Combining machine learning and metaheuristics algorithms for classification method PROAFTN

© Crown 2019. The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems

Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy

Descriptive Profiles for Sets of Alternatives in Multiple Criteria Decision Aid

International audienceIn the context of Multiple Criteria Decision Aid, a decision-maker may be faced at any time with the task of analyzing one or several sets of alternatives, irrespective of the decision he is about to make. As in this case the alternatives may express contrasting gains and losses on the criteria on which they are evaluated, and while the sets that are presented to the decision-maker may potentially be large, the task of analysing them becomes a difficult one. Therefore the need to reduce these sets to a more concise representation is very important. Classically, profiles that describe sets of alternatives may be found in the context of the sorting problem, however they are either given beforehand by the decision-maker or determined from a set of assignment examples. We would therefore like to extend such profiles, as well as propose new ones, in order to characterize any set of alternatives. For each of them, we present several approaches for extracting them, which we then compare with respect to their performance

Application of Decision Theory methods for a Community of Madrid Soil classification case

A land classification method was designed for the Community of Madrid (CM), which has lands suitable for either agriculture use or natural spaces. The process started from an extensive previous CM study that contains sets of land attributes with data for 122 types and a minimum-requirements method providing a land quality classification (SQ) for each land. Borrowing some tools from Operations Research (OR) and from Decision Science, that SQ has been complemented by an additive valuation method that involves a more restricted set of 13 representative attributes analysed using Attribute Valuation Functions to obtain a quality index, QI, and by an original composite method that uses a fuzzy set procedure to obtain a combined quality index, CQI, that contains relevant information from both the SQ and the QI methods

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets

BMJ Open

INTRODUCTION: Guidelines concerning the follow-up of subjects occupationally exposed to lung carcinogens, published in France in 2015, recommended the setting up of a trial of low-dose chest CT lung cancer screening in subjects at high risk of lung cancer. OBJECTIVE: To evaluate the organisation of low-dose chest CT lung cancer screening in subjects occupationally exposed to lung carcinogens and at high risk of lung cancer. METHODS AND ANALYSIS: This trial will be conducted in eight French departments by six specialised reference centres (SRCs) in occupational health. In view of the exploratory nature of this trial, it is proposed to test initially the feasibility and acceptability over the first 2 years in only two SRCs then in four other SRCs to evaluate the organisation. The target population is current or former smokers with more than 30 pack-years (who have quit smoking for less than 15 years), currently or previously exposed to International Agency for Research on Cancer group 1 lung carcinogens, and between the ages of 55 and 74 years. The trial will be conducted in the following steps: (1) identification of subjects by a screening invitation letter; (2) evaluation of occupational exposure to lung carcinogens; (3) evaluation of the lung cancer risk level and verification of eligibility; (4) screening procedure: annual chest CT scans performed by specialised centres and (5) follow-up of CT scan abnormalities. ETHICS AND DISSEMINATION: This protocol study has been approved by the French Committee for the Protection of Persons. The results from this study will be submitted to peer-reviewed journals and reported at suitable national and international meetings. TRIAL REGISTRATION NUMBER: NCT03562052; Pre-results

Active liquid crystal tuning of metallic nanoantenna enhanced light emission from colloidal quantum dots

A system comprising an aluminum nanoantenna array on top of a luminescent colloidal quantum dot waveguide and covered by a thermotropic liquid crystal (LC) is introduced. By heating the LC above its critical temperature, we demonstrate that the concomitant refractive index change modifies the hybrid plasmonic-photonic resonances in the system. This enables active control of the spectrum and directionality of the narrow-band (similar to 6 nm) enhancement of quantum dot photoluminescence by the metallic nanoantennas

Identification of a Gene Regulatory Network Necessary for the Initiation of Oligodendrocyte Differentiation

