Machine learning in image processing pdf
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Machine Learning in Image Processing EURASIP Journal on

machine learning in image processing pdf

Review of Image Processing and Machine Learning Techniques. Classifiers and Machine Learning Techniques for Image Processing and Computer Vision Anderson Rocha, Siome Goldenstein Institute of Computing University of Campinas (Unicamp), What You Will Learn Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software.

Machine Learning for Plant Phenotyping Needs Image Processing

AAPPPPLLIICCAATTIIOONNSS AANNDD UUSSAAGGEE. Image analysis and computer vision, which go beyond image processing, helps us to make decisions based on the contents of the image. This course, vision …, AUTOMATIC PETROGRAPHIC INSPECTION BY USING IMAGE PROCESSING AND MACHINE LEARNING petrographic inspection by using image processing and machine learning algorithms in the visible spectrum (VIS). We have implemented an automatic recognition method to analyze pebble components of an aggregate sample with a weight of nearly 1 kg to overcome the ….

Machine learning in image processing Predictive Analytics and Data Science Conference May 27-28 Parinya sanguansat Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. Machine Learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like.

revision of image-processing programs is needed. The development of image recognition sys- The development of image recognition sys- tems using machine learning techniques has been progressing with the aim of constructing The development of advanced technologies in the areas of image processing and machine learning have given us the ability to allow distinction of malignant melanoma from …

What You Will Learn Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software Letter Machine Plant Learning for Needs Phenotyping Processing Image Sotirios 2 A. Tsaftaris,1,* Massimo 3 Minervini, and Hanno Scharr We found the article by Singh et al. [1]

Machine Learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. What You Will Learn Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software

A Combination of Machine Learning and Image Processing Technologies for the Classification of Image Regions Andreas D. Lattner, Andrea Miene, and Otthein Herzog 13/01/2017 · In this paper, detection of diabetic retinopathy in fundus image is done by image processing and machine learning techniques. Probabilistic Neural Network (PNN) and Support vector machines (SVM) are the two models adopted for detection of diabetic retinopathy in fundus image and their results analyzed and compared. Accuracy of detection in SVM is 90% and that of PNN is 80%. …

Integrating OpenStreetMap with Google Street View using image processing and machine learning Ashutosh Choudhary Department of Computer Science University of Detection of Stress Using Image Processing and Machine Learning Techniques Nisha Raichur #1, Nidhi Lonakadi*2, Priyanka Mural #3 Department of Information Science …

This is a blended learning course on Machine Learning for Image Analysis, consisting of three online sessions with associated hands-on exercises prior to the workshop, a three day face-to-face workshop at EMBL Heidelberg and two optional online sessions with … CONTRIBUTION OF IMAGE PROCESSING AND MACHINE LEARNING FOR AUTOMATED ANALYSIS OF RETINAL VESSELS: A REVIEW identifying retinal vessels where the techniques of image processing and machine learning taken into consideration. Keywords- Image Registration, Image Fusion, Feature Extraction, Retinal detachment (RD), SVM, GMM, KNN. I. INTRODUCTION …

This is a blended learning course on Machine Learning for Image Analysis, consisting of three online sessions with associated hands-on exercises prior to the workshop, a three day face-to-face workshop at EMBL Heidelberg and two optional online sessions with … CONTRIBUTION OF IMAGE PROCESSING AND MACHINE LEARNING FOR AUTOMATED ANALYSIS OF RETINAL VESSELS: A REVIEW identifying retinal vessels where the techniques of image processing and machine learning taken into consideration. Keywords- Image Registration, Image Fusion, Feature Extraction, Retinal detachment (RD), SVM, GMM, KNN. I. INTRODUCTION …

emerging as a center of excellence in technical education. VIT is structured around nine schools of informatics over about 1000 IMAGE PROCESSING AND MACHINE LEARNING FOR THE DIAGNOSIS OF MELANOMA CANCER Arushi Raghuvanshi1 and Marek Perkowski Department of Electrical and Computer Engineering, Portland State University, Portland, OR 97207, U.S.A.

Special Issue of Signal Processing (Elsevier)

machine learning in image processing pdf

MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING. revision of image-processing programs is needed. The development of image recognition sys- The development of image recognition sys- tems using machine learning techniques has been progressing with the aim of constructing, Classifiers and Machine Learning Techniques for Image Processing and Computer Vision Anderson Rocha, Siome Goldenstein Institute of Computing University of Campinas (Unicamp).

