Advertisement
| data science in fashion industry: Artificial Intelligence for Fashion Industry in the Big Data Era Sébastien Thomassey, Xianyi Zeng, 2018-05-16 This book provides an overview of current issues and challenges in the fashion industry and an update on data-driven artificial intelligence (AI) techniques and their potential implementation in response to those challenges. Each chapter starts off with an example of a data-driven AI technique on a particular sector of the fashion industry (design, manufacturing, supply or retailing), before moving on to illustrate its implementation in a real-world application |
| data science in fashion industry: Big Data in Practice Bernard Marr, 2016-03-22 The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter |
| data science in fashion industry: Artificial Intelligence for Fashion Leanne Luce, 2018-12-08 Learn how Artificial Intelligence (AI) is being applied in the fashion industry. With an application focused approach, this book provides real-world examples, breaks down technical jargon for non-technical readers, and provides an educational resource for fashion professionals. The book investigates the ways in which AI is impacting every part of the fashion value chain starting with product discovery and working backwards to manufacturing. Artificial Intelligence for Fashion walks you through concepts, such as connected retail, data mining, and artificially intelligent robotics. Each chapter contains an example of how AI is being applied in the fashion industry illustrated by one major technological theme. There are no equations, algorithms, or code. The technological explanations are cumulative so you'll discover more information about the inner workings of artificial intelligence in practical stages as the book progresses. What You’ll Learn Gain a basic understanding of AI and how it is used in fashion Understand key terminology and concepts in AI Review the new competitive landscape of the fashion industry Conceptualize and develop new ways to apply AI within the workplaceWho This Book Is For Fashion industry professionals from designers, managers, department heads, and executives can use this book to learn about how AI is impacting roles in every department and profession. |
| data science in fashion industry: Information Systems for the Fashion and Apparel Industry Tsan-Ming Jason Choi, 2016-04-13 Information Systems for the Fashion and Apparel Industry brings together trends and developments in fashion information systems, industrial case-studies, and insights from an international team of authors. The fashion and apparel industry is fast-growing and highly influential. Computerized information systems are essential to support fashion business operations and recent developments in social media, mobile commerce models, radio frequency identification (RFID) technologies, and ERP systems are all driving innovative business measures in the industry. After an introductory chapter outlining key decision points and information requirements in fast fashion supply chains, Part One focuses on the principles of fashion information systems, with chapters covering how decision making in the apparel supply chains can be improved through the use of fuzzy logic, RFID technologies, evolutionary optimization techniques, and artificial neural networks. Part Two then reviews the range of applications for information systems in the fashion and apparel industry to improve customer choice, aid design, implement intelligent forecasting and procurement systems, and manage inventory and returns. - Provides systematic and comprehensive coverage of information systems for the fashion and apparel industry - Combines recent developments and industrial best-practices in apparel supply chain management in order to meet the needs of the fashion and apparel industry professionals and academics - Features input from a team of highly knowledgeable authors with a range of professional and academic experience, overseen by an editor who is a leading expert in the field - Reviews the range of applications for information systems in the fashion and apparel industry to improve customer choice, aid design, implement intelligent forecasting and procurement systems, and manage inventory and returns |
| data science in fashion industry: Data Science And Knowledge Engineering For Sensing Decision Support - Proceedings Of The 13th International Flins Conference Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre, 2018-07-30 FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to include Computational Intelligence for applied research. The contributions of the FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, with special focuses on data science and knowledge engineering for sensing decision support, both from the foundations and the applications points-of-view. |
| data science in fashion industry: Data Science and Intelligent Systems Radek Silhavy, Petr Silhavy, Zdenka Prokopova, 2021-11-16 This book constitutes the second part of refereed proceedings of the 5th Computational Methods in Systems and Software 2021 (CoMeSySo 2021) proceedings. The real-world problems related to data science and algorithm design related to systems and software engineering are presented in this papers. Furthermore, the basic research’ papers that describe novel approaches in the data science, algorithm design and in systems and software engineering are included. The CoMeSySo 2021 conference is breaking the barriers, being held online. CoMeSySo 2021 intends to provide an international forum for the discussion of the latest high-quality research results |
