Pattern recognition Books
Clanrye International Computer Vision: Advanced Techniques and Applications
£103.50
IGI Global Dark Web Pattern Recognition and Crime Analysis Using Machine Intelligence
Book SynopsisData stealing is a major concern on the internet as hackers and criminals have begun using simple tricks to hack social networks and violate privacy. Cyber-attack methods are progressively modern, and obstructing the attack is increasingly troublesome, regardless of whether countermeasures are taken. The Dark Web especially presents challenges to information privacy and security due to anonymous behaviors and the unavailability of data. To better understand and prevent cyberattacks, it is vital to have a forecast of cyberattacks, proper safety measures, and viable use of cyber-intelligence that empowers these activities.Dark Web Pattern Recognition and Crime Analysis Using Machine Intelligence discusses cyberattacks, security, and safety measures to protect data and presents the shortcomings faced by researchers and practitioners due to the unavailability of information about the Dark Web. Attacker techniques in these Dark Web environments are highlighted, along with intrusion detection practices and crawling of hidden content. Covering a range of topics such as malware and fog computing, this reference work is ideal for researchers, academicians, practitioners, industry professionals, computer scientists, scholars, instructors, and students.
£169.20
5M Books Ltd Searching for Patterns: How we can know without asking
Book SynopsisOriginal edition reissued in 2023 with new cover. This is a print on demand title and is not held in stock. The delivery leadtimes will be longer. Data mining is about finding patterns hidden inside data. It’s how the supermarket knows when your kids leave home or when your granny comes to visit; it’s how the credit card company detects fraud and how your insurance company decides whether to cover you. Searching For Patterns guides you through the techniques used to do this and the people behind them, in an easily accessible and entertaining way. Data mining is mathematical, but all the maths in this book has been kept separate from the main text, so you can skip it if you want.
£18.00
Springer London Ltd Computational Methods in Biometric Authentication: Statistical Methods for Performance Evaluation
Book SynopsisBiometrics, the science of using physical traits to identify individuals, is playing an increasing role in our security-conscious society and across the globe. Biometric authentication, or bioauthentication, systems are being used to secure everything from amusement parks to bank accounts to military installations. Yet developments in this field have not been matched by an equivalent improvement in the statistical methods for evaluating these systems. Compensating for this need, this unique text/reference provides a basic statistical methodology for practitioners and testers of bioauthentication devices, supplying a set of rigorous statistical methods for evaluating biometric authentication systems. This framework of methods can be extended and generalized for a wide range of applications and tests. This is the first single resource on statistical methods for estimation and comparison of the performance of biometric authentication systems. The book focuses on six common performance metrics: for each metric, statistical methods are derived for a single system that incorporates confidence intervals, hypothesis tests, sample size calculations, power calculations and prediction intervals. These methods are also extended to allow for the statistical comparison and evaluation of multiple systems for both independent and paired data. Topics and features: * Provides a statistical methodology for the most common biometric performance metrics: failure to enroll (FTE), failure to acquire (FTA), false non-match rate (FNMR), false match rate (FMR), and receiver operating characteristic (ROC) curves * Presents methods for the comparison of two or more biometric performance metrics * Introduces a new bootstrap methodology for FMR and ROC curve estimation * Supplies more than 120 examples, using publicly available biometric data where possible * Discusses the addition of prediction intervals to the bioauthentication statistical toolset * Describes sample-size and power calculations for FTE, FTA, FNMR and FMR Researchers, managers and decisions makers needing to compare biometric systems across a variety of metrics will find within this reference an invaluable set of statistical tools. Written for an upper-level undergraduate or master’s level audience with a quantitative background, readers are also expected to have an understanding of the topics in a typical undergraduate statistics course. Dr. Michael E. Schuckers is Associate Professor of Statistics at St. Lawrence University, Canton, NY, and a member of the Center for Identification Technology Research.Table of ContentsPart I: Introduction Introduction Statistical Background Part II: Primary Matching and Classification Measures False Non-Match Rate False Match Rate Receiver Operating Characteristic Curve and Equal Error Rate Part III: Biometric Specific Measures Failure to Enrol Failure to Acquire Part IV: Additional Topics and Appendices Additional Topics and Discussion Tables
£123.49
AvidBooks Publishing Limited Beyond The Algorithm
£16.92
Springer Nature Switzerland AG Smart Assisted Living: Toward An Open Smart-Home Infrastructure
Book SynopsisSmart Homes (SH) offer a promising approach to assisted living for the ageing population. Yet the main obstacle to the rapid development and deployment of Smart Home (SH) solutions essentially arises from the nature of the SH field, which is multidisciplinary and involves diverse applications and various stakeholders. Accordingly, an alternative to a one-size-fits-all approach is needed in order to advance the state of the art towards an open SH infrastructure.This book makes a valuable and critical contribution to smart assisted living research through the development of new effective, integrated, and interoperable SH solutions. It focuses on four underlying aspects: (1) Sensing and Monitoring Technologies; (2) Context Interference and Behaviour Analysis; (3) Personalisation and Adaptive Interaction, and (4) Open Smart Home and Service Infrastructures, demonstrating how fundamental theories, models and algorithms can be exploited to solve real-world problems.This comprehensive and timely book offers a unique and essential reference guide for policymakers, funding bodies, researchers, technology developers and managers, end users, carers, clinicians, healthcare service providers, educators and students, helping them adopt and implement smart assisted living systems.Table of ContentsPart I: Sensing and Activity Monitoring Multi-Resident Activity Monitoring in Smart Homes Through Non-Wearable Non-Intrusive SensorsSon N. Tran and Qing Zhang and Vanessa Smallbon and Mohan Karunanithi Where Am I? Comparing CNN and LSTM for Location Classification in Egocentric VideosGeorgios Kapidis, Ronald W. Poppe, Elsbeth A. van Dam, Remco C. Veltkamp, and Lucas P. J. J. Noldus A Privacy-Preserving Wearable Camera Setup for Dietary Event Spotting in Free-LivingGiovanni Schiboni, Fabio Wasner, and Oliver AmftSaving Energy on EMG-Monitoring Eyeglasses for Free-Living Eating Event Spotting Using Adaptive Duty-CyclingGiovanni Schiboni and Oliver Amft Indoor Localisation with WiFi Fingerprinting Based on a Convolutional Neural NetworkZumin Wang Unobtrusive Sensing to Assist with Post-Stroke RehabilitationChris Nugent Part II: Activity Recognition and Behaviour Analysis Energy-Based Decision Engine for Household Human Activity RecognitionAnastasios Vafeiadis, Thanasis Vafeiadis, Stelios Zikos, Stelios Krinidis, Konstantinos Votis, Dimitrios Giakoumis, Dimosthenis Ioannidis, Dimitrios Tzovaras, Liming Chen, and Raouf Hamzaoui Distributed Context Recognition, a Systematic ReviewUmar Ahmad and Luis Lopera Exercise Type Recognition Using Transfer LearningHossein Malekmohamadi Meta-Intelligence for Behaviour RecognitionXiaodong Liu and Qi Liu Part III: User Needs and Personalisation A Conceptual Framework for Adaptive User Interfaces for Older AdultsEduardo Machado, Deepika Singhy, Federico Cruciani, Liming Chen, Sten Hankey, Fernando Salvago, Johannes Kropf, and Andreas HolzingerStudying the Technological Barriers and Needs of People with Dementia: A Quantitative StudyNikolaos Liappas, Rebeca Isabel García-Betances, José Gabriel Teriús-Padrón, and María Fernanda Cabrera-Umpiérrez Adaptive Service Robot Behaviours Based on User Mood: Towards Better Personalized Support of MCI Patients at HomeDimitrios Giakoumis, Georgia Peleka, Manolis Vasileiadis, Ioannis Kostavelis, and Dimitrios Tzovaras Part IV: Ambient Assisted Living Solutions Towards Cognitive Assisted LivingClaudia Steinberger and Judith Michael Towards Self-Management of Chronic Diseases in Smart HomesJosé G. Teriús-Padrón, Georgios Kapidis, Sarah Fallmann, Erinc Merdivan, Sten Hanke, Rebeca I. García-Betances, and María Fernanda Cabrera-UmpiérrezA Deep Learning Approach for Privacy Preservation in Assisted LivingIsmini Psychoula, Erinc Merdivany, Deepika Singhy, Liming Chen, Feng Chen, Sten Hankey, Johannes Kropfy, Andreas Holzingerx, and Matthieu GeistTowards Socially Assistive Robots for the Elderly: An End-to-End Object Search FrameworkMohammad Reza Loghmani, Timothy Patten and Markus VinczeModelling Activities of Daily Living with Petri NetsMatias Garcia-Constantino, Alexandros Konios and Chris Nugent Calculus of Context-Aware Ambients for Assisted Living System ModellingFrancois Siewe
£75.99
Springer GraphBased Representations in Pattern Recognition
Book Synopsis.- Cybersecurity based on Graph models..- A Modular Triple Exchange Co-learning Framework for Anomaly Detection in Scarcely Labeled Graph Data..- Advanced Malware Detection in Code Repositories Using Graph Neural Network..- Resistance Distance Guided Node Injection Attack on Graph Neural Network..- Graph based bioinformatics..- Gene Co-Expression Networks Are Poor Proxies for Expert-Curated Gene Regulatory Networks..- Graph Neural Network Based on Molecular and Pharmacophoric Features for Drug Design Applications..- Graph-Based Representations of Almost Constant Graphs for Nanotoxicity Prediction..- Label Modulated Dynamic Graph Convolution for Subcellular Structure Segmentation from Nanoscopy Image..- Insights on Using Graph Neural Networks for Sulcal Graphs Predictive Models..- Graph Neural Networks for Multimodal Brain Connectivity Analysis in Multiple Sclerosis..- Graph similarities and graph patterns..- A Geometric Perspective on Graph Similarity Learning using Convex Hulls..- VF-GPU: Exploiting Parallel GPU Architectures to Solve Subgraph Isomorphis..- Grammatical Path Network: Unveiling Cycles Through Path Computation..- Deep QMiner: Towards a generalized DeepQ-Learning Approach for Graph Pattern Mining..- GNN: shortcomings and solutions..- An Empirical Investigation of Shortcuts in Graph Learning..- A General Sampling Framework for Graph Convolutional Network Training..- Fusion of GNN and GBDT Models for Graph and Node Classification..- Harnessing GraphSAGE for Learning Representations of Massive Transactional networks..- Entropy-Guided Graph Clustering via Rényi Optimization..- Graph learning and computer vision..- Exploring a Graph Regression Problem in River Networks..- Saliency Matters: from nodes to objects..- Hierarchical super-pixels graph neural networks for image semantic segmentation..- Lifting some Secrets about Contrast Pyramids..- An Evolution Equation Involving the Generalized Biased Infinity Laplacian on Graphs..- Doc2Graph-X: A Multilingual Graph-Based Framework for Form Understanding..- VisHubGAT: Visible Connectivity and Hub Nodes for Multimodal Entity Extraction.
