Variable selection in high-dimensional data: application in a SARS-CoV-2 pneumonia clinical data-set

As a result of the COVID-19 pandemic that collapsed hospitals in some countries, numerous studies have been carried out to understand the development of the disease and how it affects patients with different characteristics, in order to make optimal use of the available resources. This project is part of a multicentre study that aims to predict the severity of patients with SARS-CoV-2 pneumonia, for which different variables related to health, demographic and socio-economic factors and exposure to pollutants of patients have been collected. Given the number of variables contained in the data-set, it is necessary to reduce the number of variables in order to create a practical model for interpretation, as well as to reduce the amount of information that doctors have to collect on each patient. In this project, an exhaustive analysis of variable or feature selection techniques has been carried out in order to determine their performance and relevance in terms of stability, similarity and computation time. Based on the techniques that have shown the best characteristics, the most meaningful factors in preventing the severity of pneumonia have been identified, in accordance with what has been proposed by other studies

Development and applications of adaptive IIR and subband filters

Adaptive infinite impulse response (IIR) filter is a challenging research area. Identifiers and Equalizers are among the most essential digital signal processing devices for digital communication systems. In this study, we consider IIR channel both for system identification and channel equalization purposes. We focus on four different approaches: Least Mean Square (LMS), Recursive Least Square (RLS), Genetic Algorithm (GA) and Subband Adaptive Filter (SAF). ). The performance of conventional LMS and RLS based IIR system identification and channel equalization are found with the help of computer simulations. And also the convergence speed and the ability to locate the global optimum solution using a population based algorithm named Genetic Algorithm is given

Automated Machine Learning for Positive-Unlabelled Learning

Positive-Unlabelled (PU) learning is a field of machine learning that involves learning classifiers from data consisting of positive class and unlabelled instances. That is, instances that may be either positive or negative, but the label is unknown. PU learning differs from standard binary classification due to the absence of negative instances. This difference is non-trivial and requires differing classification frameworks and evaluation metrics. This thesis looks to address gaps in the PU learning literature and make PU learning more accessible to non-experts by introducing Automated Machine Learning (Auto-ML) systems specific to PU learning. Three such systems have been developed, GA-Auto-PU, a Genetic Algorithm (GA)-based Auto-ML system, BO-Auto-PU, a Bayesian Optimisation (BO)-based Auto-ML system, and EBO-Auto-PU, an Evolutionary/Bayesian Optimisation (EBO) hybrid-based Auto-ML system. These three Auto-ML systems are three primary contributions of this work. EBO, the optimiser component of EBO-Auto-PU, is by itself a novel optimisation method developed in this work that has proved effective for the task of Auto-ML and represents another contribution. EBO was developed with the aim of acting as a trade-off between GA, which achieved high predictive performance but at high computational expense, and BO, which, when utilised by the Auto-PU system, did not perform as well as the GA-based system but did execute much faster. EBO achieved this aim, providing high predictive performance with a computational runtime much faster than the GA-based system, and not substantially slower than the BO-based system. The proposed Auto-ML systems for PU learning were evaluated on three versions of 40 datasets, thus evaluated on 120 learning tasks in total. The 40 datasets consist of 20 real-world biomedical datasets and 20 synthetic datasets. The main evaluation measure was the F-measure, a popular measure in PU learning. Based on the F-measure results, the three proposed systems outperformed in general two baseline PU learning methods, usually with statistically significant results. Among the three proposed systems, there was no statistically significance difference between their results in general, whilst a version of the EBO-Auto-PU system performed overall slightly better than the other systems, in terms of F-measure. The two other main contributions of this work relate specifically to the field of PU learning. Firstly, in this work we present and utilise a robust evaluation approach. Evaluating PU learning classifiers is non-trivial and little guidance has been provided in the literature on how to do so. In this work, we present a clear framework for evaluation and use this framework to evaluate the proposed systems. Secondly, when evaluating the proposed systems, an analysis of the most frequently selected components of the optimised PU learning algorithm is presented. That is, the components that constitute the PU learning algorithms produced by the optimisers (for example, the choice of classifiers used in the algorithm, the number of iterations, etc.). This analysis is used to provide guidance on the construction of PU learning algorithms for specific dataset characteristics

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

NON-LINEAR MODEL PREDICTIVE CONTROL STRATEGIES FOR PROCESS PLANTS USING SOFT COMPUTING APPROACHES

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systemsPetroleum Training Development Fun

Evolutionary Neuro-Computing Approaches to System Identification

System models are essentially required for analysis, controller design and future prediction. System identification is concerned with developing models of physical system. Although linear system identification got enriched with several useful classical methods, nonlinear system identification always remained active area of research due to the reason that most of the real world systems are nonlinear in nature and moreover, having non-unique models. Among the several conventional system identification techniques, the Volterra series, Hammerstein-Wiener and polynomial model identification involve considerable computational complexities. The other techniques based on regression models such as nonlinear autoregressive exogenous (NARX) and nonlinear autoregressive moving average exogenous (NARMAX), also suffer from dfficulty in choosing regressors

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity