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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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Optimization advances in electric power systems

Table of Contents: “…Application to TEP Problems -- 2.3. Ant Colony Optimization -- 2.3.1. Basics Concepts -- 2.3.2. …”
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Sentient Enterprise The Evolution of Business Decision-making.

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Visual Data Mining : The VisMiner Approach. by Anderson, Russell K.

Table of Contents: “…Regression Analysis -- The Regression Model -- Correlation and Causation -- Algorithms for Regression Analysis -- Assessing Regression Model Performance -- Model Validity -- Looking Beyond R2 -- Polynomial Regression -- Artificial Neural Networks for Regression Analysis -- Dataset Preparation -- Tutorial -- A Regression Model for Home Appraisal -- Modeling with the Right Set of Observations -- Exercise 6.1 -- ANN Modeling -- The Advantage of ANN Regression -- Top-Down Attribute Selection -- Issues in Model Interpretation -- Model Validation -- Model Application -- Summary…”
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Focus on artificial neural networks

Table of Contents: “…ARTIFICIAL NEURAL NETWORKS (ANNS) -- 3. MICROEMULSIONS -- 4. APPLICATION OF ANNS IN THE DEVELOPMENT OF MICROEMULSION DRUG DELIVERY SYSTEMS -- 4.1. …”
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Industrial control systems

Table of Contents: “…EXTRACTIVE FERMENTATION PROCESS FOR BIOETHANOL PRODUCTION ; 3. PLANT MODEL BASED ON ANN ; 3.1. ANN Configurations.…”
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Media Backends : Digital Infrastructures and Sociotechnical Relations. by Parks, Lisa

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Pattern discovery in biomolecular data : tools, techniques, and applications

Table of Contents: “…Discovering patterns in DNA sequences by the algorithmic significance method / Aleksandar Milosavljevic -- Assembling blocks / Jorja G. …”
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QoS and Energy Management in Cognitive Radio Network : Case Study Approach. by Mishra, Vishram

Table of Contents: “…1.6.1 Channel Selection in CR Based Infrastructure Network1.6.2 Channel Selection in CR Based Ad-hoc Network; 1.7 MAC Protocols for Cognitive Radio Networks; 1.7.1 Random Access Based MAC Scheme; 1.7.2 Time-Slotted Based MAC Scheme; 1.8 Self-coexistence in Cognitive Radio Networks; 1.8.1 Resource Relocation Based Self-coexistence; 1.8.2 Resource Sharing Based Self-coexistence; 1.9 Discussion; References; 2 Cognitive Radio Network- A Review; 2.1 Spectrum Management; 2.1.1 Ant Colony Optimization Based Spectrum Management; 2.1.2 Non-linear Optimization Based Spectrum Management.…”
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Comorbidity in migraine

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Slantwise moves : games, literature, and social invention in nineteenth-century America by Guerra, Douglas

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Quantitative pharmacology and individualized therapy strategies in development of therapeutic proteins for immune-mediated inflammatory diseases

Table of Contents: “…Disease Interception in Autoimmune Diseases: From a Conceptual Framework to Practical Implementation / Anish Suri -- The Role of Biomarkers in Treatment Algorithms for Ulcerative Colitis (UC) / Reena Khanna, Brian G Feagan -- Mechanism and Physiologically Based PK/PD Model in Assisting Translation from Preclinical to Clinical: Understanding PK/PD of Therapeutic Proteins at Site-of-Action / Xi Chen, Weirong Wang -- Application of Minimal Anticipated Biological Effect Level (MABEL) in Human Starting Dose Selection for Immunomodulatory Protein Therapeutics -- Principles and Case Studies / Haiqing Wang, Zheng Yang, Rong Shi -- Model-Based Meta-Analysis Use in the Development of Therapeutic Proteins / Timothy J Taylor, Bill Frame, Angela D Taylor -- Utility of Joint Population Exposure-Response Modeling Approach to Assess Multiple Continuous and Categorical Endpoints in Immunology Drug Development / Chuanpu Hu, Honghui Zhou -- Modeling Approaches to Characterize Target-Mediated Pharmacokinetics and Pharmacodynamics for Therapeutic Proteins / Leonid Gibiansky, Ekaterina Gibiansky -- Tutorial: Numerical (NONMEM) Implementation of the Target-Mediated Drug Disposition Model / Leonid Gibiansky, Ekaterina Gibiansky -- Translational Considerations in Developing Bispecific Antibodies: What Can We Learn from Quantitative Pharmacology? …”
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F♯ for Machine Learning Essentials. by Mukherjee, Sudipta