£104.49
Springer Pattern Recognition
Book Synopsis.- Pattern Recognition and Machine Learning Techniques..- Detection of Concept Drift in Bayesian Networks..- Hausdorff Distance Optimization in Low-Density Point Clouds..- Extremal Topologies for the Merrifield-Simmon Index on Dendrimersin Drug Delivery..- Tailoring Bounded Instances for the Job Shop Scheduling Problemthrough Unified Particle Swarm Optimization..- Empirical Comparison of Density-Based Clustering Algorithms forLarge Datasets..- Exact versus Approximate Patterns in Dissimilarity-Based GraphEmbedding for Supervised Classification..- Optimized Early Detection of Bark Beetles through AutomatedSegmentation and Machine Learning Classification..- Handling Constraints in Clustering via Multi-Objective EvolutionaryAlgorithms..- Analysis of Childrens Emotions in Sketches Using Classical and DeepLearning Approaches..- Heuristic-Based Optimization Using Elementary Cellular Automata: APreliminary Study on the Knapsack Problem..- Language Processing and Recognition..- Dynamic Strategy for Recognizing the Mexican Sign LanguageAlphabet: Bridging Static and Dynamic Signs..- Classification of Suicidal Texts Based on Emotional Change DetectionUsing LSTM..- Synthetic Corpus of Emotions for Detection of Depression in SocialNetworks..- Deep Neural Networks and Log-Mel Spectrogram for EmotionRecognition through Spanish Speech..- Phonetic Spectral Image Representation for Yuhmu Language Analysis..- Multi-Label Classification of Texts on Harassment and Discriminationwith Neural Networks..- Exploring a Multimodal Language Model for Auto-Captioning andVisual Question Answering in Histopathology Images..- Computer Vision..- Automatic Counting System in a Region of Interest from Videos Takenby Drones..- Real-Time Image Analysis Using a Depth Camera for UAV Applications..- Extending Reference-Based Texture Transformers for Image Dehazing..- Vision-Based Formation Control Using Visual Servoing and VirtualHomographies..- Laser Beam Centroid Detection for Automatic Spatial Filtering: AComparative Analysis of Machine Vision Algorithms..- Towards End-To-End Visual Odometry for Unstructured AgriculturalEnvironments..- Medical Applications of Pattern Recognition..- Early Detection of Acute Myocardial Infarction (AMI) Risk UsingOptimized Machine Learning Models..- Deep Learning Approaches for Glaucoma Detection: A ComparativeStudy of CNN Models on Retinal Fundus Images..- Classification of CFD-Generated Aortic Flow Images Using NeuralNetworks..- Explainable Diagnosis of Bacterial Vaginosis: A Hierarchical ApproachBased on XAI..- Reinforcement Learning..- Evaluating Deep Reinforcement Learning for Robotic Navigation..- Determining Optimal Population Management with ReinforcementLearning..- Deep Learning and Neural Networks..- Hybrid Deep Learning Architecture for Automatic Detection ofCoronary Stenosis in X-Ray Videos..- Predicting Crowd Motion with Diffusion Models..- Enhancing Perceptron Learning through Bayesian Optimisation andCross-Validation..- Facial Expression Recognition Using Light Weight CNNs and Soft Voting..- Automatic Segmentation of the Major Temporal Arcade Using U-NetAttention Architecture..- A Lightweight Diffusion Model with Modified Sampler..- Unsupervised Emotion Analysis in Mexican Popular Music LyricsUsing a Bert-Based Model.
£104.49
De Gruyter Artificial Intelligence for Virtual Reality
Book SynopsisThis book explores the possible applications of Artificial Intelligence in Virtual environments. These were previously mainly associated with gaming, but have largely extended their area of application, and are nowadays used for promoting collaboration in work environments, for training purposes, for management of anxiety and pain, etc.. The development of Artificial Intelligence has given new dimensions to the research in this field.
£117.80
Springer Cutaneous Biometrics
Book SynopsisPART 1 Atopic Conditions.- New Topical Therapeutic Models in Contact Dermatitis.- Validation and Assessment Tools in Atopic Dermatitis.- The Clinical Role of the Antecubital Severity Score for Atopic Dermatitis.- Atopic Dermatis: Clinical Use of the Eczema Area and Severity Scale.- Atopic Eczema: Recent Developments in Testing of New Clinical Cutcome Measures.- PART 2 Rosacea and Dermovascular Diseases.- Clinical Assessment of Rosacea Severity.- Clinical Profiles of Adult Onset Henoch-Schoniein Purpura.- The Use of Patient's Self-Assessment Grading Scale.- PART 3 Infections and Wound Healing.- Assessments in Ulcer and Wound Healing.- Development of Skin Ulcerations.- Assessment Scales for Scar formation.- Necrotizing Soft Tissue Infection Assessments.- PART 4 Blistering and Bullous Conditions.- Development of Dermoepidermal Adhesions.- Assessment Tools for Phemigus Vulgaris.- Treatment Options and Outcomes for Phemphigus Vulgaris.- Outcome Measures for Autoimmune Blistering Disease.- PART 5 Inflammatory Skin Disease.- The Reliability of Assessment Tools in Psoriasis.- Assessing Psoriasis Patients' Response to Treatment with Biologics.- Developments in Topical Treatments for Psoriasis.- Assessment Tools to Evaluate the Severity of Disease in Psoriasis.- Usefulness of Self Reporting Questionaires for Psoriasis.- PART 6 Acne.- Acne Severity Grading Used in a Clinical Setting.- Quality of Life Assessments for Patient's with Acne.- Novel methods to improve clinical outcomes and quality of life for adolescents with acne vulgaris.- PART 7 Oncology.- Clinical Skills Assessment for Melanoma.- Advancements in the diagnostic criteria for cutaneous anaplastic large cell lymphoma.- Total Body Photography for Skin Cancer Screening.- Nail Changes and Chemotherapy.- PART 8 Autoimmune Conditions.- Assessing Clinical Responsiveness to treatment for patients with dermatomyosositis.- Measuring Outcomes of Cutaneous Sarcoidosis.- Assessment Tools for Alopecia Areata.- Assessment Tools for Evaluating Treatment Response in Patients with Chronic Spontaneous Urticaria.- Scoring and Disease Severity for Oral Lichen Planus.- Assessment Tools for Measurign the Impact of Disease in Vitiligo patients.- PART 9 Metrics, Statistics and Assessments.- Metrics and clinical relevance of percutaneous penetration and lateral spreading.- Using the physician global assessment in a clinical setting to measure and track patient outcomes.- The Importance of Creating Meaningful Patient Outcome Measures in Dermatology.- Number Needed to Treat as Derived by Ordinal Scalres.- Measurements of Transepidermal Water Loss.- The Influences Affecting Patient Discharge.- Validation of a simplified provacation instrument for diagnosis and threshold testing of symptomatic dermographism.- Consumer Quality Index Chronic Skin Disease (CQI-CSD): a new instrument to measure quality of care from the patient's perspective.- Pharmacometrics in Dermatology.- Guidelines on the measurement of ultraviolet radiationlevels in ultraviolet phototherapy.- Study design in dermatology.- Development and Validation of a clinical scale for the evaluation of skin photoaging.- PART 10 Genetics.- Improving Cutaneous Oncology Outcomes With the Use of Genetics.- Novel Genetic Screening for Psoriasis Patients to Perdict Response to Biological Treatments.
£349.12
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Nachsorge und Krankheitsverlaufsanalyse: 25.
Book SynopsisDas Rahmenthema der 25. Jahrestagung der Deutschen Gesellschaft fur Medizinische DOkumentation, Informatik und Statistik e. V. - Nachsorge und Krankheitsverlaufsanalyse - hat einen engen Bezug zu aktuellen Problemen des Gesundheitswesens. Insbesondere in Kliniken, in denen schwere Erkrankungen mit modernen erfolgversprechenden Massnahmen be- handelt werden, wird die Notwendigkeit einer systematischen weiteren Uberwachung dieser Patienten immer dringlicher erachtet. Die Beob- achtung des weiteren Schicksals, die arztliche Betreuung und die Bewertung der zeitlichen Verlaufe ergeben Ansatzpunkte fur die weitere Verbesserung der Therapie. Diese Aufgabe lasst sich nur be- waltigen, wenn die dabei auftretenden Probleme der planvollen Doku- mentation, der Informationsubermittlung, der Datenspeicherung und der statistischen Auswertung von den Vertretern unseres Faches aktiv in Angriff genommen werden. Nach 25 Jahren einer sturmischen techno- logischen und Methodenentwicklung ist unsere junge medizinische Dis- ziplin in der Lage - wenn die erforderliche apparative und perso- nelle Ausstattung zur Verfugung steht -, die Probleme der Nachsorge und Krankheitsverlaufsanalyse in Zusammenarbeit mit Klinikern und Allgemeinmedizinern wirksam zu bearbeiten und Ergebnisse zu zeitigen, die fur den Arzt relevant sind. Die Tagung soll dazu Anregungen ver- mitteln und Losungswege aufzeigen. Funf Workshops und apparative, organisatorische und methodische Pro- bleme runden den Bezug unserer Arbeit auf die Probleme der !1edizin von heute ab. Erlangen hat eine traditionelle Verbundenheit mit der technologischen Entwicklung in der Medizin. Auch heute besteht, sowohl in der Medi- zinischen wie in der Technischen Fakultat, auf deren Campus wir tragen, ein waches Interesse fur den humanen Einsatz technologischer Neuerungen, insbesondere auch der elektronischen Datenverarbeitung. L.Table of ContentsErÖffnung der 25. Jahrestagung der Deutschen Gesellschaft FÜr Medizinische Dokumentation, Informatik und Statistik.- Zufall und lebendiges Geschehen.- Nachsorge und Krankheitsverlaufsanalyse-Einführung in die Thematik.- Nachsorge nach Krebsoperationen.- Probleme der Verlaufsbeobachtung und der prognostischen Beurteilung bei Herzkrankheiten.- Prognosestellung beim Rektumkarzinom mit Hilfe des COX-Modells.- Mathematische Modelle zur Analyse des Krankheitsverlaufs von Patienten mit Hirntumoren.