Ensemble techniques are a powerful method for recognising and reacting to changes in non-stationary data. However, most researches into dynamic classification with ensembles assume that the true class label of each incoming point is available or easily obtained. This is unrealistic in most practical applications, especially in high-velocity streams where manually labeling each point is prohibitively expensive. To address this challenge, this paper proposes an algorithm, named Clustering and One-Class Classification Ensemble Learning (COCEL), which incorporates a stream clustering algorithm and an ensemble of one-class classifiers with active learning, for classification in dynamic data streams. The method exploits the intuitive relationship between clusters and one-class classifiers to cope with a small training set (or no training set) and improve with experience, self-modifying its internal state to cope with changes in the data stream. The proposed method is evaluated on synthetic data streams exhibiting concept evolution and concept drift and a collection of high-velocity real data streams where manually labeling each incoming point is infeasible or expensive and labor intensive. Finally, a comparative evaluation with peer stream classification ensembles shows that COCEL can achieve superior or comparative accuracy while typically requiring less than 0.01% of the stream labels.

The Magnetic Observatory of Tatuoca (TTB) was installed by Observatório Nacional (ON) in 1957, near Belém city in the state of Pará, Brazilian Amazon. Its history goes back to 1933, when a Danish mission used this location to collect data, due to its privileged position near the terrestrial equator. Between 1957 and 2007, TTB produced 18,000 magnetograms on paper using photographic variometers, and other associated documents like absolute value forms and yearbooks. Data was obtained manually from these graphs with rulers and grids, taking 24 average readings per day, that is, one per hour. In 2017, the Federal University of Pará (UFPA in the Portuguese acronym) and ON collaborated to rescue this physical archive. In 2022 UFPA took a step forward and proposed not only digitizing the documents but also developing an intelligent agent capable of reading and extracting the information of the curves with a resolution better than an hour, being this the central goal of the project. If the project succeeds, it will rescue 50 years of data imprisoned in paper, increasing measurement sensitivity far beyond what these sources used to give. This will also open the possibility of applying the same AI to similar documents in other observatories or disciplines like seismography. This article recaps the project, and the complex challenges faced in articulating Archival Science principles with AI and Geoscience.

Computational Intelligence (CI), originally represented by the three subjects of Evolutionary Computation (EC), Fuzzy Logic (FL) and Neural Networks (NNs), has significantly evolved to date and is ever more embedded in both software platforms and hardware devices forming intelligent systems capable of self-adaptation, decision-making and problem-solving.With a quick inspection of the scientific literature in Computer Science, one can indeed notice a significant expansion in the range of available CI tools, with, for example, modern EC optimisers making use of surrogate models (which can be based on NNs), or being used to evolve both topology and hyperparameters of neural systems.The latter systems have also grown significantly and currently offer numerous kinds of networks from, for example, recurrent, through convolutional to Generative/Adversarial deep NNs.These highly interconnected and high-level algorithms are becoming ubiquitous as their applicability has widened and grown to traverse many disciplines and application domains.In the past, the technological fields that benefited the most from applying CI techniques were in engineering, such as system control and design, robotics, telecommunication and so forth.However, the application scope of modern CI methods has widened significantly, thus making it possible to analyse large data sets, manipulate images and videos, extract sentiment and relevant information from plain text and audio recordings.Hence, modern CI turns out to be helpful in many areas which strongly impact our society, for example, medicine, finance, education, intelligent transportation, sustainability and so forth, where it is key to analyse available data, optimise processes and provide systems with extra capabilities.If placed in the right context, CI has then the potential of generating societal impact beyond enabling technological advancement per se.It can now support the deployment of technology to optimise not only the financial viability but as well the usability and benefit to the public.State-of-the-art optimisation has become focused on sustainability and waste rather than profit or cost reduction; now optimisation is critical to address the compromise between protecting society and the economic activities of small stockholders, and not just the large scale businesses.In this light, this special issue has gathered recent advances in CI addressing relevant research questions leading to societal impact and calling for the design of more intelligent systems enhancing our society in the future.

