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• Conversational Bot/QA/IR
• Knowledge graph/IE
• Machine Learning for NLP
• Machine Translation
• NLP Applications.

This two volume set of LNAI 11108 and LNAI 11109 constitutes the refereed proceedings of the 7th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2018, held in Hohhot, China, in August 2018. The 55 full papers and 31 short papers presented were carefully reviewed and selected from 308 submissions. The papers of the first volume are organized in the following topics: conversational Bot/QA/IR; knowledge graph/IE; machine learning for NLP; machine translation; and NLP applications. The papers of the second volume are organized as follows: NLP for social network; NLP fundamentals; text mining; and short papers.

• Identification of the Conjugate Pair to Estimating Object Distance: An Application of the Ant Colony Algorithm.-Design of Palm Acupuncture Points Indicator
• Low-rank Representation and Locality-constrained Regression for Robust Low-Resolution Face Recognition
• Face Recognition Benchmark with ID Photos
• Scene Relighting using a Single Reference Image through Material Constrained Layer Decomposition
• Applying Adaptive Actor-critic Learning to Human Upper Lime Lifting Motion
• A Demand-based Allocation Mechanism for Virtual Machine
• A Joint Hierarchy Model for Action Recognition Using Kinect
• QoS-Based Medical Program Evolution
• An Improved 3D Surface Reconstruction Method based on Three Wavelength Phase Shift Profilometry
• The Research on the Lung Tumor Imaging based on the Electrical Impedance Tomography
• Combining CNN and MRF for Road Detection
• The Increasing of Discrimination Accuracy of Waxed Apples based on Hyperspectral Imaging Optimized by Spectral Correlation Analysis
• A Diffeomorphic Demons Approach to Statistical Shape Modeling.

This book highlights selected papers presented at the 2nd International Symposium on Artificial Intelligence and Robotics 2017 (ISAIR2017), held in Nakamura Centenary Memorial Hall, Kitakyushu, Japan on November 25-26, 2017. Today, the integration of artificial intelligence and robotic technologies has become a topic of growing interest for both researchers and developers from academic fields and industries worldwide, and artificial intelligence is poised to become the main approach pursued in next-generation robotics research. The rapidly growing number of artificial intelligence algorithms and big data solutions has significantly extended the number of potential applications for robotic technologies. However, it also poses new challenges for the artificial intelligence community. The aim of this symposium is to provide a platform for young researchers to share the latest scientific achievements in this field, which are discussed in these proceedings.

• Cover
• Title Page
• Copyright and Credits
• About Packt
• Contributors
• Table of Contents
• Preface
• Chapter 1: Machine Learning for Cybersecurity
• Technical requirements
• Train-test-splitting your data
• Getting ready
• How to do it ...
• How it works ...
• Standardizing your data
• Getting ready
• How to do it ...
• How it works ...
• Summarizing large data using principal component analysis
• Getting ready
• How to do it ...
• How it works ...
• Generating text using Markov chains
• Getting ready
• How to do it ...
• How it works ...

• Performing clustering using scikit-learn
• Getting ready
• How to do it ...
• How it works ...
• Training an XGBoost classifier
• Getting ready
• How to do it ...
• How it works ...
• Analyzing time series using statsmodels
• Getting ready
• How to do it ...
• How it works ...
• Anomaly detection with Isolation Forest
• Getting ready
• How to do it ...
• How it works ...
• Natural language processing using a hashing vectorizer and tf-idf with scikit-learn
• Getting ready
• How to do it ...
• How it works ...
• Hyperparameter tuning with scikit-optimize
• Getting ready
• How to do it ...

• How it works ...
• Chapter 2: Machine Learning-Based Malware Detection
• Technical requirements
• Malware static analysis
• Computing the hash of a sample
• Getting ready
• How to do it ...
• How it works ...
• YARA
• Getting ready
• How to do it ...
• How it works ...
• Examining the PE header
• Getting ready
• How to do it ...
• How it works ...
• Featurizing the PE header
• Getting ready
• How to do it ...
• How it works ...
• Malware dynamic analysis
• Getting ready
• How to do it ...
• How it works ...
• Using machine learning to detect the file type

• Scraping GitHub for files of a specific type
• Getting ready
• How to do it ...
• How it works ...
• Classifying files by type
• Getting ready
• How to do it ...
• How it works ...
• Measuring the similarity between two strings
• Getting ready
• How to do it ...
• How it works ...
• Measuring the similarity between two files
• Getting ready
• How to do it ...
• How it works ...
• Extracting N-grams
• Getting ready
• How to do it ...
• How it works ...
• Selecting the best N-grams
• Getting ready
• How to do it ...
• How it works ...
• Building a static malware detector
• Getting ready

• How to do it ...
• How it works ...
• Tackling class imbalance
• Getting ready
• How to do it ...
• How it works ...
• Handling type I and type II errors
• Getting ready
• How to do it ...
• How it works ...
• Chapter 3: Advanced Malware Detection
• Technical requirements
• Detecting obfuscated JavaScript
• Getting ready
• How to do it ...
• How it works ...
• Featurizing PDF files
• Getting ready
• How to do it ...
• How it works ...
• Extracting N-grams quickly using the hash-gram algorithm
• Getting ready
• How to do it ...
• How it works ...
• See also
• Building a dynamic malware classifier

This book helps data scientists and cybersecurity experts on implementing the latest AI techniques in cybersecurity. Concrete and clear steps for implementing ML security systems are provided, saving you months in research and development. By the end of this book, you will be able to build defensive systems to curb cybersecurity threats.

• Introduction - Ethical and Sustainable Human and Artificial AI Artificial Intelligence and Robotics Applying The Benefits of AI and Identifying Challenges and Risks Starting AI - How to Build A Machine Learning Toolbox Algorithms The Management, Roles and Responsibilities of Humans and Machines AI in Use in Industry - Reimagining Everything in the Fourth Industrial Revolution AI Case Studies .
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In alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed.
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• Table of Contents Machine Learning for Trading - From Idea to Execution Market and Fundamental Data - Sources and Techniques Alternative Data for Finance - Categories and Use Cases Financial Feature Engineering - How to Research Alpha Factors Portfolio Optimization and Performance Evaluation The Machine Learning Process Linear Models - From Risk Factors to Return Forecasts The ML4T Workflow - From Model to Strategy Backtesting (N.B. Please use the Look Inside option to see further chapters).
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Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data Who this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
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• Introduction The Query Process and Barriers to Finding Information Online Online Search: An Evolution Search and Discovery Technologies: An Overview Information Access: A Spectrum of Needs and Uses Future Tense: The Next Era in Information Access and Discovery Answer Machines.
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The Answer Machine is a practical, non-technical guide to the technologies behind information seeking and analysis. It introduces search and content analytics to software buyers, knowledge managers, and searchers who want to understand and design effective online environments. The book describes how search evolved from an expert-only to an end user tool. It provides an overview of search engines, categorization and clustering, natural language processing, content analytics, and visualization technologies. Detailed profiles for Web search, eCommerce search, eDiscovery, and enterprise search contrast the types of users, uses, tasks, technologies, and interaction designs for each. These variables shape each application, although the underlying technologies are the same. Types of information tasks and the trade-offs between precision and recall, time, volume and precision, and privacy vs. personalization are discussed within this context. The book examines trends toward convenient, context-aware computing, big data and analytics technologies, conversational systems, and answer machines. The Answer Machine explores IBM Watson's DeepQA technology and describes how it is used to answer health care and Jeopardy questions. The book concludes by discussing the implications of these advances: how they will change the way we run our businesses, practice medicine, govern, or conduct our lives in the digital age.
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• Introduction The Case-Base Reasoning and Decision Making Learning Formal Aspects Summary and Beyond.
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Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the design of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges.
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• Preface Acknowledgments Introduction Related Learning Paradigms Lifelong Supervised Learning Continual Learning and Catastrophic Forgetting Open-World Learning Lifelong Topic Modeling Lifelong Information Extraction Continuous Knowledge Learning in Chatbots Lifelong Reinforcement Learning Conclusion and Future Directions Bibliography Authors' Biographies.
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Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks-which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning-most notably, multi-task learning, transfer learning, and meta-learning-because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
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• 1. Models for mobile agent computing
• Introduction
• What is a mobile agent
• Why mobile agents
• An algorithmic model for mobile agents
• Mobile agents
• Distributed networks
• Resource measures
• Mobile agent rendezvous
• Outline of the book
• Comments and bibliographic remarks.

