Cloud Detection In Satellite Imagery Using Deep Learning

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Artificial Neural Networks and Machine Learning – ICANN 2018

Author : Věra Kůrková
Publisher : Springer
ISBN 13 : 303001424X
Total Pages : 866 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Artificial Neural Networks and Machine Learning – ICANN 2018 by : Věra Kůrková

Download or read book Artificial Neural Networks and Machine Learning – ICANN 2018 written by Věra Kůrková and published by Springer. This book was released on 2018-10-02 with total page 866 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.

Neural Networks: Tricks of the Trade

Author : Grégoire Montavon
Publisher : Springer
ISBN 13 : 3642352898
Total Pages : 753 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Neural Networks: Tricks of the Trade by : Grégoire Montavon

Download or read book Neural Networks: Tricks of the Trade written by Grégoire Montavon and published by Springer. This book was released on 2012-11-14 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.

ICML 2004

Author : Russell Greiner
Publisher :
ISBN 13 : 9781581138382
Total Pages : 942 pages
Book Rating : 4.1/5 (383 download)

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Book Synopsis ICML 2004 by : Russell Greiner

Download or read book ICML 2004 written by Russell Greiner and published by . This book was released on 2004 with total page 942 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Artificial Intelligence Techniques for Satellite Image Analysis

Author : D. Jude Hemanth
Publisher : Springer Nature
ISBN 13 : 3030241785
Total Pages : 277 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Artificial Intelligence Techniques for Satellite Image Analysis by : D. Jude Hemanth

Download or read book Artificial Intelligence Techniques for Satellite Image Analysis written by D. Jude Hemanth and published by Springer Nature. This book was released on 2019-11-13 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. In particular it presents the state-of-the-art in Artificial Intelligence (AI) methodologies and shares findings that can be translated into real-time applications to benefit humankind. Interdisciplinary in its scope, the book will be of interest to both newcomers and experienced scientists working in the fields of satellite image processing, geo-engineering, remote sensing and Artificial Intelligence. It can be also used as a supplementary textbook for graduate students in various engineering branches related to image processing.

2019 IEEE CVF International Conference on Computer Vision (ICCV)

Author : IEEE Staff
Publisher :
ISBN 13 : 9781728148045
Total Pages : pages
Book Rating : 4.1/5 (48 download)

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Book Synopsis 2019 IEEE CVF International Conference on Computer Vision (ICCV) by : IEEE Staff

Download or read book 2019 IEEE CVF International Conference on Computer Vision (ICCV) written by IEEE Staff and published by . This book was released on 2019-10-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Early Vision and Sensors Color, Illumination and Texture Segmentation and Grouping Motion and Tracking Stereo and Structure from Motion Image Based Modeling Physics Based Modeling Statistical Methods and Learning in Vision Video Surveillance and Monitoring Object, Event and Scene Recognition Vision Based Graphics Image and Video Retrieval Performance Evaluation Applications

Fundamentals and Methods of Machine and Deep Learning

Author : Pradeep Singh
Publisher : John Wiley & Sons
ISBN 13 : 1119821886
Total Pages : 480 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Fundamentals and Methods of Machine and Deep Learning by : Pradeep Singh

Download or read book Fundamentals and Methods of Machine and Deep Learning written by Pradeep Singh and published by John Wiley & Sons. This book was released on 2022-02-01 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

Change Detection and Image Time Series Analysis 2

Author : Abdourrahmane M. Atto
Publisher : John Wiley & Sons
ISBN 13 : 1789450578
Total Pages : 274 pages
Book Rating : 4.7/5 (894 download)

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Book Synopsis Change Detection and Image Time Series Analysis 2 by : Abdourrahmane M. Atto

Download or read book Change Detection and Image Time Series Analysis 2 written by Abdourrahmane M. Atto and published by John Wiley & Sons. This book was released on 2021-12-29 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series. Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches. Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns. Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations, Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.

Proceedings of the 3rd Brazilian Technology Symposium

Author : Yuzo Iano
Publisher : Springer
ISBN 13 : 3319931121
Total Pages : 327 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Proceedings of the 3rd Brazilian Technology Symposium by : Yuzo Iano

Download or read book Proceedings of the 3rd Brazilian Technology Symposium written by Yuzo Iano and published by Springer. This book was released on 2018-08-14 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of the 3rd Brazilian Technology Symposium (BTSym), which is a multi/trans/interdisciplinary event offering an excellent forum for presentations and discussions of the latest scientific and technological developments in various areas of research, with an emphasis on smart design and future technologies. It brings together researchers, students and professionals from the industrial and academic sectors to discuss current technological issues. Among the main topics covered in this book, we can highlight Artificial Neural Networks, Computational Vision, Security Applications, Web Tool, Cloud Environment, Network Functions Virtualization, Software-Defined Networks, IoT, Residential Automation, Data Acquisition, Industry 4.0, Cyber-Physical Systems, Digital Image Processing, Infrared Images, Patters Recognition, Digital Video Processing, Precoding, Embedded Systems, Machine Learning, Remote Sensing, Wireless Sensor Network, Heterogeneous Networks, Unmanned Ground Vehicle, Unmanned Aerial System, Security, Surveillance, Traffic Analysis, Digital Television, 5G, Image Filter, Partial Differential Equation, Smoothing Filters, Voltage Controlled Ring Oscillator, Difference Amplifier, Photocatalysis, Photodegradation, Cosmic Radiation Effects, Radiation Hardening Techniques, Surface Electromyography, Sickle cell disease methodology, MicroRNAs, Image Processing Venipuncture, Cognitive Ergonomics, Ecosystem services, Environmental, Power Generation, Ecosystem services valuation, Solid Waste and University Extension.

