Search results for "Machine learning"
showing 10 items of 1464 documents
Toward modernizing the systematic review pipeline in genetics: efficient updating via data mining
2012
Purpose: The aim of this study was to demonstrate that modern data mining tools can be used as one step in reducing the labor necessary to produce and maintain systematic reviews. Methods: We used four continuously updated, manually curated resources that summarize MEDLINE-indexed articles in entire fields using systematic review methods (PDGene, AlzGene, and SzGene for genetic determinants of Parkinson disease, Alzheimer disease, and schizophrenia, respectively; and the Tufts Cost-Effectiveness Analysis (CEA) Registry for cost-effectiveness analyses). In each data set, we trained a classification model on citations screened up until 2009. We then evaluated the ability of the model to class…
Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression
2020
Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methods, the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM), in problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential, emphasizing the utility of the problem transformation especially with the EMLM. peerReviewed
Can we automate expert-based journal rankings? Analysis of the Finnish publication indicator
2020
The publication indicator of the Finnish research funding system is based on a manual ranking of scholarly publication channels. These ranks, which represent the evaluated quality of the channels, are continuously kept up to date and thoroughly reevaluated every four years by groups of nominated scholars belonging to different disciplinary panels. This expert-based decision-making process is informed by available citation-based metrics and other relevant metadata characterizing the publication channels. The purpose of this paper is to introduce various approaches that can explain the basis and evolution of the quality of publication channels, i.e., ranks. This is important for the academic …
Anonymization as homeomorphic data space transformation for privacy-preserving deep learning
2021
Industry 4.0 is largely data-driven nowadays. Owners of the data, on the one hand, want to get added value from the data by using remote artificial intelligence tools as services, on the other hand, they concern on privacy of their data within external premises. Ideal solution for this challenge would be such anonymization of the data, which makes the data safe in remote servers and, at the same time, leaves the opportunity for the machine learning algorithms to capture useful patterns from the data. In this paper, we take the problem of supervised machine learning with deep feedforward neural nets and provide an anonymization algorithm (based on the homeomorphic data space transformation),…
Diagnosis and prognosis of cardiovascular diseases by means of texture analysis in magnetic resonance imaging
2017
Cardiovascular diseases constitute the leading global cause of morbidity and mortality. Magnetic resonance imaging (MRI) has become the gold standard technique for the assessment of patients with myocardial infarction. However, limitations still exist thus new alternatives are open to investigation. Texture analysis is a technique that aims to quantify the texture of the images that are not always perceptible by the human eye. It has been successfully applied in medical imaging but applications to cardiac MRI (CMR) are still scarce. Therefore, the purpose of this thesis was to apply texture analysis in conventional CMR images for the assessment of patients with myocardial infarction, as an …
Accidents du travail dans l’UE-15 et méthodes d’apprentissage automatique
2020
Les méthodes d’apprentissage automatique ont été utilisées comme outil de prédiction dans denombreux domaines, mais leur utilisation en santé et sécurité au travail est relativement nouvelle.C’est la raison pour laquelle, il serait intéressant d’utiliser ces méthodes dans la prévision desaccidents du travail, sur les données d’enquêtes européennes sur les conditions de travail. L’objectifde ce travail est de tester les performances des techniques d’apprentissage automatique dans lamodélisation et la prédiction d’accidents du travail. A cette fin, nous utiliserons trois modèles :(les forêts aléatoires) (RF), Support Vector Machine (SVM), modèle logistique. Nous observonsque, la performance d…
Land surface temperature and evapotranspiration estimation in the Amazon evergreen forests using remote sensing data
2019
Amazonian tropical forests play a significant role in global water, carbon and energy cycles. Considering the relevance of this biome and the climate change projections which predict a hotter and drier climate for the region, the monitoring of the vegetation status of these forests becomes of significant importance. In this context, vegetation temperature and evapotranspiration (ET) can be considered as key variables. Vegetation temperature is directly linked with plant physiology. In addition, some studies have shown the existing relationship between this variable and the CO2 absorption capacity and biomass loss of these forests. Evapotranspiration resulting from the combined processes of …
Unsupervised network intrusion detection systems for zero-day fast-spreading network attacks and botnets
2015
Today, the occurrence of zero-day and complex attacks in high-speed networks is increasingly common due to the high number vulnerabilities in the cyber world. As a result, intrusions become more sophisticated and fast to detrimental the networks and hosts. Due to these reasons real-time monitoring, processing and intrusion detection are now among the key features of NIDS. Traditional types of intrusion detection systems such as signature base IDS are not able detect intrusions with new and complex strategies. Now days, automatic traffic analysis and anomaly intrusion detection became more efficient in field of network security however they suffer from high number of false alarms. Among all …
Ordered fuzzy rules generation based on incremental dataset
2021
This paper proposes a novel approach for building transparent knowledge-based systems by generating interpretable fuzzy rules that allow for present dependences between quantitative variables by accounting for uncertainty and the dynamics of their values. In the approach, IF-THEN rules are used to show the conditional relationship between the ordered fuzzy numbers, which contain additional information about the tendencies of variables' value changes. This paper elaborates an approach of mining ordered fuzzy rules from numerical data included in an incremental database. This approach develops the ability to record uncertainty and its change in the context of rapidly changing data. In additio…
Application of the Information Bottleneck method to discover user profiles in a Web store
2018
The paper deals with the problem of discovering groups of Web users with similar behavioral patterns on an e-commerce site. We introduce a novel approach to the unsupervised classification of user sessions, based on session attributes related to the user click-stream behavior, to gain insight into characteristics of various user profiles. The approach uses the agglomerative Information Bottleneck (IB) algorithm. Based on log data for a real online store, efficiency of the approach in terms of its ability to differentiate between buying and non-buying sessions was validated, indicating some possible practical applications of the our method. Experiments performed for a number of session sampl…