Download or read book FINANCIAL MODELING USING BIO INSPIRED ALGORITHMS written by Trilok Nath Pandey and published by Department of Political Science and Public Administration. This book was released on 2022-08-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Biologically Inspired Algorithms for Financial Modelling written by Anthony Brabazon and published by Springer Science & Business Media. This book was released on 2006-03-28 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Bio Inspired Credit Risk Analysis written by Lean Yu and published by Springer Science & Business Media. This book was released on 2008-04-24 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties.

Download or read book Bio Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment written by Alma Y Alanis and published by Elsevier. This book was released on 2024-05-03 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bio-inspired techniques such as modelling to generate control algorithms for the treatment of diabetes mellitus. The book addresses the identification of diabetes mellitus using a high-order recurrent neural network trained by the extended Kalman filter. The authors also describe the use of metaheuristic algorithms for the parametric identification of compartmental models of diabetes mellitus widely used in research works such as the Sorensen model and the Dallaman model. In addition, the book addresses the modelling of time series for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia using deep neural networks. The detection of diabetes mellitus in early stages or when current diagnostic techniques cannot detect glucose intolerance or prediabetes is proposed, carried out by means of deep neural networks in force in the literature. Readers will find leading-edge research in diabetes identification based on discrete high-order neural networks trained with the extended Kalman filter; parametric identification of compartmental models used to describe diabetes mellitus; modelling of data obtained by continuous glucose monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia; and screening for glucose intolerance using glucose tolerance test data and deep neural networks. Application of the proposed approaches is illustrated via simulation and real-time implementations for modelling, prediction, and classification. Addresses the online identification of diabetes mellitus using a high-order recurrent neural network trained online by an extended Kalman filter. Covers parametric identification of compartmental models used to describe diabetes mellitus. Provides modeling of data obtained by continuous glucose-monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia.

Download or read book Bio inspired Algorithms for Data Streaming and Visualization Big Data Management and Fog Computing written by Simon James Fong and published by Springer Nature. This book was released on 2020-08-25 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to provide some insights into recently developed bio-inspired algorithms within recent emerging trends of fog computing, sentiment analysis, and data streaming as well as to provide a more comprehensive approach to the big data management from pre-processing to analytics to visualization phases. The subject area of this book is within the realm of computer science, notably algorithms (meta-heuristic and, more particularly, bio-inspired algorithms). Although application domains of these new algorithms may be mentioned, the scope of this book is not on the application of algorithms to specific or general domains but to provide an update on recent research trends for bio-inspired algorithms within a specific application domain or emerging area. These areas include data streaming, fog computing, and phases of big data management. One of the reasons for writing this book is that the bio-inspired approach does not receive much attention but shows considerable promise and diversity in terms of approach of many issues in big data and streaming. Some novel approaches of this book are the use of these algorithms to all phases of data management (not just a particular phase such as data mining or business intelligence as many books focus on); effective demonstration of the effectiveness of a selected algorithm within a chapter against comparative algorithms using the experimental method. Another novel approach is a brief overview and evaluation of traditional algorithms, both sequential and parallel, for use in data mining, in order to provide an overview of existing algorithms in use. This overview complements a further chapter on bio-inspired algorithms for data mining to enable readers to make a more suitable choice of algorithm for data mining within a particular context. In all chapters, references for further reading are provided, and in selected chapters, the author also include ideas for future research.

Download or read book Natural Computing in Computational Finance written by Anthony Brabazon and published by Springer Science & Business Media. This book was released on 2009-03-13 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent years have seen the widespread application of Natural Computing algorithms (broadly defined in this context as computer algorithms whose design draws inspiration from phenomena in the natural world) for the purposes of financial modelling and optimisation. A related stream of work has also seen the application of learning mechanisms drawn from Natural Computing algorithms for the purposes of agent-based modelling in finance and economics. In this book we have collected a series of chapters which illustrate these two faces of Natural Computing. The first part of the book illustrates how algorithms inspired by the natural world can be used as problem solvers to uncover and optimise financial models. The second part of the book examines a number agent-based simulations of financial systems. This book follows on from Natural Computing in Computational Finance (Volume 100 in Springer’s Studies in Computational Intelligence series) which in turn arose from the success of EvoFIN 2007, the very first European Workshop on Evolutionary Computation in Finance & Economics held in Valencia, Spain in April 2007.