A mechanism for conveying Machine Learning for Signal

machine learning in image processing pdf

Machine Learning for Plant Phenotyping Needs Image Processing. is a need for mechanisms to represent the image data in a way that machine learning algorithms can use, and this process is known as feature extraction (another component bundled under pre- processing … Integrating OpenStreetMap with Google Street View using image processing and machine learning Ashutosh Choudhary Department of Computer Science University of.

machine learning in image processing pdf


A Combination of Machine Learning and Image Processing Technologies for the Classification of Image Regions Andreas D. Lattner, Andrea Miene, and Otthein Herzog The basic bundle covers how machine learning works, from the very basics all the way through deep learning, image segmentation, natural language processing, chatbots, and even strategies for applying machine learning to your business.

VJER-Vishwakarma Journal of Engineering Research www.vjer.in Volume 1 Issue 4, December 2017 ISSN: 2456-8465 101 ENHANCED SECURITY USING IMAGE PROCESSING troller controlled camera, image processing and machine learning techniques. First with the aid of First with the aid of microcontroller and four passive IR …

He uses tools from signal/image processing, probabilistic modeling, statistical inference, computer vision, computational geometry, graph theory, and machine learning to develop algorithms that allow learning from large-scale biomedical data. IMAGE PROCESSING AND MACHINE LEARNING FOR THE DIAGNOSIS OF MELANOMA CANCER Arushi Raghuvanshi1 and Marek Perkowski Department of Electrical and Computer Engineering, Portland State University, Portland, OR 97207, U.S.A.

13/01/2017 · In this paper, detection of diabetic retinopathy in fundus image is done by image processing and machine learning techniques. Probabilistic Neural Network (PNN) and Support vector machines (SVM) are the two models adopted for detection of diabetic retinopathy in fundus image and their results analyzed and compared. Accuracy of detection in SVM is 90% and that of PNN is 80%. … CONTRIBUTION OF IMAGE PROCESSING AND MACHINE LEARNING FOR AUTOMATED ANALYSIS OF RETINAL VESSELS: A REVIEW identifying retinal vessels where the techniques of image processing and machine learning taken into consideration. Keywords- Image Registration, Image Fusion, Feature Extraction, Retinal detachment (RD), SVM, GMM, KNN. I. INTRODUCTION …

This is a blended learning course on Machine Learning for Image Analysis, consisting of three online sessions with associated hands-on exercises prior to the workshop, a three day face-to-face workshop at EMBL Heidelberg and two optional online sessions with … Machine learning can be used in both image processing and computer vision but it has found more use in computer vision than in image processing. In ML the loss function can have a physical meaning in which case the features learnt can be quite informative but this …

The guest editors suggested putting together this special issue on machine learning in image processing to the editor-in-chief in November 2006. In May 2007, the guest editors and the editor-in-chief established the outline and schedule of the special issue, and the first call for papers was distributed through the Internet. The basic bundle covers how machine learning works, from the very basics all the way through deep learning, image segmentation, natural language processing, chatbots, and even strategies for applying machine learning to your business.

machine learning in intelligent image processing. To summarize, this special issue welcomes a broad range of submissions developing and using machine learning algorithms for intelligent image processing. Machine learning can be used in both image processing and computer vision but it has found more use in computer vision than in image processing. In ML the loss function can have a physical meaning in which case the features learnt can be quite informative but this …

CONTRIBUTION OF IMAGE PROCESSING AND MACHINE LEARNING FOR AUTOMATED ANALYSIS OF RETINAL VESSELS: A REVIEW identifying retinal vessels where the techniques of image processing and machine learning taken into consideration. Keywords- Image Registration, Image Fusion, Feature Extraction, Retinal detachment (RD), SVM, GMM, KNN. I. INTRODUCTION … of high performance computing. It already entered the domain of embedded systems tremendously. II. PAPER OF THE SPECIAL SESSION This special session includes two excellent contributions

machine learning in image processing pdf

Machine learning Speech, NLP Information retrieval Mathematics Computer Science Biology Engineering Physics Robotics Cognitive sciences Psychology graphics, algorithms, theory,… Image processing 3 4-Jan-16 systems, architecture, … optics . Lecture 1 - Fei-Fei Li & Andrej Karpathy & Justin Johnson Computer Vision Neuroscience Machine learning Speech, NLP Information retrieval … What You Will Learn Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software

Processing

machine learning in image processing pdf

Machine Learning in Image Processing (pdf) Paperity. The basic bundle covers how machine learning works, from the very basics all the way through deep learning, image segmentation, natural language processing, chatbots, and even strategies for applying machine learning to your business., Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image ….