- Analyse des Krankheitsverlaufs bei Prostatakarzinompatienten.- Statistische Auswertung des Krankheitsverlaufs von Tumorpatienten am Beispiel einer Studie über Karzinome der Mundhöhle.- Mehrkompartiment-Modelle in der Carcinogenese:Numerische Realisierung der Kleinste-Quadrate-Anpassung von Konzentrationsmessungen in der Maus.- Latenzzeitmessung bei Krebs am Beispiel einer Seite Fall-Kontroll-Studie an Lymphom- und Leukämiefällen in der amerikanischen Reifen- und Gummiindustrie.- Verlaufsuntersuchungen bei oralen Leukoplakien und Carcinomen.- Verteilungsfreie Teststatistiken bei Zen- sorierten Daten - Neue Entwicklungen.- Mathematisches Modell zur Prognose des Krankheitsverlaufs der Hepatitis B.- Methodische Probleme bei Langzeitstudien; insbesondere das Problem des Therapie-Abbruchs.- Nichtparametrischer Vergleich zweier Scharen von Verlaufskurven.- Anwendung eines Kompartimentmodelles zur Beurteilung von Behandlungsmethoden.- Probleme der statistischen Analyse einer Kohlenhydrat-Infusionsstudie.- Parametrische Tests für den Vergleich von Mittelwertsprofilen bei unverbundenen Beobachtungen mit homogenen Varianzen.- Variabilitätsuntersuchungen wesentlicher Spektralparameter im Verlaufe von EEG- Routine-Ableitungen.- Alternativen zur Bonferroni-Prozedur.- Variablenselektion bei multinomialen Klassifikationsproblemen.- Zur Problematik der Beurteilung abhängiger Häufigkeiten.- Explorative Datenanalyse - Schlußfolgerungen aus der Frühjahrstagung.- Die Integration der Nachsorgeorganisation und der Krankheitsverlaufsorganisation in ein allgemeines Befunddokumentationssystem.- Computerunterstützte Nachsorge und Krankheitsverlaufsanalyse – eine Komponente des medizinischen Auswertungssystems WAMAS.- Basisfunktionen für die Analyse von Verlaufsdaten.- ZEISIG Zytologisches Erfassungs- und Informationssystem in der Gynäkologie.- Betriebsärztliche Informationssysteme Schlußfolgerungen aus der Frühjahrstagung 1980.- Paket-Konzept und Refinement-Konstrukt Erste Erfahrungen mit einem Software-Entwicklungs-Instrument.- Verfahren zur Vereinheitlichung der Darstellung und Speicherung von Laborresultaten.- Implementierung eines Datenmodells auf einer operativen Intensivstation.- Das computergestützte Nachsorgesystem der I. Chirutgischen Universitätsklinik in Wien.- Computer-gestützte Nachsorge von Schrittmacher-Patienten.- Befunddokumentation in der hämostaseologischen Ambulanz der Medizinischen Hochschule Hannover.- Zur Frage des Aussagewertes einer routinemäßigen Thoraxübersichtsaufnahme bei der Diagnostik des Emphysems der Quarzstaublunge und dem Cor Pulmonale.- Auswertung von Krankheitsverläufen - Probleme und Lösungsmöglichkeiten: Dargestellt am Beispiel der akuten Virushepatitis.- Stoffwechselmetaboliten-Verlauf unter 48-stündiger Dauerinfusion von Glukose allein und in Mischung mit Sorbit, Fruktose oder Xylit bei Diabetikern.- EDV- Einsatz für die Bakteriologische Verlaufsund Befunddokumentation.- Erfassen und Auswerten von Antibiogrammen.- Institutionskarrieren schizophrener Kranker.- Das Fallregister psychisch Behinderter am PLK Weinsberg. Konzeption, Realisierung und erste Erfahrungen.- Prognose und Probleme der Verlaufsbeobachtung fokaler zerebraler Ischämie/Infarkte bei jungen Erwachsenen.- Langzeitverlauf nach Karotis-Operationen: Bedeutung der Neuropsychiatrischen Symptomatik.- Neuere Entwicklungen und Technologische Möglichkeiten der Mikroelektronik.- Ein Mikrorechner für die Eingliederung eines Analysenautomaten in dezentral organisierte Laborautomatisierungssysteme.- Mikroprozessoreinsatz im Physiologischen Labor.- Zur Bestimmung der Pulswellengeschwindigkeit.- On-line Verarbeitung von Hämoglobin-Reflexionsspektren hoher Repetitionsraten.- Anforderung an ein Mikroprozessorsystem zur Biosignalverarbeitung.- Entwurf und Aufbau eines Mikroprozessorsystems zur Biosignalverarbeitung.- Ein Mikrocomputer als Subsystem im 24-Stunden Betrieb.- Der Mikroprozessor als integrierender Bestandteil eines autonomen Meßplatzes im klinischen Laboratorium.- Implementierung des Programmes HES EKG in vor Ort auswertende Mikroprozessoren.- Erfahrungen im 3-jährigen Einsatz eines dezentralen Dokumentations- und Auskunftssystems für chronisch Kranke mit einem Minicomputer.- Ergebnisbericht der Moderatoren: Workshop 1 Mikroelektronik in der Medizin.- Die Basisdokumentation für Tumorkranke der Arbeitsgemeinschaft Deutscher Tumorzentren (ADT).- Das klinische Krebsregister des Tumorzentrums Köln.- Das Register für Onkologische Nachsorge der GBK in Münster.- Bericht über ein computergestütztes klinischpathologisches Krebsregister der ersten Ausbaustufe.- Ein klinisches Krebsregister als Basis für Nachsorge und statistische Auswertung – ein Erfahrungsbericht.- Das Dokumentations-, Kommunikations- und Organisations-System des Tumorzentrums Heidelberg/ Mannheim mit KRAZTUR.- Computerunterstützte Nachsorge und Basisdokumentation in der Radioonkologie.- Ein Patienteninformationssystem für die Strahlentherapie – Nachsorgeorganisation und Langzeitanalyse -.- Kooperative Dokumentation von Malignomen im Kindesalter.- Computerunterstütztes Magenbiopsieregister.- Computergestützte Erfassung und Nachsorge von Patienten mit kolorektalen Polypen.- Ergebnisbericht der Moderatoren: Workshop 2 Dokumentation, Datenverarbeitung und Statistik in medizinischen Krebszentren.- Therapiestudien im Kontext der Evaluationsforschung.- Organisatorische und methodische Probleme bei der Durchführung kontrollierter Psychopharmakastudien in der Praxis niedergelassener Ärzte.- Methodology and results of a long-term, controlled study of the effectiveness of immunosuppressive treatment of multiple sclerosis.- Der Wirksamkeitsnachweis in der Therapie des Ovarialkarzinoms.- Strategien zum Abbruch von kontrollierten Therapiestudien - Probleme und gegenwärtig diskutierte Ansätze.- Integrierung von Beobachtungen aus dem nichtärztlichen Bereich in die Krankheitsverlaufsanalysen.- Ergebnisbericht der Moderatoren: Workshop 3 Kontrollierte klinische Studien.- Klinische Datenverarbeitung in der Fakultät für Medizin der Technischen Universität München.- Klinische Basisdokumentation als Teil eines Informations-Systems in einem Rehabilitations-Krankenhaus Konzeption und Implementierung.- Klinische Dokumentation an einer Neurochirurgischen Klinik.- Dialogunterstützte klinische Dokumentation am Universitätsklinikum Göttingen.- Ergebnisbericht der Moderatoren: Workshop 4 Dokumentation und Verarbeitung klinischer Daten.- Verwaltung und Krankenhaus-Informationssystem Eine Strukturanalyse.- Untersuchung zur Inanspruchnahme eines Universitäts klinikums im stationären und ambulanten Bereich - durchgeführt an den Universitätskliniken Marburg.- Sind “Kurzlieger” einer Medizinischen Klinik für die Unterbringung in Hostelbetten geeignet? Die Bedeutung der Diagnosestatistik bei einer Planungsaufgabe.- Personalbedarfsplanung für den Krankenhaus-Pflegebereich mit Modellen der linearen Programmierung.- Lagerhaltung verderblicher medizinischer Güter.- Bedarfsgesteuerte Blutspenden mit TRAMIDIS.- Ergebnisbericht der Moderatoren: Workshop 5 Medizinökonomie.- Autorenverzeichnis.
£45.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Pattern Recognition
£73.94
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Neural Networks: A Systematic Introduction
Book SynopsisNeural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.Trade Review"If you want a systematic and thorough overview of neural networks, need a good reference book on this subject, or are giving or taking a course on neural networks, this book is for you." Computing ReviewsTable of Contents1. The Biological Paradigm.- 1.1 Neural computation.- 1.1.1 Natural and artificial neural networks.- 1.1.2 Models of computation.- 1.1.3 Elements of a computing model.- 1.2 Networks of neurons.- 1.2.1 Structure of the neurons.- 1.2.2 Transmission of information.- 1.2.3 Information processing at the neurons and synapses.- 1.2.4 Storage of information — learning.- 1.2.5 The neuron — a self-organizing system.- 1.3 Artificial neural networks.- 1.3.1 Networks of primitive functions.- 1.3.2 Approximation of functions.- 1.3.3 Caveat.- 1.4 Historical and bibliographical remarks.- 2. Threshold Logic.- 2.1 Networks of functions.- 2.1.1 Feed-forward and recurrent networks.- 2.1.2 The computing units.- 2.2 Synthesis of Boolean functions.- 2.2.1 Conjunction, disjunction, negation.- 2.2.2 Geometric interpretation.- 2.2.3 Constructive synthesis.- 2.3 Equivalent networks.- 2.3.1 Weighted and unweighted networks.- 2.3.2 Absolute and relative inhibition.- 2.3.3 Binary signals and pulse coding.- 2.4 Recurrent networks.- 2.4.1 Stored state networks.- 2.4.2 Finite automata.- 2.4.3 Finite automata and recurrent networks.- 2.4.4 A first classification of neural networks.- 2.5 Harmonic analysis of logical functions.- 2.5.1 General expression.- 2.5.2 The Hadamard—Walsh transform.- 2.5.3 Applications of threshold logic.- 2.6 Historical and bibliographical remarks.- 3.Weighted Networks — The Perceptron.- 3.1 Perceptrons and parallel processing.- 3.1.1 Perceptrons as weighted threshold elements.- 3.1.2 Computational limits of the perceptron model.- 3.2 Implementation of logical functions.- 3.2.1 Geometric interpretation.- 3.2.2 The XOR problem.- 3.3 Linearly separable functions.- 3.3.1 Linear separability.- 3.3.2 Duality of input space and weight space.- 3.3.3 The error function in weight space.- 3.3.4 General decision curves.- 3.4 Applications and biological analogy.- 3.4.1 Edge detection with perceptrons.- 3.4.2 The structure of the retina.- 3.4.3 Pyramidal networks and the neocognitron.- 3.4.4 The silicon retina.- 3.5 Historical and bibliographical remarks.- 4. Perceptron Learning.