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Springer The Computer Animation Dictionary Including Related Terms Used in Computer Graphics Film and Video Production and Desktop Publishing
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£85.49
Springer LaTeX for Linux
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£44.99
Springer Current Issues in Computational Linguistics In Honour of Don Walker 9 Linguistica Computazionale
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£85.49
Springer Current Issues in Computational Linguistics In Honour of Don Walker 9 Linguistica Computazionale
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£85.49
Springer Applied Logic How What and Why Logical Approaches to Natural Language 247 Synthese Library
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£123.49
Springer Groupware and the World Wide Web
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Springer Natural Language Information Retrieval 7 Text Speech and Language Technology
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Springer Breadth and Depth of Semantic Lexicons 10 Text Speech and Language Technology
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£123.49
Springer Computing Meaning Volume 1 73 Studies in Linguistics and Philosophy
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£123.49
Springer Parallel Text Processing Alignment and Use of Translation Corpora 13 Text Speech and Language Technology
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£170.99
Springer Databases and Information Systems Fourth International Baltic Workshop Baltic DBIS 2000 Vilnius Lithuania May 15 2000 Selected Papers
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£123.49
Springer Digital Libraries and Multimedia
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Springer Automatic Learning Techniques in Power Systems
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Springer Cooperative Computeraided Authoring and Learning A Systems Approach
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£170.99
Amazon Digital Services LLC - Kdp The Cranky Mans Guide to LoRA QLoRA
£37.22
S.G. POTTER Media Publishing House The Novel Machine
£22.49
Boone Cutler Media Enterprise, LLC How to Fight Artificial Intelligence AI
£26.09
Evolution Personnelle Chat GPT
£18.99
Mercury Learning and Information AI Horizons
Book Synopsis
£38.67
De Gruyter AI Revealed
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£31.05
iUniverse Designing Ai Companions: Designing Ai Companions
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£34.95
Aesthetic Calculations I AI
£18.99
It Governance Publishing Ltd Digital Ethics in the Age of AI
£30.35
Packt Publishing Limited Python Artificial Intelligence Projects for Beginners: Get up and running with Artificial Intelligence using 8 smart and exciting AI applications
Book SynopsisBuild smart applications by implementing real-world artificial intelligence projectsKey Features Explore a variety of AI projects with Python Get well-versed with different types of neural networks and popular deep learning algorithms Leverage popular Python deep learning libraries for your AI projects Book DescriptionArtificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence.This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library.By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progressWhat you will learn Build a prediction model using decision trees and random forest Use neural networks, decision trees, and random forests for classification Detect YouTube comment spam with a bag-of-words and random forests Identify handwritten mathematical symbols with convolutional neural networks Revise the bird species identifier to use images Learn to detect positive and negative sentiment in user reviews Who this book is forPython Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with codeTable of ContentsTable of Contents Building Your Own Prediction Models Prediction with Random Forests Application for comment classification Neural Networks Deep Learning
£24.50
Packt Publishing Limited Mastering spaCy: An end-to-end practical guide to implementing NLP applications using the Python ecosystem
