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Biologically Inspired Algorithms for Financial 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.
Biological and artificial intelligence environments
The book reports the proceedings of the 15th Italian workshop on neural networks issued by the Italian Society on Neural Networks SIREN. The longevity recipe of this conference stands in three main points that normally renders the reading of these proceedings so interesting as appealing. 1. The topics of the neural networks is considered an attraction pole for a set of researches centered on the inherent paradigm of the neural networks, rather than on a specific tool exclusively. Thus, the subsymbolic management of the data information content constitutes the key feature of papers in various fields such as Pattern Recognition, Stochastic Optimization, Learning, Granular Computing, and so on, with a special bias toward bioinformatics operational applications. An excerpt of all these matters may be found in the book. 2. Though managed at domestic level, the conference attracts contributions from foreign researchers as well, so that in the book the reader may capture the flavor of the state of the art in the international community. 3. The conference is a meeting of friends as well. Thus the papers generally reflect a relaxed atmosphere where researchers meet to generously exchange their thought and explain their actual results in view of a common cultural growing of the community.
Bio-inspired modeling of cognitive tasks ; 2nd International Work-conference on the interplay between natural and artificial computation, IWINAC 2007, La Manga del Mar Menor, Spain, June 18-21, 2007, Proceedings, Part I
This volume includes all the contributions mainly related with theoretical, conceptual and methodological aspects linking AI and knowledge engineering with neurophysiology, clinics and cognition.
Bio-inspired information and communication technologies ; 12th EAI International Conference, BICT 2020, Shanghai, China, July 7-8, 2020, Proceedings
This book constitutes the refereed conference proceedings of the 12th International Conference on Bio-inspired Information and Communications Technologies, held in Shanghai, China, in July 2020. Due to the safety concerns and travel restrictions caused by COVID-19, BICT 2020 took place online in a live stream. BICT 2020 aims to provide a world-leading and multidisciplinary venue for researchers and practitioners in diverse disciplines that seek the understanding of key principles, processes and mechanisms in biological systems and leverage those understandings to develop novel information and communications technologies (ICT).
Bio-inspired computing and communication ; 1st Workshop on Bio-inspired design of networks, BIOWIRE 2007 Cambridge, UK, April 2-5, 2007 Revised Selected Papers
The book constitutes the thoroughly refereed post-workshop proceedings of the First Workshop on Bio-Inspired Design of Networks, BIOWIRE 2007, held in Cambridge, UK, in April 2007.
Bio-inspired computational intelligence and applications ; International conference on life system modeling, and simulation, LSMS 2007, Shanghai, China, September 14-17, 2007. Proceedings
It covers both micro and macro c- ponents ranging from cells, tissues and organs across to organisms and ecologic niches. These interact and evolve to produce an overall complex system whose beh- ior is difficult to comprehend and predict.The arrival of the 21st century has been marked by a resurgence of research interest both in arriving at a systems-level und- standing of biology and in applying such knowledge in complex real-world appli- tions. Consequently, computational methods and intelligence in systems, biology, as well as bio-inspired computational intelligence, have emerged as key drivers for new computational methods. For this reason papers dealing with theory, techniques and real-world applications relating to these two themes were especially solicited.
Bioinformatics research and applications ; 3rd International Symposium,ISBRA 2007, Atlanta, GA, USA, May 7-10, 2007, Proceedings
This book including clustering and classification, gene expression analysis, gene networks, genome analysis, motif finding, pathways, protein structure prediction, protein domain interactions, phylogenetics, and software tools.
Bioinformatics of genome regulation and structure II
The conference was organized by the Laboratory of Theoretical Genetics, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia. The material covers the most recent topics in bioinformatics, including (i) regulatory genomic sequences: databases, knowledge bases, computer analysis, modeling, and recognition; (ii) large-scale genome analysis and functional annotation; (iii) gene structure detection and prediction; (iv) comparative and evolutionary genomics; (v) computer analysis of genome polymorphism and evolution; computer analysis and modeling of transcription, splicing, and translation; structural computational biology: structure- function organization of genomic DNA, RNA, and proteins; (vi) gene networks, signal transduction pathways, and genetically controlled metabolic pathways: databases, knowledge bases, computer analysis, and modeling; principles of organization, operation, and evolution; (vii) data warehousing, knowledge discovery and data mining; and (viii) analysis of basic patterns of genome operation, organization, and evolution.
