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

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

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

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

A hybrid Delphi multi-criteria sorting approach for polypharmacy evaluations

With the intensification of chronical disease within older people, concurrent use of different drugs (polypharmacy) is becoming increasingly frequent. However, there is no established manner to determine whether polypharmacy is appropriate or not. We propose an original method of classifying polypharmacy using a Delphi survey results and multi-criteria decision-aid methods. To do this, we provided clinicians with a list of drugs that could be potentially prescribed to the typical elderly person suffering from three diseases (diabetes, chronic obstructive pulmonary disease, and heart failure). Clinicians expressed their opinions on a 5-point Likert scale, allowing for hesitation between two or more answers. They evaluated risks, benefits, and impacts of each drug on the patient’s quality of life. We then aggregated these evaluations in order to obtain, for each drug, a multi-criteria evaluation vector representing the collective opinion of the clinicians consulted. Subsequently, ELECTRE TriC and ELECTRE Tri multi-criteria sorting methods were used to evaluate and assign the polypharmacy to one of the following three categories: appropriate, more or less appropriate, or not appropriate. The proposed approach is innovative and enables the integration of a variety of conflicting criteria in the evaluation of polypharmacy quality. It also allows clinicians to express their opinion, and their hesitation where relevant, linguistically

Automatic Documents Analyzer and Classifier

Military organizations have to deal with an increasing number of documents coming from different sources and in various formats (paper, fax, e-mail messages, electronic documents). These documents have to be screened, analyzed and categorized in order to interpret their content and gain situation awareness. These documents should be categorized according to their content to enable efficient storage and retrieval. In this context, intelligent techniques and tools should be provided to support this information management process that is currently partly manual. Integrating the recently acquired knowledge in different fields in a system for analyzing, diagnosing, filtering, classifying and clustering documents with a limited human intervention would improve efficiently the quality of information management with reduced human resources. A better categorization and management of information would facilitate correlation of information from different sources, avoid information redundancy, improve access to relevant information, and thus better support decision-making processes. The RDDC-Valcartier's ADAC system (Automatic Documents Analyzer and Classifier) incorporates several techniques and tools for document summarizing and semantic analysis based on ontology of a certain domain (e.g. terrorism), and algorithms of diagnostic, classification and clustering. In this paper, we describe the architecture of the system and the techniques and tools used at each step of the document processing. For the first prototype implementation, the focus has been concentrated on the terrorism domain to develop document corpus and related ontology.Les organisations militaires doivent faire face \ue0 un nombre croissant de documents provenant de diverses sources et dans divers formats (papier, t\ue9l\ue9copies, courriels, documents \ue9lectroniques). Ces documents doivent \ueatre v\ue9rifi\ue9s, analys\ue9s et cat\ue9goris\ue9s afin d'en interpr\ue9ter le contenu et de prendre connaissance de la situation. Ils devraient \ueatre cat\ue9goris\ue9s selon leur contenu pour permettre un entreposage et une r\ue9cup\ue9ration efficaces. Dans cette optique, des technologies et outils intelligents devraient \ueatre fournis afin de soutenir la gestion de l'information qui se fait en partie manuellement. En int\ue9grant les connaissances r\ue9cemment acquises dans divers domaines \ue0 un syst\ue8me qui analyse, diagnostique, filtre, classifie et regroupe les documents avec une intervention humaine limit\ue9e, on am\ue9liorerait convenablement la qualit\ue9 de la gestion de l'information avec moins d'effectifs. Une meilleure cat\ue9gorisation et gestion de l'information faciliteraient la corr\ue9lation de l'information issue de diff\ue9rentes sources, \ue9viteraient la redondance, am\ue9lioreraient l'acc\ue8s \ue0 de l'information pertinente et permettraient donc de mieux soutenir les processus d\ue9cisionnels. Le syst\ue8me ADAC (Analyseur et classificateur automatiques pour les documents) de RDDC Valcartier comprend plusieurs techniques et outils pour r\ue9sumer les documents et effectuer des analyses s\ue9mantiques qui se basent sur l'ontologie d'un domaine particulier (p. ex. le terrorisme) ainsi que les algorythmes de diagnostic, de classification et de regroupement. Dans ce document, nous d\ue9crivons l'architecture du syst\ue8me ainsi que les techniques et outils utilis\ue9s \ue0 chaque \ue9tape du traitement des documents. Pour la r\ue9alisation du premier prototype, nous nous sommes concentr\ue9s sur le domaine du terrorisme pour \ue9laborer le corps du document et l'ontologie qui s'y rattache.NRC publication: Ye

Critical optical coupling between a GaAs disk and a nanowaveguide suspended on the chip

We report on an integrated GaAs disk/waveguide system. A millimeter-long waveguide is suspended and tapered on the chip over a length of 25 m to evanescently couple to high Q optical whispering gallery modes of a GaAs disk. The critical coupling regime is obtained both by varying the disk/guide gap distance and the width of the suspended nanoscale taper. Experimental results are in good agreement with predictions from coupled mode theory