Book SynopsisBuild end-to-end industrial-strength NLP models using advanced morphological and syntactic features in spaCy to create real-world applications with easeKey Features Gain an overview of what spaCy offers for natural language processing Learn details of spaCy's features and how to use them effectively Work through practical recipes using spaCy Book DescriptionspaCy is an industrial-grade, efficient NLP Python library. It offers various pre-trained models and ready-to-use features. Mastering spaCy provides you with end-to-end coverage of spaCy's features and real-world applications.You'll begin by installing spaCy and downloading models, before progressing to spaCy's features and prototyping real-world NLP apps. Next, you'll get familiar with visualizing with spaCy's popular visualizer displaCy. The book also equips you with practical illustrations for pattern matching and helps you advance into the world of semantics with word vectors. Statistical information extraction methods are also explained in detail. Later, you'll cover an interactive business case study that shows you how to combine all spaCy features for creating a real-world NLP pipeline. You'll implement ML models such as sentiment analysis, intent recognition, and context resolution. The book further focuses on classification with popular frameworks such as TensorFlow's Keras API together with spaCy. You'll cover popular topics, including intent classification and sentiment analysis, and use them on popular datasets and interpret the classification results.By the end of this book, you'll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create your own NLP apps.What you will learn Install spaCy, get started easily, and write your first Python script Understand core linguistic operations of spaCy Discover how to combine rule-based components with spaCy statistical models Become well-versed with named entity and keyword extraction Build your own ML pipelines using spaCy Apply all the knowledge you've gained to design a chatbot using spaCy Who this book is forThis book is for data scientists and machine learners who want to excel in NLP as well as NLP developers who want to master spaCy and build applications with it. Language and speech professionals who want to get hands-on with Python and spaCy and software developers who want to quickly prototype applications with spaCy will also find this book helpful. Beginner-level knowledge of the Python programming language is required to get the most out of this book. A beginner-level understanding of linguistics such as parsing, POS tags, and semantic similarity will also be useful.Table of ContentsTable of Contents Getting Started with spaCy Core Operations with spaCy Linguistic Features Rule-Based Matching Working with Word Vectors and Semantic Similarity Putting Everything Together: Semantic Parsing with spaCy Customizing spaCy Models Text Classification with spaCy spaCy and Transformers Putting Everything Together: Designing Your Chatbot with spaCy
£37.99
Packt Publishing Limited Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models
Book SynopsisBuild cutting edge machine and deep learning systems for the lab, production, and mobile devices.Purchase of the print or Kindle book includes a free eBook in PDF format.Key Features Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples Implement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learning Learn cutting-edge machine and deep learning techniques Book DescriptionDeep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.What you will learn Learn how to use the popular GNNs with TensorFlow to carry out graph mining tasks Discover the world of transformers, from pretraining to fine-tuning to evaluating them Apply self-supervised learning to natural language processing, computer vision, and audio signal processing Combine probabilistic and deep learning models using TensorFlow Probability Train your models on the cloud and put TF to work in real environments Build machine learning and deep learning systems with TensorFlow 2.x and the Keras API Who this book is forThis hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems.Some machine learning knowledge would be useful. We don't assume TF knowledge.Table of ContentsTable of Contents Neural Networks Foundations with TF Regression and Classification Convolutional Neural Networks Word Embeddings Recurrent Neural Network Transformers Unsupervised Learning Autoencoders Generative Models Self-Supervised Learning Reinforcement Learning Probabilistic TensorFlow An Introduction to AutoML The Math Behind Deep Learning Tensor Processing Unit Other Useful Deep Learning Libraries Graph Neural Networks Machine Learning Best Practices TensorFlow 2 Ecosystem Advanced Convolutional Neural Networks
£37.99
Packt Publishing Limited Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4