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Java Deep Learning Projects : Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs. by Karim, Rezaul

Table of Contents: “…; Artificial Neural Networks; Biological neurons; A brief history of ANNs; How does an ANN learn?; ANNs and the backpropagation algorithm; Forward and backward passes; Weights and biases; Weight optimization; Activation functions.…”
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Data clustering in C++ : an object-oriented approach by Gan, Guojun, 1979-

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Mastering machine learning with scikit-learn : learn to implement and evaluate machine learning solutions with scikit-learn by Hackeling, Gavin

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Advances in time series forecasting. Volume 2

Table of Contents: “…INTRODUCTION -- CLASSICAL TIME SERIES FORECASTING MODELS -- ARTIFICIAL NEURAL NETWORKS FOR FORECASTING TIME SERIES -- A NEW ARTIFICIAL NEURAL NETWORK WITH DETERMINISTIC COMPONENTS -- APPLICATIONS -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A Fuzzy Time Series Approach Based on Genetic Algorithm with Single Analysis Process -- Ozge Cagcag Yolcu* -- INTRODUCTION -- FUZZY TIME SERIES -- RELATED METHODS -- Genetic Algorithm (GA) -- Single Multiplicative Neuron Model -- PROPOSED METHOD -- APPLICATIONS -- CONCLUSION AND DISCUSSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Forecasting Stock Exchanges with Fuzzy Time Series Approach Based on Markov Chain Transition Matrix -- Cagdas Hakan Aladag1,* and Hilal Guney2 -- INTRODUCTION -- FUZZY TIME SERIES -- TSAUR 'S FUZZY TIME SERIES MARKOV CHAIN MODEL -- THE IMPLEMENTATION -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A New High Order Multivariate Fuzzy Time Series Forecasting Model -- Ufuk Yolcu* -- INTRODUCTION -- RELATED METHODOLOGY -- The Fuzzy C-Means (FCM) Clustering Method -- Single Multiplicative Neuron Model Artificial Neural Network (SMN-ANN) -- Fuzzy Time Series -- THE PROPOSED METHOD -- APPLICATIONS -- CONCLUSIONS AND DISCUSSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Fuzzy Functions Approach for Time Series Forecasting -- Ali Z. …”
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PRINCIPLES OF QUANTUM ARTIFICIAL INTELLIGENCE. by Wichert, Andreas

Table of Contents: “…Computation; 2.1 Entscheidungsproblem; 2.1.1 Cantor's diagonal argument; 2.1.2 Reductio ad absurdum; 2.2 Complexity Theory; 2.2.1 Decision problems; 2.2.2 P and NP; 2.3 Church-Turing Thesis; 2.3.1 Church-Turing-Deutsch principle; 2.4 Computers; 2.4.1 Analog computers; 2.4.2 Digital computers; 2.4.3 Von Neumann architecture; 3. …”
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Pulmonary arterial hypertension : diagnosis and evidence-based treatment

Table of Contents: “…Combination therapy in pulmonary arterial hypertension / Anne Keogh and Marius Hoeper -- Interventional and surgical modalities of treatment for pulmonary arterial hypertension / Julio Sandoval and Ramona Doyle -- End points and clinical trial design in pulmonary arterial hypertension : clinical and regulatory perspectives / Andrew J. …”
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Pattern Recognition in Computational Molecular Biology : Techniques and Approaches by Elloumi, Mourad, Iliopoulos, Costas, Wang, Jason T. L., Zomaya, Albert Y.

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