Expert systems / knowledge-based systems Books
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Advances in Data Mining: Applications and
Book SynopsisThis book constitutes the refereed proceedings of the 13th Industrial Conference on Data Mining, ICDM 2013, held in New York, NY, in July 2013. The 22 revised full papers presented were carefully reviewed and selected from 112 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, finance and telecommunication, in medicine and agriculture, and in process control, industry and society.Table of ContentsTheoretical aspects of data mining; applications of data mining in multimedia data.- Applications of data mining in marketing and in finance.- Applications of data mining in telecommunication.- Applications of data mining in medicine and agriculture.- Applications of data mining in process control, industry and society.
£999.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Uncertainty Modeling for Data Mining: A Label
Book SynopsisMachine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.
£80.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Chatbots in der Kundenkommunikation
Book SynopsisDas Internet bietet Unternehmen und Kunden ganz neue Kommunikationsmöglichkeiten. Via Corporate-Website der Unternehmen können sich Kunden 24 Stunden am Tag mit Informationen über das Unternehmen versorgen. Dies ermöglicht Unternehmen direkten Einfluss auf ihre Kunden auszuüben und gleichzeitig umfangreiche Informationen über diese zu sammeln. Auf der anderen Seite belastet der Anspruch der permanenten Verfügbarkeit zur Gewährleistung eines optimalen Kundenservices das Kundenservice-Potential der Unternehmen. Chatbots entlasten Call-Center und Customer-Support-Abteilungen, da sie bereits 80 % der gestellten Fragen direkt via Website beantworten können. Sie sind 24 Stunden am Tag verfügbar, Kosten werden reduziert. Ferner wird den Benutzern der Umgang mit der Website erleichtert. Die automatisch dokumentierten Unterhaltungen liefern zudem konkrete Einblicke in den tatsächlichen Informationsbedarf der Kunden.Table of Contents1 Einleitung.- 1.1 Zustandsbeschreibung.- 1.2 Problembeschreibung.- 1.3 Problemanalyse.- 1.3.1 Sprachproblematik.- 1.3.2 Divergenz der Bedürfnisse.- 1.3.3 Komplexität verteilter Systeme.- 1.4 Lösungsansatz.- 1.5 Abgrenzung des Themas.- 1.6 Vorgehen.- 2 Grundlagen.- 2.1 Begriffsabgrenzung.- 2.1.1 User Interface.- 2.1.2 Usability.- 2.1.3 Agenten.- 2.1.4 Chatbot.- 2.1.5 Künstliche Intelligenz.- 2.2 Historische Entwicklung.- 2.2.1 Aufweichung der Grenze Mensch-Maschine.- 2.2.2 Human-Computer Interaction (HCI).- 2.2.3 Entwicklung von Chatbots.- 2.2.4 Chatbots im Zentrum der Kritik.- 2.3 Warum Chatbots?.- 2.3.1 Interaktion in natürlicher Sprache.- 2.3.2 Aktive Gesprächsführung.- 2.3.3 Informationsstrukturierung.- 2.3.4 Realisierung von Einsparungspotenzialen.- 2.3.5 Gewinnung von Kundeninformationen.- 2.3.6 Gesteigerte Zugänglichkeit.- 2.4 Formen von Chatbots.- 2.4.1 Verständnisebenen von Chatbots.- 2.4.2 Beispiele.- 3 Gestaltungsgrundsätze.- 3.1 Implikationen adaptiver Funktionalität.- 3.2 Natürliche Sprache.- 3.3 Repräsentierung.- 3.3.1 Tool oder Team?.- 3.3.2 Höflichkeit.- 3.3.3 Komplimente.- 3.3.4 Form der Repräsentierung.- 3.3.5 Persönlichkeit.- 3.3.6 Spezialisten.- 3.3.7 Geschlechts-Stereotypen.- 3.3.8 Verwendung von Stimmen / Personifizierung.- 3.3.9 Bewegung.- 3.3.10 Synchronismus.- 3.4 Glaubhaftigkeit und Vertrauenswürdigkeit.- 3.5 Konzeptionelle Implementierungsrichtlinien.- 3.5.1 Klare Kommunikation.- 3.5.2 Anreize schaffen.- 3.5.3 Konsistenten Multichannel-Kontakt ermöglichen / Integration.- 3.5.4 Kontinuierliche Weiterentwicklung.- 4 Evaluation.- 4.1 Moralische Einwände.- 4.1.1 Evaluation der Kritik.- 4.1.2 Folgerungen.- 4.2 Verständnisproblematik: Kontext und Gesunder Menschenverstand.- 4.2.1 Evaluation der Kritik.- 4.2.2 Folgerungen.- 4.3 Personifizierte Interfaces.- 4.3.1 Evaluation der Kritik.- 4.3.2 Folgerungen.- 4.4 Ineffizienz indirekter Manipulation.- 4.4.1 Evaluation der Kritik.- 4.4.2 Folgerungen.- 4.5 Erkenntnisse aus dem Praxiseinsatz.- 4.5.1 Coca-Cola.- 4.5.2 Defense Logistics Information Service.- 4.5.3 Deutsche Direktbank.- 4.5.4 Deutscher Getränkefabrikant.- 4.5.5 Direkt Anlage Bank.- 4.5.6 Ford.- 4.5.7 Hannoversche Lebensversicherung.- 4.5.8 Interact Commerce.- 4.5.9 Net-tissimo.com.- 4.5.10 One2One.- 4.5.11 Pioneer Investment.- 4.5.12 Schwäbisch-Hall.- 4.5.13 Zusammenfassung.- 5 Anwendungsszenarien.- 6 Ausblick.- 7 Anhang.- 7.1 Übersicht: Anbieter, Anwender, Forschungsgebiete.- 7.2 Interviews.- 7.2.1 Coca-Cola.- 7.2.2 Defense Logistics Information Service.- 7.2.3 Deutsche Direktbank.- 7.2.4 Deutscher Getränkefabrikant.- 7.2.5 Direkt Anlage Bank.- 7.2.6 Hannoversche Lebensversicherung.- 7.2.7 Net-tissimo.com.- 7.2.8 Pioneer Investment.- 7.2.9 Schwäbisch-Hall.- 7.3 AIML-Spezifikation.- 7.3.1 Grundaufbau AIML.- 7.3.2 Reduktion.- 7.3.3 SRAI.- 7.3.4 Lernen (Think).- 7.3.5 Variablen.- 7.3.6 Eigenschaften (Predicates).- 7.3.7 Scripts.- Literatur.
£999.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Transactions on Large-Scale Data- and
Book SynopsisThe LNCS journal Transactions on Large-scale Data and Knowledge-centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 54th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains three fully revised and extended papers and two additional extended keynotes selected from the 38th conference on Data Management - Principles, Technologies and Applications, BDA 2022. The topics cover a wide range of timely data management research topics on temporal graph management, tensor-based data mining, time-series prediction, healthcare analytics over knowledge graphs, and explanation of database query answers.Table of ContentsClock-G: Temporal graph management system.- TSPredIT: Integrated tuning of data preprocessing and time series prediction models.- A guide to the Tucker tensor decomposition for data mining: exploratory analysis, clustering and classification.- Challenges for Healthcare Data Analytics over Knowledge Graphs.- From Database Repairs to Causality in Databases and Beyond.
£49.49
Springer Verlag, Singapore Machine Learning
Book SynopsisMachine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest. The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning.Trade Review“The book is full of cross-references, making the reader well aware of tight interconnections between many of the different approaches and methods. … the book is written in a very comprehensible and readable way. Its comprehensibility is further encreased through frequent marginal notes and through consistently illustrating all presented kinds of methods using the same toy example, and through historical notes to all addressed areas … the book explains also several quite advanced subjects … .” (Martin Holeňa, zbMATH 1479.68001, 2022)Table of Contents1 Introduction.- 2 Model Selection and Evaluation.- 3 Linear Models.- 4 Decision Trees.- 5 Neural Networks.- 6 Support Vector Machine.- 7 Bayes Classifiers.- 8 Ensemble Learning.- 9 Clustering.- 10 Dimensionality Reduction and Metric Learning.- 11 Feature Selection and Sparse Learning.- 12 Computational Learning Theory.- 13 Semi-Supervised Learning.- 14 Probabilistic Graphical Models.- 15 Rule Learning.- 16 Reinforcement Learning.
£49.49
Springer Verlag, Singapore Enabling Smart Urban Services with GPS Trajectory
Book SynopsisWith the proliferation of GPS devices in daily life, trajectory data that records where and when people move is now readily available on a large scale. As one of the most typical representatives, it has now become widely recognized that taxi trajectory data provides rich opportunities to enable promising smart urban services. Yet, a considerable gap still exists between the raw data available, and the extraction of actionable intelligence. This gap poses fundamental challenges on how we can achieve such intelligence. These challenges include inaccuracy issues, large data volumes to process, and sparse GPS data, to name but a few. Moreover, the movements of taxis and the leaving trajectory data are the result of a complex interplay between several parties, including drivers, passengers, travellers, urban planners, etc. In this book, we present our latest findings on mining taxi GPS trajectory data to enable a number of smart urban services, and to bring us one step closer to the vision of smart mobility. Firstly, we focus on some fundamental issues in trajectory data mining and analytics, including data map-matching, data compression, and data protection. Secondly, driven by the real needs and the most common concerns of each party involved, we formulate each problem mathematically and propose novel data mining or machine learning methods to solve it. Extensive evaluations with real-world datasets are also provided, to demonstrate the effectiveness and efficiency of using trajectory data. Unlike other books, which deal with people and goods transportation separately, this book also extends smart urban services to goods transportation by introducing the idea of crowdshipping, i.e., recruiting taxis to make package deliveries on the basis of real-time information. Since people and goods are two essential components of smart cities, we feel this extension is bot logical and essential. Lastly, we discuss the most important scientific problems and open issues in mining GPS trajectory data.Table of Contents1. Trajectory data map-matching 1.1 Introduction 1.2 Definitions and problem formulation 1.3 SD-Matching algorithm 1.4 Evaluations 1.5 Conclusions and discussions 2. Trajectory data compression 2.1 Introduction 2.2 Basic concepts and system overview 2.3 HCC algorithm 2.4 System implementation 2.5 Evaluations 2.6 Conclusions 3. Trajectory data protection 3.1 Introduction 3.2 Preliminary 3.3 Trajectory protection mechanism 3.4 Performance evaluations 3.5 Conclusions Part II: Enabling Smart Urban Services: Travellers 4. TripPlanner: Personalized trip planning leveraging heterogeneous trajectory data 4.1 Introduction 4.2 TripPlanner System 4.3 Dynamic network modelling 4.4 The two-phase approach 4.5 System evaluations 4.6 Conclusions and future work 5. ScenicPlanner: Recommending the most beautiful driving routes 5.1 Introduction 5.2 Preliminary 5.3 The two-phase approach 5.4 Experimental evaluations 5.5 Conclusion and future work Part III: Enabling Smart Urban Services: Drivers 6. GreenPlanner: Planning fuel-efficient driving routes 6.1 Introduction 6.2 Basic concepts and problem formulation 6.3 Personal fuel consumption model building 6.4 Fuel-efficient driving route planning 6.5 Evaluations 6.6 Conclusions and future work 7. Hunting or waiting: Earning more by understanding taxi service strategies 7.1 Introduction 7.2 Empirical study 7.3 Taxi strategy formulation 7.4 Understanding taxi service strategies 7.5 Conclusions Part IV: Enabling Smart Urban Services: Passengers 8. iBOAT: Real-time detection of anomalous taxi trajectories from GPS traces 8.1 Introduction 8.2 Preliminaries and problem definition 8.3 Isolation-based online anomalous trajectory detection 8.4 Empirical evaluations 8.5 Fraud behaviour analysis 8.6 Conclusions and future work 9. Real-Time imputing trip purpose leveraging heterogeneous trajectory data 9.1 Introduction 9.2 Basic concepts and problem statement 9.3 Imputing trip purposes 9.4 Enabling real-time response 9.5 Evaluations 9.6 Conclusions and future work Part V: Enabling Smart Urban Services: Urban Planners 10. GPS environment friendliness estimation with trajectory data 10.1 Introduction 10.2 Basic concepts 10.3 Methodology 10.4 Experiments 10.5 Limitations and future work 10.6 Conclusions 11. B-Planner: Planning night bus routes using taxi trajectory data 11.1 Introduction 11.2 Candidate bus stop identification 11.3 Bus route selection 11.4 Experimental evaluations 11.5 Conclusions and future work 12. VizTripPurpose: Understanding city-wide passengers’ travel behaviours 12.1 Introduction 12.2 System overview 12.3 Trip2Vec model 12.4 User interfaces 12.5 Case studies 12.6 Conclusions and future work Part VI: Enabling Smart Urban Services: Beyond People Transportation 13. CrowdDeliver: Arriving as soon as possible 13.1 Introduction 13.2 Basic concepts, assumptions and problem statement 13.3 Overview of CrowdDeliver 13.4 Two-phase approach 13.5 Evaluations 13.6 Conclusions and future work 14. CrowdExpress: Arriving by the user-specified deadline 14.1 Introduction 14.2 Preliminary, problem statement and system overview 14.3 Offline package transport network building 14.4 Online taxi scheduling and package routing 14.5 Experimental evaluations 14.6 Conclusions and future work Part VII: Open Issues and Conclusions 15. Open Issues 16. Conclusions
£999.99
Springer Verlag, Singapore Data Science: 7th International Conference of
Book SynopsisThis two volume set (CCIS 1451 and 1452) constitutes the refereed proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021 held in Taiyuan, China, in September 2021.The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis; social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo.Table of ContentsSocial Media and Recommendation Systems.- Data Security and Privacy.- Applications of Data Science.- Education research, methods and materials for data science and engineering.- Research demo.