• 2. Deterministic rendezvous in a ring
• Introduction
• A single stationary token
• The feasibility of rendezvous
• The time complexity of rendezvous
• Memory tradeoff for rendezvous with detection
• Limits to the memory trade-off
• Movable tokens
• Comments and bibliographic remarks.

• 3. Multiple agent rendezvous in a ring
• Introduction
• Impossibility of rendezvous
• Rendezvous with detection
• Conditional solutions
• Comments and bibliographic remarks.

• 4. Randomized rendezvous in a ring
• Introduction
• Random walk algorithm
• Randomization and tokens
• Time/memory trade-offs
• Coin half tour algorithm
• Approximate counting algorithm
• Comments and bibliographic remarks.

• 5. Other models
• Introduction
• Leader election and rendezvous
• Rendezvous with failing tokens
• Rendezvous when tokens fail upon release
• Rendezvous when tokens can fail at any time
• The cost of token failure
• Flickering tokens
• Asynchronous rendezvous
• Look-compute-move
• Model and terminology
• Impossibility results
• Gathering configurations with a single multiplicity
• Gathering rigid configurations
• Gathering an odd number of robots
• Dangerous networks
• Black-hole search in an asynchronous ring
• Rendezvous in asynchronous rings in spite of a black-hole
• Comments and bibliographic remarks.

• 6. Other topologies
• Introduction
• Synchronous torus
• Memory lower bounds for rendezvous
• Rendezvous algorithms
• Trees
• Arbitrary graphs
• Comments and bibliographic remarks.

• Bibliography
• Glossary
• Authors' biographies
• Index.

Mobile agent computing is being used in fields as diverse as artificial intelligence, computational economics and robotics. Agents' ability to adapt dynamically and execute asynchronously and autonomously brings potential advantages in terms of fault-tolerance, flexibility and simplicity. This monograph focuses on studying mobile agents as modelled in distributed systems research and in particular within the framework of research performed in the distributed algorithms community. It studies the fundamental question of how to achieve rendezvous, the gathering of two or more agents at the same node of a network. Like leader election, such an operation is a useful subroutine in more general computations that may require the agents to synchronize, share information, divide up chores, etc. The work provides an introduction to the algorithmic issues raised by the rendezvous problem in the distributed computing setting. For the most part our investigation concentrates on the simplest case of two agents attempting to rendezvous on a ring network. Other situations including multiple agents, faulty nodes and other topologies are also examined. An extensive bibliography provides many pointers to related work not covered in the text. The presentation has a distinctly algorithmic, rigorous, distributed computing flavor and most results should be easily accessible to advanced undergraduate and graduate students in computer science and mathematics departments.

• Automating Inquiry Uncertainty Sampling Searching Through the Hypothesis Space Minimizing Expected Error and Variance Exploiting Structure in Data Theory Practical Considerations.
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The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose ""queries, "" usually in the form of unlabeled data instances to be labeled by an ""oracle"" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or ""query selection frameworks."" We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities.
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• Markov Decision Processes Value Prediction Problems Control For Further Exploration.
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Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.
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• Preface Planning and Autonomous Behavior Classical Planning: Full Information and Deterministic Actions Classical Planning: Variations and Extensions Beyond Classical Planning: Transformations Planning with Sensing: Logical Models MDP Planning: Stochastic Actions and Full Feedback POMDP Planning: Stochastic Actions and Partial Feedback Discussion Bibliography Author's Biography.
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Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective.
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• Introduction Rational Agents Strategic Games Coordination Partial Observability Mechanism Design Learning.
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Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
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• Ensembles Discovered Predictive Learning and Decision Trees Model Complexity, Model Selection and Regularization Importance Sampling and the Classic Ensemble Methods Rule Ensembles and Interpretation Statistics Ensemble Complexity.
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Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges - from investment timing to drug discovery, and fraud detection to recommendation systems - where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization - today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods - bagging, random forests, and boosting - to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners.
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• Support Vector Machines for Classification Kernel-based Models Learning with Kernels.
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Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data.
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• Introduction Ranking-Based Clustering Classification of Heterogeneous Information Networks Meta-Path-Based Similarity Search Meta-Path-Based Relationship Prediction Relation Strength-Aware Clustering with Incomplete Attributes User-Guided Clustering via Meta-Path Selection Research Frontiers.
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Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this book, we investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including: (1) rank-based clustering and classification; (2) meta-path-based similarity search and mining; (3) relation strength-aware mining, and many other potential developments. This book introduces this new research frontier and points out some promising research directions.
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• Introduction MDPs Fundamental Algorithms Heuristic Search Algorithms Symbolic Algorithms Approximation Algorithms Advanced Notes.
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Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems.
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• Preface Sentiment Analysis: A Fascinating Problem The Problem of Sentiment Analysis Document Sentiment Classification Sentence Subjectivity and Sentiment Classification Aspect-Based Sentiment Analysis Sentiment Lexicon Generation Opinion Summarization Analysis of Comparative Opinions Opinion Search and Retrieval Opinion Spam Detection Quality of Reviews Concluding Remarks Bibliography Author Biography.
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Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.
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• Introduction Preference Modeling and Reasoning Uncertainty in Preference Reasoning Aggregating Preferences Stable Marriage Problems.
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Computational social choice is an expanding field that merges classical topics like economics and voting theory with more modern topics like artificial intelligence, multiagent systems, and computational complexity. This book provides a concise introduction to the main research lines in this field, covering aspects such as preference modelling, uncertainty reasoning, social choice, stable matching, and computational aspects of preference aggregation and manipulation. The book is centered around the notion of preference reasoning, both in the single-agent and the multi-agent setting. It presents the main approaches to modeling and reasoning with preferences, with particular attention to two popular and powerful formalisms, soft constraints and CP-nets. The authors consider preference elicitation and various forms of uncertainty in soft constraints. They review the most relevant results in voting, with special attention to computational social choice. Finally, the book considers preferences in matching problems. The book is intended for students and researchers who may be interested in an introduction to preference reasoning and multi-agent preference aggregation, and who want to know the basic notions and results in computational social choice.
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• Introduction Example: Bidding on eBay Auction Fundamentals Continuous Double Auctions Interdependent Markets Conclusion.
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Automated trading in electronic markets is one of the most common and consequential applications of autonomous software agents. Design of effective trading strategies requires thorough understanding of how market mechanisms operate, and appreciation of strategic issues that commonly manifest in trading scenarios. Drawing on research in auction theory and artificial intelligence, this book presents core principles of strategic reasoning that apply to market situations. The author illustrates trading strategy choices through examples of concrete market environments, such as eBay, as well as abstract market models defined by configurations of auctions and traders. Techniques for addressing these choices constitute essential building blocks for the design of trading strategies for rich market applications. The lecture assumes no prior background in game theory or auction theory, or artificial intelligence.
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• Introduction to Web Page Recommender Systems Preprocessing for Web Page Recommender Models Pattern Extraction Evaluation Metrics.
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One of the application areas of data mining is the World Wide Web (WWW or Web), which serves as a huge, widely distributed, global information service for every kind of information such as news, advertisements, consumer information, financial management, education, government, e-commerce, health services, and many other information services. The Web also contains a rich and dynamic collection of hyperlink information, Web page access and usage information, providing sources for data mining. The amount of information on the Web is growing rapidly, as well as the number of Web sites and Web pages per Web site. Consequently, it has become more difficult to find relevant and useful information for Web users. Web usage mining is concerned with guiding the Web users to discover useful knowledge and supporting them for decision-making. In that context, predicting the needs of a Web user as she visits Web sites has gained importance. The requirement for predicting user needs in order to guide the user in a Web site and improve the usability of the Web site can be addressed by recommending pages to the user that are related to the interest of the user at that time. This monograph gives an overview of the research in the area of discovering and modeling the users' interest in order to recommend related Web pages. The Web page recommender systems studied in this monograph are categorized according to the data mining algorithms they use for recommendation.
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• Table of Contents An Overview of Amazon EMR Exploring the Architecture and Deployment Options Common Use Cases and Architecture Patterns Big Data Applications and Notebooks Available in Amazon EMR Setting Up and Configuring EMR Clusters Monitoring, Scaling, and High Availability Understanding Security in Amazon EMR Understanding Data Governance in Amazon EMR Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark Implementing Real-Time Streaming with Amazon EMR and Spark Streaming Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA Migrating On-Premises Hadoop Workloads to Amazon EMR Best Practices and Cost Optimization Techniques.
• (source: Nielsen Book Data)

