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data science conferences 2023: Proceedings of the International Conference on Big Data, IoT, and Machine Learning Mohammad Shamsul Arefin, M. Shamim Kaiser, Anirban Bandyopadhyay, Md. Atiqur Rahman Ahad, Kanad Ray, 2021-12-03 This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2021), held in Cox’s Bazar, Bangladesh, during 23–25 September 2021. The book covers research papers in the field of big data, IoT and machine learning. The book will be helpful for active researchers and practitioners in the field. |
data science conferences 2023: Next Generation Data Science Henry Han, Erich Baker, 2024 Zusammenfassung: This book constitutes the refereed proceedings of the Sescond Southwest Data Science Conference, SDSC 2023, held in Waco, TX, USa, during March 24-25, 2023. The 16 full and 1 short paper included in this book were carefully reviewed and selected from 72 submissions. They were oragnized in topical sections named: Business social and foundation data science; and applied data science, artifiicial intelligence and data engineering. |
data science conferences 2023: The Ethical Algorithm Michael Kearns, Aaron Roth, 2020 Algorithms have made our lives more efficient and entertaining--but not without a significant cost. Can we design a better future, one in which societial gains brought about by technology are balanced with the rights of citizens? The Ethical Algorithm offers a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. |
data science conferences 2023: International Conference on Intelligent and Smart Computing in Data Analytics Siddhartha Bhattacharyya, Janmenjoy Nayak, Kolla Bhanu Prakash, Bighnaraj Naik, Ajith Abraham, 2021-03-12 This book is a collection of best selected research papers presented at International Conference on Intelligent and Smart Computing in Data Analytics (ISCDA 2020), held at K L University, Guntur, Andhra Pradesh, India. The primary focus is to address issues and developments in advanced computing, intelligent models and applications, smart technologies and applications. It includes topics such as artificial intelligence and machine learning, pattern recognition and analysis, computational intelligence, signal and image processing, bioinformatics, ubiquitous computing, genetic fuzzy systems, hybrid evolutionary algorithms, nature-inspired smart hybrid systems, Internet of things, industrial IoT, health informatics, human–computer interaction and social network analysis. The book presents innovative work by leading academics, researchers and experts from industry. |
data science conferences 2023: Predictive Analytics Eric Siegel, 2016-01-12 Mesmerizing & fascinating... —The Seattle Post-Intelligencer The Freakonomics of big data. —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a |
data science conferences 2023: Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications; Volume 2 Amit Kumar, |
data science conferences 2023: Proceedings of International Conference on Data Science and Applications Mukesh Saraswat, Sarbani Roy, Chandreyee Chowdhury, Amir H. Gandomi, 2021-11-22 This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2021), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from April 10 to 11, 2021. 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 conferences 2023: Information Management and Big Data Juan Antonio Lossio-Ventura, Denisse Muñante, Hugo Alatrista-Salas, 2019 This book constitutes the refereed proceedings of the 5th International Conference on Information Management and Big Data, SIMBig 2018, held in Lima, Peru, in September 2018. The 34 papers presented were carefully reviewed and selected from 101 submissions. The papers address issues such as data mining, artificial intelligence, Natural Language Processing, information retrieval, machine learning, web mining. |
data science conferences 2023: Creativity in Intelligent Technologies and Data Science Alla G. Kravets, Maxim V. Shcherbakov, Peter P. Groumpos, 2023-11-14 This book constitutes the proceedings of the 5th Conference on Creativity in Intellectual Technologies and Data Science, CIT&DS 2023, held in Volgograd, Russia, in September 2023. The 40 regular papers and 2 keynote papers presented were carefully reviewed and selected from 148 submissions. The papers are organized in the following topical sections: Artificial intelligence and deep learning technologies for creative tasks. Knowledge discovery in patent and open sources; Artificial intelligence & Deep Learning Technologies for Creative tasks. Open science semantic technologies; Artificial intelligence and deep learning technologies for creative tasks. Computer vision and knowledge-based control; Cyber-physical systems and big data-driven control: pro-active modeling in intelligent decision making support; Cyber-Physical Systems & Big Data-driven world. Industrial creativity in CASE/CAI/CAD/PDM; Cyber-Physical Systems & Big Data-driven world. Intelligent Internet of Services and Internet of Things; Intelligent Technologies in Social Engineering. Data Science in Social Networks Analysis and Cyber Security; Intelligent Technologies in Social Engineering. Creativity & Game-Based Learning; Intelligent Technologies in Social Engineering. Intelligent Technologies in Medicine& Healthcare; Intelligent Technologies in Social Engineering. Intelligent technologies in Urban Design&Computing. |
