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Sustainable Finance, Digitalization and the Role of Technology: Proceedings of The International Conference on Business and Technology (ICBT 2021) (Lecture Notes in Networks and Systems #487)
by Allam Hamdan Bahaaeddin AlareeniThis book constitutes the refereed proceedings of the International Conference on Business and Technology (ICBT2021) organized by EuroMid Academy of Business & Technology (EMABT), held in Istanbul, between 06–07 November 2021. In response to the call for papers for ICBT2021, 485 papers were submitted for presentation and inclusion in the proceedings of the conference. After a careful blind refereeing process, 292 papers were selected for inclusion in the conference proceedings from forty countries. Each of these chapters was evaluated through an editorial board, and each chapter was passed through a double-blind peer-review process.The book highlights a range of topics in the fields of technology, entrepreneurship, business administration, accounting, and economics that can contribute to business development in countries, such as learning machines, artificial intelligence, big data, deep learning, game-based learning, management information system, accounting information system, knowledge management, entrepreneurship, and social enterprise, corporate social responsibility and sustainability, business policy and strategic management, international management and organizations, organizational behavior and HRM, operations management and logistics research, controversial issues in management and organizations, turnaround, corporate entrepreneurship, innovation, legal issues, business ethics, and firm governance, managerial accounting and firm financial affairs, non-traditional research, and creative methodologies.These proceedings are reflecting quality research contributing theoretical and practical implications, for those who are wise to apply the technology within any business sector. It is our hope that the contribution of this book proceedings will be of the academic level which even decision-makers in the various economic and executive-level will get to appreciate.
Sustainable Hybrid Energy Systems: Carbon Neutral Approaches, Modeling, and Case Studies
by Jiuping Xu Fengjuan WangSustainable Hybrid Energy Systems Discovering comprehensive approaches to build sustainable hybrid energy systems Hybridization is the eternal theme of human energy utilization. However, it has never been more important than it is now because of the urgency of promoting energy transition and achieving carbon neutrality. Therefore, exploring the design, combustion, operation, and policy challenges of sustainable hybrid energy systems becomes increasingly important. Sustainable Hybrid Energy Systems: Carbon Neutral Approaches, Modeling, and Case Studies provides a detailed explanation of these aspects. Dividing hybrid energy systems into three categories—co-located, co-combusted, and co-operated, this book emphasizes the deployment optimization, emission quota allocation, scheduling coordination, and renewable portfolio standards implementation of these systems. The results are essential tools for understanding the current and future of multi-input single-output hybrid energy systems. Sustainable Hybrid Energy Systems readers will also find: Clear logical framework that reveals the constitutes of hybrid energy systems.Systematic technical scheme for building an economic, environmental, flexible, and resilient future energy system.Extensive case studies from single power plant level, multiple power plant level, and grid level.Effective guidelines for wider application of the proposed carbon neutral approaches. Sustainable Hybrid Energy Systems is ideal for power engineers, electrical engineers, scientists in industry, and environmental researchers looking to understand these energy solutions. It will also provide collectible value for libraries.
Sustainable IoT and Data Analytics Enabled Machine Learning Techniques and Applications (Contributions to Environmental Sciences & Innovative Business Technology)
by V. Ajantha DeviThis book provides a structured presentation of machine learning related to vision, speech, and natural language processing. It addresses the tools, techniques, and challenges of machine learning algorithm implementation, computation time, and the complexity of reasoning and modeling of different types of data. The book covers diverse topics such as semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, natural language processing, traffic and signaling, driverless driving, and radiology. The majority of smart applications have a need for a sustainable Internet of things (IoT) and artificial intelligence. Active research trends and future directions of machine learning under big data analytics are also discussed. Machine learning is a class of artificial neural networks that have become dominant in various computer vision tasks, attracting interest across a variety of domains as they are a type of deep neural networks efficient in extracting meaningful information from visual imagery.