Download or read book Bio Inspired Computational Algorithms and Their Applications written by Shangce Gao and published by BoD – Books on Demand. This book was released on 2012-03-07 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bio-inspired computational algorithms are always hot research topics in artificial intelligence communities. Biology is a bewildering source of inspiration for the design of intelligent artifacts that are capable of efficient and autonomous operation in unknown and changing environments. It is difficult to resist the fascination of creating artifacts that display elements of lifelike intelligence, thus needing techniques for control, optimization, prediction, security, design, and so on. Bio-Inspired Computational Algorithms and Their Applications is a compendium that addresses this need. It integrates contrasting techniques of genetic algorithms, artificial immune systems, particle swarm optimization, and hybrid models to solve many real-world problems. The works presented in this book give insights into the creation of innovative improvements over algorithm performance, potential applications on various practical tasks, and combination of different techniques. The book provides a reference to researchers, practitioners, and students in both artificial intelligence and engineering communities, forming a foundation for the development of the field.

Download or read book Biologically Inspired Techniques for Knowledge Discovery and Data Mining written by Alam, Shafiq and published by IGI Global. This book was released on 2014-05-31 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book AI and Blockchain Optimization Techniques in Aerospace Engineering written by Vignesh, U. and published by IGI Global. This book was released on 2024-03-05 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: The amalgamation of artificial intelligence (AI), optimization techniques, and blockchain is revolutionizing how to conceptualize, design, and operate aerospace systems. While optimization techniques are pivotal in streamlining aerospace processes, security challenges have recently surfaced. AI and Blockchain Optimization Techniques in Aerospace Engineering delves into the transformative impact of technologies on various facets of the aerospace industry, offering a multidimensional solution to overcome security concerns and enhance the overall efficiency of aerospace systems The book explores how machine learning reshapes aerospace systems by automating complex tasks through self/reinforced learning methods. From air traffic data analysis to flight scheduling, geographical information, and navigation, machine learning has become an indispensable tool, offering valuable insights that enhance aerospace operations. Simultaneously, blockchain technology, with its inherent characteristics of decentralization and tamper-proof ledgers, ensures transparency, accountability, and security in transactions, providing an innovative approach to data integrity and system resilience. Designed for technology development professionals, academicians, data scientists, industrial experts, researchers, and students, the book offers a panoramic view of the latest innovations in the field.

Download or read book Biologically Inspired Techniques for Financial Modeling written by and published by . This book was released on 2019 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bio Inspired Optimization Techniques in Blockchain Systems written by Vignesh, U. and published by IGI Global. This book was released on 2024-01-29 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the dynamic landscape of bioinformatics and blockchain technology, a profound challenge is evident: ensuring secure exchange and analysis of complex biological data while maintaining data integrity and ownership. Traditional methods fall short in seamlessly transferring genomic data, spurring the fusion of blockchain innovation and optimization algorithms as a groundbreaking solution. Biology-Inspired Optimization Techniques in Blockchain Systems directly addresses the data integrity and ownership dilemma in bioinformatics and blockchain. Despite the intricacies of genomic data, blockchain's potential solution faces obstacles like data volume and slow transactions. These challenges are adeptly overcome through optimization algorithms. The book, authored by experts in bioinformatics, blockchain, and optimization, offers a comprehensive guide, showcasing how blockchain architecture and biological data intricacies can harmonize. It provides a blueprint for using blockchain to store genomic variants and aligned reads. This work empowers developers, data scientists, and researchers to overcome technological barriers, redefining the landscape of bioinformatics and beyond.

Download or read book Nature Inspired Design of Hybrid Intelligent Systems written by Patricia Melin and published by Springer. This book was released on 2016-12-08 with total page 817 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights recent advances in the design of hybrid intelligent systems based on nature-inspired optimization and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction, and optimization of complex problems. The book is divided into seven main parts, the first of which addresses theoretical aspects of and new concepts and algorithms based on type-2 and intuitionistic fuzzy logic systems. The second part focuses on neural network theory, and explores the applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The book’s third part presents enhancements to meta-heuristics based on fuzzy logic techniques and describes new nature-inspired optimization algorithms that employ fuzzy dynamic adaptation of parameters, while the fourth part presents diverse applications of nature-inspired optimization algorithms. In turn, the fifth part investigates applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. The sixth part examines new optimization algorithms and their applications. Lastly, the seventh part is dedicated to the design and application of different hybrid intelligent systems.

