Il modello del Kreislauf nel sistema teorico dottrinale di Egidio Giannessi / Valerio Antonelli

• Record Nr.: UNISA990002809870203316
• Autore: ANTONELLI, Valerio
• Pubbl/distr/stampa: [s.l.] : [s.n.], 1995
• Descrizione fisica: P. 508-521 ; 24 cm
• Disciplina: 338.092
• Soggetti: Giannessi, Egidio
• Collocazione: P08 1983
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Estratto da "Rivista italiana di ragioneria e di economia aziendale", anno 1995, n.9/10

Workshop proceedings

• Record Nr.: UNISA996204447703316
• Pubbl/distr/stampa: [Place of publication not identified], : Institute of Electrical and Electronics Engineers, 1996
• Disciplina: 621.3815/422
• Soggetti: Liquid crystal displays - Congresses; Thin film transistors - Congresses; Electrical & Computer Engineering; Engineering & Applied Sciences; Electrical Engineering
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph

Self-adaptive systems for machine intelligence [[electronic resource] /] / Haibo He

• Record Nr.: UNINA9910139631803321
• Autore: He Haibo <1976->
• Pubbl/distr/stampa: Hoboken, N.J., : Wiley-Interscience, 2011
• ISBN: 1-283-17569-X; 9786613175694; 1-118-02559-8; 1-118-02560-1; 1-118-02558-X
• Descrizione fisica: 1 online resource (248 p.)
• Classificazione: COM044000
• Disciplina: 006.3/1; 006.31
• Soggetti: Machine learning; Self-organizing systems; Artificial intelligence; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: SELF-ADAPTIVE SYSTEMS FOR MACHINE INTELLIGENCE; CONTENTS; Preface; Acknowledgments; 1 Introduction; 1.1 The Machine Intelligence Research; 1.2 The Two-Fold Objectives: Data-Driven and Biologically Inspired Approaches; 1.3 How to Read This Book; 1.3.1 Part I: Data-Driven Approaches for Machine Intelligence (Chapters 2, 3, and 4); 1.3.2 Part II: Biologically-Inspired Approaches for Machine Intelligence (Chapters 4, 5, and 6); 1.4 Summary and Further Reading; References; 2 Incremental Learning; 2.1 Introduction; 2.2 Problem Foundation; 2.3 An Adaptive Incremental Learning Framework; 2.4 Design of the Mapping Function2.4.1 Mapping Function Based on Euclidean Distance; 2.4.2 Mapping Function Based on Regression Learning Model; 2.4.3 Mapping Function Based on Online Value System; 2.4.3.1 A Three-Curve Fitting (TCF) Technique; 2.4.3.2 System-Level Architecture for Online Value Estimation; 2.5 Case Study; 2.5.1 Incremental Learning from Video Stream; 2.5.1.1 Feature Representation; 2.5.1.2 Experimental Results; 2.5.1.3 Concept Drifting Issue in Incremental Learning; 2.5.2 Incremental Learning for Spam E-mail Classification; 2.5.2.1 Data Set Characteristic and System Configuration2.5.2.2 Simulation Results; 2.6 Summary; References; 3 Imbalanced Learning; 3.1 Introduction; 3.2 The Nature of Imbalanced Learning; 3.3 Solutions for Imbalanced Learning; 3.3.1 Sampling Methods for Imbalanced Learning; 3.3.1.1 Random Oversampling and Undersampling; 3.3.1.2 Informed Undersampling; 3.3.1.3 Synthetic Sampling with Data Generation; 3.3.1.4 Adaptive Synthetic Sampling; 3.3.1.5 Sampling with Data Cleaning Techniques; 3.3.1.6 Cluster-Based Sampling Method; 3.3.1.7 Integration of Sampling and Boosting; 3.3.2 Cost-Sensitive Methods for Imbalanced Learning3.3.2.1 Cost-Sensitive Learning Framework; 3.3.2.2 Cost-Sensitive Data Space Weighting with Adaptive Boosting; 3.3.2.3 Cost-Sensitive Decision Trees; 3.3.2.4 Cost-Sensitive Neural Networks; 3.3.3 Kernel-Based Methods for Imbalanced Learning; 3.3.3.1 Kernel-Based Learning Framework; 3.3.3.2 Integration of Kernel Methods with Sampling Methods; 3.3.3.3 Kernel Modification Methods for Imbalanced Learning; 3.3.4 Active Learning Methods for Imbalanced Learning; 3.3.5 Additional Methods for Imbalanced Learning; 3.4 Assessment Metrics for Imbalanced Learning3.4.1 Singular Assessment Metrics; 3.4.2 Receiver Operating Characteristics (ROC) Curves; 3.4.3 Precision-Recall (PR) Curves; 3.4.4 Cost Curves; 3.4.5 Assessment Metrics for Multiclass Imbalanced Learning; 3.5 Opportunities and Challenges; 3.6 Case Study; 3.6.1 Nonlinear Normalization; 3.6.2 Data Sets Distribution; 3.6.3 Simulation Results and Discussions; 3.7 Summary; References; 4 Ensemble Learning; 4.1 Introduction; 4.2 Hypothesis Diversity; 4.2.1 Q-Statistics; 4.2.2 Correlation Coefficient; 4.2.3 Disagreement Measure; 4.2.4 Double-Fault Measure
• Sommario/riassunto: "This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of ; the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications"--; "This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain"--

