existence of chaos and control in the context of biological models. Texts in agroforestry, economic development, and wastewater trea...
AI-Powered Productivity
AI-Powered Productivity is a guide to understanding and using AI and generative tools in professional settings. Chapter 1 introduces AI basics, its impact on various sectors, and an overview of generative AI tools. Chapter 2 delves into large language models exploring their integration with multimodal technologies and effects on productivity. Chapter 3 offers a practical guide to mastering LLM prompting and customization, with tutorials on crafting effective prompts and advanced techniques, including real-world examples of AI applications. Chapter 4 examines how AI can enhance individual productivity, focusing on professional and personal benefits, ethical use, and future trends. Chapter 5 a...
Data Engineering for Smart Systems
This book features original papers from the 3rd International Conference on Smart IoT Systems: Innovations and Computing (SSIC 2021), organized by Manipal University, Jaipur, India, during January 22–23, 2021. It discusses scientific works related to data engineering in the context of computational collective intelligence consisted of interaction between smart devices for smart environments and interactions. Thanks to the high-quality content and the broad range of topics covered, the book appeals to researchers pursuing advanced studies.
Big Data Meets LLMs: A New Era of Incident Monitoring
As data-driven enterprises tackle increasingly complex systems, the fusion of Big Data and Large Language Models (LLMs) is transforming incident monitoring and response. Big Data Meets LLMs: A New Era of Incident Monitoring explores how this groundbreaking synergy enhances real-time analysis, anomaly detection, and proactive system management. This book provides a deep dive into the core frameworks of Big Data, from Hadoop to Spark, while demonstrating the integration of LLMs to deliver unprecedented contextual insights. Learn how these models elevate monitoring systems, enabling organizations to move beyond reactive incident management to predictive and adaptive solutions. With cutting-edge...