The unfolding coronavirus (COVID-19) pandemic has highlighted the global need for robust predictive and containment tools and strategies. COVID-19 continues to cause widespread economic and social turmoil, and while the current focus is on both minimising the spread of the disease and deploying a range of vaccines to save lives, attention will soon turn to future proofing. In line with this, this paper proposes a prediction and containment model that could be used for pandemics and natural disasters. It combines selective lockdowns and protective cordons to rapidly contain the hazard while allowing minimally impacted local communities to conduct "business as usual" and/or offer support to highly impacted areas. A flexible, easy to use data analytics model, based on Self Organising Maps, is developed to facilitate easy decision making by governments and organisations. Comparative tests using publicly available data for Great Britain (GB) show that through the use of the proposed prediction and containment strategy, it is possible to reduce the peak infection rate, while keeping several regions (up to 25% of GB parliamentary constituencies) economically active within protective cordons.

This article presents an intelligent system using deep learning algorithms and the transfer learning approach to detect oil palm units in multispectral photographs taken with unmanned aerial vehicles. Two main contributions come from this piece of research. First, a dataset for oil palm units detection is carefully produced and made available online. Although being tailored to the palm detection problem, the latter has general validity and can be used for any classification application. Second, we designed and evaluated a state-of-the-art detection system, which uses a convolutional neural network to extract meaningful features, and a classifier trained with the images from the proposed dataset. Results show outstanding effectiveness with an accuracy peak of 99.5% and a precision of 99.8%. Using different images for validation taken from different altitudes the model reached an accuracy of 97.5% and a precision of 98.3%. Hence, the proposed approach is highly applicable in the field of precision agriculture.

Lethal Wilt (Marchitez Letal) is a disease which affects Etaeis Guineensis, a plant used in the production of palm oil. The disease is increasingly common but the spatial-dynamics of the infection spread remain poorly understood. It is particularly dangerous due to the speed at which it spreads and the speed at which infected plants show symptoms and die. Early identification, or even better, accurate prediction of areas at high risk of infection can slow the spread of the disease and limit crop waste. This study is based on data collected over a five-year period from an affected plantation in Colombia. The aim of the study is to analyse the collected data to better understand how the disease spreads and then to model the behaviour. Based on insights from the initial analysis a multi-agent-based system is proposed to model the pattern of infection. The model is comprised of two steps; first Kernel Density Estimation is used to create an estimation of the distribution from which newly infected plants are drawn and this density estimation is then used to direct agents on a biased-walk of the surrounding areas. Results show that the model can approximate the behaviour of the disease and can predict areas which are at high risk of future infection.

Real-world problems such as landmine detection require multiple sources of information to reduce the uncertainty of decision-making. A novel approach to solve these problems includes distributed systems, as presented in this work based on hardware and software multi-agent systems. To achieve a high rate of landmine detection, we evaluate the performance of a trained system over the distribution of samples between training and validation sets. Additionally, a general explanation of the data set is provided, presenting the samples gathered by a cooperative multi-agent system developed for detecting improvised explosive devices. The results show that input samples affect the performance of the output decisions, and a decision-making system can be less sensitive to sensor noise with intelligent systems obtained from a diverse and suitably organised training set.

A data stream is a continuously arriving sequence of data and clustering data streams requires additional considerations to traditional clustering. A stream is potentially unbounded, data points arrive online and each data point can be examined only once. This imposes limitations on available memory and processing time. Furthermore, streams can be noisy and the number of clusters in the data and their statistical properties can change over time. This paper presents an online, bio-inspired approach to clustering dynamic data streams. The proposed ant colony stream clustering (ACSC) algorithm is a density-based clustering algorithm, whereby clusters are identified as high-density areas of the feature space separated by low-density areas. ACSC identifies clusters as groups of micro-clusters. The tumbling window model is used to read a stream and rough clusters are incrementally formed during a single pass of a window. A stochastic method is employed to find these rough clusters, this is shown to significantly speeding up the algorithm with only a minor cost to performance, as compared to a deterministic approach. The rough clusters are then refined using a method inspired by the observed sorting behavior of ants. Ants pick-up and drop items based on the similarity with the surrounding items. Artificial ants sort clusters by probabilistically picking and dropping micro-clusters based on local density and local similarity. Clusters are summarized using their constituent micro-clusters and these summary statistics are stored offline. Experimental results show that the clustering quality of ACSC is scalable, robust to noise and favorable to leading ant clustering and stream-clustering algorithms. It also requires fewer parameters and less computational time.

