Fractal based adaptive boosting algorithm for cognitive detection of computer malware

This paper employed the intelligent approach based on machine learning categorized as base and ensemble methods in classifying the disaster risk in the Philippines. It focused on the Decision Trees, Support Vector Machine, Adaptive Boosting Algorithm with Decision Trees, and Support Vector Machine as base estimators. The research used the Exponential Regression for missing value imputation and converted the number of casualties, damaged houses, and properties into five (5) risk levels using Quantile Method. The 10-fold cross-validation was used to validate the proposed algorithms. The experiment shows that Decision Trees and Adaptive Decision Trees are the most suitable models for the disaster data with the score of more than 90%, more than 75%, more than 75% in all the classification metrics (accuracy, precision, recall f1-score) when applied to classification risk levels of casualties, damaged houses and damaged properties respectively.

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AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning

Neurocomputing ◽

10.1016/j.neucom.2020.03.064 ◽

2020 ◽

Vol 404 ◽

pp. 351-366 ◽

Cited By ~ 2

Author(s):

Aboozar Taherkhani ◽

Georgina Cosma ◽

T.M. McGinnity

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Imbalanced Datasets ◽

Adaptive Boosting ◽

Boosting Algorithm

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Machine Vision Application on Science and Industry

Developing and Applying Optoelectronics in Machine Vision - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-0632-4.ch005 ◽

2017 ◽

pp. 146-179

Author(s):

Oleg Starostenko ◽

Claudia Cruz-Perez ◽

Vicente Alarcon-Aquino ◽

Viktor I. Melnik ◽

Vera Tyrsa

Keyword(s):

Face Detection ◽

Recognition Rate ◽

Adaptive Boosting ◽

Human Face Detection ◽

Machine Vision Application ◽

Face Detection And Recognition ◽

Actual Field ◽

Boosting Algorithm ◽

Saturated Color ◽

Correct Recognition Rate

Face detection, tracking and recognition is still actual field of human centered technologies used for developing more natural communication between computing artefacts and users. Analyzing modern trends and advances in this field, two approaches for face sensing and recognition have been proposed. The first color/shape-based approach uses sets of fuzzy saturated color regions providing face detection by Fourier descriptors and recognition by SVM. The second approach provides fast face detection by adaptive boosting algorithm, and recognition based on SIFT key point extraction into eye-nose-mouth regions has been improved using Bayesian approach. Designed systems have been tested in order to evaluate capability of the proposed approaches to detect, trace and interpret faces of known individuals registered into facial standard databases providing correct recognition rate in range of 94.5-99.0% with recall up to 46%. The conducted tests ensure that both approaches have satisfactory performance achieving less than 3 seconds for human face detection and recognition in live video streams.

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An Adaptive Boosting Algorithm for Image Denoising

Mathematical Problems in Engineering ◽

10.1155/2019/8365932 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14

Author(s):

Zhuang Fang ◽

Xuming Yi ◽

Liming Tang

Keyword(s):

Image Denoising ◽

Noise Level ◽

Signal To Noise Ratio ◽

Visual Quality ◽

Estimation Algorithm ◽

Noisy Image ◽

Adaptive Boosting ◽

Noise Level Estimation ◽

Parameter Configuration ◽

Boosting Algorithm

Image denoising is an important problem in many fields of image processing. Boosting algorithm attracts extensive attention in recent years, which provides a general framework by strengthening the original noisy image. In such framework, many classical existing denoising algorithms can improve the denoising performance. However, the boosting step is fixed or nonadaptive; i.e., the noise level in iteration steps is set to be a constant. In this work, we propose a noise level estimation algorithm by combining the overestimation and underestimation results. Based on this, we further propose an adaptive boosting algorithm that excludes intricate parameter configuration. Moreover, we prove the convergence of the proposed algorithm. Experimental results that are obtained in this paper demonstrate the effectiveness of the proposed adaptive boosting algorithm. In addition, compared with the classical boosting algorithm, the proposed algorithm can get better performance in terms of visual quality and peak signal-to-noise ratio (PSNR).

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A Study in Weather Model Output Postprocessing: Using the Boosting Method for Thunderstorm Detection

Weather and Forecasting ◽

10.1175/2008waf2007047.1 ◽

2009 ◽

Vol 24 (1) ◽

pp. 211-222 ◽

Cited By ~ 3

Author(s):

Donat Perler ◽

Oliver Marchand

Keyword(s):

Substantial Improvement ◽

Probability Of Detection ◽

Model Output ◽

Adaptive Boosting ◽

Linear Discriminant ◽

Output Surface ◽

Running System ◽

Weather Model ◽

Boosting Algorithm ◽

Boosting Method

Abstract In this work, a new approach to weather model output postprocessing is presented. The adaptive boosting algorithm is used to train a set of simple base classifiers with historical data from weather model output, surface synoptic observation (SYNOP) messages, and lightning data. The resulting overall method then can be used to classify weather model output to identify potential thunderstorms. The method generates a certainty measure between −1 and 1, describing how likely a thunderstorm is to occur. Using a threshold, the measure can be converted to a binary decision. When compared to a linear discriminant and a method currently employed in an expert system from the German Weather Service, boosting achieves the best validation scores. A substantial improvement of the probability of detection of up to 72% and a decrease of the false alarm rate down to 34% can be achieved for the identification of thunderstorms in model analysis. Independent of the verification results, the method has several useful properties: good cross-validation results, short learning time (≤10 min sequential run time for the experiments on a standard PC), comprehensible inner values of the underlying statistical analysis, and the simplicity of adding predictors to a running system. This paper concludes with a set of possible other applications and extensions to the presented example of thunderstorm detection.

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