After decades of basic research and more promises than impressive applications, artificial intelligence (AI) is starting to deliver benefits. A convergence of advances is motivating this new surge of AI development and applications. Computer capability as evolved from high throughput and high performance computing systems is increasing. AI models and operations research adaptations are becoming more matured, and the world is breeding big data not only from the web and social media but also from the Internet of Things. This is a very distinctive book which discusses important applications using a variety of paradigms from AI and outlines some of the research to be performed. The work supersedes similar books that do not cover as diversified a set of sophisticated applications. The authors present a comprehensive and articulated view of recent developments, identifies the applications gap by quoting from the experience of experts, and details suggested research areas. The book is organized into 14 chapters which provide a perspective of the field of AI. Areas covered in these selected papers include a broad range of applications, such as manufacturing, autonomous systems, healthcare, medicine, advanced materials, parallel distributed computing, and electronic commerce. AI paradigms utilized in this book include unsupervised learning, ensembles, neural networks, deep learning, fuzzy logic, support-vector machines, genetic algorithms, genetic programming, particle swarm optimization, agents, and case-based reasoning. A synopsis of the chapters follow: • Clustering Techniques: Novel research in clustering techniques are essential to improve the required exploratory analysis for revealing hidden patterns, where label information is unknown. Ramazan Ünlü in the chapter “Unsupervised Ensemble Learning” discusses unsupervised ensemble learning, or consensus clustering which is a method to improve the selection of the most suitable clusterization algorithm. The goal of this combination process is to increase the average quality of individual clustering methods. Through this chapter, the main concepts of clustering methods are introduced viii Luis Rabelo, Sayli Bhide and Edgar Gutierrez first and then the basics of ensemble learning are given. Finally, the chapter concludes with a summary of the novel progresses in unsupervised learning. • Deep Learning and a Complex Application in Parallel Distributed Simulation: is introduced in the chapter by Edwin Cortes and Luis Rabelo entitled “Using Deep Learning to Configure Parallel Distributed Discrete-Event Simulators.” The authors implemented a pattern recognition scheme to identify the best time management and synchronization scheme to execute a particular parallel discrete simulation (DES) problem. This innovative pattern recognition method measures the software complexity. It characterizes the features of the network and hardware configurations to quantify and capture the structure of the Parallel Distributed DES problem. It is an innovative research in deep belief network models. • Autonomous Systems: The area of autonomous systems as represented by autonomous vehicles and deep learning in particular Convolutional Neural Networks (CNNs) are presented in the chapter “Machine Learning Applied to Autonomous Vehicles” by Olmer García and Cesar Díaz. This chapter presents an application of deep learning for the architecture of autonomous vehicles which are a good example of a multiclass classification problem. The authors argue that the use of AI in this domain requires two hardware/software systems: one for training in the cloud and the other one in the autonomous vehicle. This chapter demonstrates that deep learning can create sophisticated models which are able to generalize with relative small datasets. • Genetic Algorithms & Support Vector Machines: The utilization of Genetic Algorithms (GAs) to select which learning parameters of AI paradigms can actually assist researchers in automating the learning process is discussed in the chapter “Evolutionary Optimization of Support Vector Machines Using Genetic Algorithms”. Fred Gruber uses a GA to find an optimized parameter set for support vector machines. GAs and cross validation increase the generalization performance of support vector machines (SVMs). When doing this, it should be noted that the processing time increases. However, this drawback can be reduced by finding configurations for SVMs that are more efficient. • Texture Descriptors for the Generic Pattern Classification Problem: In the chapter “Texture Descriptors for the Generic Pattern Classification Problem”, Loris Nanni, Sheryl Brahnam, and Alessandra Lumini propose a framework that employs a matrix representation for extracting features from patterns that can be effectively applied to very different classification problems. Under texture analysis, the chapter goes through experimental analysis showing the advantages of their approach. They also report the results of experiments that examine the performance outcomes from extracting different texture descriptors from matrices that were generated by reshaping the original feature vector. Their new methods outperformed SVMs. • Simulation Optimization: The purpose of simulation optimization in predicting supply chain performance is addressed by Alfonso Sarmiento and Edgar Gutierrez in the chapter “Simulation Optimization Using a Hybrid Scheme with Particle Swarm Preface ix Optimization for a Manufacturing Supply Chain.” The methodology uses particle swarm optimization (PSO) in order to find stability in the supply chain using a system dynamics model of an actual situation. This is a classical problem where asymptotic stability has been listed as one of the problems to solve. The authors show there are many factors that affect supply chain dynamics including: shorter product life cycles, timing of inventory decisions, and environmental regulations. Supply chains evolve with these changing dynamics which causes the systems to behave non-linearly. The impacts of these irregular behaviors can be minimized when the methodology solves an optimization problem to find a stabilizing policy using PSO (that outperformed GAs in the same task). To obtain a convergence, a hybrid algorithm must be used. By incorporating a theorem that allows finding ideal equilibrium levels, enables a broader search to find stabilizing policies. • Cutting Forces: Accurate prediction of cutting forces has a significant impact on quality of product in manufacturing. The chapter “Estimation of Cutting Forces in turning of Inconel 718 Assisted with High Pressure Coolant using Bio-Inspired Artificial Neural Networks” aims at utilizing