Machine learning Books
Springer International Publishing AG Revealing Media Bias in News Articles: NLP
Book SynopsisThis open access book presents an interdisciplinary approach to reveal biases in English news articles reporting on a given political event. The approach named person-oriented framing analysis identifies the coverage’s different perspectives on the event by assessing how articles portray the persons involved in the event. In contrast to prior automated approaches, the identified frames are more meaningful and substantially present in person-oriented news coverage. The book is structured in seven chapters: Chapter 1 presents a few of the severe problems caused by slanted news coverage and identifies the research gap that motivated the research described in this thesis. Chapter 2 discusses manual analysis concepts and exemplary studies from the social sciences and automated approaches, mostly from computer science and computational linguistics, to analyze and reveal media bias. This way, it identifies the strengths and weaknesses of current approaches for identifying and revealing media bias. Chapter 3 discusses the solution design space to address the identified research gap and introduces person-oriented framing analysis (PFA), a new approach to identify substantial frames and to reveal slanted news coverage. Chapters 4 and 5 detail target concept analysis and frame identification, the first and second component of PFA. Chapter 5 also introduces the first large-scale dataset and a novel model for target-dependent sentiment classification (TSC) in the news domain. Eventually, Chapter 6 introduces Newsalyze, a prototype system to reveal biases to non-expert news consumers by using the PFA approach. In the end, Chapter 7 summarizes the thesis and discusses the strengths and weaknesses of the thesis to derive ideas for future research on media bias. This book mainly targets researchers and graduate students from computer science, computational linguistics, political science, and further social sciences who want to get an overview of the relevant state of the art in the other related disciplines and understand and tackle the issue of bias from a more effective, interdisciplinary viewpoint.Table of Contents1. Introduction.- 2. Media Bias Analysis.- 3. Person-Oriented Framing Analysis.- 4. Target Concept Analysis.- 5. Frame Analysis.- 6. Prototype.- 7. Conclusion.
£31.49
Springer International Publishing AG Machine Learning and Data Analytics for Solving
Book SynopsisThis book presents advances in business computing and data analytics by discussing recent and innovative machine learning methods that have been designed to support decision-making processes. These methods form the theoretical foundations of intelligent management systems, which allows for companies to understand the market environment, to improve the analysis of customer needs, to propose creative personalization of contents, and to design more effective business strategies, products, and services. This book gives an overview of recent methods – such as blockchain, big data, artificial intelligence, and cloud computing – so readers can rapidly explore them and their applications to solve common business challenges. The book aims to empower readers to leverage and develop creative supervised and unsupervised methods to solve business decision-making problems.Table of ContentsIntroduction.- Supervised and unsupervised methods for customer segmentation.- Supervised and unsupervised methods for supply chain management.- Supervised and unsupervised methods for logistics improvement.- Design of recommender systems.- Supervised and unsupervised methods for e-marketing.- Analysis of Blockchain data.- Supervised and unsupervised methods applied in banking.- Cryptocurrency analysis.- Supervised and unsupervised methods to improve operational processes.- Big data analysis and summarization.- Intelligent Financial analysis and sales forecasting.- Financial data modeling and decision making.- Integration of learning methods in Smart ERP systems.- E-commerce recommender systems.- Social media and E-business analysis.- Intelligent Business control and monitoring systems.- Conclusion.
£116.99
Springer International Publishing AG Advances in Neural Computation, Machine Learning,
Book SynopsisThis book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain–computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXIV International Conference on Neuroinformatics, held on October 17–21, 2022, in Moscow, Russia.Table of ContentsPart I: Neuroinformatics and Artificial Intelligence.- Tree Inventory with LiDAR Data.- Towards Reliable Solar Atmospheric Parameters Neural-Based Inference.- Addressing Task Prioritization in Model-based Reinforcement Learning.- Automatic Generation of Conversational Skills from Dialog Datasets.- Part II: Neural Networks and Cognitive Sciences. Adaptive Behavior and Evolutionary Simulation.- Individual Topology Structure of Eye Movement Trajectories.- Neural Network Providing the Involvement of Voluntary Attention into the Processing and Conscious Perception of Sensory Information.- Alpha Rhythm Dynamics During Spoken Word Recognition.- Robotic Devices Control Based on Neuromorphic Classifiers of Imaginary Motor Commands.- A Software System for Training Motor Imagery in Virtual Reality.- On the Importance of Diversity.- “MYO-chat” – A New Computer Control System for People with Disabilities.- Low-bit Quantization of Transformer for Audio Speech Recognition.- A Model of Predicting and Using Regularities by an Autonomous Agent.- A Review of One-Shot Neural Architecture Search Methods.- Does a Recurrent Neural Network Use Reflection During a Reflexive Game?.- A Gender Genetic Algorithm and its Comparison with Conventional Genetic Algorithm.- Associations of Morphometric Changes of the Brain with the Levels of IGF1, a Multifunctional Growth Factor, and with Systemic Immune Parameters Reflect the Disturbances of Neuroimmune Interactions in Patients with Schizophrenia.- Part III: Modern Methods and Technologies in Neurobiology.- Dynamics of Background and Evoked Activity of Neurons in the Auditory Cortex of the Unanaesthetized Cat.- Search for Markers of Moderate Cognitive Disorders through Phase Synchronization between Rhythmic Photostimulus and EEG Pattern.- Astrocytes Enhance Image Representation Encoded in Spiking Neural Network.- Classification of Neuron Type Based on Average Activity.- Comparative Analysis of Statistical and Neural Network Classification Methods on the Example of Synthesized Data in the Stimulus-Independent Brain-Computer Interface Paradigm.- Shunting Effect of Synaptic Channels Located on Presynaptic Terminal.- Analysis of Appearances, Formation and Evolution of Biological Functional Systems.- The Reinforcement Learning Theory, Value Function, and the Nature of Value Function Calculation by the Insular Cortex.- To the Role of Inferior Olives in Cerebellar Neuromechanics.- Individual Differences in Mismatch-Induced c-Fos Expression in the Retrosplenial Cortex in Rats: Shift in Activity is Layer-Specific.- Sleep of Poor and Good Nappers under the Afternoon Exposure to Weak 2-Hz/8-Hz Electromagnetic Fields.- Part IV: Applications of Neural Networks.- Classification of Light Microscopy Image Using Probabilistic Bayesian Neural Network.- SPICE Model of Analog Content-Addressable Memory Based on 2G FeFET Crossbar.- IQ-GAN: Instance Quantized Image Synthesis.- Specifics of Crossbar Resistor Arrays.- Recurrent and Graph Neural Networks for Particle Tracking at the BM@N Experiment.- Modeling of a Neural Network Algorithm for Suppressing Non-Stationary Interference in an Adaptive Antenna Array.- Learning Various Locomotion Skills from Scratch with Deep Reinforcement Learning.- Center3dAugNet: Effect of Rotation Representation on One-Stage Joint Car Detection and 6D-Pose Estimation.- Global memory transformer for processing long documents.- Development of the Convolutional Neural Network for Defining the Renal Pathology Using Computed Tomography Images.- Possibility of Using Various Architectures of Convolutional Neural Networks in the Problem of Determining the Type of Rhythm.- DeepPavlov Topics: Topic Classification Dataset for Conversational Domain in English.- Multi-Input Convolutional Neural Networks in Real-Time Semantic Segmentation Tasks.- Integration of Data and Algorithms in Solving Inverse Problems of Spectroscopy of Solutions by Machine Learning Methods.- Investigation of Pareto Front of Neural Network Approximation of Solution of Laplace Equation in Two Statements: with Discontinuous Initial Conditions or with Measurement Data?.- Multitask learning for extensive object description to improve scene understanding on monocular video.- Use of Classification Algorithms to Predict the Grade of Geomagnetic Disturbance.- Information processing in spiking neuron-astrocyte network in ageing.- Multilingual Case-insensitive Named Entity Recognition.- Multi-level Pipeline for Data Mining with Similar Structure.- Creating a Brief Review of Judicial Practice Using Clustering Methods.- Part V: Neural Network Theory, Concepts and Architectures.- "Gas” instead of “Liquid”: which Liquid State Machine is Better?.- Using a Resistor Array to Tackle Optimization Problems.- Generative Adversarial Networks as an Approach to Unsupervised Link Prediction Problem.- DGAC: Dialog Graph AutoConstruction up on Data with a Regular Structure.- Relay System of Differential Equations with Delay as a Perceptron Model.- Analysis of Predictive Capabilities of Adaptive Multilayer Models with Physics-Based Architecture for Duffing Oscillator?.- An Attempt to Formalize the Formulation of the Network Architecture Search Problem for Convolutional Neural Networks.- Use of Conditional Variational Autoencoders and Partial Least Squares in Solving an Inverse Problem of Spectroscopy.- On the Similarities between Denoising Diffusion Models and Autoencoders.
