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USING MULTIPLE REGRESSION, NEURAL NETWORKS AND SUPPORT VECTOR MACHINES TO PREDICT LAMB CARCASSES COMPOSITION
"... The objective of this work was to use a Data Mining (DM) approach to predict, using as predictors the carcass mea-surements taken at slaughter line, the composition of lamb carcasses. One hundred and twenty five lambs of Churra Galega Bragançana breed were slaughtered, and carcasses were weighed (HC ..."
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The objective of this work was to use a Data Mining (DM) approach to predict, using as predictors the carcass mea-surements taken at slaughter line, the composition of lamb carcasses. One hundred and twenty five lambs of Churra Galega Bragançana breed were slaughtered, and carcasses were weighed (HCW) approximately 30 minutes after exsan-guination. During carcasses quartering, a caliper was used to perform subcutaneous fat measurements, over the maxi-mum depth of longissimus muscle (LM), between the 12th and 13th ribs (C12), and between the 1st and 2nd lumbar
SUPPORT VECTOR MACHINES IN MECHANICAL PROPERTIES PREDICTION OF JET GROUTING COLUMNS
, 2011
"... mining, support vector machines, sensitivity analysis. Strength and stiffness are the mechanical properties currently used in geotechnical works design, namely in jet grouting (JG) treatments. However, when working with this soil improvement technology, due to its inherent geological complexity and ..."
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mining, support vector machines, sensitivity analysis. Strength and stiffness are the mechanical properties currently used in geotechnical works design, namely in jet grouting (JG) treatments. However, when working with this soil improvement technology, due to its inherent geological complexity and high number of variables involved, such design is a hard, perhaps very hard task. To help in such task, support vector machine (SVM), which is a data mining algorithm especially adequate to explore high number of complex data, can be used to learn the complex relationship between mechanical properties of JG samples extracted from real JG columns (JGS) and its contributing factors. In the present paper, the high capabilities of SVM in Uniaxial Compressive Strength (UCS) and Elastic Young Modulus estimation of JG laboratory formulations are summarized. After that, the performance reached by the same algorithm in the study of JGS are presented and discussed. It is shown, by performing a detailed sensitivity analysis, that the relation between mixture porosity and the volumetric content of cement, as well as the JG system are the key variables in UCS prediction of JGS. Furthermore, it is underlined the exponential effect of the age of the mixture in UCS estimation as well as the high iteration between these two key variables.
Real-time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine
"... Abstract This work aims to support doctor's decision-making on predicting sepsis level and the best treatment for patients with microbiological problems. A set of Data Mining (DM) models was developed using forecasting techniques and classification models which will enable doctors' decisi ..."
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Abstract This work aims to support doctor's decision-making on predicting sepsis level and the best treatment for patients with microbiological problems. A set of Data Mining (DM) models was developed using forecasting techniques and classification models which will enable doctors' decisions about the appropriate therapy to apply, as well as the most successful one. The data used in DM models were collected at the Intensive Care Unit (ICU) of the Centro Hospitalar do Porto, in Oporto, Portugal. Classification models where considered to predict sepsis level and therapeutic plan for patients with sepsis in a supervised learning approach. Models were induced making use of the following algorithms: Decision Trees, Support Vector Machines and Naïve Bayes classifier. Confusion Matrix, including associated metrics, and Cross-validation were used for the evaluation. Analysis of the total error rate, sensitivity, specificity and accuracy were the associated metrics used to identify the most relevant measures to predict sepsis level and treatment plan under study. In conclusion, it was possible to predict with great accuracy the sepsis level (2 nd and 3 rd ), but not the therapeutic plan. Although the good results attained for sepsis (accuracy: 100%), therapeutic plan does not present the same level of accuracy (best: 62.8%).
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. ROBUST IMAGE ALIGNMENT FOR TAMPERING DETECTION 1 Robust Image Alignment for Tampering Detection
"... The widespread use of classic and newest technologies available on Internet (e.g., emails, social networks, digital repositories) has induced a growing interest on systems able to protect the visual content against malicious manipulations that could be performed during their transmission. One of the ..."
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The widespread use of classic and newest technologies available on Internet (e.g., emails, social networks, digital repositories) has induced a growing interest on systems able to protect the visual content against malicious manipulations that could be performed during their transmission. One of the main problems addressed in this context is the authentication of the image received in a communication. This task is usually performed by localizing the regions of the image which have been tampered. To this aim the aligned image should be first registered with the one at the sender by exploiting the information provided by a specific component of the forensic hash associated to the image. In this paper we propose a robust alignment method which makes use of an image hash component based on the Bag of Features paradigm. The proposed signature is attached to the image before transmission and then analyzed at destination to recover the geometric transformations which have been applied to the received image. The estimator is based on a voting procedure in the parameter space of the model used to recover the geometric transformation occurred into the manipulated image. The proposed image hash encodes the spatial distribution of the image features to deal with highly textured and contrasted tampering patterns. A block-wise tampering detection which exploits an histograms of oriented gradients representation is
Strong Image Alignment for Meddling Recognision Purpose
"... ABSTRACT: The Vast use of classic and modern technologies of internet causes increase the interest on systems that will protect in visual images against the wrongful manipulation that may be processed during the execution / transmission.One reason behind this problem is the verification of image rec ..."