- 4.1 Learning algorithms for neural networks.- 4.1.1 Classes of learning algorithms.- 4.1.2 Vector notation.- 4.1.3 Absolute linear separability.- 4.1.4 The error surface and the search method.- 4.2 Algorithmic learning.- 4.2.1 Geometric visualization.- 4.2.2 Convergence of the algorithm.- 4.2.3 Accelerating convergence.- 4.2.4 The pocket algorithm.- 4.2.5 Complexity of perceptron learning.- 4.3 Linear programming.- 4.3.1 Inner points of polytopes.- 4.3.2 Linear separability as linear optimization.- 4.3.3 Karmarkar’s algorithm.- 4.4 Historical and bibliographical remarks.- 5. Unsupervised Learning and Clustering Algorithms.- 5.1 Competitive learning.- 5.1.1 Generalization of the perceptron problem.- 5.1.2 Unsupervised learning through competition.- 5.2 Convergence analysis.- 5.2.1 The one-dimensional case — energy function.- 5.2.2 Multidimensional case — the classical methods.- 5.2.3 Unsupervised learning as minimization problem.- 5.2.4 Stability of the solutions.- 5.3 Principal component analysis.- 5.3.1 Unsupervised reinforcement learning.- 5.3.2 Convergence of the learning algorithm.- 5.3.3 Multiple principal components.- 5.4 Some applications.- 5.4.1 Pattern recognition.- 5.4.2 Image compression.- 5.5 Historical and bibliographical remarks.- 6. One and Two Layered Networks.- 6.1 Structure and geometric visualization.- 6.1.1 Network architecture.- 6.1.2 The XOR problem revisited.- 6.1.3 Geometric visualization.- 6.2 Counting regions in input and weight space.- 6.2.1 Weight space regions for the XOR problem.- 6.2.2 Bipolar vectors.- 6.2.3 Projection of the solution regions.- 6.2.4 Geometric interpretation.- 6.3 Regions for two layered networks.- 6.3.1 Regions in weight space for the XOR problem.- 6.3.2 Number of regions in general.- 6.3.3 Consequences.- 6.3.4 The Vapnik—Chervonenkis dimension.- 6.3.5 The problem of local minima.- 6.4 Historical and bibliographical remarks.- 7. The Backpropagation Algorithm.- 7.1 Learning as gradient descent.- 7.1.1 Differentiable activation functions.- 7.1.2 Regions in input space.- 7.1.3 Local minima of the error function.- 7.2 General feed-forward networks.- 7.2.1 The learning problem.- 7.2.2 Derivatives of network functions.- 7.2.3 Steps of the backpropagation algorithm.- 7.2.4 Learning with backpropagation.- 7.3 The case of layered networks.- 7.3.1 Extended network.- 7.3.2 Steps of the algorithm.- 7.3.3 Backpropagation in matrix form.- 7.3.4 The locality of backpropagation.- 7.3.5 Error during training.- 7.4 Recurrent networks.- 7.4.1 Backpropagation through time.- 7.4.2 Hidden Markov Models.- 7.4.3 Variational problems.- 7.5 Historical and bibliographical remarks.- 8. Fast Learning Algorithms.- 8.1 Introduction — classical backpropagation.- 8.1.1 Backpropagation with momentum.- 8.1.2 The fractal geometry of backpropagation.- 8.2 Some simple improvements to backpropagation.- 8.2.1 Initial weight selection.- 8.2.2 Clipped derivatives and offset term.- 8.2.3 Reducing the number of floating-point operations.- 8.2.4 Data decorrelation.- 8.3 Adaptive step algorithms.- 8.3.1 Silva and Almeida’s algorithm.- 8.3.2 Delta-bar-delta.- 8.3.3 Rprop.- 8.3.4 The Dynamic Adaption algorithm.- 8.4 Second-order algorithms.- 8.4.1 Quickprop.- 8.4.2 QRprop.- 8.4.3 Second-order backpropagation.- 8.5 Relaxation methods.- 8.5.1 Weight and node perturbation.- 8.5.2 Symmetric and asymmetric relaxation.- 8.5.3 A final thought on taxonomy.- 8.6 Historical and bibliographical remarks.- 9. Statistics and Neural Networks.- 9.1 Linear and nonlinear regression.- 9.1.1 The problem of good generalization.- 9.1.2 Linear regression.- 9.1.3 Nonlinear units.- 9.1.4 Computing the prediction error.- 9.1.5 The jackknife and cross-validation.- 9.1.6 Committees of networks.- 9.2 Multiple regression.- 9.2.1 Visualization of the solution regions.- 9.2.2 Linear equations and the pseudoinverse.- 9.2.3 The hidden layer.- 9.2.4 Computation of the pseudoinverse.- 9.3 Classification networks.- 9.3.1 An application: NETtalk.- 9.3.2 The Bayes property of classifier networks.- 9.3.3 Connectionist speech recognition.- 9.3.4 Autoregressive models for time series analysis.- 9.4 Historical and bibliographical remarks.- 10. The Complexity of Learning.- 10.1 Network functions.- 10.1.1 Learning algorithms for multilayer networks.- 10.1.2 Hilbert’s problem and computability.- 10.1.3 Kolmogorov’s theorem.- 10.2 Function approximation.- 10.2.1 The one-dimensional case.- 10.2.2 The multidimensional case.- 10.3 Complexity of learning problems.- 10.3.1 Complexity classes.- 10.3.2 NP-complete learning problems.- 10.3.3 Complexity of learning with AND-OR networks.- 10.3.4 Simplifications of the network architecture.- 10.3.5 Learning with hints.- 10.4 Historical and bibliographical remarks.- 11. Fuzzy Logic.- 11.1 Fuzzy sets and fuzzy logic.- 11.1.1 Imprecise data and imprecise rules.- 11.1.2 The fuzzy set concept.- 11.1.3 Geometric representation of fuzzy sets.- 11.1.4 Fuzzy set theory, logic operators, and geometry.- 11.1.5 Families of fuzzy operators.- 11.2 Fuzzy inferences.- 11.2.1 Inferences from imprecise data.- 11.2.2 Fuzzy numbers and inverse operation.- 11.3 Control with fuzzy logic.- 11.3.1 Fuzzy controllers.- 11.3.2 Fuzzy networks.- 11.3.3 Function approximation with fuzzy methods.- 11.3.4 The eye as a fuzzy system — color vision.- 11.4 Historical and bibliographical remarks.- 12. Associative Networks.- 12.1 Associative pattern recognition.- 12.1.1 Recurrent networks and types of associative memories.- 12.1.2 Structure of an associative memory.- 12.1.3 The eigenvector automaton.- 12.2 Associative learning.- 12.2.1 Hebbian learning — the correlation matrix.- 12.2.2 Geometric interpretation of Hebbian learning.- 12.2.3 Networks as dynamical systems — some experiments.- 12.2.4 Another visualization.- 12.3 The capacity problem.- 12.4 The pseudoinverse.- 12.4.1 Definition and properties of the pseudoinverse.- 12.4.2 Orthogonal projections.- 12.4.3 Holographic memories.- 12.4.4 Translation invariant pattern recognition.- 12.5 Historical and bibliographical remarks.- 13. The Hopfield Model.- 13.1 Synchronous and asynchronous networks.- 13.1.1 Recursive networks with stochastic dynamics.- 13.1.2 The bidirectional associative memory.- 13.1.3 The energy function.- 13.2 Definition of Hopfield networks.- 13.2.1 Asynchronous networks.- 13.2.2 Examples of the model.- 13.2.3 Isomorphism between the Hopfield and Ising models.- 13.3 Converge to stable states.- 13.3.1 Dynamics of Hopfield networks.- 13.3.2 Covergence proof.- 13.3.3 Hebbian learning.- 13.4 Equivalence of Hopfield and perceptron learning.- 13.4.1 Perceptron learning in Hopfield networks.- 13.4.2 Complexity of learning in Hopfield models.- 13.5 Parallel combinatorics.- 13.5.1 NP-complete problems and massive parallelism.- 13.5.2 The multiflop problem.- 13.5.3 The eight rooks problem.- 13.5.4 The eight queens problem.- 13.5.5 The traveling salesman.- 13.5.6 The limits of Hopfield networks.- 13.6 Implementation of Hopfield networks.- 13.6.1 Electrical implementation.- 13.6.2 Optical implementation.- 13.7 Historical and bibliographical remarks.- 14. Stochastic Networks.- 14.1 Variations of the Hopfield model.- 14.1.1 The continuous model.- 14.2 Stochastic systems.- 14.2.1 Simulated annealing.- 14.2.2 Stochastic neural networks.- 14.2.3 Markov chains.- 14.2.4 The Boltzmann distribution.- 14.2.5 Physical meaning of the Boltzmann distribution.- 14.3 Learning algorithms and applications.- 14.3.1 Boltzmann learning.- 14.3.2 Combinatorial optimization.- 14.4 Historical and bibliographical remarks.- 15. Kohonen Networks.- 15.1 Self-organization.- 15.1.1 Charting input space.- 15.1.2 Topology preserving maps in the brain.- 15.2 Kohonen’s model.- 15.2.1 Learning algorithm.- 15.2.2 Mapping high-dimensional spaces.- 15.3 Analysis of convergence.- 15.3.1 Potential function — the one-dimensional case.- 15.3.2 The two-dimensional case.- 15.3.3 Effect of a unit’s neighborhood.- 15.3.4 Metastable states.- 15.3.5 What dimension for Kohonen networks?.- 15.4 Applications.- 15.4.1 Approximation of functions.- 15.4.2 Inverse kinematics.- 15.5 Historical and bibliographical remarks.- 16. Modular Neural Networks.- 16.1 Constructive algorithms for modular networks.- 16.1.1 Cascade correlation.- 16.1.2 Optimal modules and mixtures of experts.- 16.2 Hybrid networks.- 16.2.1 The ART architectures.- 16.2.2 Maximum entropy.- 16.2.3 Counterpropagation networks.- 16.2.4 Spline networks.- 16.2.5 Radial basis functions.- 16.3 Historical and bibliographical remarks.- 17. Genetic Algorithms.- 17.1 Coding and operators.- 17.1.1 Optimization problems.- 17.1.2 Methods of stochastic optimization.- 17.1.3 Genetic coding.- 17.1.4 Information exchange with genetic operators.- 17.2 Properties of genetic algorithms.- 17.2.1 Convergence analysis.- 17.2.2 Deceptive problems.- 17.2.3 Genetic drift.- 17.2.4 Gradient methods versus genetic algorithms.- 17.3 Neural networks and genetic algorithms.- 17.3.1 The problem of symmetries.- 17.3.2 A numerical experiment.- 17.3.3 Other applications of GAs.- 17.4 Historical and bibliographical remarks.- 18. Hardware for Neural Networks.- 18.1 Taxonomy of neural hardware.- 18.1.1 Performance requirements.- 18.1.2 Types of neurocomputers.- 18.2 Analog neural networks.- 18.2.1 Coding.- 18.2.2 VLSI transistor circuits.- 18.2.3 Transistors with stored charge.- 18.2.4 CCD components.- 18.3 Digital networks.- 18.3.1 Numerical representation of weights and signals.- 18.3.2 Vector and signal processors.- 18.3.3 Systolic arrays.- 18.3.4 One-dimensional structures.- 18.4 Innovative computer architectures.- 18.4.1 VLSI microprocessors for neural networks.- 18.4.2 Optical computers.- 18.4.3 Pulse coded networks.- 18.5 Historical and bibliographical remarks.