Machine Learning and Medical Imaging 1st Edition

RECURSIVE DEEP LEARNING A DISSERTATION. emerging as a center of excellence in technical education. VIT is structured around nine schools of informatics over about 1000, In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations..

13/01/2017 · In this paper, detection of diabetic retinopathy in fundus image is done by image processing and machine learning techniques. Probabilistic Neural Network (PNN) and Support vector machines (SVM) are the two models adopted for detection of diabetic retinopathy in fundus image and their results analyzed and compared. Accuracy of detection in SVM is 90% and that of PNN is 80%. … troller controlled camera, image processing and machine learning techniques. First with the aid of First with the aid of microcontroller and four passive IR …

Machine Learning for Dental Image Analysis Young-jun Yu Department of Computer Science Yonsei University aaorkr@gmail.com 1.Introduction The field of pathology diagnosis has steadily advanced with the Machine learning in image processing Predictive Analytics and Data Science Conference May 27-28 Parinya sanguansat Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising.

In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations. troller controlled camera, image processing and machine learning techniques. First with the aid of First with the aid of microcontroller and four passive IR …

Machine learning made in a minute The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use . Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image …

13/01/2017 · In this paper, detection of diabetic retinopathy in fundus image is done by image processing and machine learning techniques. Probabilistic Neural Network (PNN) and Support vector machines (SVM) are the two models adopted for detection of diabetic retinopathy in fundus image and their results analyzed and compared. Accuracy of detection in SVM is 90% and that of PNN is 80%. … CONTRIBUTION OF IMAGE PROCESSING AND MACHINE LEARNING FOR AUTOMATED ANALYSIS OF RETINAL VESSELS: A REVIEW identifying retinal vessels where the techniques of image processing and machine learning taken into consideration. Keywords- Image Registration, Image Fusion, Feature Extraction, Retinal detachment (RD), SVM, GMM, KNN. I. INTRODUCTION …

Our image processing pipeline is a series of steps: convolution, max-pooling, and finally a fully-connected network. When solving problems in the real world, these steps can be combined and IJCA Special Issue on “Novel Aspects of Digital Imaging Applications” DIA, 2011 79 Position Detection with Face Recognitionusing Image Processing and Machine Learning Techniques

Letter Machine Plant Learning for Needs Phenotyping Processing Image Sotirios 2 A. Tsaftaris,1,* Massimo 3 Minervini, and Hanno Scharr We found the article by Singh et al. [1] VJER-Vishwakarma Journal of Engineering Research www.vjer.in Volume 1 Issue 4, December 2017 ISSN: 2456-8465 101 ENHANCED SECURITY USING IMAGE PROCESSING

troller controlled camera, image processing and machine learning techniques. First with the aid of First with the aid of microcontroller and four passive IR … Learning Image Processing with OpenCV Exploit the amazing features of OpenCV to create powerful image processing applications through easy-to-follow examples

8/28/2012 1 11-755/18-797 Machine Learning for Signal Processing Machine Learning for Signal Processing Lecture 1: Signal Representations Class 1. 27 August 2012 CSCI 8810 Course Project MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING By Kaan Tariman M.S. in Computer Science Outline Introduction to Machine Learning The example application Machine Learning Methods Decision Trees Artificial Neural Networks Instant Based Learning What is Machine Learning Machine Learning (ML) is constructing computer

The development of advanced technologies in the areas of image processing and machine learning have given us the ability to allow distinction of malignant melanoma from … Our image processing pipeline is a series of steps: convolution, max-pooling, and finally a fully-connected network. When solving problems in the real world, these steps can be combined and

which effectively amalgamates image processing and machine learning. In the 1st stage, the image of the skin condition is subject to numerous types of pre-processing techniques followed by feature extraction. The extracted features for each image are then converted to a feature vector. In the second stage, the feature vectors are fed to a machine learning algorithm (artificial neural networks Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image …