£161.99
Springer International Publishing AG Fundamentals of Machine Learning and Deep
Book SynopsisThis book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid pace. Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader’s learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge. This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional prerequisites. Table of ContentsIntroduction.- Mathematical Modeling of Medical Data.- Linear Learning.- Nonlinear Learning.- Multi-Layer Perceptrons.- Convolutional Neural Networks.- Recurrent Neural Networks.- Autoencoders.- Generative Adversarial Networks.- Reinforcement Learning.
£71.24
Springer International Publishing AG Artificial Intelligence over Infrared Images for
Book SynopsisThis book constitutes the refereed proceedings of the First Workshop on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2022, and the First Workshop on Medical Image Assisted Biomarker Discovery, MIABID 2022, both held in conjunction with MICCAI 2022, Singapore, during September 18 and 22, 2022.For MIABID 2022, 7 papers from 10 submissions were accepted for publication. This workshop created a forum to discuss this specific sub-topic at MICCAI and promote this novel area of research among the research community that has the potential to hugely impact our society.For AIIIMA 2022, 10 papers from 15 submissions were accepted for publication. The first workshop on AIIIMA aimed to create a forum to discuss this specific sub-topic of AI over Infrared Images for Medical Applications at MICCAI and promote this novel area of research that has the potential to hugely impact our society, among the research community.
£42.74
Springer International Publishing AG System Design for Epidemics Using Machine
Book SynopsisThis book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.Table of Contents1. Pandemic effect of COVID-19: Identification, Present scenario and preventive measures using Machine learning model..- 2. A Comprehensive Review of the Smart Health Records to prevent Pandemic.- 3. Automation of COVID-19 Disease Diagnosis from Radiograph.- 4. Applications of Artificial Intelligence in the attainment of Sustainable Development Goals.- 5. A Novel Model for IoT Blockchain Assurance Based Compliance to COVID Quarantine.- 6. DEEP LEARNING BASED CONVOLUTIONALNEURAL NETWORK WITH RANDOM FOREST APPROACH FOR MRI BRAIN TUMOUR SEGMENTATION .- 7. Expert systems for improving the effectiveness of remote health monitoring in Covid-19 Pandemic - A Critical Review.- 8. Artificial Intelligence-based predictive tools for Life-threatening diseases.- 9. Deep Convolutional Generative Adversarial Network for Metastatic Tissue Diagnosis in Lymph Node Section.- 10. Transformation in Health Sector during Pandemic by Photonics Devices .- 11. DIAGNOSIS OF COVID-19 FROM CT IMAGES AND RESPIRATORY SOUND SIGNALS USING DEEP LEARNING STRATEGIES.- 12. The Role of Edge Computing in Pandemic and Epidemic Situations with its Solutions.- 13. Advances and application of Artificial Intelligence and Machine learning in the field of cardiovascular diseases and its role during the Pandemic condition.- 14. Effective Health Screening and Prompt Vaccination to Counter the Spread of Covid-19 and Minimize its Adverse Effects.- 15. CROWD DENSITY ESTIMATION USING NEURAL NETWORK FOR COVID’19 AND FUTURE PANDEMICS.- 16. “Role of digital healthcare in rehabilitation during pandemic”.- 17. AN EPIDEMIC OF NEURODEGENERATIVE DISEASE ANALYSIS USING MACHINE LEARNING TECHNIQUES.- 18. Covid-19 Growth Curve Forecasting for India using Deep Learning Techniques.
£134.99
Springer International Publishing AG Learning to Quantify
Book SynopsisThis open access book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learning to quantify is a task of its own related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier is known to often return inaccurate (“biased”) class proportion estimates. The book introduces learning to quantify by looking at the supervised learning methods that can be used to perform it, at the evaluation measures and evaluation protocols that should be used for evaluating the quality of the returned predictions, at the numerous fields of human activity in which the use of quantification techniques may provide improved results with respect to the naive use of classification techniques, and at advanced topics in quantification research. The book is suitable to researchers, data scientists, or PhD students, who want to come up to speed with the state of the art in learning to quantify, but also to researchers wishing to apply data science technologies to fields of human activity (e.g., the social sciences, political science, epidemiology, market research) which focus on aggregate (“macro”) data rather than on individual (“micro”) data.Table of Contents- 1. The Case for Quantification. - 2. Applications of Quantification. - 3. Evaluation of Quantification Algorithms. - 4. Methods for Learning to Quantify. - 5. Advanced Topics. - 6. The Quantification Landscape. - 7. The Road Ahead.
£31.49
Springer International Publishing AG Artificial Neural Networks in Pattern Recognition: 10th IAPR TC3 Workshop, ANNPR 2022, Dubai, United Arab Emirates, November 24–26, 2022, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 10th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2022, held in Dubai, UAE, in November 2022. The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The conference presents papers on subject such as pattern recognition and machine learning based on artificial neural networks. Table of ContentsTransformer-Encoder generated context-aware embeddings for spell correction.- Graph Augmentation for Neural Networks Using Matching-Graphs.- Wavelet Scattering Transform Depth Benefit, An Application for Speaker Identification.- Assessment of Pharmaceutical Patent Novelty using Siamese Neural Network.- A Review of Capsule Networks in Medical Image Analysis.- Multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisions.- Introducing an Atypical Loss: A Perceptual Metric Learning for Image Pairing.- A Study on the Autonomous Detection of Impact Craters.- Minimizing Cross Intersections in Graph Drawing via Linear Splines.- Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech.- Do Minimal Complexity Least Squares Support Vector Machines Work?.- A Novel Representation of Graphical Patterns for Graph Convolution Networks.- Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi Utilization of Vision Transformer for Classification and Ranking of Video Distortions.- White Blood Cell Classification of Porcine Blood Smear Images.- Medical Deepfake Detection using 3-Dimensional Neural Learning.
£47.49
Springer International Publishing AG Machine Learning in Medical Imaging: 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings
Book SynopsisThis book constitutes the proceedings of the 13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with MICCAI 2022, in Singapore, in September 2022. The 48 full papers presented in this volume were carefully reviewed and selected from 64 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.Table of ContentsFunction MRI Representation Learning via Self-Supervised Transformer for Automated Brain Disorder Analysis.- Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images using Deep Learning.- Region-Guided Channel-Wise Attention Network for Accelerated MRI Reconstruction.- Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation.- Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN.- 3D Segmentation with Fully Trainable Gabor Kernels and Pearson's Correlation Coefficient.- A More Design-flexible Medical Transformer for Volumetric Image Segmentation.- Dcor-VLDet: A Vertebra Landmark Detection Network for Scoliosis Assessment with Dual Coordinate System.- Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping.- A Coarse-To-Fine Network for Craniopharyngioma Segmentation.- Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring.- AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine.- Memory transformers for full context and high-resolution 3D Medical Segmentation.- Whole Mammography Diagnosis via Multi-instance Supervised Discriminative Localization and Classification.- Cross Task Temporal Consistency for Semi Supervised Medical Image Segmentation.- U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration.- UNet-eVAE: Iterative refinement using VAE embodied learning for endoscopic image segmentation.- Dynamic Linear Transformer for 3D Biomedical Image Segmentation.- Automatic Grading of Emphysema by Combining 3D Lung Tissue Appearance and Deformation Map Using a Two-stream Fully Convolutional Neural Network.- A Novel Two-Stage Multi-View Low-Rank Sparse Subspace Clustering Approach to Explore the Relationship between Brain Function and Structure.- Fast Image-Level MRI Harmonization via Spectrum Analysis.- CT2CXR: CT-based CXR Synthesis for Covid-19 Pneumonia Classification.- Harmonization of Multi-Site Cortical Data Across the Human Lifespan.- Head and neck vessel segmentation with connective topology using affinity graph.- Coarse Retinal Lesion Annotations Refinement via Prototypical Learning.- Nuclear Segmentation and Classification: On Color & Compression Generalization.- Understanding Clinical Progression of Late-Life Depression to Alzheimer’s Disease Over 5 Years with Structural MRI.- ClinicalRadioBERT: Knowledge-Infused Few Shot Learning for Clinical Notes Named Entity Recognition.- Graph Representation Neural Architecture Search for Optimal Spatial/Temporal Functional Brain Network Decomposition.- Driving Points Prediction For Abdominal Probabilistic Registration.- CircleSnake: Instance Segmentation with Circle Representation.- Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle.- Coronary Ostia Localization Using Residual U-Net with Heatmap Matching and 3D DSNT.- AMLP-Conv, a 3D Axial Long-range Interaction Multilayer Perceptron for CNNs.- Neural State-Space Modeling with Latent Causal-Effect Disentanglement.- Adaptive Unified Contrastive Learning for Imbalanced Classification.- Prediction of HPV-Associated Genetic Diversity for Squamous Cell Carcinoma of Head and Neck Cancer based on 18F-FDG PET/CT.- TransWS: Transformer-based Weakly Supervised Histology Image Segmentation.- Contextual Attention Network: Transformer Meets U-Net.- Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis.- A New Lightweight Architecture and a Class Imbalance Aware Loss Function for Multi-label Classification of Intracranial Hemorrhages.- Spherical Transformer on Cortical Surfaces.- Accurate localization of inner ear regions of interests using deep reinforcement learning.- Shifted Windows Transformers for Medical Image Quality Assessment.- Multi-scale Multi-structure Siamese Network (MMSNet) for Primary Open-angle Glaucoma Prediction.- HealNet - Self-Supervised Acute Wound Heal-Stage Classification.- Federated Tumor Segmentation with Patch-wise Deep Learning Model.- Multi-scale and Focal Region Based Deep Learning Network for Fine Brain Parcellation.
