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Entrepreneurship Education: Experiments with Curriculum, Pedagogy and Target Groups
The book provides an overview of developments in the field of entrepreneurship education, with special reference to global perspectives on innovations and best practices, as well as research in the emerging economy context. It focuses on various experiments in curriculum design, review and reform in addition to the innovative processes adopted for developing new content for entrepreneurship courses, in many cases with an assessment of their impact on students' entrepreneurial performance. Further, it discusses the pedagogical methods introduced by teachers and trainers to enhance the effectiveness of students' learning and their development as future entrepreneurs. It explains the various initiatives generally undertaken to broaden the scope of entrepreneurship education by extending it beyond regular students and offering it to other groups such as professionals, technicians, artisans, war veterans, and the unemployed. The book is a valuable resource for researchers and academics working in the field of entrepreneurship education as well as for trainers, consultants, mentors and policy makers. Springer Nature Singapore Pte Ltd. 2017. All rights are reserved. -
(Hi)stories of desire: Sexualities and culture in modern India
(Hi)Stories of Desire situates questions of sexuality in the larger domain where they are conditioned by and, in turn, also condition historically and culturally produced landscapes of being, doing and desiring. The book draws upon multi-disciplinary frameworks of analysis - including history, anthropology, literary studies, queer studies and psychoanalysis - to provide a pan-Indian account of the making of sexual cultures. Based on original research, the chapters foreground sexuality as a significant site for the making of regional, national and personal modernities. The volume addresses the modern paradox where sexuality is assigned a central significance in human life and yet its study tends to remain unconnected from the political, religious, social and economic contexts that produce human subjectivity. It will be of interest to a wide range of readership, opening up the topic to complex yet accessible ways of understanding the culture of sexualities and the sexuality of culture. Indian Institute of Advanced Studies, Shimla 2020. -
Advanced Machine Vision Paradigms for Medical Image Analysis
Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs. 2020 Elsevier Inc. All rights reserved. -
Intelligent Environmental Data Monitoring for Pollution Management: A volume in Intelligent Data-Centric Systems
Intelligent Environmental Data Monitoring for Pollution Management discusses evolving novel intelligent algorithms and their applications in the area of environmental data-centric systems guided by batch process-oriented data. Thus, the book ushers in a new era as far as environmental pollution management is concerned. It reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, followed by a review of intelligent tools and techniques which can be used in this direction. In addition, it discusses novel intelligent algorithms for effective environmental pollution data management that are on par with standards laid down by the World Health Organization. To learn more about Elseviers Series, Intelligent Data-Centric Systems, please visit this link: https://www.elsevier.com/books-and-journals/book-series/intelligent-data-centric-systems-sensor-collected-intelligence. 2021 Elsevier Inc. All rights reserved. -
Hybrid Computational Intelligence: Challenges and Applications
Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of hybrid computation as well as the history of their development. Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems. 2020 Elsevier Inc. -
Quantum machine learning
Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices. New trends in Machine Learning based on Quantum Computing and Quantum Algorithms Examples on real life applications Illustrative diagrams and coding examples. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Deep learning: Research and applications
This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition. Tutorials on deep learning framework with focus on tensor flow, keras etc. Numerous worked out examples on real life applications Illustrative diagrams and coding examples. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications
The field of computational intelligence has grown tremendously over that past five years, thanks to evolving soft computing and artificial intelligent methodologies, tools and techniques for envisaging the essence of intelligence embedded in real life observations. Consequently, scientists have been able to explain and understand real life processes and practices which previously often remain unexplored by virtue of their underlying imprecision, uncertainties and redundancies, and the unavailability of appropriate methods for describing the incompleteness and vagueness of information represented. With the advent of the field of computational intelligence, researchers are now able to explore and unearth the intelligence, otherwise insurmountable, embedded in the systems under consideration. Computational Intelligence is now not limited to only specific computational fields, it has made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design to name a few. Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications explores the use of this computational paradigm across a wide range of applied domains which handle meaningful information. Chapters investigate a broad spectrum of the applications of computational intelligence across different platforms and disciplines, expanding our knowledge base of various research initiatives in this direction. This volume aims to bring together researchers, engineers, developers and practitioners from academia and industry working in all major areas and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing. 2021 Elsevier Inc. All rights reserved. -
Quality of Life: An Interdisciplinary Perspective
Quality of Life: An Interdisciplinary Perspective presents the Quality of Life using a contemporary and interdisciplinary approach. Various socio-cultural, spiritual, technological, and human factors aspects, which have an immense bearing on our lives, are an integral part of this book. This book highlights cultural differences in terms of Quality of Life. It recognizes the presence of cultural differences resulting from the social status attributed to an individuals age, gender, class, race, and ethnicity. It can be used as a guide in the field of global well-being and for future research. It presents clues to complex problems and empirical materials, and attempts to bring out a more comprehensive picture of global and contemporary Quality of Life and well-being. This book can also fill a gap in teaching and research. Those who will find this book useful are researchers, academicians, practitioners, and students of management, behavioral science, human factors, psychology, health economics, sociology, public health, and politics. 2022 Taylor & Francis Group, LLC. -