£75.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Biometrie: Das Konstruktionsprinzip des Kniegelenks, des Hüftgelenks, der Beinlänge und der Körpergröße
Book SynopsisDie Biometrie ist eine neue Arbeitsmethode zur Aufklärung der Ursachen des Phänomens der reproduzierbaren Bewegung in der Biologie. Die Arbeitsweise der Biometrie macht es möglich, aus dem Längenverhältnis der Kreuzbänder des Kniegelenkes und der konkreten Längenangabe des hinteren Kreuzbandes das Kniesteuersystem a priori räumlich zu entwickeln, die Beinlänge und die dazugehörende Körpergröße zu bestimmen und das Hüftgelenk aus dem Kniesteuersystem abzuleiten. Die Erkenntnis, daß die unbekannten biologischen Bewegungssysteme selbstverwirklichte biometrische kräftefreie Gesetzlichkeiten verkörpern, ist richtungsweisend für alle Disziplinen, die sich mit dem Lebendigen beschäftigen.Table of ContentsEinführung.- I Empirisch-geometrische Untersuchungen des Kniegelenks. Historischer Überblick, Problemstellung, Bandstrukturen, Beuge- und Streckbewegung sowie Transversalbewegung.- 1 Historischer Überblick und Problemstellung.- 2 Klassische und relativistische Denkweise in der Physik.- 3 Grundsätzliches zur Analyse unbekannter Bewegungssysteme.- 3.1 Die nicht zielführende Analyse eines unbekannten Bewegungssystems.- 3.2 Die zielführende Analyse eines „unbekannten“ technischen Bewegungssystems.- 3.2.1 Untersuchung aus der Bewegung heraus.- 3.2.2 Untersuchung von Bewegungssystemen in Ruhelage.- 3.3 Die zielführende Analyse eines unbekannten, vom menschlichen Geist nicht erfundenen biologischen Bewegungssystems (Kniegelenk).- 4 Kinematik der Beuge- und Streckbewegung des Kniegelenks.- 4.1 Kinematik der Kreuzbänder.- 4.2 Achsen des Kniegelenks und Krümmungsmittelpunkte der Oberschenkelkondylen (Koppelhüllkurven und Hüllflächen).- 4.3 Kondylenformen.- 4.4 Dach der Fossa intercondylaris.- 4.5 Retroposition des Condylus femoris und des Tibiaplateaus.- 4.6 Abroll- und Gleitbewegung von Ober- und Unterschenkelkondylen.- 5 Die orthogonale Kraftübertragung an den Berührungsstellen der Gelenkflächen.- 6 Die Geschwindigkeitsverteilung bei der Bewegung des Unterschenkels.- 7 Das Kniegelenk — ein stufenloses Getriebe.- 8 Die Schlußrotation und die sekundäre Verformung der Oberschenkelkondylen.- 8.1 Schlußrotation.- 8.2 Condylus lateralis femoris.- 8.3 Condylus medialis femoris.- 9 Die kinematische Beziehung der Kreuzbänder (Steuersystem) zu den Kollateralbändern — Scheitel- und Angelkubik.- 9.1 Lig. collaterale mediale.- 9.2 Das Lig. collaterale laterale.- 9.3 Der Ball-Punkt.- 9.4 Definition der Scheitel- und Angelkubik.- 9.5 Zur Konstruktion der Scheitel- und Angelkubik.- 9.6 Symmetrische Winkelschleife.- 10 Das Kniegelenk — ein Vierstabgetriebe.- 11 Die Synoviapumpe des Kniegelenks.- 11.1 Die Gelenkkapsel.- 12 Pro- und Supination des Unterschenkels und die Gegenbewegung des Oberschenkels.- 12.1 Das „nichtdurchschlagende Gelenkviereck“.- 12.2 Die Drehachsen für die Transversalbewegung (Polkurven des nichtdurchschlagenden Gelenkvierecks“).- 12.3 Der „Nachlauf des Kniegelenks.- 12.4 Die Asymptoten des „nichtdurchschlagenden Gelenkvierecks“.- II Inversion r·?r = ± c2, die grundsätzliche Beziehung des ruhenden zum bewegten System. Elementargeometrie mit Anwendungsbeispielen aus der Physik.- 13 Die inverse Transformation, das Ordnungsprinzip, der Algorithmus der Vertebraten.- 13.1 Reproduzierbare ebene Bewegung und Inversion.- 13.1.1 Ableitung der Euler-Savary-Gleichung.- 13.2 Die Inversion-das Plücker-Rechenverfahren (1834).- 13.3 Antiparallele.- 13.4 Der Inversionskreis „i“.- 13.5 Polarität.- 13.6 Potenz.- 13.7 Inversion einer Geraden.- 13.8 Winkeltreue der Inversion.- 13.9 Winkel- und Längenverhältnisse am Einheitskreis.- 13.10 Abteilung der 2. Elementargleichung.- 13.11 Das Abstandslängenverhältnis inverser Punktepaare P und P?.- 13.12 Das Längenverhältnis r: z?.- 13.13 Die duale Bedeutung von „?,“.- 13.14 Inversion eines Punktes mit dem Zirkel.- 13.15 Symmetrische Teilung einer Strecke AB mit dem Zirkel.- 13.16 Inversion einer Geraden mit dem Zirkel.- 13.17 Inversion eines Kreises mit dem Zirkel.- 13.18 Die Verhältniszahl ? vom Punkt „S“ aus betrachtet.- 13.18.1 Konstruktion der Leitlinien der Parabel mit dem Zirkel..- 13.19 Das Längenverhältnis ? und die Ellipse.- 14 Fokalkegelschnitt.- 14.1 Die Beziehung der Parameter der Ellipse und Hyperbel eines Fokalkegelschnitts.- 14.2 Die inversen Beziehungen der Parameter eines Fokalkegelschnitts.- 14.3 Scheitelkreise.- 14.4 Die Beziehung der Halbparameter pb und pe.- 14.5 Inversion der Kegelschnitte.- 15 Inversion und Influenz.- 16 Inversion und Ohm-Widerstand.- 17 Der „goldene Schnitt“ — ein Spezialfall der Inversion.- III Konstruktion des Steuersystems des Kniegelenks (windschiefes Gelenkviereck) — kinetostatische Untersuchung.- 18 Das Steuersystem der Beuge- und Streckbewegung.- 19 Die konstruktive Entwicklung des Steuersystems im Aufriß (überschlagenes Gelenkviereck).- 19.1 Das Längenverhältnis ? des vorderen und hinteren Kreuzbandes und seine damit bestimmten Winkel ?, ?, ?.- 19.2 Der Abstand f des Tibiaplateaus vom Dach der Fossa intercondylaris.- 19.3 Das Tibiaplateau p ist das inverse Abbild der Scheitelkubik des inversen Steuersystems.- 19.4 Die Wälznormale n0 und Wälztangente ?0.- 19.5 Konstruktion des Parameters h.- 19.6 Entwicklung des kleinen Steuersystems ABA*B*.- 19.7 Die Radien der zerfallenen Scheitel-und Angelkubik.- 19.8 Die Radien der Scheitelkubik rs und der Angelkubik rA durch ? und hk ausgedrückt.- 19.9 Der Winkel ? durch ? ausgedrückt.- 19.10 Der Wendekreis w und sein Durchmesser ?.- 19.11 Tibiaplateau und Dach der Fossa intercondylaris.- 19.12 Der Proportionalitätsfaktor ?.- 19.13 Der Proportionalitätsfaktor ? durch ? ausgedrückt.- 19.14 Der Durchmesser 2rA der zerfallenen Angelkubik Ak des kleinen Steuersystems und der Wendekreisdurchmesser ?0 des großen Steuersystems.- 19.15 Das Verhältnis der Konstanten sv und shk und die entsprechenden Winkel ?1 und ?2.- 19.16 Die Retroversion des Tibiaplateaus.- 20 Das Steuersystem der Transversalbewegung. Ableitung des Grundrisses — „nichtdurchschlagendes Gelenkviereck“ — aus dem Aufriß — „überschlagenes Gelenkviereck“.- 20.1 Konstruktion der Wälztangente nach Bobillier.- 20.2 Die Beziehung von rv und $$ {r_{{{h_k}}}} $$ bzw. $$ {r_{{v{d_0}}}} $$.- und $$ {r_{{hk{d_0}}}} $$ im Aufriß.- 20.3 Entwicklung des nichtdurchschlagenden Gelenkvierecks (Grundriß des Steuersystems).- 20.4 Darstellung der räumlich versetzten Kreuzbandursprünge und Ansätze (Abstandslängen a? und b?).- 20.5 Die Beziehung von d0 und d?0.- 20.6 Lage der Wälztangenten t(As) (der Winkel o).- 20.7 Die Länge des vorderen und hinteren Kreuzbandes im Grundriß.- 20.8 Die Beziehung der Koppel p0 im Aufriß zum Steg d?0 im Grundriß.- 20.9 Die Beziehung zwischen ? und ?.- 20.10 Die Projektion der Asymptotenflächen t(As) des Grundrisses im Auf- und Seitenriß.- 20.10.1 Der Winkel ? im Aufriß.- 20.11 Der Winkel ? im Seitenriß.- 20.12 Die Krümmungsverwandtschaft X?X* des „nichtdurchschlagenden Gelenkvierecks“.- 20.13 Das räumliche Steuersystem.- 20.13.1 Die wahre Länge des vorderen Kreuzbandes ?* und des hinteren Kreuzbandes hk*.- 20.13.2 Darstellung der Kreuzungswinkel ? zwischen dem vorderen Kreuzband v0* und dem hinteren Kreuzband $$ h_{{{k_o}}}^{ * } $$ durch die entsprechenden Richtungskosinusse.- IV Die konstruktive Entwicklung der Beinlänge und Körpergröße aus dem Längenverhältnis ? der Kreuzbänder.- 21 Die Beinlänge und das Bewegungssystem Oberschenkel-Unterschenkel.- 21.1 Ober-und Unterschenkellänge.- 21.2 Der „Einbau“ des Steuersystems des Kniegelenks (kleines System) in das Bewegungssystem OSCH-USCH (großes System).- 21.3 Überstreckbarkeit des Bewegungssystems OSCH-USCH.- 21.4 Das hinfällige „Nußknackerprinzip“ des Kniegelenks.- 21.5 Die Fußhöhe und die Bewegungssysteme des Beines.- 21.6 Körpergröße.- V Entwicklung des proximalen Femurendes aus den Parametern des Kniegelenks.- 22 Entwicklung des Hüftgelenks aus dem Steuersystem des Kniegelenks.- 22.1 Das geometrische Grundkonzept des Hüftkopfes und der Hüftpfanne.- 22.2 Die individuelle Form des Hüftkopfes und der Hüftpfanne.- 22.3 Die Pascal-Schnecke als meridiane Schnittfigur der Kugelkonchoide und der Rotationskugelkonchoide.- 22.4 Kugelkonchoide.- 22.5 Rotationskugelkonchoid.- 22.6 Die Beziehung von Hüftkopf und Hüftpfanne.- 22.7 Der Drehpunkt des Hüftgelenks bei Schwingbewegungen.- 22.8 Berechnung der Scheitelkreisradien der Hüftpfanne und des Hüftkopfes.- 22.9 Inversion in der Gauß-Zahlenebene.- 22.10 Hyperbolische Inversion.- 22.11 Elliptische Inversion.- 22.12 Warum ist der Hüftkopf von Masse (Zellmasse) erfüllt?.- 22.13 Warum bildet die Hüftpfanne einen Hohlraum, an dem sich die Zellmassen außen anlagern?.- 22.14 Fovea capitis femoris.- 22.15 Antetorsion des proximalen Femurendes.- 22.16 Die konstruktive Lösung der Anlenkung des Hüftkopfes an den Schenkelhals und seine Beziehung zur Oberschenkelschaftachse..- 22.17 Die analytische Lösung der Anlenkung des Hüftkopfes an den Schenkelhals und seine Beziehung zum Oberschenkelschaft (Die Beziehung von CCD-Winkel und AT-Winkel).- 22.17.1 Zentralprojektion.- 22.17.2 Die konstruktive Entwicklung des AT-Winkels und die Länge des Schenkelhalses im Grundriß aus den Parametern ah (Kehlkreis) und ?1 (Öffnungswinkel des Richtkegels).- 22.17.3 Entwicklung des CCD-Winkels und die Schenkelhalslänge im Aufriß.- 22.17.4 Konstruktive Entwicklung des CCD- und AT-Winkels und die Schenkelhalslänge aus dem Längenverhältnis der Kreuzbänder ? und dem Hüftkopfparameter ah.- 22.17.5 Der AT-Winkel und die Ursache der verschiedenen Meßwerte.- 22.18 Schwankungsbreite der Winkelwerte der reellen CCD-Winkel.- 22.18.1 Mittelwert des CCD-Winkels.- 22.18.2 Obere Grenze der mittleren Schwankungsbreite des CCD-Winkels.- 22.18.3 Untere Grenze der mittleren Schwankungsbreite des CCD-Winkels.- 22.18.4 Oberer Extremwert des CCD-Winkels.- 22.18.5 Unterer Extremwert des CCD-Winkels.- Schlußwort und Zusammenfassung.