Density based methods have been shown to be an effective approach for clustering non-stationary data streams. The number of clusters does not need to be known a priori and density methods are robust to noise and changes in the statistical properties of the data. However, most density approaches require sensitive, data dependent parameters. These parameters greatly affect the clustering performance and in a dynamic stream a good set of parameters at time t are not necessarily the best at time t+1. Furthermore, these parameters are global and so restrict the algorithm to finding clusters of the same density. In this paper, we propose a density based algorithm with adaptive parameters which are local to each discovered cluster. The algorithm, denoted Ant Colony Multi-Density Clustering (ACMDC), uses artificial ants to form nests in dense areas of the data. As the ants move between nests, their collective memory is stored in the form of pheromone trails. Clusters are identified as groups of similar nests. The proposed algorithm is evaluated across a number of synthetic data streams containing overlapping and embedded multi-density clusters. The performance of the algorithm is shown to be favourable to a leading density based stream-clustering algorithm despite requiring no tunable parameters.

Minimizing the ongoing impact of train delays has benefits to both the users of the railway system and the railway stakeholders. However, the efficient rescheduling of trains after a perturbation is a complex real-world problem. The complexity is compounded by the fact that the problem may be both dynamic and multi-objective. The aim of this research is to investigate the ability of ant colony optimization algorithms to solve a simulated dynamic multi-objective railway rescheduling problem and, in the process, to attempt to identify the features of the algorithms that enable them to cope with a multi-objective problem that is also dynamic. Results showed that, when the changes in the problem are large and frequent, retaining the archive of non-dominated solution between changes and updating the pheromones to reflect the new environment play an important role in enabling the algorithms to perform well on this dynamic multi-objective railway rescheduling problem.

Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization.To enhance the selection of channel with less noise among the white spaces (idle channels), the a priory knowledge of Radio Frequency (RF) power is very important.Computational Intelligence (CI) techniques cans be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS).In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) and Support Vector Regression (SVR) models for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) FM and TV bands.Sensitivity analysis was used to reduce the input vector of the prediction models.The inputs of the ANN and SVR consist of only time domain data and past RF power without using any RF power related parameters, thus forming a nonlinear time series prediction model.The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate.Since CR are embedded communication devices with memory constrain limitation, the models used, implemented a novel and innovative initial weight optimization of the ANN's through the use of compact differential evolutionary (cDE) algorithm variants which are memory efficient.This was found to enhance the accuracy and generalization of the ANN model.

This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules.

The planning of resources within a supply chain can prove to be a deciding factor in the success or failure of an operation. This research continues the authors' previous work using an extended Interval Type-2 Fuzzy Logic supply chain model, with an Evolutionary Algorithm to search for good resource plans. A set of enhanced experiments is conducted to validate our novel approach with optimal configurations, and determine an appropriate Evolutionary Algorithm set up for the given problem.

Our research focuses on the use of sound to enhance the control of an autonomous indoor helicopter - Flyper. One of the many challenging problems in this project is managing the uncertainty which is present in input data and the control actions; this paper focuses on managing the uncertainty in the input data. We present a fuzzy logic system which infers how much confidence should be placed on a control decision based on the data which was used to make that decision. The input data is a supervised and sound based position estimate of the flying robot. The output of the fuzzy inference system provides us with a confidence parameter used to attenuate the position control of the autonomous helicopter. We performed test flights with and without the fuzzy confidence parameter and with and without artificial disturbance in form of concurrent speech. We employed a motion tracker to capture the helicopter's movement during all test flights. The analysis of the data collected shows encouraging results.

