WEIBULL MULTIPLICATIVE MODEL AND MACHINE LEARNING MODELS FOR FULL-AUTOMATIC DARK-SPOT DETECTION FROM SAR IMAGES

As a major aspect of marine pollution, oil release into the sea has serious biological and environmental impacts. Among remote sensing systems (which is a tool that offers a non-destructive investigation method), synthetic aperture radar (SAR) can provide valuable synoptic information about the position and size of the oil spill due to its wide area coverage and day/night, and all-weather capabilities. In this paper we present a new automated method for oil-spill monitoring. A new approach is based on the combination of Weibull Multiplicative Model and machine learning techniques to differentiate between dark spots and the background. First, the filter created based on Weibull Multiplicative Model is applied to each sub-image. Second, the sub-image is segmented by two different neural networks techniques (Pulsed Coupled Neural Networks and Multilayer Perceptron Neural Networks). As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approaches were tested on 20 ENVISAT and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall dataset, the average accuracies of 94.05 % and 95.20 % were obtained for PCNN and MLP methods, respectively. The average computational time for dark-spot detection with a 256 × 256 image in about 4 s for PCNN segmentation using IDL software which is the fastest one in this field at present. Our experimental results demonstrate that the proposed approach is very fast, robust and effective. The proposed approach can be applied to the future spaceborne SAR images

Oil and gas industry hydrotechnical structures design

The gradual depletion of oil and gas reserves on land and the aggravation of the global energy crisis necessitated the ever wider development of the oil and gas resources of the seabed, which contains almost three times more oil and gas than on land. Offshore oil and gas production projects will be actively developed in the coming years, given the clear global need for large volumes of fossil fuels, as evidenced by the ongoing energy crisis in Europe and shortages in the markets

Multilayer perceptron neural networks model for meteosat second generation SEVIRI daytime cloud masking

A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm) with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery

QSPR Modeling using Catalan Solvent and Solute Parameters

A área de correlação quantitativa entre estrutura e propriedade (QSPR) pode beneficiar-se de descritores moleculares que representam interações intermoleculares. Catalan desenvolveu um método de escalas solvatocrômicas para solventes que pode ser explorado para esta finalidade. Neste trabalho, escalas de solvente de Catalan foram usadas como descritores moleculares para o desenvolvimento de modelos QSPR, e para o cálculo de novos descritores de soluto para uso posterior em QSPR. As escalas Catalan para o solvente e os descritores de soluto derivados foram recentemente comparados com o método de descritores de Abraham, em termos da qualidade do QSPR desenvolvido. Os parâmetros Catalan para solventes, que mostraram uma correlação modesta com os correspondentes descritores de Abraham, mostraram-se bem sucedidos para modelar temperatura de fusão, temperatura de ebulição, ponto de ignição, índice de refração, tensão superficial, densidade e parâmetro de solubilidade dos solventes, com médias geométricas dos desvios relativos (GMRD) de 7,1, 6,6, 4,9, 3,8, 9,1, 6,0 e 4,2%, respectivamente. Os descritores do soluto foram obtidos a partir das equações de regressão entre a solubilidade de um soluto em diferentes solventes com um GMRD total de 30,0%. Os descritores de soluto obtidos desta maneira superam o modelo de solvatação geral de Abraham no cálculo de solubilidade em meio aquoso de 27 solutos de várias famílias químicas. Os descritores Catalan podem ser considerados como um recurso valioso para modelagem QSPR. The field of quantitative structure-property relationship (QSPR) can greatly benefit from molecular descriptors that particularly represent the intermolecular interactions. Catalan has developed a set of solvatochromic scales for solvents, which could be exploited for this purpose. In this work, Catalan solvent scales were explored as molecular descriptors for the development of QSPR models, and for the calculation of new solute descriptors for further use in QSPR. Catalan solvent scales and the newly derived solute descriptors were compared with the commonly used set of Abraham descriptors in terms of the quality of the developed QSPRs. Catalan solvent parameters, which showed modest correlation with the corresponding Abraham descriptors, proved to be successful in modeling melting point, boiling point, flash point, refractive index, surface tension, density, and solubility parameter of the solvents with geometric mean relative deviations (GMRD) of 7.1, 6.6, 4.9, 3.8, 9.1, 6.0, and 4.2%, respectively. The solute descriptors were obtained from regression equations between a solute's solubility in different solvents with an overall GMRD of 30.0%. The solute descriptors obtained in this way outperformed Abraham general solvation model in the calculation of aqueous solubility for 27 solutes of broad chemical ranges. It was concluded that Catalan descriptors can be regarded as a valuable resource for QSPR modeling