| data science in fashion industry: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| data science in fashion industry: Data Science and Applications Satyasai Jagannath Nanda, |
| data science in fashion industry: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data science in fashion industry: Big Data , 2011 |
| data science in fashion industry: Data Science and Big Data Analytics Durgesh Mishra, |
| data science in fashion industry: Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications Abhishek Majumder, Joy Lal Sarkar, Arindam Majumder, 2023-08-16 Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications captures the state of the art in usage of artificial intelligence in different types of recommendation systems and predictive analysis. The book provides guidelines and case studies for application of artificial intelligence in recommendation from expert researchers and practitioners. A detailed analysis of the relevant theoretical and practical aspects, current trends and future directions is presented. The book highlights many use cases for recommendation systems: · Basic application of machine learning and deep learning in recommendation process and the evaluation metrics · Machine learning techniques for text mining and spam email filtering considering the perspective of Industry 4.0 · Tensor factorization in different types of recommendation system · Ranking framework and topic modeling to recommend author specialization based on content. · Movie recommendation systems · Point of interest recommendations · Mobile tourism recommendation systems for visually disabled persons · Automation of fashion retail outlets · Human resource management (employee assessment and interview screening) This reference is essential reading for students, faculty members, researchers and industry professionals seeking insight into the working and design of recommendation systems. |
| data science in fashion industry: Process Innovation in the Global Fashion Industry Byoungho Ellie Jin, Elena Cedrola, 2019-03-28 Process innovations - an improved way of doing things – help firms achieve higher-level performance by reducing the time and cost to produce a product or perform a service, and increasing productivity and growth. This book provides a comprehensive examination of process innovations occurring in the global fashion industry, with a focus on fashion brands from USA, Italy, and Japan. It offers practical insights for enhancing efficiency in the supply chain as well as management process such as work routines, information flow, and organization structures. Using case analyses, this book will help readers to grasp how successful fashion companies optimize their operations and advance their competitive position by integrating process innovations into their supply chain and management systems. |
| data science in fashion industry: Social Media for Fashion Marketing Wendy K. Bendoni, 2020-08-06 Social Media for Fashion Marketing uses cutting edge case studies and detailed interviews to show how the business of fashion is changing in the digital landscape. Bendoni (@BendoniStyle) also considers the psychological impact of being a hyper-connected consumer and the generational gaps in social media communication. Using academic research, alongside her 25 years of fashion marketing experience, Bendoni offers a clear picture of the changing narrative of storytelling, social confirmation, digital nesting and how to use data to shape a brand's online presence. With practical and critical thinking activities to hone your skills into professional practice, this is the ultimate guide to social marketing, promotion, SEO, branding and communication. Featured topics - Rules of Digital Storytelling - Rethinking Gamification - Strategic Digital Marketing - The Role of Citizen Journalists - The Social Media Looking Glass - World of Influencer Marketing - Visual Consumption Economy - Global Perspective of Social Media |
| data science in fashion industry: Handbook of Research on Global Fashion Management and Merchandising Vecchi, Alessandra, 2016-05-03 Innovation and novel leadership strategies have aided the successful growth of the fashion industry around the globe. However, as the dynamics of the industry are constantly changing, a deficit can emerge in the overall comprehension of industry strategies and practices. The Handbook of Research on Global Fashion Management and Merchandising explores the various facets of effective management procedures within the fashion industry. Featuring research on entrepreneurship, operations management, marketing, business modeling, and fashion technology, this publication is an extensive reference source for practitioners, academics, researchers, and students interested in the dynamics of the fashion industry. |
| data science in fashion industry: ARTIFICIAL INTELLIGENCE in FASHION INDUSTRY Sabeer Mehta, 2024-05-01 Al in the Fashion Industry uncovers the revolutionary impact of artificial intelligence on the fashion world, illustrating how Al technologies are reshaping everything from design and production to marketing and customer experience. This compelling book provides a detailed examination of how Al-driven innovations like predictive analytics, virtual fitting rooms, and automated supply chains are enabling fashion brands to enhance efficiency, creativity, and personalization. Through an array of case studies and expert insights, the book offers a comprehensive look at how AI is driving unprecedented transformation and setting new standards in the fashion industry. |