£58.49
Springer International Publishing AG Proceedings of ELM 2021: Theory, Algorithms and
Book SynopsisThis book contains papers from the International Conference on Extreme Learning Machine 2021, which was held in virtual on December 15–16, 2021. Extreme learning machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental `learning particles’ filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc.) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. This conference provides a forum for academics, researchers, and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. This book covers theories, algorithms, and applications of ELM. It gives readers a glance of the most recent advances of ELM.Table of ContentsPretrained E-commerce Knowledge Graph Model for Product Classification.- A Novel Methodology for Object Detection in Highly Cluttered Images.- Extreme learning Machines for Offline Forged Signature Identification.- Randomized model structure selection approach for Extreme Learning Machine applied to Acid sulfate soils detection.- Online label distribution learning based on kernel extreme learning machine.
£170.99
Springer International Publishing AG Smart Multimedia: Third International Conference,
Book SynopsisThis book constitutes the proceedings of the Third International Conference on Smart Multimedia, ICSM 2022, which was held in Marseille, France, during August 25–27, 2022.The 30 full papers and 4 short paper presented in this volume were carefully reviewed and selected from 68 submissions. The contributions were organized in topical sections as follows: Machine Learning for Multimedia; Image Processing; Multimedia Applications; Multimedia for Medicine and Health-Care; Smart Homes; Multimedia Environments and Metaverse; Deep Learning on Video and Music; Haptic; Industrial.Table of ContentsMachine Learning for Multimedia.- Normalizing Flow Based Surface Defect Detection.- FUNet: Flow Based Conference Video Background Subtraction.- IARG: Improved Actor Relation Graph-based Group Activity Recognition.- SPGNet: Spatial Projection Guided 3D Human Pose Estimation in Low Dimensional Space.- Image Processing.- Unsupervised Face Frontalization GAN Driven by 3D Rotation and Symmetric Filling.- Infrared and Visible Image Fusion Based on Multi-Scale Gaussian Rolling Guidance Filter Decomposition.- Multi-Directional Edge Detection Algorithm Based on Fuzzy Logic Judgment.- Multimedia Applications.- Security Concerns and Citizens' Privacy Implications in Smart Multimedia Applications.- Metric Learning on Complex Projective Spaces.- Gamified Smart Grid Implementation through Pico, Nano, and Microgrids in a Sustainable Campus.- Product Re-Identification System in Fully Automated Defect Detection.- Multimedia for Medicine and Health-Care.- A Real-Time Fall Classification Model Based on Frame Series Motion Deformation.- GradXcepUNet: Explainable AI Based Medical Image Segmentation.- Non-Invasive Anemia Detection from Conjunctival Images.- 3D Segmentation and Visualization of Human Brain CT Images for Surgical Training - a VTK Approach.- Smart Homes.- The Energy 4.0 Concept and Its Relationship with the S3 Framework.- A Real-Time Adaptive Thermal Comfort Model for Sustainable Energy in Interactive Smart Homes: Part I.- A Real-Time Adaptive Thermal Comfort Model for Sustainable Energy in Interactive Smart Homes: Part II.- Multimedia Environments and Metaverse.- Including Grip Strength Activities into Tabletop Training Environments.- Matrix World – A Programmable 3D Multichain Metaverse.- Matrix Syncer - A Multi-Chain Data Aggregator for Supporting Blockchain-Based Metaverses.- Construction and Design of Food Traceability Based on Blockchain Technology Applying in the Metaverse.- Deep Learning on Video and Music.- Motion Segmentation Based on Pixel Distribution Learning on Unseen Videos.- Estimation of Music Recording Quality to Predict Automatic Music Transcription Performance.- Unleashing the Potential of Data Analytics through Music.- Haptic.- Impact of PGM Training on Reaction Time and Sense of Agency.- Epidural Motor Skills Measurements for Haptic Training.- Sensorless Force Approximation Control of 3-DOF Passive Haptic Devices.- Passive Haptic Learning as a Reinforcement Modality for Information.- Industrial.- Lighting Enhancement Using Self-Attention Guided HDR Reconstruction.- MoCap Trajectory-Based Animation Synthesis and Perplexity Driven Compression.- Hyperspectral Image Denoising Based on Dual Low-Rank Structure Preservation.- SimFormer: Real-To-Sim Transfer with Recurrent Restoration.- Spatio-Frequency Analysis for High-Frequency Surface Wave Radar Ship Target Detection.
£61.74
Springer International Publishing AG A Guide to Applied Machine Learning for
Book SynopsisThis textbook is an introductory guide to applied machine learning, specifically for biology students. It familiarizes biology students with the basics of modern computer science and mathematics and emphasizes the real-world applications of these subjects. The chapters give an overview of computer systems and programming languages to establish a basic understanding of the important concepts in computer systems. Readers are introduced to machine learning and artificial intelligence in the field of bioinformatics, connecting these applications to systems biology, biological data analysis and predictions, and healthcare diagnosis and treatment. This book offers a necessary foundation for more advanced computer-based technologies used in biology, employing case studies, real-world issues, and various examples to guide the reader from the basic prerequisites to machine learning and its applications.Table of Contents1. Basics of Modern Computer Systems (Unix/Linux Centric) a. Computer Hardware Basics b. Operating System c. Files & Directories d. Programs and Shells e. Programming Languages f. Troubleshooting Computer Problems (How to Google issues.) 2. The Python Programming Language ( A tool to enter the world of Machine Learning.) a. Short History b. The Python Interpreter c. Basic Syntax d. Working with Popular Libraries (Modules) e. Optimization f. Advanced Concepts g. Introduction to ML libraries 3. Basic Math a. Overview b. Linear Algebra Basics c. Calculus Basics d. Probability e. Use cases of above three in ML 4. Introduction to the World of Bioinformatics a. Laying the Foundation b. A Brief History c. Goals of Bioinformatics d. Genomes, Genes, Sequences e. Protein and structures f. Databases g. Bioinformatics Tools 5. Introduction to Artificial Intelligence & ML a. Machine Learning i. History of Machine Learning ii. Why Machine Learning? iii. Machine Learning Approaches iv. Machine Learning Applications b. Artificial Intelligence i. What is AI? ii. Basic Principles iii. General applications of Artificial Intelligence 6. Fundamentals of ML a. Types of learning i. Supervised ii. Unsupervised b. Popular Algorithms c. Deep learning and related concepts d. Model Training and Testing e. Summary 7. Applications in the field of Bioinformatics a. In Systems Biology b. Biological Data Analysis and Predictions c. In Healthcare, Diagnosis and Treatment 8. Future Prospects 9. Further Readings
£53.99
Springer International Publishing AG Fusion of Machine Learning Paradigms: Theory and
Book SynopsisThis book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The book consists of an editorial note and an additional eight chapters and is organized into two parts, namely: (i) Recent Application Areas of Fusion of Machine Learning Paradigms and (ii) Applications that can clearly benefit from Fusion of Machine Learning Paradigms. This book is directed toward professors, researchers, scientists, engineers and students in Machine Learning-related disciplines, as the hybridism presented, and the case studies described provide researchers with successful approaches and initiatives to efficiently address complex classification or regression problems. It is also directed toward readers who come from other disciplines, including Engineering, Medicine or Education Sciences, and are interested in becoming versed in some of the most recent Machine Learning-based technologies. Extensive lists of bibliographic references at the end of each chapter guide the readers to probe further into the application areas of interest to them.Table of ContentsEditorial Note.- Artificial Intelligence as Dual-Use Technology.- Diabetic Retinopathy Detection using Transfer and Reinforcement Learning with effective image preprocessing and data augmentation techniques.