£999.99
Springer Verlag, Singapore Data Science: 7th International Conference of
Book SynopsisThis two volume set (CCIS 1451 and 1452) constitutes the refereed proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021 held in Taiyuan, China, in September 2021.The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis; social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo.Table of ContentsBig Data Management and Applications.- Social Media and Recommendation Systems.- Infrastructure for Data Science.- Basic Theory and Techniques for Data Science.- Machine Learning for Data Science.- Multimedia Data Management and Analysis.
£999.99
Springer Verlag, Singapore Network Behavior Analysis: Measurement, Models,
Book SynopsisThis book provides a comprehensive overview of network behavior analysis that mines Internet traffic data in order to extract, model, and make sense of behavioral patterns in Internet “objects” such as end hosts, smartphones, Internet of things, and applications. The objective of this book is to fill the book publication gap in network behavior analysis, which has recently become an increasingly important component of comprehensive network security solutions for data center networks, backbone networks, enterprise networks, and edge networks.The book presents fundamental principles and best practices for measuring, extracting, modeling and analyzing network behavior for end hosts and applications on the basis of Internet traffic data. In addition, it explains the concept and key elements (e.g., what, who, where, when, and why) of communication patterns and network behavior of end hosts and network applications, drawing on data mining, machine learning, information theory, probabilistic graphical and structural modeling to do so. The book also discusses the benefits of network behavior analysis for applications in cybersecurity monitoring, Internet traffic profiling, anomaly traffic detection, and emerging application detections.The book will be of particular interest to researchers and practitioners in the fields of Internet measurement, traffic analysis, and cybersecurity, since it provides a spectrum of innovative techniques for summarizing behavior models, structural models, and graphic models of Internet traffic, and explains how to leverage the results for a broad range of real-world applications in network management, security operations, and cyber-intelligent analysis. After finishing this book, readers will 1) have learned the principles and practices of measuring, modeling, and analyzing network behavior on the basis of massive Internet traffic data; 2) be able to make sense of network behavior for a spectrum of applications ranging from cybersecurity and network monitoring to emerging application detection; and 3) understand how to explore network behavior analysis to complement traditional perimeter-based firewall and intrusion detection systems in order to detect unusual traffic patterns or zero-day security threats using data mining and machine learning techniques. To ideally benefit from this book, readers should have a basic grasp of TCP/IP protocols, data packets, network flows, and Internet applications.Table of ContentsChapter 1: Introduction.- Chapter 2: Background of Network Behavior Modeling and Analysis.- Chapter 3: Behavior Modeling of Network Traffic.- Chapter 4: Structural Modeling of Network Traffic.- Chapter 5: Graphic Modeling of Network Traffic.- Chapter 6: Real-Time Network Behavior Analysis.- Chapter 7: Applications.- Chapter 8: Research Frontiers of Network Behavior Analysis.
£113.99
Springer Verlag, Singapore Smart Data Intelligence: Proceedings of ICSMDI
Book SynopsisThis book presents high-quality research papers presented at 2nd International Conference on Smart Data Intelligence (ICSMDI 2022) organized by Kongunadu College of Engineering and Technology at Trichy, Tamil Nadu, India, during April 2022. This book brings out the new advances and research results in the fields of algorithmic design, data analysis, and implementation on various real-time applications. It discusses many emerging related fields like big data, data science, artificial intelligence, machine learning, and deep learning which have deployed a paradigm shift in various data-driven approaches that tends to evolve new data-driven research opportunities in various influential domains like social networks, healthcare, information, and communication applications.Table of ContentsDetection and Analysis of Sentiments on Twitter Using Machine Learning Algorithm.- Malware Attack Detection on IoT Devices Using Machine Learning.- BeSafe: IoT Based Safety Band.- Stock Market Analysis and forecasting with Statistical and Deep Learning Methods.- The paradigm shift in Higher Education from traditional learning to digitalisation.- Designing a socially intelligent system by cognitive modeling of human-environment interaction.- Web-Based recycle Waste Management for Ecommerce.