Sustainable Life Insurance: Managing Risk Appetite for Insurance Savings and Retirement Products (Chapman and Hall/CRC Financial Mathematics Series)
by Aymeric Kalife Ludovic Goudenège Tan Xiaolu Mouti Saad Mounir BellmaneSustainable Life Insurance: Managing Risk Appetite for Insurance Savings and Retirement Products gives an overview of all relevant aspects of traditional and non-traditional savings and retirement products from both insurers’ and policyholders’ respective risk appetites. Examples of such products include general accounts, whole life, annuities (variable, fixed and fixed indexed, structured), index-linked products, CPPI-based products, etc. The book contains technical details associated with both practice and theory, specifically related to modelling, product design, investments and risk management challenges and solutions, tailored to both insurers’ and policyholders’ perspectives. Features The book offers not only theoretical background but also concrete, cutting-edge "quick wins" across strategic and operational business axes. It will be an asset for professionals in the insurance industry, and a great teaching/learning resource for courses in risk management, insurance modelling, and more. The book highlights the operational challenges encountered across modelling, product designs and hedging.
Sustainable Management Through Knowledge and Innovation: How to Develop a Strong Strategy in the Wine Industry (SpringerBriefs in Applied Sciences and Technology)
by Javier Martínez-Falcó Bartolomé Marco-Lajara Eduardo Sánchez-García Luis A. Millán-TudelaThis book explores multiple types of innovation within the modern wine industry and how it has developed historically. The book provides and extensive examination of the existing knowledge in this subject. This highlights the evolution of historical and contemporary trends and signposts the authoritative literature published and most important researchers active in the field. With this comprehensive approach, the book is a versatile resource for both scholars and industry professionals, akin to a "Swiss Army Knife" for all aspects of green innovation in the wine sector.
Sustainable Neighbourhoods for Ageing in Place: An Interdisciplinary Voice Against Global Crises
by Nestor Asiamah Hafiz T. A. Khan Pablo Villalobos Dintrans Mohammad Javad Koohsari Emmanuel Mogaji Edgar Ramos Vieira Ruth Lowry Henry Kofi MensahThis timely book provides an understanding of how an ageing population can maintain health in the ageing process in their preferred homes and neighbourhoods while coping with global crises of climate change events, infectious diseases, systemic violence, and radical or extreme industrialisation. It is the first-known volume to consider the four crises as health and social threats to healthy longevity from a sustainability perspective. The book is a collection of commentaries, theoretical frameworks, case studies, and empirical evidence that: (1) provides an analysis of how the crises affect neighbourhood attributes and the ability of residents to use them to maintain health while living in their preferred neighbourhoods, and (2) suggests potential interventions for enabling residents to utilise these attributes for health while living at home in contexts experiencing the crises. Contributions are authored by scholars and practitioners from various disciplines including public health, health care, architecture, engineering, human resources development, information technology, and finance. Among the topics covered:The Impact of Crises on Older Adults’ Health and Function: An Intergenerational Perspective A Behavioural Approach to Sustainable Neighbourhoods: A Philosophical Construction of a Friendly NeighbourhoodAssistive Technologies for Ageing in Place: A Theoretical Proposition of Human Development Postulates “Sustainable Ageing” in a World of CrisesSustainable Neighbourhoods for Ageing in Place: An Interdisciplinary Voice Against Global Crises serves as both a primary and secondary text particularly suited for post-graduate level study (e.g., MSc, PhD). Each chapter richly describes events, phenomena and models in a way that fits contemporary curricula for students and instructors in sociology, gerontology, architecture, environmental science studies, sustainability, ageing studies, and public health. Researchers in a broad range of disciplines can use the book as a research guide to design their studies based on models and insights described in its contents. With theoretical frameworks and recommendations from this book, stakeholders can understand what a sustainable neighbourhood is in the context of crises by presenting problems and solutions from different countries and disciplines.
Sustainable Practices in Italian Businesses: Environmental, Social and Economic Aspects (SpringerBriefs in Environmental Science)
by Fabiola Riccardini Silvia Biffignandi Samuel AshongThis SpringerBrief describes the development and use of a synthetic indicator to assess different degrees of sustainability adoption by economic sector and businesses size. To make this analysis a theoretical framework which involves variables common to alternative frameworks (specifically ESG, GRI and Istat) is proposed. The empirical analysis focuses on the environmental, social and economic variables of the Italian businesses. In this analysis, all three pillars of sustainability – economic, environmental, and social – are considered. The work begins with a review of business sustainability literature and a look into institutional frameworks for the development and measurement of the phenomena. Connections between businesses and the SDGs are examined and comparison of the classifications of sustainable activities defined by GRI and ESG international standards is used to define a framework to be adopted to analyse ISTAT Business Census. Selected indicator variables are aggregated with a synthetic indicator and the results are presented (this is a new proposal of a synthetic indicator useful for the type of data used and published by ISTAT – Italian National Statistical Institute), discussing pros and cons of using it. This study provides two important innovative contributions. The first one is about how to approach the theoretical framework of businesses sustainability at firms aggregated level. The basic idea to work on a set of variables common to different approaches is interesting from the interpretative point of view. The second one, is about the specific empirical analysis, i.e. the Italian businesses sustainability situation. The investigation based on this new theoretical classification/framework and the new proposed indicator provides some interesting substantive results.
