Abstract This study focuses on an industrial facility’s time-slot allotment (TSA) problem, where loading and unloading docks are assigned to the incoming lorries, reducing the number of them waiting for service. Several constraints apply to the different dock stations, including disparate timetables and task duration, various capacities of simultaneous services, etc. Evolutionary algorithms cope with the enormous variability of the combinatorial problem, maximizing the number of accepted lorries while decreasing the queue at the entrance. Interestingly, the problem’s structure led to unconventional operator probabilities, which also analyzes the evolutionary techniques and operators included in this study. Comparative analysis with state-of-the-art methods highlights the algorithm’s effectiveness, though computational demands rise with population size.

Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, being the most frequently diagnosed cancer among women and the second leading cause of cancer-related mortality. Precise tumor segmentation is essential for breast cancer assessment, as it enables accurate estimation of tumor size, monitoring of disease progression, and evaluation of treatment effectiveness. Despite the importance of this task, the development of reliable automatic methods is hindered by the scarcity of fully annotated datasets, which makes manual labeling both time-consuming and subject to inter-observer variability. In this study, we propose an unsupervised 3D tumor segmentation method based on Fuzzy C-Means (FCM) clustering, specifically designed for volumetric Dynamic Contrast-Enhanced MRI (DCE-MRI) of the breast. Unlike supervised deep learning approaches, our method does not require manual annotations for training, making it especially valuable in scenarios with limited labeled data. The proposed pipeline combines preprocessing, region-of-interest extraction, and FCM-based clustering to generate accurate segmentation masks with minimal human intervention. We evaluated our approach using clinical data from the ACRIN-6698 dataset, comparing the automatic segmentations against expert manual annotations. The method achieved high performance across multiple metrics, including accuracy, precision, recall, specificity, Dice-Sørensen coefficient (DSC), and Jaccard index (IoU). These results demonstrate the feasibility of unsupervised clustering techniques for volumetric breast tumor segmentation, offering a promising alternative to supervised methods in clinical contexts where annotated data is limited.

Function optimization represents a remarkable challenge in industry and society, aiming to find reasonable solutions –even if they are suboptimal– for everyday problems. Metaheuristics drive the optimization search towards the goals using a specific algorithm inspired by different concepts: from industrial processes to the behaviour of living beings in nature, from mathematical ideas to physics notions. This research proposes a new metaheuristic inspired by the Slime Mould and its foraging behaviours. On the one hand, an exploitation stage mimics the greedy amoeba’s conduct when food is plenty. On the other hand, an exploration stage copies the fruity aggregation of the cells and the subsequent spore dissemination. This study compares the most cited metaheuristics and the Slime Mould Optimization in two different experimentation stages: on the one hand, the optimization of standard benchmarking functions; on the other hand, solving the robot path planning problem. Moreover, a hybridization of the SMO and the WOA is presented, which keeps the SMO’s convergence speed and the WOA’s good performance in finding the best solutions.

The rapid adoption of Industry 4.0 has seen Information Technology (IT) networks increasingly merged with Operational Technology (OT) networks, which have traditionally been isolated on air-gapped and fully trusted networks. This increased attack surface has resulted in compromises of Cyber-Physical Systems (CPS) with significant economic and life safety consequences. This paper proposes a hybrid model of anomaly detection of security threats to CPS by blending the signature-based and threshold-based Intrusion Detection Systems (IDS) commonly used in IT networks, with a Machine Learning (ML) model designed to detect behaviour-based anomalies in OT networks. This hybrid model achieves more rapid detection of known threats through signature-based and threshold-based detection strategies, and more accurate detection of unknown threats via behaviour-based anomaly detection using ML algorithms.

In this research, a coevolutionary collision free multi-robot path planning that makes use of A* is proposed. To find collision-free paths for all robots, we generate a route for each of robot using A* path finding but introducing restrictions for each collision found. Afterward, a co-evolutionary optimization process is implemented for introducing changes in the initial paths to find a combination of routes that is collision-free. The approach has been tested in mazes with increasing the number of robots, showing a robust performance although at high time expenses. Nevertheless, several enhancements are proposed to tackle this issue.