Design scalable big data solutions using Hadoop, Spark, and AWS cloud native services Key Features Build data pipelines that require distributed processing capabilities on a large volume of data Discover the security features of EMR such as data protection and granular permission management Explore best practices and optimization techniques for building data analytics solutions in Amazon EMR Book DescriptionAmazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS. What you will learn Explore Amazon EMR features, architecture, Hadoop interfaces, and EMR Studio Configure, deploy, and orchestrate Hadoop or Spark jobs in production Implement the security, data governance, and monitoring capabilities of EMR Build applications for batch and real-time streaming data analytics solutions Perform interactive development with a persistent EMR cluster and Notebook Orchestrate an EMR Spark job using AWS Step Functions and Apache Airflow Who this book is forThis book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and Amazon EMR. Prior experience in either Python programming, Scala, or the Java programming language and a basic understanding of Hadoop and AWS will help you make the most out of this book.
(source: Nielsen Book Data)

"Academic, industry, and government experts provide a global survey of the financial services sector's transformation by AI, data science, and blockchain"-- Provided by publisher.

• Table of Contents Introduction to Software Architecture API Design Data Modeling The Data Layer The Twelve-Factor App Methodology Web Server Structures Event-Driven Structures Advanced Event-Driven Structures Microservices vs Monolith Testing and TDD Package Management Logging Metrics Profiling Debugging Ongoing Architecture.
• (source: Nielsen Book Data)

Make the best of your test suites by using cutting-edge software architecture patterns in Python Key Features Learn how to create scalable and maintainable applications Build a web system for micro messaging using concepts in the book Use profiling to find bottlenecks and improve the speed of the system Book DescriptionDeveloping large-scale systems that continuously grow in scale and complexity requires a thorough understanding of how software projects should be implemented. Software developers, architects, and technical management teams rely on high-level software design patterns such as microservices architecture, event-driven architecture, and the strategic patterns prescribed by domain-driven design (DDD) to make their work easier. This book covers these proven architecture design patterns with a forward-looking approach to help Python developers manage application complexity-and get the most value out of their test suites. Starting with the initial stages of design, you will learn about the main blocks and mental flow to use at the start of a project. The book covers various architectural patterns like microservices, web services, and event-driven structures and how to choose the one best suited to your project. Establishing a foundation of required concepts, you will progress into development, debugging, and testing to produce high-quality code that is ready for deployment. You will learn about ongoing operations on how to continue the task after the system is deployed to end users, as the software development lifecycle is never finished. By the end of this Python book, you will have developed "architectural thinking": a different way of approaching software design, including making changes to ongoing systems. What you will learn Think like an architect, analyzing software architecture patterns Explore API design, data storage, and data representation methods Investigate the nuances of common architectural structures Utilize and interoperate elements of patterns such as microservices Implement test-driven development to perform quality code testing Recognize chunks of code that can be restructured as packages Maintain backward compatibility and deploy iterative changes Who this book is forThis book will help software developers and architects understand the structure of large complex systems and adopt architectural patterns that are scalable. Examples in the book are implemented in Python so a fair grasp of basic Python concepts is expected. Proficiency in any programming languages such as Java or JavaScript is sufficient.
(source: Nielsen Book Data)

"This book examines artistic practices that use machine learning and computational technologies through historical perspectives surrounding adaptive systems from the 1950s onwards"-- Provided by publisher.

• Frontmatter
• Preface
• Contents
• List of Contributors
• 1. Feature selection in biomedical signal classification process and current software implementations / Jovic, Alan
• 2. An overview of skin lesion segmentation, features engineering, and classification / Sabri, My Abdelouahed / Filali, Youssef / Ennouni, Assia / Yahyaouy, Ali / Aarab, Abdellah
• 3. Brain tumor image segmentation and classification using SVM, CLAHE, and ARKFCM / Ishita, Banerjee / Madhumathy, P. / Kavitha, N.
• 4. Coronary Heart Disease prediction using genetic algorithm based decision tree / Sivaranjani, Reddi / Naresh, Vankamamidi S. / Murthy, Nistala V.E.S.
• 5. Intelligent approach for retinal disease identification / Rajyaguru, Vipul C. / Vithalani, Chandresh H. / Thanki, Rohit M.
• 6. Speech separation for interactive voice systems / Wiem, Belhedi / Anouar, Ben Messaoud Mohamed / Aïcha, Bouzid
• 7. Machine vision for human-machine interaction using hand gesture recognition / Verma, Varnita / Rajput, Anshuman / Chauhan, Piyush / Rathore, Harshit / Goyal, Piyush / Gupta, Mukul Kumar
• Index

Intelligent prediction and decision support systems are based on signal processing, computer vision (CV), machine learning (ML), software engineering (SE), knowledge based systems (KBS), data mining, artificial intelligence (AI) and include several systems developed from the study of expert systems (ES), genetic algorithms (GA), artificial neural networks (ANN) and fuzzy-logic systems The use of automatic decision support systems in design and manufacturing industry, healthcare and commercial software development systems has the following benifits: Cost savings in companies, due to employment of expert system technology. Fast decision making, completion of projects in time and development of new products. Improvement in decision making capability and quality. Usage of Knowledge database and Preservation of expertise of individuals Eases complex decision problems. Ex: Diagnosis in Healthcare To address the issues and challenges related to development, implementation and application of automatic and intelligent prediction and decision support systems in domains such as manufacturing, healthcare and software product design, development and optimization, this book aims to collect and publish wide ranges of quality articles such as original research contributions, methodological reviews, survey papers, case studies and/or reports covering intelligent systems, expert prediction systems, evaluation models, decision support systems and Computer Aided Diagnosis (CAD).
(source: Nielsen Book Data)

• Title page; Preface; Contents; Part I. Foundations of Well-Being; Part II. Supporting the Well-Being Through Care Taking in Smart Environments; Part III. Improving the Well-Being Through Life-Style and Entertainment; Subject Index; Author Index.

The notion of well-being is one which is crucial to many aspects of our daily lives. In addition to providing one of the cornerstones to a healthy lifestyle, the concept of well-being extends to the selection of the type of environment we live in, our interaction with other people and the things we do to realize our plans for the future. Well-being is so intrinsic to our daily lives that it plays a fundamental role at all times and in all places, so it is important that it is taken into account when designing the ubiquitous computing technologies which pervade our lives nowadays. This book contains the papers presented at the 2009 Behaviour Monitoring and Interpretation (BMI) workshop, third in the annual series launched in 2007, and co-located with the German conference on Artificial Intelligence. The focus of the 2009 workshop on the topic of well-being reflects the significant interest in this area of research and the book offers state-of-the-art contributions in the application area of well-being from diverse disciplines such as engineering and philosophy. This overview of a wide range of research projects will be of interest to researchers and developers whose work necessitates a consideration of well-being, either as part of the task of implementing current applications or of designing the systems of the future.
(source: Nielsen Book Data)

"The first book that provides a systematic and rigorous logical theory of causality"-- Provided by publisher.

"A collection of critical essays dealing with the social and ethical impacts of AI including issues of trust, reliability, and bias"-- Provided by publisher.

• Database and Distributed Machine Learning.- Querying and Mining Strings Made Easy Distributed Training Large-Scale Deep Architectures.- Fault Detection and Localization in Distributed Systems using Recurrent Convolutional Neural Networks.- Discovering Group Skylines with Constraints by Early Candidate Pruning.- Comparing MapReduce-Based k-NN Similarity Joins On Hadoop For High-dimensional Data.- A Higher-Fidelity Frugal Quantile Estimator.- Recommender System.- Fair Recommendations Through Diversity Promotion.- A Hierarchical Bayesian Factorization Model for Implicit and Explicit Feedback Data.- Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities.- Group Recommender Model Based on Preference Interaction.- Identification of Grey Sheep Users By Histogram Inter
• section In Recommender Systems.- Social Network and Social Media.- A Feature-based Approach for the Redefined Link Prediction Problem in Signed Networks.- From Mutual Friends to Overlapping Community Detection: A Non-negative Matrix Factorization Approach.- Calling for Response: Automatically Distinguishing Situation-aware Tweets During Crises.- Efficient Revenue Maximization for Viral Marketing in Social Networks.- Generating Life Course Trajectory Sequences with Recurrent Neural Networks and Application to Early Detection on Social Disadvantage.- FRISK: A Multilingual Approach to Find twitteR InterestS via wiKipedia.- A Solution to Tweet-Based User Identification across Online Social Networks.- Machine Learning.- Supervised Feature Selection Algorithm Based on Low-Rank and Manifold Learning.- Mixed Membership Sparse Gaussian Conditional Random Fields.- Effects of Dynamic Subspacing in Random Forest.- Diversity and Locality in Multi-Component, Multi-Layer Predictive Systems: A Mutual Information Based Approach.- Hybrid Subspace Mixture Models For Prediction and Anomaly Detection in High Dimensions.- Classification and Clustering Methods.- StruClus: Scalable Structural Graph Set Clustering with Representative Sampling.- Employing Hierarchical Clustering and Reinforcement Learning for Attribute-based Zero-Shot Classification.- Environmental Sound Recognition using Masked Conditional Neural Networks.- Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure.- A Framework for Clustering and Dynamic Maintenance of XML Documents.- Language-independent Twitter Classification using Character-based Convolutional Networks.- Behavior Modeling and User Profiling.- Modeling Check-in Behavior with Geographical Neighborhood Influence of Venues.- An empirical study on collective online behaviors of extremist supporters. -Your Moves, Your Device: Establishing Behavior Profiles using Tensors.- An Approach for Identifying Author Profiles of Blogs.- Generating Topics of Interests for Research Communities.- An Evolutionary Approach for Learning Conditional Preference Network from Inconsistent Examples.- Bioinformatic and Medical Data Analysis.- Predicting Clinical Outcomes of Alzheimer's Disease from Complex Brain Networks.- Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties.- Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets.- Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers.- Spatio-temporal Data.- People-Centric Mobile Crowdsensing Platform for Urban Design.- Long-Term User Location Prediction Using Deep Learning and Periodic Pattern Mining.- An Intelligent Weighted Fuzzy Time Series Model Based on A Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting.- Mobile Robot Scheduling with Multiple Trips and Time Windows.- Natural Language Processing and Text Mining.- Feature Analysis for Duplicate Detection in Programming QA Communities.- A Joint Human/Machine Process for Coding Events and Conflict Drivers.- Quality Prediction of Newly Proposed Questions in CQA by Leveraging Weakly Supervised Learning.- Improving Chinese Sentiment Analysis via Segmentation-based Representation Using Parallel CNN.- Entity Recognition by Distant Supervision with Soft List Constraint.- Structured Sentiment Analysis.- Data Mining Applications.- Improving Real-Time Bidding Using a Constrained Markov Decision Process.- PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network.- Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments.- Color-sketch simulator: a guide for color-based visual known-item search.- Applications.- Making Use of External Company Data to Improve the Classification of Bank Transactions.- Mining Load Profile Patterns for Australian Electricity Consumers.- STA: a Spatio-temporal Thematic Analytics Framework for Urban Ground Sensing.- Privacy and Utility Preservation for Location Data Using Stay Region Analysis.- Location-aware Human Activity Recognition.- Demos.- SWYSWYK: a new Sharing Paradigm for the Personal Cloud.- Tools and Infrastructure for Supporting Enterprise Knowledge Graphs.- An Interactive Web-based Toolset for Knowledge Discovery from Short Text Log Data.- Carbon: Forecasting Civil Unrest Events by Monitoring News and Social Media.- A system for Querying and Analyzing Urban Regions.- Detect tracking behavior among trajectory data.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos. .
(source: Nielsen Book Data)

• Requirements analysis.-Business analysis:- Ambient Assisted Living:- Computing for Healthcare:- Data Science:-Data Analysis.- Business Intelligence.- Ontology.- Software Quality.- Model Quality.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of five workshops and a symposium, held at the 36th International Conference on Conceptual Modeling, ER 2017, in Valencia, Spain in November 2017. The 21 revised full papers were carefully reviewed and selected out of 47 submissions to the following events: AHA 2017 - 3rd International Workshop on Modeling for Ambient Assistance and Healthy Ageing MoBiD 2017 - 6th International Workshop on Modeling and Management of Big Data MREBA 2017 - 4th International Workshop on Conceptual Modeling in Requirements and Business Analysis OntoCom 2017 - 5th International Workshop on Ontologies and Conceptual Modeling QMMQ 2017 - 4th Workshop on Quality of Models and Models of Quality.
(source: Nielsen Book Data)

• Classification and deep learning.- Social network and graph mining.- Privacy-preserving mining and security/risk applications.- Spatio-temporal and sequential data mining.- Clustering and anomaly detection.- Recommender system.- Feature selection.- Text and opinion mining.- Clustering and matrix factorization.- Dynamic, stream data mining.- Novel models and algorithms.- Behavioral data mining.- Graph clustering and community detection.- Dimensionality reduction.
• (source: Nielsen Book Data)

This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.
(source: Nielsen Book Data)

• Part I: Online learning of a weighted selective naive Bayes classifier with non-convex optimization
• On making skyline queries resistant to outliers
• Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures
• Exact and Approximate Minimal Pattern Mining
• Part II: Comparison of proximity measures for a topological discrimination
• Comparison of linear modularization criteria using the relational formalism, an approach to easily identify resolution limit
• A novel approach to feature selection based on quality estimation metrics
• Ultrametricity of Dissimilarity Spaces and Its Significance for Data Mining
• Part III: SMERA: Semantic Mixed Approach for Web Query Expansion and Reformulation
• Multi-layer ontologies for integrated 3D shape segmentation and annotation
• Ontology Alignment Using Web Linked Ontologies as Background Knowledge
• LIAISON: reconciLIAtion of Individuals profiles across SOcial Networks
• Clustering of Links and Clustering of Nodes: Fusion of Knowledge in Social Networks.

This book presents a collection of representative and novel work in the field of data mining, knowledge discovery, clustering and classification, based on expanded and reworked versions of a selection of the best papers originally presented in French at the EGC 2014 and EGC 2015 conferences held in Rennes (France) in January 2014 and Luxembourg in January 2015. The book is in three parts: The first four chapters discuss optimization considerations in data mining. The second part explores specific quality measures, dissimilarities and ultrametrics. The final chapters focus on semantics, ontologies and social networks. Written for PhD and MSc students, as well as researchers working in the field, it addresses both theoretical and practical aspects of knowledge discovery and management.

• A Combined Approach for Ontology Enrichment from Textual and Open Data.- C-SPARQL Extension for Sampling RDF Graphs Streams.- Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks.
• (source: Nielsen Book Data)

This book is a collection of representative and novel works in the field of data mining, knowledge discovery, clustering and classification. Discussing both theoretical and practical aspects of "Knowledge Discovery and Management" (KDM), it is intended for researchers interested in these fields, including PhD and MSc students, and researchers from public or private laboratories. The contributions included are extended and reworked versions of six of the best papers that were originally presented in French at the EGC'2016 conference held in Reims (France) in January 2016. This was the 16th edition of this successful conference, which takes place each year, and also featured workshops and other events with the aim of promoting exchanges between researchers and companies concerned with KDM and its applications in business, administration, industry and public organizations. For more details about the EGC society, please consult egc.asso.fr.
(source: Nielsen Book Data)

• Chapter 1 Memristor Emulators A Note on Modeling.-
• Chapter 2 A Simple Oscillator using Memristor.-
• Chapter 3 A Hyperjerk Memristive System with Hidden Attractors.-
• Chapter 4 A Memristive System with Hidden Attractors and its Engineering Application.-
• Chapter 5 Adaptive Control, Synchronization and Circuit Simulation of a Memristor-Based.-
• Chapter 6 Modern System Design using Memristors.-
• Chapter 7 RF/Microwave Applications of Memristors.-
• Chapter 8 Theory, Modeling and Design of Memristor-Based Min-Max Circuits.-
• Chapter 9 Analysis of a 4-D Hyperchaotic Fractional-Order Memristive System with Hidden Attractors.-
• Chapter 10 Adaptive Control and Synchronization of a Memristor-Based Shinriki's System.
• (source: Nielsen Book Data)