Sustainable Statistical and Data Science Methods and Practices: Reports from LISA 2020 Global Network, Ghana, 2022 (STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health)
by O. Olawale Awe Eric A. VanceThis volume gathers papers presented at the LISA 2020 Sustainability Symposium in Kumasi, Ghana, May 2–6, 2022. They focus on sustainable methods and practices of using statistics and data science to address real-world problems. From utilizing social media for statistical collaboration to predicting obesity among rural women, and from analyzing inflation in Nigeria using machine learning to teaching data science in Africa, this book explores the intersection of data, statistics, and sustainability. With practical applications, code snippets, and case studies, this book offers valuable insights for researchers, policymakers, and data enthusiasts alike.The LISA 2020 Global Network aims to enhance statistical and data science capability in developing countries through the creation of a network of collaboration laboratories (also known as “stat labs”). These stat labs are intended to serve as engines for development by training the next generation of collaborative statisticians and data scientists, providing research infrastructure for researchers, data producers, and decision-makers, and enabling evidence-based decision-making that has a positive impact on society. The research conducted at LISA 2020 focuses on practical methods and applications for sustainable growth of statistical capacity in developing nations.
Sustained Simulation Performance 2012
by Erich Focht Hiroaki Kobayashi Michael M. Resch Wolfgang Bez Xin WangThe book presents the state of the art in high performance computing and simulation on modern supercomputer architectures. It covers trends in hardware and software development in general and specifically the future of high performance systems and heterogeneous architectures. The application contributions cover computational fluid dynamics, material science, medical applications and climate research. Innovative fields like coupled multi-physics or multi-scale simulations are presented. All papers were chosen from presentations given at the 14th Teraflop Workshop held in December 2011 at HLRS, University of Stuttgart, Germany and the Workshop on Sustained Simulation Performance at Tohoku University in March 2012.
Sustained Simulation Performance 2022: Proceedings of the Joint Workshop on Sustained Simulation Performance, High-Performance Computing Center Stuttgart (HLRS), University of Stuttgart and Tohoku University, May and October 2022
by Michael M. Resch Wolfgang Bez Hiroaki Kobayashi Hiroyuki Takizawa Johannes GebertThis book presents the state of the art in High-Performance Computing on modern supercomputer architectures. It addresses trends in hardware and software development in general. The contributions cover a broad range of topics, from performance evaluations in context with power efficiency to Computational Fluid Dynamics and High-Performance Data Analytics. In addition, they explore new topics like the use of High-Performance Computers in the field of Artificial Intelligence and Machine Learning. All contributions are based on selected papers presented in 2022 at the 33rd Workshop on Sustained Simulation Performance, WSSP33, held at HLRS in Stuttgart, Germany, and WSSP34, held at Tohoku University in Sendai, Japan.
Swarm Intelligence: 11th International Conference, ANTS 2018, Rome, Italy, October 29–31, 2018, Proceedings (Lecture Notes in Computer Science #11172)
by Marco Dorigo Mauro Birattari Christian Blum Anders L. Christensen Andreagiovanni Reina Vito TrianniThis book constitutes the proceedings of the 11th International Conference on Swarm Intelligence, ANTS 2018, held in Rome, Italy, in October 2018. The 24 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 69 submissions. They are devoted to the field of swarm intelligence as a whole, without any bias towards specific research directions.
