Artificial intelligence and soft computing : 16th International Conference, ICAISC 2017, Zakopane, Poland, June 11-15, 2017, Proceedings. Part I / Leszek Rutkowski, Marcin Korytkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.).

By: (16th : ICAISC (Conference) (16th : 2017 : Zakopane, Poland)
Contributor(s): Rutkowski, Leszek [editor.] | Korytkowski, Marcin [editor.] | Scherer, Rafał [editor.] | Tadeusiewicz, Ryszard [editor.] | Zadeh, Lotfi A. (Lotfi Asker) [editor.] | Zurada, Jacek M [editor.]
Material type: TextTextSeries: SerienbezeichnungLecture notes in computer science: 10245.; LNCS sublibrary: Publisher: Cham, Switzerland : Springer, 2017Description: 1 online resource (xxiv, 776 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9783319590639; 3319590634; 3319590626; 9783319590622Other title: ICAISC 2017Subject(s): Artificial intelligence -- Congresses | Soft computing -- Congresses | Computers -- Computer Vision & Pattern Recognition | Computers -- Programming -- Algorithms | Computers -- Logic Design | Computers -- Database Management -- Data Mining | Computers -- Expert Systems | Computer vision | Algorithms & data structures | Computer architecture & logic design | Data mining | Expert systems -- knowledge-based systems | Computers -- Intelligence (AI) & Semantics | Artificial intelligence | Artificial intelligence | Soft computingGenre/Form: Electronic books. | Conference papers and proceedings. Additional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q334 | .I23 2017ebOnline resources: Click here to access online
Contents:
Intro; Preface; Organization; Contents -- Part I; Contents -- Part II; Neural Networks and Their Applications; Author Profiling with Classification Restricted Boltzmann Machines; 1 Introduction; 2 Author Profile Dimensions; 3 Restricted Boltzmann Machines; 4 Probabilities and Gradients; 4.1 Discriminative Training; 4.2 Generative Training; 5 Evaluation Datasets; 6 Experiments and Results; 6.1 Overall Results; 7 Conclusions; References; Parallel Implementation of the Givens Rotations in the Neural Network Learning Algorithm; 1 Introduction; 2 Givens Elimination Step; 3 Givens QR Decomposition.
4 QR Decomposition in Neural Network Weights Update5 Parallel Implementation; 6 Simulation Results; 7 Conclusion; References; Parallel Levenberg-Marquardt Algorithm Without Error Backpropagation; 1 Introduction; 2 Parallel Realisation; 2.1 Calculating the Weight Derivatives Without Error Backpropagation; 2.2 Calculating the A Matrix and the Gradient Vector; 2.3 The QR Decomposition Based on the Householser Reflections; 3 Computational Results; 4 Conclusions; References; Spectral Analysis of CNN for Tomato Disease Identification; Abstract; 1 Introduction; 2 Related Works.
3 Spectral Analysis of CNN for Tomato Disease3.1 Deep Visualization of CNN; 3.2 Color Sensitivity of RGB Images; 3.3 Sensitivity to Color with Different Wavelength Values; 3.3.1 Visible Spectrum of Images; 4 Experimental Results; 4.1 Dataset Description; 4.2 CNN Activations and Features Visualization; 4.2.1 Activations of Neurons; 4.2.2 RGB Color Sensitivity; 4.2.3 Feature Maps; 5 Conclusion and Future Work; Acknowledgments; References; From Homogeneous Network to Neural Nets with Fractional Derivative Mechanism; 1 Introduction; 2 Weight Distribution with Fractional Calculus.
3 Fractional Derivative Inside Neuron Transfer Function4 The Fractional Mechanism Within 2D Homogeneous Network; 5 Conclusion; References; Neurons Can Sort Data Efficiently; 1 Introduction; 2 Models of Neurons, Receptors, and the Senses; 2.1 Sensory Fields and Sensors; 2.2 Extreme, Sensory and Object Neurons; 3 Simplistic Sequential Neural Associative Sorting; 4 Conclusions and Remarks; References; Avoiding Over-Detection: Towards Combined Object Detection and Counting; 1 Introduction; 2 Related Work; 2.1 Deep Learning Methods for Object Detection; 2.2 Deep Learning Methods for Cell Detection.
3 Method3.1 Loss Function; 3.2 Model Architecture; 4 Results; 5 Conclusion; References; Echo State Networks Simulation of SIR Distributed Control; 1 Introduction; 2 Echo State Networks; 3 SIR Model with Delay and Spatial Diffusions; 3.1 Distributed Optimal Control Problem; 4 Discretisation and Adaptive Critic Neural Networks Solution of the Distributed Optimal Control; 4.1 Numerical Simulation; 5 Conclusion; References; The Study of Architecture MLP with Linear Neurons in Order to Eliminate the ``vanishing Gradient'' Problem; 1 Introduction; 2 Nonlinearity capabilities of deep neural networks.
Summary: The two-volume set LNAI 10245 and LNAI 10246 constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, held in Zakopane, Poland in June 2017. The 133 revised full papers presented were carefully reviewed and selected from 274 submissions. The papers included in the first volume are organized in the following five parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; computer vision, image and speech analysis; and bioinformatics, biometrics and medical applications.
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Includes author index.