£107.99
Springer International Publishing AG Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II
Book SynopsisThis volume constitutes the papers of several workshops which were held in conjunction with the International Workshops of ECML PKDD 2022 on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, held in Grenoble, France, during September 19–23, 2022. The 73 revised full papers and 6 short papers presented in this book were carefully reviewed and selected from 143 submissions. ECML PKDD 2022 presents the following five workshops:Workshop on Data Science for Social Good (SoGood 2022)Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2022)Workshop on Explainable Knowledge Discovery in Data Mining (XKDD 2022)Workshop on Uplift Modeling (UMOD 2022)Workshop on IoT, Edge and Mobile for Embedded Machine Learning (ITEM 2022)Workshop on Mining Data for Financial Application (MIDAS 2022)Workshop on Machine Learning for Cybersecurity (MLCS 2022)Workshop on Machine Learning for Buildings Energy Management (MLBEM 2022) Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2022)Workshop on Data Analysis in Life Science (DALS 2022)Workshop on IoT Streams for Predictive Maintenance (IoT-PdM 2022)Table of ContentsWorkshop on Mining Data for Financial Application (MIDAS 2022).- Preface from the workshop organisers.- Multi-Task Learning for Features Extraction in Financial Annual Reports.- What to do with your sentiments in finance.- On the development of a European tracker of societal issues and economic activities using alternative data.- Privacy-preserving machine learning in life insurance risk prediction.- Financial Distress Model Prediction using Machine Learning: A Case Study on Indonesia’s Consumers Cyclical Companies.- Improve default prediction in highly unbalanced context.- Towards Explainable Occupational Fraud Detection.- Towards Data-Driven Volatility Modeling with Variational Autoencoders.- Auto-Clustering of Financial Reports Based on Formatting Style and Author’s Fingerprint.- InFi-BERT 1.0: Transformer-based language model for Indian Financial Volatility Prediction.- Workshop on Machine Learning for Cybersecurity (MLCS 2022).- Preface from the workshop organisers.- Intrusion Detection using Ensemble Models.- Domain Adaptation with Maximum Margin Criterion with application to network traffic classification.- Evaluation of Detection Limit in Network Dataset Quality Assessment with Permutation Testing.- Towards a General Model for Intrusion Detection: An Exploratory Study.- Workshop on Machine Learning for Buildings Energy Management (MLBEM 2022).- Preface from the workshop organisers.- Conv-NILM-Net, a causal and multi-appliance model for energy source separation.- Domestic Hot Water Forecasting for Individual Housing with Deep Learning.- Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2022).- Preface from the workshop organisers.- Detecting Drift in Healthcare AI Models based on Data Availability.- Assessing Different Feature Selection Methods applied to a bulk RNA Sequencing Dataset with regard to Biomedical Relevance.- Predicting Drug Treatment for Hospitalized Patients with Heart Failure.- A Workflow for Generating Patient Counterfactuals in Lung Transplant Recipients.- Few-Shot Learning for Identification of COVID-19 Symptoms Using Generative Pre-Trained Transformer Language Models.- A Light-weight Deep Residual Network for Classification of Abnormal Heart Rhythms on Tiny Devices.- Workshop on Data Analysis in Life Science (DALS 2022).- Preface from the workshop organisers.- I-CONVEX: Fast and Accurate de Novo Transcriptome Recovery from Long Reads.- Italian debate on measles vaccination: how Twitter data highlight communities and polarity.- Workshop on IoT Streams for Predictive Maintenance (IoT-PdM 2022).- Preface from the workshop organisers.- Online Anomaly Explanation: A Case Study on Predictive Maintenance.- Fault forecasting using data-driven system modeling: a case study for Metro do Porto data set.- An online data-driven predictive maintenance approach for railway switches.- curr2vib: Modality Embedding Translation for Broken-Rotor Bar Detection.- Incorporating Physics-based Models into Data-Driven Approaches for Air Leak Detection in City Buses.- Towards Geometry-Preserving Domain Adaptation for Fault Identification.- A systematic approach for tracking the evolution of XAI as a field of research.- Frequent Generalized Subgraph Mining via Graph Edit Distances.
£62.99
Springer International Publishing AG Big Data Analytics: 10th International Conference, BDA 2022, Hyderabad, India, December 19–22, 2022, Proceedings
Book SynopsisThis book constitutes the proceedings of the 10th International Conference on Big Data Analytics, BDA 2022, which took place in Hyderabad, India, in December 2022.The 7 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 36 submissions. The book also contains 4 keynote talks in full-paper length. The papers are organized in the following topical sections: Big Data Analytics: Vision and Perspectives; Data Science: Architectures; Data Science: Applications; Graph Analytics; Pattern Mining; Predictive Analytics in Agriculture.Table of ContentsBig Data Analytics: Vision and Perspectives.- Data Challenges and Societal Impacts – the case in favor of the Blueprint for an AI Bill of Rights.- Big Data in Cognitive Neuroscience: Opportunities and Challenges.- Data Science: Architectures.- A Novel Feature Selection Based Text Classification using Multi-layer ELM.- ARCORE: Software Requirements Dataset for Service Identification.- ARCORE: Software Requirements Dataset for Service Identification.- Learning enhancement using Question-Answer generation for e-book using contrastive fine-tuned T5.- Data Science: Applications.- A Machine and Deep Learning Framework to Retain Customers based on their Lifetime Value.- A Deep Learning based Approach to Automate Clinical Coding of Electronic Health Records.- Determining the severity of Dementia using ensemble learning.- Determining the severity of Dementia using ensemble learning.- A distributed ensemble machine learning technique for emotion classification from vocal cues.- Graph Analytics. -Drugomics: Knowledge Graph & AI to Construct Physicians' Brain Digital Twin to Prevent Drug Side-effects and Patient Harm.- Extremely Randomized Tree based Sentiment Polarity Classification on Online Product Reviews.- Community Detection in Large Directed Graphs.- Pattern Mining.- FastTIRP: Efficient discovery of Time-Interval Related Patterns.- Discovering Top-K Periodic Patterns in Temporal Databases.- Hui2Vec: Learning Transaction Embedding Through High Utility Itemsets.- Predictive Analytics in Agriculture.- A Data-driven, Farmer-oriented Agricultural Crop Recommendation Engine (ACRE).- Analyze the Impact of Weather Parameters for Crop Yield Prediction using Deep Learning.- Analysis of Weather Condition based Reuse among Agromet Advisory: A Validation Study.
£47.49
Springer International Publishing AG Machine Learning, Image Processing, Network Security and Data Sciences: 4th International Conference, MIND 2022, Virtual Event, January 19–20, 2023, Proceedings, Part II
Book SynopsisThis two-volume set (CCIS 1762-1763) constitutes the refereed proceedings of the 4th International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2022, held in Bhopal, India, in December 2022. The 64 papers presented in this two-volume set were thoroughly reviewed and selected from 399 submissions. The papers are organized according to the following topical sections: machine learning and computational intelligence; data sciences; image processing and computer vision; network and cyber security.Table of ContentsMachine Learning and Computational Intelligence.- Data Sciences.- Image Processing and Computer Vision.- Network and Cyber Security.
£58.49
Springer International Publishing AG Computer Vision – ECCV 2022 Workshops: Tel Aviv,
Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.
£80.74
Springer International Publishing AG Computer Vision – ECCV 2022 Workshops: Tel Aviv,
Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.
£125.99
Springer International Publishing AG Computer Vision – ECCV 2022 Workshops: Tel Aviv,
Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.
£80.74
Springer International Publishing AG Computer Vision – ECCV 2022 Workshops: Tel Aviv,
Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.
£80.74
Springer International Publishing AG Computer Vision – ACCV 2022: 16th Asian
Book SynopsisThe 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022. The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; optimization methods; Part II: applications of computer vision, vision for X; computational photography, sensing, and display; Part III: low-level vision, image processing; Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics; Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis; Part VI: biomedical image analysis; deep learning for computer vision; Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods.
£80.74
Springer International Publishing AG Machine Learning and Knowledge Discovery in
Book SynopsisThe multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022.The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track. Table of ContentsTime series.- Financial machine learning.- Applications.- Applications: transportation.- Demo track.