Strategic Management During a Pandemic
The COVID- 19 pandemic changed world dynamics, working scenarios, as well as professional and emotional dimensions. The virus has emerged as a significant threat for the continuity of business. Keeping the gravity of the problem in mind, companies must understand the need for change and must now update their strategy to account for pandemics. The next pandemic may be more severe than the current one, meaning that organizations need to devise mechanisms and business models to fight with these situations and maintain business continuity. They should not only look forward to saving plants, machinery and infrastructure, but also concentrate on employee welfare, customer engagement and satisfaction during this crisis time. The book will not only present the evidence of various effective solutions to run a business in the time of a pandemic, but also put forward the new models and practices of business being followed by people at the time of crisis. It aims to create a bridge between existing business models and proposed business solutions, focusing on existing theories and most importantly case studies from the recent happenings. This rich collection of chapters will provide insights regarding the business challenges, opportunities and practices during pandemic situations like COVID- 19, making it particularly valuable to researchers, academics and students in the fields of strategic management, leadership and disaster management. 2022 selection and editorial matter, Vikas Kumar and Gaurav Gupta. -
Intelligent Multi-modal Data Processing
A comprehensive review of the most recent applications of intelligent multi-modal data processing Intelligent Multi-Modal Data Processing contains a review of the most recent applications of data processing. The Editors and contributors noted experts on the topic offer a review of the new and challenging areas of multimedia data processing as well as state-of-the-art algorithms to solve the problems in an intelligent manner. The text provides a clear understanding of the real-life implementation of different statistical theories and explains how to implement various statistical theories. Intelligent Multi-Modal Data Processing is an authoritative guide for developing innovative research ideas for interdisciplinary research practices. Designed as a practical resource, the book contains tables to compare statistical analysis results of a novel technique to that of the state-of-the-art techniques and illustrations in the form of algorithms to establish a pre-processing and/or post-processing technique for model building. The book also contains images that show the efficiency of the algorithm on standard data set. This important book: Includes an in-depth analysis of the state-of-the-art applications of signal and data processing Contains contributions from noted experts in the field Offers information on hybrid differential evolution for optimal multilevel image thresholding Presents a fuzzy decision based multi-objective evolutionary method for video summarisation Written for students of technology and management, computer scientists and professionals in information technology, Intelligent Multi-Modal Data Processing brings together in one volume the range of multi-modal data processing. 2021 John Wiley & Sons Ltd. All rights reserved. -
Statistical modelling of software source code
This book will focus on utilizing statistical modelling of the software source code, in order to resolve issues associated with the software development processes. Writing and maintaining software source code is a costly business; software developers need to constantly rely on large existing code bases. Statistical modelling identifies the patterns in software artifacts and utilize them for predicting the possible issues. Statistic tool for the software engineer. 2021 Walter de Gruyter GmbH, Berlin/Boston. -
Smart healthcare system design: Security and privacy aspects
SMART HEALTHCARE SYSTEM DESIGN This book deeply discusses the major challenges and issues for security and privacy aspects of smart health-care systems. The Internet-of-Things (IoT) has emerged as a powerful and promising technology, and though it has significant technological, social, and economic impacts, it also poses new security and privacy challenges. Compared with the traditional internet, the IoT has various embedded devices, mobile devices, a server, and the cloud, with different capabilities to support multiple services. The pervasiveness of these devices represents a huge attack surface and, since the IoT connects cyberspace to physical space, known as a cyber-physical system, IoT attacks not only have an impact on information systems, but also affect physical infrastructure, the environment, and even human security. The purpose of this book is to help achieve a better integration between the work of researchers and practitioners in a single medium for capturing state-of-the-art IoT solutions in healthcare applications, and to address how to improve the proficiency of wireless sensor networks (WSNs) in healthcare. It explores possible automated solutions in everyday life, including the structures of healthcare systems built to handle large amounts of data, thereby improving clinical decisions. The 14 separate chapters address various aspects of the IoT system, such as design challenges, theory, various protocols, implementation issues, as well as several case studies. Smart Healthcare System Design covers the introduction, development, and applications of smart healthcare models that represent the current state-of-the-art of various domains. The primary focus is on theory, algorithms, and their implementation targeted at real-world problems. It will deal with different applications to give the practitioner a flavor of how IoT architectures are designed and introduced into various situations. Audience: Researchers and industry engineers in information technology, artificial intelligence, cyber security, as well as designers of healthcare systems, will find this book very valuable. 2021 Scrivener Publishing LLC. All rights reserved. -
Methodologies and Applications of Computational Statistics for Machine Intelligence