data science conferences 2023: Big Data Meets Survey Science Craig A. Hill, Paul P. Biemer, Trent D. Buskirk, Lilli Japec, Antje Kirchner, Stas Kolenikov, Lars E. Lyberg, 2020-09-29 Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources. |
data science conferences 2023: Soft Computing in Data Science Marina Yusoff, Tao Hai, Murizah Kassim, Azlinah Mohamed, Eisuke Kita, 2023-03-16 This book constitutes the refereed proceedings of the 7th International Conference on Soft Computing in Data Science, SCDS 2023, which was held virtually in January 2023. The 21 revised full papers presented were carefully reviewed and selected from 61 submissions. The papers are organized in topical sections on artificial intelligence techniques and applications; computing and optimization; data analytics and technologies; data mining and image processing; mathematical and statistical learning. |
data science conferences 2023: Blockchain, Internet of Things, and Artificial Intelligence Naveen Chilamkurti, T. Poongodi, Balamurugan Balusamy, 2021-04-02 Blockchain, Internet of Things, and Artificial Intelligence provides an integrated overview and technical description of the fundamental concepts of blockchain, IoT, and AI technologies. State-of-the-art techniques are explored in depth to discuss the challenges in each domain. The convergence of these revolutionized technologies has leveraged several areas that receive attention from academicians and industry professionals, which in turn promotes the book's accessibility more extensively. Discussions about an integrated perspective on the influence of blockchain, IoT, and AI for smart cities, healthcare, and other business sectors illuminate the benefits and opportunities in the ecosystems worldwide. The contributors have focused on real-world examples and applications and highlighted the significance of the strengths of blockchain to transform the readers’ thinking toward finding potential solutions. The faster maturity and stability of blockchain is the key differentiator in artificial intelligence and the Internet of Things. This book discusses their potent combination in realizing intelligent systems, services, and environments. The contributors present their technical evaluations and comparisons with existing technologies. Theoretical explanations and experimental case studies related to real-time scenarios are also discussed. FEATURES Discusses the potential of blockchain to significantly increase data while boosting accuracy and integrity in IoT-generated data and AI-processed information Elucidates definitions, concepts, theories, and assumptions involved in smart contracts and distributed ledgers related to IoT systems and AI approaches Offers real-world uses of blockchain technologies in different IoT systems and further studies its influence in supply chains and logistics, the automotive industry, smart homes, the pharmaceutical industry, agriculture, and other areas Presents readers with ways of employing blockchain in IoT and AI, helping them to understand what they can and cannot do with blockchain Provides readers with an awareness of how industry can avoid some of the pitfalls of traditional data-sharing strategies This book is suitable for graduates, academics, researchers, IT professionals, and industry experts. |
data science conferences 2023: German Medical Data Sciences 2023 — Science. Close to People. R. Röhrig, N. Grabe, M. Haag, 2023-10-19 The Covid-19 pandemic affected the daily lives of all of us on many levels. Epidemiology suddenly became a personal matter and general interest in many aspects of medical data science became much more widespread. And physical distance became the new normal. This book presents the full paper part of the proceedings of GMDS 2023, the 68th annual meeting of the German Association for Medical Informatics, Biometry and Epidemiology, held from 17 to 21 September 2023 in Heilbronn, Germany. The theme of the conference was, Science. Close to People, a particularly appropriate theme for the first of these annual