“…Tim McNamara, author of Rust in Action, is an experienced programmer with a deep interest in natural language processing, text mining, and wider forms of machine learning and artificial intelligence. …”

“…Sand, clay and rock have to be excavated for a variety of purposes, such as dredging, trenching, mining (including deep sea mining), drilling, tunnel boring and many other applications. …”

“…This open access book focuses on investigating predicting precursor information and key points of rockburst in mining engineering through laboratory experiment, theoretical analysis, numerical simulation and case studies. …”

Tabla de Contenidos: “…7.3.8 Deep Learning Algorithm -- 7.3.9 Deep Learning Application -- 7.4 Unsupervised Learning Algorithms for Medical Data Analysis -- 7.4.1 Clustering Algorithm -- 7.4.2 Principal Component Analysis Algorithm -- 7.4.3 Independent Component Analysis Algorithm -- 7.4.4 Association Rule Mining Algorithm -- 7.5 Applications of Machine-Learning Algorithms in Medical Data Analysis -- 7.6 Limitations and Challenges of Machine Learning Algorithms in Medical Data Analysis -- 7.7 Future Research Directions and Machine Learning Developments in the Realm of Medical Data Analysis -- 7.8 Conclusion -- References -- Part II: Applications and Analytics -- Chapter 8 Fog Computing in Healthcare: Application Taxonomy, Challenges and Opportunities -- 8.1 Introduction -- 8.2 Research Methodology -- 8.3 Application Taxonomy in FC-Based Healthcare -- 8.3.1 Diagnosis -- 8.3.2 Monitoring -- 8.3.3 Notification -- 8.3.4 Zest of Applications of FC in Healthcare -- 8.4 Challenges in FC-Based Healthcare -- 8.4.1 QoS Optimization -- 8.4.2 Patient Authentication and Access Control -- 8.4.3 Data Processing -- 8.4.4 Data Privacy Preservation -- 8.4.5 Energy Efficiency -- 8.5 Research Opportunities -- 8.5.1 Research Opportunity in Computing -- 8.5.2 Research Opportunity in Security -- 8.5.3 Research Opportunity in Services -- 8.5.4 Research Opportunity in Implementation -- 8.6 Conclusion -- References -- Chapter 9 IoT-Driven Predictive Maintenance Approach in Industry 4.0: A Fiber Bragg Grating (FBG) Sensor Application -- 9.1 Introduction -- 9.2 Review of Related Research Articles -- 9.2.1 Studies on FBG Sensors and Their Role in Industry 4.0 -- 9.2.1.1 Magnetostrictive Material -- 9.2.1.2 Magneto-Optical (MO) Materials -- 9.2.1.3 Magnetic Fluid (MF) Materials -- 9.2.1.4 Magnetically Sensitive Materials and Their Application -- 9.2.1.5 Optical Fiber Current Sensors…”

Tabla de Contenidos: “…VoiceXML application development using WebSphere Transcoding Publisher -- 9.1 WebSphere Transcoding Publisher -- 9.2 How WebSphere Transcoding Publisher can be used -- 9.2.1 Converting XML to VoiceXML -- 9.2.2 HTML to VoiceXML transcoding -- 9.2.3 Mining content from HTML pages -- 9.2.4 The HTML-to-VoiceXML transcoder -- 9.3 Using annotation process -- 9.3.1 Content clipping -- 9.3.2 Form simplification -- 9.4 HTML to VoiceXML transcoding limitations…”

Tabla de Contenidos: “…1.8.7 The Trusted Execution Environment (TEE) -- 1.8.8 Game-Based Smart Contracts (GBSC) -- 1.9 Challenges of Blockchain -- 1.9.1 Scalability -- 1.9.2 Privacy Outflow -- 1.9.3 Selfish Mining -- 1.9.4 Security -- 1.10 Conclusion -- References -- 2 Toward Smart Cities Based on the Internet of Things -- 2.1 Introduction -- 2.2 Smart City Emergence -- 2.2.1 A Term Popularized by Private Foundations -- 2.2.2 Continuation of Ancient Reflections on the City of the Future -- 2.3 Smart and Sustainable City -- 2.4 Smart City Areas (Sub-Areas) -- 2.4.1 Technology and Data -- 2.4.2 Economy -- 2.4.3 Population -- 2.5 IoT -- 2.5.1 A New Dimension for the Internet and Objects -- 2.5.2 Issues Raised by the IoT -- 2.5.2.1 IoT Scale -- 2.5.2.2 IoT Heterogeneity -- 2.5.2.3 Physical World Influence on the IoT -- 2.5.2.4 Security and Privacy -- 2.5.3 Applications of the IoT That Revolutionize Society -- 2.5.3.1 IoT in the Field of Health -- 2.5.3.2 Digital Revolution in Response to Energy Imperatives -- 2.5.3.3 Home Automation (Connected Home) -- 2.5.3.4 Connected Industry -- 2.5.3.5 IoT in Agriculture -- 2.5.3.6 Smart Retail or Trendy Supermarkets -- 2.5.3.7 Smart and Connected Cities -- 2.5.3.8 IoT at the Service of Road Safety -- 2.5.3.9 Security Systems -- 2.5.3.10 Waste Management -- 2.6 Examples of Smart Cities -- 2.6.1 Barcelona, a Model Smart City -- 2.6.2 Vienna, the Smartest City in the World -- 2.7 Smart City Benefits -- 2.7.1 Security -- 2.7.2 Optimized Management of Drinking and Wastewater -- 2.7.3 Better Visibility of Traffic/Infrastructure Issues -- 2.7.4 Transport -- 2.8 Analysis and Discussion -- 2.9 Conclusion and Perspectives -- References -- 3 Integration of Blockchain and Artificial Intelligence in Smart City Perspectives -- 3.1 Introduction -- 3.2 Concept of Smart Cities, Blockchain Technology, and Artificial Intelligence…”