Swarm Intelligence: 14th International Conference, ANTS 2024, Konstanz, Germany, October 9–11, 2024, Proceedings (Lecture Notes in Computer Science #14987)
by Marco Dorigo Heiko Hamann Andreagiovanni Reina Leslie Pérez Cáceres Jonas Kuckling Tanja Katharina Kaiser Mohammad Soorati Ken Hasselmann Eduard BussThis book constitutes the proceedings of the 14th International Conference on Swarm Intelligence, ANTS 2024, which took place in Konstanz, Germany, during October 9-11, 2024. The 14 ull papers and 5 short papers included in this book were carefully reviewed and selected from 33 submissions. They deal with self-organizing processes both in nature and in artificial systems.
Swarm Intelligence: Principles, Advances, and Applications
by Aboul Ella Hassanien Eid EmarySwarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then: Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possible Discusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizers Depicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function design Details the similarities, differences, weaknesses, and strengths of each swarm optimization method Draws parallels between the operators and searching manners of the different algorithms Swarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB® package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.
Swarm Intelligence: From Social Bacteria to Humans
by Andrew SchumannThe notion of swarm intelligence was introduced for describing decentralized and self-organized behaviors of groups of animals. Then this idea was extrapolated to design groups of robots which interact locally to cumulate a collective reaction. Some natural examples of swarms are as follows: ant colonies, bee colonies, fish schooling, bird flocking, horse herding, bacterial colonies, multinucleated giant amoebae Physarum polycephalum, etc. In all these examples, individual agents behave locally with an emergence of their common effect. An intelligent behavior of swarm individuals is explained by the following biological reactions to attractants and repellents. Attractants are biologically active things, such as food pieces or sex pheromones, which attract individuals of swarm. Repellents are biologically active things, such as predators, which repel individuals of swarm. As a consequence, attractants and repellents stimulate the directed movement of swarms towards and away from the stimulus, respectively. It is worth noting that a group of people, such as pedestrians, follow some swarm patterns of flocking or schooling. For instance, humans prefer to avoid a person considered by them as a possible predator and if a substantial part of the group in the situation of escape panic (not less than 5%) changes the direction, then the rest follows the new direction, too. Some swarm patterns are observed among human beings under the conditions of their addictive behavior such as the behavior of alcoholics or gamers. The methodological framework of studying swarm intelligence is represented by unconventional computing, robotics, and cognitive science. In this book we aim to analyze new methodologies involved in studying swarm intelligence. We are going to bring together computer scientists and cognitive scientists dealing with swarm patterns from social bacteria to human beings. This book considers different models of simulating, controlling, and predicting the swarm behavior of different species from social bacteria to humans.
Swarm Intelligence Algorithms: Modifications and Applications
by Adam SlowikNature-based algorithms play an important role among artificial intelligence algorithms. Among them are global optimization algorithms called swarm intelligence algorithms. These algorithms that use the behavior of simple agents and various ways of cooperation between them, are used to solve specific problems that are defined by the so-called objective function. Swarm intelligence algorithms are inspired by the social behavior of various animal species, e.g. ant colonies, bird flocks, bee swarms, schools of fish, etc. The family of these algorithms is very large and additionally includes various types of modifications to enable swarm intelligence algorithms to solve problems dealing with areas other than those for which they were originally developed. This book presents 24 swarm algorithms together with their modifications and practical applications. Each chapter is devoted to one algorithm. It contains a short description along with a pseudo-code showing the various stages of its operation. In addition, each chapter contains a description of selected modifications of the algorithm and shows how it can be used to solve a selected practical problem. This book should also be useful for undergraduate and postgraduate students studying nature-based optimization algorithms, and can be a helpful tool for learning these algorithms, along with their modifications and practical applications. In addition, it can be a useful source of knowledge for scientists working in the field of artificial intelligence, as well as for engineers interested in using this type of algorithms in their work. If the reader wishes to expand his knowledge beyond the basics of swarm intelligence algorithms presented in this book and is interested in more detailed information, we recommend the book "Swarm Intelligence Algorithms: A Tutorial" (Edited by A. Slowik, CRC Press, 2020). It contains a detailed explanation of how each algorithm works, along with relevant program codes in Matlab and the C ++ programming language, as well as numerical examples illustrating step-by-step how individual algorithms work.
