Search results for "data set"
showing 10 items of 154 documents
A topological substructural approach for the prediction of P-glycoprotein substrates
2006
A topological substructural molecular design approach (TOPS-MODE) has been used to predict whether a given compound is a P-glycoprotein (P-gp) substrate or not. A linear discriminant model was developed to classify a data set of 163 compounds as substrates or nonsubstrates (91 substrates and 72 nonsubstrates). The final model fit the data with sensitivity of 82.42% and specificity of 79.17%, for a final accuracy of 80.98%. The model was validated through the use of an external validation set (40 compounds, 22 substrates and 18 nonsubstrates) with a 77.50% of prediction accuracy; fivefold full cross-validation (removing 40 compounds in each cycle, 80.50% of good prediction) and the predictio…
SIOPRED performance in a Forecasting Blind Competition
2012
In this paper we present the results obtained by applying our automatic forecasting support system, named SIOPRED, over a data set of time series in a Forecasting Blind Competition. In order to apply our procedure for providing point forecasts it has been necessary to develop an interactive strategy for the choice of the suitable length of the seasonal cycle and the seasonality form for a generalized exponential smoothing method, which have been obtained using SIOPRED. For the choice of those essential characteristics of forecasting methods, also a certain multi-objective formulation which minimizes several measures of fitting is used. Once these specifications are established, the model pa…
Algorithms for Image Reconstruction
2010
Three-dimensional (3D) imaging is becoming one of the most important applications of radioactive materials in medicine. It offers good spatial resolution, a 3D insight into the human body, and a high sensitivity in the picomolar range because markers for biological processes can be detected well when labeled with radioactive materials. In addition, the technical equipment has undergone many technological achievements. This is true for single-photon emission computed tomography (SPECT), positron emission tomography (PET), and X-ray computed tomography (CT), which is often used in connection with the nuclear medical imaging systems, as also described in chapter 5 about sources in nuclear medi…
Validation of a Reinforcement Learning Policy for Dosage Optimization of Erythropoietin
2007
This paper deals with the validation of a Reinforcement Learning (RL) policy for dosage optimization of Erythropoietin (EPO). This policy was obtained using data from patients in a haemodialysis program during the year 2005. The goal of this policy was to maintain patients' Haemoglobin (Hb) level between 11.5 g/dl and 12.5 g/dl. An individual management was needed, as each patient usually presents a different response to the treatment. RL provides an attractive and satisfactory solution, showing that a policy based on RL would be much more successful in achieving the goal of maintaining patients within the desired target of Hb than the policy followed by the hospital so far. In this work, t…
On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization
2016
Surrogate-assisted evolutionary multiobjective optimization algorithms are often used to solve computationally expensive problems. But their efficacy on handling constrained optimization problems having more than three objectives has not been widely studied. Particularly the issue of how feasible and infeasible solutions are handled in generating a data set for training a surrogate has not received much attention. In this paper, we use a recently proposed Kriging-assisted evolutionary algorithm for many-objective optimization and investigate the effect of infeasible solutions on the performance of the surrogates. We assume that constraint functions are computationally inexpensive and consid…
Distance-constrained data clustering by combined k-means algorithms and opinion dynamics filters
2014
Data clustering algorithms represent mechanisms for partitioning huge arrays of multidimensional data into groups with small in–group and large out–group distances. Most of the existing algorithms fail when a lower bound for the distance among cluster centroids is specified, while this type of constraint can be of help in obtaining a better clustering. Traditional approaches require that the desired number of clusters are specified a priori, which requires either a subjective decision or global meta–information knowledge that is not easily obtainable. In this paper, an extension of the standard data clustering problem is addressed, including additional constraints on the cluster centroid di…
INTEGRATED SFM TECHNIQUES USING DATA SET FROM GOOGLE EARTH 3D MODEL AND FROM STREET LEVEL
2017
Abstract. Structure from motion (SfM) represents a widespread photogrammetric method that uses the photogrammetric rules to carry out a 3D model from a photo data set collection. Some complex ancient buildings, such as Cathedrals, or Theatres, or Castles, etc. need to implement the data set (realized from street level) with the UAV one in order to have the 3D roof reconstruction. Nevertheless, the use of UAV is strong limited from the government rules. In these last years, Google Earth (GE) has been enriched with the 3D models of the earth sites. For this reason, it seemed convenient to start to test the potentiality offered by GE in order to extract from it a data set that replace the UAV …
Compression-based classification of biological sequences and structures via the Universal Similarity Metric: experimental assessment.
2007
Abstract Background Similarity of sequences is a key mathematical notion for Classification and Phylogenetic studies in Biology. It is currently primarily handled using alignments. However, the alignment methods seem inadequate for post-genomic studies since they do not scale well with data set size and they seem to be confined only to genomic and proteomic sequences. Therefore, alignment-free similarity measures are actively pursued. Among those, USM (Universal Similarity Metric) has gained prominence. It is based on the deep theory of Kolmogorov Complexity and universality is its most novel striking feature. Since it can only be approximated via data compression, USM is a methodology rath…
Improved polyhedral descriptions and exact procedures for a broad class of uncapacitated p-hub median problems
2019
Abstract This work focuses on a broad class of uncapacitated p-hub median problems that includes non-stop services and setup costs for the network structures. In order to capture both the single and the multiple allocation patterns as well as any intermediate case of interest, we consider the so-called r-allocation pattern with r denoting the maximum number of hubs a terminal can be allocated to. We start by revisiting an optimization model recently proposed for the problem. For that model, we introduce several families of valid inequalities as well as optimality cuts. Moreover, we consider a relaxation of the model that contains several sets of set packing constraints. This motivates a pol…
Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms
2006
This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of our approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical …