Search Results - (((((((ant OR manthe) OR mind) OR semantic) OR cantor) OR anne) OR halten) OR rantiing) algorithms.

Practical Strategies in Geriatric Mental Health : Cases and Approaches. by Dunn, Laura B.

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Biological computation by Lamm, Ehud

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A Primer on Machine Learning Applications in Civil Engineering by Deka, Paresh Chandra

Table of Contents: “…Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- A Primer on Machine Learning Applications in Civil Engineering -- Author -- 1: Introduction -- 1.1 Machine Learning -- 1.2 Learning from Data -- 1.3 Research in Machine Learning: Recent Progress -- 1.4 Artificial Neural Networks -- 1.5 Fuzzy Logic (FL) -- 1.6 Genetic Algorithms -- 1.7 Support Vector Machine (SVM) -- 1.8 Hybrid Approach (HA) -- Bibliography -- 2: Artificial Neural Networks -- 2.1 Introduction to Fundamental Concepts and Terminologies -- 2.2 Evolution of Neural Networks -- 2.3 Models of ANN -- 2.4 McCulloch-Pitts Model -- 2.5 Hebb Network -- 2.6 Summary -- 2.7 Supervised Learning Network -- 2.7.1 Perceptron Network -- 2.7.2 Adaptive Linear Neuron -- 2.7.3 Back-Propagation Network -- 2.7.4 Radial Basis Function Network -- 2.7.5 Generalized Regression Neural Networks -- 2.7.6 Summary -- 2.8 Unsupervised Learning Networks -- 2.8.1 Introduction -- 2.8.2 Kohonen Self-Organizing Feature Maps -- 2.8.3 Counter Propagation Network -- 2.8.4 Adaptive Resonance Theory Network -- 2.8.5 Summary -- 2.9 Special Networks -- 2.9.1 Introduction -- 2.9.2 Gaussian Machine -- 2.9.3 Cauchy Machine -- 2.9.4 Probabilistic Neural Network -- 2.9.5 Cascade Correlation Neural Network -- 2.9.6 Cognitive Network -- 2.9.7 Cellular Neural Network -- 2.9.8 Optical Neural Network -- 2.9.9 Summary -- 2.10 Working Principle of ANN -- 2.10.1 Introduction -- 2.10.2 Types of Activation Function -- 2.10.3 ANN Architecture -- 2.10.4 Learning Process -- 2.10.5 Feed-Forward Back Propagation -- 2.10.6 Strengths of ANN -- 2.10.7 Weaknesses of ANN -- 2.10.8 Working of the Network -- 2.10.9 Summary -- Bibliography -- 3: Fuzzy Logic -- 3.1 Introduction to Classical Sets and Fuzzy Sets -- 3.1.1 Classical Sets -- 3.1.2 Fuzzy Sets -- 3.1.3 Summary.…”
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Artificial intelligence research and development : current challenges, new trends and applications

Table of Contents: “…Step 1: DrugBank -- Unsupervised Systems-Level Learning Mechanism Based on Bayesian-Inference -- Towards a Universal Neural Network Encoder for Time Series -- Determining Classic Versus Modern Style in Fashion -- Data Science, Recommender Systems and Case-Based Reasoning -- Big Data Analytics for Obesity Prediction -- A Method to Integrate Semantic Criteria into a Recommender System Based on ELECTRE Outranking Relations -- A Study on Contextual Influences on Automatic Playlist Continuation.…”
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DEEP LEARNING FOR HEALTHCARE DECISION MAKING.

