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A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesInterpret real-world data, including cardiovascular disease data and the COMPAS recidivism scoresBuild your interpretability toolkit with global, local, model-agnostic, and model-specific methodsAnalyze and extract insights from complex models from CNNs to BERT to time series modelsBook DescriptionInterpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.What you will learnProgress from basic to advanced techniques, such as causal inference and quantifying uncertaintyBuild your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformersUse monotonic and interaction constraints to make fairer and safer modelsUnderstand how to mitigate the influence of bias in datasetsLeverage sensitivity analysis factor prioritization and factor fixing for any modelDiscover how to make models more reliable with adversarial robustnessWho this book is forThis book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It's also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.Table of ContentsInterpretation, Interpretability and Explainability; and why does it all matter?Key Concepts of InterpretabilityInterpretation ChallengesGlobal Model-agnostic Interpretation MethodsLocal Model-agnostic Interpretation MethodsAnchors and Counterfactual ExplanationsVisualizing Convolutional Neural NetworksInterpreting NLP TransformersInterpretation Methods for Multivariate Forecasting and Sensitivity AnalysisFeature Selection and Engineering for InterpretabilityBias Mitigation and Causal Inference MethodsMonotonic Constraints and Model Tuning for InterpretabilityAdversarial RobustnessWhat's Next for Machine Learning Interpretability?

This book constitutes the proceedings of the 19th International Workshop on Security and Trust Management, STM 2023, co-located with the 28th European Symposium on Research in Computer Security, ESORICS 2023, held in The Hague, The Netherlands, during September 28th, 2023 The 5 full papers together with 4 short papers included in this volume were carefully reviewed and selected from 15 submissions. The workshop presents papers with topics such as security and privacy, trust models, security services, authentication, identity management, systems security, distributed systems security, privacy-preserving protocols.

This book provides a new model to explore discoverability and enhance the meaning of information. The authors have coined the term epidata, which includes items and circumstances that impact the expression of the data in a document, but are not part of the ordinary process of retrieval systems.  Epidata affords pathways and points to details that cast light on proximities that might otherwise go unknown.  In addition, epidata are clues to mis-and dis-information discernment.  There are many ways to find needed information; however, finding the most useable information is not an easy task.  The book explores the uses of proximity and the concept of epidata that increases the probability of  finding functional information.  The authors sketch a constellation of proximities, present examples of attempts to accomplish proximity, and provoke a discussion of the role of proximity in the field. In addition, the authors suggest that proximity is a thread between retrieval constructs based on known topics, predictable relations, and types of information seeking that lie outside constructs such as browsing, stumbling, encountering, detective work, art making, and translation.

In ancient games such as chess or go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work? With easy-to-understand explanations and clear examples in Java and Greenfoot, you can acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.Klling, King's College London) and the hamster model (D. Bohles, University of Oldenburg) are simple but also powerful didactic tools that were developed to convey basic programming concepts. The result is an accessible introduction into machine learning that  concentrates on reinforcement learning. Taking the reader through the steps of developing intelligent agents, from the very basics to advanced aspects, touching on a variety of machine learning algorithms along the way, one is allowed to play along, experiment, and add their own ideas and experiments.  