£67.49
Springer International Publishing AG Symbols: An Evolutionary History from the Stone
Book SynopsisFor millennia humans have used visible marks to communicate information. Modern examples of conventional graphical symbols include written language, and non-linguistic symbol systems such as mathematical symbology or traffic signs. The latter kinds of symbols convey information without reference to language. This book presents the first systematic study of graphical symbol systems, including a history of graphical symbols from the Paleolithic onwards, a taxonomy of non-linguistic systems – systems that are not tied to spoken language – and a survey of more than 25 such systems. One important feature of many non-linguistic systems is that, as in written language, symbols may be combined into complex “messages” if the information the system represents is itself complex. To illustrate, the author presents an in-depth comparison of two systems that had very similar functions, but very different structure: European heraldry and Japanese kamon. Writing first appeared in Mesopotamia about 5,000 years ago and is believed to have evolved from a previous non-linguistic accounting system. The exact mechanism is unknown, but crucial was the discovery that symbols can represent the sounds of words, not just the meanings. The book presents a novel neurologically-inspired hypothesis that writing evolved in an institutional context in which symbols were “dictated”, thus driving an association between symbol and sound, and provides a computational simulation to support this hypothesis. The author further discusses some common fallacies about writing and non-linguistic systems, and how these relate to widely cited claims about statistical “evidence” for one or another system being writing. The book ends with some thoughts about the future of graphical symbol systems. The intended audience includes students, researchers, lecturers, professionals and scientists from fields like Natural Language Processing, Machine Learning, Archaeology and Semiotics, as well as general readers interested in language and/or writing systems and symbol systems.Trade Review“The book is the first systematic study of graphical symbol systems, ranging from the imagery found in Paleolithic cave paintings, through ancient and contemporary writing systems employing both phonetic and logographic symbols, to modern language-independent symbols such as meteorological icons and emoji.” (Andrew Robinson, Science, science.org, Vol. 382 (6669), October 27, 2023)Table of ContentsPreface1 Introduction 1.1 What’s in a Symbol? 1.2 Syntax 1.3 What this book is about 2 Semiotics 2.1 Introduction 2.2 The Field of Semiotics 2.3 Iconicity 2.4 Syntax 2.5 Articulation 3 Taxonomy 3.1 Introduction 3.2 History 3.3 Preliminary Taxonomy 3.4 Examples of systems 3.5 Kamon/Heraldry 3.5.1 Kamon 3.5.2 British heraldry 3.5.3 Structural Differences: Summary 3.A Symbol system survey (A detailed analysis of 26 symbol systems) 3.B Statistics of kamon 4 Writing Systems 4.1 Introduction 4.2 Writing 4.2.1 Preliminaries 4.2.2 Types of Writing Systems 4.2.3 Blissymbolics 4.3 Limitations of writing 4.3.1 Inclusiveness 4.3.2 Graphocentrism 4.3.3 Summary 4.4 Writing: A summary 5 Symbols in the Brain 5.1 Brain areas 5.2 Meaning in the brain 5.3 Reading in the brain 5.3.1 The letterbox 5.3.2 Summary: the evolution of the letterbox 5.4 Non-linguistic symbols in the brain 5.5 A Hypothesis 6 The Evolution of Writing 6.1 Evolution 6.2 A Hypothesis 6.3 Schools 7 Simulations 7.1 Prior work 7.2 Simulation 7.2.1 Description of the model 7.2.2 Simulation of evolution 7.2.3 Summary and discussion 7.3 Pre-writing 7.4 Summary 7.A Details 7.A.1 Data Generation 7.A.2 Model 7.B Compounds 7.B.1 Monosyllabic cases 7.B.2 Sesquisyllabic cases 7.B.3 Disyllabic cases 8 Misrepresentations 8.1 Introduction 8.2 What does it mean to say something "Looks like writing"? 8.3 Statistics 8.3.1 Statistical analysis of the Indus Valley inscriptions 8.3.2 More on structure in the Indus inscriptions 8.3.3 Variations of distributions of symbols 8.4 Summary 9 The Future 9.1 The Dream of a Universal Written Language 9.2 Semasiography 9.3 The Prestige of Writing 9.4 Final Thoughts
£31.49
Springer International Publishing AG Advances in Information Retrieval: 45th European
Book SynopsisThe three-volume set LNCS 13980, 13981 and 13982 constitutes the refereed proceedings of the 45th European Conference on IR Research, ECIR 2023, held in Dublin, Ireland, during April 2-6, 2023. The 65 full papers, 41 short papers, 19 demonstration papers, 12 reproducibility papers consortium papers, 7 tutorial papers, and 10 doctorial consortium papers were carefully reviewed and selected from 489 submissions. The book also contains, 8 workshop summaries and 13 CLEF Lab descriptions. The accepted papers cover the state of the art in information retrieval focusing on user aspects, system and foundational aspects, machine learning, applications, evaluation, new social and technical challenges, and other topics of direct or indirect relevance to search.Table of ContentsFull Papers.- Automatic Summarization of Financial Earnings Calls Transcript.- Parameter-Efficient Sparse Retrievers and Rerankers using Adapters.- Feature Differentiation and Fusion for Semantic Text Matching.- Multivariate Powered Dirichlet-Hawkes Process.- Fragmented Visual Attention in Web Browsing: Weibull Analysis of Item Visit Times.- Topic-Enhanced Personalized Retrieval-based Chatbot.- Improving the Generalizability of the Dense Passage Retriever Using Generated Datasets.- SegmentCodeList: Unsupervised Representation Learning for Human Skeleton Data Retrieval.- Knowing What and How: A Multi-modal Aspect-Based Framework for Complaint Detection.- What is your cause for concern? Towards Interpretable Complaint Cause Analysis.- DeCoDE: DEtection of COgnitive Distortion and Emotion cause extraction in clinical conversations.- Domain-aligned Data Augmentation for Low-resource and Imbalanced Text Classification.- Privacy-Preserving Fair Item Ranking.- Multimodal Geolocation Estimation of News Photos.- Topics in Contextualised Attention Embeddings.- New Metrics to Encourage Innovation and Diversity in Information Retrieval Approaches.- Probing BERT for Ranking Abilities.- Clustering of Bandit with Frequency-Dependent Information Sharing.- Contrastive Graph Learning with Positional Representation for Recommendation.- Domain Adaptation for Anomaly Detection on Heterogeneous Graphs in E-Commerce.- Short PapersImproving Neural Topic Models with Wasserstein Knowledge Distillation.- Towards Effective Paraphrasing for Information Disguise.- Generating Topic Pages for Scientific Concepts Using Scientific Publications.- Relevance Judgements for Fair Ranking.- A Study of Term-Topic Embeddings for Ranking.- Topic Refinement in Multi-Level Hate Speech Detection.- Is Cross-modal Information Retrieval Possible without Training?.- Adversarial Adaptation for French Named Entity Recognition.- Exploring Fake News Detection with Heterogeneous Social Media Context Graphs.- Justifying Multi-Label Text Classifications for Healthcare Applications.- Doc2Query–: When Less is More.- Towards Quantifying The Privacy Of Redacted Text. -Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study.- Leveraging Comment Retrieval for Code Summarization.- CPR: Cross-domain Preference Ranking with User Transformation.- Colbert-FairPRF: Towards Fair Pseudo-Relevance Feedback in Dense Retrieval.- C2LIR: Continual Cross-lingual Transfer for Low-Resource Information Retrieval.- Joint Extraction and Classification of Danish Competences for Job Matching.- A Study on FGSM Adversarial Training for Neural Retrieval.- Dialogue-to-Video Retrieval.- Time-dependent next-basket recommendations.- Investigating the Impact of Query Representation on Medical Information Retrieval.- Where a Little Change Makes a Big Difference: A Preliminary Exploration of Children’s Queries.- Multi-document QA with GPT-3 and Neural Reranking .- Towards Detecting Interesting Ideas Expressed in Text.- Towards Linguistically Informed Multi-Objective Transformer Pre-Training for Natural Language Inference.- Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion Networks.- Consumer Health Question Answering Using Off-the-shelf Components.- MOO-CMDS+NER: Named Entity Recognition-based Extractive Comment-oriented Multi-document Summarization.- Don’t Raise Your Voice, Improve Your Argument: Learning to Retrieve Convincing Arguments.- Learning Query-Space Document Representations for High-Recall Retrieval.- Investigating Conversational Search Behavior For Domain Exploration.- Evaluating Humorous Response Generation to Playful Shopping Requests.- Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents.- Trigger or not Trigger: Dynamic Thresholding for Few Shot Event Detection.- The Impact of a Popularity Punishing Hyperparameter on ItemKNN Recommendation Performance.- Neural Ad hoc Retrieval Meets Information Extraction.- Augmenting Graph Convolutional Networks with Textual Data for Recommendations.- Utilising Twitter Metadata for Hate Classification.- Evolution of Filter Bubbles and Polarization in News Recommendation.- Capturing Cross-platform Interaction for Identifying Coordinated Accounts of Misinformation Campaigns.
£71.99
Springer International Publishing AG Neural Networks and Deep Learning: A Textbook
Book SynopsisThis 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. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories: The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2.Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections 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. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12. The textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well.Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition.Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.Table of ContentsAn Introduction to Neural Networks.- The Backpropagation Algorithm.- Machine Learning with Shallow Neural Networks.- Deep Learning: Principles and Training Algorithms.- Teaching a Deep Neural Network to Generalize.- Radial Basis Function Networks.- Restricted Boltzmann Machines.- Recurrent Neural Networks.- Convolutional Neural Networks.- Graph Neural Networks.- Deep Reinforcement Learning.- Advanced Topics in Deep Learning.
£53.99
Springer International Publishing AG Machine Learning Governance for Managers
Book SynopsisMachine Learning Governance for Managers provides readers with the knowledge to unlock insights from data and leverage AI solutions. In today's business landscape, most organizations face challenges in scaling and maintaining a sustainable machine learning model lifecycle. This book offers a comprehensive framework that covers business requirements, data generation and acquisition, modeling, model deployment, performance measurement, and management, providing a range of methodologies, technologies, and resources to assist data science managers in adopting data and AI-driven practices. Particular emphasis is given to ramping up a solution quickly, detailing skills and techniques to ensure the right things are measured and acted upon for reliable results and high performance. Readers will learn sustainable tools for implementing machine learning with existing IT and privacy policies, including versioning all models, creating documentation, monitoring models and their results, and assessing their causal business impact. By overcoming these challenges, bottom-line gains from AI investments can be realized. Organizations that implement all aspects of AI/ML model governance can achieve a high level of control and visibility over how models perform in production, leading to improved operational efficiency and a higher ROI on AI investments. Machine Learning Governance for Managers helps to effectively control model inputs and understand all the variables that may impact your results. Don't let challenges in machine learning hinder your organization's growth - unlock its potential with this essential guide. Table of Contents1. Understanding Business Goals.- 2. Measure the Right Things.- 3. Searching for the Right Tools.- 4. MLOps Governance & Architecting the Data Science Solution.- 5. Unifying Organizations’ Machine Learning Vision.
