Predicting the future for financial gain is a difficult, sometimes profitable activity. The focus of this book is the application of biologically inspired algorithms (BIAs) to financial modelling. In a detailed introduction, the authors explain computer trading on financial markets and the difficulties faced in financial market modelling. Then Part I provides a thorough guide to the various bioinspired methodologies – neural networks, evolutionary computing (particularly genetic algorithms and grammatical evolution), particle swarm and ant colony optimization, and immune systems. Part II brings the reader through the development of market trading systems. Finally, Part III examines real-world case studies where BIA methodologies are employed to construct trading systems in equity and foreign exchange markets, and for the prediction of corporate bond ratings and corporate failures. The book was written for those in the finance community who want to apply BIAs in financial modelling, and for computer scientists who want an introduction to this growing application domain.

newlineThe basis for this research originally stemmed from my passion for developing better and efficient methods to predict the time series financial data. As the world moves further into globalization and in this digital age, generating vast amounts of financial data and born digital content, there will be a greater need to access accurately the financial information about a country, so that it will help in economic growth of that country. Previously it is very difficult to get the parameters and technical indicators that affects the economy of a country. In most of the research works the researchers have used technical indicators as the parameters to predict the stock index and exchange rate of any country. These data are biased so they affect the prediction performance. It has been observed from the analysis of global market that the exchange rate and stock index of any country depends on the major stock indices and exchange rates of developed countries. Therefore, we have designed datasets by considering major stock indices of the world and exchange rates of developed G-7 countries to predict the future values of stock index and exchange rate of another country. In this research work, we have experimentally concluded that we can use the major stock indices of the world and exchange rates of developed countries as predictors. newlineMoreover, from the deep analysis, it has been observed that radial basis function neural networks are capable of universal approximation and are performing better than the other traditional prediction models for predicting the financial data. However, in many cases/instance, it is difficult to obtained the optimal parameters for the radial basis function neural network. Therefore, we have concentrated on designing and improving the efficiency of radial basis function neural networks by using bio-inspired algorithms. In this globalization era the economy of most of the country depends on the financial stability of other country. The prediction of financial data can be done more accurately if we could use better algorithms for prediction purpose. Researchers have suggested that neural networks based algorithms are performing better than traditional statistical algorithms and all most all the researchers are agreed that radial basis function network can be used as a universal approximator. Therefore, in our research work we have used radial basis function neural network as our prediction algorithm and then, we have improved its performance by fine tuning the parameters of the radial basis function neural network by using bio-inspired algorithm. One of the most popular bio-inspired algorithm is particle newlinevii newlineswarm optimization algorithm. It is widely used for solving optimization problems due to its simplicity and less number of parameters. Hence, we have considered canonical particle swarm optimization algorithm to fine tune the parameters of radial basis function neural network. From the experimental results we have observed that the performance of particle swarm optimized radial basis function neural network is performing better than the traditional radial basis function neural network algorithm. However, in this approach we have selected the particles randomly and the initial weights are updated by using the random number generator function. Further, we have analyzed that chaotic functions have better statistical and dynamical behavior than the random number generator function, which basically follows the normal distribution. Therefore, to improve the performance of the above model we have considered chaotic function instead of random number generator function to fine tune the inertia weights. Finally, based on the experimental results, we have compared our proposed model with other models. We have applied our proposed model to the three different areas in financial sector such as stock index prediction.

The chapters in this book illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics. The eleven chapters were selected following a rigorous, peer-reviewed, selection process.

Biologically-inspired data mining has a wide variety of applications in areas such as data clustering, classification, sequential pattern mining, and information extraction in healthcare and bioinformatics. Over the past decade, research materials in this area have dramatically increased, providing clear evidence of the popularity of these techniques. Biologically-Inspired Techniques for Knowledge Discovery and Data Mining exemplifies prestigious research and shares the practices that have allowed these areas to grow and flourish. This essential reference publication highlights contemporary findings in the area of biologically-inspired techniques in data mining domains and their implementation in real-life problems. Providing quality work from established researchers, this publication serves to extend existing knowledge within the research communities of data mining and knowledge discovery, as well as for academicians and students in the field.

"The objective of the book is to introduce and bring together well-known circuit design aspects, as well as to cover up-to-date outcomes of theoretical studies in decision-making, biologically-inspired, and artificial intelligent learning techniques"--Provided by publisher.

Research on applying principles of quantum computing to improve the engineering of intelligent systems has been launched since late 1990s. This emergent research field concentrates on studying on quantum computing that is characterized by certain principles of quantum mechanics such as standing waves, interference, quantum bits, coherence, superposition of states, and concept of interference, combined with computational intelligence or soft computing approaches, such as artificial neural networks, fuzzy systems, evolutionary computing, swarm intelligence and hybrid soft computing methods. This volume offers a wide spectrum of research work developed using soft computing combined with quantum computing systems.

While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques. This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals. Key Features: This book contains examples and illustrations to demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security. It showcases important security aspects and current trends in the field. It provides an insight of the future research directions in the field. Contents of this book help to prepare the students for exercising better defense in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation using machine learning based approaches in better manner.