An Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2011, Volume and Issue: 50(3), P. 894 - 909

Published: Aug. 25, 2011

The artificial immune network (AIN), a computational intelligence model based on systems inspired by the vertebrate system, has been widely utilized for pattern recognition and data analysis. However, due to inherent complexity of current AIN models, their application multi-/hyperspectral remote sensing image classification severely restricted. This paper presents novel supervised AIN-namely, antibody (ABNet), theory-aimed at performing classification. To construct ABNet, population (AB) was utilized. AB is set antibodies where each two attributes-its center vector recognizing radius-thus can recognize all antigens within its radius. In contrast traditional model, ABNet adaptively obtain these parameters evolving without relying user-defined in training step. During process training, enlarge range, operators (such as clone, mutation, selection) were used enhance find better feature space, which may much antigen possible. After process, trained classify image, exhibiting superior learning abilities. Three experiments with different types images performed evaluate performance proposed algorithm comparison other algorithms: minimum distance, Gaussian maximum likelihood, back-propagation neural network, our previously developed classifiers-resource-limited multiple-valued classifier. experimental results demonstrate that remarkable accuracy ability provide effective imagery, methods.

Language: Английский

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Comparison of Different Classification Techniques Using WEKA for Breast Cancer

IFMBE proceedings, Journal Year: 2007, Volume and Issue: unknown, P. 520 - 523

Published: Jan. 1, 2007

Language: Английский

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Empirical Mode Decomposition of Hyperspectral Images for Support Vector Machine Classification

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2010, Volume and Issue: unknown

Published: Oct. 5, 2010

This paper presents the utilization of empirical mode decomposition (EMD) hyperspectral images to increase classification accuracy using support vector machine (SVM)-based classification. EMD has been shown in literature be particularly suitable for nonlinear and nonstationary signals is used this decompose image bands into several intrinsic functions (IMFs) a final residue. utilized improve hyperspectral-image-classification by effectively exploiting feature that performs spatially adaptive with respect features. two different approaches improved making use EMD. In first approach, IMFs corresponding each band are obtained sums lower order as new features SVM. second pieces information contained combined composite kernels SVM higher accuracy.

Language: Английский

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A support vector machine approach for detecting gene‐gene interaction

Genetic Epidemiology, Journal Year: 2007, Volume and Issue: 32(2), P. 152 - 167

Published: Oct. 29, 2007

Abstract Although genetic factors play an important role in most human diseases, multiple genes or and environmental may influence individual risk. In order to understand the underlying biological mechanisms of complex it is relationships that control process. this paper, we consider different perspectives, from each optimization, complexity analysis, algorithmic design, which allows us describe a reasonable applicable computational framework for detecting gene‐gene interactions. Accordingly, support vector machine combinatorial optimization techniques (local search algorithm) were tailored fit within framework. proposed approach computationally expensive, our results indicate promising tool identification characterization high gene‐environment We have demonstrated several advantages method, including strong power classification, less concern overfitting, ability handle unbalanced data achieve more stable models. would like make accessible epidemiologists, promote use extension these powerful approaches. Genet. Epidemiol . 2008. © 2007 Wiley‐Liss, Inc.

Language: Английский

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Automated Vulnerability Analysis: Leveraging Control Flow for Evolutionary Input Crafting

Published: Dec. 1, 2007

We present an extension of traditional "black box" fuzz testing using a genetic algorithm based upon dynamic Markov model fitness heuristic. This heuristic allows us to "intelligently" guide input selection feedback concerning the "success" past inputs that have been tried. Unlike many software tools, our implementation is strictly binary code and does not require source be available. Our evaluation on Windows server program shows this approach superior random black box fuzzing for increasing coverage depth penetration into control flow logic. As result, technique may beneficial development future automated vulnerability analysis tools.

Language: Английский

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Real-Time Gesture Detection Based on Machine Learning Classification of Continuous Wave Radar Signals

IEEE Sensors Journal, Journal Year: 2020, Volume and Issue: 21(6), P. 8310 - 8322

Published: Dec. 17, 2020

Classical signal processing methodologies have been infiltrated by machine learning (ML) approaches for a long time, where the ML are in particular applied when it comes to gesture recognition. In this paper, we investigate naïve recognition and compare classical novel (nML) algorithms. The considered gestures simple human such as swiping hand or kicking with foot. For sake of comparability, algorithms assessed respect their true positive rate (TPR), false-positive (FPR), real-time capability together required computational power, implementability on low-cost hardware. Two different data sets utilized separately training process algorithms, both recorded making use radar results show that all superior methodologies, e.g., threshold detection. allow almost assured However, our primary contribution is design approach scalable neural networks (NNs) be executable microcontroller units (MCUs).