Swarm Intelligence Algorithms: A Tutorial
by Adam SlowikSwarm intelligence algorithms are a form of nature-based optimization algorithms. Their main inspiration is the cooperative behavior of animals within specific communities. This can be described as simple behaviors of individuals along with the mechanisms for sharing knowledge between them, resulting in the complex behavior of the entire community. Examples of such behavior can be found in ant colonies, bee swarms, schools of fish or bird flocks. Swarm intelligence algorithms are used to solve difficult optimization problems for which there are no exact solving methods or the use of such methods is impossible, e.g. due to unacceptable computational time. This book thoroughly presents the basics of 24 algorithms selected from the entire family of swarm intelligence algorithms. Each chapter deals with a different algorithm describing it in detail and showing how it works in the form of a pseudo-code. In addition, the source code is provided for each algorithm in Matlab and in the C ++ programming language. In order to better understand how each swarm intelligence algorithm works, a simple numerical example is included in each chapter, which guides the reader step by step through the individual stages of the algorithm, showing all necessary calculations. This book can provide the basics for understanding how swarm intelligence algorithms work, and aid readers in programming these algorithms on their own to solve various computational problems. This book should also be useful for undergraduate and postgraduate students studying nature-based optimization algorithms, and can be a helpful tool for learning the basics of these algorithms efficiently and quickly. In addition, it can be a useful source of knowledge for scientists working in the field of artificial intelligence, as well as for engineers interested in using this type of algorithms in their work. If the reader already has basic knowledge of swarm intelligence algorithms, we recommend the book: "Swarm Intelligence Algorithms: Modifications and Applications" (Edited by A. Slowik, CRC Press, 2020), which describes selected modifications of these algorithms and presents their practical applications.
Swarm Intelligence Algorithms (Two Volume Set): A Tutorial
by Adam SlowikSwarm intelligence algorithms are a form of nature-based optimization algorithms. Their main inspiration is the cooperative behavior of animals within specific communities. This can be described as simple behaviors of individuals along with the mechanisms for sharing knowledge between them, resulting in the complex behavior of the entire community. Examples of such behavior can be found in ant colonies, bee swarms, schools of fish or bird flocks. Swarm intelligence algorithms are used to solve difficult optimization problems for which there are no exact solving methods or the use of such methods is impossible, e.g. due to unacceptable computational time. This set comprises two volumes: Swarm Intelligence Algorithms: A Tutorial and Swarm Intelligence Algorithms: Modifications and Applications. The first volume thoroughly presents the basics of 24 algorithms selected from the entire family of swarm intelligence algorithms. It contains a detailed explanation of how each algorithm works, along with relevant program codes in Matlab and the C ++ programming language, as well as numerical examples illustrating step-by-step how individual algorithms work. The second volume describes selected modifications of these algorithms and presents their practical applications. This book presents 24 swarm algorithms together with their modifications and practical applications. Each chapter is devoted to one algorithm. It contains a short description along with a pseudo-code showing the various stages of its operation. In addition, each chapter contains a description of selected modifications of the algorithm and shows how it can be used to solve a selected practical problem.
Swarm Intelligence and Evolutionary Computation: Theory, Advances and Applications in Machine Learning and Deep Learning
by Georgios N. KouziokasThe aim of this book is to present and analyse theoretical advances and also emerging practical applications of swarm and evolutionary intelligence. It comprises nine chapters. Chapter 1 provides a theoretical introduction of the computational optimization techniques regarding the gradient-based methods such as steepest descent, conjugate gradient, newton and quasi-Newton methods and also the non-gradient methods such as genetic algorithm and swarm intelligence algorithms. Chapter 2, discusses evolutionary computation techniques and genetic algorithm. Swarm intelligence theory and particle swarm optimization algorithm are reviewed in Chapter 3. Also, several variations of particle swarm optimization algorithm are analysed and explained such as Geometric PSO, PSO with mutation, Chaotic PSO with mutation, multi-objective PSO and Quantum mechanics – based PSO algorithm. Chapter 4 deals with two essential colony bio-inspired algorithms: Ant colony optimization (ACO) and Artificial bee colony (ABC). Chapter 5, presents and analyses Cuckoo search and Bat swarm algorithms and their latest variations. In chapter 6, several other metaheuristic algorithms are discussed such as: Firefly algorithm (FA), Harmony search (HS), Cat swarm optimization (CSO) and their improved algorithm modifications. The latest Bio-Inspired Swarm Algorithms are discussed in chapter 7, such as: Grey Wolf Optimization (GWO) Algorithm, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA) and other algorithm variations such as binary and chaotic versions. Chapter 8 presents machine learning applications of swarm and evolutionary algorithms. Illustrative real-world examples are presented with real datasets regarding neural network optimization and feature selection, using: genetic algorithm, Geometric PSO, Chaotic Harmony Search, Chaotic Cuckoo Search, and Evolutionary Algorithm and also crime forecasting using swarm optimized SVM. In chapter 9, applications of swarm intelligence on deep long short-term memory (LSTM) networks and Deep Convolutional Neural Networks (CNNs) are discussed, including LSTM hyperparameter tuning and Covid19 diagnosis from chest X-Ray images. The aim of the book is to present and discuss several state-of-theart swarm intelligence and evolutionary algorithms together with their variances and also several illustrative applications on machine learning and deep learning.