If you are aware of the potential of AI and ChatGPT and you want to learn how to make an income, but you don't know where to start, then this book is for you.In this book, I'll break down the complexities of AI into understandable, easy-to-apply concepts that anyone can grasp. Whether you are an entrepreneur, freelancer, student, or a 9-5 employee, this book can help you adapt and know how to monetize. We provide different business examples where you can use AI to scale quickly.Here are a few things we cover in the book:Various Plugins and Extensions: Discover the utility of various tools to enhance your use of ChatGPT.Unique Business Examples: Real-life examples of successful online businesses that can leverage AI to scale quicklyPersonalized Business Search: Learn techniques to identify and evaluate online business opportunities tailored to your skills, interests, and market trends.7 Life-Altering Hacks: The hacks are specific ways and tools to enhance your productivity, transform your life and improve your lifestyle. They have the power to revolutionize your life.Extensive Prompt Examples: We provide a diverse collection of prompt examples meticulously curated for various aspects of progress.Master Prompt Creation: on top of the examples, you get a practical guide for creating effective prompts, including handling lengthy and complicated ones for different aspects of life and business.Adapt AI to Life's Many Facets: Discover comprehensive strategies to integrate ChatGPT and AI into different aspects of life ( Business, learning productivity, and creativity)This book comes with a bonus gift, which is a downloadable PDF aimed at assisting you in exploring various career paths available to you, both within and outside of college. The PDF is loaded with valuable information about different industries, diverse jobs, and career trajectories that you could potentially use.This book will help you adapt and generate income streams with relevant skills you possess or could develop.You may be speculating whether this book will work for you or not. Let me tell you it will give you a foundation of what to do.If you're thinking..."Idk if it would help me or not": This book isn't just theoretical; We provide plenty of practical insights about AI and income. We provide you with business examples, all the ways to catch the AI trend, personalized business searches, and many other things that are practical. Take it, personalize it for you, and enhance it."I don't have a technical background": You don't need it. This book explains the concepts in a simple, easy-to-understand language, and AI is very simple to use."AI is a passing trend": NOT EVEN. AI is still in its infancy, and it's not going anywhere. On the contrary, it will become a more part of our lives as time passes. By 2050 AI will be even more prominent than it is now."The book's contents might become outdated quickly" The content in this book will still be relevant for years to come; we cover many aspects of how the world is going to look like in 2030 and beyond-expected changes, ways to make money that will still exist, and how to be prepared for the new economy.No matter your stage in life or level of AI knowledge, you stand to gain from this book. If you're ready to step into the world of AI and unlock a prosperous future. Make your move, and add this valuable resource to your cart today.

This book constitutes the refereed proceedings of the 16th International Conference on Similarity Search and Applications, SISAP 2023, held in A Coruña, Spain, during October 9¿11, 2023.The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 33 submissions. They were organized in topical sections as follows: similarity queries, similarity measures, indexing and retrieval, data management, feature extraction, intrinsic dimensionality, efficient algorithms, similarity in machine learning and data mining.

The two-volume set CCIS 1896 and 1897 constitutes the refereed post-conference proceedings of the 5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023, which took place in Haikou, China during August 8-10, 2023. The 45 revised full papers presented in these proceedings were carefully reviewed and selected from 93 submissions. The papers are organized in the following topical sections: Part I: Anomaly detection on blockchain; edge intelligence and metaverse services; blockchain system security; empirical study and surveys; federated learning for blockchain. Part II: AI for blockchain; blockchain applications; blockchain architecture and optimization; protocols and consensus.

Securing Next-Generation Connected Healthcare Systems: Artificial Intelligence Technologies focuses on the crucial aspects of IoT security in a connected environment, which will not only benefit from cutting-edge methodological approaches but also assist in the rapid scalability and improvement of these systems. This book shows how to utilize technologies like blockchain and its integration with IoT for communication, data security, and trust management. It introduces the security aspect of next generation technologies for healthcare, covering a wide range of security and computing methodologies.Researchers, data scientists, students, and professionals interested in the application of artificial intelligence in healthcare management, data security of connected healthcare systems and related fields, specifically on data intensive secured systems and computing environments, will finds this to be a welcomed resource.

This concise textbook introduces a systems approach to technology, describing tribological, mechatronic, cyber-physical systems, and the technologic concept of Industry 4.0 to students in a range of engineering domains. "e;Technology"e; in this book refers to the totality of human-made, benefit-oriented products, based on engineered combinations of material, energy and information. Dr. Czichos examines technology in this volume in the context of systems thinking with regard to the following main technology areasTechnical systems with "e;interacting surfaces in relative motion"e; especially in mechanical engineering, production, and transport; including the analysis of friction-induced energy losses and wear-induced materials dissipation. Technical systems that require a combination of mechanics, electronics, controls, and computer engineering for needs of industry and society. Technical systems with a combination of mechatronics and internet communication. Cyber-physical Systems for the digitalization of Industry in the development project Industry 4.0.Considers technology as combination of the physical world and the digital virtual world of information and communication.Describes the product cycle of technical systems and the corner stones of technology: material, energy and information.Presents a holistic view of technology and engineering.