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R. This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including: Importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms / Curation and delivery of biological metadata for use in statistical modeling and interpretation. / Statistical analysis of high-throughput data, including machine learning and visualization,modeling and visualization of graphs and networks. This book is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.
Bilinear integrable systems : From classical to quantum, continuous to discrete ; Proceedings of the NATO Advanced Research Workshop on Bilinear Integrable Systems: From Classical to Quantum, Continuous to Discrete St. Petersburg, Russia, 15-19 September 2002
Trained as a physicistin his home university Kyushu University, Professor Hirota earned his PhD in’61 at Northwestern University with Professor Siegert in the field of “QuantumStatistical mechanics”. He wrote a widely appreciated Doctoral dissertation on“Functional Integral representation of the grand partition function”. As a youngresearcher, he entered the RCA Company in Tokyo to do research on semi-conductor plasmas. Professor Hirota was led to model the Toda lattice as a non-linear networkof ladder-type LC circuits. The self-dual case led to equations very reminiscentof the Sine-Gordon equation, with much the same features (existence of onesoliton, soliton-soliton interaction, etc)
Big data-enabled internet of things
Covers analytical techniques for handling the huge amount of data generated by the Internet of Things, from architectures and platforms to security and privacy issues, applications, and challenges as well as future directions. The fields of Big Data and the Internet of Things (IoT) have seen tremendous advances, developments, and growth in recent years. The IoT is the inter-networking of connected smart devices, buildings, vehicles and other items which are embedded with electronics, software, sensors and actuators, and network connectivity that enable these objects to collect and exchange data. The IoT produces a lot of data. Big data describes very large and complex data sets that traditional data processing application software is inadequate to deal with, and the use of analytical methods to extract value from data. This edited book covers analytical techniques for handling the huge amount of data generated by the Internet of Things, from architectures and platforms to security and privacy issues, applications, and challenges as well as future directions.
Big Data Science in Finance
Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides
Big data and artificial intelligence in digital finance : Increasing personalization and trust in digital finance using big data and AI
This book presents how cutting-edge digital technologies like Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTechs, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also introduces some of the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data.
Big Data : Conceptual Analysis and Applications
The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used.
Beyond the Worst-Case Analysis of Algorithms
There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.
Beyond the limits to growth : New ideas for sustainability from Japan
This book offers an optimistic view of the future and provides a road map for societies to get there. Drawing upon extensive research and many years as a thought leader in environmental and sustainability issues in Japan and internationally, Hiroshi Komiyama analyzes the most pressing challenges to the attainment of sustainability of economically advanced nations and argues forcefully for Japan to lead them out of the present dilemma through active promotion of creative consumer and societal demand. He shows how an active industry–government–academic partnership can provide the environment needed to promote such new creative demand and illustrates its potential through presentation of a Platinum Society Network that was launched on a regional basis in Japan in 2010 to facilitate the solution of common issues through the exchange of information and ideas.
Beginning Ubuntu Server Administration : From novice to professional
You love it as the world's most popular desktop Linux distribution, and now Ubuntu is available at a server near you. Embracing the very same features desktop users have grown to love, system administrators are rapidly adopting Ubuntu due to their ability to configure, deploy, and manage network services more effectively than ever. Beginning Ubuntu Server Administration guides you through all of the key configuration and administration tasks you'll need to know. Whether you're interested in adopting Ubuntu within a Fortune 500 environment or just want to use Ubuntu to manage your home network, this book is your go–to guide to using the distribution securely for a wide variety of network services. Topics include file, print, web, and FTP management, command–line tips and tricks, automated installation, configuration and deployment processes, and kernel management.
Beginning Python : From novice to professional
Gain a fundamental understanding of Python's syntax and features with the second edition of Beginning Python, an up–to–date introduction and practical reference. Covering a wide array of Python–related programming topics, including addressing language internals, database integration, network programming, and web services, you'll be guided by sound development principles. Ten accompanying projects will ensure you can get your hands dirty in no time. Updated to reflect the latest in Python programming paradigms and several of the most crucial features found in Python 3.0 (otherwise known as Python 3000), advanced topics, such as extending Python and packaging/distributing Python applications, are also covered
Beginning deep learning with TensorFlow : Work with Keras, MNIST data sets, and advanced neural networks
Stats with an introduction to AI, where you’ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you’ll jump into simple classification programs for hand-writing analysis. Once you’ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you’ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! You will: Develop using deep learning algorithms Build deep learning models using TensorFlow 2 Create classification systems and other, practical deep learning applications
Bayesian networks and Influence diagrams : A guide to construction and analysis
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty.
