Search Results - (((((((want OR when) OR semantic) OR art) OR cantor) OR anne) OR maarten) OR santis) algorithms.

Convergence : the redesign of design by Deutsch, Randy

Table of Contents: “…The Point Where All of the Intelligence Converges ENDNOTES; IMAGES; CHAPTER 4 PARAMETRICS AND COMPUTATION ; BIM Tools ; Challenges to the Convergence ; Computational Tools ; On the Evolution of a Parametric/Computational Convergence ; Simulation and Automation ; The Algorithm of Everything ; Algorithms and Machine Learning ; ENDNOTES; IMAGES; CHAPTER 5 VIRTUAL AND PHYSICAL ; Advances in Virtual Reality ; VR and Reality Capture ; Reality Computing ; Uses for VR in Architecture ; VR and AR in Architecture, Engineering, and Construction ; Leveraging Video Games in AEC.…”
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Establishing Sufficient Temporal Independence Efficiently : a Monitoring Approach. by Neukirchner, Moritz

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Delphi High Performance : Build fast Delphi applications using concurrency, parallel programming and memory management. by Gabrijelčič, Primož

Table of Contents: “…; Different types of speed; Algorithm complexity; Big O and Delphi data structures; Data structures in practice; Mr. …”
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Management practice for wastewater pipelines : synthesis report by Sinha, Sunil K., Ge, Shaoqing, Ott, Dann, Nelson, Doug, Oram, Peter, Patton, Shawn, Rose, Duncan

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The linguistic cerebellum

Table of Contents: “…VOICE RECORDING MATERIALVOICE RECORDING SESSION; ANALYSIS ALGORITHM AND EFFECTS; SPINOCEREBELLAR ATAXIA DIAGNOSIS USING SPEECH ANALYSIS; ACOUSTIC FINDINGS IN CEREBELLAR PATIENTS; CONCLUSION; REFERENCES; 8 -- Cerebellum and Writing; INTRODUCTION; WRITING; AGRAPHIA; CEREBELLUM; DISCUSSION; CONCLUSION; REFERENCES; 9 -- The Role of the Cerebellum in Developmental Dyslexia; INTRODUCTION; IS THE CEREBELLUM PART OF THE READING NETWORK?…”
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Approximate dynamic programming : solving the curses of dimensionality by Powell, Warren B., 1955-

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Cooperative Control of Multi-Agent Systems : Theory and Applications

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Informatics for Health by Randell, R.

Table of Contents: “…An Automatic Approach for Analyzing Treatment Effectiveness Based on Medication Hierarchy -- The Myocardial Infarction Case Study -- Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data -- Dermatology Disease Prediction Based on Two Step Cascade Genetic Algorithm Optimization of ANFIS Parameters -- Querying EHRs with a Semantic and Entity-Oriented Query Language -- Evaluation of the Terminology Coverage in the French Corpus LiSSa -- Linked Data Applications Through Ontology Based Data Access in Clinical Research -- Epidemiological Models Lacking Process Noise Can Be Overconfident -- Disentangling Prognostic and Predictive Biomarkers Through Mutual Information -- IntegrIT -- Towards Utilizing the Swedish National Health Information Exchange Platform for Clinical Research -- Introducing a Method for Transformation of Paper-Based Research Data into Concept-Based Representation with openEHR -- The P̀EARL' Data Warehouse: Initial Challenges Faced with Semantic and Syntactic Interoperability -- Combining Different Privacy-Preserving Record Linkage Methods for Hospital Admission Data -- Application of Correspondence Analysis to Graphically Investigate Associations Between Foods and Eating Locations -- Data Driven Quality Improvement of Health Professions Education: Design and Development of CLUE -- An Interactive Curriculum Data Visualization Tool -- Developing Healthcare Data Analytics APPs with Open Data Science Tools -- Fast and Efficient Feature Engineering for Multi-Cohort Analysis of EHR Data -- Development and Evaluation of a Case-Based Retrieval Service -- Learning Differentially Expressed Gene Pairs in Microarray Data -- Developing a Manually Annotated Corpus of Clinical Letters for Breast Cancer Patients on Routine Follow-Up -- Automated Diagnosis Coding with Combined Text Representations.…”
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Mount Sinai expert guides. Gastroenterology

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Code halos : how the digital lives of people, things, and organizations are changing the rules of business by Frank, Malcolm

Table of Contents: “…The "Skinterface" Will Change the Consumer ExperienceIndustrial Amplifiers: Harnessing the Power of the Internet of Things; Smart Appliances Become Amplifiers in the Home; The Application Interface: Window to a Beautiful Experience; The Algorithm: Data Becomes Meaning; Facebook's EdgeRank Algorithm Makes It Personal; Successful Algorithms Power Code Connections and Create Insight; All Data, Great and Small; Netflix Lassos Data to Win in the Market; New Business Models Showcase Code Halo Value; Why Didn't They Tune in the Zune?…”
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Frames and Harmonic Analysis. by Kim, Yeonhyang

Table of Contents: “…Examples; References; The Kaczmarz algorithm, row action methods, and statistical learning algorithms; 1. …”
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Optimisation in Economic Analysis. by Mills, Gordon

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Human Behavior Analysis for Library and Information Science. by Greifender, Elke

Table of Contents: “…Covers; Editorial advisory board; Guest editorial; Developing a context-aware annotation system for Hakka culture-specific language learning; Is there any efficient reading strategy when using text signals for navigation in a long document?…”
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Computer and network security essentials

Table of Contents: “…SECURITY APPLICATIONS -- Chapter 25. Generic Semantics Specification and Processing for Inter-System Information Flow Tracking -- Chapter 26. …”
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Machine learning for protein subcellular localization prediction by Wan, Shibiao, Mak, M. W.

