Deep Learning Applications Volume 3

Deep Learning Applications  Volume 3

This book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as ...

Author: M. Arif Wani

Publisher: Springer

ISBN: 9811633568

Category: Technology & Engineering

Page: 322

View: 734

This book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.
Categories: Technology & Engineering

Deep Learning Applications Volume 3

Deep Learning Applications  Volume 3

In: Deep Learning Applications, vol. 2, pp. 1–23. Springer (2021) 2. Ben, Z., Long, X., Zeng, X., Liu, J.: Model compression for image classification based on low-rank sparse quantization. In: 2019 IEEE 4th International Conference on ...

Author: M. Arif Wani

Publisher: Springer Nature

ISBN: 9789811633577

Category: Artificial intelligence

Page: 328

View: 490

This book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.
Categories: Artificial intelligence

Deep Learning

Deep Learning

Already published in the series Volume 6: Quantum Machine Learning Siddhartha Bhattacharyya, Indrajit Pan, ... e-ISBN (PDF) 978-3-11-067070-7 e-ISBN (EPUB) 978-3-11-067072-1 Volume 5: Machine Learning Applications S. Bhattacharyya, ...

Author: Siddhartha Bhattacharyya

Publisher: Walter de Gruyter GmbH & Co KG

ISBN: 9783110670905

Category: Computers

Page: 161

View: 104

This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.
Categories: Computers

Building Cognitive Applications with IBM Watson Services Volume 3 Visual Recognition

Building Cognitive Applications with IBM Watson Services  Volume 3 Visual Recognition

If your goal is to become more familiar with Watson in relation to your current environment, or if you are evaluating cognitive computing, this collection can serve as a powerful learning tool. This IBM Redbooks® publication, Volume 3, ...

Author: Dr. Azeddine Elhassouny

Publisher: IBM Redbooks

ISBN: 9780738442570

Category: Computers

Page: 144

View: 777

The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM® Watson cognitive computing services. The series includes an overview of specific Watson services with their associated architectures and simple code examples. Each volume describes how you can use and implement these services in your applications through practical use cases. The series includes the following volumes: Volume 1 Getting Started, SG24-8387 Volume 2 Conversation, SG24-8394 Volume 3 Visual Recognition, SG24-8393 Volume 4 Natural Language Classifier, SG24-8391 Volume 5 Language Translator, SG24-8392 Volume 6 Speech to Text and Text to Speech, SG24-8388 Volume 7 Natural Language Understanding, SG24-8398 Whether you are a beginner or an experienced developer, this collection provides the information you need to start your research on Watson services. If your goal is to become more familiar with Watson in relation to your current environment, or if you are evaluating cognitive computing, this collection can serve as a powerful learning tool. This IBM Redbooks® publication, Volume 3, introduces the IBM Watson® Visual Recognition service. The Watson Visual Recognition service uses deep learning algorithms to analyze images for scenes, objects, faces, and other content. This book introduces concepts that you need to understand in order to use this Watson service and provides simple code examples to illustrate the use of the APIs. This book includes examples of applications that demonstrate how to use the Watson Visual Recognition service in practical use cases. You can develop and deploy the sample applications by following along in a step-by-step approach and using provided code snippets. Alternatively, you can download an existing Git project to more quickly deploy the application.
Categories: Computers

Internet of Things and Machine Learning in Agriculture

Internet of Things and Machine Learning in Agriculture

Already published in the series Volume 7: Deep Learning Siddhartha Bhattacharyya, Vaclav Sasel, ... e-ISBN (PDF) 978-3-11-067070-7 e-ISBN (EPUB) 978-3-11-067072-1 Volume 5: Machine Learning Applications S. Bhattacharyya, R. Das, ...

Author: Jyotir Moy Chatterjee

Publisher: Walter de Gruyter GmbH & Co KG

ISBN: 9783110691276

Category: Computers

Page: 424

View: 441

Agriculture is one of the most fundamental human activities. As the farming capacity has expanded, the usage of resources such as land, fertilizer, and water has grown exponentially, and environmental pressures from modern farming techniques have stressed natural landscapes. Still, by some estimates, worldwide food production needs to increase to keep up with global food demand. Machine Learning and the Internet of Things can play a promising role in the Agricultural industry, and help to increase food production while respecting the environment. This book explains how these technologies can be applied, offering many case studies developed in the research world.
Categories: Computers

Internet of Things and Analytics for Agriculture Volume 3

Internet of Things and Analytics for Agriculture  Volume 3

3. Machine. Learning. and. Deep. Learning. for. AI. Application. Artificial intelligence is a broad topic and covers wide variety of techniques. This chapter focuses machine learning and deep learning techniques as AI tools.

Author: Prasant Kumar Pattnaik

Publisher: Springer Nature

ISBN: 9789811662102

Category: Technology & Engineering

Page: 380

View: 406

The book discusses one of the major challenges in agriculture which is delivery of cultivate produce to the end consumers with best possible price and quality. Currently all over the world, it is found that around 50% of the farm produce never reaches the end consumer due to wastage and suboptimal prices. The authors present solutions to reduce the transport cost, predictability of prices on the past data analytics and the current market conditions, and number of middle hops and agents between the farmer and the end consumer using IoT-based solutions. Again, the demand by consumption of agricultural products could be predicted quantitatively; however, the variation of harvest and production by the change of farm's cultivated area, weather change, disease and insect damage, etc., could be difficult to be predicted, so that the supply and demand of agricultural products has not been controlled properly. To overcome, this edited book designed the IoT-based monitoring system to analyze crop environment and the method to improve the efficiency of decision making by analyzing harvest statistics. The book is also useful for academicians working in the areas of climate changes.
Categories: Technology & Engineering

Cyber Crime and Forensic Computing

Cyber Crime and Forensic Computing

... (EPUB) 978-3-11-069128-3 Volume 7: Deep Learning. Research and Applications Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy (Eds.) ISBN 978-3-11-067079-0, e-ISBN (PDF) 978-3-11-067090-5, ...