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Amazon Digital Services LLC - Kdp Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs Part 1
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Springer Neural Information Processing
Book SynopsisDefend from Scratch: A Diffusion-based Proactive Defense Method for Unauthorized Speech Synthesis.- Transformers As Approximations of Solomonoff Induction.- Interpreting Decision Transformer: Insights from Continuous Control Tasks.- Flexible-order Feature-interaction for Mixed Continuous and Discrete Variables with Group-level Interpretability.- Critical Feature Sifting and Dynamic Aggregation for Anomalous Audio Sequence Detection.- Parallel Interpretation Network via Semantic Visual Probe and Counterfactual Verification.- Real-Time Decentralized M2M Decision-Making via Deep Learning and Incremental Learning.- Explainable Federated Stacking Models with Encrypted Gradients for Secure Kidney Medical Imaging Diagnosis.- DDFGNN: Dual-dimensionality Fusion Graph Neural Network for Social Bot Detection.- A Motif-based Graph Convolution Network for Stock Trend Prediction.- VAGNN: Advancing the Generalization of Graph Neural Networks.- TrajAngleNet: Transformer-based Trajectory Prediction through Multi-Task Learning with Angle Prediction.- Correlation Disentangling and Spatio-Temporal Cooperative Optimizing Network for Temperature Prediction Revision.- Hierarchical Adaptive Position Encoding-based Transformer for Point Cloud Analysis.- In-context Learning for Temperature Field Reconstruction under Multiple Layouts.- Loosely coupled oscillators as a correlate of behavioral control circuits within the central complex of the fruit fly.- EL-LSTM: A Multivariate Time Series Forecasting Model Combining Spiking Neurons and Long Short-Term Memory Networks.- A Two-Stage Network for Enhanced Intracranial Artery 3D Segmentation in TOF-MRA Volume.- Independence Constrained Disentangled Representation Learning from pistemological Perspective.- Utilizing Small and Large Spectral Radii for Appropriate Reservoir Computing Design.- Noisy Deep Ensemble: Accelerating Deep Ensemble Learning via Noise Injection.- LCNet: Lightning Hierarchical Convolution for Occupancy Flow Prediction.- FedTS: Leveraging Teacher-Student Architecture in Federated Learning against Model Heterogeneity in Edge Computing Scenarios.- Physics-informed antisymmetric recurrent neural networks for solving nonlinear partial differential equations.- APS: An Adaptive Policy Switching Framework to Improve the Generalization of Branching.- Effcient Pruning and Compression Techniques for Convolutional Neural Networks to Preserve Knowledge and Optimize Performance.- Enhancing Convnets with Pruning and Symmetry-Based Filter Augmentation.- Improved Approximation Algorithms for the Cumulative Vehicle Routing Problem.
£64.99
Springer Neural Information Processing
Book SynopsisNetwork structure and recurrent dynamics achieved by maximizing information transfer and minimizing maintenance costs of the network.- Outlier-Robust Range-Based Method for Estimating the Location and Velocity of a Moving Source Using Lagrange Programming Neural Network.- Spatial Analysis Techniques in Recognition and Localization of Mouse Neuronal Activity.- ScaleMixer: A Multi-Scale MLP-Mixer Model for Long-Term Time Series Forecasting.- Application of Pseudometric Functions in Clustering and a Novel Similarity Measure Based on Path Information Discrepancy.- USAM-Net: A U-Net based network for improved stereo correspondence and scene depth estimation using features from a pre-trained image segmentation network.- TaW-PeRCNN:Time-adaptive Weights Physics-encoded Recurrent Convolutional Neural Network for Solving Partial Differential Equations.- An Explainable Error Detection Approach for Machine Learning.- T-GET3D: A Generative Model of High-Quality 3D Textured Shapes Guided by Texts.- Conformal Adversarial Generative Ensemble.- Virtual Command Allocation: Enhancing Hexapod Robot Locomotion through Goal-Conditioned Reinforcement Learning.- Adaptive Retrieval-based Gradient Planning for Offine Multi-context Model-based Optimization.- RBHAR: Role-Based Heterogeneous Action Representation in Multi-Agent Reinforcement Learning.- Deep mixtures of variational autoencoders model for representation learning and clustering tasks.- TempoKGAT: A Novel Graph Attention Network Approach for Temporal Graph Analysis.- Direct Correlational Spike-Timing-Dependent Plasticity Learning Applied to Classification Tasks.- Wave-RVFL: A Randomized Neural Network Based on Wave Loss Function.- Dual Cross Fusion Deep-unfolding Transformer for Hyperspectral Image Reconstruction.- A weight averaging neural network for semi-supervised data stream learning.- obust Noise Tolerant Algorithm for Randomized Neural Network.- Tackling Periodic Distribution Shifts in Federated Learning with Half-cycle Knowledge Distillation.- Multi-Scale Attention Convolutional Network and Reinforcement Learning for Flexible Job Shop Scheduling.- Temporal State Prediction and Sequence Recovery for Multi-Agent Reinforcement Learning.- Data Augmentation with Variational Autoencoder for Imbalanced Dataset.- Performance Analysis of Quantum-Enhanced Kernel Classifiers Based on Feature Maps: A Case Study on EEG-BCI Data.- Certified Patch Defense via Dual Mask-Preservation Prediction.- Proximal Point Method for Online Saddle Point Problem.- Fast Preserving Local Distances and Topology in Auto-Encoders.- Neural Collapse Inspired Regularization for Deep Graph Neural Networks.
£64.99
Springer Neural Information Processing
Book SynopsisFreeFlow: A Unified Viewpoint on Diffusion Probabilistic Models via Optimal Transport and Fluid Mechanics.- Optimizing CNNs with Gram Schmidt Non-Iterative Learning for Image Recognition.- Improving Multilingual Speech Recognition with Tucker-compressed Mixture of LoRAs.- MetaFix: Semi-Supervised Model Agnostic Meta-Learning using Consistency Regularization.- Towards Private and Fair Machine Learning: Group-Specific Differentially Private Stochastic Gradient Descent with Threshold Optimization.- LogMoE: Optimizing Mixture of Experts for Log Anomaly Detection via Knowledge Distillation.- Cross-Domain Few-Shot Learning with Equiangular Embedding and Dynamic Adversarial Augmentation.- ∞-Net: An Unsupervised Model for Online Graph Time-Series Denoising.- On Learnable Parameters of Optimal and Suboptimal Deep Learning Models.- Aero-engine Condition-Based Maintenance Planning Using Reinforcement Learning.- Multi-Timescale Processing with Heterogeneous Assembly Echo StateNetworks.- ADERec: Adaptive Data Augmentation Sequence Recommendation Based on Dual Network Architecture.- Pruning neural network parameters using recurrent neural networks.- MA-Mamba: Multi-Agent Reinforcement Learning with State Space Model.- Decentralized Extension for Centralized Multi-Agent Reinforcement Learning via Online Distillation.- Advancing RVFL networks: Robust classification with the HawkEye loss function.- An Enhanced MILP-based Verifier for Adversary Robustness of Neural Networks.- Hide-and-Seek GANs for Generation with Limited Data.- Unsupervised Robust Hypergraph Correlation Hashing for MultimediaRetrieval.- Emotional Atmosphere Soft Label for Emotion Recognition in Conversations.- CCATS: Moving Forward with Class-Conditional Time Series Generation.- M3ixTS: Mixing of Multi-patch and Multi-view For Time Series Forecasting.- CSTFormer: Cross Spatial-Temporal Learning Transformer withDynamic Sign Language Recognition through an Augmented Reality Environment.- MmFormer: A Novel Multi-Scale and Multi-Period Transformer Model for Irregular periodic Network Traffc Prediction.- Time Series Anomaly Detection via Temporal Dependencies and Multivariate Correlations Integrating.- Transformer-Based Long Time Series Forecasting with Decoupled Information Extraction and Information Complementarity.
£64.99
Springer Neural Information Processing
Book SynopsisMultimodal Emotion Recognition by Fusing Video Semantics in Video Learning Scenarios.- STGCN-DHD: Spatio-Temporal Graph Convolutional Network for EEG-Based Driving Hazard Detection.- Machine Learning for Raman Spectroscopy-based Cyber-Marine Fish Biochemical Composition Analysis.- Granular-ball Representation Learning for Deep CNN on Learning with Label Noise.- Computational Intelligence for Optimizing UAV Positioning and Task Scheduling in UAV-assisted MEC Systems.- Feed-Forward Optimization With Delayed Feedback for Neural Network Training.- Fusion of Multi-level Information: Solve Large-scale Traveling Salesman Problem with an Effcient Framework.- A Novel Elitism-Based Genetic Algorithm with Gradient-based Local Search for Seeking Local Nash Equilibrium in Non-Cooperative Game.- Massive Multi-Agent Mean-Field Game Using Online Federated Adaptive Critic-Density Learning.- Combining Explicit Priors and Set Attention Driven Implicit Priors for Demonstration-Guided Reinforcement Learning.- Mixed Time-State Dependent Distributed Event-Triggered Consensus Protocol of a DC Microgrids Cluster.- Computing Approximate Nash Equilibrium in Two-Team Zero-Sum Games by NashConv Descent.- Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory.- GATE: Guided Contrastive State Space for Multi-Agent Reinforcement Learning.- A Leaky Wang kWTA.- Semantic Mapping and Reconstruction from Brain Activation to Natural Images Using LDM and LLM.- Enhancing Angular Resolution via Directionality Encoding and Geometric Constraints in Brain Diffusion Tensor Imaging.- Critical roles of Contours in Intermediate-Level Neural Representation: Comparative study between Primate V4 and DCNN.- Generalized knowledge-enhanced framework for biomedical entity and relation extraction.- CPG: Channel Pruning with DFS Guided Grouping for Effcient Medical Image Segmentation.- Attention based multi-scale feature conservation network for medical image segmentation.- DSNet: A Decoupled Siamese Network for ECG Classification.- A Lightweight Multi-Scale Effcient Model for Breast Cancer Detection and Classification in Mammograms.- HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affnity Prediction.- Graph-Augmented Sparse Attention for Medical Image Segmentation.- Active Learning by Feature Perturbation for Medical Image Classification.- A Multi-Encoder Pyramid U-Net for Multimodal Brain Tumor Segmentation.- MambaFuse: Fusing Multi-Scale Mamba and CNN Features for Seizure Prediction.- An Encoder-Decoder Based Approach for ECG Delineation.