Table of Contents: “…5.2.2 Problem transformation methods -- 5.2.3 Multi-label classification in bioinformatics -- 5.3 mGOASVM: A predictor for both single- and multi-location proteins -- 5.3.1 Feature extraction -- 5.3.2 Multi-label multiclass SVM classification -- 5.4 AD-SVM: An adaptive decision multi-label predictor -- 5.4.1 Multi-label SVM scoring -- 5.4.2 Adaptive decision for SVM (AD-SVM) -- 5.4.3 Analysis of AD-SVM -- 5.5 mPLR-Loc: A multi-label predictor based on penalized logistic regression -- 5.5.1 Single-label penalized logistic regression -- 5.5.2 Multi-label penalized logistic regression -- 5.5.3 Adaptive decision for LR (mPLR-Loc) -- 5.6 Summary -- 6 Mining deeper on GO for protein subcellular localization -- 6.1 Related work -- 6.2 SS-Loc: Using semantic similarity over GO -- 6.2.1 Semantic similarity measures -- 6.2.2 SS vector construction -- 6.3 HybridGO-Loc: Hybridizing GO frequency and semantic similarity features -- 6.3.1 Hybridization of two GO features -- 6.3.2 Multi-label multiclass SVM classification -- 6.4 Summary -- 7 Ensemble random projection for large-scale predictions -- 7.1 Random projection -- 7.2 RP-SVM: A multi-label classifier with ensemble random projection -- 7.2.1 Ensemble multi-label classifier -- 7.2.2 Multi-label classification -- 7.3 R3P-Loc: A compact predictor based on ridge regression and ensemble random projection -- 7.3.1 Limitation of using current databases -- 7.3.2 Creating compact databases -- 7.3.3 Single-label ridge regression -- 7.3.4 Multi-label ridge regression -- 7.4 Summary -- 8 Experimental setup -- 8.1 Prediction of single-label proteins -- 8.1.1 Datasets construction -- 8.1.2 Performance metrics -- 8.2 Prediction of multi-label proteins -- 8.2.1 Dataset construction -- 8.2.2 Datasets analysis -- 8.2.3 Performance metrics -- 8.3 Statistical evaluation methods -- 8.4 Summary.…”
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Clinical Guide to Cardiology. by Camm, Christian Fielder

Table of Contents: “…Common diagnosis 2 -- Common diagnosis 3 -- Common diagnosis 4 -- Common diagnosis 5 -- Common diagnosis 6 -- Diagnosis to consider 1 -- Diagnosis to consider 2 -- 2.6 Key investigations -- Bedside -- Blood tests -- Imaging -- Special -- 2.7 When to call a senior -- Situations -- What should be completed by the time the senior arrives -- What should be arranged by the time the senior arrives -- Key clinical trials -- Key trial 2.1 -- Key trial 2.2 -- Key trial 2.3 -- Key trial 2.4 -- Guidelines -- 3 Shortness of Breath -- 3.1 Definition -- 3.2 Diagnostic algorithm -- 3.3 Differentials list…”
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Artificial intelligence for big data : complete guide to automating big data solutions using artificial intelligence techniques. by Deshpande, Anand

Table of Contents: “…Snowball stemming -- Lancaster stemming -- Lovins stemming -- Dawson stemming -- Lemmatization -- N-grams -- Feature extraction -- One hot encoding -- TF-IDF -- CountVectorizer -- Word2Vec -- CBOW -- Skip-Gram model -- Applying NLP techniques -- Text classification -- Introduction to Naive Bayes' algorithm -- Random Forest -- Naive Bayes' text classification code example -- Implementing sentiment analysis -- Frequently asked questions -- Summary -- Chapter 7: Fuzzy Systems -- Fuzzy logic fundamentals -- Fuzzy sets and membership functions -- Attributes and notations of crisp sets -- Operations on crisp sets -- Properties of crisp sets -- Fuzzification -- Defuzzification -- Defuzzification methods -- Fuzzy inference -- ANFIS network -- Adaptive network -- ANFIS architecture and hybrid learning algorithm -- Fuzzy C-means clustering -- NEFCLASS -- Frequently asked questions -- Summary -- Chapter 8: Genetic Programming -- Genetic algorithms structure -- KEEL framework -- Encog machine learning framework -- Encog development environment setup -- Encog API structure -- Introduction to the Weka framework -- Weka Explorer features -- Preprocess -- Classify -- Attribute search with genetic algorithms in Weka -- Frequently asked questions -- Summary -- Chapter 9: Swarm Intelligence -- Swarm intelligence -- Self-organization -- Stigmergy -- Division of labor -- Advantages of collective intelligent systems -- Design principles for developing SI systems -- The particle swarm optimization model -- PSO implementation considerations -- Ant colony optimization model -- MASON Library -- MASON Layered Architecture -- Opt4J library -- Applications in big data analytics -- Handling dynamical data -- Multi-objective optimization -- Frequently asked questions -- Summary -- Chapter 10: Reinforcement Learning -- Reinforcement learning algorithms concept.…”
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Handbook on soft computing for video surveillance

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Predictive Analytics For Dummies. by Bari, Anasse

Table of Contents: “…Recognizing Your Data TypesStructured and unstructured data; Static and streamed data; Identifying Data Categories; Attitudinal data; Behavioral data; Demographic data; Generating Predictive Analytics; Data-driven analytics; User-driven analytics; Connecting to Related Disciplines; Statistics; Data mining; Machine learning; Chapter 4 Complexities of Data; Finding Value in Your Data; Delving into your data; Data validity; Data variety; Constantly Changing Data; Data velocity; High volume of data; Complexities in Searching Your Data; Keyword-based search; Semantic-based search.…”
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Practical data analysis by Cuesta, Hector

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