Search results for "Data mining"
showing 10 items of 907 documents
A New Approach to the Stock Location Assignment Problem by Multidimensional Scaling and Seriation
1999
The problem of the best stock location assignment in a warehouse has a fundamental role while optimising picking activities. In the present paper, this problem has been faced by considering seven variables to compute similarity between items. In this context, the problem of the choice of the most adequate similarity (or dissimilarity) measure between units while applying Multidimensional Scaling (MDS), has been examined. Besides the right metric, the possibility of applying a Seriation algorithm has been also considered. By using both MDS and seriation not just a single target can be considered, but we are able to manage with a plenty of variables; on the contrary with techniques used in li…
Hydrodynamic ultrasonic maxillary sinus lift : review of a new technique and presentation of a clinical case
2010
Objectives: Placing implants in the posterior maxillary area has the drawback of working with scarce, poor quality bone in a significant percentage of cases. Numerous advanced surgical techniques have been developed to overcome the difficulties associated with these limitations. Subsequent to reports on the elevation of the maxillary sinus through the lateral approach, there were reports on the use of the crestal approach, which is less aggressive but requires a minimal amount of bone. Furthermore, it is more sensitive to operator technique, as the integrity of the sinus membrane is checked indirectly. The aim of this paper is to review the technical literature on minimally invasive sinus l…
GWideCodeML: A python package for testing evolutionary hypotheses at the genome-wide level
2020
One of the most widely used programs for detecting positive selection, at the molecular level, is the program codeml, which is implemented in the Phylogenetic Analysis by Maximum Likelihood (PAML) package. However, it has a limitation when it comes to genome-wide studies, as it runs on a gene-by-gene basis. Furthermore, the size of such studies will depend on the number of orthologous genes the genomes have income and these are often restricted to only account for instances where a one-to-one relationship is observed between the genomes. In this work, we present GWideCodeML, a Python package, which runs a genome-wide codeml with the option of parallelization. To maximize the number of analy…
Therapeutic Drug Monitoring of Kidney Transplant Recipients Using Profiled Support Vector Machines
2007
This paper proposes a twofold approach for therapeutic drug monitoring (TDM) of kidney recipients using support vector machines (SVMs), for both predicting and detecting Cyclosporine A (CyA) blood concentrations. The final goal is to build useful, robust, and ultimately understandable models for individualizing the dosage of CyA. We compare SVMs with several neural network models, such as the multilayer perceptron (MLP), the Elman recurrent network, finite/infinite impulse response networks, and neural network ARMAX approaches. In addition, we present a profile-dependent SVM (PD-SVM), which incorporates a priori knowledge in both tasks. Models are compared numerically, statistically, and in…
A SYNTHETIC MEASURE FOR THE ASSESSMENT OF THE PROJECT PERFORMANCE
2009
The present paper aims to offer a synthetic project performance indicator (PPI) that aggregates two input parameters obtained by the Earned Value Analysis. The PPI is calculated by using a Fuzzy Inference System (FIS) able to single out a measure based on the input parameters, instead of formulating a mathematical model that could be a troublesome task whenever complex relations among the input variables exist. The purpose is to communicate the project performance to the stakeholders in a clear and complete way, for example, describing the PPI by means of contour lines.
The decision support system for telemedicine based on multiple expertise
1998
This paper discusses the application of artificial intelligence in telemedicine and some of our research results in this area. The main goal of our research is to develop methods and systems to collect, analyse, distribute and use medical diagnostics knowledge from multiple knowledge sources and areas of expertise. Use of modern communication tools enable a physician to collect and analyse information obtained from experts worldwide with the help of a decision support medical system. In this paper we discuss a multilevel representation and processing of medical data using a system which evaluates and exploits knowledge about the behaviour of statistical diagnostics methods. The presented te…
Search strategies for ensemble feature selection in medical diagnostics
2003
The goal of this paper is to propose, evaluate, and compare four search strategies for ensemble feature selection, and to consider their application to medical diagnostics, with a focus on the problem of the classification of acute abdominal pain. Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to get higher accuracy, sensitivity, and specificity, which are often not achievable with single models. One technique, which proved to be effective for ensemble construction, is feature selection. Lately, several strategies for ensemble feature selection were proposed, including random subspacing, hill-climbing-based se…
Multiple imputation of rainfall missing data in the Iberian Mediterranean context
2017
Abstract Given the increasing need for complete rainfall data networks, in recent years have been proposed diverse methods for filling gaps in observed precipitation series, progressively more advanced that traditional approaches to overcome the problem. The present study has consisted in validate 10 methods (6 linear, 2 non-linear and 2 hybrid) that allow multiple imputation, i.e., fill at the same time missing data of multiple incomplete series in a dense network of neighboring stations. These were applied for daily and monthly rainfall in two sectors in the Jucar River Basin Authority (east Iberian Peninsula), which is characterized by a high spatial irregularity and difficulty of rainfa…
DeepSRE: Identification of sterol responsive elements and nuclear transcription factors Y proximity in human DNA by Convolutional Neural Network anal…
2021
SREBP1 and 2, are cholesterol sensors able to modulate cholesterol-related gene expression responses. SREBPs binding sites are characterized by the presence of multiple target sequences as SRE, NFY and SP1, that can be arranged differently in different genes, so that it is not easy to identify the binding site on the basis of direct DNA sequence analysis. This paper presents a complete workflow based on a one-dimensional Convolutional Neural Network (CNN) model able to detect putative SREBPs binding sites irrespective of target elements arrangements. The strategy is based on the recognition of SRE linked (less than 250 bp) to NFY sequences according to chromosomal localization derived from …
An empirical study of recommendations in OLAP reporting tool
2015
This paper presents the results of the experimental study that was performed in laboratory settings in the context of the OLAP reporting tool developed and put to operation at the University. The study was targeted to explore which of the modes for generating recommendations in the OLAP reporting tool has a deeper impact on users (i.e. produces more accurate recommendations). Each of the modes of the recommendation component â report structure, user activity, and semantic â employs a separate content-based method that takes advantage of OLAP schema metadata and aggregate functions. Gained data are assessed (i) quantitatively by means of the precision/recall and other metrics from the lo…