Book SynopsisOpenAI’s GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book. Get a taste of the future of transformers, including computer vision tasks and code writing and assistance. Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Improve your productivity with OpenAI’s ChatGPT and GPT-4 from prompt engineering to creating and analyzing machine learning models Pretrain a BERT-based model from scratch using Hugging Face Fine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your data Book DescriptionTransformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs? Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses. You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model. If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides. The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details). You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using DALL-E 2, ChatGPT, and GPT-4. By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective.What you will learn Discover new techniques to investigate complex language problems Compare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformers Carry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3 Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-E Learn the mechanics of advanced prompt engineering for ChatGPT and GPT-4 Who this book is forIf you want to learn about and apply transformers to your natural language (and image) data, this book is for you. You'll need a good understanding of Python and deep learning and a basic understanding of NLP to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters. And don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community to help guide you on your transformers journey!Table of ContentsTable of Contents What are Transformers? Getting Started with the Architecture of the Transformer Model Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer The Rise of Suprahuman Transformers with GPT-3 Engines Applying Transformers to Legal and Financial Documents for AI Text Summarization Matching Tokenizers and Datasets Semantic Role Labeling with BERT-Based Transformers Let Your Data Do the Talking: Story, Questions, and Answers Detecting Customer Emotions to Make Predictions Analyzing Fake News with Transformers Interpreting Black Box Transformer Models From NLP to Task-Agnostic Transformer Models The Emergence of Transformer-Driven Copilots The Consolidation of Suprahuman Transformers with OpenAI’s ChatGPT and GPT-4' Appendix I — Terminology of Transformer Models Appendix II — Hardware Constraints for Transformer Models Appendix III — Generic Text Completion with GPT-2 Appendix IV — Custom Text Completion with GPT-2 Appendix V — Answers to the Questions
£73.93
Packt Publishing Limited HandsOn Machine Learning with C
£37.99
Packt Publishing Limited Modern Generative AI with ChatGPT and OpenAI Models: Leverage the capabilities of OpenAI's LLM for productivity and innovation with GPT3 and GPT4
Book SynopsisHarness the power of AI with innovative, real-world applications, and unprecedented productivity boosts, powered by the latest advancements in AI technology like ChatGPT and OpenAI Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore the theory behind generative AI models and the road to GPT3 and GPT4 Become familiar with ChatGPT’s applications to boost everyday productivity Learn to embed OpenAI models into applications using lightweight frameworks like LangChain Book DescriptionGenerative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You’ll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data. Next, you’ll explore use cases where ChatGPT can boost productivity and enhance creativity. You’ll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster. You’ll also discover enterprise-level scenarios that leverage OpenAI models’ APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you’ll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications. By the end of this book, you’ll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models’ APIs in your own projects.What you will learn Understand generative AI concepts from basic to intermediate level Focus on the GPT architecture for generative AI models Maximize ChatGPT’s value with an effective prompt design Explore applications and use cases of ChatGPT Use OpenAI models and features via API calls Build and deploy generative AI systems with Python Leverage Azure infrastructure for enterprise-level use cases Ensure responsible AI and ethics in generative AI systems Who this book is forThis book is for individuals interested in boosting their daily productivity; businesspersons looking to dive deeper into real-world applications to empower their organizations; data scientists and developers trying to identify ways to boost ML models and code; marketers and researchers seeking to leverage use cases in their domain – all by using Chat GPT and OpenAI Models. A basic understanding of Python is required; however, the book provides theoretical descriptions alongside sections with code so that the reader can learn the concrete use case application without running the scripts.Table of ContentsTable of Contents Introduction to Generative AI OpenAI and ChatGPT – Beyond the Market Hype Getting Familiar with ChatGPT Understanding Prompt Design Boosting Day-to-Day Productivity with ChatGPT Developing the Future with ChatGPT Mastering Marketing with ChatGPT Research Reinvented with ChatGPT OpenAI and ChatGPT for Enterprises – Introducing Azure OpenAI Trending Use Cases for Enterprises Epilogue and Final Thoughts