conferences to be held face-to-face since 2019. A total of 227 scientific contributions were submitted to GMDS 2023, including 41 full papers for this volume in Studies in HTI. Of these, 30 papers are included here, following a rigorous two-stage review process, which represents an acceptance rate of 73%. The 30 papers in this book are grouped under 8 headings: FAIRification; research software engineering for research infrastructure & study data management; human factors; data quality; clinical decision support & artificial intelligence; evaluation of healthcare IT; biosignals; and interoperability. Providing a broad overview of current developments in the disciplines of medical informatics, biometry and epidemiology, the book will be of interest to all those working in these fields. |
data science conferences 2023: R for Everyone Jared P. Lander, 2017-06-13 Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available. |
data science conferences 2023: Web Metrics Jim Sterne, 2003-05-12 There now exists a wealth of tools and techniques that can determine if and how a Web site is providing business value to its owners. This book is a survey of those metrics and is as important to IT executives as it is to marketing professionals. Jim Sterne is recognized worldwide as a leading Internet business expert and is the author of several Wiley books, including WWW Marketing, Third Edition (0-471-41621-5) Explains the criteria for building a successful site, surveying the tools, services, techniques, and standards for Web measurement, and fully integrating those metrics with the customer experience Companion Web site contains links to online tools, resources, and white papers |
data science conferences 2023: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
data science conferences 2023: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
data science conferences 2023: Data Science and Artificial Intelligence Chutiporn Anutariya, |
data science conferences 2023: Neuromorphic Photonics Paul R. Prucnal, Bhavin J. Shastri, 2017-05-08 This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of neuromorphic photonics. It includes a thorough discussion of evolution of neuromorphic photonics from the advent of fiber-optic neurons to today’s state-of-the-art integrated laser neurons, which are a current focus of international research. Neuromorphic Photonics explores candidate interconnection architectures and devices for integrated neuromorphic networks, along with key functionality such as learning. It is written at a level accessible to graduate students, while also intending to serve as a comprehensive reference for experts in the field. |
data science conferences 2023: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
data science conferences 2023: Data Science—Analytics and Applications Peter Haber, Thomas J. Lampoltshammer, Manfred Mayr, 2024-01-03 Based on the overall digitalization in all spheres of our lives, Data Science and Artificial Intelligence (AI) are nowadays cornerstones for innovation, problem solutions, and business transformation. Data, whether structured or unstructured, numerical, textual, or audiovisual, put in context with other data or analyzed and processed by smart algorithms, are the basis for intelligent concepts and practical solutions. These solutions address many application areas such as Industry 4.0, the Internet of Things (IoT), smart cities, smart energy generation, and distribution, and environmental management. Innovation dynamics and business opportunities for effective solutions for the essential societal, environmental, or health challenges, are enabled and driven by modern data science approaches. However, Data Science and Artificial Intelligence are forming a new field that needs attention and focused research. Effective data science is only achieved in a broad and diverse discourse – when data science experts cooperate tightly with application domain experts and scientists exchange views and methods with engineers and business experts. Thus, the 5th International Data Science Conference (iDSC 2023) brings together researchers, scientists, business experts, and practitioners to discuss new approaches, methods, and tools made possible by data science. |
data science conferences 2023: ICDSMLA 2019 Amit Kumar, Marcin Paprzycki, Vinit Kumar Gunjan, 2020-05-19 This book gathers selected high-impact articles from the 1st International Conference on Data Science, Machine Learning & Applications 2019. It highlights the latest developments in the areas of Artificial Intelligence, Machine Learning, Soft Computing, Human–Computer Interaction and various data science & machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise. |