With the field of computational statistics growing rapidly, there is a need for capturing the advances and assessing their impact. Advances in simulation and graphical analysis also add to the pace of the statistical analytics field. Computational statistics play a key role in financial applications, particularly risk management and derivative pricing, biological applications including bioinformatics and computational biology, and computer network security applications that touch the lives of people. With high impacting areas such as these, it becomes important to dig deeper into the subject and explore the key areas and their progress in the recent past. Methodologies and Applications of Computational Statistics for Machine Intelligence serves as a guide to the applications of new advances in computational statistics. This text holds an accumulation of the thoughts of multiple experts together, keeping the focus on core computational statistics that apply to all domains. Covering topics including artificial intelligence, deep learning, and trend analysis, this book is an ideal resource for statisticians, computer scientists, mathematicians, lecturers, tutors, researchers, academic and corporate libraries, practitioners, professionals, students, and academicians. 2021, IGI Global. All rights reserved. -
Neuro-Systemic applications in learning
Neuroscience research deals with the physiology, biochemistry, anatomy and molecular biology of neurons and neural circuits and especially their association with behavior and learning. Of late, neuroscience research is playing a pivotal role in industry, science writing, government program management, science advocacy, and education. In the process of learning as experiencing knowledge, the human brain plays a vital role as the central governing system to map the images of learning in the human brain which may be called educational neuroscience. It provides means to develop a common language and bridge the gulf between educators, psychologists and neuroscientists. The emerging field of educational neuroscience presents opportunities as well as challenges for education, especially when it comes to assess the learning disorders and learning intentions of the students. The most effective learning involves recruiting multiple regions of the brain for the learning task. These regions are associated with such functions as memory, the various senses, volitional control, and higher levels of cognitive functioning. By considering biological factors, research has advanced the understanding of specific learning difficulties, such as dyslexia and dyscalculia. Likewise, neuroscience is uncovering why certain types of learning are more rewarding than others. Of late, a lot of research has gone in the field of neural networks and deep learning. It is worthwhile to consider these research areas in investigating the interplay between the human brain and human formal/natural learning. This book is intended to bring together the recent advances in neuroscience research and their influence on the evolving learning systems with special emphasis on the evolution of a learner-centric framework in outcome based education by taking into cognizance the learning abilities and intentions of the learners. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Deep Learning for Sustainable Agriculture
The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm. 2022 Elsevier Inc. All rights reserved. -
Blockchain Technology for Emerging Applications: A Comprehensive Approach
Blockchain Technology for Emerging Applications: A Comprehensive Approach explores recent theories and applications of the execution of blockchain technology. Chapters look at a wide range of application areas, including healthcare, digital physical frameworks, web of-things, smart transportation frameworks, interruption identification frameworks, ballot-casting, architecture, smart urban communities, and digital rights administration. The book addresses the engineering, plan objectives, difficulties, constraints, and potential answers for blockchain-based frameworks. It also looks at blockchain-based design perspectives of these intelligent architectures for evaluating and interpreting real-world trends. Chapters expand on different models which have shown considerable success in dealing with an extensive range of applications, including their ability to extract complex hidden features and learn efficient representation in unsupervised environments for blockchain security pattern analysis. 2022 Elsevier Inc. All rights reserved. -
Cyber-Physical Systems: AI and COVID-19
Cyber-Physical Systems: AI and COVID-19 highlights original research which addresses current data challenges in terms of the development of mathematical models, cyber-physical systems-based tools and techniques, and the design and development of algorithmic solutions, etc. It reviews the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS) and reviews tools and techniques that can be used. This book will act as a resource to guide COVID researchers as they move forward with clinical and epidemiological studies on this outbreak, including the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS). The major problem in the identification of COVID-19 is detection and diagnosis due to non-availability of medicine. In this situation, only one method, Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been widely adopted and used for diagnosis. With the evolution of COVID-19, the global research community has implemented many machine learning and deep learning-based approaches with incremental datasets. However, finding more accurate identification and prediction methods are crucial at this juncture. 2022 Elsevier Inc. All rights reserved. -
Data Analytics for Social Microblogging Platforms
Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data. 2023 Elsevier Inc. All rights reserved. -
PUNCHING UP IN STAND-UP COMEDY: Speaking Truth to Power
Punching Up in Stand-Up Comedy explores the new forms, voices and venues of stand-up comedy in different parts of the world and its potential role as a counterhegemonic tool for satire, commentary and expression of identity especially for the disempowered or marginalised. The title brings together essays and perspectives on stand-up and satire from different cultural and political contexts across the world which raise pertinent issues regarding its role in contemporary times, especially with the increased presence of OTT platforms and internet penetration that allows for easy access to this art form. It examines the theoretical understanding of the different aspects of the humour, aesthetics and politics of stand-up comedy, as well as the exploration of race, gender, politics and conflicts, urban culture and LGBTQ+ identities in countries such as Indonesia, Finland, France, Iran, Italy, Morocco, India and the USA. It also asks the question whether, along with contesting and destabilising existing discursive frameworks and identities, a stand-up comic can open up a space for envisaging a new social, cultural and political order? This book will appeal to people interested in performance studies, media, popular culture, digital culture, sociology, digital sociology and anthropology, and English literature. Chapter 9 of this book is freely available as a downloadable Open Access PDF at https://www.taylorfrancis.com under a Creative Commons (CC-BY) 4.0 license. Funded by the University of Helsinki. 2023 selection and editorial matter, Rashi Bhargava and Richa Chilana; individual chapters, the contributors.