Letter Machine Plant Learning for Needs Phenotyping Processing Image Sotirios 2 A. Tsaftaris,1,* Massimo 3 Minervini, and Hanno Scharr We found the article by Singh et al. [1] Classifiers and Machine Learning Techniques for Image Processing and Computer Vision Anderson Rocha, Siome Goldenstein Institute of Computing University of Campinas (Unicamp)

The Accord.NET Image Processing and Machine Learning Framework. Accord.NET is a framework for scientific computing in .NET. The framework is comprised of multiple librares encompassing a wide range of scientific computing applications, such as statistical data processing, machine learning, pattern recognition, including but emerging as a center of excellence in technical education. VIT is structured around nine schools of informatics over about 1000

Learning the image processing pipeline Haomiao Jiang, Qiyuan Tian, Joyce Farrell, Brian Wandell Department of Electrical Engineering, Stanford University Psychology Department, Stanford University Abstract—Many creative ideas are being proposed for image sensor designs, and these may be useful in applications ranging from consumer photography to computer vision. To understand and evaluate Machine learning made in a minute The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use .

What You Will Learn Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software revision of image-processing programs is needed. The development of image recognition sys- The development of image recognition sys- tems using machine learning techniques has been progressing with the aim of constructing

Editorial This volume of the journal “Research in Computing Science” contains selected papers related to image processing and machine learning. This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing.

My Top 9 Favorite Python Libraries for Building Image

machine learning in image processing pdf

Accord.NET Machine Learning Framework. VJER-Vishwakarma Journal of Engineering Research www.vjer.in Volume 1 Issue 4, December 2017 ISSN: 2456-8465 101 ENHANCED SECURITY USING IMAGE PROCESSING, For a list of free machine learning books available for download, go here. For a list of (mostly) free machine learning courses available online, go here. For a list of blogs on data science and machine learning, go here. For a list of free-to-attend meetups and local events, go here.

Introduction Accord.NET Machine Learning in C#

machine learning in image processing pdf

Machine Learning Technology Applied to Production Lines. troller controlled camera, image processing and machine learning techniques. First with the aid of First with the aid of microcontroller and four passive IR … Our image processing pipeline is a series of steps: convolution, max-pooling, and finally a fully-connected network. When solving problems in the real world, these steps can be combined and.

machine learning in image processing pdf

  • GitHub josephmisiti/awesome-machine-learning A curated
  • Automated System for Prediction of Skin Disease using
  • ENHANCED SECURITY USING IMAGE PROCESSING AND MACHINE LEARNING

  • Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image … Machine learning made in a minute The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use .

    of the machine learning algorithm may benefit by knowing how the features are extracted from the image, and the feature extracting may be more successful if the type of machine learning algorithm to be used is known. IJCA Special Issue on “Novel Aspects of Digital Imaging Applications” DIA, 2011 79 Position Detection with Face Recognitionusing Image Processing and Machine Learning Techniques

    Introducing Machine Learning Using Supervised Learning to Predict Heart Attacks Suppose clinicians want to predict whether someone will have a heart attack within a year.Supervised Learning The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. Typical applications include medical imaging. changes in temperature or 8/28/2012 1 11-755/18-797 Machine Learning for Signal Processing Machine Learning for Signal Processing Lecture 1: Signal Representations Class 1. 27 August 2012

    Representing images as NumPy arrays is not only computational and resource efficient, but many other image processing and machine learning libraries use NumPy array representations as well. Furthermore, by using NumPy’s built-in high-level mathematical functions, we can quickly perform numerical analysis on an image. revision of image-processing programs is needed. The development of image recognition sys- The development of image recognition sys- tems using machine learning techniques has been progressing with the aim of constructing

    In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations. Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image …

    About the Author Gavin Hackeling develops machine learning services for large-scale documents and image classification at an advertising network in New York. Image Processing, Facial Expression, Machine conditions such as illumination and occlusions also affect Learning, Python Programming, OpenCV facial appearances. In 1. INTRODUCTION What is an emotion? An emotion is a mental and physiological state which is subjective and private it involves a lot of behaviors, actions, thoughts and feelings. Initial research carried out on emotions can be