| data science in fashion industry: Proceedings of International Conference on Data Science and Applications Mukesh Saraswat, Chandreyee Chowdhury, Chintan Kumar Mandal, Amir H. Gandomi, 2023-02-06 This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing. |
| data science in fashion industry: Product Innovation in the Global Fashion Industry Byoungho Jin, Elena Cedrola, 2017-11-27 As an initial attempt to understand innovation in fashion, this volume focuses on product innovations, realizing that this industry is truly an innovative sector in which diverse technologies, science, art, and tradition have been merged, synthesized, and utilized to solve the needs and concerns of the end-users. In doing so, this book categorizes product innovation into three levels—materials, style and product development—and aims to present the broader scope of innovation in the global fashion industry with the hope that other sectors can learn from these developments and be inspired. |
| data science in fashion industry: Fashion Trend Forecasting Gwyneth Holland, Rae Jones, 2017-08-08 An understanding of trends is a fundamental skill for anyone working in the fashion industry. In this book Gwyneth Holland and Rae Jones look at how to produce a well-researched trend, from initial inspiration to concrete idea and, eventually, real product. Illustrated throughout with insights from practicing trend forecasters and industry insiders, it is an invaluable guide for fashion students and practitioners alike. |
| data science in fashion industry: Advanced Studies in Classification and Data Science Tadashi Imaizumi, Akinori Okada, Sadaaki Miyamoto, Fumitake Sakaori, Yoshiro Yamamoto, Maurizio Vichi, 2020-09-25 This edited volume focuses on the latest developments in classification and data science and covers a wide range of topics in the context of data analysis and related areas, e.g. the analysis of complex data, analysis of qualitative data, methods for high-dimensional data, dimensionality reduction, data visualization, multivariate statistical methods, and various applications to real data in the social sciences, medical sciences, and other disciplines. In addition to sharing theoretical and methodological findings, the book shows how to apply the proposed methods to a variety of problems — e.g. in consumer behavior, decision-making, marketing data and social network structures. Both methodological aspects and applications to a wide range of areas such as economics, behavioral science, marketing science, management science and the social sciences are covered. The book is chiefly intended for researchers and practitioners who are interested in the latest developments and practical applications in these fields, as well as applied statisticians and data analysts. Its combination of methodological advances with a wide range of real-world applications gathered from several fields makes it of unique value in helping readers solve their research problems. |
| data science in fashion industry: Clothing Appearance and Fit J Fan, W Yu, L Hunter, 2004-09-20 Fashion and beauty have helped shape history and today more than ever, we find ourselves under increasing pressure to think about what we wear, what we look good in and how best to enhance our body shape and size. Behind this seemingly superficial industry, however, lies a technical thinking firmly grounded in science and technology. In one fully comprehensive book, Clothing appearance and fit: Science and technology provides a critical appreciation of the technological developments and scientific understanding of the appearance and fit of clothing. The authors bridge the science of beauty and fashion design with garment evaluation technology, garment drape and human anthropometrics and sizing.The ten chapters of the book provide a detailed coverage of clothing appearance and fit. Chapter 1 considers body attractiveness and how it relates to clothing material and design parameters and discusses classical and contemporary theories of beauty. Chapters 2 and 3 present the industry's techniques, methods and standards for assessing clothing appearance and fit and Chapters 4 and 5 review the research and development of objective measurement technologies for evaluating clothing appearance and fit. Fabric objective measurement, fabric properties and garment drape are covered in Chapters 6 and 7 and the R & D of body measurement, anthropometrics and sizing systems are detailed in Chapters 8 and 9. The final chapter reviews published work on garment design and pattern alteration for achieving good clothing appearance and fit.This book is an essential reference for researchers, academics, professionals and students in clothing and textile academia and industry. It includes many industrial standards, techniques and practices. - Offers a critical appreciation of technological developments - Incorporates user-friendly illustrations and photographs - Valuable reference for students, researchers and professionals in the clothing and textile industries |
| data science in fashion industry: Data Scientists at Work Sebastian Gutierrez, 2014-12-12 Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is the sexiest job in the 21st century, according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients. |