Differentiation of oligodendrocyte progenitor cells (OPCs) into mature oligodendrocytes requires extensive changes in gene expression, which are partly mediated by post-translational modifications of nucleosomal histones. An essential modification for oligodendrocyte differentiation is the removal of acetyl groups from lysine residues which is catalyzed by histone deacetylases (HDACs). The transcriptional targets of HDAC activity within OPCs however, have remained elusive and have been identified in this study by interrogating the oligodendrocyte transcriptome. Using a novel algorithm that allows clustering of gene transcripts according to expression kinetics and expression levels, we defined major waves of co-regulated genes. The initial overall decrease in gene expression was followed by the up-regulation of genes involved in lipid metabolism and myelination. Functional annotation of the down-regulated gene clusters identified transcripts involved in cell cycle regulation, transcription, and RNA processing. To define whether these genes were the targets of HDAC activity, we cultured rat OPCs in the presence of trichostatin A (TSA), an HDAC inhibitor previously shown to inhibit oligodendrocyte differentiation. By overlaying the defined oligodendrocyte transcriptome with the list of ‘TSA sensitive’ genes, we determined that a high percentage of ‘TSA sensitive’ genes are part of a normal program of oligodendrocyte differentiation. TSA treatment increased the expression of genes whose down-regulation occurs very early after induction of OPC differentiation, but did not affect the expression of genes with a slower kinetic. Among the increased ‘TSA sensitive’ genes we detected several transcription factors including Id2, Egr1, and Sox11, whose down-regulation is critical for OPC differentiation. Thus, HDAC target genes include clusters of co-regulated genes involved in transcriptional repression. These results support a de-repression model of oligodendrocyte lineage progression that relies on the concurrent down-regulation of several inhibitors of differentiation

Objects recognition using SIFT and fuzzy similarity measure

Multimedia database has been an extremely active area of research over the last 20 years. This research aims to develop techniques for searching and recognizing multimedia documents based on their content. For objects recognition, make works proposed different techniques to extract the visual contents such as color, shape, texture, etc. By comparing these visual contents, we can determine whether or not the image data contains some specific object. Since the Euclidean distance has always been employed to compare visual contents so far, using other approaches for the comparison is an interesting research way that still needs to be explored. In this paper, we propose fuzzy similarity measures as alternatives for the Euclidean distance. Visual contents to be compared are based on the Scale Invariant Feature Transform (SIFT). This approach has been applied to coil databases. Our experimentation shows that this method is more realistic than objects recognition method obtained by classical distances.La base de donn\ue9es multim\ue9dias constitue un secteur de recherche extr\ueamement actif depuis les 20 derni\ue8res ann\ue9es. Cette \ue9tude vise \ue0 \ue9laborer des techniques de recherche et de reconnaissance de documents multim\ue9dias en fonction de leur contenu. Pour la reconnaissance des objets, des travaux ont propos\ue9 diff\ue9rentes techniques d'extraction de contenus visuels, comme la couleur, la forme et la texture. En comparant ces contenus visuels, il est possible de d\ue9terminer si les donn\ue9es d'image comprennent un objet particulier. Comme la distance euclidienne n'a servi jusqu'\ue0 maintenant qu'\ue0 comparer des contenus visuels, le recours \ue0 d'autres m\ue9thodes pour la comparaison offre un cheminement de recherche int\ue9ressant qu'il reste encore \ue0 explorer. Ce document propose des mesures de similarit\ue9 floue en remplacement de la distance euclidienne. Les contenus visuels \ue0 comparer se fondent sur une transformation de caract\ue9ristiques invariantes \ue0 l'\ue9chelle (SIFT). Cette m\ue9thode a \ue9t\ue9 appliqu\ue9e \ue0 des bases de donn\ue9es COIL. Notre exp\ue9rience d\ue9montre qu'elle est plus r\ue9aliste que la reconnaissance des objets fond\ue9e sur les distances classiques.NRC publication: Ye

Application de la méthode PROAFTN pour assister le diagnostic des tumeurs astrocytaires en utilisant les paramètres générés par microscopie assistée par ordinateur

info:eu-repo/semantics/publishe