Usually, many high-skilled human resources are required to create sophisticated control systems. Automatic generation of control systems can overcome these requirements. Because of their versatility and flexibility neural networks gained an important role for this task. While evolutionary methods have been relatively successful in generating neural networks, they have some limitations, in addition to being computationally expensive, because they rely on adapting populations instead of individuals. Reinforcement methods on the other hand can improve and adapt the behaviour of an individual; the reinforcement methods that are presented in this paper can grow a neural network during operation. We show that neural networks can be created for various domains without changing any parameters. Additionally, our neural network can learn the action selection policy and the value function locally within the neurons. These features make our neural network highly flexible and distinguish it from other reinforcement based constructive neural networks.

A genetic algorithm (GA) presents an excellent method for controller parameter tuning. In our work, we evolved the heading as well as the altitude controller for a small lightweight helicopter. We use the real flying robot to evaluate the GA's individuals rather than an artificially consistent simulator. By doing so we avoid the ldquoreality gaprdquo, taking the controller from the simulator to the real world. In this paper we analyze the evolutionary aspects of this technique and discuss the issues that need to be considered for it to perform well and result in robust controllers.

In this paper we evolve the parameters of a proportional, integral, and derivative (PID) controller for an unstable, complex and nonlinear system. The individuals of the applied genetic algorithm (GA) are evaluated on the actual system rather than on a simulation of it, thus avoiding the ldquoreality gaprdquo. This makes implicit a formal model identification for the implementation of a simulator. This also calls for the GA to be approached in an unusual way, where we need to consider new aspects not normally present in the usual situations using an unnaturally consistent simulator for fitness evaluation. Although elitism is used in the GAs, no monotonic increase in fitness is exhibited by the algorithm. Instead, we show that the GApsilas individuals converge towards more robust solutions.

This paper describes the design and construction of the first hardware running a generalised type-2 fuzzy logic system. A rationale for the design is given and hardware representations are discussed. An integrated development environment, also developed as part of this project, for type-2 fuzzy system is described along with the software which links this IDE to the novel hardware. The performance of the novel type-2 development board is tested under two scenarios.

Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network controllers for complex systems while minimising the design effort. Using a robot task as a case study, we have shown that using the feedback from the robot itself, the system can learn from experience, or example provided by an expert. We present a system where the processing of the feedback is integrated entirely in the growing of a spiking neural network system. The feedback is extracted from a measurement of a reward interpretation system provided by the designer, which takes into consideration the robot actions without the need for external explicit inputs. Starting with a small basic neural network, new connections are created. The connections are separated into artificial dendrites, which are mainly used for classification issues, and artificial axons, which are responsible for selecting appropriate actions. New neurons are then created using a special connection structure and the current reward interpretation of the robot. We show that dyadic connections can also make an artificial neural network acting and learning faster because they reduce the total number of neurons and connections needed in the resulting neural system. The main contribution of this research is the creation of a novel unsupervised learning system where the designer needs to define only the interface between the robot and the neural network in addition to the feedback system which includes a calculation of a reward value depending on the performance of the robot (or task aim of the system being developed).

This paper presents a novel methodology for the analysis of the data at Birmingham Airport to provide effective and useful information about the dwell-time that passengers have between different points of their visit to the Airport. Birmingham Airport has sensors that anonymously count the number of people passing through crucial access routes, including boarding gates and security points. These sensors provide an enormous amount of crude data which contains valuable information reflecting the time people spend on different parts of the premises, but extracting this information requires a complex processing of the data. The methodology presented in this work uses a Genetic Paradigm which is able to process that data using a compact and robust simulation model, so that the time spent by the visitors to the airport can be extracted from the raw data produced by the sensors.