scholarly journals Intelligent Computing, Cognitive Graphics, Neural Networks in Computer Models of Forest Fires

2019 ◽  
Author(s):  
Valery B. Taranchuk
Keyword(s):  
Neural Networks ◽  
Forest Fires ◽  
Computer Models ◽  
Intelligent Computing ◽  
Cognitive Graphics

Forest Fires Segmentation using Deep Convolutional Neural Networks

2021 ◽  
Author(s):  
Rafik Ghali ◽  
Moulay A. Akhloufi ◽  
Marwa Jmal ◽  
Wided Souidene Mseddi ◽  
Rabah Attia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Forest Fires ◽  
Deep Convolutional Neural Networks

scholarly journals Towards Ubiquitous Intelligent Computing: Heterogeneous Distributed Deep Neural Networks

2018 ◽  
pp. 1-1 ◽  
Author(s):  
Zongpu Zhang ◽  
Tao Song ◽  
Liwei Lin ◽  
Yang Hua ◽  
Xufeng He ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Intelligent Computing

Evolutionary heuristic with Gudermannian neural networks for the nonlinear singular models of third kind

2021 ◽  
Author(s):  
Zulqurnain Sabir ◽  
Hafiz Abdul Wahab
Keyword(s):  
Neural Networks ◽  
Differential Operators ◽  
Layer Structure ◽  
Research Work ◽  
Merit Function ◽  
Third Order ◽  
Intelligent Computing ◽  
Computing Platform ◽  
Hybrid Heuristics ◽  
Hidden Layer

Abstract The presented research work articulates a new design of heuristic computing platform with artificial intelligence algorithm by exploitation of modeling with feed-forward Gudermannian neural networks (FFGNN) trained with global search viability of genetic algorithms (GA) hybrid with speedy local convergence ability of sequential quadratic programing (SQP) approach, i.e., FFGNN-GASQP for solving the singular nonlinear third order Emden-Fowler (SNEF) models. The proposed FFGNN-GASQP intelligent computing solver Gudermannian kernel unified in the hidden layer structure of FFGNN systems of differential operators based on the SNEF that are arbitrary connected to represent the error-based merit function. The optimization objective function is performed with hybrid heuristics of GASQP. Three problems of the third order SNEF are used to evaluate the correctness, robustness and effectiveness of the designed FFGNN-GASQP scheme. Statistical assessments of the performance of FFGNN-GASQP are used to validate the consistent accuracy, convergence and stability.

Introduction

2013 ◽  
pp. 1-5
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Intelligence ◽  
Evolutionary Computing ◽  
Historical Background ◽  
Intelligent Computing ◽  
Biometric Security ◽  
Introductory Overview ◽  
Over Time

This chapter presents an introductory overview of the application of computational intelligence in biometrics. Starting with the historical background on artificial intelligence, the chapter proceeds to the evolutionary computing and neural networks. Evolutionary computing is an ability of a computer system to learn and evolve over time in a manner similar to humans. The chapter discusses swarm intelligence, which is an example of evolutionary computing, as well as chaotic neural network, which is another aspect of intelligent computing. At the end, special concentration is given to a particular application of computational intelligence—biometric security.

scholarly journals Revisiting Batch Normalization for Training Low-Latency Deep Spiking Neural Networks From Scratch

2021 ◽  
Vol 15 ◽  
Author(s):  
Youngeun Kim ◽  
Priyadarshini Panda
Keyword(s):  
Neural Networks ◽  
Temporal Dynamics ◽  
Temporal Characteristic ◽  
Spiking Neural Networks ◽  
Time Step ◽  
Intelligent Computing ◽  
Early Exit ◽  
Batch Normalization ◽  
Binary Event ◽  
Neuromorphic Hardware

Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield huge energy efficiency benefits on neuromorphic hardware. However, SNNs convey temporally-varying spike activation through time that is likely to induce a large variation of forward activation and backward gradients, resulting in unstable training. To address this training issue in SNNs, we revisit Batch Normalization (BN) and propose a temporal Batch Normalization Through Time (BNTT) technique. Different from previous BN techniques with SNNs, we find that varying the BN parameters at every time-step allows the model to learn the time-varying input distribution better. Specifically, our proposed BNTT decouples the parameters in a BNTT layer along the time axis to capture the temporal dynamics of spikes. We demonstrate BNTT on CIFAR-10, CIFAR-100, Tiny-ImageNet, event-driven DVS-CIFAR10 datasets, and Sequential MNIST and show near state-of-the-art performance. We conduct comprehensive analysis on the temporal characteristic of BNTT and showcase interesting benefits toward robustness against random and adversarial noise. Further, by monitoring the learnt parameters of BNTT, we find that we can do temporal early exit. That is, we can reduce the inference latency by ~5 − 20 time-steps from the original training latency. The code has been released at https://github.com/Intelligent-Computing-Lab-Yale/BNTT-Batch-Normalization-Through-Time.

Neural Networks as Forest Fires

2014 ◽  
pp. 481-487
Author(s):  
Pascal Wallisch ◽  
Michael E. Lusignan ◽  
Marc D. Benayoun ◽  
Tanya I. Baker ◽  
Adam S. Dickey ◽  
...  
Keyword(s):  
Neural Networks ◽  
Forest Fires

scholarly journals Pattern Recognition of Spiking Neural Networks Based on Visual Mechanism and Supervised Synaptic Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiumin Li ◽  
Hao Yi ◽  
Shengyuan Luo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Visual Cortex ◽  
Image Classification ◽  
Learning Rule ◽  
Spiking Neural Networks ◽  
Layer By Layer ◽  
Intelligent Computing ◽  
Training Samples ◽  
Feed Forward Network

Electrophysiological studies have shown that mammalian primary visual cortex are selective for the orientations of visual stimuli. Inspired by this mechanism, we propose a hierarchical spiking neural network (SNN) for image classification. Grayscale input images are fed through a feed-forward network consisting of orientation-selective neurons, which then projected to a layer of downstream classifier neurons through the spiking-based supervised tempotron learning rule. Based on the orientation-selective mechanism of the visual cortex and tempotron learning rule, the network can effectively classify images of the extensively studied MNIST database of handwritten digits, which achieves 96 % classification accuracy based on only 2000 training samples (traditional training set is 60000 ). Compared with other classification methods, our model not only guarantees the biological plausibility and the accuracy of image classification but also significantly reduces the needed training samples. Considering the fact that the most commonly used deep learning neural networks need big data samples and high power consumption in image recognition, this brain-inspired computational neural network model based on the layer-by-layer hierarchical image processing mechanism of the visual cortex may provide a basis for the wide application of spiking neural networks in the field of intelligent computing.

scholarly journals Redes Neurais Artificiais na Previsão de Queimadas e Incêndios no Pantanal (Artificial Neural Networks in Prediction of Forest Fires and Burns in the Pantanal)

2017 ◽  
Vol 10 (5) ◽  
pp. 1355
Author(s):  
Hevelyne Henn da Gama Viganó ◽  
Celso Correia de Souza ◽  
Marcia Ferreira Cristaldo ◽  
Leandro De Jesus
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Forest Fires ◽  
Multilayer Perceptron ◽  
Environmental Variables ◽  
Meteorological Variables ◽  
Burned Area ◽  
Artificial Neural

O bioma pantaneiro é acometido anualmente por um grande número de queimadas e incêndios. Prever esses eventos é de suma importância, uma vez que, prejuízos à fauna e à flora poderiam ser minimizados e catástrofes evitadas. Uma intervenção imediata do poder público na mitigação desses eventos passa, essencialmente, pela previsão do número de focos e das áreas queimadas, e posteriormente, na localização desses focos e na identificação das áreas. Dados precisos sobre as variáveis ambientais, em tempo real, podem ser obtidos através do sensoriamento remoto, aliado aos sistemas de informações geográficas, às técnicas de inteligência artificial e estatística aplicada, favorecendo às tomadas de decisão na previsão. O objetivo deste estudo foi aplicar a técnica de Redes Neurais Artificiais (RNAs) para a previsão dos focos e das áreas queimadas no Pantanal Sul-Mato-Grossense. O centro de previsão de tempo e estudos climáticos do Instituto Nacional de Pesquisas Espaciais (INPE), bem como, o Instituto Nacional de Meteorologia (INMET) possuem bancos de dados do Pantanal, dos quais, as variáveis envolvidas nesse processo foram extraídas. Utilizando as RNAs do tipo Multilayer Perceptron, com algoritmo Retropropagation de aprendizagem foi possível prever o valor do número de focos com um ajuste de 84,8% utilizando um conjunto de variáveis meteorológicas como preditoras e, de 99,4% usando como preditora, somente a série temporal do número de focos. No entanto, o ajuste passou a ser 90,3% ao se realizar a previsão da área queimada, utilizando as mesmas variáveis, e de 98,6%, usando como preditora, somente os dados da área queimada.  A B S T R A C TOn a year basis the Pantanal biome is affected by a large number of fires and fire points. Predicting these events is of paramount importance, since damage to fauna and flora could be minimized and disasters might be avoided. Immediate intervention of the government in mitigating these events essentially depends on the identification and location of fire outbreaks. Accurate and reliable data on environmental variables in real time can be obtained by means of remote sensing coupled with geographic information systems, techniques of artificial intelligence and applied statistics, which would favor the decision-making in foreseeing fire foci and burned area. The aim of this study was to apply the technique of artificial neural networks to predict the fire foci and burned areas in the Pantanal Sul-Mato-Grossense. The center for weather forecast and climate studies of the National Institute for Space Research (INPE) and the National Institute of Meteorology (INMET) afford meteorological database of the Pantanal region, from which the environmental variables involved in this process were extracted. By using Multilayer Perceptron Artificial Neural Networks with Backpropagation algorithm, it was possible to predict the value of the number of fire foci with an adjustment of 84,8% using a set of meteorological variables as predictors and of 99,4% using as a predictor only the time series of the number fire foci.  However, the adjustment became 90,3% when the forecast of the burned area, using the same meteorological variables, and 98,6%, using as a predictor, only the time series of the burned area.Keywords: fire forecasting, environmental monitoring,artificial intelligence. 

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