data science conferences 2023: Artificial Intelligence Stuart Russell, Peter Norvig, 2016-09-10 Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. |
data science conferences 2023: Data Science and Intelligent Applications Ketan Kotecha, Vincenzo Piuri, Hetalkumar N. Shah, Rajan Patel, 2020-06-17 This book includes selected papers from the International Conference on Data Science and Intelligent Applications (ICDSIA 2020), hosted by Gandhinagar Institute of Technology (GIT), Gujarat, India, on January 24–25, 2020. The proceedings present original and high-quality contributions on theory and practice concerning emerging technologies in the areas of data science and intelligent applications. The conference provides a forum for researchers from academia and industry to present and share their ideas, views and results, while also helping them approach the challenges of technological advancements from different viewpoints. The contributions cover a broad range of topics, including: collective intelligence, intelligent systems, IoT, fuzzy systems, Bayesian networks, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, speech processing, machine learning and deep learning, and intelligent applications and systems. Helping strengthen the links between academia and industry, the book offers a valuable resource for instructors, students, industry practitioners, engineers, managers, researchers, and scientists alike. |
data science conferences 2023: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
data science conferences 2023: ICDSMLA 2020 Amit Kumar, Sabrina Senatore, Vinit Kumar Gunjan, 2021-11-08 This book gathers selected high-impact articles from the 2nd International Conference on Data Science, Machine Learning & Applications 2020. It highlights the latest developments in the areas of artificial intelligence, machine learning, soft computing, human–computer interaction and various data science and machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise. |
data science conferences 2023: The Mathematics of Data Michael W. Mahoney, John C. Duchi, Anna C. Gilbert, 2018-11-15 Nothing provided |
data science conferences 2023: Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23) Sergey Kovalev, Igor Kotenko, Andrey Sukhanov, 2023-10-22 This book contains the works connected with the key advances in Industrial Artificial Intelligence presented at IITI 2023, the Seventh International Scientific Conference on Intelligent Information Technologies for Industry held on September 25-30, 2023 in St. Petersburg, Russia. The works were written by the experts in the field of applied artificial intelligence including topics such as Machine Learning, Explainable AI, Decision-Making, Fuzzy Logic, Multi-Agent and Bioinspired Systems. The following industrial application domains were touched: railway automation, cyber security, intelligent medical systems, navigation and energetic systems. The editors believe that this book will be helpful for all scientists and engineers interested in the modern state of applied artificial intelligence. |
data science conferences 2023: Data Management Technologies and Applications Slimane Hammoudi, Christoph Quix, Jorge Bernardino, 2021-07-22 This book constitutes the thoroughly refereed proceedings of the 9th International Conference on Data Management Technologies and Applications, DATA 2020, which was supposed to take place in Paris, France, in July 2020. Due to the Covid-19 pandemic the event was held virtually. The 14 revised full papers were carefully reviewed and selected from 70 submissions. The papers deal with the following topics: datamining; decision support systems; data analytics; data and information quality; digital rights management; big data; knowledge management; ontology engineering; digital libraries; mobile databases; object-oriented database systems; data integrity. |
data science conferences 2023: Data Science John D. Kelleher, Brendan Tierney, 2018-04-13 A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects. |
data science conferences 2023: Proceedings of the 23rd European Conference on Cyber Warfare and Security Dr Martti Lehto, 2024-06-27 These proceedings represent the work of contributors to the 23rd European Conference on Cyber Warfare and Security (ECCWS 2024), supported by University of Jyväskylä, and JAMK University of Applied Sciences, Finland on 27-28 June 2024. The Conference Chair is Dr Martti Lehto from the University of Jyväskylä, Finland, and the Programme Chair is Dr Mika Karjalainen from JAMK University of Applied Sciences, Finland. ECCWS is a well-established event on the academic research calendar and now in its 23rd year conference remains the opportunity for participants to network and share ideas. The