£64.99
Springer Neural Information Processing
Book SynopsiscPER2P: Parameter-Effcient Single-cell LLM for Translated Proteome Profiles.- CRAFT: Consistent Representational Fusion of Three Molecular Modalities.- AMPCL: Adaptive Meta-Path Selection and Contrastive Learning for miRNA-Disease Prediction.- Video-Driven Comprehensive 3D Hip Joint Motion Model for FAI Auxiliary Diagnosis.- LungCANet: A Novel Deep Co-Attention Convolutional Neural Network Architecture for High-Precision Lung Cancer Morphological Analysis and Classification.- ATFN: An Effcient Multi-Modal Depression Assistance Diagnostic Model Based on Multi-Channel Attention Mechanism.- Domain Knowledge Based Temporal-spatial Graph Convolution Network for ECG Recognition.- Adaptive Constrained ICABMGGMM: application to ECG blind source separation.- CRA-Eformer: Cross-scale Residual Attention-based Edge-guide Transformer for Low-Dose CT Denoising.- Improving Text Representation for Disease Detection From Social Media via Self-augmentation and Contrastive Learning.- Improving Healthcare Outcomes by Identifying Populations with Higher Risk of Lung Cancer from Primary Care Data.- Split Learning on Multi-source Cross-streams.- Seizure Prediction based on Multi-scale Fusion-attention Transformer.- Dynamic Self-Attention Gated Spatial-Temporal Graph Convolutional Network for Skeleton-based Human Activity Recognition.- G-SwinHAR: Swin Transformer for Smartphone-Based Human Activity Recognition Using Gramian Angular Field.- Cross-feature Interactive Fusion for Speech Emotion Recognition.- Temporal-contextual Event Learning for Pedestrian Crossing Intent Prediction.- PoseRAC: Enhancing Repetitive Action Counting with Salient Poses.- Spatio-Temporal Graph Convolutional Networks for Pedestrian Trajectory Prediction.- Dual-branch StarNet with Mutual Attention and U-Net Denoising for Simultaneously Recognizing Keywords and Speakers.- Unsupervised Personalized Deep Learning for Wearable Human Activity Recognition.- The Role of AI in Optimizing Human-centered Complex Systems.- A Global Interactive and Bottleneck Fusion Model for Multi-Intent Spoken Language Understanding.- GloveTyping: A Hand Gesture Recognition System for Text Input Using a Hierarchical Framework with Attention Mechanism.- Impacts of Prompt Perturbation on Reducing Bias and Hallucination of Large Language Models.- A Multi-task Emotion Recognition Model based on Continuously Labeled EEG Signals.- MUR: Multimodal Unified Refinement for Multimedia Recommendation.- Identifying Misaligned Features for Cross-Domain Cold-start Recommendation.- Temporal Semantic Scoring Path aware Multi-Embedding Sequential Recommendation.- Online Labor Market Task Recommendation via Time-weighted Diffusion Model.- Multi-Pattern Joint Denoising Sequential Recommendation with Diffusion Model.- ProFetch: Accelerate Deep Recommendation System Training with Proactively Designed Data Layout and Dynamic Prefetching.
£66.49
Springer Neural Information Processing
Book SynopsisAnchor STARK: Query Design for Transformer-Based Target Tracking.- DACG-Net: A Dual Attention and Classifier Guided Network for Low-Light Image Enhancement.- Improved Aggregated Contextual Transformations Based on U-Net for Image Inpainting.- Mitigating Vanishing Activations in Deep CapsNets Using Channel Pruning.- A Novel Data Synthesis Method by Integration of Diffusion Model and GAN for Object Detection Task.- MOSSE-YOLOv8: A Two-Stage Approach for Small-Target Arc Detection in High-Speed Railways.- SAM-FL:Enhanced Generalizable Medical Image Segmentation via Sharpness-Aware Minimization and Focal Loss.- CP2PNet: A General End-to-End Framework for Plant Organs Counting and Phenological Stage Prediction.- Hierarchical Prompt-Enhanced Image Generation Using Hyperbolic Space.- Efficient Conditional Diffusion Model for Accurate PedestrianTrajectory Prediction.- MonoViM: Enhancing Self-supervised Monocular Depth Estimation via Mamba.- An End-to-End rPPG-Based Face Anti-Spoofing Network with Deception Enhancement Module.- Region-Aware Instruction-Guided Image Editing with Attention-Weighted Feature Fusion.- Multi-view Self-supervised 3D Human Pose and Shape Estimation on SMPL.- WT-based Feature Enhancement Network for Camouflaged Object Detection.- Multi-Headed Graph-based Attention aided U-Net Model for Nuclei Segmentation.- Research and Implementation of Fine-Grained Bird Image Classification.- LSC-YOLO: Small Target Defects Detection Model for Wind Turbine Blade based on YOLOv9.- SAU: A Dual-Branch Network to Enhance Long-Tailed Recognition via Generative Models.- Leveraging local similarity for token merging in Vision Transformers.- Semi-Supervised Domain Adaptation for All Weather Point Cloud Semantic Segmentation.- Federated Learning for Blind Image Super-Resolution.- Towards Better Text-to-Image Generation Alignment via Attention Modulation.- CLOFAI: A Dataset of Real And Fake Image Classification Tasks for Continual Learning.- AMSA-UNet: An Asymmetric Multiple Scales U-net Based on Self-attention for Deblurring.- MT-Net: A Dual-Encoder Multiscale Medical Segmentation Model.- ENHANCING ADVERSARIAL ROBUSTNESS OF DIFFUSION DENOISED SMOOTHING VIA IMAGE SUPER-RESOLUTION.- A simultaneous hierarchical count data clustering and feature selection based on Multinomial Nested Dirichlet Mixture using the Minorization-Maximization framework.
£75.99
Springer Neural Information Processing
Book SynopsisFine-tuning Fine-tuned Models: Towards a Practical Methodology for Sentiment Analysis with Small In-domain Supervised Dataset.- End-to-end Knowledge Graph Construction System Powered by LargeLanguage Models.- EPRVR: Efficient Partially Relevant Video Retrieval with Disentangled Video Representation Learning.- Graph-Based Data Augmentation and Label Noise Identification forEntity Resolution.- Patient Mortality prediction Using Clinical Notes.- ScaleDoc: A Two-Stage Approach for Scale-Aware Document Dewarping.- CCUH:CLIP-Based Clustering Method for Unsupervised Hashing Multi-Modal Retrieval.- A Privacy-Preserving Image Classification Framework with Transformer.- Reversible Data Hiding in Dual Encrypted Images with Dual Data Embedding.- A Dual-Layer Reversible Data Hiding Scheme Based on Optimal Neighbor Mean Interpolation (ONMI) and Histogram Shifting.- Threat Intelligence Entity Recognition Based On Large Language Model With Contrastive Learning.- GTSD: Generative Text Steganography Based on Diffusion Model.- Enhanced Autoencoder Model for Robust Anomaly Detection in Financial Fraud with Imbalanced Data.- Membership Inference Attacks in Text Classification Tasks.- PURVEY-CE: A Complex texture adaptive image steganography based on channel attention.- Air-Sniffing Analytics Enhancing Wi-Fi Device Identification with Robust and Accurate Techniques.- Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices.- Control ControlNet: Multidimensional Backdoor Attack based on ControlNet.- CPANet: Convolutional Parameter Adapter Network for ImageCopy-Move Forgery Detection and Localization.- AO-UAP: An Adaptive Universal Adversarial Perturbation Generation for Speech Recognition Models.- A Hilbert-Curve based Encoding scheme for Privacy-preserving Nearest-Neighbor Classification.- ZKP-HGNN: A Study on Improving Zero- Knowledge Proof (ZKP) Based on Heterogeneous Graph Neural Networks for Anonymous Digital Identity Sharing in Blockchain.- Adversarial Knowledge Extraction via Steering Diffusion Models.- Solving the Thinnest Path Problem with Hypergraph Learning.- AISSGR: Attack Investigation Based on Self-Supervised Graph Representation Learning.- Two-stage optimized adversarial patch for attacking infrared vehicle detectors in the physical world.- Deep Learning-Based Detection of Code Execution Vulnerabilities in Binary Programs.- Towards Real-Time Audio Deepfake Detection in Resource-LimitedEnvironments.- Detecting Audio Deepfakes through Emotional Fingerprinting.- Constructing Multi-Detector Decision Forest for Fake Speech Detection.- KDAE: Kernel Density Auto-Encoder for Semi-Supervised Anomaly Detection with Limited Labeled Data.
£75.99
Springer Neural Information Processing
Book SynopsisIntegrating Hierarchical Fine-Grained and Global Information for Multimodal Sentiment Analysis.- Elemental Discourse Unit Guidance Based Model for Multimodal Sentence Summarization.- DeemCLIP: Multimodal Emotion Information Enhanced Human Action Recognition.- Can Multimodal Large Language Model Think Analogically?.- PKRD-CoT: A unified Chain-of-thought Prompting for Multi-Modal Large Language Models in Autonomous Driving.- Multi-Granularity Multimodal Information Interaction for Knowledge Graph Completion.- Graph Enhanced Cross-Modal Retrieval based on Visual-Language Knowledge Distillation.- A Simple Interactive Attention for Multimodal Named Entity Recognition.- Multimodal Polarity-semantic Coupling Network for Sarcasm Analysis.- A Two-Stage Multi-Domain Collaborative Optimization Network for 3D Human Mesh Recovery.- Supporting Event Sentence Coreference Identification with Progressive Prompt-guided Implicit knowledge Distillation.- CRGAT: Contextualized Relational Graph Attention Network for Knowledge Graph Completion.- Leveled Learning: An Interpolation-Based Data Augmentation Method on Few-Shot Text Classification.- Prompt-tuning for Clickbait Detection via Text Summarization.- Optimizing BERT for Superior NLP Performance: Balancing Efficiency with Advanced Pre-Training Techniques.- EBPL: Financial Event Causality Extraction Based on Prompt Learning.- TCAN: Triple Context-Aware Network for Multi-Modal Conversational Emotion Recognition.- DDKG: Dual attention KG-to-text Generation with Dual-view Graph Attention.- The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claims.- MulLog: A Software Defect Prediction Approach Based on Multi-Label Contrastive Learning and Line Property Graph Learning.- Video Piracy Websites Detection using Continual Learning with Elastic Weight Consolidation.- MetaRAED: Meta Learning Prototype-based Retrieval Augmented Few-shot Event Detection.- Cantonese Dialect Transcription in Diverse Sophisticated Scenarios via the OpenAI Whisper Speech Recognition Model.- Cross-lingual Sentence Representations via Focus Learning.- SPSEAE: Soft Prompt with Relevant Context Aggregation for Sentence-Level Event Argument Extraction.- HinglishCap: A Code Mixed Hindi-English Image Captioning Framework.- Silent Intruders: Dissecting Textual Backdoor Attacks in FederatedDialog Systems.- SBoRA: Low-Rank Adaptation with Regional Weight Updates.- Improving Document-level Event Coreference Resolution with Knowledge Distillation.- Improving In-Context Learning with Inquiry Style Classification in Table Question Answering.- Role-playing based on Large Language Models via Style Extraction.