£39.99
Packt Publishing The AI Optimization Playbook
£33.99
Packt Publishing Limited Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
Book SynopsisGet to grips with the LangChain framework from theory to deployment and develop production-ready applications. Code examples regularly updated on GitHub to keep you abreast of the latest LangChain developments. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Learn how to leverage LLMs’ capabilities and work around their inherent weaknesses Delve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality Book DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learn Understand LLMs, their strengths and limitations Grasp generative AI fundamentals and industry trends Create LLM apps with LangChain like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks Who this book is forThe book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena. Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily.Table of ContentsTable of Contents What Is Generative AI? LangChain for LLM Apps Getting Started with LangChain Building Capable Assistants Building a Chatbot like ChatGPT Developing Software with Generative AI LLMs for Data Science Customizing LLMs and Their Output Generative AI in Production The Future of Generative Models
£66.02
Packt Publishing Limited LLM Design Patterns
£41.99
Institution of Engineering and Technology Generative AI for Sign Language Recognition and Translation
£109.25
ISTE Ltd and John Wiley & Sons Inc Collaborative Annotation for Reliable Natural Language Processing: Technical and Sociological Aspects
Book SynopsisThis book presents a unique opportunity for constructing a consistent image of collaborative manual annotation for Natural Language Processing (NLP). NLP has witnessed two major evolutions in the past 25 years: firstly, the extraordinary success of machine learning, which is now, for better or for worse, overwhelmingly dominant in the field, and secondly, the multiplication of evaluation campaigns or shared tasks. Both involve manually annotated corpora, for the training and evaluation of the systems. These corpora have progressively become the hidden pillars of our domain, providing food for our hungry machine learning algorithms and reference for evaluation. Annotation is now the place where linguistics hides in NLP. However, manual annotation has largely been ignored for some time, and it has taken a while even for annotation guidelines to be recognized as essential. Although some efforts have been made lately to address some of the issues presented by manual annotation, there has still been little research done on the subject. This book aims to provide some useful insights into the subject. Manual corpus annotation is now at the heart of NLP, and is still largely unexplored. There is a need for manual annotation engineering (in the sense of a precisely formalized process), and this book aims to provide a first step towards a holistic methodology, with a global view on annotation.Table of ContentsPreface ix List of Acronyms xi Introduction xiii Chapter 1. Annotating Collaboratively 1 1.1. The annotation process (re)visited 1 1.1.1. Building consensus 1 1.1.2. Existing methodologies 3 1.1.3. Preparatory work 7 1.1.4. Pre-campaign 13 1.1.5. Annotation 17 1.1.6. Finalization 21 1.2. Annotation complexity 24 1.2.1. Example overview 25 1.2.2. What to annotate? 28 1.2.3. How to annotate? 30 1.2.4. The weight of the context 36 1.2.5. Visualization 38 1.2.6. Elementary annotation tasks 40 1.3. Annotation tools 43 1.3.1. To be or not to be an annotation tool 43 1.3.2. Much more than prototypes 46 1.3.3. Addressing the new annotation challenges 49 1.3.4. The impossible dream tool 54 1.4. Evaluating the annotation quality 55 1.4.1. What is annotation quality? 55 1.4.2. Understanding the basics 56 1.4.3. Beyond kappas 63 1.4.4. Giving meaning to the metrics 67 1.5. Conclusion 75 Chapter 2. Crowdsourcing Annotation 77 2.1. What is crowdsourcing and why should we be interested in it? 77 2.1.1. A moving target 77 2.1.2. A massive success 80 2.2. Deconstructing the myths 81 2.2.1. Crowdsourcing is a recent phenomenon 81 2.2.2. Crowdsourcing involves a crowd (of non-experts) 83 2.2.3. “Crowdsourcing involves (a crowd of) non-experts” 87 2.3. Playing with a purpose 93 2.3.1. Using the players’ innate capabilities and world knowledge 94 2.3.2. Using the players’ school knowledge 96 2.3.3. Using the players’ learning capacities 97 2.4. Acknowledging crowdsourcing specifics 101 2.4.1. Motivating the participants 101 2.4.2. Producing quality data 107 2.5. Ethical issues 109 2.5.1. Game ethics 109 2.5.2. What’s wrong with Amazon Mechanical Turk? 111 2.5.3. A charter to rule them all 113 Conclusion 115 Appendix 117 Glossary 141 Bibliography 143 Index 163