| data science in fashion industry: Trends in Applied Knowledge-Based Systems and Data Science Hamido Fujita, Moonis Ali, Ali Selamat, Jun Sasaki, Masaki Kurematsu, 2016-07-13 This book constitutes the refereed conference proceedings of the 29th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, held in Morioka, Japan, in August 2-4, 2016. The 80 revised full papers presented were carefully reviewed and selected from 168 submissions. They are organized in topical sections: data science; knowledge base systems; natural language processing and sentiment analysis; semantic Web and social networks; computer vision; medical diagnosis system and bio-informatics; applied neural networks; innovations in intelligent systems and applications; decision support systems; adaptive control; soft computing and multi-agent systems; evolutionary algorithms and heuristic search; system integration for real-life applications. |
| data science in fashion industry: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science in fashion industry: Structural Dynamics and Resilience in Supply Chain Risk Management Dmitry Ivanov, 2017-11-07 This book offers an introduction to structural dynamics, ripple effect and resilience in supply chain disruption risk management for larger audiences. In the management section, without relying heavily on mathematical derivations, the book offers state-of-the-art concepts and methods to tackle supply chain disruption risks and designing resilient supply chains in a simple, predictable format to make it easy to understand for students and professionals with both management and engineering background. In the technical section, the book constitutes structural dynamics control methods for supply chain management. Real-life problems are modelled and solved with the help of mathematical programming, discrete-event simulation, optimal control theory, and fuzzy logic. The book derives practical recommendations for management decision-making with disruption risk in the following areas: How to estimate the impact of possible disruptions on performance in the pro-active stage? How to generate efficient and effective stabilization and recovery policies? When does one failure trigger an adjacent set of failures? Which supply chain structures are particular sensitive to ripple effect? How to measure the disruption risks in the supply chain? |
| data science in fashion industry: Advanced Fashion Technology and Operations Management Vecchi, Alessandra, 2017-03-01 Fashion has been steadily moving from the brick and mortar to the digital market. As such, it is increasingly vital to research new methods that will help businesses to grow and succeed in this new sphere. Advanced Fashion Technology and Operations Management is a pivotal reference source for the latest development management strategies, fashion marketing, international business, and fashion entrepreneurship. Featuring extensive coverage across a range of relevant perspectives and topics, such as online shopping behavior, digital fashion, and e-commerce, this book is ideally designed for professionals, entrepreneurs, students, and researchers. |
| data science in fashion industry: Data Science, Analytics and Machine Learning with R Luiz Paulo Favero, Patricia Belfiore, Rafael de Freitas Souza, 2023-01-23 Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear. - Presents a comprehensive and practical overview of machine learning, data mining and AI techniques for a broad multidisciplinary audience - Serves readers who are interested in statistics, analytics and modeling, and those who wish to deepen their knowledge in programming through the use of R - Teaches readers how to apply machine learning techniques to a wide range of data and subject areas - Presents data in a graphically appealing way, promoting greater information transparency and interactive learning |
| data science in fashion industry: Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions Matt Taddy, 2019-08-23 Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science. |
| data science in fashion industry: Proceedings of Data Analytics and Management Abhishek Swaroop, Zdzislaw Polkowski, Sérgio Duarte Correia, Bal Virdee, 2024-01-29 This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2023), held at London Metropolitan University, London, UK, during June 2023. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into four volumes. |
| data science in fashion industry: The Psychology of Fashion Carolyn Mair, 2018-04-09 The Psychology of Fashion offers an insightful introduction to the exciting and dynamic world of fashion in relation to human behaviour, from how clothing can affect our cognitive processes to the way retail environments manipulate consumer behaviour. The book explores how fashion design can impact healthy body image, how psychology can inform a more sustainable perspective on the production and disposal of clothing, and why we develop certain shopping behaviours. With fashion imagery ever present in the streets, press and media, The Psychology of Fashion shows how fashion and psychology can make a positive difference to our lives. |