Swarm Intelligence Based Optimization
by Patrick Siarry Lhassane Idoumghar Julien LepagnotThis book constitutes the thoroughly refereed post-conference proceedings of the 1st International Conference on Swarm Intelligence Based Optimization, ICSIBO 2014, held in Mulhouse, France, in May 2014. The 20 full papers presented were carefully reviewed and selected from 48 submissions. Topics of interest presented and discussed in the conference focuses on the theoretical progress of swarm intelligence metaheuristics and their applications in areas such as: theoretical advances of swarm intelligence metaheuristics, combinatorial, discrete, binary, constrained, multi-objective, multi-modal, dynamic, noisy, and large-scale optimization, artificial immune systems, particle swarms, ant colony, bacterial foraging, artificial bees, fireflies algorithm, hybridization of algorithms, parallel/distributed computing, machine learning, data mining, data clustering, decision making and multi-agent systems based on swarm intelligence principles, adaptation and applications of swarm intelligence principles to real world problems in various domains.
Swarm Intelligence for Cloud Computing
by Indrajit PanSwarm Intelligence in Cloud Computing is an invaluable treatise for researchers involved in delivering intelligent optimized solutions for reliable deployment, infrastructural stability, and security issues of cloud-based resources. Starting with a bird’s eye view on the prevalent state-of-the-art techniques, this book enriches the readers with the knowledge of evolving swarm intelligent optimized techniques for addressing different cloud computing issues including task scheduling, virtual machine allocation, load balancing and optimization, deadline handling, power-aware profiling, fault resilience, cost-effective design, and energy efficiency. The book offers comprehensive coverage of the most essential topics, including: Role of swarm intelligence on cloud computing services Cloud resource sharing strategies Cloud service provider selection Dynamic task and resource scheduling Data center resource management. Indrajit Pan is an Associate Professor in Information Technology of RCC Institute of Information Technology, India. He received his PhD from Indian Institute of Engineering Science and Technology, Shibpur, India. With an academic experience of 14 years, he has published around 40 research publications in different international journals, edited books, and conference proceedings. Mohamed Abd Elaziz is a Lecturer in the Mathematical Department of Zagazig University, Egypt. He received his PhD from the same university. He is the author of more than 100 articles. His research interests include machine learning, signal processing, image processing, cloud computing, and evolutionary algorithms. Siddhartha Bhattacharyya is a Professor in Computer Science and Engineering of Christ University, Bangalore. He received his PhD from Jadavpur University, India. He has published more than 230 research publications in international journals and conference proceedings in his 20 years of academic experience.
Swarm Intelligence Methods for Statistical Regression
by Soumya MohantyA core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization problems. The large data volume that must be handled in Big Data applications further increases the difficulty of these problems. Swarm Intelligence Methods for Statistical Regression describes methods from the field of computational swarm intelligence (SI), and how they can be used to overcome the optimization bottleneck encountered in statistical analysis. Features Provides a short, self-contained overview of statistical data analysis and key results in stochastic optimization theory Focuses on methodology and results rather than formal proofs Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO) Uses concrete and realistic data analysis examples to guide the reader Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges
Swarm Systems in Art and Architecture: State of the Art (Computational Synthesis and Creative Systems)
by Mahsoo SalimiThis book presents the recent computational developments inspired by swarms in art known as swarm art and discusses applying swarm intelligence concepts in architecture. Non-human art is a great leap in the evolution of contemporary art, removing the requirement of an artist’s production from the creative process. Furthermore, it is a critical declaration in opposition to the anthropomorphic vision which is so destructive for all other life forms and the planet’s ecology. When accepted and integrated into human culture, non-human art done by artificial systems or machines boosts creativity and stimulates innovative fusions. We analyze 120 swarm systems with unique and diverse conceptual contexts, agent design, and audience engagement that can be utilized as inspiration for future projects or to design new swarm algorithms by artists, architects, or computer scientists.
