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Extract patterns and knowledge from your data in easy way using MATLAB About This Book * Get your first steps into machine learning with the help of this easy-to-follow guide * Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB * Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn * Learn the introductory concepts of machine learning. * Discover different ways to transform data using SAS XPORT, import and export tools, * Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. * Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. * Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. * Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. * Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
(source: Nielsen Book Data)

Extract patterns and knowledge from your data in easy way using MATLAB About This Book * Get your first steps into machine learning with the help of this easy-to-follow guide * Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB * Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn * Learn the introductory concepts of machine learning. * Discover different ways to transform data using SAS XPORT, import and export tools, * Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. * Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. * Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. * Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. * Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
(source: Nielsen Book Data)

• A Study of Energy Reduction Strategies in Renewable Hybrid Grid.- Classification and Watermarking of Brain Tumor using Artificial and Convolutional Neural Networks.- A Proposal for a Deep Learning Model to Enhance Student Guidance and Reduce Dropout.- EduBot: An Unsupervised Domain-Specific Chatbot for Educational Institutions.- SQL Generation from Natural Language using Supervised Learning and Recurrent Neural Networks.- Toward Intelligent Solution to Identify Learner Attitude from Source Code.
• (source: Nielsen Book Data)

This book gathers selected papers from Artificial Intelligence and Industrial Applications (A2IA'2020), the first installment of an annual international conference organized by ENSAM-Meknes at Moulay Ismail University, Morocco. The 29 papers presented here were carefully reviewed and selected from 141 submissions by an international scientific committee. They address various aspects of artificial intelligence such as digital twin, multiagent systems, deep learning, image processing and analysis, control, prediction, modeling, optimization and design, as well as AI applications in industry, health, energy, agriculture, and education. The book is intended for AI experts, offering them a valuable overview and global outlook for the future, and highlights a wealth of innovative ideas and recent, important advances in AI applications, both of a foundational and practical nature. It will also appeal to non-experts who are curious about this timely and important subject.
(source: Nielsen Book Data)

• A Fuzzy Ontology Based Approach To Support Product Eco-Design.-
• A Genetic-based SVM Approach for Quality Data Classification.- Towards a platform to implement an intelligent and predictive maintenance in the context of industry.- Towards a Prediction Analysis in an Industrial Context.- Methodology for Implementation of Industry . Technologies in Supply Chain for SMEs.- A Deep Reinforcement Learning DRL Decision Model For Heating Process Parameters Identification In Automotive Glass Manufacturing
• Analytic Hierarchy Process AHP for supply chain . risks management.- SmartDFRelevance: a Holonic Agent based System for Engineering Industrial Projects in Concurrent Engineering Context.- A Cyber-Physical Warehouse Management System Architecture in an.- Industry . context.- PLM and Smart technologies for product and supply chain design.- Production systems simulation considering Non-Productive Times and Human Factors.- Distributed and Embedded System to Control Traffic Collision Based on Artificial Intelligence.- The Emergence and Decision Support in Complex System with Fuzzy Logic Control.- Markov Decision Processes with Discounted Costs over a Finite Horizon: Action Elimination.- Robust adaptive fuzzy path tracking control of Differential Wheeled Mobile Robot.- Deep Learning approach for Automated Guided Vehicle System.- Path Planning Using Particle Swarm Optimization And Fuzzy Logic
• Prediction of Robot localization states using Hidden Markov Models.- A New Approach for Multi-Agent Reinforcement Learning.- Recommender System for Most Relevant k Pick-Up Points.- Feature Detection and Tracking for Visual Effects: Augmented Reality and Video Stabilization.- Spectral image recognition using artificial dynamic neural network in information resonance mode.- U-Net Based Model for Obfuscated Human Faces Reconstruction.- A Machine Learning Assistant for Choosing Operators and Tuning Their Parameters in Image Processing Tasks.- Convergence and parameters setting of continuous Hopfield neural networks applied to image restoration problem.- The Ibn Battouta Air Traffic Control Corpus with Real Life ADS-B and METAR data.- Weed Recognition System For Low-Land Rice Precision Farming Using Deep Learning Approach.- A Comparative Study Between Mixture and Stepwise Regression to Model The Parameters Of The Composting Process.- Deep Learning Based Sponge Gourd Diseases Recognition For Commercial Cultivation in Bangladesh.- Exploitation of vegetation indices and Random Forest for cartography of rosemary cover: application to Gourrama region, Morocco.
• (source: Nielsen Book Data)