| data science in fashion industry: Ethical Data Science Anne L. Washington, 2023 Can data science truly serve the public interest? Data-driven analysis shapes many interpersonal, consumer, and cultural experiences yet scientific solutions to social problems routinely stumble. All too often, predictions remain solely a technocratic instrument that sets financial interests against service to humanity. Amidst a growing movement to use science for positive change, Anne L. Washington offers a solution-oriented approach to the ethical challenges of data science. Ethical Data Science empowers those striving to create predictive data technologies that benefit more people. As one of the first books on public interest technology, it provides a starting point for anyone who wants human values to counterbalance the institutional incentives that drive computational prediction. It argues that data science prediction embeds administrative preferences that often ignore the disenfranchised. The book introduces the prediction supply chain to highlight moral questions alongside the interlocking legal and commercial interests influencing data science. Structured around a typical data science workflow, the book systematically outlines the potential for more nuanced approaches to transforming data into meaningful patterns. Drawing on arts and humanities methods, it encourages readers to think critically about the full human potential of data science step-by-step. Situating data science within multiple layers of effort exposes dependencies while also pinpointing opportunities for research ethics and policy interventions. This approachable process lays the foundation for broader conversations with a wide range of audiences. Practitioners, academics, students, policy makers, and legislators can all learn how to identify social dynamics in data trends, reflect on ethical questions, and deliberate over solutions. The book proves the limits of predictive technology controlled by the few and calls for more inclusive data science. |
| data science in fashion industry: Data Science and Network Engineering Suyel Namasudra, Munesh Chandra Trivedi, Ruben Gonzalez Crespo, Pascal Lorenz, 2023-11-02 This book includes research papers presented at the International Conference on Data Science and Network Engineering (ICDSNE 2023) organized by the Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India, during July 21–22, 2023. It includes research works from researchers, academicians, business executives, and industry professionals for solving real-life problems by using the advancements and applications of data science and network engineering. This book covers many advanced topics, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), computer networks, blockchain, security and privacy, Internet of things (IoT), cloud computing, big data, supply chain management, and many more. Different sections of this book are highly beneficial for the researchers, who are working in the field of data science and network engineering. |
| data science in fashion industry: Machine Learning, Optimization, and Data Science Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Giorgio Jansen, Vincenzo Sciacca, Panos Pardalos, Giovanni Giuffrida, Renato Umeton, 2021-01-06 This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications. |
| data science in fashion industry: Fashion Supply Chain Management Tsan-Ming Choi, 2012 This book focuses on reporting both quantitative research on FSCM and exploratory studies on emerging supply chain management issues in the fashion industry--Provided by publisher. |
| data science in fashion industry: Digitalization in the Luxury Fashion Industry Anna Cabigiosu, 2020-07-13 The luxury fashion industry is one of the best performing and fastest growing industries in today’s business landscape, and is set to continue expanding over the next years. Exploring the effects of digitalization, this book aims to increase our understanding of the key drivers of internal growth and competitiveness in luxury fashion firms. With a focus on the development of new brand strategies brought about by digitalization, the author outlines the need for business models to be redesigned in order to make use of social media and satisfy Millennial consumers. Offering case studies on leading luxury fashion brands, this timely book evaluates new digital technologies and strategies including omnichannel marketing, 3D printing and smart textiles. A must-read for those researching digital marketing and branding, as well as luxury or fashion management, this book provides a much-needed and up-to-date analysis of a successful and digitally aware industry. |
| data science in fashion industry: Artificial Intelligence and Knowledge Processing Hemachandran K, Raul V. Rodriguez, Umashankar Subramaniam, Valentina Emilia Balas, 2023-09-06 Artificial Intelligence and Knowledge Processing play a vital role in various automation industries and their functioning in converting traditional industries to AI-based factories. This book acts as a guide and blends the basics of Artificial Intelligence in various domains, which include Machine Learning, Deep Learning, Artificial Neural Networks, and Expert Systems, and extends their application in all sectors. Artificial Intelligence and Knowledge Processing: Improved Decision-Making and Prediction, discusses the designing of new AI algorithms used to convert general applications to AI-based applications. It highlights different Machine Learning and Deep Learning models for various applications used in healthcare and wellness, agriculture, and automobiles. The book offers an overview of the rapidly growing and developing field of AI applications, along with Knowledge of Engineering, and Business Analytics. Real-time case studies are included across several different fields such as Image Processing, Text Mining, Healthcare, Finance, Digital Marketing, and HR Analytics. The book also introduces a statistical background and probabilistic framework to enhance the understanding of continuous distributions. Topics such as Ensemble Models, Deep Learning Models, Artificial Neural Networks, Expert Systems, and Decision-Based Systems round out the offerings of this book. This multi-contributed book is a valuable source for researchers, academics, technologists, industrialists, practitioners, and all those who wish to explore the applications of AI, Knowledge Processing, Deep Learning, and Machine Learning. |