aims and scope of the conference is to be a forum for technical, theoretical and practical exchange about the study, management, development and implementation of systems and concepts to improve cyber security and combat cyber warfare. The opening keynote presentation is given by Stefan Lee, from Ministry of Transport and Communications, Finland, on the topic of Geopolitics and Cyberspace: Key Implications for National Cybersecurity Policies and Strategies. The second day of the conference will open with an address by Colonel Janne Jokinen, Finnish Defence Force, Finland speaking on Ten Practical Hindrances to Building Cyber Defence. With an initial submission of 171 abstracts, after the double blind, peer review process there are 180 Academic research papers, 11 PhD research papers, 6 Masters research paper and 2 work-in-progress papers published in these Conference Proceedings. These papers represent research from Australia, Austria, Belgium, Canada, Czech Republic, Estonia, Finland, Germany, Ireland, Japan, Kingdom of Saudi Arabia, Lithuania, Norway, Oman, Poland, Portugal, Romania, South Africa, Spain, The Czech republic, United Arab Emirates, UK and USA. |
data science conferences 2023: Data Analytics and Management Ashish Khanna, Deepak Gupta, Zdzisław Pólkowski, Siddhartha Bhattacharyya, Oscar Castillo, 2021-01-04 This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2020), held at Jan Wyzykowski University, Poland, during June 2020. 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. |
data science conferences 2023: Responsible Artificial Intelligence Virginia Dignum, 2019-11-04 In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens. |
data science conferences 2023: Graph Data Management George Fletcher, Jan Hidders, Josep Lluís Larriba-Pey, 2018-10-31 This book presents a comprehensive overview of fundamental issues and recent advances in graph data management. Its aim is to provide beginning researchers in the area of graph data management, or in fields that require graph data management, an overview of the latest developments in this area, both in applied and in fundamental subdomains. The topics covered range from a general introduction to graph data management, to more specialized topics like graph visualization, flexible queries of graph data, parallel processing, and benchmarking. The book will help researchers put their work in perspective and show them which types of tools, techniques and technologies are available, which ones could best suit their needs, and where there are still open issues and future research directions. The chapters are contributed by leading experts in the relevant areas, presenting a coherent overview of the state of the art in the field. Readers should have a basic knowledge of data management techniques as they are taught in computer science MSc programs. |
data science conferences 2023: SQL Pocket Guide Alice Zhao, 2021-08-26 If you use SQL in your day-to-day work as a data analyst, data scientist, or data engineer, this popular pocket guide is your ideal on-the-job reference. You'll find many examples that address the language's complexities, along with key aspects of SQL used in Microsoft SQL Server, MySQL, Oracle Database, PostgreSQL, and SQLite. In this updated edition, author Alice Zhao describes how these database management systems implement SQL syntax for both querying and making changes to a database. You'll find details on data types and conversions, regular expression syntax, window functions, pivoting and unpivoting, and more. Quickly look up how to perform specific tasks using SQL Apply the book's syntax examples to your own queries Update SQL queries to work in five different database management systems NEW: Connect Python and R to a relational database NEW: Look up frequently asked SQL questions in the How Do I? chapter |
data science conferences 2023: Spatio-Temporal Statistics with R Christopher K. Wikle, Andrew Zammit-Mangion, Noel Cressie, 2019-02-18 The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these big data that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps. Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book: Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation Provides a gradual entry to the methodological aspects of spatio-temporal statistics Provides broad coverage of using R as well as R Tips throughout. Features detailed examples and applications in end-of-chapter Labs Features Technical Notes throughout to provide additional technical detail where relevant Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data. |
