Early stopping of iterative algorithms is a widely-used form of regularization in statistics, commonly used in conjunction with boosting and related gradient-type algorithms. Although consistency results have been established in some settings, such estimators are less well-understood than their analogues based on penalized regularization. In this paper, for a relatively broad class of loss func...

The main objective of this paper is a texture based solution to the problem of acute stroke tissue recognition on computed tomography images. Our proposed method of early stroke indication was based on two fundamental steps: i) segmentation of potential areas with distorted brain tissue (selection of regions of interest), ii) acute stroke tissue recognition by extracting and then classifying a ...

We propose an automatic algorithm for phase labeling that relies on the intensity changes in anatomical regions due to the contrast agent propagation. The regions (specified by aorta, vena cava, liver, and kidneys) are first detected by a robust learning-based discriminative algorithm. The intensities inside each region are then used in multi-class LogitBoost classifiers to independently estima...

Different cultivars of seeds may have different properties. Therefore, distinguishing be important for seed processing and product quality. This study was aimed at revealing the usefulness innovative models developed based on selected image textures built using traditional machine algorithms cultivar classification quince seeds. The belonging to four ‘Uspiech’, ‘Leskovac’, ‘Bereczki’, ‘Kaszczen...

Brownboost is an adaptive, continuous time boosting algorithm based on the Boost-by-Majority (BBM) algorithm. Though it has been little studied at the time of writing, it is believed that it should prove especially robust with respect to noisy data sets. This would make it a very useful boosting algorithm for real-world applications. More familiar algorithms such as Adaboost, or its successor L...

Abstract The emergence of the Internet Things (IoT) concept as a new direction technological development raises problems such valid and timely identification devices, security vulnerabilities that can be exploited for malicious activities, management devices. communication IoT devices generates traffic has specific features differences with respect to conventional This research seeks analyze po...

The aim of this study was to develop the procedure for varietal discrimination date palm fruit using image analysis and traditional machine learning techniques. images ‘Mejhoul’, ‘Boufeggous’, ‘Aziza’, ‘Assiane’, ‘Bousthammi’ varieties, converted individual color channels, were processed extract texture parameters. After performing attribute selection, textures used build models intended differ...

The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development simple, non-invasive models. However, different were utilized conjunction with various data, either alone or combination other biophysical and lifestyle variables. It is essential to assess impacts chosen models using simple measurements. We developed tested 13 methods neura...

Groundwater potential assessment is essential for optimum utilization and recharge of groundwater resources the proper development management an area. The main aim this study to develop accurate map Dak Nong Province (Vietnam) using hybrid artificial intelligence models, which are a combination Random Forest (RF) its Ensemble Framework (AdaBoost - ABRF, Bagging BRF LogitBoost LBRF). In study, t...

ABSTRACT The ESA’s X-ray Multi-mirror Mission (XMM–Newton) created a new high-quality version of the XMM–Newton serendipitous source catalogue, 4XMM-DR9, which provides wealth information for observed sources. 4XMM-DR9 catalogue is correlated with Sloan Digital Sky Survey (SDSS) DR12 photometric data base and AllWISE base; we then get sources from X-ray, optical, and/or infrared bands obtain XM...