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Microwave and millimeter wave circuits and systems : emerging design, technologies, and applications

Table of Contents: “…1.1.7 MBF Model -- the Memoryless PA Behavioural Model of ChoiceAcknowledgements; References; 2 Artificial Neural Network in Microwave Cavity Filter Tuning; 2.1 Introduction; 2.2 Artificial Neural Networks Filter Tuning; 2.2.1 The Inverse Model of the Filter; 2.2.2 Sequential Method; 2.2.3 Parallel Method; 2.2.4 Discussion on the ANN's Input Data; 2.3 Practical Implementation -- Tuning Experiments; 2.3.1 Sequential Method; 2.3.2 Parallel Method; 2.4 Influence of the Filter Characteristic Domain on Algorithm Efficiency; 2.5 Robots in the Microwave Filter Tuning; 2.6 Conclusions; Acknowledgement…”
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The Big Bang Theory and Philosophy : Rock, Paper, Scissors, Aristotle, Locke

Table of Contents: “…Cover; Contents; Title; Copyright; Acknowledgments; Dedication; Introduction: "Unraveling the Mysteries"; Part One: "It All Began on A Warm Summer's Evening in Greece"; Chapter 1: Aristotle on Sheldon Cooper; The Life of the Mind; The Ancient Greek and the Modern Geek; The Joy of Geekdom; Geeky Fun and the Purpose of Life; Trial of a Nerd; The Aristotle-Cooper Evaluation; Chapter 2: "You're a Sucky, Sucky Friend"; "Do You Have Any Books about Making Friends?"…”
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D Cookbook. by D. Ruppe, Adam

Table of Contents: “…Performing type conversionsFinding the largest files in a directory; Creating a network client and server; Using Base64 to create a data URI; Generating random numbers; Normalizing a string and performing Unicode comparisons; Searching with regular expressions; Writing a digest utility; Using std.zlib compression; Using the std.json module; Chapter 3: Ranges; Introduction; Using ranges when implementing an algorithm; Creating an input range; Creating an output range; Creating a higher-order range; Putting a range interface on a collection; Creating an input range over a tree structure…”
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Towards Cognitive Autonomous Networks : Network Management Automation for 5G and Beyond. by Mwanje, Stephen S.

Table of Contents: “…Cover -- Title Page -- Copyright -- Contents -- List of Contributors -- Foreword I -- Foreword II -- Preface -- Chapter 1 The Need for Cognitive Autonomy in Communication Networks -- 1.1 Complexity in Communication Networks -- 1.1.1 The Network as a Graph -- 1.1.2 Planes, Layers, and Cross-Functional Design -- 1.1.3 New Network Technology -- 5G -- 1.1.4 Processes, Algorithms, and Automation -- 1.1.5 Network State Changes and Transitions -- 1.1.6 Multi-RAT Deployments -- 1.2 Cognition in Network Management Automation -- 1.2.1 Business, Service and Network Management Systems…”
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Exploratory data analysis with MATLAB by Martinez, Wendy L., Martinez, Angel R., Solka, Jeffrey

Table of Contents: “…5.5.2 Probabilistic Latent Semantic Analysis5.6 Minimum Spanning Trees and Clustering; 5.6.1 Definitions; 5.6.2 Minimum Spanning Tree Clustering; 5.7 Evaluating the Clusters; 5.7.1 Rand Index; 5.7.2 Cophenetic Correlation; 5.7.3 Upper Tail Rule; 5.7.4 Silhouette Plot; 5.7.5 Gap Statistic; 5.7.6 Cluster Validity Indices; 5.8 Summary and Further Reading; Exercises; Chapter 6 Model-Based Clustering; 6.1 Overview of Model-Based Clustering; 6.2 Finite Mixtures; 6.2.1 Multivariate Finite Mixtures; 6.2.2 Component Models -- Constraining the Covariances; 6.3 Expectation-Maximization Algorithm.…”
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Contemporary Ergonomics 1998. by Hanson, Margaret A.