£31.49
Springer International Publishing AG Engineering of Additive Manufacturing Features
Book SynopsisThis book is a comprehensive guide to the latest developments in data-driven additive manufacturing (AM). From data mining and pre-processing to signal processing, computer vision, and more, the book covers all the essential techniques for preparing AM data. Readers willl explore the key physical and synthetic sources of AM data throughout the life cycle of the process and learn about feature engineering techniques, pipelines, and resulting features, as well as their applications at each life cycle phase. With a focus on featurization efforts from reviewed literature, this book offers tabular summaries for major data sources and analyzes feature spaces at the design, process, and structure phases of AM to uncover trends and insights specific to feature engineering techniques. Finally, the book discusses current challenges and future directions, including AI/ML/DL readiness of AM data. Whether you're an expert or newcomer to the field, this book provides a broader summary of the status and future of data-driven AM technology.Table of ContentsIntroduction.- Feature Engineering in AM.- Applications in Data-driven AM.- Analyzing AM Feature Spaces.- Challenges and Opportunities in AM Data Preparation.- Summary.
£33.24
Springer International Publishing AG Mathematical Principles of Topological and
Book SynopsisThis book explores and demonstrates how geometric tools can be used in data analysis. Beginning with a systematic exposition of the mathematical prerequisites, covering topics ranging from category theory to algebraic topology, Riemannian geometry, operator theory and network analysis, it goes on to describe and analyze some of the most important machine learning techniques for dimension reduction, including the different types of manifold learning and kernel methods. It also develops a new notion of curvature of generalized metric spaces, based on the notion of hyperconvexity, which can be used for the topological representation of geometric information.In recent years there has been a fascinating development: concepts and methods originally created in the context of research in pure mathematics, and in particular in geometry, have become powerful tools in machine learning for the analysis of data. The underlying reason for this is that data are typically equipped with some kind of notion of distance, quantifying the differences between data points. Of course, to be successfully applied, the geometric tools usually need to be redefined, generalized, or extended appropriately.Primarily aimed at mathematicians seeking an overview of the geometric concepts and methods that are useful for data analysis, the book will also be of interest to researchers in machine learning and data analysis who want to see a systematic mathematical foundation of the methods that they use. Table of ContentsIntroduction.- Topological foundations, hypercomplexes and homology.- Weighted complexes, cohomology and Laplace operators.- The Laplace operator and the geometry of graphs.- Metric spaces and manifolds.- Linear methods: Kernels, variations, and averaging.- Nonlinear schemes: Clustering, feature extraction and dimension reduction.- Manifold learning, the scheme of Laplacian eigenmaps.- Metrics and curvature.
£53.99
Springer International Publishing AG Mitosis Domain Generalization and Diabetic Retinopathy Analysis: MICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18–22, 2022, Proceedings
Book SynopsisThis book constitutes two challenges that were held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which took place in Singapore during September 18-22, 2022. The peer-reviewed 20 long and 5 short papers included in this volume stem from the following three biomedical image analysis challenges: Mitosis Domain Generalization Challenge (MIDOG 2022), Diabetic Retinopathy Analysis Challenge (CRAC 2022) The challenges share the need for developing and fairly evaluating algorithms that increase accuracy, reproducibility and efficiency of automated image analysis in clinically relevant applications.Table of ContentsPreface DRAC 2022.- nnU-Net Pre- and Postprocessing Strategies for UW-OCTA Segmentation Tasks in Diabetic Retinopathy Analysis.- Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias.- Bag of Tricks for Diabetic Retinopathy Grading of Ultra-wide Optical Coherence Tomography Angiography Images.- Deep convolutional neural network for image quality assessment and diabetic retinopathy grading.- Diabetic Retinal Overlap Lesion Segmentation Network.- An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images.- Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data Scarcity.- Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images.- Deep Learning-based Multi-tasking System for Diabetic Retinopathy in UW-OCTA images.- Semi-Supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment.- Image Quality Assessment based on Multi-Model Ensemble Class-Imbalance Repair Algorithm for Diabetic Retinopathy UW-OCTA Images.- An improved U-Net for diabetic retinopathy segmentation.- A Vision transformer based deep learning architecture for automatic diagnosis of diabetic retinopathy in optical coherence tomography angiography.- Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy.- Data Augmentation by Fourier Transformation for Class-Imbalance : Application to Medical Image Quality Assessment.- Automatic image quality assessment and DR grading method based on convolutional neural network.- A transfer learning based model ensemble method for image quality assessment and diabetic retinopathy grading.- Automatic Diabetic Retinopathy Lesion Segmentation in UW-OCTA Images using Transfer Learning.- Preface MIDOG 2022.- Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge.- Radial Prediction Domain Adaption Classifier for the MIDOG 2022 challenge.- Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge.- Tackling Mitosis Domain Generalization in Histopathology Images with Color Normalization.- "A Deep Learning based Ensemble Model for Generalized Mitosis Detection in H&E stained Whole Slide Images".- Fine-Grained Hard-Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset.- Multi-task RetinaNet for mitosis detection.
£47.49
Springer International Publishing AG Formal Concept Analysis: 17th International
Book SynopsisThis book constitutes the proceedings of the 17th International Conference on Formal Concept Analysis, ICFCA 2023, which took place in Kassel, Germany, in July 2023.The 13 full papers presented in this volume were carefully reviewed and selected from 19 submissions. The International Conference on Formal Concept Analysis serves as a platform for researchers from FCA and related disciplines to showcase and exchange their research findings. The papers are organized in two topical sections, first "Theory" and second "Applications and Visualization".Table of ContentsTheory: Approximating fuzzy relation equations through concept lattices.- Doubly-Lexical Order Supports Standardisation and Recursive Partitioning of Formal Context.- Graph-FCA Meets Pattern Structures.- On the commutative diagrams among Galois connections involved in closure structures.- Scaling Dimension.- Three Views on Dependency Covers from an FCA Perspective.- A Triadic Generalisation of the Boolean Concept Lattice.- Applications and Visualization: Computing witnesses for centralising monoids on a three-element set.- Description Quivers for Compact Representation of Concept Lattices and Ensembles of Decision Trees.- Examples of clique closure systems.- On the maximal independence polynomial of the covering graph of the hypercube up to n=6.- Relational Concept Analysis in Practice: Capitalizing on Data Modeling using Design Patterns.- Representing Concept Lattices with Euler Diagrams.
£42.74
Springer International Publishing AG Fundamentals of Reinforcement Learning
Book SynopsisArtificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization.This book provides an introduction to AI, specifies machine learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner. It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges.Understanding the Fundamentals of Reinforcement Learning will allow you to: Understand essential AI concepts Gain professional experience Interpret sequential decision problems and solve them with reinforcement learning Learn how the Q-Learning algorithm works Practice with commented Python code Find advantageous directions Table of ContentsChapter. 1. IntroductionChapter. 2. ConceptsChapter. 3. Q-Learning algorithmChapter. 4. Development toolsChapter. 5. Practice with codeChapter. 6. Recent applications and future researchIndex.
£56.99
Springer International Publishing AG Document Analysis and Recognition – ICDAR 2023
Book SynopsisThis two-volume set LNCS 14193-14194 constitutes the proceedings of International Workshops co-located with the 17th International Conference on Document Analysis and Recognition, ICDAR 2023, held in San José, CA, USA, during August 21–26, 2023. The total of 43 regular papers presented in this book were carefully selected from 60 submissions. Part I contains 22 regular papers that stem from the following workshops: ICDAR 2023 Workshop on Computational Paleography (IWCP); ICDAR 2023 Workshop on Camera-Based Document Analysis and Recognition (CBDAR); ICDAR 2023 International Workshop on Graphics Recognition (GREC); ICDAR 2023 Workshop on Automatically Domain-Adapted and Personalized Document Analysis (ADAPDA); Part II contains 21 regular papers that stem from the following workshops: ICDAR 2023 Workshop on Machine Vision and NLP for Document Analysis (VINALDO); ICDAR 2023 International Workshop on Machine Learning (WML). Table of ContentsTypefaces and Ligatures in Printed Arabic Text: A Deep Learning-Based OCR Perspective.- Leveraging Knowledge Graph Embeddings to Enhance Contextual Representations for Relation Extraction.- Extracting Key-Value Pairs in Business Documents.- Long-Range Transformer Architectures for Document Understanding.-Pre-training transformers for Corporate Documents Understanding.- Transformer-Based Neural Machine Translation for Post-OCR Error Correction in Cursive Text.- Arxiv Tables: Document Understanding Challenge Linking Texts and Tables.- Subgraph-Induced Extraction Technique for Information (SETI) from Administrative Documents.- Document Layout Annotation: Database and Benchmark in the Domain of Public Affairs.- A Clustering Approach Combining Lines and Text Detection for Table Extraction.- Absformer: Transformer-Based Model for Unsupervised Multi-Document Abstractive Summarization.- A Comparison of Demographic Attributes Detection from Handwriting Based on Traditional and Deep Learning Methods.- A New Optimization Approach to Improve an Ensemble Learning Model: Application to Persian/Arabic Handwritten Character Recognition.- BN-DRISHTI: Bangla Document Recognition Through Instance-level Segmentation of Handwritten Text Images.- Text Line Detection and Recognition of Greek Polytonic Documents.- A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet.- Local Style Awareness of Font Images.- Fourier Feature-Based CBAM and Vision Transformer for Text Detection in Drone Images.- Document Binarization with Quaternionic Double Discriminator Generative Adversarial Network.- Crosslingual Handwritten Text Generation Using GANs.- Knowledge Integration inside Multitask Network for Analysis of Unseen ID Types.
