A new method for feature selection based on fuzzy similarity measures using multi objective genetic algorithm

Feature selection (FS) is considered to be an important preprocessing step in machine learning and pattern recognition, and feature evaluation is the key issue for constructing a feature selection algorithm. Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on fuzzy similarity measures by multi objective genetic algorithm (FSFSM - MOGA) is introduced and performance of the proposed method on published data sets from UCI was evaluated. The results show the efficiency of the method is compared with the conventional version. When this method multi-objective genetic algorithms and fuzzy similarity measures used in CFS method can improve it

Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602

Artificial Intelligence for the prediction of weaning readiness outcome in a multi-centrical clinical cohort of mechanically ventilated patients

Quando un paziente soffre di insufficienza respiratoria acuta, viene praticata la ventilazione meccanica (VM) finché questa non riesce a respirare di nuovo in autonomia. Il medico di Terapia Intensiva verifica ogni giorno se la VM può essere interrotta. Questo screening consiste in una prima fase, il Readiness Test (RT), che è composta da vari parametri clinici. Se questo test ha esito positivo, si sottopone il paziente a 30 minuti di respirazione spontanea (SBT). Se anche l'SBT viene superato con successo, la VM viene interrotta. Al contrario, se l’RT o l’SBT falliscono, il paziente rimane in VM e verrà rivalutato il giorno successivo. Quindi ogni giorno possono verificarsi tre scenari mutuamente esclusivi: l’SBT non verrà tentato, l’SBT fallirà o l’SBT avrà successo (portando quindi all’estubazione del paziente). Il modello di intelligenza artificiale sviluppato, è progettato per dedurre fin dalle prime ore del mattino quale dei tre scenari si verificherà probabilmente nel corso della giornata, partendo dai dati clinici del paziente, dalle informazioni raccolte nel diario clinico dei giorni precedenti e dall'intera storia di registrazione minuto-per-minuto dei vari parametri del ventilatore meccanico, provenienti da uno studio osservazionale retrospettivo multicentrico, condotto in Italia nel corso di 27 mesi. Questi dati vengono elaborati con un approccio di Deep Learning, attraverso una topologia di rete neurale multi-sorgente, alimentata da architetture ricorrenti multiple. Gli iper-parametri sono ottimizzati per selezionare il modello desiderato attraverso la convalida incrociata, riservando 36 pazienti su 182 per testare le prestazioni finali del modello su una serie di metriche, tra cui uno score personalizzato progettato per evidenziare l'impatto clinico. Il modello di intelligenza artificiale finale mostra un'accuratezza del 79% [74, 83%], uno score personalizzato di 0,01 [-0,04, 0,05], un MCC di 0,28 [0,17, 0,39], ottenendo un punteggio migliore rispetto agli altri modelli di confronto, tra cui XG Boost, addestrato solo sui dati clinici giornalieri del giorno precedente, che ha avuto un'accuratezza del 61% [56%, 66%], un MCC di 0,14 [0,06, 0,2] e uno score personalizzato di -0,05 [-0,08, -0,01]. Complessivamente, il modello di intelligenza artificiale è in grado di approssimare bene l'attuale gestione clinica giorno per giorno, fornendo suggerimenti al mattino presto. Inoltre, c'è ancora spazio per migliorare l'utilità clinica del modello considerando ulteriori dati di addestramento personalizzati.When someone suffers from acute respiratory failure, mechanical ventilation (MV) is performed until they can breathe on their own again. The doctor checks every day whether the MV can be stopped. This screening consists of a first phase, the Readiness Testing (RT), which includes various clinical parameters. If this test is successful, 30 minutes of spontaneous breathing (SBT) is attempted. If also the SBT is passed successfully, the VM is stopped. On the contrary, if RT or SBT fails, the patient will be re-evaluated the next day. So, every day three mutually exclusive scenarios may happen: SBT will not be attempted, SBT will fail, or SBT will succeed. Our artificial intelligence model is designed to infer early in the morning which of the three scenarios will probably occur during the day, starting from the patient's clinical data, from the information collected in the previous day’s clinical diary, and from whole minute-by-minute recording history of the various parameters of the mechanical ventilator, coming from a retrospective observational multi-centrical study, conducted in Italy over a course of 27 months. Those data are processed with a deep learning approach, through a multi-source neural network topology, powered by multiple recurrent architectures. Hyper-parameters are optimized to select the purposed model through cross-validation, setting aside 36 out of 182 patients for testing final model performance over a variety of metrics, including a custom score designed to highlight clinical impact. The final AI model had an accuracy of 79% [74, 83%], a custom score of 0.01 [-0.04, 0.05], a MCC of 0.28 [0.17, 0.39], scoring better than the other comparison models, including XG Boost that was trained on daily and baseline clinical data of the previous day only, which had an accuracy of 61% [56%, 66%], a MCC of 0.14 [0.06, 0.2] and a custom score of -0.05 [-0.08, -0.01]. Overall, AI model could approximate well what is the current clinical management throughout day-by-day providing suggestions early in the morning. Moreover, there are still space to improve the model clinical utility considering additional tailored training data

Multiobjective and Stochastic Optimization

This book is divided into four main sections, the first of which contains five papers dealing with the theoretical aspects of multiobjective and stochastic optimization. The seven papers included in Section II are concerned with those aspects of multiobjective analysis which have a direct relationship to decision making (some papers in Sections III and IV are also linked to decision making or decision support, although not so directly). Section III contains four papers dealing with uncertainties and multiobjective analysis. The first and last papers in this section also present solution techniques which are illustrated by means of examples. The final section contains papers which concentrate on solution techniques and indicate how they can be applied to practical problems; the software presented in this section can be regarded as a step toward computerized decision support systems. Naturally, some of the papers in other sections also touch on applications of multiobjective and stochastic optimization: examples are drawn from a wide range of activities, including regional planning, environmental control, wage negotiation and energy planning