Table of Contents: “…Development of a questionnaire to measure attitudes towards virtual reality -- Orientation of blind users on the World Wide Web -- F̃lash, splash and crash:̃ Human factors and the implementation of innovative Web technologies -- Determining ergonomic factors in stress from work demands of nurses -- A risk assessment and control cycle approach to managing workplace stress -- Teleworking: Assessing the risks -- Evaluating teleworking -- case study -- Team organisational mental models: an integrative framework for research -- The impact of ITT on virtual team working in the European automotive industry -- The effect of communication processes upon workers and job efficiency -- A case study of job design in a steel plant -- The effects of age and habitual physical activity on the adjustment to nocturnal shiftwork -- Job design for university technicians: work activity and allocation of function -- Allocation of functions and manufacturing job design based on knowledge requirements -- The need to specify cognition within system requirements -- Analysis of complex communication tasks -- Health and safety as the basis for specifying information systems design requirements -- Cognitive algorithms -- Rapid prototyping in foam of 3D anthropometric computer models in functional postures -- The use of high and low level prototyping methods for product user interfaces -- Creative collaboration in engineering design teams -- Pleasure and product semantics -- A survey of usability practice and needs in Europe -- Cultural influence in usability assessment -- Interface display designs based on operator knowledge requirements -- Understanding what makes icons effective: how subjective ratings can inform design -- Representing uncertainty in decision support systems: the state of the art.…”
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Leadership and cultural webs in organisations : Weavers' tales by McLean, A. J. (Adrian J.)

Table of Contents: “…Front Cover; Leadership and Cultural Webs in Organisations: Weavers' Tales; Copyright Page; Contents; Preface; ORGsimONE Cards; References; Acknowledgements; Chapter 1 Beyond the Holy Grail; Three Envelopes; Know Your Maps; A Quiet Revolution; A Lesson in Hubris; Bateson's Dog; Structure of the Book; References; Chapter 2 Seeing with Our Minds; In Search of Meaning ... ; Early Excursions; Meaning is Neither Universal Nor Fixed; Cultures as Webs of Signification; Behaviour Is Symbolic ... …”
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A cabinet of philosophical curiosities : a collection of puzzles, oddities, riddles, and dilemmas by Sorensen, Roy A.

Table of Contents: “…; Emily Dickinson's Hummingbird; Plato's Packing Problem; Telepathy for the Absent-Minded; Order of Absence versus Absence of Order; Neglect of the Absent; Child Proof; Wittgenstein's Parallelograms; Knowing the Area of a Parallelogram; Freud versus the Dreaming Logicians; Do Butterflies Dream?…”
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Pediatric incontinence : evaluation and clinical management

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Knowledge mining using intelligent agents

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Digitalization of Society and Socio-Political Issues. 1, Digital, Communication, and Culture

Table of Contents: “…The Digitalization of Cultural Policies in France 149; Anne BELLON 14.1.…”
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Decision Intelligence for Dummies by Baker, Pamela

Table of Contents: “…. -- Doing the right math but asking the wrong question -- Dealing with conflicting outputs -- Battling AIs -- Chapter 10 Meddling Data and Mindful Humans -- Engaging with Decision Theory -- Working with your gut instincts -- Looking at the role of the social sciences -- Examining the role of the managerial sciences -- The Role of Data Science in Decision Intelligence -- Fitting data science to decision intelligence -- Reimagining the rules -- Expanding the notion of a data source -- Where There's a Will, There's a Way -- Chapter 11 Decisions at Scale -- Plugging and Unplugging AI into Automation -- Dealing with Model Drifts and Bad Calls -- Reining in AutoML -- Seeing the Value of ModelOps -- Bracing for Impact -- Decide and dedicate -- Make decisions with a specific impact in mind -- Chapter 12 Metrics and Measures -- Living with Uncertainty -- Making the Decision -- Seeing How Much a Decision Is Worth -- Matching the Metrics to the Measure -- Leaning into KPIs -- Tapping into change data.…”
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Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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Theoretical Computer Science : Proceedings of the 10th Italian Conference on ICTCS '07. by Italiano, Giuseppe F.

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Selection of main mechanical ventilators for underground coal mines : a case study by Jha, Ajay Kumar

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