£75.99
Springer Neural Information Processing
Book SynopsisEvaluating Large Language Models for Depression Detection in Text: A Comparative Analysis.- A Self-Sampling Data Augmentation Method for Low-Resource Neural Machine Translation.- RLKGE: Trustworthiness Measurement for Knowledge Graph Triples based on Reinforcement Learning.- Improving Empathetic Dialogue Generation via Response Attention Guidance.- Advanced Stock Market Forecasting Using Synergic of Sentiment Analysis and Association Rule Mining.- Exploit the Emotional Dynamics for Better Conversational Emotion Recognition.- Knowledge Enhanced Sentence-Level Fine-grained Relation Extraction via Multi-Agent Collaborative Generation.- Multi-view Adaptive Fusion Model for Multimodal Fake News Detection.- A Hybrid Prompt Method for Few-shot Named Entity Recognition.- GDBT: A Joint Model for Overlapping Relational Triple Extraction Based Global Detection and Bidirectional Tagging.- Retrieval-Enhanced Method Using Siamese Networks and Graph Kernel Functions for Code Summarization.- Enhancing Prompt Tuning for Smaller Pretrained Models via Knowledge Distillation.- AcademicMT: Boosting Performance of Large Language Models in Academic Translation.- Improving Neural Machine Translation by Multi-Step Teacher-Assisant Knowledge Distillation.- Enhancing Robustness in Large Language Models Prompting for Mitigating the Impact of Irrelevant Information.- TUMS: Enhancing Tool-use Abilities of LLMs with Multi-structure Handlers.- A Legal Case Matching Model Using Dual LLMs, BGE, and Mamba-2.- Fine-grained Controllable Generation of Latent Language Diffusion Models 252.- Can Dynamic Prompt Help Sentiment Style Transfer?.- Label-template based Few-Shot Text Classification with Contrastive Learning.- Curriculum Learning with Difficulty Division for Metaphor Detection.- Open-Source Large Language Models Excel in Named Entity Recognition.- Precision Where It Matters: A Novel Spike Aware Mixed-PrecisionQuantization Strategy for LLaMA-based Language Models.- Hypergraph Contrastive Learning for Evidence-Aware Fake News Detection.- SmartPL: An Integrated Approach for Platoons Driving onMixed-Traffic Freeways.- Optimizing Training Speed with Novel Adaptive Exploration Techniquein Simulation and Real-World Robotics for Visual Path Following.- Modeling and Adaptive Sliding Mode Control of Autonomous Underwater Vehicles.- Causality-Aware Transformer Networks for Robotic Navigation.
£75.99
Springer Neural Information Processing
Book SynopsisUtilizing Deep Learning to address Temporal and Spatial Dependencies in Weather Forecasting.- Imagined Digits Recognition Based on Masked Electroencephalography Modeling.- THGCN:Temporal Hypergraph Convolutional Network for Subject Independent EEG Emotion Recognition.- ANN-Based Pollution Forecasting Through Short-Term Spatio-Temporal Analysis: A North Island, New Zealand Case Study.- Detection of Animal Movement from Weather Radar using Self-Supervised Learning.- From Concrete to Abstract: A Multimodal Generative Approach to Abstract Concept Learning.- Analysis on Artificial Representations of a Trained AlexNet Model Using the CIFAR-10 Dataset.- Modelling the influence of temperature and rainfall on the spread of African swine fever in Australia.- An EEG-based Spatial-Temporal Hybrid Architecture for Cognitive Load Detection.- Decoding Psychological Stress during Laparoscopic Surgery Training: Insights from EEG.- A Comparison between baseline models and a transformer network for SOC prediction of lithium-ion batteries.- Insights into Long-term Electrical Load Forecasting: Explainable AI approach on Multivariate LSTM.- Artificial Intelligence and Climate Change: A Review of Causes and Opportunities.- Towards a machine learning model to predict cognitive ability using EEG data and virtual spatial navigation task scores in intellectually disabled adults.- HyPeFL: Tackling Data Heterogeneity via Hypernetwork in Personalized Federated Learning.- NeuroGeMS: An open-source GUI software for multimodal modelling in biomedical research and applications.- Multimodal Multiview Graph Convolution Network for the Diagnosis of Alzheimer’s Disease.- DNA-PRIME: Advanced DNA Sequence Compression through Enhanced Feature Fusion and Weight Hashing.- SnE-VNet: A Deep Learning Model with Squeeze and Excitation for Improved 3D Stroke Lesion Segmentation.- Morphology-Guided 3D Skull Gender Identification with Point-BERT.- Cuffless Blood Pressure Measurement From Photoplethysmography through High and low Frequency Information Fusion Attention Mechanism.- Hybrid EEG-fNIRS decoding for fine joint motor imagery of Unilateral Upper Limb with Two-Stage Hybrid Training.- A Neural Network-Augmented Case-Based Reasoning Framework for Weather Risk Modeling using Remote Sensing Data.- Autonomous Design of Floor Plan Based on Architectural Drawings Example without Neighbour Relation.- Using ensemble learning algorithms to integrate multisource remote sensing data for mapping regional forest canopy height.- MTDS: Meta-Path Context Enhanced Drug Combination Synergy Prediction.- A Federated Learning Approach for Genomic Selection in Pigs.- TOP-EEG: a robust software to predict the outcomes of therapies for depression using EEG signals in DGMD domain.- Neural Network as Surrogate Model for Sleep EEG Trajectories and Insomnia Disorder Classification.
£75.99
Springer Neural Information Processing
Book SynopsisOptimisation of Fibre Selection For Tubes Production in Manufacturing of Optic Cables.- Cross-Domain Evaluation of CNN-based and Generative Adversarial Networks Models’ Generalisability for (D)DoS Attack Detection in CPS and IoT.- Robust Design of Echo State Networks for Soft Sensor ApplicationsBased on Risk-Aware Optimization and Stability Testing.- Logic Error Localization in Student Programming Assignments Using Pseudocode and Graph Neural Networks.- FISHER: An Efficient Sim2sim Training Framework Dedicated in Multi-AUV Target Tracking via Learning from Demonstrations.- Revisiting Cross-Domain Problem for LiDAR-based 3D Object Detection.- Self-Supervised Pretraining-Enhanced Intelligent Quality Control for Ocean Observations with Limited Historical Data.- SHAPE: Smart Shaping with Adaptation Physically Excited Networks.- Accelerating Attentional Generative Adversarial Networks with Sampling Blocks.- Weak Supervision Techniques towards Enhanced ASR Models in Industry-level CRM Systems.- Guided Safe Diffusion: Prohibiting Diffusion Models from Generating Inappropriate Content.- A Fine-Tuned Multi-Classifier Optimization Framework towards Safety-Critical Classes.- Behavior-Driven Data Augmentation for Non-Intrusive Load Monitoring.- Data-Driven Approach to assess and identify gaps in healthcare set up in South Asia.- A Robust Tensor Decomposition Model for Traffic Data Imputation with Capped Frobenius Norm in Smart City.- A Federated Domain Generalization Method by Enhancing Knowledge Distillation With Stylistic Feature Dispatcher.- RetailEye: Supervised Contrastive Learning with Compliance Matchingfor Retail Shelf Monitoring.- Solving Expensive Dynamic Multi-Objective Problem via Cross-Problem Knowledge Transfer.- XImgCom: Fine-tuned Text-Guided X-ray Image Synthesis for Airport Logistics Based on Hypercomplex Attention.- Multiclass semantic segmentation of satellite Imagery usingconvolutional neural networks.- PPDA: A Privacy Preserving Framework for Distributed Graph Learning.- MonoTCM: Semantic-Depth Fusion Transformer for Monocular 3D Object Detection with Token Clustering and Merging.- Illumination Estimation and Fourier-Guided Component Prediction forEnhancing Low-Light Images.- Efficient Visual Object Tracking with Temporal Context-Aware TokenLearning and Scale Adaptive Token Pruning.- Towards Unveiling the Potential of Fuzzy Values as Features: A Comparative Study in Cybercrime Text Analysis.- Hybrid Niching Differential Evolution with Restart Strategy for Multimodal Optimization.- StreetSyn: A Full Radiance Field Solution for Street and Vehicle Free-View Synthesis.
£54.99
Springer Advanced Palmprint Authentication
Book SynopsisChapter 1 Towards Next-Generation Palmprint Recognition.- Part I CONTACT-BASED PALMPRINT RECOGNITION.- Chapter 2 Jointly Heterogeneous Palmprint Discriminant Feature Learning.- Chapter 3 Rich Orientation Coding for Large-Scale Palmprint Image Analysis.- Chapter 4 Hybrid Fusion Combining Palmprint and Palm Vein for Large-scale Palm-based Recognition.- Part II CONTACTLESS PALMPRINT RECOGNITION.- Chapter 5 Keypoint Localization Neural Network for Touchless Palmprint Recognition Based on Edge-Aware Regression.- Chapter 6 Hand-Geometry Aware Image Quality Assessment Framework for Contactless Palmprint Recognition.- Chapter 7 Touchless Palmprint Recognition Based on 3D Gabor Template and Block Feature Refinement.- Chapter 8 Aligned Multilevel Gabor Convolution Network for Palmprint Recognition.- Chapter 9 Contactless Palmprint Recognition System based on Dual-camera Alignment.- Part III MULTIPLE PALMPRINT SENSING SYSTEMS.- Chapter 10 Multi-camera System for High Speed Touchless Palm Recognition.- Chapter 11 Line-Scan Palmprint Acquisition System.- Chapter 12 Person Recognition Using 3-D Palmprint Data Based on Full-Field Sinusoidal FringeProjection.- Chapter 13 Complete Binary Representation for 3-D Palmprint Recognition.- Chapter 14 Book Reivew and Future Work.
£170.99
Amazon Digital Services LLC - Kdp AI GenAI Robotics and IoT for Operations Management
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Independently Published The Future of AI Chatbots
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Amazon Digital Services LLC - Kdp The Ultimate AI Handbook for Beginners
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Amazon Digital Services LLC - Kdp Machine Learning on Mobile Devices
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Amazon Digital Services LLC - Kdp Deep Learning for Computer Vision with Keras and PyTorch
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Amazon Digital Services LLC - Kdp Understanding the Mechanics and Sensors Behind Robots
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Amazon Digital Services LLC - Kdp AI Image Generation with Prompt Engineering
£24.58
Independently Published The Neuromorphic Age
£14.63
Amazon Digital Services LLC - Kdp NLP Essentials with Hugging Face
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Independently Published The Smart AI Leader
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Independently Published The New Agentic AI Bible
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Independently Published The 2026 Pivot
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Independently Published Neural Rendering for Vr AR and the Metaverse
£28.88
Independently Published The Mindful Tech That Harnesses Technology Without Losing Yourself
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