£125.06
Qasas.Pub The Prompt Engineers Handbook
£17.99
£12.99
£19.99
Milne Open Textbooks The Future is Now
£36.90
SereneWisdom Works AI For Beginners
£30.12
Issachar Coll3ctive Redeeming Sundar
£17.95
Millsico More Human Than Human
£22.49
Millsico More Human Than Human
£15.19
Data Analytics Curriculum AI Rookies Labs Learn Text Analytics and NLP using R
£30.99
Data Analytics Curriculum AI Rookies Labs Learn Text Analytics and NLP using R
£36.09
Amazon Digital Services LLC - Kdp Lila and Andy learn about Artificial Intelligence
£15.84
Andriy Burkov The Hundred-Page Machine Learning Book
£34.95
Books Explorer Teaching Using AI
£39.97
Springer Nature Switzerland AG Computational Data and Social Networks: 10th International Conference, CSoNet 2021, Virtual Event, November 15–17, 2021, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 10th International Conference on Computational Data and Social Networks, CSoNet 2021, which was held online during November 15-17, 2021. The conference was initially planned to take place in Montreal, Quebec, Canada, but changed to an online event due to the COVID-19 pandemic. The 24 full and 8 short papers included in this book were carefully reviewed and selected from 57 submissions. They were organized in topical sections as follows: Combinatorial optimization and learning; deep learning and applications to complex and social systems; measurements of insight from data; complex networks analytics; special track on fact-checking, fake news and malware detection in online social networks; and special track on information spread in social and data networks. Table of ContentsCombinatorial Optimization and Learning.- Streaming algorithms for maximizing non-submodular functions on the integer lattice.- Causal Inference for Influence Propagation --- Identifiability of the In-dependent Cascade Model.- Streaming algorithms for Budgeted $k$-Submodular Maximization problem.- Approximation algorithms for the lower bounded correlation clustering problem.- Approximation Algorithm for Maximizing Nonnegative Weakly Mono-tonic Set Functions.- Differentially Private Submodular Maximization over Integer Lattice.- Maximizing the sum of a supermodular function and a monotone DR-submodular function subject to a knapsack constraint on the integer lattice.- Deep Learning and Applications to Complex and Social Systems.- A Framework for Accelerating Graph Convolution Networks on Massive Datasets.- AdvEdge: Optimizing Adversarial Perturbations against Interpretable Deep Learning.- Incorporating Transformer Models for Sentiment Analysis and News Classification in Khmer.- Deep Bangla Authorship Attribution using Transformer Models.- A Deep Learning Based Traffic Sign Detection for Intelligent Transportation Systems.- Detecting Hate Speech Contents Using Embedding Models.- MIC Model for Cervical Cancer Risk Factors Deep Association Analysis.- Power Grid Cascading Failure Prediction Based on Transforme.- Measurements of Insight from Data.- Security Breaches in the Healthcare Domain: A Spatiotemporal Analysis.- Social and Motivational Factors for the Spread of Physical Activities in a Health Social Network.- Understanding the Issues Surrounding COVID-19 Vaccine Roll Out Via User Tweets.- Complex Networks Analytics.- Minimize Travel Time with Traffic Flow Density Equilibrium on Road Network.- Network based Framework to Compare Vaccination Strategies.- Groups Influence with Minimum Cost in Social Network.- Recovering communities in temporal networks using persistent edges.- Community Detection using Semilocal Topological Features and Label Propagation Algorithm.- Twitter Analysis of Covid-19 Misinformation in Spain.- Comparing Community-aware Centrality Measures in Online Social Networks.- Two-Tier Cache-Aided Full-Duplex Content Delivery in Satellite-Terrestrial Networks.- Special Track: Fact-Checking, Fake News and Malware Detection in Online Social Networks.- Mean User-Text Agglomeration (MUTA): Practical User Representation and Visualization for Detection of Online Influence Operations.- The Role of Information Organization and Knowledge Structuring in Combatting Misinformation: A Literary Analysis.- Fake News Detection using LDA Topic Modelling and K-Nearest Neighbor Classifier.- Special Track: Information Spread in Social and Data Networks.- Summarization Algorithms for News: a Study of the Coronavirus Theme and its Impact on the News Extracting Algorithm.- Social cohesion during stay-at-home phase during the first wave of COVID-19 in Poland.- Influence and Activation Thresholds Target Set Selection within Community Structure.
£64.99