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

Development of band ratioing algorithms and neural networks to detection of oil spills using Landsat ETM+ data

Accurate knowledge of the spatial extents and distributions of an oil spill is very impor-tant for efficient response. This is because most petroleum products spread rapidly on the water surface when released into the ocean, with the majority of the affected area becoming covered by very thin sheets. This article presents a study for examining the feasibility of Landsat ETM+ images in order to detect oil spills pollutions. The Landsat ETM+ images for 1st, 10th, 17th May 2010 were used to study the oil spill in Gulf of Mexico. In this article, an attempt has been made to perform ratio operations to enhance the feature. The study concluded that the bands difference between 660 and 560 nm, division at 660 and 560 and division at 825 and 560 nm, normalized by 480 nm provide the best result. Multilayer perceptron neural network classifier is used in order to perform a pixel-based supervised classification. The result indicates the potential of Landsat ETM+ data in oil spill detection. The promising results achieved encourage a further analysis of the potential of the optical oil spill detection approach

MEASURING LAND USES ACCESSIBILITY BY USING FUZZY MAJORITY GIS-BASED MULTICRITERIA DECISION ANALYSIS CASE STUDY: MALAYER CITY

Public spaces accessibility has become one of the important factors in urban planning. Therefore, considerable attention has been given to measure accessibility to public spaces on the UK, US and Canada, but there are few studies outside the anglophone world especially in developing countries such as Iran. In this study an attempt has been made to measure objective accessibility to public spaces (parks, school, library and administrative) using fuzzy majority GIS-based multicriteria decision analysis. This method is for defining the priority for distribution of urban facilities and utilities as the first step towards elimination of social justice. In order to test and demonstrate the presented model, the comprehensive plan of Malayer city has been considered for ranking in three objectives and properties in view of index per capital (Green space, sport facilities and major cultural centers like library and access index). The results can be used to inform the local planning process and the GIS approach can be expanded into other local authority domains. The results shows that the distribution of facilities in Malayer city has followed on the base of cost benefit law and the human aspect of resource allocation programming of facilities (from centre to suburbs of the city)

The impact of training set data distributions for modelling of passive intestinal absorption.

This study presents regression and classification models to predict human intestinal absorption of 645 drug and drug like compounds using percentage human intestinal values from the published dataset by Hou et al. (2007c). The problem with this dataset and other datasets in the literature is there are more highly than poorly absorbed compounds. Any models developed using these datasets will be biased towards highly absorbed compounds and not applicable for use in industry where now more compounds are likely to be poorly absorbed. The study compared two training sets, TS1, a balanced (50:50) distribution of highly and poorly absorbed compounds created by under-sampling the majority high absorption compounds, with TS2, a randomly selected training set with biased distribution towards highly absorbed compounds. The regression results indicate that the best models were those developed using the balanced dataset (TS1). Also for classification, TS1 led to the most accurate models and the highest specificity value of 0.949. In comparison, TS2 led to the highest sensitivity with a value of 0.939. Thus, under-sampling the majority class of the highly absorbed compounds leads to a balanced training set (TS1) that can achieve more applicable in silico regression and classification models for the use in the industry. © 2012 Elsevier B.V. All rights reserved

Weibull multiplicative model and machine learning models for full-automatic dark-spot detection from SAR images

As a major aspect of marine pollution, oil release into the sea have serious biological and environmental impacts. Among remote sensing systems (which is a tool that offers a non-destructive investigation method), synthetic aperture radar (SAR) can provide valuable synoptic information about the position and size of the oil spill due to its wide area coverage and day/night, and all-weather capabilities. In this paper we present a new automated method for oil-spill monitoring. A new approach is based on the combination of Weibull Multiplicative Model and neural network techniques to differentiate between dark spots and the background. First, the filter created based on Weibull Multiplicative Model is applied to each sub-image. Second, the sub-image is segmented by two different neural networks techniques (Pulsed Coupled Neural Networks and Multilayer Perceptron Neural Networks). As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approaches were tested on 20 ENVISAT and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall dataset, the average accuracies of 94.05 % and 95.20 % were obtained for PCNN and MLP methods, respectively. The average computational time for dark-spot detection with a 256×256 image in about 4 s for PCNN segmentation using IDL software which is the fastest one in this field at present. Our experimental results demonstrate that the proposed approach is very fast, robust and effective. The proposed approach can be applied to the future spaceborne SAR images