Front Line Extremity and Orthopaedic Surgery : A Practical Guide / / edited by Lawrence B. Bone, Christiaan N. Mamczak

• Record Nr.: UNINA9910300337303321
• Pubbl/distr/stampa: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
• ISBN: 3-642-45337-6
• Edizione: [1st ed. 2014.]
• Descrizione fisica: 1 online resource (209 p.)
• Disciplina: 610; 617.1; 617.47
• Soggetti: Orthopedics; Traumatology; Surgical Orthopedics; Traumatic Surgery
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references at the end of each chapters and index.
• Nota di contenuto: Pre-hospital and en-route care -- Combat triage and mass casualty management -- Initial management and priorities -- Damage control resuscitation – Blast injuries -- Foot and ankle injuries -- Tibia fractures -- B.K. amputations -- Vascular injuries of the lower extremity -- Use of fasciotomies of the lower extremity -- Distal femur fractures -- A.K. amputations -- Femur fractures -- Pelvic injuries and hip dislocations -- Hand injuries -- Arm and forearm Injuries -- Shoulder girdle injuries -- Upper extremity vascular injuries -- Spinal fractures -- Burns and extremity fractures.
• Sommario/riassunto: This book is designed as an easy to read reference and practical guide to the management of combat extremity injuries, which account for a high percentage of the injuries sustained in recent and current conflicts. The surgical techniques appropriate to the full range of extremity injuries and some other frequent injuries, such as trauma to the spine and pelvis, are clearly described with the aid of helpful illustrations. In each chapter a “bottom line up front” approach is adopted, providing key messages first; a further important feature is the emphasis placed on case-based information and lessons learned from practice. Care has been taken to ensure that the advice provided is straightforward and in line with military clinical practice guidelines. This guide will be relevant to all physicians working in forward surgical teams, combat surgical hospitals, or the “Charlie Med”. The authors are without exception experienced surgeons who have been deployed to Iraq or Afghanistan at least once, and the editors are ideally suited to their task: Dr. Lawrence Bone has been an orthopaedic and general surgical trauma surgeon for more than 30 years and has had military training and experience in combat casualty care, while Dr. Christiaan Mamczak is an Attending Orthopaedic Trauma Surgeon in the United States Navy and has served as Head Orthopaedic Trauma Surgeon at a NATO Multinational Medical Unit in Afghanistan.