neural networks to predict cutting forces in turning of a nickel-based alloy Inconel 718 assisted with high pressure coolant. Djordje Cica and Davorin Kramar discuss a study that employs two bio-inspired algorithms; namely GAs and PSO, as training methods of neural networks. Further, they compare the results obtained from the GA-based and PSO-based neural network models with the most commonly used back propagation-based neural networks for performance. • Predictive Analytics using Genetic Programming: The chapter “Predictive Analytics using Genetic Programming” by Luis Rabelo, Edgar Gutierrez, Sayli Bhide, and Mario Marin focus on predictive analytics using genetic programming (GP). The authors describe with detail the methodology of GP and demonstrate its advantages. It is important to highlight the use of the decile table to classify better predictors and guide the evolutionary process. An actual application to the Reinforced Carbon-Carbon structures of the NASA Space Shuttle is used. This example demonstrates how GP has the potential to be a better option than regression/classification trees due to the fact that GP has more operators which include the ones from regression/classification trees. In addition, GP can help create synthetic variables to be used as input to other AI paradigms. • Managing Overcrowding in Healthcare using Fuzzy Logic: The chapter “Managing Overcrowding in Healthcare using Fuzzy Logic” focuses on the overcrowding problem frequently observed in the emergency departments (EDs) of healthcare systems. The hierarchical fuzzy logic approach is utilized by Abdulrahman Albar, Ahmad Elshennawy, Mohammed Basingab, and Haitham Bahaitham to develop a framework for quantifying overcrowding. The purpose of this research was to develop a quantitative measurement tool for evaluating ED crowding which captures healthcare experts’ opinions and other ED stakeholder’s perspectives. This framework has the x Luis Rabelo, Sayli Bhide and Edgar Gutierrez ability to be applied in variety of healthcare systems. The methodology developed is the first of this kind. • Simulation Modeling: can be used as an important methodology to capture and develop knowledge and complement the implementation of intelligent system. The chapter “The Utilization of Case-Based Reasoning: A Case Study of the Healthcare Sector Using Simulation Modeling” applies a combination of discrete event simulations (DES) and case based reasoning (CBR) to assist in solving new cases in healthcare systems. An important objective of this approach is that it can improve the stakeholders’ involvement by eliminating the need for simulation or statistical knowledge or experience. A case study on EDs which face multiple resource constraints including financial, labor, and facilities is explained by Khaled Alshareef, Ahmad Rahal, and Mohammed Basingab. The applications of DES-CBR provided solutions that were realistic, robust, and more importantly the results were scrutinized, and validated by field experts. • Agent Based Modeling and Simulation and its Application to E-commerce: by Oloruntomi Joledo, Edgar Gutierrez, and Hathim Bukhari presents an application for a peer-to-peer lending environment. The authors seek to find how systems performance is affected by the actions of stakeholders in an ecommerce system. Dynamic system complexity and risk are considered in this research. When systems dynamics and neural networks are combined along with at the strategy level and agent- based models of consumer behavior allows for a business model representation that leads to reliable decision-making. The presented framework shares insights into the consumer-to- consumer behavior in ecommerce systems. • Artificial Intelligence for the Modeling and Prediction of the Bioactivities of Complex Natural Products: by Jose Prieto presents neural networks as a tool to predict bioactivities for very complex chemical entities such as natural products, and suggests strategies on the selection of inputs and conditions for the in silico experiments. Jose Prieto explains that neural networks can become reliable, fast and economical tools for the prediction of anti-inflammatory, antioxidant, antimicrobial and anti-inflammatory activities, thus improving their use in medicine and nutrition. • Predictive Analytics: is one of the most advanced forms of analytics and AI paradigms that are the core of these predictive systems. The chapter “Predictive Analytics for Thermal Coal Prices using Neural Networks and Regression Trees” by Mayra Bornacelli and Edgar Gutierrez aims to deliver price predictive analytics models. A necessity for many industries. This chapter is targeted towards predicting prices of thermal coal. By implementing the Delphi methodology along with neural networks, conclusions can be reached about global market tendencies and variables. Although neural networks outperformed regression trees, the latter created models which can be easily visualized and understood. Overall, the research found that even though the market of thermal coal is dynamic and the history of its prices is not a good predictive for future Preface xi prices; the general patterns that were found, hold more importance than the study of individual prices and that the methodology that was used applies to oligopolistic markets. • Explorations of the Transhuman Dimension of Artificial Intelligence: The final chapter provides a very important philosophical discussion of AI and its ‘transhuman’ dimension, which is “here understood as that which goes beyond the human, to the point of being wholly different from it.” In “Explorations of the ‘Transhuman’ Dimension of Artificial Intelligence”, Bert Olivier examines the concept of intelligence as a function of artificially intelligent beings. However, these artificially intelligent beings are recognized as being ontologically distinct from humans as “embodied, affective, intelligent beings.” These differences are the key to understand the contrast between AI and being-human. His examination involves contemporary AI-research as well as projections of possible AI developments. This is a very important chapter with important conclusions for AI and its future. We would like to acknowledge the individuals who contributed to this effort. First and foremost, we would like to express our sincere thanks to the contributors of the chapters for reporting their research and also for their time, and promptness. Our thanks are due to Nova for publishing this book, their advice, and patience. We believe that this book is an important contribution to the community in AI. We hope this book will serve as a motivation for continued research and development in AI.