£999.99
Springer Verlag, Singapore Proceedings of International Conference on Data
Book SynopsisThis book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.Table of ContentsCancer Prognosis by Using Machine Learning and Data Science: A systematic review.- Understanding Agriculture Scenario of Punjab: A Qualitative Research of Crop parameter in relation to fertilizer usage.- Face Detection Based Border Security System Using Haar-Cascade and LBPH Algorithm.- Proposed Experimental Design of a Portable COVID-19 Screening Device Using Cough Audio Samples.- Big Data Framework for Analytics Business Intelligence.- Technological Impacts of AI on Hospitality and Tourism Industry.- Improvement of Real-Time Kinematic Positioning Using Kalman Filter-Based Singular Spectrum Analysis during Geomagnetic Storm for Thailand sector.
£999.99
Springer Verlag, Singapore Low Power Architectures for IoT Applications
Book SynopsisThis book provides comprehensive coverage of different aspects of low-power circuit synthesis for IoT applications at various levels of the design hierarchy, starting from the layout level to the system level. For a seamless understanding of the subject, the basics of MOS circuits have been introduced at the transistor, gate and circuit level, followed by various low-power design methodologies, such as supply voltage scaling, switched capacitance minimization techniques, and leakage power minimization approaches. The contents of this book are useful to students, researchers, as well as practicing engineers. Low-power architectures refer to the latest development in computer microchips which are created by integrating hundreds of thousands of transistors on one chip for different IoT applications. Emerging research in this area has the potential to uncover further applications for IoT in addition to system advancements.Table of ContentsEnergy Efficient and Temperature Aware Scheduling Strategies on Mixed Criticality Tasks for IoT based Systems.- Dynamic Power Management using Dynamic Voltage and Frequency Scaling and Leakage Current Optimization Techniques.- Performance-Based FPGA Implementation of Different Adders.- Computer Mediated Communication in Second Language Education for Different Iot Application.- Efficient FPGS Based Implementation of Image Segmentation Algorithms for IoT Applications.- Energy Harvesting, Challenges and Power Management for IoT.- Application of IoT in agriculture and challenges in implementation & development of soil moisture sensor node.
£123.49
Springer Verlag, Singapore Data Science: 9th International Conference of
Book SynopsisThis two-volume set (CCIS 1879 and 1880) constitutes the refereed proceedings of the 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023 held in Harbin, China, during September 22–24, 2023. The 52 full papers and 14 short papers presented in these two volumes were carefully reviewed and selected from 244 submissions. The papers are organized in the following topical sections:Part I: Applications of Data Science, Big Data Management and Applications, Big Data Mining and Knowledge Management, Data Visualization, Data-driven Security, Infrastructure for Data Science, Machine Learning for Data Science and Multimedia Data Management and Analysis.Part II: Data-driven Healthcare, Data-driven Smart City/Planet, Social Media and Recommendation Systems and Education using big data, intelligent computing or data mining, etc.Table of ContentsApplications of Data Science.- Construction of Software Design and Programming Practice Course in Information and Communication Engineering.- A Self-Attention-Based Stock Prediction Method Using Long Short-Term Memory Network Architecture.- CAD-based Research on the Design of a Standard Unit Cabinet for Custom Furniture of the Cabinet Type.- An Improved War Strategy Optimization Algorithm for Big Data Analytics.- Research on Path Planning of Mobile Robots Based on Dyna-RQ.- Small Target Helmet Wearing Detection Algorithm based on Improved YOLO V5.- Research on Dance Evaluation Technology based on Human Posture Recognition.- Multiple-channel Weight-based CNN Fault Diagnosis Method.- Big Data Management and Applications.- Design and Implementation of Key-Value Database for Ship Virtual Test Platform Based on Distributed System.- Big Data Mining and Knowledge Management.- Research on Multi-modal Time Series Data Prediction Method Based on Dualstage Attention Mechanism.- Prediction of Time Series Data with Low Latitude Features.- Lightweight and Efficient Attention-based Superresolution Generative Adversarial Networks.- The Multisource Time Series Data Granularity Conversion Method.- Outlier Detection Model Based on Autoencoder and Data Augmentation for High-Dimensional Sparse Data.- Dimension Reduction Based on Sampling.- Complex Time Series Analysis Based on Conditional Random Fields.- Feature Extraction of Time Series Data Based on CNNCBAM.- Optimization of a Network Topology Generation Algorithm based on Spatial Information Network.- Data Visualization.- MBTIviz: A Visualization System for Research on Psycho-demographics and Personality.- Data-driven Security.- Distributed Implementation of SM4 Block Cipher Algorithm based on SPDZ Secure Multi-party Computation Protocol.- DP-ASSGD: Differential Privacy Protection Based on Stochastic Gradient Descent Optimization.- Study on Tourism Workers ’ Intercultural Communication Competence.- A Novel Federated Learning with Bidirectional Adaptive Differential Privacy.- Chaos-Based Construction of LWEs in Lattice-Based Cryptosystems.- Security Compressed Sensing Image Encryption Algorithm Based on Elliptic Curve.- Infrastructure for Data Science.- Two-dimensional Code Transmission System Based on Side Channel Feedback.- An Updatable and Revocable Decentralized Identity Management Scheme based on Blockchain.- Cloud-Edge Intelligent Collaborative Computing Model based on Transfer Learning in IoT.- Design and Validation of a Hardware-in-the-loop based Automated Driving Simulation Test Platform.- Machine Learning for Data Science.- Improving Transferability Reversible Adversarial Examples based on Flipping Transformation.- Rolling Iterative Prediction for Correlated Multivariate Time Series.- Multimedia Data Management and Analysis.- Video Popularity Prediction Based on Knowledge Graph and LSTM Network.- Design and Implementation of Speech Generation and Demonstration Research Based on Deep Learning.- Testing and Improvement of OCR Recognition Technology in Export-OrientedChinese Dictionary APP.
