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000823494 019__ $$a1002418535$$a1002821260$$a1005136647$$a1012078901
000823494 020__ $$a9783319569048$$q(electronic book)
000823494 020__ $$a331956904X$$q(electronic book)
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000823494 0247_ $$a10.1007/978-3-319-56904-8$$2doi
000823494 035__ $$aSP(OCoLC)on1002643862
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000823494 049__ $$aISEA
000823494 050_4 $$aQA76.87
000823494 08204 $$a006.32$$223
000823494 24500 $$aMultidisciplinary approaches to neural computing /$$cAnna Esposito [and 3 others], editors.
000823494 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2018]
000823494 300__ $$a1 online resource :$$billustrations
000823494 336__ $$atext$$btxt$$2rdacontent
000823494 337__ $$acomputer$$bc$$2rdamedia
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000823494 4901_ $$aSmart innovation, systems, and technologies,$$x2190-3018 ;$$vvolume 69
000823494 504__ $$aIncludes bibliographical references.
000823494 5050_ $$aPreface; Organization; SIREN Executive Committee; SIREN International Advisory Committee; Program Committee; Sponsoring Institutions; Contents; Introduction; 1 Redefining Information Processing Through Neural Computing Models; Abstract; 1.1 Introduction; 1.2 Content of This Book; 1.3 Conclusion; References; Algorithms; 2 A Neural Approach for Hybrid Events Discrimination at Stromboli Volcano; Abstract; 2.1 Introduction; 2.2 Seismic Signals at Stromboli Volcano; 2.3 Data Parametrization; 2.4 MLP Technique; 2.5 Results; 2.6 Conclusions; References
000823494 5058_ $$a3 Fully Automatic Multispectral MR Image Segmentation of Prostate Gland Based on the Fuzzy C-Means Clustering AlgorithmAbstract; 3.1 Introduction; 3.2 Related Works; 3.3 Patients and Methods; 3.3.1 Patient Dataset Composition; 3.3.2 The Proposed Prostate Segmentation Method; 3.4 Segmentation Results; 3.4.1 Evaluation Metrics and Achieved Experimental Results; 3.5 Conclusions and Future Works; References; 4 Integrating QuickBundles into a Model-Guided Approach for Extracting "Anatomically-Coherent'' and "Symmetry-Aware'' White Matter Fiber-Bundles; 4.1 Introduction
000823494 5058_ $$a5.6 ConclusionsReferences; 6 Effective Blind Source Separation Based on the Adam Algorithm; 6.1 Introduction; 6.2 The Blind Source Separation Problem; 6.3 The Adam Algorithm; 6.4 Modified InfoMax Algorithm; 6.5 Experimental Results; 6.6 Conclusions; References; ANN Applications; 7 Depth-Based Hand Pose Recognizer Using Learning Vector Quantization; 7.1 Introduction; 7.2 The Hand Pose Recognizer; 7.2.1 Segmentation Module; 7.2.2 Feature Extractor; 7.2.3 The Classifier; 7.3 Experimental Results; 7.4 Conclusions; References
000823494 5058_ $$a8 Correlation Dimension-Based Recognition of Simple Juggling Movements8.1 Introduction; 8.2 Correlation Dimension; 8.3 Juggler's Arm Movement Recognition; 8.3.1 Feature Extraction and Classification Phases; 8.4 Experimental Results; 8.5 Conclusions; References; 9 Cortical Phase Transitions as an Effect of Topology of Neural Network; 9.1 Introduction; 9.2 The Model; 9.2.1 Learning and Pruning Procedures; 9.2.2 Shuffling Procedure; 9.3 Results; 9.4 Conclusion; References; 10 Human Fall Detection by Using an Innovative Floor Acoustic Sensor; 10.1 Introduction; 10.1.1 Related Work
000823494 506__ $$aAccess limited to authorized users.
000823494 520__ $$aThis book presents a collection of contributions in the field of Artificial Neural Networks (ANNs). The themes addressed are multidisciplinary in nature, and closely connected in their ultimate aim to identify features from dynamic realistic signal exchanges and invariant machine representations that can be exploited to improve the quality of life of their end users. Mathematical tools like ANNs are currently exploited in many scientific domains because of their solid theoretical background and effectiveness in providing solutions to many demanding tasks such as appropriately processing (both for extracting features and recognizing) mono- and bi-dimensional dynamic signals, solving strong nonlinearities in the data and providing general solutions for deep and fully connected architectures. Given the multidisciplinary nature of their use and the interdisciplinary characterization of the problems they are applied to – which range from medicine to psychology, industrial and social robotics, computer vision, and signal processing (among many others) – ANNs may provide a basis for redefining the concept of information processing. These reflections are supported by theoretical models and applications presented in the chapters of this book. This book is of primary importance for: (a) the academic research community, (b) the ICT market, (c) PhD students and early-stage researchers, (d) schools, hospitals, rehabilitation and assisted-living centers, and (e) representatives of multimedia industries and standardization bodies.
000823494 588__ $$aOnline resource; title from PDF title page (viewed Sept. 12, 2017).
000823494 650_0 $$aNeural networks (Computer science)
000823494 650_0 $$aNatural computation.
000823494 650_0 $$aArtificial intelligence.
000823494 7001_ $$aEsposito, Anna,$$eeditor.
000823494 77608 $$iPrint version: $$z9783319569031$$z3319569031$$w(OCoLC)978290067
000823494 830_0 $$aSmart innovation, systems, and technologies ;$$vv. 69.
000823494 852__ $$bebk
000823494 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-56904-8$$zOnline Access$$91397441.1
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