This book reports on the latest advances in and applications of memristors, memristive devices and systems. It gathers 20 contributed chapters by subject experts, including pioneers in the field such as Leon Chua (UC Berkeley, USA) and R.S. Williams (HP Labs, USA), who are specialized in the various topics addressed in this book, and covers broad areas of memristors and memristive devices such as: memristor emulators, oscillators, chaotic and hyperchaotic memristive systems, control of memristive systems, memristor-based min-max circuits, canonic memristors, memristive-based neuromorphic applications, implementation of memristor-based chaotic oscillators, inverse memristors, linear memristor devices, delayed memristive systems, flux-controlled memristive emulators, etc. Throughout the book, special emphasis is given to papers offering practical solutions and design, modeling, and implementation insights to address current research problems in memristors, memristive devices and systems. As such, it offers a valuable reference book on memristors and memristive devices for graduate students and researchers with a basic knowledge of electrical and control systems engineering.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 15th International Conference on Practical Applications of Scalable Multi-Agent Systems, PAAMS 2017, held in Porto, Portugal, in June 2017. The 11 revised full papers, 11 short papers, and 17 Demo papers were carefully reviewed and selected from 63 submissions. The papers report on the application and validation of agent-based models, methods, and technologies in a number of key application areas, including day life and real world, energy and networks, human and trust, markets and bids, models and tools, negotiation and conversation, scalability and resources. .
(source: Nielsen Book Data)

• General.- Normality from Monte Carlo Simulation for Statistical Validation of Computer Intensive Algorithms.- Are Android Smartphones Ready to Locally Execute Intelligent Algorithms.- Reasoning and Multi-Agent Systems.- Large Scale Reasoning Using Allen's Interval Algebra.- Towards the Distributed Logic Programming of Intelligent Visual Surveillance Applications.- An efficient expert system for diabetes with a Bayesian inference engine.- iEnsemble: A Framework for Committee Machine based On Multiagent Systems with Reinforcement Learning.- Exploring Complex Networks with Failure-prone Agents.- On the Conception of Intelligent Power Plants Based on Multiple Agent Systems.- Neural Networks and Deep Learning.- Author Profiling with doc2vec Neural Network-Based Document Embeddings.- Neural network modelling for dissolved oxygen effects in extensive Litopenaeus vannamei culture.- Neural-network based algorithm for algae detection in automatic inspection of underwater pipelines.- Evolutionary Algorithms.- Study on the Development of Complex Network for Evolutionary and Swarm based Algorithms.- Estimation of Distribution Algorithms based on the Beta Distribution for Bounded Search Spaces.- Automated Analog Synthesis with an Estimation of the Distribution Algorithm.- Mathematical model of glucose metabolism by symbolic regression alpha beta.- Machine Learning.- Jensen inequality with subdifferential for Sugeno integral.- Towards a more general XCS: Classifier fusion and don't care in actions.- A Novel Artificial Hydrocarbon Networks Based Value Function Approximation in Hierarchical Reinforcement Learning.- Wind power forecasting for the Villonaco wind farm using AI techniques.- Predicting the need of mechanical ventilation in Guillain-Barre patients using machine learning algorithms with relevant features.- A Robust Machine learning Approach to Microprocessor Instructions Identification Final.- Classification and Clustering.- Stochastic Semantic-Based Multi-Objective Genetic Programming Optimisation for Classification of Imbalanced Data.- Consensus Clustering for Binning Metagenome Sequences.- Algorithm for Clustering of Web Search Results from a Hyper-Heuristic approach.- Grouping Business Process Models Based on Multimodal Search and Covering Arrays.- Optimization.- A new method to optimize dynamic environments with global changes using the chickens-hen' algorithm.- Transit Network Frequencies-Setting Problem Solved Using a new Multi-Objective Global-Best Harmony Search Algorithm and Discrete Event Simulation.- Optimal pricing model: case of study for convenience stores.- Method of musical composition for portfolio optimization problem.- Metaheuristic hybridized applied to solve the Capacity Vehicle Routing Problem.- ABC-PSO: An Efficient Bioinspired Metaheuristic for Parameter Estimation in Nonlinear Regression.- Data Mining.- Data mining in EEG wave trains in early stages of Parkinson's disease.- Data Mining in the Analysis of Ocean-Atmosphere Dynamics in Colombia's Central Caribbean Ocean.- Molecular docking based on ligand by Complexity LMC.- Integrating Information of Films by a Multi-Source Combining Framework.- Graph-Based Algorithms.- Computing the clique-width of Polygonal tree graphs.- A New Approach to Weakening and Destruction of Malicious Internet Networks.- Intelligent Learning Environments.- Toward Optimal Pedagogical Action Patterns by means of Partially Observable Markov Decision Process.- Data-driven construction of a student model using Bayesian networks in an electrical domain.- Strategic Learning Meta-model: A Selection Model of Learning Activities.- CodeTraining: An Authoring Tool for a Gamified Programming Learning Environment.- Generating a Logical Structure for Virtualizing Physiotherapy Instructions through NLP.- Building a Corpus and a Local Binary Pattern Recognizer for Learning-Centered Emotions.- Affective Learning System for Algorithmic Logic Applying Gamification. .
• (source: Nielsen Book Data)

The two-volume set LNAI 10061 and 10062 constitutes the proceedings of the 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, held in Cancun, Mexico, in October 2016. The total of 86 papers presented in these two volumes was carefully reviewed and selected from 238 submissions. The contributions were organized in the following topical sections: Part I: natural language processing; social networks and opinion mining; fuzzy logic; time series analysis and forecasting; planning and scheduling; image processing and computer vision; robotics. Part II: general; reasoning and multi-agent systems; neural networks and deep learning; evolutionary algorithms; machine learning; classification and clustering; optimization; data mining; graph-based algorithms; and intelligent learning environments. .
(source: Nielsen Book Data)

• Spatial and temporal databases.- Current research: Concepts, tools, techniques.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 15th International Symposium on Spatial and Temporal Databases, SSTD 2017, held in Arlington, VA, USA, in August 2017.The 19 full papers presented together with 8 demo papers and 5 vision papers were carefully reviewed and selected from 90 submissions. The papers are organized around the current research on concepts, tools, and techniques related to spatial and temporal databases. .
(source: Nielsen Book Data)