    In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations. revision of image-processing programs is needed. The development of image recognition sys- The development of image recognition sys- tems using machine learning techniques has been progressing with the aim of constructing

    Machine learning in image processing Predictive Analytics and Data Science Conference May 27-28 Parinya sanguansat Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. emerging as a center of excellence in technical education. VIT is structured around nine schools of informatics over about 1000

    is a need for mechanisms to represent the image data in a way that machine learning algorithms can use, and this process is known as feature extraction (another component bundled under pre- processing … He uses tools from signal/image processing, probabilistic modeling, statistical inference, computer vision, computational geometry, graph theory, and machine learning to develop algorithms that allow learning from large-scale biomedical data.

    Representing images as NumPy arrays is not only computational and resource efficient, but many other image processing and machine learning libraries use NumPy array representations as well. Furthermore, by using NumPy’s built-in high-level mathematical functions, we can quickly perform numerical analysis on an image. Machine learning Speech, NLP Information retrieval Mathematics Computer Science Biology Engineering Physics Robotics Cognitive sciences Psychology graphics, algorithms, theory,… Image processing 3 4-Jan-16 systems, architecture, … optics . Lecture 1 - Fei-Fei Li & Andrej Karpathy & Justin Johnson Computer Vision Neuroscience Machine learning Speech, NLP Information retrieval …

    Learning the image processing pipeline Haomiao Jiang, Qiyuan Tian, Joyce Farrell, Brian Wandell Department of Electrical Engineering, Stanford University Psychology Department, Stanford University Abstract—Many creative ideas are being proposed for image sensor designs, and these may be useful in applications ranging from consumer photography to computer vision. To understand and evaluate IJCA Special Issue on “Novel Aspects of Digital Imaging Applications” DIA, 2011 79 Position Detection with Face Recognitionusing Image Processing and Machine Learning Techniques

    CONTRIBUTION OF IMAGE PROCESSING AND MACHINE LEARNING FOR AUTOMATED ANALYSIS OF RETINAL VESSELS: A REVIEW identifying retinal vessels where the techniques of image processing and machine learning taken into consideration. Keywords- Image Registration, Image Fusion, Feature Extraction, Retinal detachment (RD), SVM, GMM, KNN. I. INTRODUCTION … Machine Learning Projects for $25 - $50. Image Enhancement 2 follows the previous one.... Post a Project image enhancement pdf, I have extensive experience in the domains of Natural Language Processing, Recommender Systems,Machine learning,Data Minning, Deep Learning, Computer Vision, and Reinforcement Learning. While working in the domains men

    machine learning in intelligent image processing. To summarize, this special issue welcomes a broad range of submissions developing and using machine learning algorithms for intelligent image processing. Disponible en ligne sur www.sciencedirect.com IRBM 34 (2013) 196–203 Original article TeleOphta: Machine learning and image processing methods for teleophthalmology

    Editorial This volume of the journal “Research in Computing Science” contains selected papers related to image processing and machine learning. Machine Learning Projects for $25 - $50. Image Enhancement 2 follows the previous one.... Post a Project image enhancement pdf, I have extensive experience in the domains of Natural Language Processing, Recommender Systems,Machine learning,Data Minning, Deep Learning, Computer Vision, and Reinforcement Learning. While working in the domains men

    of high performance computing. It already entered the domain of embedded systems tremendously. II. PAPER OF THE SPECIAL SESSION This special session includes two excellent contributions CSCI 8810 Course Project MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING By Kaan Tariman M.S. in Computer Science Outline Introduction to Machine Learning The example application Machine Learning Methods Decision Trees Artificial Neural Networks Instant Based Learning What is Machine Learning Machine Learning (ML) is constructing computer

    machine learning in image processing pdf

    Francesco Camastra Alessandro Vinciarelli Machine Learning for Audio, Image and Video Analysis SPIN Springer’s internal project number October 5, 2007 CONTRIBUTION OF IMAGE PROCESSING AND MACHINE LEARNING FOR AUTOMATED ANALYSIS OF RETINAL VESSELS: A REVIEW identifying retinal vessels where the techniques of image processing and machine learning taken into consideration. Keywords- Image Registration, Image Fusion, Feature Extraction, Retinal detachment (RD), SVM, GMM, KNN. I. INTRODUCTION …

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