£56.99
Springer International Publishing AG Cancer Prevention Through Early Detection: Second
Book SynopsisThis book constitutes the refereed proceedings of the second International Workshop on Cancer Prevention through Early Detection, CaPTion, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2023, in Vancouver, Canada, in October 2023.The 11 papers presented at CaPTion 2023 were carefully reviewed and selected from 12 submissions. The workshop invites researchers to submit their work in the field of medical image analysis around the central theme of cancer and early cancer detection, progression, inflammation understanding, multimodality data, and computer-aided navigation.
£75.99
Springer International Publishing AG Artificial Intelligence over Infrared Images for Medical Applications: Second MICCAI Workshop, AIIIMA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 2, 2023, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the Second Workshop on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2023 held in conjunction with MICCAI 2023, held in Vancouver, BC, Canada, on October 2, 2023. The 10 full papers presented in this book were carefully peer reviewed and selected from 15 submissions. The second workshop on AIIIMA, similarily to the first, aimes to create a forum to discuss the specific sub-topic of AI over Infrared Images for Medical Applications at MICCAI and promote this novel area of research, that has the potential to hugely impact our society, among the research community.Table of ContentsArtificial Intelligence over Infrared Images for Medical Applications.- The Socioeconomic Impact of Artificial Intelligence Applications in Diagnostic Medical Thermography: A Comparative Analysis with Mammography in Breast Cancer Detection and other diseases early detection.- 3D-BreastNet: A Self-supervised Deep Learning Network for Reconstruction of 3D Breast Surface from 2D Thermal Images.- Modeling the 3D Breast Surface Using Thermography.- 3D Convolutional Neural Networks for Dynamic Breast Infrared Imaging Classification.- Could the consideration of symmetry be statistically significant for breast infrared analysis?.- Performance Evaluation of Convolutional Segmentation Models with Human Hand Thermal Images (H2TI) Dataset.- A Generative Approach for Image Registration of Visible-Thermal (VT) Cancer Faces.- Relationship between thermography assessment and hamstring isometric test in amateur soccer players.- Evaluation of Deep Learning Models for Lower Extremity Muscle Segmentation in Thermal Imaging.- Radiomics feature selection from Thyroid thermal images to improve thyroid nodules interpretations.
£75.99
Springer International Publishing AG Shape in Medical Imaging: International Workshop,
Book SynopsisThis volume comprises the proceedings of the International Workshop, ShapeMI 2023, which took place alongside MICCAI 2023 on October 8, 2023, in Vancouver, British Columbia, Canada.The 23 selected full papers deal with all aspects of leading methods and applications for advanced shape analysis and geometric learning in medical imaging.Table of ContentsAnatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstruction.- C3Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy.- Anatomy-Aware Masking for Inpainting in Medical Imaging.- Particle-Based Shape Modeling for Arbitrary Regions-of-Interest.- Optimal coronary artery segmentation based on transfer learning and UNet architecture.- Unsupervised Learning of Cortical Surface Registration using Spherical Harmonics.- Unsupervised correspondence with combined geometric learning and imaging for radiotherapy applications.- ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images.- Body Fat Estimation from Surface Meshes using Graph Neural Networks.- Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors.- On the Localization of Ultrasound Image Slices within Point Distribution Models.- FSJP-Net: Foreground and Shape Joint Perception Network for Glomerulus Detection.- Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models.- Muscle volume quantification: guiding transformers with anatomical priors.- Geodesic Logistic Analysis of Lumbar Spine Intervertebral Disc Shapes in Supine and Standing Positions.- SlicerSALT: From medical images to quantitative insights of anatomy.- Predicting Shape Development: A Riemannian Method.- AReg IOS: Automatic Registration on IntraOralScans.- Modeling Longitudinal Optical Coherence Tomography Images for Monitoring and Analysis of Glaucoma Progression.- IcoConv : Explainable brain cortical surface analysis for ASD classification.- DeCA: A Dense Correspondence Analysis Toolkit for Shape Analysis.- 3D Shape Analysis of Scoliosis.- SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction.
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Springer International Publishing AG Machine Learning Crash Course for Engineers
Book SynopsisMachine Learning Crash Course for Engineers is a reader-friendly introductory guide to machine learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement machine learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of machine learning quickly.Table of ContentsIntroduction to Machine Learning.- Evaluation Criteria and Model Selection.- Machine Learning Algorithms.- Applications of Machine Learning: Signal/Image Processing.- Applications of Machine Learning: Energy Systems.- Applications of Machine Learning: Robotics.- State of the Art of Machine Learning.
£49.49
Springer International Publishing AG Lifelong and Continual Learning Dialogue Systems
Book SynopsisThis book introduces the new paradigm of lifelong and continual learning dialogue systems to endow dialogue systems with the ability to learn continually by themselves through their own self-initiated interactions with their users and the working environments. The authors present the latest developments and techniques for building such continual learning dialogue systems. The book explains how these developments allow systems to continuously learn new language expressions, lexical and factual knowledge, and conversational skills through interactions and dialogues. Additionally, the book covers techniques to acquire new training examples for learning new tasks during the conversation. The book also reviews existing work on lifelong learning and discusses areas for future research. Table of Contents1 Introduction.- 2 Open-world Continual Learning: A Framework.- 3 Continuous Factual Knowledge Learning in Dialogues.- 4 Continuous and Interactive Language Learning and Grounding.- 5 Continual Learning in Chit-chat Systems.- 6 Continual Learning for Task-oriented Dialogue Systems.- 7 Continual Learning of Conversational Skills.- 8 Conclusion and Future Directions.
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Springer International Publishing AG Big Data and Artificial Intelligence: 11th
Book SynopsisThis book constitutes the proceedings of the 11th International Conference on Big Data and Artificial Intelligence, BDA 2023, held in Delhi, India, during December 7–9, 2023. The17 full papers presented in this volume were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical sections: Keynote Lectures, Artificial Intelligence in Healthcare, Large Language Models, Data Analytics for Low Resource Domains, Artificial Intelligence for Innovative Applications and Potpourri. Table of ContentsKeynote Lectures.- Representation Learning for Dialog Models.- Sparsity, modularity, and structural plasticity in deep neural networks.- Artificial Intelligence in Healthcare.- Tuberculosis disease diagnosis using controlled super resolution.- GREAT AI in Medical Appropriateness and Value-Based-Care.- Large Language Models.- KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models.- SciPhyRAG - Retrieval Augmentation to Improve LLMs on Physics Q&A.- Revolutionizing High School Physics Education: A Novel Dataset.- Context-Enhanced Language Models for Generating Multi-Paper Citations.- GEC-DCL: Grammatical Error Correction Model with Dynamic Context Learning for Paragraphs & Scholarly Papers.- Data Analytics for Low Resource Domains.- A Deep Learning Emotion Classification Framework for Low Resource Languages.- Assessing the Efficacy of Synthetic Data for Enhancing Machine Translation Models in Low Resource Domains.- Artificial Intelligence for Innovative Applications.- Evaluation of Hybrid Quantum Approximate Inference Methods on Bayesian Networks.- IndoorGNN: A Graph Neural Network based approach for Indoor Localization using WiFi RSSI.- Ensemble-Based Road Surface Crack Detection: A Comprehensive Approach.- Potpourri.- Fast similarity search in large-scale Iris databases using high-dimensional hashing.- Explaining Finetuned Transformers on Hate Speech Predictions using Layerwise Relevance Propagation.- Multilingual Speech Sentiment Recognition using Spiking Neural Networks.- FopLAHD: Federated optimization using Locally Approximated Hessian Diagonal.- A Review of Approaches on Facets for Building IT-based Career Guidance Systems.