• Multi-objective Optimization.- A Parametric Study of Crossover Operators in Pareto-based Multiobjective Evolutionary Algorithm.- Non-dominated Sorting and Crowding Distance Based Multi-objective Chaotic Evolution.- On Performance Improvement based on Restart Meta-Heuristic Implementation for Solving Multi-Objective Optimization Problems.- Using multi-objective evolutionary algorithm to solve dynamic environment and economic dispatch with EVs.- Improved Interval Multi-objective Evolutionary Optimization Algorithm based on Directed Graph.- A Novel Linear Time Invariant Systems Order Reduction Approach based on Cooperative Multi-Objective Genetic Algorithm.- Solving Constrained Multi-objective Optimization Problems with Evolutionary Algorithms.- Portfolio Optimization.- Multi-objective Comprehensive Learning Bacterial Foraging Optimization for Portfolio Optimization.- Metaheuristics for Portfolio Optimization.- Community Detection.- Community Detection under Exponential Random Graph Model: A Metaheuristic Approach.- An Enhanced Particle Swarm Optimization Based on Physarum Model for Community Detection.- The Design and Development of the Virtual Learning Community for Teaching Resources Personalized Recommendation.- The Effect of Event Sentiment on Product Recommendation in a Microblog Platform.- Multi-Agent Systems and Swarm Robotics.- Solar Irradiance Forecasting Based on the Multi-agent Adaptive Fuzzy Neuronet.- Passive Field Dynamics Method: an Advanced Physics-based Approach for Formation Control of Robot Swarm.- Adaptive Potential Fields Model for Solving Distributed Area Coverage Problem in Swarm Robotics.- Swarm-based Spreading Points.- A Survivability Enhanced Swarm Robotic Searching System Using Multi-Objective Particle Swarm Optimization.- Autonomous Coordinated Navigation of Virtual Swarm Bots in Dynamic Indoor Environments by Bat Algorithm.- Building Fractals with a Robot Swarm.- A Stigmergy Based Search Method for Swarm Robots.- Cooperative Control of Multi-Robot System Using Mobile Agent for Multi Source Localization.- Hybrid Optimization Algorithms and Applications.- Evolutionary Fuzzy Control of Three Robots Cooperatively Carrying an Object for Wall Following through the Fusion of Continuous ACO and PSO.- Optimal Operational Planning of Energy Plants by Multi-population Differential Evolutionary Particle Swarm Optimization.- A Review on Hybridization of Particle Swarm Optimization with Artificial Bee Colony.- Study on Greedy Search to Improve Simulated Annealing Algorithm for Large-scale Traveling Salesman Problem.- A Hybrid Swarm based Composition for Chinese Music.- Fuzzy and Swarm Approach.- Fuzzy Logic Controller Design for Tuning the Cooperation of Bio-Inspired Algorithms.- Making Capital Budgeting Decisions for Project Abandonment by Fuzzy Approach.- An Imputation for Missing Data Features based on Fuzzy Swarm Approach in Heart Disease Classification.- Clustering and Forecast.- Total Optimization of Smart City Using Initial Searching Points Generation Based on k-means Algorithm.- Clustering Analysis of ECG Data Streams.- A Novel Multi-cell Multi-Bernoulli Tracking Method Using Local Fractal Feature Estimation.- An Improved Locality Preserving Projection Method for Dimensionality Reduction with Hyperspectral Image.- Applying a Classification Model for Selecting Postgraduate Programs.- University Restaurant Sales Forecast Based on BP neural Network
• InShanghai Jiao Tong University Case.- Classification and Detection.- Swarm ANN/SVR-based modeling method for warfarin dose prediction in Chinese.- A Novel HPSOSA for Kernel Function Type and Parameter Optimization of SVR in Rainfall Forecasting.- An Improved Weighted ELM with Krill Herd Algorithm for Imbalanced Learning.- Fast Pseudo Random Forest using Discrimination Hyper-space.- A Fast Video Vehicle Detection Approach based on Improved Adaboost Classifier.- Detection of Repetitive Forex Chart Patterns.- Damage Estimation from Cues of Image Change.- Identifying Deceptive Review Comments with Rumor and Lie Theories.- Identifying Fake Review Comments for Hostel Industry.- Planning and Routing Problems.- Multi-UAV Cooperative Path Planning for Sensor Placement Using Cooperative Coevolving Genetic Strategy.- Optimal Micro-Siting Planning Considering Long-Term Electricity Demand.- A Hyper-Heuristic Method for UAV Search Planning.- An Efficient MVMO-SH Method for Optimal Capacitor Allocation in Electric Power Distribution Systems.- A Method of New Workers' Accurate Acceptance of Tasks Using Capable Sensing.- A Genetic Mission Planner for Solving Temporal Multi-agent Problems with Concurrent Tasks.- Reformulation and Metaheuristic for the Team Orienteering Arc Routing Problem.- Application of Smell Detection Agent Based Algorithm for Optimal Path Identification by SDN Controllers.- A Comparison of Heuristic Algorithms for Bus Dispatch.- Simulation and Application of Algorithms CVRP to Optimize the Transport of Minerals Metallic and Nonmetallic by Rail for Export.- Dialog System Applications.- User Intention Classification in An Entities Missed In-vehicle Dialog System.- An Exploratory Study of Factors Affecting Number of Fans onFacebook Based on Dialogic Theory.- Assembling Chinese-Mongolian Speech Corpus via Crowdsourcing.- Robotic Control.- Developing Robot Drumming Skill with Listening-Playing Loop.- Evaluation of Parameters of Transactions When Remote Robot Control.- Desktop Gestures Recognition for Human Computer Interaction.- Approach to the Diagnosis and Configuration of Servo Drives in Heterogeneous Machine Control Systems.- Other Applications.- Gravitational Search Algorithm in Recommendation Systems.- A Driver Model Based on Emotions.- A Binaural Signal Synthesis Approach for Fast Rendering of Moving Sound.- Semantic Evolutionary Visualization.

This book and its companion volumes, LNCS volumes 10385 and 10386, constitute the proceedings of the 8th International Conference on Swarm Intelligence, ICSI 2017 held from July 27 to August 1, 2017, in Fukuoka, Japan. ICSI 2017 was held in conjunctionwith the Second International Conference on Data Mining and Big Data (DMBD 2017)at Fukuoka, Japan, to share common mutual ideas, promote transverse fusion, and stimulate innovation. ICSI 2017 received 267 submissions from about 512 authors in 35 countries and regions across six continents. Each submission was reviewed by at least two, and on average 2.9 reviewers. Based on rigorous reviews by the Program Committee members and reviewers, 133 high-quality papers were selected for publication in this proceedings volume, with an acceptance rate of 49.81%. The papers are organized in 24 cohesive sections covering all major topics of swarm intelligence and computational intelligence research and development.

• Urban Systems Modelling.- Towards an Agent-Based Simulation of Housing in Urban Beirut.- Simulating Urban Growth with Raster and Vector models: A case study for the city of Can Tho, Vietnam.- Integrating Behavior and Microsimulation Models.- Agent-Based Modelling for Urban Planning Current Limitations and Future Trends.- Traffic Simulation in Urban Modelling.- Software Architecture for a Transparent and Versatile Traffic Simulation.- A Generic Software Framework for Carsharing Modelling based on a Large-Scale Multi-Agent Traffic Simulation Platform.- Mapping bicycling patterns with an agent-based model, census and crowdsourced data.- Transportation in Agent-Based Urban Modelling.- Applications.- Simulation-aided Crowd Management: a Multi-scale Model for an Urban Case Study.- A National Heat Demand Model for Germany.- How Smart is the Smart City? Assessing the Impact of ICT on Cities.
• (source: Nielsen Book Data)

This book constitutes revised, selected, and invited papers from the First International Workshop on Agent Based Modelling of Urban Systems, ABMUS 2016, held in conjunction with AAMAS 2016 in Singapore in May 2016. The 11 papers presented in this volume were carefully reviewed and selected from 20 submissions. They were organized in topical sections named: urban systems modeling; traffic simulation in urban modeling; and applications. .
(source: Nielsen Book Data)

• Graphs that arise from the Web and various user activities on the Web.- The development of high performance algorithms and applications that exploit graphs.- Graph-theoretic and algorithmic aspects of related complex networks.- Social networks, citation networks, biological networks
• molecular networks.- Other networks arising from the Internet.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 14th International Workshop Algorithms and Models for the Web Graph, WAW 2017, held in Toronto, ON, Canada, in June 2017. The 7 full papers presented in this volume were carefully reviewed and selected from 14 submissions. The papers are organized around topics such as graphs that arise from the Web and various user activities on the Web; the development of high Performance algorithms and applications that exploit these graphs; graph-theoretic and algorithmic aspects of related complex networks; social networks, citation networks, biological networks; molecular networks, and other networks arising from the Internet.
(source: Nielsen Book Data)

• Multi-Message Broadcast in Dynamic Radio Networks.- Global Synchronization and Consensus Using Beeps in a Fault-Prone MAC 16.- Vertex Coloring with Communication and Local Memory Constraints in Synchronous Broadcast Networks.- A New Kind of Selectors, and Their Applications to Conflict Resolution in Wireless Multi-channels Networks.- The Impact of the Gabriel Sub-graph of the Visibility Graph on the Gathering of Mobile Autonomous Robots.- Search-and-Fetch with One Robot on a Disk.- A 2-Approximation Algorithm for Barrier Coverage by Weighted Non-uniform Sensors on a Line.- Flexible Cell Selection in Cellular Networks.- The Euclidean k-Supplier Problem in IR2.
• (source: Nielsen Book Data)

This book constitutes revised selected papers from the 12th International Symposium on Algorithms and Experiments for Wireless Sensor Networks, ALGOSENSORS 2016, held in Aarhus, Denmark, in August 2016. The 9 full papers presented in this volume were carefully reviewed and selected from 19 submissions. This year papers were solicited into three tracks: Distributed and Mobile, Experiments and Applications, and Wireless and Geometry.
(source: Nielsen Book Data)