Download or read book Computational Intelligence Paradigms for Optimization Problems Using MATLAB SIMULINK written by S. Sumathi and published by CRC Press. This book was released on 2018-09-03 with total page 731 pages. Available in PDF, EPUB and Kindle. Book excerpt: Considered one of the most innovative research directions, computational intelligence (CI) embraces techniques that use global search optimization, machine learning, approximate reasoning, and connectionist systems to develop efficient, robust, and easy-to-use solutions amidst multiple decision variables, complex constraints, and tumultuous environments. CI techniques involve a combination of learning, adaptation, and evolution used for intelligent applications. Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® explores the performance of CI in terms of knowledge representation, adaptability, optimality, and processing speed for different real-world optimization problems. Focusing on the practical implementation of CI techniques, this book: Discusses the role of CI paradigms in engineering applications such as unit commitment and economic load dispatch, harmonic reduction, load frequency control and automatic voltage regulation, job shop scheduling, multidepot vehicle routing, and digital image watermarking Explains the impact of CI on power systems, control systems, industrial automation, and image processing through the above-mentioned applications Shows how to apply CI algorithms to constraint-based optimization problems using MATLAB® m-files and Simulink® models Includes experimental analyses and results of test systems Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® provides a valuable reference for industry professionals and advanced undergraduate, postgraduate, and research students.

Download or read book Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio Inspired Algorithms for Time Series Prediction written by Jesus Soto and published by Springer. This book was released on 2017-11-19 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. This book describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series prediction. Interval type-2 and type-1 fuzzy systems are used to integrate the outputs of the Ensemble of Interval Type-2 Fuzzy Neural Network models. Genetic Algorithms and Particle Swarm Optimization are the Bio-Inspired algorithms used for the optimization of the fuzzy response integrators. The Mackey-Glass, Mexican Stock Exchange, Dow Jones and NASDAQ time series are used to test of performance of the proposed method. Prediction errors are evaluated by the following metrics: Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Percentage Error and Mean Absolute Percentage Error. The proposed prediction model outperforms state of the art methods in predicting the particular time series considered in this work.

Download or read book Computer and Cyber Security written by Brij B. Gupta and published by CRC Press. This book was released on 2018-11-19 with total page 865 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a monumental reference for the theory and practice of computer security. Comprehensive in scope, this text covers applied and practical elements, theory, and the reasons for the design of applications and security techniques. It covers both the management and the engineering issues of computer security. It provides excellent examples of ideas and mechanisms that demonstrate how disparate techniques and principles are combined in widely-used systems. This book is acclaimed for its scope, clear and lucid writing, and its combination of formal and theoretical aspects with real systems, technologies, techniques, and policies.

Download or read book Expert Systems in Finance written by Noura Metawa and published by Routledge. This book was released on 2019-05-10 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: Throughout the industry, financial institutions seek to eliminate cumbersome authentication methods, such as PINs, passwords, and security questions, as these antiquated tactics prove increasingly weak. Thus, many organizations now aim to implement emerging technologies in an effort to validate identities with greater certainty. The near instantaneous nature of online banking, purchases, transactions, and payments puts tremendous pressure on banks to secure their operations and procedures. In order to reduce the risk of human error in financial domains, expert systems are seen to offer a great advantage in big data environments. Besides their efficiency in quantitative analysis such as profitability, banking management, and strategic financial planning, expert systems have successfully treated qualitative issues including financial analysis, investment advisories, and knowledge-based decision support systems. Due to the increase in financial applications’ size, complexity, and number of components, it is no longer practical to anticipate and model all possible interactions and data processing in these applications using the traditional data processing model. The emergence of new research areas is clear evidence of the rise of new demands and requirements of modern real-life applications to be more intelligent. This book provides an exhaustive review of the roles of expert systems within the financial sector, with particular reference to big data environments. In addition, it offers a collection of high-quality research that addresses broad challenges in both theoretical and application aspects of intelligent and expert systems in finance. The book serves to aid the continued efforts of the application of intelligent systems that respond to the problem of big data processing in a smart banking and financial environment.

Download or read book Biologically Inspired Techniques in Many Criteria Decision Making written by Satchidananda Dehuri and published by Springer Nature. This book was released on 2020-01-21 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. In many real-world problems, there are several different objectives that need to be taken into account. Solving these problems is a challenging task and requires careful consideration. In real applications, often simple and easy to understand methods are used; as a result, the solutions accepted by decision makers are not always optimal solutions. On the other hand, algorithms that would provide better outcomes are very time consuming. The greatest challenge facing researchers is how to create effective algorithms that will yield optimal solutions with low time complexity. Accordingly, many current research efforts are focused on the implementation of biologically inspired algorithms (BIAs), which are well suited to solving uni-objective problems. This book introduces readers to state-of-the-art developments in biologically inspired techniques and their applications, with a major emphasis on the MCDM process. To do so, it presents a wide range of contributions on e.g. BIAs, MCDM, nature-inspired algorithms, multi-criteria optimization, machine learning and soft computing.