Deep Learning with Keras

Author : Antonio Gulli
Publisher : Packt Publishing Ltd
ISBN 13 : 1787129039
Total Pages : 310 pages
Book Rating : 4.7/5 (871 download)

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Book Synopsis Deep Learning with Keras by : Antonio Gulli

Download or read book Deep Learning with Keras written by Antonio Gulli and published by Packt Publishing Ltd. This book was released on 2017-04-26 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Who This Book Is For If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book. What You Will Learn Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm Fine-tune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network (RNN) solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning In Detail This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. Style and approach This book is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras.

Building Intelligent Cloud Applications

Author : John Biggs
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492052272
Total Pages : 154 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Building Intelligent Cloud Applications by : John Biggs

Download or read book Building Intelligent Cloud Applications written by John Biggs and published by "O'Reilly Media, Inc.". This book was released on 2019-09-10 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Serverless computing is radically changing the way we build and deploy applications. With cloud providers running servers and managing machine resources, companies now can focus solely on the application’s business logic and functionality. This hands-on book shows experienced programmers how to build and deploy scalable machine learning and deep learning models using serverless architectures with Microsoft Azure. You’ll learn step-by-step how to code machine learning into your projects using Python and pretrained models that include tools such as image recognition, speech recognition, and classification. You’ll also examine issues around deployment and continuous delivery, including scaling, security, and monitoring. This book is divided into three parts with application examples woven throughout: Cloud-based development: Learn the basics of serverless computing with machine learning, Functions-as-a-Service (FaaS), and the use of APIs Adding intelligence: Create serverless applications using Azure Functions; learn how to use prebuilt machine learning and deep learning models Deployment and continuous delivery: Get up to speed with Azure Kubernetes Service, Azure Security Center, and Azure Monitoring

Deep Learning for Coders with fastai and PyTorch

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Clouds and Climate

Author : A. Pier Siebesma
Publisher :
ISBN 13 : 9781107447738
Total Pages : pages
Book Rating : 4.4/5 (477 download)

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Book Synopsis Clouds and Climate by : A. Pier Siebesma

Download or read book Clouds and Climate written by A. Pier Siebesma and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Clouds have always fascinated humans, but never has the need to understand them been so vital. As global surface temperature increases, human activities influence particulate matter in the atmosphere, and the properties of the land surface, clouds are expected to change, with manifold consequences for the climate. Clouds influence climate through their regulation of radiant energy transfer, through their role in convective energy transport, and in mediating the water cycle. Cloud research has never been so exciting. It is a topic with many new opportunities. New satellite observations from space with active instruments such as lidar and radar allow for the first time to reconstruct the three-dimensional distribution of clouds across Earth. Likewise, numerical simulations are beginning to globally resolve the three spatial dimensions of clouds as well as their dynamic evolution. This is enabling researchers, for the first time, to link the smallscale cloud processes and the basic laws that govern them to the general circulation of the atmosphere. These new types of observations, and simulation, are enabling researchers to distinguish how different cloud types influence the climate, and are guiding conceptual representations of their collective behaviour"--

Machine Learning for Ecology and Sustainable Natural Resource Management

Author : Grant Humphries
Publisher : Springer
ISBN 13 : 3319969781
Total Pages : 442 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Machine Learning for Ecology and Sustainable Natural Resource Management by : Grant Humphries

Download or read book Machine Learning for Ecology and Sustainable Natural Resource Management written by Grant Humphries and published by Springer. This book was released on 2018-11-05 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

Deep Learning for the Earth Sciences

Author : Gustau Camps-Valls
Publisher : John Wiley & Sons
ISBN 13 : 1119646162
Total Pages : 436 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Deep Learning for the Earth Sciences by : Gustau Camps-Valls

Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-18 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Practical Machine Learning for Computer Vision

Author : Valliappa Lakshmanan
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098102339
Total Pages : 481 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Practical Machine Learning for Computer Vision by : Valliappa Lakshmanan

Download or read book Practical Machine Learning for Computer Vision written by Valliappa Lakshmanan and published by "O'Reilly Media, Inc.". This book was released on 2021-07-21 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Author : Wojciech Samek
Publisher : Springer Nature
ISBN 13 : 3030289540
Total Pages : 435 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by : Wojciech Samek

Download or read book Explainable AI: Interpreting, Explaining and Visualizing Deep Learning written by Wojciech Samek and published by Springer Nature. This book was released on 2019-09-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM)

Author : IEEE Staff
Publisher :
ISBN 13 : 9781538653227
Total Pages : pages
Book Rating : 4.6/5 (532 download)

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Book Synopsis 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM) by : IEEE Staff

Download or read book 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM) written by IEEE Staff and published by . This book was released on 2018-09-13 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The conference solicits high quality original research papers in any aspect of multimedia big data Topics include, but are not limited to New theory and models for multimedia big data computing Ultra high efficiency compression, coding and transmission for multimedia big data Content analysis and mining for multimedia big data Semantic retrieval of multimedia big data Deep learning and cloud computing for multimedia big data Green computing for multimedia big data (e g, high efficiency storage) Security and privacy in multimedia big data Multimedia big data systems Novel and incentive applications of multimedia big data in various fields (e g, search, healthcare, transportation, retail)