data science conferences 2023: Artificial Intelligence and the Legal Profession Michael Legg, Felicity Bell, 2020-11-26 How are new technologies changing the practice of law? With examples and explanations drawn from the UK, US, Canada, Australia and other common law countries, as well as from China and Europe, this book considers the opportunities and implications for lawyers as artificial intelligence systems become commonplace in legal service delivery. It examines what lawyers do in the practice of law and where AI will impact this work. It also explains the important continuing role of the lawyer in an AI world. This book is divided into three parts: Part A provides an accessible explanation of AI, including diagrams, and contrasts this with the role and work of lawyers. Part B focuses on six different aspects of legal work (litigation, transactional, dispute resolution, regulation and compliance, criminal law and legal advice and strategy) where AI is making a considerable impact and looks at how this is occurring. Part C discusses how lawyers and law firms can best utilise the promise of AI, while also acknowledging its limitations. It also discusses ethical and regulatory issues, including the lawyer's role in upholding the rule of law. |
data science conferences 2023: Foundations of Machine Learning, second edition Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, 2018-12-25 A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition. |
data science conferences 2023: Blockchain + Antitrust Schrepel, Thibault, 2021-09-21 This innovative and original book explores the relationship between blockchain and antitrust, highlighting the mutual benefits that stem from cooperation between the two and providing a unique perspective on how law and technology could cooperate. |
data science conferences 2023: Never Enough Mike Hayes, 2021-02-09 In Never Enough, Mike Hayes—former Commander of SEAL Team TWO—helps readers apply high-stakes lessons about excellence, agility, and meaning across their personal and professional lives. Mike Hayes has lived a lifetime of once-in-a-lifetime experiences. He has been held at gunpoint and threatened with execution. He’s jumped out of a building rigged to explode, helped amputate a teammate’s leg, and made countless split-second life-and-death decisions. He’s written countless emails to his family, telling them how much he loves them, just in case those were the last words of his they’d ever read. Outside of the SEALs, he’s run meetings in the White House Situation Room, negotiated international arms treaties, and developed high-impact corporate strategies. Over his many years of leadership, he has always strived to be better, to contribute more, and to put others first. That’s what makes him an effective leader, and it’s the quality that he’s identified in all of the great leaders he’s encountered. That continual striving to lift those around him has filled Mike’s life with meaning and purpose, has made him secure in the knowledge that he brings his best to everything he does, and has made him someone others can rely on. In Never Enough, Mike Hayes recounts dramatic stories and offers battle- and boardroom-tested advice that will motivate readers to do work of value, live lives of purpose, and stretch themselves to reach their highest potential. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnationa…
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 …
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 …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
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 …
Automatic shoe embossing machine integrated with real-time …
Automatic shoe embossing machine integrated with real-time data cloud system to improve SMEs footwear productivity Iskandar1*, I Made Arsana1, Rizdana Galih Pambudi1, Yuli Sutoto …
Presentation Board with Quad Chart Guidelines
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Optimization and Analysis of Female Foot Anthropometry
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ABSTRACTS - cdn.ymaws.com
Paper Session 1: Engaging in Systems Science to Reduce Tobacco-Related Health Disparities and Promote Equity Paper Session 2: Rapid Fire: Developing and Evaluating Risk Messages …
2023 IEEE 39th International Conference on Data
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Massive Scale Data Analytics at LCLS-II - epj-conferences.org
LCLS Data Systems has developed a world-leading capability in high throughput data analytics consistent with the leap to 1 MHz operation, and providing fast feedback capabilities, real-time …
A Big Data Platform for International Academic Conferences …
Electronics 2023, 12, 1182 3 of 18 • A comprehensive evaluation of the conference and its series was conducted by using indicators such as the number of followers, page views, number of ...