• Reconciling Humans and Technology: The Role of Ambient Intelligence.- Engineering a Cyber-Physical Intersection Management.- Exploring Design Opportunities for Intelligent Worker Assistance: A new Approach Using Projection-Based AR and a Novel Hand-Tracking Algorithm.- AuthentiCap - A Touchless Vehicle Authentication and Personalization System.- Indoor Localization Based on Passive Electric Field Sensing.- Safety Services in Smart Environments using Depth Cameras.- Contextual Requirements Prioritization and its Application to Smart Homes.- Voices and Views of Informal Caregivers: Investigating Ambient Assisted Living Technologies.- Easy to Install Indoor Positioning System that Parasitizes Home Lighting.- Exploring the Use of Ambient WiFi Signals to Find Vacant Houses.- A Framework for Distributed Interaction in Intelligent Environments.- Which mobile health toolkit should a service provider choose? A comparative evaluation of Apple HealthKit, Google Fit, and Samsung Digital Health Platform.- Visual End-User Programming of Personalized AAL in the Internet of Things.- Opportunities for Biometric Technologies in Smart Environments.- New approach for optimizing the usage of situation recognition algorithms within IoT domains.- HUDConCap - Automotive Head-Up Display Controlled with Capacitive Proximity Sensing.- E-textile Couch: Towards Smart Garments integrated Furniture.- Context-Aware Monitoring Agents For Ambient Assisted Living Applications.- Mobility competencies of people with Down syndrome supported by technical assistance - Results of the Requirement Analysis in POSEIDON Project.- An Exploratory Study on Electric Field Sensing.- A Framework for Responsive Environments.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th European Conference on Ambient Intelligence, AmI 2017, held in Malaga, Spain, in April 2017. The 16 revised full papers presented together with 4 short papers and 1 keynote paper were carefully reviewed and selected from 48 submissions. The papers cover topics such as: Enabling technologies, methods and platforms; objectives and approaches of ambient intelligence and internet of things; from information design to interaction and experience design, and application areas of AmI and IoT.
(source: Nielsen Book Data)

• EvoSET: Hybrid Algorithms Based on Integer Programming for the Search of Prioritized Test Data in Software Product Lines.- On the Use of Smelly Examples to Detect Code Smells in JavaScript.- Deep Parameter Tuning of Concurrent Divide and Conquer Algorithms in Akka.- Focusing Learning-based Testing away from Known Weaknesses.- Polytypic Genetic Programming.- Evolving Rules for Action Selection in Automated Testing via Genetic Programming - A First Approach.- EvoSTOC: A New Multi-swarm Particle Swarm Optimization for Robust Optimization over Time.- The Static and Stochastic VRP with Time Windows and Both Random Customers and Reveal Times.- Pre-Scheduled Colony Size Variation in Dynamic Environments.- An Online Packing Heuristic for the Three-dimensional Container Loading Problem in Dynamic Environments and the Physical Internet.- Advancing Dynamic Evolutionary Optimization Using In-Memory Database Technology.- Road Traffic Rules Synthesis Using Grammatical Evolution.- Solving Dynamic Graph Coloring Problem Using Dynamic Pool Based Evolutionary Algorithm.- General: Meta-Heuristics for Improved RF Emitter Localization.- Automated Design of Genetic Programming Classification Algorithms Using a Genetic Algorithm.
• (source: Nielsen Book Data)

The two volumes LNCS 10199 and 10200 constitute the refereed conference proceedings of the 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017, held in Amsterdam, The Netherlands, in April 2017, colocated with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented together with 26 poster papers were carefully reviewed and selected from 108 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoKNOW (knowledge incorporation in evolutionary computation), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoROBOT (evolutionary robotics), EvoSET (nature-inspired algorithms in software engineering and testing), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments). .
(source: Nielsen Book Data)

• EvoSET: Hybrid Algorithms Based on Integer Programming for the Search of Prioritized Test Data in Software Product Lines.- On the Use of Smelly Examples to Detect Code Smells in JavaScript.- Deep Parameter Tuning of Concurrent Divide and Conquer Algorithms in Akka.- Focusing Learning-based Testing away from Known Weaknesses.- Polytypic Genetic Programming.- Evolving Rules for Action Selection in Automated Testing via Genetic Programming - A First Approach.- EvoSTOC: A New Multi-swarm Particle Swarm Optimization for Robust Optimization over Time.- The Static and Stochastic VRP with Time Windows and Both Random Customers and Reveal Times.- Pre-Scheduled Colony Size Variation in Dynamic Environments.- An Online Packing Heuristic for the Three-dimensional Container Loading Problem in Dynamic Environments and the Physical Internet.- Advancing Dynamic Evolutionary Optimization Using In-Memory Database Technology.- Road Traffic Rules Synthesis Using Grammatical Evolution.- Solving Dynamic Graph Coloring Problem Using Dynamic Pool Based Evolutionary Algorithm.- General: Meta-Heuristics for Improved RF Emitter Localization.- Automated Design of Genetic Programming Classification Algorithms Using a Genetic Algorithm.
• (source: Nielsen Book Data)

The two volumes LNCS 10199 and 10200 constitute the refereed conference proceedings of the 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017, held in Amsterdam, The Netherlands, in April 2017, colocated with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented together with 26 poster papers were carefully reviewed and selected from 108 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoKNOW (knowledge incorporation in evolutionary computation), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoROBOT (evolutionary robotics), EvoSET (nature-inspired algorithms in software engineering and testing), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments). .
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Symposium on Applied Reconfigurable Computing, ARC 2017, held in Delft, The Netherlands, in April 2017. The 17 full papers and 11 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They are organized in topical sections on adaptive architectures, embedded computing and security, simulation and synthesis, design space exploration, fault tolerance, FGPA-based designs, neural neworks, and languages and estimation techniques.
(source: Nielsen Book Data)

• Architectures.- Mathematical foundations.- Algorithms.- Safety.- Understanding.- Human cognition.- Philosophy.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 10th International Conference on Artificial General Intelligence, AGI 2017, held in Melbourne, VIC, Australia, in August 2017. The 24 regular papers presented in this book together with 1 short paper were carefully reviewed and selected from 35 submissions. They cover topics such as architectures; mathematical foundations; algorithms; safety; understanding; human cognition; and philosophy.
(source: Nielsen Book Data)

• Intro; Preface; Organization; Contents
• Part I; Contents
• Part II; Neural Networks and Their Applications; Author Profiling with Classification Restricted Boltzmann Machines; 1 Introduction; 2 Author Profile Dimensions; 3 Restricted Boltzmann Machines; 4 Probabilities and Gradients; 4.1 Discriminative Training; 4.2 Generative Training; 5 Evaluation Datasets; 6 Experiments and Results; 6.1 Overall Results; 7 Conclusions; References; Parallel Implementation of the Givens Rotations in the Neural Network Learning Algorithm; 1 Introduction; 2 Givens Elimination Step; 3 Givens QR Decomposition.

• 4 QR Decomposition in Neural Network Weights Update5 Parallel Implementation; 6 Simulation Results; 7 Conclusion; References; Parallel Levenberg-Marquardt Algorithm Without Error Backpropagation; 1 Introduction; 2 Parallel Realisation; 2.1 Calculating the Weight Derivatives Without Error Backpropagation; 2.2 Calculating the A Matrix and the Gradient Vector; 2.3 The QR Decomposition Based on the Householser Reflections; 3 Computational Results; 4 Conclusions; References; Spectral Analysis of CNN for Tomato Disease Identification; Abstract; 1 Introduction; 2 Related Works.

• 3 Spectral Analysis of CNN for Tomato Disease3.1 Deep Visualization of CNN; 3.2 Color Sensitivity of RGB Images; 3.3 Sensitivity to Color with Different Wavelength Values; 3.3.1 Visible Spectrum of Images; 4 Experimental Results; 4.1 Dataset Description; 4.2 CNN Activations and Features Visualization; 4.2.1 Activations of Neurons; 4.2.2 RGB Color Sensitivity; 4.2.3 Feature Maps; 5 Conclusion and Future Work; Acknowledgments; References; From Homogeneous Network to Neural Nets with Fractional Derivative Mechanism; 1 Introduction; 2 Weight Distribution with Fractional Calculus.

• 3 Fractional Derivative Inside Neuron Transfer Function4 The Fractional Mechanism Within 2D Homogeneous Network; 5 Conclusion; References; Neurons Can Sort Data Efficiently; 1 Introduction; 2 Models of Neurons, Receptors, and the Senses; 2.1 Sensory Fields and Sensors; 2.2 Extreme, Sensory and Object Neurons; 3 Simplistic Sequential Neural Associative Sorting; 4 Conclusions and Remarks; References; Avoiding Over-Detection: Towards Combined Object Detection and Counting; 1 Introduction; 2 Related Work; 2.1 Deep Learning Methods for Object Detection; 2.2 Deep Learning Methods for Cell Detection.