This book constitutes the refereed post-conference proceedings of the Fifth IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2022, held in Amsterdam in October 2022.The 20 full papers presented were carefully reviewed and selected from 36 submissions. The papers are organized in the following topical sections: IoT for Smart Villages, Security and Safety, Smart Home, Development, Engineering, Machine Learning, and Applications.

In both the database and machine learning communities, data quality has become a serious issue which cannot be ignored. In this context, we refer to data with quality problems as ¿dirty data.¿ Clearly, for a given data mining or machine learning task, dirty data in both training and test datasets can affect the accuracy of results. Accordingly, this book analyzes the impacts of dirty data and explores effective methods for dirty data processing.Although existing data cleaning methods improve data quality dramatically, the cleaning costs are still high. If we knew how dirty data affected the accuracy of machine learning models, we could clean data selectively according to the accuracy requirements instead of cleaning all dirty data, which entails substantial costs. However, no book to date has studied the impacts of dirty data on machine learning models in terms of data quality. Filling precisely this gap, the book is intended for a broad audience ranging from researchers inthe database and machine learning communities to industry practitioners.Readers will find valuable takeaway suggestions on: model selection and data cleaning; incomplete data classification with view-based decision trees; density-based clustering for incomplete data; the feature selection method, which reduces the time costs and guarantees the accuracy of machine learning models; and cost-sensitive decision tree induction approaches under different scenarios. Further, the book opens many promising avenues for the further study of dirty data processing, such as data cleaning on demand, constructing a model to predict dirty-data impacts, and integrating data quality issues into other machine learning models. Readers will be introduced to state-of-the-art dirty data processing techniques, and the latest research advances, while also finding new inspirations in this field.

This book constitutes the refereed proceedings of the 7th International Joint Conference on Rules and Reasoning, RuleML+RR 2023, held in Oslo, Norway, during September 18¿20, 2023. The 13 full papers and 3 short papers included in these proceedings were carefully reviewed and selected from 46 submissions. They focus on all aspects of theoretical advances; novel technologies; innovative applications; knowledge representation; reasoning with rules; and research, development, applications of rule-based systems.

This book constitutes the proceedings of the 26th International Conference on Discovery Science, DS 2023, which took place in Porto, Portugal, in October 2023. The 37 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 133 submissions. They were organized in topical sections as follows: Machine learning methods and applications; natural language processing and social media analysis; interpretability and explainability in AI; data analysis and optimization; fairness, privacy and security in AI; control and spatio-temporal modeling; graph theory and network analysis; time series and forecasting; healthcare and biological data analysis; anomaly, outlier and novelty detection.

Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications focuses on recent, up-to-date technologies, combining other intelligent tools with swarm intelligence techniques to yield robust and failsafe solutions to real world problems. This book aims to provide audiences with a platform to learn and gain insights into the latest developments in hybrid swarm intelligence. It will be useful to researchers, engineers, developers, practitioners, and graduate students working in the major and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing. With the advent of data-intensive applications, the elimination of redundancy in disseminated information has become a serious challenge for researchers who are on the lookout for evolving metaheuristic algorithms which can explore and exploit the information feature space to derive the optimal settings for specific applications. Swarm intelligence algorithms have developed as one of the most widely used metaheuristic techniques for addressing this challenge in an effective way. Inspired by the behavior of a swarm of bees, these swarm intelligence techniques emulate the corresponding natural instincts to derive optimal solutions for data-intensive applications.

Maschinelles Lernen (ML) ist zu einem alltäglichen Element in unserem Leben und zu einem Standardwerkzeug für viele Bereiche der Wissenschaft und Technik geworden. Um ML optimal nutzen zu können, ist es wichtig, die zugrunde liegenden Prinzipien zu verstehen. In diesem Buch wird ML als die rechnerische Umsetzung des wissenschaftlichen Prinzips betrachtet. Dieses Prinzip besteht darin, ein Modell eines gegebenen datenerzeugenden Phänomens kontinuierlich anzupassen, indem eine Form des Verlustes, der durch seine Vorhersagen entsteht, minimiert wird.Das Buch schult den Leser darin, verschiedene ML-Anwendungen und -Methoden in drei Komponenten (Daten, Modell und Verlust) aufzuschlüsseln, und hilft ihm so, aus dem riesigen Angebot an vorgefertigten ML-Methoden auszuwählen.Der Drei-Komponenten-Ansatz des Buches erlaubt eine einheitliche und transparente Darstellung verschiedener ML-Techniken. Wichtige Methoden zu Regularisierung, zum Schutz der Privatsphäre und zur Erklärbarkeit von ML-Methoden sind Spezialfälle dieses Drei-Komponenten-Ansatz.