This book gathers the refereed proceedings of the Artificial Intelligence and Industrial Applications (A2IA'2020), the first installment of an annual international conference organized by the ENSAM-Meknes at Moulay Ismail University, Morocco. The 30 papers presented here were carefully reviewed and selected from 141 submissions by an international scientific committee. They address various aspects of artificial intelligence such as smart manufacturing, smart maintenance, smart supply chain management, supervised learning, unsupervised learning, reinforcement learning, graph-based and semi-supervised learning, neural networks, deep learning, planning and optimization, and other AI applications. The book is intended for AI experts, offering them a valuable overview of the status quo and a global outlook for the future, with many new and innovative ideas and recent important developments in AI applications, both of a foundational and practical nature. It will also appeal to non-experts who are curious about this timely and important subject.
(source: Nielsen Book Data)

• 1. Introduction.-
• 2. Machine Learning Methods for Spatial and Temporal Parameter Estimation.-
• 3. Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms.-
• 4. Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing and Biomedicine.-
• 5. Advances in Deep Learning for Hyperspectral Image Analysis - Addressing Challenges Arising in Practical Imaging Scenarios.-
• 6. Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis.-
• 7. Supervised, Semi Supervised and Unsupervised Learning for Hyperspectral Regression.-
• 8. Sparsity-based Methods for Classification.-
• 9. Multiple Kernel Learning for Hyperspectral Image Classification.-
• 10. Low Dimensional Manifold Model in Hyperspectral Image Reconstruction.-
• 11. Deep Sprase Band Selection for Hyperspectral Face Recognition.-
• 12. Detection of Large-Scale and Anomalous Changes.-
• 13. Recent Advances in Hyperspectral Unmixing Using Sparse Techniques and Deep Learning.-
• 14. Chapter Hyperspectral-Multispectral Image Fusion Enhancement Based on Deep Learning.-
• 15. Automatic Target Detection for Sparse Hyperspectral Images.
• (source: Nielsen Book Data)

This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful. .
(source: Nielsen Book Data)

• 1. The vision of sociable robots
• 2. Robot in society: a question of interface
• 3. Insights from developmental psychology
• 4. Designing sociable robots
• 5. The physical robot
• 6. The vision system
• 7. The auditory system
• 8. The motivation system
• 9. The behavior system
• 10. Facial animation and expression
• 11. Expressive vocalization system
• 12. Social constraints on animate vision
• 13. Grand challenges of building sociable robots.

Cynthia Breazeal here presents her vision of the sociable robot of the future, a synthetic creature and not merely a sophisticated tool. A sociable robot will be able to understand us, to communicate and interact with us, to learn from us and grow with us. It will be socially intelligent in a humanlike way. Eventually sociable robots will assist us in our daily lives, as collaborators and companions. Because the most successful sociable robots will share our social characteristics, the effort to make sociable robots is also a means for exploring human social intelligence and even what it means to be human. Breazeal defines the key components of social intelligence for these machines and offers a framework and set of design issues for their realization. Much of the book focuses on a nascent sociable robot she designed named Kismet. Breazeal offers a concrete implementation for Kismet, incorporating insights from the scientific study of animals and people, as well as from artistic disciplines such as classical animation. This blending of science, engineering, and art creates a lifelike quality that encourages people to treat Kismet as a social creature rather than just a machine. The book includes a CD-ROM that shows Kismet in action.
(source: Nielsen Book Data)

• 1. A Comprehensive Introduction to Photometric 3D-Reconstruction.-
• 2. Perspective Shape from Shading an Exposition on Recent Works with New Experiments.-
• 3. RGBD-Fusion: Depth Refinement for Diffuse and Specular Objects.-
• 4. Non-Rigid Structure from Motion and Shading.-
• 5. On the Well-Posedness of Uncalibrated Photometric Stereo Under General Lighting.-
• 6. Recent Progress in Shape from Polarization.-
• 7. Estimating Facial Aging using Light Scattering Photometry.
• (source: Nielsen Book Data)

This book presents the latest advances in photometric 3D reconstruction. It provides the reader with an overview of the state of the art in the field, and of the latest research into both the theoretical foundations of photometric 3D reconstruction and its practical application in several fields (including security, medicine, cultural heritage and archiving, and engineering). These techniques play a crucial role within such emerging technologies as 3D printing, since they permit the direct conversion of an image into a solid object. The book covers both theoretical analysis and real-world applications, highlighting the importance of deepening interdisciplinary skills, and as such will be of interest to both academic researchers and practitioners from the computer vision and mathematical 3D modeling communities, as well as engineers involved in 3D printing. No prior background is required beyond a general knowledge of classical computer vision models, numerical methods for optimization, and partial differential equations. .
(source: Nielsen Book Data)

• Biological and cognitive vision
• Hardware-implemented and real-time vision systems
• High-level vision
• Learning and adaptation
• Robot vision.- Vision systems applications.