£71.24
Amazon Digital Services LLC - Kdp Artificial Intelligence and Ethics
Book Synopsis
£12.58
Amazon Digital Services LLC - Kdp Large Language Models in Trading
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£12.68
Amazon Digital Services LLC - Kdp Claude 3 3.5 Crash Course
Book Synopsis
£11.17
Amazon Digital Services LLC - Kdp Chronos
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£9.97
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Unleashing The Power of ChatGPT: A Real World
Book SynopsisExplore the role ChatGPT can play in business, including operations, marketing, sales, and delivery. This concise book illustrates how ChatGPT is changing the way individuals interact with machines and how you can take advantage of its capabilities for business. The book starts with an overview of ChatGPT and its impact in the realm of of conversational AI. You will then dive into the technical aspects of ChatGPT and gain an understanding of how machine learning algorithms and natural language processing work in the background. Various business applications of ChatGPT are then discussed, followed by how it can get integrated into your business operations. To wrap things up, you will gain insight into the data and privacy elements that need to be considered while using ChatGPT, and how to maintain its integrity. After completing this book, you will understand the ChatGPT framework and how to integrate it into your own ventures. What You Will Learn Understand the various technologies and techniques utilized in ChatGPT Gain insight into the future of human-machine interaction Analyze the advantages and disadvantages of ChatGPT for your industry Explore the ethical implications of using AI Who This Book Is For Tech enthusiasts who are passionate about artificial intelligence (AI), machine learning, and human-machine interaction.Table of ContentsChapter 1: Introduction to ChatGPT.- Chapter 2: Understand ChatGPT’s Underlying Technology. - Chapter 3: Real World Applications for ChatGPT.- Chapter 4: Enhancing Business Communication with ChatGPT.- Chapter 5: Implementing AI Conversation in Business.- Chapter 6: Security and Ethical Considerations When Using ChatGPT.
£999.99
Independently Published Why AI Is Smartest... Is Dangerous... Is Divine
Book Synopsis
£14.96
Amazon Digital Services LLC - Kdp The Only ChatGPT Prompts Book Youll Ever Need
Book Synopsis
£999.99
Flexxable Ltd The Instant AI Agency
Book Synopsis
£35.76
John Wiley & Sons Inc Mathematical Models for Speech Technology
Book SynopsisPresents the motivations for, intuitions behind, and basic mathematical models of natural spoken language communication. This book offers an overview of various aspects of the problem from the physics of speech production through the hierarchy of linguistic structure and ending with some observations on language and mind.Trade Review"...a succinct presentation of the most important mathematical technology of speech technology and the author's ideas for overcoming the limitations of these techniques…" (Mathematical Reviews, 2005j)Table of ContentsAuthor's preface. 1 Introduction 2 Preliminaries 2.1 The physics of speech production 2.2 The source-filter model 2.3 Information-bearing features of the speech signal 2.4 Time-frequency representations 2.5 Classifications of acoustic patterns in speech 2.6 Temporal invariance and stationarity 2.7 Taxonomy of linguistic structure 3 Mathematical models of linguistic structure 3.1 Probabilistic functions of a discrete Markov process 3.2 Formal grammars and abstract automata 4 Syntactic analysis 4.1 Deterministic parsing algorithms 4.2 Probabilistic parsing algorithms 4.3 Parsing natural language 5 Grammatical inference 5.1 Exact inference and Gold's theorem 5.2 Baum's algorithm for regular grammars 5.3 Event counting in parse trees 5.4 Baker's algorithm for context-free grammars 6 Information-theoretic analysis of speech communication 6.1 The Miller et al. experiments 6.2 Entropy of an information source 6.3 Recognition error rates and entropy 7 Automatic speech recognition and constructive theories of language 7.1 Integrated architectures 7.2 Modular architectures 7.3 Parameter estimation from fluent speech 7.4 System performance 7.5 Other speech technologies 8 Automatic speech understanding and semantics 8.1 Transcription and comprehension 8.2 Limited domain semantics 8.3 The semantics of natural language 8.4 System architectures 8.5 Human and machine performance 9 Theories of mind and language 9.1 The challenge of automatic natural language understanding 9.2 Metaphors for mind 9.3 The artificial intelligence program 10 A speculation on the prospects for a science of the mind 10.1 The parable of the thermos bottle: measurements and symbols 10.2 The four questions of science 10.3 A constructive theory of the mind 10.4 The problem of consciousness 10.5 The role of sensorimotor function, associative memory and reinforcement learning in automatic acquisition of spoken language by an autonomous robot 10.6 Final thoughts: predicting the course of discovery
£110.25
Hanser Publications The Handbook of Data Science and AI
Book Synopsis
£76.49
Murphy & Moore Publishing Intelligent Systems Handbook
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£97.68
ISTE Ltd and John Wiley & Sons Inc Markov Decision Processes in Artificial