This book constitutes the refereed post-conference proceedings of the Fifth IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2022, held virtually, in March 2022.The 28 revised full papers presented were carefully reviewed and selected from 96 submissions. The papers cover topics such as computational intelligence for text analysis; computational intelligence for image and video analysis; blockchain and data science.

This book provides a principled data-driven framework that progressively constructs, enriches, and applies taxonomies without leveraging massive human annotated data. Traditionally, people construct domain-specific taxonomies by extensive manual curations, which is time-consuming and costly. In today's information era, people are inundated with the vast amounts of text data. Despite their usefulness, people haven't yet exploited the full power of taxonomies due to the heavy curation needed for creating and maintaining them. To bridge this gap, the authors discuss automated taxonomy discovery and exploration, with an emphasis on label-efficient machine learning methods and their real-world usages. Taxonomy organizes entities and concepts in a hierarchy way. It is ubiquitous in our daily life, ranging from product taxonomies used by online retailers, topic taxonomies deployed by news outlets and social media, as well as scientific taxonomies deployed by digital libraries across various domains. When properly analyzed, these taxonomies can play a vital role for science, engineering, business intelligence, policy design, e-commerce, and more. Intuitive examples are used throughout enabling readers to grasp concepts more easily.

Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems.Other areas of note include optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision-making mechanisms realized under uncertainty.

Smart Spaces covers the latest concepts and technologies surrounding smart spaces, providing technical personnel engaged in smart space related research and industries a more in-depth understanding of smart spaces. This book can be used as a reference for practicing this emerging discipline, but it will also be useful for researchers, scientists, developers, practitioners, and graduate students working in the fields of smart spaces and artificial intelligence. It combines the study of working or living spaces with computing, information equipment, and multimodal sensing devices, and with natural and convenient interactive interfaces to support how people can easily obtain services from computer systems. People's work and life in smart spaces use computer systems; it is a process of uninterrupted interaction between people and the computer system. In this process, the computer is no longer just an information processing tool that passively executes explicit human operation commands but a collaborator with people to complete tasks - a partner to human beings. International research on smart spaces is quite extensive, which shows the important role of smart spaces in ubiquitous computing research.

Sustainable development is based on the idea that societies should advance without compromising their future development requirements. This book explores how the application of data analytics and digital technologies can ensure that development changes are executed on the basis of factual data and information. It addresses how innovations that rely on digital technologies can support sustainable development across all sectors and all social, economic, and environmental aspects and help us achieve the Sustainable Development Goals (SDGs). The book also highlights techniques, processes, models, tools, and practices used to achieve sustainable development through data analysis.The various topics covered in this book are critically evaluated, not only theoretically, but also from an application perspective. It will be of interest to researchers and students, especially those in the fields of applied data analytics, business intelligence and knowledge management.

"e;This book is a comprehensive text for the design of safety critical, hard real-time embedded systems. It offers  a splendid example for the balanced, integrated treatment of systems and software engineering, helping readers tackle the hardest problems of advanced real-time system design, such as determinism, compositionality, timing and fault management. This book is an essential reading for advanced undergraduates and graduate students in a wide range of disciplines impacted by embedded computing and software. Its conceptual clarity, the style of explanations and the examples make the abstract concepts accessible for a wide audience."e;Janos Sztipanovits, DirectorE. Bronson Ingram Distinguished Professor of EngineeringInstitute for Software Integrated SystemsVanderbilt UniversityReal-Time Systems focuses on hard real-time systems, which are computing systems that must meet their temporal specification in all anticipated load and fault scenarios. The book stresses the system aspects of distributed real-time applications, treating the issues of real-time, distribution and fault-tolerance from an integral point of view. A unique cross-fertilization of ideas and concepts between the academic and industrial worlds has led to the inclusion of many insightful examples from industry to explain the fundamental scientific concepts in a real-world setting.  Compared to the Second Edition, new developments in communication standards for time-sensitive networks, such as TSN and Time-Triggered Ethernet are addressed. Furthermore, this edition includes a new chapter on real-time aspects in cloud and fog computing.The book is written as a standard textbook for a high-level undergraduate or graduate course on real-time embedded systems or cyber-physical systems.  Its practical approach to solving real-time problems, along with numerous summary exercises, makes it an excellent choice for researchers and practitioners alike.