Swing Kings: The Inside Story of Baseball's Home Run Revolution
by Jared Diamond"The best baseball book I’ve read in years." — Sam Walker • "An exhilarating story of innovation." — Ben Reiter • "Swing Kings feels like a spiritual successor to Moneyball." — Baseball ProspectusFrom the Wall Street Journal’s national baseball writer, the captivating story of the home run boom, following a group of players who rose from obscurity to stardom and the rogue swing coaches who helped them usher the game into a new age.We are in a historic era for the home run. The 2019 season saw the most homers ever, obliterating a record set just two years before. It is a shift that has transformed the way the game is played, contributing to more strikeouts, longer games, and what feels like the logical conclusion of the analytics era. In Swing Kings, Wall Street Journal national baseball writer Jared Diamond reveals that the secret behind this unprecedented shift isn’t steroids or the stitching of the baseballs, it’s the most elemental explanation of all: the swing. In this lively narrative romp, he tracks a group of baseball’s biggest stars—including Aaron Judge, J.D. Martinez, and Justin Turner—who remade their swings under the tutelage of a band of renegade coaches, and remade the game in the process. These coaches, many of them baseball washouts who have reinvented themselves as swing gurus, for years were one of the game’s best-kept secrets. Among their ranks are a swimming pool contractor, the owner of a billiards hall, and an ex-hippie whose swing insights draw from surfing and the technique of Japanese samurai. Now, as Diamond artfully charts, this motley cast has moved from the baseball margins to its center of power. They are changing the way hitting is taught to players of all ages, and major league clubs are scrambling for their services, hiring them in record numbers as coaches and consultants. And Diamond himself, whose baseball career ended in high school, enlists the tutelage of each swing coach he profiles, with an aim toward starring in the annual Boston-New York media game at Yankee Stadium.Swing Kings is both a rollicking history of baseball’s recent past and a deeply reported, character-driven account of a battle between opponents as old as time: old and new, change and stasis, the establishment and those who break from it. Jared Diamond has written a masterful chronicle of America’s pastime at the crossroads.
Swiss National Forest Inventory – Methods and Models of the Fourth Assessment (Managing Forest Ecosystems #35)
by Christoph Fischer Berthold TraubThe Swiss National Forest Inventory (NFI) is a forest survey on national level which started in 1982 and has already reached its 5th survey cycle (NFI5). It can be characterized as a multisource and multipurpose inventory where information is mainly collected from terrestrial field surveys using permanent sample plots. In addition, data from aerial photography, GIS and forest service questionnaires are also included.The NFI's main objective is to provide statistically reliable and sound figures to stakeholders such as politicians, researchers, ecologists, forest service, timber industry, national and international organizations as well as to international projects such as the Forest Resources Assessment of the United Nations. For Switzerland, NFI results are typically reported on national and regional level.State of the art methods are applied in all fields of data collection which have been proven to be of international interest and have even served as a basis for other European NFIs. The presented methods are applicable to any sample based forest inventory around the globe.In 2001 the Swiss NFI published its methods for the first time. Since then, many methodological changes and improvements have been introduced. This book describes the complete set of methods and revisions since NFI2. It covers various topics ranging from inventory design and statistics to remote sensing, field survey methods and modelling. It also describes data quality concepts and the software framework used for data storage, statistical analysis and result presentation.
Switched Time-Delay Systems
by Magdi S. MahmoudThis book is about stability analysis and control design methodologies for a new class of systems, switched time-delay systems (STDs). The author presents an introductory, yet comprehensive, treatment of STD systems by jointly combining the two fundamental attributes: the system dynamics which possesses an inherent time-delay and the system operational mode which undergoes switching among different modes. The material integrates the two main issues of switched systems in a systematic way.