Book SynopsisMarkov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in artificial intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, reinforcement learning, partially observable MDPs, Markov games and the use of non-classical criteria). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.Trade Review"As an overall conclusion, this book is an extensive presentation of MDPs and their applications in modeling uncertain decision problems and in reinforcement learning." (Zentralblatt MATH, 2011) "The range of subjects covered is fascinating, however, from game-theoretical applications to reinforcement learning, conservation of biodiversity and operations planning. Oriented towards advanced students and researchers in the fields of both artificial intelligence and the study of algorithms as well as discrete mathematics." (Book News, September 2010)Table of ContentsPreface xvii List of Authors xix PART 1. MDPS: MODELS AND METHODS 1 Chapter 1. Markov Decision Processes 3 Frédérick GARCIA and Emmanuel RACHELSON 1.1. Introduction 3 1.2. Markov decision problems 4 1.3. Value functions 9 1.4. Markov policies 12 1.5. Characterization of optimal policies 14 1.6. Optimization algorithms for MDPs 28 1.7. Conclusion and outlook 37 1.8. Bibliography 37 Chapter 2. Reinforcement Learning 39 Olivier SIGAUD and Frédérick GARCIA 2.1. Introduction 39 2.2. Reinforcement learning: a global view 40 2.3. Monte Carlo methods 45 2.4. From Monte Carlo to temporal difference methods 45 2.5. Temporal difference methods 46 2.6. Model-based methods: learning a model 59 2.7. Conclusion 63 2.8. Bibliography 63 Chapter 3. Approximate Dynamic Programming 67 Rémi MUNOS 3.1. Introduction 68 3.2. Approximate value iteration (AVI) 70 3.3. Approximate policy iteration (API) 77 3.4. Direct minimization of the Bellman residual 87 3.5. Towards an analysis of dynamic programming in Lp-norm 88 3.6. Conclusions 93 3.7. Bibliography 93 Chapter 4. Factored Markov Decision Processes 99 Thomas DEGRIS and Olivier SIGAUD 4.1. Introduction 99 4.2. Modeling a problem with an FMDP 100 4.3. Planning with FMDPs 108 4.4. Perspectives and conclusion 122 4.5. Bibliography 123 Chapter 5. Policy-Gradient Algorithms 127 Olivier BUFFET 5.1. Reminder about the notion of gradient 128 5.2. Optimizing a parameterized policy with a gradient algorithm 130 5.3. Actor-critic methods 143 5.4. Complements 147 5.5. Conclusion 150 5.6. Bibliography 150 Chapter 6. Online Resolution Techniques 153 Laurent PÉRET and Frédérick GARCIA 6.1. Introduction 153 6.2. Online algorithms for solving an MDP 155 6.3. Controlling the search 167 6.4. Conclusion 180 6.5. Bibliography 180 PART 2. BEYOND MDPS 185 Chapter 7. Partially Observable Markov Decision Processes 187 Alain DUTECH and Bruno SCHERRER 7.1. Formal definitions for POMDPs 188 7.2. Non-Markovian problems: incomplete information 196 7.3. Computation of an exact policy on information states 202 7.4. Exact value iteration algorithms 207 7.5. Policy iteration algorithms 222 7.6. Conclusion and perspectives 223 7.7. Bibliography 225 Chapter 8. Stochastic Games 229 Andriy BURKOV, Laëtitia MATIGNON and Brahim CHAIB-DRAA 8.1. Introduction 229 8.2. Background on game theory 230 8.3. Stochastic games 245 8.4. Conclusion and outlook 269 8.5. Bibliography 270 Chapter 9. DEC-MDP/POMDP 277 Aurélie BEYNIER, François CHARPILLET, Daniel SZER and Abdel-Illah MOUADDIB 9.1. Introduction 277 9.2. Preliminaries 278 9.3. Multi agent Markov decision processes 279 9.4. Decentralized control and local observability 280 9.5. Sub-classes of DEC-POMDPs 285 9.6. Algorithms for solving DEC-POMDPs 295 9.7. Applicative scenario: multirobot exploration 310 9.8. Conclusion and outlook . . . 312 9.9. Bibliography 313 Chapter 10. Non-Standard Criteria 319 Matthieu BOUSSARD, Maroua BOUZID, Abdel-Illah MOUADDIB, Régis SABBADIN and Paul WENG 10.1. Introduction 319 10.2. Multicriteria approaches 320 10.3. Robustness in MDPs 327 10.4. Possibilistic MDPs 329 10.5. Algebraic MDPs 342 10.6. Conclusion 354 10.7. Bibliography 355 PART 3. APPLICATIONS 361 Chapter 11. Online Learning for Micro-Object Manipulation 363 Guillaume LAURENT 11.1. Introduction 363 11.2. Manipulation device 364 11.3. Choice of the reinforcement learning algorithm 367 11.4. Experimental results 370 11.5. Conclusion 373 11.6. Bibliography 373 Chapter 12. Conservation of Biodiversity 375 Iadine CHADÈS 12.1. Introduction 375 12.2. When to protect, survey or surrender cryptic endangered species 376 12.3. Can sea otters and abalone co-exist? 381 12.4. Other applications in conservation biology and discussions 391 12.5. Bibliography 392 Chapter 13. Autonomous Helicopter Searching for a Landing Area in an Uncertain Environment 395 Patrick FABIANI and Florent TEICHTEIL-KÖNIGSBUCH 13.1. Introduction 395 13.2. Exploration scenario 397 13.3. Embedded control and decision architecture 401 13.4. Incremental stochastic dynamic programming 404 13.5. Flight tests and return on experience 407 13.6. Conclusion 410 13.7. Bibliography 410 Chapter 14. Resource Consumption Control for an Autonomous Robot 413 Simon LE GLOANNEC and Abdel-Illah MOUADDIB 14.1. The rover’s mission 414 14.2. Progressive processing formalism 415 14.3. MDP/PRU model 416 14.4. Policy calculation 418 14.5. How to model a real mission 419 14.6. Extensions 422 14.7. Conclusion 423 14.8. Bibliography 423 Chapter 15. Operations Planning 425 Sylvie THIÉBAUX and Olivier BUFFET 15.1. Operations planning 425 15.2. MDP value function approaches 433 15.3. Reinforcement learning: FPG 442 15.4. Experiments 446 15.5. Conclusion and outlook 448 15.6. Bibliography 450 Index 453
£145.30
Springer International Publishing AG Recommender Systems: The Textbook
Book SynopsisThis book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.Trade Review“Charu Aggarwal, a well-known, reputable IBM researcher, has taken the time to distill the advances in the design of recommender systems since the advent of the web … . Extensive bibliographic notes at the end of each chapter and more than 700 references in the book bibliography make this monograph an excellent resource for both practitioners and researchers. … Without a doubt, this is an excellent addition to my bookshelf!” (Fernando Berzal, Computing Reviews, February, 2017)Table of ContentsAn Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.