Author: Gulshan Shrivastava

Publisher: Walter de Gruyter GmbH & Co KG

ISBN: 9783110677478

Category: Computers

Page: 240

View: 236

This book presents a comprehensive study of different tools and techniques available to perform network forensics. Also, various aspects of network forensics are reviewed as well as related technologies and their limitations. This helps security practitioners and researchers in better understanding of the problem, current solution space, and future research scope to detect and investigate various network intrusions against such attacks efficiently. Forensic computing is rapidly gaining importance since the amount of crime involving digital systems is steadily increasing. Furthermore, the area is still underdeveloped and poses many technical and legal challenges. The rapid development of the Internet over the past decade appeared to have facilitated an increase in the incidents of online attacks. There are many reasons which are motivating the attackers to be fearless in carrying out the attacks. For example, the speed with which an attack can be carried out, the anonymity provided by the medium, nature of medium where digital information is stolen without actually removing it, increased availability of potential victims and the global impact of the attacks are some of the aspects. Forensic analysis is performed at two different levels: Computer Forensics and Network Forensics. Computer forensics deals with the collection and analysis of data from computer systems, networks, communication streams and storage media in a manner admissible in a court of law. Network forensics deals with the capture, recording or analysis of network events in order to discover evidential information about the source of security attacks in a court of law. Network forensics is not another term for network security. It is an extended phase of network security as the data for forensic analysis are collected from security products like firewalls and intrusion detection systems. The results of this data analysis are utilized for investigating the attacks. Network forensics generally refers to the collection and analysis of network data such as network traffic, firewall logs, IDS logs, etc. Technically, it is a member of the already-existing and expanding the field of digital forensics. Analogously, network forensics is defined as "The use of scientifically proved techniques to collect, fuses, identifies, examine, correlate, analyze, and document digital evidence from multiple, actively processing and transmitting digital sources for the purpose of uncovering facts related to the planned intent, or measured success of unauthorized activities meant to disrupt, corrupt, and or compromise system components as well as providing information to assist in response to or recovery from these activities." Network forensics plays a significant role in the security of today’s organizations. On the one hand, it helps to learn the details of external attacks ensuring similar future attacks are thwarted. Additionally, network forensics is essential for investigating insiders’ abuses that constitute the second costliest type of attack within organizations. Finally, law enforcement requires network forensics for crimes in which a computer or digital system is either being the target of a crime or being used as a tool in carrying a crime. Network security protects the system against attack while network forensics focuses on recording evidence of the attack. Network security products are generalized and look for possible harmful behaviors. This monitoring is a continuous process and is performed all through the day. However, network forensics involves post mortem investigation of the attack and is initiated after crime notification. There are many tools which assist in capturing data transferred over the networks so that an attack or the malicious intent of the intrusions may be investigated. Similarly, various network forensic frameworks are proposed in the literature.
Categories: Computers

Computer Vision

Computer Vision

Applications of Visual AI and Image Processing Pancham Shukla, Rajanikanth Aluvalu, Shilpa Gite, Uma Maheswari. Volume 9: Machine Learning for Sustainable Development Kamal Kant Hiran, Deepak Khazanchi, Ajay Kumar Vyas, ...

Author: Pancham Shukla

Publisher: Walter de Gruyter GmbH & Co KG

ISBN: 9783110756821

Category: Computers

Page: 457

View: 372

This book focuses on the latest developments in the fields of visual AI, image processing and computer vision. It shows research in basic techniques like image pre-processing, feature extraction, and enhancement, along with applications in biometrics, healthcare, neuroscience and forensics. The book highlights algorithms, processes, novel architectures and results underlying machine intelligence with detailed execution flow of models.
Categories: Computers

Blockchain 3 0 for Sustainable Development

Blockchain 3 0 for Sustainable Development

Already published in the series Volume 9: Machine Learning for Sustainable Development Kamal Kant Hiran, Deepak Khazanchi, ... e-ISBN (PDF) 978-3-11-067070-7, e-ISBN (EPUB) 978-3-11-067072-1 Volume 5: Machine Learning Applications.

Author: Deepak Khazanchi

Publisher: Walter de Gruyter GmbH & Co KG

ISBN: 9783110702507

Category: Computers

Page: 172

View: 337

This book will focus on the use of Blockchain 3.0 for sustainable development. This tool is invaluable for achieving transparency and trust, but possibilities to benefit society more broadly are emerging that will bring a bright future for sustainable development, too. The adoption of blockchain in agriculture, healthcare, infrastructure, education, environment, energy, communication will provide revolutionary changes in the digital era.
Categories: Computers

Neural Networks and Deep Learning

Neural Networks and Deep Learning

This book covers both classical and modern models in deep learning.

Author: Charu C. Aggarwal

Publisher: Springer

ISBN: 9783319944630

Category: Computers

Page: 497

View: 500

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
Categories: Computers