Attending to the Cultures of Data Science Work
Apr 3, 2023 · 20 YEARS OF DATA SCIENCE ESSAY CORRESPONDING AUTHOR: Lindsay Poirier Smith College, US lpoirier@smith.edu KEYWORDS: data culture; data infrastructure; …
The growing influence of industry in AI research
conferences in 2000 to 38% in 2020 (see the second figure). Alternate definitions of what ... ties (where we are able to get the best data) show that computer science PhD graduates …
NIH STRATEGIC PLAN FOR DATA SCIENCE 2023-2028
The 2023-2028 NIH Strategic Plan for Data Science builds on accomplishments from significant collaborations of NIH ICOs under the initial NIH Strategic Plan for Data Science3. Experiences …
FY 2024 Conference Report to the Inspector General - NASA
Dubai, UAE 12/6/2023 12/12/2023 $203,131 20 NASA is an expert in climate and Earth science. While its role is not to set climate policy or prescribe responses or solutions to climate change, …
The World’s Most Popular Data Science Platform - Anaconda
Anaconda to power their data science and AI workflows, making Anaconda the world’s most popular data science platform and the foundation of modern machine learning. Anaconda has …
IEEE CASE Automation for
IEEE CASE AUCKLAND2023 19TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING 26 – 29 August 2023 | Cordis, Auckland, New Zealand …
Your brilliance, connected Content Coverage Guide
Updated March 2023. 27,950 active titles: (see section 4.1) ... •˚Advanced Data Science ... humanities and social science and conferences are more broadly covered in computer …
Green Technology and Environmental Sustainability ... - e3s …
documents were located, a data cleaning process was run to look for missing or incorrectly recorded information. In this method, two specific processes were used: (1) validating the data …
Statistics and Chair of the Data Science program at the …
since 2023. Currently editor or editorial board member for four international statistics journals. Author of numerous publications on development and evaluation of new statistical methods, in …
Exploring Explainable Artificial Intelligence for Transparent …
Exploring Explainable Artificial Intelligence for Transparent Decision Making Dr D David Winster Praveenraj 1 Mr Melvin Victor 2 C. Vennila 3 Ahmed Hussein Alawadi 4 Pardaeva Diyora5N. …
Applied Generative AI for Beginners - AI Unplugged
is a seasoned senior data architect with 13 years of experience in cloud services, big data, and data engineering. Dilip has a strong background in designing and developing ETL solutions, …
The Influence Factors of Short Video Marketing on Consumer …
a certain psychological distance will be produced when consumers perceive the supply of short video marketing products. If the psychological distance becomes shorter, it
ICDSA2024 Brochure - Malaviya National Institute of …
on Data Science and Applications (ICDSA 2024) in technical support of the Soft Computing Research. Society (SCRS) from July 17-19, 2024. The ICDSA 2024 aims to bring the …
Gas Leakage Detection System Using IoT And cloud ... - e3s …
Gas Leakage Detection System Using IoT And cloud Technology : A Review V Praveen Sharma1*, Dr Raman Dugyala2, Dr V Padmavathi3, Vijendar Reddy Gurram4 1Department of …
2023’s Top Employers: Making a happy workplace for …
For over 20 years, Science has surveyed its readership to identify and celebrate the best pharma and biotech companies as part of its annual Top Employers survey. This survey received …
Jinchi Lv Curriculum Vitae - Amazon Web Services
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Indian Library Association - Indian Library Association, Delhi
Digital Open Data- Open Science- Big Data- Data Mining. Call for paper : Original contributions based on practice, theoretical and empirical research, case studies etc. are invited on the …
YEARLY STATUS REPORT - 2023-2024
Science and Technology (KSCST) 2023 0.045 Lakhs Mr. Elaiyaraja P Student Project Programme (SPP) Karnataka State Council for Science and Technology (KSCST) 2023 0.04 Lakhs Dr. …
Cross-disciplinary collaboration in neuroscience: Genetics, …
data science, and clinical practice 10.1. Introduction Cross-disciplinary collaboration can heighten the success of efforts to tackle highly ... 2023; Lee et al., 2023; Martinez et al., 2024). Further …
Jordan H. McAllister - University of Kentucky
EXPERIENCE Biomedical Data Science Assistant, University of Kentucky ... Washington University in St. Louis 2018 – 2023 B.A. in International Studies, Centre College 2014 – 2018 …
Climate Change 2023 Synthesis Report - IPCC
Climate Change 2023 Synthesis Report IPCC, 2023: Sections. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report …
Attending to the Cultures of Data Science Work