£999.99
Springer Fachmedien Wiesbaden 100 Rezepte für MS-DOS 6.0: Tips, Tricks und
Book SynopsisHandlungsorientierte Darstellung Mit diesem Buch führen wir Sie in einer aufbauenden Folge von Rezepten in die Welt des Betriebssystems MS DOS in der Version 6 ein. An einfachen Beispielen erleben Sie Problemlösungen mit dieser Generation des weit verbreiteten Einplatz Betriebssystems. Hilfe Sie lernen die mitgelieferten Hilfeprogramme zu nutzen. Wenn Sie mehr Informa tionen wollen, lesen Sie, wie Sie die Microsoft Support CDs und den Zugang zu CompuServe bekommen und dort das Forum MSDOS nutzen. Für alle zusätzli chen Hilfen finden Sie Bezugsadressen. Brücken Wir zeigen Ihnen Aussichtspunkte, von denen Sie die Blicke in die Nachbarwelt von Windows 3.1 werfen können. Viele Tabellen veranschaulichen Handlungsal ternativen an der DOS-Eingabeaufforderung, der DOS-Shell und Windows. Alles automatisch? Mit Batch-Programmen und Makros gestalten Sie eigene Abläufe. Sie erhalten viele Tips und Anregungen für die Organisation und Planung Ihrer Arbeit mit DOS. Bilderbuch Die zahlreichen exakten Abbildungen von Bildschirmen und Ausdrucken vermit teln Ihnen auch ohne PC jederzeit eine genaue Vorstellung von den Beispielen und der Arbeit mit DOS. Beispiele Sie finden durchweg einfache und übersichtliche abgeschlossene Beispiele, die Sie auch noch am Feierabend bequem nachvollziehen können. Danke ... Wir danken CompuServe, Creative Daten Systeme, der GD Gesellschaft für Datenkommunikation, Microsoft und Andreas Müller Software für die vielfältige Unterstützung. Viel Erfolg! Wir wünschen Ihnen viel Freude und Erfolg beim Lesen und Arbeiten mit diesem Rezept-Buch und MS DOS 6.0. Über Ihre Anregungen freuen wir uns.Table of Contents1 DOS installieren.- 1–1 Festplatte vorbereiten.- 1–2 Automatisches SETUP.- 1–3 Dateien expandieren.- 1–4 Dateien schreibschützen.- 1–5 Verzeichnisse verbergen.- 1–6 Notstartdiskette einrichten.- 1–7 Alte DOS-Programme.- 1–8 Systemdateien.- 1–9 Systeminformationen mit MSD.- 2 Anwendungen installieren.- 2–1 Verzeichnisse/Pfade einrichten.- 2–2 Suchpfade erweitern.- 2–3 Suchpfade automatisch laden.- 2–4 DOS-Version vorgaukeln.- 2–5 Programme starten (Überblick).- 2–6 Programmzuordnungen einrichten.- 2–7 Parameter in der Programmzuordnung.- 2–8 Programmumschaltung.- 3 Datenträger und Datenstrukturen.- 3–1 Verzeichnisse umbenennen.- 3–2 Verzeichnisse löschen.- 3–3 Verzeichnisstruktur sehen und ausdrucken.- 3–4 Zerstörte Daten wiederherstellen.- 3–5 Festplattenplatz schaffen.- 3–6 Zugriffzeit verkürzen (Defragmentieren).- 4 Mit Dateien arbeiten.- 4–1 Stellvertreterzeichen (Joker) verwenden.- 4–2 Mehrere Dateien markieren..- 4–3 Dateien kopieren.- 4–4 Kopieren und Verschieben mit der Maus und den Funktionstasten.- 4–5 Datenträger, Verzeichnisse und Dateien umbenennen.- 4–6 Dateien verschieben.- 4–7 Dateien vergleichen.- 4–8 Dateien löschen.- 4–9 Dateibewegungen verfolgen.- 4–10 Dateien wiederherstellen.- 4–11 Verzeichnisse wiederherstellen.- 4–12 Dateien verknüpfen.- 4–13 Eingaben umlenken.- 4–14 Ausgaben umlenken.- 4–15 Daten übergeben.- 5 Textverarbeitung.- 5–1 Textdateien ansehen.- 5–2 Textdateien ausdrucken.- 5–3 Textdateien erstellen mit COPY CON ....- 5–4 Textdateien bearbeiten (EDLIN).- 5–5 Textdateien bearbeiten (EDIT).- 5–6 Textdateien verbinden.- 5–7 Textdateien ergänzen.- 5–8 Zeichen in Texten suchen.- 5–9 Textzeilen sortieren.- 6 Stapelprogramme.- 6–1 Kommentare.- 6–2 Fallunterscheidungen.- 6–3 Beendigungscodes.- 6–4 Hilfefunktionen programmieren.- 6–5 Unterprogramme verwenden.- 6–6 Benutzereingaben.- 6–7 Menüführung für Benutzer.- 6–8 ANSI-Escape-Befehle.- 6–9 Vorteile inverser Zeichendarstellung.- 6–10 Parameter verwenden.- 6–11 Pausen im Programmablauf.- 7 Hohe Schule der Konfiguration.- 7–1 Speicherbereiche und -begriffe.- 7–2 Speicherbelegung ansehen.- 7–3 Speicherbereiche aktivieren.- 7–4 Programme in bestimmte Speicherbereiche laden.- 7–5 Speicher für Egoisten.- 7–6 Speicheroptimierung mit MemMaker.- 7–7 64 k-Bereich meiden.- 7–8 FASTOPEN verwenden.- 7–9 Festplattenpuffer (Cache).- 7–10 Konfigurationsbefehle übergehen.- 7–11 AUTOEXEC.BAT protokollieren.- 7–12 Variable Konfiguration.- 7–13 Variable AUTOEXEC.BAT.- 7–14 Konfigurationszeilen ausklammern.- 7–15 Prompt gestalten.- 7–16 Ländereinstellung.- 7–17 Laufwerke umlenken.- 7–18 Serielle Schnittstellen konfigurieren.- 7–19 Parallele Schnittstelle umleiten.- 7–20 Bildschirm konfigurieren.- 7–21 Zeichenwiederholung einstellen.- 7–22 Systeminformationen (DEBUG).- 7–23 Maschinenprogramme schreiben (DEBUG).- 8 Arbeitsvereinfachung.- 8–1 Online-Hilfe.- 8–2 Befehlswiederholung (DOSKey).- 8–3 Stapelprogramme mit DOSKey.- 8–4 DOSKey-Makros.- 8–5 Gleichnamige DOS-Befehle und Makros.- 8–6 DOS-Funktionstasten.- 8–7 Funktionstasten belegen.- 8–8 Tasten umlegen.- 8–9 Tastaturlayout umschalten.- 8–10 Rechner vernetzen.- 8–11 Rechner fernbedienen.- 9 Systemsicherheit.- 9–1 Laufwerke blockieren und umleiten.- 9–2 Alte Datensicherungen zurückspeichern.- 9–3 Datenträgernamen überprüfen.- 9–4 Dateien endgültig löschen.- 9–5 Nutzungszeiten protokollieren.- 9–6 Paßwortabfrage programmieren.- 9–7 Abbruch der AUTOEXEC.BAT verhindern.- 9–8 Programmbenutzer registrieren.- Anhang A — Symbole und Tabellen.- Anhang B — Hilfe aus CompuServe.- Anhang C — Weitere Hilfe von CD.- Anhang D — Adressen und Bestellinformationen.
£41.39
Ergon Verlag TKE 96: Terminology and Knowledge Engineering -
Book Synopsis
£29.48
Ergon Verlag TKE 90: Terminology and Knowledge Engineering -
Book Synopsis
£34.76
Springer International Publishing AG Big Data and Artificial Intelligence: 11th
Book SynopsisThis book constitutes the proceedings of the 11th International Conference on Big Data and Artificial Intelligence, BDA 2023, held in Delhi, India, during December 7–9, 2023. The17 full papers presented in this volume were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical sections: Keynote Lectures, Artificial Intelligence in Healthcare, Large Language Models, Data Analytics for Low Resource Domains, Artificial Intelligence for Innovative Applications and Potpourri. Table of ContentsKeynote Lectures.- Representation Learning for Dialog Models.- Sparsity, modularity, and structural plasticity in deep neural networks.- Artificial Intelligence in Healthcare.- Tuberculosis disease diagnosis using controlled super resolution.- GREAT AI in Medical Appropriateness and Value-Based-Care.- Large Language Models.- KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models.- SciPhyRAG - Retrieval Augmentation to Improve LLMs on Physics Q&A.- Revolutionizing High School Physics Education: A Novel Dataset.- Context-Enhanced Language Models for Generating Multi-Paper Citations.- GEC-DCL: Grammatical Error Correction Model with Dynamic Context Learning for Paragraphs & Scholarly Papers.- Data Analytics for Low Resource Domains.- A Deep Learning Emotion Classification Framework for Low Resource Languages.- Assessing the Efficacy of Synthetic Data for Enhancing Machine Translation Models in Low Resource Domains.- Artificial Intelligence for Innovative Applications.- Evaluation of Hybrid Quantum Approximate Inference Methods on Bayesian Networks.- IndoorGNN: A Graph Neural Network based approach for Indoor Localization using WiFi RSSI.- Ensemble-Based Road Surface Crack Detection: A Comprehensive Approach.- Potpourri.- Fast similarity search in large-scale Iris databases using high-dimensional hashing.- Explaining Finetuned Transformers on Hate Speech Predictions using Layerwise Relevance Propagation.- Multilingual Speech Sentiment Recognition using Spiking Neural Networks.- FopLAHD: Federated optimization using Locally Approximated Hessian Diagonal.- A Review of Approaches on Facets for Building IT-based Career Guidance Systems.
£47.49
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Model-Based Engineering of Embedded Systems: The
Book SynopsisEmbedded systems have long become essential in application areas in which human control is impossible or infeasible. The development of modern embedded systems is becoming increasingly difficult and challenging because of their overall system complexity, their tighter and cross-functional integration, the increasing requirements concerning safety and real-time behavior, and the need to reduce development and operation costs.This book provides a comprehensive overview of the Software Platform Embedded Systems (SPES) modeling framework and demonstrates its applicability in embedded system development in various industry domains such as automation, automotive, avionics, energy, and healthcare. In SPES 2020, twenty-one partners from academia and industry have joined forces in order to develop and evaluate in different industrial domains a modeling framework that reflects the current state of the art in embedded systems engineering.The content of this book is structured in four parts. Part I “Starting Point” discusses the status quo of embedded systems development and model-based engineering, and summarizes the key requirements faced when developing embedded systems in different application domains. Part II “The SPES Modeling Framework” describes the SPES modeling framework. Part III “Application and Evaluation of the SPES Modeling Framework” reports on the validation steps taken to ensure that the framework met the requirements discussed in Part I. Finally, Part IV “Impact of the SPES Modeling Framework” summarizes the results achieved and provides an outlook on future work.The book is mainly aimed at professionals and practitioners who deal with the development of embedded systems on a daily basis. Researchers in academia and industry may use it as a compendium for the requirements and state-of-the-art solution concepts for embedded systems development.Table of ContentsPart I Starting Situation.- Challenges in Engineering for Software-Intensive Embedded Systems.- Requirements from the Application Domains.- Part II The SPES Modeling Framework.- Introduction to the SPES Modeling Framework.- Requirements Viewpoint.- Functional Viewpoint.- Logical Viewpoint.- Technical Viewpoint.- Modeling Quality Aspects: Safety.- Modeling Quality Aspects: Real-Time.- Part III Application and Evaluation of the SPES Modeling Framework.- Overview of the SPES Evaluation Strategy.- Application and Evaluation in the Automation Domain.- Application and Evaluation in the Automotive Domain.- Application and Evaluation in the Avionics Domain.- Application and Evaluation in the Energy Domain.- Application and Evaluation in the Healthcare Domain.- Evaluation Summary.- Part IV Impact of the SPES Modeling Framework.- Lessons Learned.- Outlook.
£999.99
Springer Fachmedien Wiesbaden Netzbasierte Ansätze zur natürlichsprachlichen
Book SynopsisFür Leser, die bereits die Grundlagen der Wissensverarbeitung und Computernetzwerke beherrschen, gibt das Buch einen Überblick über innovative Verfahren, die die automatisierte Suche, Recherche, Klassifikation und Verwaltung von Texten im Kontext dezentraler Systeme und vor allem im WWW erlauben. Besondere Aufmerksamkeit wird dabei auf eine personalisierte Verarbeitung gerichtet, die auch zeitliche Aspekte, wie z. B. das digitale Vergessen, einbeziehen. An vielen Stellen werden auf interessante und neuartige Art und Weise Analogien aus anderen Wissensgebieten, so z. B. zur Verarbeitung von Informationen und zum Lernen im menschlichen Gehirn sowie der Natur schlechthin genutzt.Table of ContentsWissensverarbeitung im menschlichen Gehirn - Lernen - Netzwerke für die Textanalyse - Digitale Updates und digitales Vergessen - Exploration von Netzwerkstrukturen - Konzepte des Text Minings in dezentralen Systemen - Informationsmanagement im Web
£26.59
