Species Separation by a Clustering Mean towards Multimodal Function Optimization

Present paper introduces a new evolutionary technique for multimodal real-valued optimization which uses a clustering method for separating the individuals within a population into species that are each connected to different optima from the search space. It is applied for a set of benchmark functions both for uni- and multimodal optimization and it proves to be very efficient as regards both the accuracy of the obtained results and the costs regarding the fitness evaluation calls that are spent.Article / Letter to editorLeiden Inst. Advanced Computer Science

Bioinformatics and Medicine in the Era of Deep Learning

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic

Sensor networks security based on sensitive robots agents. A conceptual model

Multi-agent systems are currently applied to solve complex problems. The security of networks is an eloquent example of a complex and difficult problem. A new model-concept Hybrid Sensitive Robot Metaheuristic for Intrusion Detection is introduced in the current paper. The proposed technique could be used with machine learning based intrusion detection techniques. The new model uses the reaction of virtual sensitive robots to different stigmergic variables in order to keep the tracks of the intruders when securing a sensor network.Comment: 5 page

A cooperative evolutionary algorithm for classification

An evolutionary algorithm based on cooperative coevolution is applied to a classification problem, the Pima Indian diabetes diagnosis problem. Previous cooperative coevolution algorithms were developed for function optimization [1], optimizing agents behaviour [2] or modelling the behaviour of a robot in an unknown environment [3]. The aim of this paper is to integrate the cooperative approach into a learning classifier system and use it for solving a real-world problem of classification. To the best of our knowledge, there have been no attempts on applying cooperative coevolution specifically to classification. For each category of the classification problem, a sub-population evolves specific rules using a classical genetic algorithm. Sub-populations evolve simultaneously but independently; cooperation between them takes place only when the fitness of an individual in computed. Obtained experimental results encourage further investigation.Algorithms and the Foundations of Software technolog

Support vector machine learning with an evolutionary engine

The paper presents a novel evolutionary technique constructed as an alternative of the standard support vector machines architecture. The approach adopts the learning strategy of the latter but aims to simplify and generalize its training, by offering a transparent substitute to the initial black-box. Contrary to the canonical technique, the evolutionary approach can at all times explicitly acquire the coefficients of the decision function, without any further constraints. Moreover, in order to converge, the evolutionary method does not require the positive (semi-)definition properties for kernels within nonlinear learning. Several potential structures, enhancements and additions are proposed, tested and confirmed using available benchmarking test problems. Computational results show the validity of the new approach in terms of runtime, prediction accuracy and flexibility

An evolutionary approximation for the coefficients of decision functions within a support vector machine learning strategy

Support vector machines represent a state-of-the-art paradigm, which has nevertheless been tackled by a number of other approaches in view of the development of a superior hybridized technique. It is also the proposal of present chapter to bring support vector machines together with evolutionary computation, with the aim to offer a simplified solving version for the central optimization problem of determining the equation of the hyperplane deriving from support vector learning. The evolutionary approach suggested in this chapter resolves the complexity of the optimizer, opens the ’blackbox’ of support vector training and breaks the limits of the canonical solving component

Automated detection of presymptomatic conditions in spinocerebellar ataxia type 2 using monte carlo dropout and deep neural network techniques with electrooculogram signals

Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should also be thoroughly quantified. This paper therefore addresses the employment of Monte Carlo dropout within the DL structure to automatically discriminate presymptomatic signs of spinocerebellar ataxia type 2 in saccadic samples obtained from electrooculograms. The current work goes beyond the common incorporation of this special type of dropout into deep neural networks and uses the uncertainty derived from the validation samples to construct a decision tree at the register level of the patients. The decision tree built from the uncertainty estimates obtained a classification accuracy of 81.18% in automatically discriminating control, presymptomatic and sick classes. This paper proposes a novel method to address both uncertainty quantification and explainability to develop reliable healthcare support systems.</jats:p

Sonographic Evaluation of the Mechanism of Active Labor (SonoLabor Study): Observational study protocol regarding the implementation of the sonopartogram

Introduction Over the last decades, a large body of literature has shown that intrapartum clinical digital pelvic estimations of fetal head position, station and progression in the pelvic canal are less accurate, compared with ultrasound (US) scan. Given the increasing evidence regarding the advantages of using US to evaluate the mechanism of labour, our study protocol aims to develop sonopartograms for fetal cephalic presentations. They will allow for a more objective evaluation of labour progression than the traditional labour monitoring, which could enable more rapid decisions regarding the mode of delivery. Methods/analysis This is a prospective observational study performed in three university hospitals, with an unselected population of women admitted in labour at term. Both clinical and US evaluations will be performed assessing fetal head position, descent and rotation. Specific US parameters regarding fetal head position, progression and rotation will be recorded to develop nomograms in a similar way that partograms were developed. The primary outcome is to develop nomograms for the longitudinal US assessment of labour in unselected nulliparous and multiparous women with fetal cephalic presentation. The secondary aims are to assess the sonopartogram differences in occiput anterior and posterior deliveries, to compare the labour trend from our research with the classic and other recent partogram models and to investigate the capability of the US labour monitoring to predict the outcome of spontaneous vaginal delivery. Ethics and dissemination All protocols and the informed consent form comply with the Ministry of Health and the professional society ethics guidelines. University ethics committees approved the study protocol. The trial results will be published in peer-reviewed journals and at the conference presentations. The study will be implemented and reported in line with the Strengthening the Reporting of Observational Studies in Epidemiology statement. © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