Language: Английский

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Analysis of support vector machine and maximum likelihood classifiers in land cover classification using Sentinel-2 images

DELETED, Journal Year: 2022, Volume and Issue: 88(2), P. 213 - 227

Published: June 1, 2022

Remote sensing data has been widely applied to classify the land cover more frequently and on a near real-time basis for updating as it is economic, less time consuming compared ground based survey. Accurate classification of use/cover classes such water body, cropland, built-up area, scrub land, fallow forest etc., one biggest challenges in natural resource inventory, management monitoring. As accuracy remote affected by many parameters which include type data, presence heterogeneous landscapes study approaches satellite imagery complex nature. Many classifiers have developed tested remotely sensed better classification. Classification mainly divided into two categories supervised unsupervised. In classification, decision boundaries feature space are determined training samples. Two namely maximum likelihood (ML) classifier, parametric classifier that assumes be normally distributed, support vector machine (SVM) non-parametric used present study, these studied five different sets Sentinel-2 image years sessions accommodate intra inter annual variations datasets. images covering part Nagpur, located Maharashtra, India were Classifier calculated using overall kappa statistics truth information. The result obtained carefully examined comparing accuracies then visual analysis. shows SVM gives coefficients its average value outputs 91.78% 0.89 order far than ML gave 87.07% 0.83 respectively. experimental results from clear produced classifying optical significant potential various use/land conditions tropical regime.

Language: Английский

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An Online Adaptive Model for Location Prediction

Springer eBooks, Journal Year: 2010, Volume and Issue: unknown, P. 64 - 78

Published: Jan. 1, 2010

Language: Английский

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Strategies for Simplifying Reactive Transport Models: A Bayesian Model Comparison

Water Resources Research, Journal Year: 2020, Volume and Issue: 56(11)

Published: Oct. 24, 2020

Abstract For simulating reactive transport on aquifer scale, various modeling approaches have been proposed. They vary considerably in their computational demands and the amount of data needed for calibration. Typically, more complex a model is, are required to sufficiently constrain its parameters. In this study, we assess set five models that simulate aerobic respiration denitrification heterogeneous at quasi steady state. probabilistic framework, test whether simplified can be used as alternatives most detailed model. The simplifications achieved by neglecting processes such dispersion or biomass dynamics, replacing spatial discretization with travel‐time‐based coordinates. We use justifiability analysis proposed Schöniger, Illman, et al. (2015, https://doi.org/10.1016/j.jhydrol.2015.07.047 ) determine how similar reference This rests principles Bayesian selection performs tradeoff between goodness‐of‐fit complexity, which is important reliability predictions. Results show that, principle, able reproduce predictions considered scenario. Yet, it became evident challenging define appropriate ranges effective parameters models. issue lead overly wide predictive distributions, counteract apparent simplicity found performing case simplification an objective comprehensive approach suitability candidate different levels detail.

Language: Английский

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Characteristics of preceding Ia activity on postactivation depression in health and disease

Journal of Neurophysiology, Journal Year: 2015, Volume and Issue: 113(10), P. 3751 - 3758

Published: April 23, 2015

Previous activation of the soleus Ia afferents causes a depression in amplitude H-reflex. This mechanism is referred to as postactivation (PAD) and suggested be presynaptically mediated. With use paired reflex paradigm (eliciting two H-reflexes with conditioning-test intervals from 80 ms 300 ms), PAD was examined group healthy individuals hemiplegic patients. Healthy showed substantial test H-reflex at all intervals. Although patient substantially less intervals, increasing interval between reflexes sharply reduced depression. In separate experiment, we varied size conditioning against constant individuals, by H-reflex, exponentially decreased. group, however, this pattern dependent on interval; caused an exponential decrease shorter than 150 ms. similar that individuals. However, conducting same protocol longer (300 ms) these patients resulted abnormal (instead reflex, exaggerated responses were observed). Fisher discriminant analysis patterns (which differed only timing stimuli) different each other. Therefore, it stroke could contributing factor for pathophysiology spasticity.

Language: Английский

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