| data science in fashion industry: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 A guide to the usefulness of data science covers such topics as algorithms, logistic regression, financial modeling, data visualization, and data engineering. |
| data science in fashion industry: Industry and Innovation: Textile Industry José Moleiro Martins, |
| data science in fashion industry: Supply Chain Analytics and Modelling Nicoleta Tipi, 2021-04-03 An incredible volume of data is generated at a very high speed within the supply chain and it is necessary to understand, use and effectively apply the knowledge learned from analyzing data using intelligent business models. However, practitioners and students in the field of supply chain management face a number of challenges when dealing with business models and mathematical modelling. Supply Chain Analytics and Modelling presents a range of business analytics models used within the supply chain to help readers develop knowledge on a variety of topics to overcome common issues. Supply Chain Analytics and Modelling covers areas including supply chain planning, single and multi-objective optimization, demand forecasting, product allocations, end-to-end supply chain simulation, vehicle routing and scheduling models. Learning is supported by case studies of specialist software packages for each example. Readers will also be provided with a critical view on how supply chain management performance measurement systems have been developed and supported by reliable and accurate data available in the supply chain. Online resources including lecturer slides are available. |
| data science in fashion industry: Digital Fashion Dr Sukhvir Singh, Rikhil Nagpal, 2023-10-20 Digital Fashion, authored by the esteemed Professor Dr. Sukhvir Singh & Mr. Rikhil Nagpal, is a visionary exploration of the transformative intersection of technology and the fashion industry. This illuminating book encapsulates the dynamic evolution of fashion, guided by the forces of digitalization. From the fusion of 3D printing and virtual modelling to the integration of artificial intelligence, sustainability, and ethical considerations, This Masterpiece offers a comprehensive and insightful view of the digital fashion landscape. Through compelling insights, it delves into the ethical, ecological, and technological dimensions, serving as a compelling guide for navigating the vibrant future of fashion. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
The Influence of Social Media on Fashion Trends and …
daily lives, profoundly impacting various aspects, including the fashion industry. From Instagram to TikTok, platforms have transformed the way people discover, engage with and consume fashion …
The Future of Fashion: How Technology is shaping the …
The fashion industry is undergoing a transformative shift driven by advancements in technology. From smart textiles and wearable tech to AI- ... design tools analyze vast amounts of data to …
Digital Transformation of Supply Chain Management in the Fast …
04019 * Corresponding author: 1706240521@xy.dlpu.edu.cn Digital Transformation of Supply Chain Management in the Fast Fashion Industry: A Case Study of Zara Ruojia 2Li1,*, Wenxin Liu, and …
International Journal of Data and Network Science - Growing …
The effect of Instagram on millennials consumer’s purchase intentions in the fashion industry Shafig Al-Haddad a, Mohammad Al-Khasawneh a, Abdel-Aziz Ahmad Sharabati b*, ... S. Al-Haddad et …
Cultural Appropriation in the Fashion Industry: A Critical …
The fashion industry is a dynamic and ever-evolving sector that plays a significant role in the global economy. Examining trends and implications within the fashion industry provides valuable insights
The Academic Research Community Publication
The fashion industry is the largest contributor to pollution and waste in the world, therefore this industry should apply sustainability to every stage of its production process, starting from R&D ...
Decarbonising Fashion - UNFCCC
• Fashion on Climate (Global Fashion Agenda + McKinsey) • Preferred Fibre & Materials: Market Report 2020 (Textile Exchange) • Financing the Transformation in the Fashion Industry (Fashion …
Digital product passport for the textile sector - European …
industry, in the context of the EU strategy for sustainable and circular textiles. The fashion and textile industry faces significant environmental, social and economic challenges. This research is …
applied in the fashion and apparel industry - ResearchGate
fashion and apparel industry. Articles were retrieved from two popular databases “Scopus” and “Web of Science” and the article screening was completed in five phases, resulting in 149 ...
Development of Data Mining Expert System Using Naïve Bayes …
The study implemented the Data Science approach and techniques to see how reliable ... 2021; AlHamad et al., 2021, 2022; Ali et al., 2021). The fashion and clothing industry constitutes high …
The Analysis of Chanel’s Marketing Strategies - ResearchGate
to help Chanel sustain the top status in the fashion world. In 2019, the company's financial returns billed at ten billion euros with one of the largest social media following in the fashion industry.