This book constitutes the refereed proceedings of the 10th International Conference on Computer Vision Systems, ICVS 2015, held in Copenhagen, Denmark, in July 2015. The 48 papers presented were carefully reviewed and selected from 92 submissions. The paper are organized in topical sections on biological and cognitive vision; hardware-implemented and real-time vision systems; high-level vision; learning and adaptation; robot vision; and vision systems applications.

• Pattern Recognition and Machine Learning
• Spatiotemporal Stacked Sequential Learning for Pedestrian Detection
• Social Signaling Descriptor for Group Behaviour Analysis
• A New Smoothing Method for Lexicon-Based Handwritten Text Keyword Spotting
• Empirical Evaluation of Different Feature Representations for Social Circles Detection
• Source-Target-Source Classification Using Stacked Denoising Autoencoders
• On the Impact of Distance Metrics in Instance-Based Learning Algorithms
• Multi-class Boosting for Imbalanced Data
• Computer Vision
• Object Discovery Using CNN Features in Egocentric Videos
• Prototype Generation on Structural Data Using Dissimilarity Space Representation: A Case of Study
• Estimation and Tracking of Partial Planar Templates to Improve VSLAM
• Human Centered Scene Understanding Based on 3D Long-Term Tracking Data
• Fast Head Pose Estimation for Human-Computer Interaction
• Structured Light System Calibration for Perception in Underwater Tanks
• Dense 3D SLAM in Dynamic Scenes Using Kinect
• Scene Recognition Invariant to Symmetrical Reflections and Illumination Conditions in Robotics
• System for Medical Mask Detection in the Operating Room Through Facial Attributes
• Image and Signal Processing
• New Method for Obtaining Optimal Polygonal Approximations
• Noise Decomposition Using Polynomial Approximation
• Color Correction for Image Stitching by Monotone Cubic Spline Interpolation
• Structured Output Prediction with Hierarchical Loss Functions for Seafloor Imagery Taxonomic Categorization
• Escaping Path Approach with Extended Neighborhood for Speckle Noise Reduction
• Adaptive Line Matching for Low-Textured Images
• Unsupervised Approximation of Digital Planar Curves
• On the Modification of Binarization Algorithms to Retain Grayscale Information for Handwritten Text Recognition
• Applications
• Improving the Minimum Description Length Inference of Phrase-Based Translation Models
• A Kinect-Based System to Assess Lymphedema Impairments in Breast Cancer Patients
• Arabic Writer Identification Using Local Binary Patterns (LBP) of Handwritten Fragments
• A Fuzzy C-Means Algorithm for Fingerprint Segmentation
• Word-Graph Based Applications for Handwriting Documents: Impact of Word-Graph Size on Their Performances
• Temporal Segmentation of Digital Colposcopies
• Goal-Driven Phenotyping Through Spectral Imaging for Grape Aromatic Ripeness Assessment
• Medical Image
• Robust 3D Active Shape Model for the Segmentation of the Left Ventricle in MRI
• Kernel-Based Feature Relevance Analysis for ECG Beat Classification
• Spatial-Dependent Similarity Metric Supporting Multi-atlas MRI Segmentation
• Color Detection in Dermoscopy Images Based on Scarce Annotations
• Pattern Recognition and Machine Learning
• Spectral Clustering Using Friendship Path Similarity
• R-Clustering for Egocentric Video Segmentation
• Applying Basic Features from Sentiment Analysis for Automatic Irony Detection
• Exploiting the Bin-Class Histograms for Feature Selection on Discrete Data
• Time-Series Prediction Based on Kernel Adaptive Filtering with Cyclostationary Codebooks
• Latent Topic Encoding for Content-Based Retrieval
• Dissimilarity-Based Learning from Imbalanced Data with Small Disjuncts and Noise
• Binary and Multi-class Parkinsonian Disorders Classification Using Support Vector Machines
• Peripheral Nerve Segmentation Using Speckle Removal and Bayesian Shape Models
• Measuring Scene Detection Performance
• Threshold of Graph-Based Volumetric Segmentation
• Brain Neural Data Analysis with Feature Space Defined by Descriptive Statistics
• Extremely Overlapping Vehicle Counting
• Sentence Clustering Using Continuous Vector Space Representation
