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Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))
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DEEP LEARNING TECHNIQUES: CLUSTER ANALYSIS and PATTERN RECOGNITION with NEURAL NETWORKS. Examples with MATLAB
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Pattern Recognition with Neural Networks in C++
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Guide to Convolutional Neural Networks for Computer Vision (Synthesis Lectures on Computer Vision)
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Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists
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Adaptive Pattern Recognition and Neural Networks
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Fundamentals of CNN in Deep Learning As a result of its capacity to handle massive volumes of data, Deep Learning has become an important t…
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Artificial neural networks are computational systems vaguely inspired by design of natural neural networks (NNN). These systems are also called connectionist systems. Learn what is Artificial Neural Networks in detail through this blog.
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What must man do to prevent the destruction of the planet, because today it is one of the most real threats. Not being able to stop the course of world history, we are still free in choosing the direction of its development, and do not have to carelessly go with the flow. Is poverty, division between poor and rich, war and destruction due to natural disasters an integral part of the divine plan? Or maybe it is the result of the imperfection of human nature, and can still be corrected? And then the incomprehensible and uncontrollable elements will we be able to digitally control and thoroughly investigate all punctures? But bio and digital technologies give us great opportunities for that. • Author: Larissa Mironova • ISBN:9786138920793 • Format:Paperback • Publication Date:2020-02-03
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One of the most challenging issues in modelling today’s large-scale computational systems is to effectively manage highly parametrised distributed environments such as computational grids, clouds, ad hoc networks and P2P networks. Next-generation computational grids mustprovide a wide range of services and high performance computing infrastructures. Various types of information and data processed in the large-scale dynamic grid environment may be incomplete, imprecise, and fragmented, which complicates the specification of proper evaluation criteria and which affects both the availability of resources and the final collective decisions of users. The complexity of grid architectures and grid management may also contribute towards higher energy consumption. All of these issues necessitate the development of intelligent resource management techniques, which are capable of capturing all of this complexity and optimising meaningful metrics for a wide range of grid applications. This book covers hot topics in the design, administration and management of dynamic grid environments with a special emphasis on the preferences and autonomous decisions of system users, secure access to the processed data and services, and application of green technologies. It features advanced research related to scalable genetic-based heuristic approaches to grid scheduling, whereby new scheduling criteria, such as system reliability, security, and energy consumption are incorporated into a general scheduling model. This book may be a valuable reference for students, researchers, and practitioners who work on – or who are interested in joining — interdisciplinary research efforts in the areas of distributed and evolutionary computation. • Author: Joanna Kolodziej • ISBN:9783642436611 • Format:Paperback • Publication Date:2014-08-09
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This book provides an introduction to the use of automated methods for gathering competitive strategic intelligence. It examines the gathering of knowledge that appears of paramount importance to organizations. Introduction.- Research Foundations.- Competitive Intelligence Capturing Systems.- Research Topics and Applications.- Conclusion. • Author: Cai-Nicolas Ziegler • ISBN:9783642277139 • Format:Hardcover • Publication Date:2012-03-14
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Learning sequential data with the help of linear systems.- A spiking neural network for personalised modelling of Electrogastogrophy (EGG).- Improving generalization abilities of maximal average margin classifiers.- Finding small sets of random Fourier features for shift-invariant kernel approximation.- Incremental construction of low-dimensional data representations.- Soft-constrained nonparametric density estimation with artificial neural networks.- Density based clustering via dominant sets.- Co-training with credal models.- Interpretable classifiers in precision medicine: feature selection and multi-class categorization.- On the evaluation of tensor-based representations for optimum-pathforest classification.- On the harmony search using quaternions.- Learning parameters in deep belief networks through firefly algorithm.- Towards effective classification of imbalanced data with convolutional neural networks.- On CPU performance optimization of restricted Boltzmann machine and convolutional RBM.- Comparing incremental learning strategies for convolutional neural networks.- Approximation of graph edit distance by means of a utility matrix.- Time series classification in reservoir- and model-space: a comparison.- Objectness scoring and detection proposals in forward-Looking sonar images with convolutional neural networks.- Background categorization for automatic animal detection in aerial videos using neural networks.- Predictive segmentation using multichannel neural networks in Arabic OCR system.- Quad-tree based image segmentation and feature extraction to recognize online handwritten Bangla characters.- A hybrid recurrent neural network/dynamic probabilistic graphical model predictor of the disulfide bonding state of cysteines from the primary structure of proteins.- Using radial basis function neural networks for continuous anddiscrete pain estimation from bio-physiological signals.- Active learning for speech event detection in HCI.- Emotion recognition in speech with deep learning architectures.- On gestures and postural behavior as a modality in ensemble methods.- Machine learning driven heart rate detection with camera photoplethysmography in time domain. • ISBN:9783319461816 • Format:Paperback • Publication Date:2016-09-09
Pharmaceutical Research
Gesture recognition is one of the most popular techniques in the field of computer vision today. In recent years, many algorithms for gesture recognition have been proposed, but most of them do not have a good balance between recognition efficiency and accuracy. Therefore, proposing a dynamic gesture recognition algorithm that balances efficiency and accuracy is still a meaningful work. Currently, most of the commonly used dynamic gesture recognition algorithms are based on 3D convolutional neural networks. Although 3D convolutional neural networks consider both spatial and temporal features, the networks are too complex, which is the main reason for the low efficiency of the algorithms. To improve this problem, we propose a recognition method based on a strategy combining 2D convolutional neural networks with feature fusion. The original keyframes and optical flow keyframes are used to represent spatial and temporal features respectively, which are then sent to the 2D convolutional neural network for feature fusion and final recognition. To ensure the quality of the extracted optical flow graph without increasing the complexity of the network, we use the fractional-order method to extract the optical flow graph, creatively combine fractional calculus and deep learning. Finally, we use Cambridge Hand Gesture dataset and Northwestern University Hand Gesture dataset to verify the effectiveness of our algorithm. The experimental results show that our algorithm has a high accuracy while ensuring low network complexity.