• 3 Method3.1 Loss Function; 3.2 Model Architecture; 4 Results; 5 Conclusion; References; Echo State Networks Simulation of SIR Distributed Control; 1 Introduction; 2 Echo State Networks; 3 SIR Model with Delay and Spatial Diffusions; 3.1 Distributed Optimal Control Problem; 4 Discretisation and Adaptive Critic Neural Networks Solution of the Distributed Optimal Control; 4.1 Numerical Simulation; 5 Conclusion; References; The Study of Architecture MLP with Linear Neurons in Order to Eliminate the ``vanishing Gradient'' Problem; 1 Introduction; 2 Nonlinearity capabilities of deep neural networks.

The two-volume set LNAI 10245 and LNAI 10246 constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, held in Zakopane, Poland in June 2017. The 133 revised full papers presented were carefully reviewed and selected from 274 submissions. The papers included in the second volume are organized in the following five parts: data mining; artificial intelligence in modeling, simulation and control; various problems of artificial intelligence; special session: advances in single-objective continuous parameter optimization with nature-inspired algorithms; special session: stream data mining.
(source: Nielsen Book Data)
The two-volume set LNAI 10245 and LNAI 10246 constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, held in Zakopane, Poland in June 2017. The 133 revised full papers presented were carefully reviewed and selected from 274 submissions. The papers included in the first volume are organized in the following five parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; computer vision, image and speech analysis; and bioinformatics, biometrics and medical applications.
(source: Nielsen Book Data)

• Intro; Preface; Organization; Contents
• Part II; Contents
• Part I; Data Mining; Computer Based Stylometric Analysis of Texts in Polish Language; 1 Introduction; 2 Features Used in Stylometry; 3 Performed Stylometric Analysis; 3.1 Data Sets and Classes; 3.2 Feature Generation Methods; 3.3 Classification; 4 Results; 4.1 Stylometric Features; 4.2 Training Set Size; 4.3 Classifiers Accuracy; 4.4 Statistical Features Importance; 4.5 Grammatical Classes Importance; 5 Summary; References; Integration Base Classifiers Based on Their Decision Boundary; 1 Introduction; 2 Basic Concept.

• 3 Proposed Method4 Experimental Studies; 5 Conclusion; References; Complexity of Rule Sets Induced by Two Versions of the MLEM2 Rule Induction Algorithm; 1 Introduction; 2 Incomplete Data Sets; 3 Probabilistic Approximations; 4 Rule Induction; 4.1 True MLEM2; 4.2 Emulated MLEM2; 5 Experiments; 6 Conclusions; References; Spark-Based Cluster Implementation of a Bug Report Assignment Recommender System; 1 Introduction; 2 Related Work; 3 The Recommender System; 3.1 Datasets; 3.2 Cleansing; 3.3 Preprocessing and Feature Reduction; 3.4 Training the Recommender; 3.5 Implementation.

• 4 Results and Discussion4.1 Results of Dimensionality Reduction Techniques; 4.2 Choice of SVM Kernel; 4.3 Comparison with Previous Approaches and Scalability; 5 Conclusion; References; The Bag-of-Words Method with Dictionary Analysis by Evolutionary Algorithm; 1 Introduction; 2 Description of Proposed Methods; 3 Experimental Research; 4 Conclusions; References; The Novel Method of the Estimation of the Fourier Transform Based on Noisy Measurements; 1 Introduction; 2 Orthogonal Series Estimation of Regression Function and Its Spectrum.

• 3 Nonparametric Orthogonal Series Estimation of Fourier Transform4 Simulation Example; 5 Remarks and Extensions; References; A Complete Efficient FFT-Based Algorithm for Nonparametric Kernel Density Estimation; 1 Introduction; 2 Problem Demonstration; 3 The Improved FFT-Based Algorithm for Density Estimation; 4 Binning; 5 Experiments; 6 Conclusions; References; A Framework for Business Failure Prediction; 1 Introduction; 2 Related Work; 3 The Dataset and the Proposed Framework; 3.1 Details of Dataset; 3.2 Proposed Framework; 4 Performance Evaluation Results; 5 Conclusions; References.

• Fuzzy Clustering with -Hyperballs and Its Application to Data Classification1 Introduction; 2 Fuzzy Clustering with -Hyperballs; 3 FCH Based Classification; 4 Results and Discussion; 5 Conclusions; References; Two Modifications of Yinyang K-means Algorithm; 1 Introduction; 2 Yinyang K-means; 3 Modifications of the Algorithm; 3.1 Regrouping of Centroids; 3.2 Initial Grouping and Regrouping Using Same-Size K-means; 4 Experimental Results; 5 Conclusions; References; Detection of the Innovative Logotypes on the Web Pages; 1 Introduction; 2 Related Works vs. Proposed Approach.

The two-volume set LNAI 10245 and LNAI 10246 constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, held in Zakopane, Poland in June 2017. The 133 revised full papers presented were carefully reviewed and selected from 274 submissions. The papers included in the second volume are organized in the following five parts: data mining; artificial intelligence in modeling, simulation and control; various problems of artificial intelligence; special session: advances in single-objective continuous parameter optimization with nature-inspired algorithms; special session: stream data mining.
(source: Nielsen Book Data)

• Artificial Life and Computational Intelligence.- Extending the Delaunay Triangulation Based Density Measurement to Many-objective Optimization.- Emotion, Trustworthiness and Altruistic Punishment in a Tragedy of the Commons Social Dilemma.- Equity Option Strategy Discovery and Optimization Using a Memetic Algorithm.- Co-Evolving Line Drawings with Hierarchical Evolution.- Reliability estimation of individual multi-target regression predictions.- Feedback Modulated Attention Within a Predictive Framework.- A Batch Infill Strategy for Computationally Expensive Optimization Problems.- Automatic Clustering and Summarisation of Microblogs: A Multi-Subtopic Phrase Reinforcement Algorithm.- Generation and exploration of architectural form using a composite Cellular Automata.- Wrapper Feature Construction for Figure-ground Image Segmentation Using Genetic Programming.- Surrogate-assisted Multi-swarm Particle Swarm Optimization of Morphing Airfoils.- Applying Dependency Patterns in Causal Discovery of Latent Variable Models.- An Evolutionary Multi-criteria Journey Planning Algorithm for Multi-modal Transportation Networks.- Estimating Passenger Preferences Using Implicit Relevance Feedback for Personalized Journey.- Quantitative Assessment of Hearts Function: A Hybrid Mechanism for Left Ventricles Segmentation from Cine MRI Sequences.- A Hybrid feature selection scheme based on local compactness and global separability for improving roller bearing diagnostic performance.- Reliable Fault Diagnosis of Bearings Using Distance and Density Similarity on an Enhanced k-NN.- Towards Solving TSPN with Arbitrary Neighborhoods: A Hybrid Solution.- Detectable Genetic Algorithms-based techniques for solving Dynamic Optimisation Problem with Unknown Active Variables.- Neighbourhood analysis: a case study on Google Machine Reassignment Problem.- Optimisation Algorithms and Applications.- Multi-Objective Optimisation with Multiple Preferred Regions.- An Adaptive Memetic Algorithm for the Architecture Optimisation Problem.- Resource Constrained Job Scheduling with Parallel Constraint-based ACO.- An Iterated Local Search with Guided Perturbation for the Heterogeneous Fleet Vehicle Routing Problem with Time Windows and Three-Dimensional Loading Constraints.- A Memetic Cooperative Co-evolution Model for Large Scale Continuous Optimization.- Investigating the Generality of Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling with Machine Breakdown.- Exploratory Analysis of Clustering Problems Using a Comparison of Particle Swarm Optimization and Differential Evolution.- A PSO-based Reference Point Adaption Method for Genetic Programming Hyper-heuristic in Many-Objective Job Shop Scheduling.- Optimal power allocation of wireless sensor networks with multi-operator based constrained differential evolution.- CEMAB: A Cross-Entropy-based Method for Large-Scale Multi-Armed Bandits.- Binary PSO for Web Service Location-Allocation.- A MOEA/D with Non-uniform Weight Vector Distribution Strategy for Solving the Unit Commitment Problem in Uncertain Environment.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Third Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017, held in Geelong, VIC, Australia, in January/February 2017. The 32 papers presented in this volume were carefully reviewed and selected from 47 submissions. They were organized in topical sections named: artificial life and computational intelligence and optimization algorithms and applications. .
(source: Nielsen Book Data)