• Online Learning of Stochastic Bi-automaton to Model Dialogues
• Single-Channel Separation Between Stationary and Non-stationary Signals Using Relevant Information
• Computer Vision
• A New Trajectory Based Motion Segmentation Benchmark Dataset (UdG-MS15)
• Escritoire: A Multi-touch Desk with e-Pen Input for Capture, Management and Multimodal Interactive Transcription of Handwritten Documents
• Person Enrollment by Face-Gait Fusion
• Homographic Class Template for Logo Localization and Recognition
• Multimodal Object Recognition Using Random Clustering Trees
• Videogrammetry System for Wind Turbine Vibration Monitoring
• Canonical Views for Scene Recognition in Mobile Robotics
• Crater Detection in Multi-ring Basins of Mercury
• Iterative Versus Voting Method to Reach Consensus Given Multiple Correspondences of Two Sets
• Extracting Categories by Hierarchical Clustering Using Global Relational Features
• A Calibration Algorithm for Multi-camera Visual Surveillance Systems Based on Single-View Metrology
• 3D-Guided Multiscale Sliding Window for Pedestrian Detection
• A Feature-Based Gaze Estimation Algorithm for Natural Light Scenarios
• Image and Signal Processing
• Fast Simple Linear Iterative Clustering by Early Candidate Cluster Elimination
• Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture Decomposition
• PPG Beat Reconstruction Based on Shape Models and Probabilistic Templates for Signals Acquired with Conventional Smartphones
• Peripheral Nerves Segmentation in Ultrasound Images Using Non-linear Wavelets and Gaussian Processes
• Improving Diffusion Tensor Estimation Using Adaptive and Optimized Filtering Based on Local Similarity
• Dimension Reduction of Hyperspectral Image with Rare Event Preserving
• Applications
• Clustering of Strokes from Pen-Based Music Notation: An Experimental Study
• Image Analysis-Based Automatic Detection of Transmission Towers Using Aerial Imagery
• A Sliding Window Framework for Word Spotting Based on Word Attributes
• Combining Statistical and Semantic Knowledge for Sarcasm Detection in Online Dialogues
• Estimating Fuel Consumption from GPS Data
• A Bag-of-phonemes Model for Homeplace Classification of Mandarin Speakers
• A Gaussian Process Emulator for Estimating the Volume of Tissue Activated During Deep Brain Stimulation
• Genetic Seam Carving: A Genetic Algorithm Approach for Content-Aware Image Retargeting
• Analysis of Expressiveness of Portuguese Sign Language Speakers
• Automatic Eye Localization; Multi-block LBP vs. Pyramidal LBP Three-Levels Image Decomposition for Eye Visual Appearance Description
• Clinical Evaluation of an Automatic Method for Segmentation and Characterization of the Thoracic Aorta with and Without Aneurysm Patients
• Combined MPEG7 Color Descriptors for Image Classification: Bypassing the Training Phase
• A Multi-platform Graphical Software for Determining Reproductive Parameters in Fishes Using Histological Image Analysis.

This book constitutes the proceedings of the 7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015, held in Santiage de Compostela, Spain, in June 2015. The 83 papers presented in this volume were carefully reviewed and selected from 141 submissions. They were organized in topical sections named: Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; Applications; Medical Image; Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; and Applications.

• Clustering and Quantum Methods.- Motion and Tracking.- Image Processing and Segmentation.- Color, Shading and Reflectance of Light.- Propagation and Time-evolution.- Inference, Labeling, and Relaxation.
• (source: Nielsen Book Data)

This volume constitutes the refereed proceedings of the 11th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2017, held in Venice, Italy, in October/November 2017. The 37 revised full papers were carefully reviewed and selected from 51 submissions. The papers are organized in topical sections on Clustering and Quantum Methods; Motion and Tracking; Image Processing and Segmentation; Color, Shading and Reflectance of Light; Propagation and Time-evolution; and Inference, Labeling, and Relaxation.
(source: Nielsen Book Data)