Li Zhao, Ph.D. EDUCATION ACADEMIC APPOINTMENTS
The rise of fashion informatics: A case of data mining based social network analysis in fashion. Clothing and Textiles Research Journal. 37(2), 87-102, [SSCI Indexed]. 14. Sun, L. & Zhao, L. …
Image-based fashion recommender systems - DiVA
Master Programme in Data Science 2021 Luleå University of Technology Department of Computer Science, Electrical and Space Engineering ... (C. Guan et al., 2017) studied fashion …
Halal Fashion Industry in Challenges in the Digital Age
2. The Potential of Indonesia’s Halal Fashion Industry The halal fashion industry has enormous potential in Indonesia. This is an implication of Indonesia’s vast Muslim population. In 2019, …
Advances in Social Science, Education and Humanities …
Source: author based on Web data . This paper analyzes the innovation of H&M supply chain management from the following aspects: （ 1）Products design：First, H&M is a fashion …
The Power of Marketing in Fashion: The Reality of The Fast …
fashion industry has on the environment and developing coun-tries, so the research paper aims to give an in-depth analysis on the fast fashion industry as a whole. This paper delves into the …
Artificial Intelligence in Business-to-Customer Fashion Retail: …
Mathematics 2023, 11, 2943 5 of 32 A total of 392 references were discovered using these search terms, including 108 du-plicates between the two sources; 124 were in Web of Science and 268 …
Roadmap: Fashion Merchandising - Bachelor of Science CA …
Roadmap: Fashion Merchandising - Bachelor of Science CA-BS-FM College of the Arts School of Fashion Design and Merchandising Catalog Year: 2016-2017 . ... FDM 20263 Fashion Retail …
APPAREL AND FOOTWEAR SECTOR - Science Based Targets …
Overview of the Apparel and Footwear Industry 12 2.1 ... as fast fashion and growing consumption in emerging middle-income economies. ... emissions, the sector should actively mitigate GHG …
Fashion Meets Science: Analysing Students’ Views on …
preparedness in the fashion industry. Figure 1 Level of Interest (n%) The bar graph in Figure 1 illustrating the “Level of Interest” among science students towards fashion and pattern-making …
Kosuke Takemoto, Koshinaka Takafumi The Graduate School …
The rise of e-commerce in the fashion industry has created a strong demand for the development of virtual try-on systems that enhance the online shopping experience by allowing cus-tomers to …
Implementing The Use of AI for Analysis and Prediction in the …
Fashion Industry Luri Renaningtyas1, Putri Dwitasari2, ... Deepfashion is a data set specifically made for fashion analytic needs, containing a collection of photos classified by type of garment or …
GLOBALIZATION OF THE FASHION INDUSTRY AND ITS …
which employs observation and interview for data collection. The population studied comprises fashion producers who have their workshops within twelve suburbs in Kumasi.
Blockchain adoption in the fashion sustainable supply chain ...
%PDF-1.4 %âãÏÓ 337 0 obj > endobj xref 337 60 0000000016 00000 n 0000002115 00000 n 0000002364 00000 n 0000002400 00000 n 0000002679 00000 n 0000002798 00000 n …
Environmental Impacts in the Fashion Industry - JSTOR
%PDF-1.6 %âãÏÓ 712 0 obj > endobj xref 712 84 0000000016 00000 n 0000003615 00000 n 0000003720 00000 n 0000004380 00000 n 0000004513 00000 n 0000004655 00000 n …
Development of Data Mining Expert System Using Naïve Bayes …
The study implemented the Data Science approach and techniques to see how reliable ... 2021; AlHamad et al., 2021, 2022; Ali et al., 2021). The fashion and clothing industry constitutes high …
Development of Data Mining Expert System Using Naïve Bayes …
The study implemented the Data Science approach and techniques to see how reliable ... 2021; AlHamad et al., 2021, 2022; Ali et al., 2021). The fashion and clothing industry constitutes high …
Traditional vs. big-data fashion trend forecasting: an …
Using big data in the fashion industry ... of data science to target upcoming trends and allow companies to more quickly create popular and best-sell-ing items based on concrete numbers …
Development of Data Mining Expert System Using Naïve Bayes …
The study implemented the Data Science approach and techniques to see how reliable ... 2021; AlHamad et al., 2021, 2022; Ali et al., 2021). The fashion and clothing industry constitutes high …
Tien Tran CONSUMER BEHAVIOUR IN SUSTAINABLE …
Therefore it is likely that sustainability will shape the fashion industry even further in the future. The fashion industry is highly dependent on consumers, thus it is critical that consumers receive …
YUSAN LIN