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INTERNSHIP & PLACEMENT BROCHURE - 2023 - Indian …
6 For more details about the B.S. Degree Program, please visit https://study.iitm.ac.in/ds B.S. Degree in Data Science and Applications IIT Madras IIT Madras, India’s premier technology …
arXiv:2303.16750v1 [cs.IR] 23 Mar 2023
We use this data to compare several popular algorithms currently employed in computer science conferences and come up with recommendations for stakeholders. ... [cs.IR] 23 Mar 2023. …
Data science curriculum v5 - ALX Africa
Data Science Programme Curriculum In partnership with EXPLORE Al www.alxafrica.com . Data Science Programme Curriculum Module 1: Explore 101 (1 Week) IN PARTNERSHIP WITH …
Preface of the Conference Proceeding of the 9th ISCPMS …
The 9th ISCPMS 2023, taking place on August 29-30, 2023, at The Patra Bali Resorts and Villas, Bali, converges under the overarching theme - "Application of Artificial Intelligences and Data …
Graduate Data Science Programme Booklet
2022 to 2023. The data science graduate programme is an exciting opportunity for graduates to work at the heart of public sector data science. ... • Attending data science conferences and …
The borders of science - Royal Society
science at the cutting edge, and ensures that the benefits of research feed downstream into society. This report looks at the UK's visitor visa arrangements for individuals who attend …
June 23 – 27, 2025 Singapore ISPAC 2025 - ispac …
Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Kingdom of Saudi Arabia Nikolaos.Hadjichristidis@kaust.edu.sa Polymers …
Meet the Mentors – IEEE/CVF CVPR 2025
E stefa n í a Ta l ave ra is an Assistant Professor in the Data Management and Biometrics group at the University of Twente. Her research interests include ... She has a PhD in Computer …
Department of Energy Fiscal Year 2023 Conference Activity
In Fiscal Year (FY) 2023, eleven (11) Program Offices within DOE conducted a total of fifteen (15) Agency-Sponsored reportable conferences. The conference themes supported their …
School Year 2023-2024 Elementary School Assessments …
May 23, 2010 · Updated 8/7/2023 . Diagnostic Assessments: Administered throughout the year to gather data to adjust and enhance instruction. For more information, please see the . ...
2023 Data and AI Trends Report - Google Search
VP of Informatics, Data Science, and AI at Moderna 2023 Data and AI Trends Report Usher in the age of the open data ecosystem 16 78% of executive management believe that using external …
Deep Learning for Multispectral, Multiresolution and …
meetup, Finland, 2023. • F. Farahnakian, “Deep Learning for Data Fusion“, Tutorial of IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2021. • F. …
Program Chairs’ Report on Peer Review at ACL 2023
the *ACL conferences work, (c) provide useful data for the future chairs and workshop organizers, and also academic work on peer review, and (d) provide useful context for the final program, …
The Art & Science - AI at Wharton
The Art & Science of A/B Testing 2021 Wharton Analytics Conference Supported by Wharton AI for Business, Analytics@Wharton. Welcome & Introduction. 3 Ph.D. Candidate ... Prior …
INDIANA UNIVERSITY DATA SCIENCE - Luddy School of …
Sep 11, 2023 · As a Master of Data Science student, you have the option of focusing on one of the following four distinct tracks: (1) Applied Data Science; (2) Big Data Systems; (3) …
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Organising webinars, conferences and FDPs Page 60/145 11-08-2023 03:18:14. ... Science, History, Sociology and Foundation Course. The college has zero tolerance policy against …
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Making progress on the science of “soft” skills requires stronger theory and measurement paradigms • The Harvard Skills Lab – funded in part by a two-year, $1.5m gift from Schmidt …
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Ozone Data Analysis The 2019, 2020 and 2021 AQS hourly ozone data were used to calculate the daily 8-hour maximum concentration for each ozone-monitoring site . The hourly averaged …
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Upcoming Conferences of Note December 7–8, 2023: Military Scholarship of Teaching and Learning Forum Quantico, VA ... EduData Summit is a premier forum for data-driven …
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conferences and meetings has been that the key challenges data science communities face ‘are not technical but social’ and that, as a community, we need to be focused on building social ...
Educational Data mining and Learning Analytics: An updated …
• Educational Data Science (EDS) is defined as the use of data gathered from educational environments/settings for solving educational problems (Romero & Ventura, 2017). ... EDM …