Tabla de Contenidos: “…6.3.3.2 Gradient Boosting Classifier -- 6.3.3.3 K-Nearest Neighbors -- 6.3.3.4 Logistic Regression -- 6.3.3.5 Artificial Neural Networks -- 6.3.3.6 Support Vector Machines (SVM) -- 6.3.3.7 Naïve Bayes Classifier -- 6.4 Results -- 6.5 Conclusions -- References -- Chapter 7 Detection of Malicious Emails and URLs Using Text Mining -- 7.1 Introduction -- 7.2 Related Works -- 7.3 Dataset Description -- 7.4 Proposed Architecture -- 7.5 Methodology -- 7.5.1 Methodology for the URL Dataset -- 7.5.2 Methodology for the Email Dataset -- 7.5.2.1 Overcoming the Overfitting Problem -- 7.5.2.2 Tokenization -- 7.5.2.3 Applying Machine Learning Algorithms -- 7.5.3 Detecting Presence of Malicious URLs in Otherwise Non-Malicious Emails -- 7.5.3.1 Preparation of Dataset -- 7.5.3.2 Creation of Features -- 7.5.3.3 Applying Machine Learning Algorithms -- 7.6 Results -- 7.6.1 URL Dataset -- 7.6.2 Email Dataset -- 7.6.3 Final Dataset -- 7.7 Conclusion -- References -- Chapter 8 Quantum Data Traffic Analysis for Intrusion Detection System -- 8.1 Introduction -- 8.2 Literature Overview -- 8.3 Methodology -- 8.3.1 Autoviz -- 8.3.2 Dataset -- 8.3.3 Proposed Models -- 8.3.3.1 Decision Tree -- 8.3.3.2 Random Forest Classifier Algorithm -- 8.3.3.3 AdaBoost Classifier -- 8.3.3.4 Ridge Classifier -- 8.3.3.5 Logistic Regression -- 8.3.3.6 SVM-Linear Kernel -- 8.3.3.7 Naive Bayes -- 8.3.3.8 Quadratic Discriminant Analysis -- 8.4 Results -- 8.5 Conclusion -- References -- Chapter 9 Quantum Computing in Netnomy: A Networking Paradigm in e-Pharmaceutical Setting -- 9.1 Introduction -- 9.2 Discussion -- 9.2.1 Exploring Market Functioning via Quantum Network Economy -- 9.2.1.1 Internal Networking Marketing -- 9.2.1.2 Layered Marketing -- 9.2.1.3 Role of Marketing in Pharma Network Organizations -- 9.2.1.4 Role of Marketing in Vertical Networking Organizations…”

“…SCADA (Supervisory Control and Data Acquisition) systems are at the heart of the modern industrial enterprise ranging from mining plants, water and electrical utility installations to oil and gas plants. …”
>Acceso al texto completo en ebscohost

“…This edition of Structural Statistics for Industry and Services provides official annual data for detailed industrial sectors (mining and quarrying, manufacturing, utilities, and construction) as well as for detailed service sectors, covering such variables as production, value added, investment, employment, wages and salaries, hours worked and number of establishments or enterprises…”

“…She is a rat, and she detects land-mines by smelling them. She was born in the training lab of the Belgian company, Apopo, in Tanzania. …”

“…Studies commercial routes, mining development, indigenous resistance, and infiltration of North American capitalist enterprises to determine population policies between the 18th- and mid-20th centuries"--Handbook of Latin American Studies, v. 58.…”

“…Contributed by leading “big data” experts, the cases in The Applied Business Analytics Casebook covers: Forecasting and statistical analysis: time series forecasting models, regression models, data visualization, and hypothesis testing Optimization and simulation: linear, integer, and nonlinear programming; Monte Carlo simulation and risk analysis; and stochastic optimization Decision analysis: decision making under uncertainty; expected value of perfect information; decision trees; game theory models; AHP; and multi-criteria decision making Advanced business analytics: data warehousing/mining; text mining; neural networks; financial analytics; CRM analytics; and revenue management models…”

“…The question of how best to adapt text mining tools and their use by humanities researchers is at the heart of the impresso enterprise…”

“…The Special Issue on ‘Intelligent Computing for Big Data’ highlighted a number of recent studies related to the use of intelligent computing techniques in the processing of big data for text mining, autism diagnosis, behaviour recognition, and blockchain-based storage…”