£47.49
Springer International Publishing AG Learning Techniques for the Internet of Things
Book SynopsisThe book is structured into thirteen chapters; each comes with its own dedicated contributions and future research directions. Chapter 1 introduces IoT and the use of Edge computing, particularly cloud computing, and mobile edge computing. This chapter also mentions the use of edge computing in various real-time applications such as healthcare, manufacturing, agriculture, and transportation. Chapter 2 motivates mathematical modeling for federated learning systems with respect to IoT and its applications. Further Chapter 3 extends the discussion of federated learning for IoT, which has emerged as a privacy-preserving distributed machine learning approach. Chapter 4 provides various machine learning techniques in Industrial IoT to deliver rapid and accurate data analysis, essential for enhancing production quality, sustainability, and safety. Chapter discusses the potential role of data-driven technologies, such as Artificial Intelligence, Machine Learning, and Deep Learning, focuses on their integration with IoT communication technologies. Chapter 6 presents the requirements and challenges to realize IoT deployments in smart cities, including sensing infrastructure, Artificial Intelligence, computing platforms, and enabling communications technologies such as 5G networks. To highlight these challenges in practice, the chapter also presents a real-world case study of a city-scale deployment of IoT air quality monitoring within Helsinki city. Chapter 7 uses digital twins within smart cities to enhance economic progress and facilitate prompt decision-making regarding situational awareness. Chapter 8 provides insights into using Multi-Objective reinforcement learning in future IoT networks, especially for an efficient decision-making system. Chapter 9 offers a comprehensive review of intelligent inference approaches, with a specific emphasis on reducing inference time and minimizing transmitted bandwidth between IoT devices and the cloud. Chapter 10 summarizes the applications of deep learning models in various IoT fields. This chapter also presents an in-depth study of these techniques to examine new horizons of applications of deep learning models in different areas of IoT. Chapter 11 explores the integration of Quantum Key Distribution (QKD) into IoT systems. It delves into the potential benefits, challenges, and practical considerations of incorporating QKD into IoT networks. In chapter 12, a comprehensive overview regarding the current state of quantum IoT in the context of smart healthcare is presented, along with its applications, benefits, challenges, and prospects for the future. Chapter 13 proposes a blockchain-based architecture for securing and managing IoT data in intelligent transport systems, offering advantages like immutability, decentralization, and enhanced security.Table of ContentsChapter. 1. Edge Computing for IoTChapter. 2. Federated Learning Systems: Mathematical modelling and Internet of ThingsChapter. 3. Federated Learning for Internet of ThingsChapter. 4. Machine Learning Techniques for Industrial Internet of ThingsChapter. 5. Exploring IoT Communication Technologies and Data-Driven SolutionsChapter. 6. Towards Large-Scale IoT Deployments in Smart Cities: Requirements and ChallengesChapter. 7. Digital Twin and IoT for Smart City MonitoringChapter. 8. Multiobjective and Constrained Reinforcement Learning for IoTChapter. 9. Intelligence Inference on IoT DevicesChapter. 10. Applications of Deep Learning models in diverse streams of IoTChapter. 11. Quantum Key Distribution in Internet of ThingsChapter. 12. Quantum Internet of Things for Smart HealthcareChapter. 13. Enhancing Security in Intelligent Transport Systems: A Blockchain-Based Approach for IoT Data ManagementIndex
£125.99
Springer International Publishing AG Advances in Information Systems, Artificial
Book SynopsisThis book constitutes the refereed proceedings of the 6th International Conference on Information and Knowledge Systems, ICIKS 2023, held in Portsmouth, UK, during June 22–23, 2023.The 18 full papers and 6 short papers included in this book were carefully reviewed and selected from 58 submissions. They were organized in topical sections as follows: Decision Making, Recommender Systems, and Information Support Systems; Information Systems and Machine Learning; Knowledge Management, Context and Ontology; Cybersecurity and Intelligent Systems; and Natural Language Processing for Decision Systems.Table of ContentsDecision Making, Recommender Systems, and Information Support Systems.- Models-simulators in business decision-making processes for pharmaceutical enterprises.- Decision and Information Support System to implement the Framework of Twelve steps for building decision models.- Graph Representation Learning for Recommendation Systems: A short review.- FITradeoff Decision Support System applied to solve a Supplier Selection Problem.- A Step-by-Step Decision Process to Support Application Migration to the Cloud-Native Architecture.- Information Systems and Machine Learning.- ACTIVE SMOTE for Imbalanced Medical Data Classification.- Evolutionary Graph-Clustering vs Evolutionary Cluster-Detection Approaches for Community Identification in PPI Networks.- Predictive monitoring of business process execution delays.- An accurate Random Forest-based Action Recognition Technique using only velocity and landmarks’ distances.- Efficient topic Detection using an Adaptive Neural Network architecture.- Exploiting Machine Learning Technique for attack detection in Intrusion Detection System (IDS) based on Protocol.- Robust Aggregation Function in Federated Learning.- Knowledge Management, Context and Ontology.- Should I Share or Should I Go? A Study of Tacit Knowledge Sharing Behaviors in Extended Enterprises.- Designing a User Contextual Profile Ontology: A Focus on the Vehicle Sales Domain.- Management of Implicit Ontology Changes Generated by Non-conservative JSON Instance Updates in the τJOWL Environment.- A Model Driven Architecture approach for implementing sensitive business processes.- Extension of the functional dimension of BPMN based on MDA approach for sensitive business processes execution.- Inclusive Mobile Health System for Yoruba Race in Nigeria.- Cybersecurity and Intelligent Systems.- Epistemology for Cyber Security: A Controlled Natural Language Approach.- Application of fuzzy decision support systems in IT industry functioning.- Moving towards explainable AI using Fuzzy Networks in decision making process.- Natural Language Processing for Decision Systems.- Sentiment Analysis: Effect of Combining BERT as an Embedding Technique with CNN Model for Tunisian Dialect.- An Enhanced Machine Learning-Based Analysis of Teaching and Learning Process for Higher Education System.- Topic Modelling of Legal Texts using bidirectional encoder representations from Sentence Transformers.
£56.99
Springer Probability and Statistics for Machine Learning
Book SynopsisChapter. 1. Probability and Statistics: An Introduction.- Chapter. 2. Summarizing and Visualizing Data.- Chapter. 3. Probability Basics and Random Variables.- Chapter. 4. Probability Distributions.- Chapter. 5. Hypothesis Testing and Confidence Intervals.- Chapter. 6. Reconstructing Probability Distributions from Data.- Chapter. 7. Regression.- Chapter. 8. Classification: A Probabilistic View.- Chapter. 9. Unsupervised Learning: A Probabilistic View.- Chapter. 10. Discrete State Markov Processes.- Chapter. 11. Probabilistic Inequalities and Extreme Value Analysis.- Bibliography.- Index.
£49.49
Springer AI for People Democratizing AI
Book Synopsis
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Springer International Publishing AG Energy Informatics
Book SynopsisThe two-volume set LNCS 15271 and 15272 constitutes the proceedings of the 4th Energy Informatics Academy Conference, EI.A 2024, held inKuta, Bali, Indonesia, during October 2325, 2024. The 40 full papers and 8 short papers included in these proceedings were carefully reviewed and selected from 64 submissions. They are categorized under the topical sections as follows:Part I:IoT Edge Computing, and Software Innovations in Energy,Big Data Analytics and Cybersecurity in Energy,Digital Twin Technology and Energy Simulations,Energy data and consumer behaviors, and Digitalization of District Heating and Cooling Systems. Part II:Smart Buildings and Energy Communities,Energy Pricing, Trading, and Market Dynamics,Demand Flexibility and Energy Conservation Strategies,Optimization of Energy Systems and Renewable Integration andEnergy System Resilience and Reliability. Chapter 14 and chapter 15 is available open access under a Creative Commons Attribution 4.0 International License vialink.springer.com.
£56.99
Springer AI Foundations and Applications with MATLAB
Book SynopsisChapter 1 Introduction.- Chapter 2 Learning and Decision Making Process.- Chapter 3 Fuzzy Logic Inference System.- Chapter 4 Introduction to Machine Learning.- Chapter 5 Introduction to Regression Algorithms.- Chapter 6 Introduction to Classification Algorithms.- Chapter 7 Neural Networks and Deep Learning.- Chapter 8 Introduction to Unsupervised Learning.- Chapter 9 Introduction to Reinforcement Learning.- Chapter 10 Introduction to Adaptive Neuro Fuzzy Inference System.- Chapter 11 Case Study Projects on Fuzzy Logic Technology.- Chapter 12 Case Study Projects on Deep Learning.- Appendix A.
£53.99
Springer Structured Representation Learning
Book Synopsis
£31.49
Springer Domaininformed Machine Learning for Smart
Book SynopsisIntroduction.- Domain-informed Feature Engineering for Smart Manufacturing.- Domain-informed.- Dimension Reduction for Smart Manufacturing.- Fabrication-Aware Machine.- Learning Models for Additive Manufacturing.- Domain-Informed Machine Learning.- Models for Nanomanufacturing.- Engineering-Informed Transfer Learning.- Engineering-Informed.- Process Compensation and Adjustment.- Domain-informed Data Pre-Processing in Additive Manufacturing.- Future Perspective for Domain-informed Machine.- Learning for Smart Manufacturing.
£67.49
Springer Linear Algebra and Optimization for Machine Learning
Book SynopsisPreface.- 1 Linear Algebra and Optimization: An Introduction.- 2 Linear Transformations and Linear Systems.- 3 Eigenvectors and Diagonalizable Matrices.- 4 Optimization Basics: A Machine Learning View.- 5 Advanced Optimization Solutions.- 6 Constrained Optimization and Duality.- 7 Singular Value Decomposition.- 8 Matrix Factorization.- 9 The Linear Algebra of Similarity.- 10 The Linear Algebra of Graphs.- 11 Optimization in Computational Graphs.- Index.
£58.49
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