Online resource; title from PDF title page (SpringerLink, viewed June 9, 2017).

The two-volume set LNAI 10245 and LNAI 10246 constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, held in Zakopane, Poland in June 2017. The 133 revised full papers presented were carefully reviewed and selected from 274 submissions. The papers included in the first volume are organized in the following five parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; computer vision, image and speech analysis; and bioinformatics, biometrics and medical applications.

Intro; Preface; Organization; Contents -- Part I; Contents -- Part II; Neural Networks and Their Applications; Author Profiling with Classification Restricted Boltzmann Machines; 1 Introduction; 2 Author Profile Dimensions; 3 Restricted Boltzmann Machines; 4 Probabilities and Gradients; 4.1 Discriminative Training; 4.2 Generative Training; 5 Evaluation Datasets; 6 Experiments and Results; 6.1 Overall Results; 7 Conclusions; References; Parallel Implementation of the Givens Rotations in the Neural Network Learning Algorithm; 1 Introduction; 2 Givens Elimination Step; 3 Givens QR Decomposition.

4 QR Decomposition in Neural Network Weights Update5 Parallel Implementation; 6 Simulation Results; 7 Conclusion; References; Parallel Levenberg-Marquardt Algorithm Without Error Backpropagation; 1 Introduction; 2 Parallel Realisation; 2.1 Calculating the Weight Derivatives Without Error Backpropagation; 2.2 Calculating the A Matrix and the Gradient Vector; 2.3 The QR Decomposition Based on the Householser Reflections; 3 Computational Results; 4 Conclusions; References; Spectral Analysis of CNN for Tomato Disease Identification; Abstract; 1 Introduction; 2 Related Works.

3 Spectral Analysis of CNN for Tomato Disease3.1 Deep Visualization of CNN; 3.2 Color Sensitivity of RGB Images; 3.3 Sensitivity to Color with Different Wavelength Values; 3.3.1 Visible Spectrum of Images; 4 Experimental Results; 4.1 Dataset Description; 4.2 CNN Activations and Features Visualization; 4.2.1 Activations of Neurons; 4.2.2 RGB Color Sensitivity; 4.2.3 Feature Maps; 5 Conclusion and Future Work; Acknowledgments; References; From Homogeneous Network to Neural Nets with Fractional Derivative Mechanism; 1 Introduction; 2 Weight Distribution with Fractional Calculus.

3 Fractional Derivative Inside Neuron Transfer Function4 The Fractional Mechanism Within 2D Homogeneous Network; 5 Conclusion; References; Neurons Can Sort Data Efficiently; 1 Introduction; 2 Models of Neurons, Receptors, and the Senses; 2.1 Sensory Fields and Sensors; 2.2 Extreme, Sensory and Object Neurons; 3 Simplistic Sequential Neural Associative Sorting; 4 Conclusions and Remarks; References; Avoiding Over-Detection: Towards Combined Object Detection and Counting; 1 Introduction; 2 Related Work; 2.1 Deep Learning Methods for Object Detection; 2.2 Deep Learning Methods for Cell Detection.

3 Method3.1 Loss Function; 3.2 Model Architecture; 4 Results; 5 Conclusion; References; Echo State Networks Simulation of SIR Distributed Control; 1 Introduction; 2 Echo State Networks; 3 SIR Model with Delay and Spatial Diffusions; 3.1 Distributed Optimal Control Problem; 4 Discretisation and Adaptive Critic Neural Networks Solution of the Distributed Optimal Control; 4.1 Numerical Simulation; 5 Conclusion; References; The Study of Architecture MLP with Linear Neurons in Order to Eliminate the ``vanishing Gradient'' Problem; 1 Introduction; 2 Nonlinearity capabilities of deep neural networks.

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