Apr 18, 2021 · Fashion Industry Using Named Entity Recognition, IEEE/ACM International Conference on Ad-vances in Social Networks Analysis and Mining (ASONAM 16), San Francisco, …
Mapping the fashion research landscape: a bibliometric analysis
of a combination of Web of Science and Scopus database. We used the Bibliometrix package on the R studio software to perform the data analysis of 686 papers. We identified four clusters #1 …
LIFE SCIENCES INDUSTRY REPORT 2025 - pharmaphorum.com
www.pharmaphorum.com Pharma market overview – US & Europe 2 Contents 03 Editors’ introduction 04 Life Sciences Industry Report 2025 Part 1: Pharma market overview – US & Europe
GLOBALIZATION OF THE FASHION INDUSTRY AND ITS …
This paper investigates the globalization of the fashion industry and its effects on Ghanaian in-dependent fashion designers and finds feasible strategies that can be employed for the improve …
What is the Economic Impact of Fast Fashion? - IJSRST
The fashion industry, of which fast fashion is a part, concerns the livelihoods of about 15% of the world’s workforce. That is, 1 in 6 people employed globally has their income attached to fashion. …
The impact of social media on fashion industry - IJRCS
impact on the fashion industry. 1. The Impact of Social Media on the Fashion Industry: Empirical Investigation from Karachiites(2015) - By-Nawaz Ahmad, Atif Salman, Rubab Ashiq: This project …
Leveraging Artificial Intelligence for Sustainability in the …
May 6, 2021 · Leveraging Artificial Intelligence for Sustainability in the Textile and Fashion Industry. 8(3): 2021. JTSFT.MS.ID.000688. DOI: 10.33552/JTSFT.2021.07.000688. Journal of Textile …
Analytical assortment optimization - McKinsey & Company
Granular data points such as sales per week, per store, or per basket give retailers a more useful metric for a product’s current and potential total economic performance. — Uniqueness: SKU …
BLOCKCHAIN IN THE FASHION INDUSTRY OPPORTUNITIES …
fashion industry is constituted by many small companies that strive to remain in the black due to the fierce industry competition. In this regard, Eurostat data16 shows that in the traditional …
The State of Fashion 2020 - McKinsey & Company
impact the fashion market. The year ahead will open with the industry in a state of high nervousness and uncertainty, with most executives across fashion and the wider business world bracing for a …
(Impact of Social Media on Consumer Behaviour) (Fashion …
International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356 www.ijres.org Volume 9 Issue 8 ǁ 2021 ǁ PP. 81-88 ... fashion industry. …
The Impact of Technology on Fashion Design: From Concept …
evolve, its integration within the fashion industry has revolutionized traditional practices, fostering innovation, sustainability, and accessibility. Through an interdisciplinary approach, this paper ...
ISSN: 2641-192X DOI: Journal of Textile Science & Fashion …
with the heightened visibility of ultrafast fashion around 2017, led by brands such as Boohoo and ASOS. These companies took the fast fashion model to new extremes, capable of adding …
SBTi MONITORING REPORT 2023 - Science Based Targets …
financial institutions setting science-based targets in 2023, together with the SBTi’s major updates and publications during the year. It uses a number of data sources as detailed in Appendix 1. In …
YUSAN LIN
Nov 22, 2022 · Fashion Industry Using Named Entity Recognition, IEEE/ACM International Conference on Ad-vances in Social Networks Analysis and Mining (ASONAM 16), San Francisco, …
The State of Fashion 2021 - McKinsey & Company
FASHION SYSTEM 58—99 06: Less is More 59 A More Circular Fashion Industry Will Require a Collective Effort 63 07: Opportunistic Investment 67 08: Deeper Partnerships 70 Shahi Exports: …
Technological and Social Impact on Hong Kong's Young …
Jun 7, 2024 · the purchase trends of different casual fashion products. Also, this work examines how online retailing and e-commerce platforms make retail buying easier compared to the …
The Impact of Social Media Marketing on Brand Loyalty in the …
the fashion industry by examining the mediating role of brand love. Data was collected through a survey of 532 social media users of fashion brands. The results indicate that customization, …
Influence of Social Media on Consumer Purchase Decisions …
Fashion is one industry that is thought to be particularly well-suited to social media (Ahmad et al., 2015). According to Ciarniene and Vienazindiene (2014) fashion is the style of cloths and …
YUSAN LIN
Nov 22, 2022 · Fashion Industry Using Named Entity Recognition, IEEE/ACM International Conference on Ad-vances in Social Networks Analysis and Mining (ASONAM 16), San Francisco, …