This book constitutes proceedings of the 18th European Conference on Logics in Artificial Intelligence, JELIA 2023, held in Dresden, Germany, in September 2023.The 41 full papers and 11 short papers included in this volume were carefully reviewed and selected from 111 submissions. The accepted papers span a number of areas within Logics in AI, including: argumentation; belief revision; reasoning about actions, causality, and change; constraint satisfaction; description logics and ontological reasoning; non-classical logics; and logic programming (answer set programming).

This book provides awareness of different evolutionary methods used for automatic generation and optimization of test data in the field of software testing. While the book highlights on the foundations of software testing techniques, it also focuses on contemporary topics for research and development. This book covers the automated process of testing in different levels like unit level, integration level, performance level, evaluation of testing strategies, testing in security level, optimizing test cases using various algorithms, and controlling and monitoring the testing process etc. This book aids young researchers in the field of optimization of automated software testing, provides academics with knowledge on the emerging field of AI in software development, and supports universities, research centers, and industries in new projects using AI in software testing.Supports the advancement in the artificial intelligence used in software development;Advances knowledge on artificial intelligence based metaheuristic approach in software testing;Encourages innovation in traditional software testing field using recent artificial intelligence.* 

Contents have been created by stabilizing into entry a diverse amount of intelligence forming core, to which was/is able to process entire categories into conformity to structural powers. Therein, the control room (communications), being together in template form representative of (most) known/classic subject matter, binding: nursing as resources/reference, and sanctuary as references/resources.By converting contents into applicability to your subject of proficiency, you are able to use a guideline mapping, coordination, an overall strategic embodiment, enabling potentially the creation of an AI securities with display-room-control center.

This book introduces readers to advanced data science techniques for signal mining in connection with agriculture. It shows how to apply heuristic modeling to improve farm-level efficiency, and how to use sensors and data intelligence to provide closed-loop feedback, while also providing recommendation techniques that yield actionable insights.The book also proposes certain macroeconomic pricing models, which data-mine macroeconomic signals and the influence of global economic trends on small-farm sustainability to provide actionable insights to farmers, helping them avoid financial disasters due to recurrent economic crises.The book is intended to equip current and future software engineering teams and operations research experts with the skills and tools they need in order to fully utilize advanced data science, artificial intelligence, heuristics, and economic models to develop software capabilities that help to achieve sustained food security for future generations.

This book constitutes the refereed proceedings of the 8th International Conference on Information, Communication and Computing Technology, ICICCT 2023, held in New Delhi, India, during May 27, 2023.The 14 full papers included in this book were carefully reviewed and selected from 60 submissions. They were organized in topical sections as follows: global platform for researchers, scientists and practitioners from both academia and industry to present their research and development activities in all the aspects of Pattern Recognition and computational Intelligence techniques.

Ethics in Online AI-Based Systems: Risks and Opportunities in Current Technological Trends creates a space to explore the ethical relevance that new technologies under development may have. Stimulating reflection and considerations with respect to the design, deployment, and use of technology helps readers guide current and future technological advancements from an ethically informed position to ensure that such advancements contribute towards solving current global and social challenges that we, as a society, have today. This will not only be useful for researchers and professional engineers, but also for educators, policy makers, and ethicists. Recent technological advancements have deeply transformed society and the way people interact with each other. Instantaneous communication platforms have allowed connections with other people, forming global communities and creating unprecedented opportunities in many sectors, making access to online resources more ubiquitous by reducing limitations imposed by geographical distance and temporal constrains. These technological developments bear ethically relevant consequences with their deployment, and legislations often lag behind such advancements. Because the appearance and deployment of these technologies happen much faster than legislative procedures, the way these technologies affect social interactions have profound ethical effects before any legislative regulation can be built in order to prevent and mitigate those effects.