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ABSTRACT: The Vast use of classic and modern technologies of internet causes increase the interest on systems that will protect in visual images against the wrongful manipulation that may be processed during the execution / transmission.One reason behind this problem is the verification of image received during communication. This work will be performed by strong image, and for this the image must be first registered by taking advantage of information provided by specific part of connected image. We describe strong image setting method in which there is a use of hash element (signatures). The required signature is also attached with image before the transmission of image as well as before the image will send at destination place to get the graphical transformation of the received image. The accessor is based on the selecting the image which is having highest preference in the parameter space to recovered the graphical transformation which is used to manipulate image. The required image encodes the spaces occurred to deal with textures and contrasted strong image types. A block-wise strong image will be detected which occurs a graphical representation showing the visual impression of distributed of data with directed slope can be also proposed. This can be also used to build the signature for each strong image block. This new technique shows that it gives nice result as compared with state-of-art method.
Research Track Poster Regression Error Characteristic Surfaces
"... This paper presents a generalization of Regression Error Characteristic (REC) curves. REC curves describe the cumulative distribution function of the prediction error of models and can be seen as a generalization of ROC curves to regression problems. REC curves provide useful information for analyzi ..."
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This paper presents a generalization of Regression Error Characteristic (REC) curves. REC curves describe the cumulative distribution function of the prediction error of models and can be seen as a generalization of ROC curves to regression problems. REC curves provide useful information for analyzing the performance of models, particularly when compared to error statistics like for instance the Mean Squared Error. In this paper we present Regression Error Characteristic (REC) surfaces that introduce a further degree of detail by plotting the cumulative distribution function of the errors across the distribution of the target variable, i.e. the joint cumulative distribution function of the errors and the target variable. This provides a more detailed analysis of the performance of models when compared to REC curves. This extra detail is particularly relevant in applications with non-uniform error costs, where it is important to study the performance of models for specific ranges of the target variable. In this paper we present the notion of REC surfaces, describe how to use them to compare the performance of models, and illustrate their use with an important practical class of applications: the prediction of rare extreme values.
Applying REC Analysis to Ensembles of Sigma-Point Kalman Filters
"... The Sigma-Point Kalman Filters (SPKF) is a family of filters that achieve very good performance when applied to time series. Currently most researches involving time series forecasting use the Sigma-Point Kalman Filters, however they do not use an ensemble of them, which could achieve a better perfo ..."
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The Sigma-Point Kalman Filters (SPKF) is a family of filters that achieve very good performance when applied to time series. Currently most researches involving time series forecasting use the Sigma-Point Kalman Filters, however they do not use an ensemble of them, which could achieve a better performance. The REC analysis is a powerful technique for visualization and comparison of regression models. The objective of this work is to advocate the use of REC curves in order to compare the SPKF and ensembles of them and select the best model to be used. 1.
Ranking-Based Evaluation of Regression Models
"... We suggest the use of ranking-based evaluation measures for regression models, as a complement to the commonly used residual-based evaluation. We argue that in some cases, such as the case study we present, ranking can be the main underlying goal in building a regression model, and ranking performan ..."
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We suggest the use of ranking-based evaluation measures for regression models, as a complement to the commonly used residual-based evaluation. We argue that in some cases, such as the case study we present, ranking can be the main underlying goal in building a regression model, and ranking performance is the correct evaluation metric. However, even when ranking is not the contextually correct performance metric, the measures we explore still have significant advantages: They are robust against extreme outliers in the evaluation set; and they are interpretable. The two measures we consider correspond closely to non-parametric correlation coefficients commonly used in data analysis (Spearman’s ρ and Kendall’s τ); and they both have interesting graphical representations, which, similarly to ROC curves, offer useful “partial ” model performance views, in addition to a one-number summary in the area under the curve. We illustrate our methods on a case study of evaluating IT Wallet size estimation models for IBM’s customers. 1
Using Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networks
"... Abstract. Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regression functions simultaneously in a single graph. The objective of this work is to present a new approach for ..."
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Abstract. Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regression functions simultaneously in a single graph. The objective of this work is to present a new approach for model selection in ensembles of Neural Networks, in which we propose the use of REC curves in order to select a good threshold value, so that only residuals greater than that value are considered as errors. The algorithm was empirically evaluated and its results were analyzed also by means of REC curves. 1