Abstract Photosensitivity, in relation to epilepsy, is a genetically determined condition in which patients have epileptic seizures of different severity provoked by visual stimuli. It can be diagnosed by detecting epileptiform discharges in their electroencephalogram (EEG), known as photoparoxysmal responses (PPR). The most accepted PPR detection method—a manual method—considered as the standard one, consists in submitting the subject to intermittent photic stimulation (IPS), i.e. a flashing light stimulation at increasing and decreasing flickering frequencies in a hospital room under controlled ambient conditions, while at the same time recording her/his brain response by means of EEG signals. This research focuses on introducing virtual reality (VR) in this context, adding, to the conventional infrastructure a more flexible one that can be programmed and that will allow developing a much wider and richer set of experiments in order to detect neurological illnesses, and to study subjects’ behaviours automatically. The loop includes the subject, the VR device, the EEG infrastructure and a computer to analyse and monitor the EEG signal and, in some cases, provide feedback to the VR. As will be shown, AI modelling will be needed in the automatic detection of PPR, but it would also be used in extending the functionality of this system with more advanced features. This system is currently in study with subjects at Burgos University Hospital, Spain.

Fall detection is a very challenging task that has a clear impact in the autonomous living of the elderly individuals: suffering a fall with no support increases the fears of the elderly population to continue living by themselves. This study proposes the use of a non-invasive tri-axial accelerometer device placed on a wrist to measure the movements of the participant. The novelty of this study is two fold: on the one hand, the use of a Long-Short Term Memory Neural Network (LSTM) for classification of the Time Series and, on the other hand, the proposal of a novel data augmentation stage that introduces variability in the training by merging the Time Series gathered from both human activities of daily living. The experimentation shows that the combination of a LSTM model together with the data augmentation produces more robust and accurate models that perfectly cope with the validation stage; the high impact fall event detection can be considered solved.

One of the biggest problems in EEG recordings is the contamination of the signal caused by artifacts because these interferences hinder the analysis of real neural information. Thus, their elimination while preserving as much brain data as possible is a key procedure before the study of the EEG. To address the automatic removal of craniofacial artifacts, this paper proposes a two-stages procedure: the former one is the detection stage - where both a MLP neural network and a dynamic threshold method are applied to detect the contaminated areas of the EEG-, while the latter is the removal stage - combining CCA and EEMD algorithms to remove the artifact data only. Experimental results show that both detection methods are comparable, but with the dynamic threshold detection slightly outperforming the MLP. Also, the combined technique can completely remove those artifacts scattered in all the EEG channels. This study will be extended to ocular artifacts, where more complex models would be required.

Abstract Detecting falls in the elderly population is a very important issue that is related with the time of recovery. This study focuses on using wearable smart watches to monitor the movements of the user in order to detect patterns that might be related to fall events. The proposed solution explores Symbolic Aggregate approXimation (SAX) Time Series representation, together with two information retrieval techniques enriched with transfer learning (TL). The solution is user centred; that is, a model is developed for each specific user. Basically, the fall detection approach makes use of a finite‐state machine to detect peaks; the time series window embedding these peaks are represented using SAX. Assuming the data from the public fall detection data sets are valid, a dictionary is prepared using the most relevant words. This dictionary is then introduced as previous knowledge to an online learning classifier that is trained with normal activities of daily living. The two classifiers are evaluated and compared with two classical approaches. Before this comparison, two clustering approaches are studied to produce the bag of relevant words. A complete experimentation is included, which makes use of several publicly available data sets and also with a data set developed by the research group. Comparisons are performed for all the data sets, showing how the TL stage empowers the classifier. The results show that this solution produces high detection rates and at the same time performed similarly for all the individuals tested. Furthermore, the positive effects of TL in this context are clearly remarked.

The identification and the modeling of epilepsy convulsions during everyday life using wearable devices would enhance patient anamnesis and monitoring. The psychology of the epilepsy patient penalizes the use of user-driven modeling, which means that the probability of identifying convulsions is driven through generalized models. Focusing on clonic convulsions, this pre-clinical study proposes a method for generating a type of model that can evaluate the generalization capabilities. A realistic experimentation with healthy participants is performed, each with a single 3D accelerometer placed on the most affected wrist. Unlike similar studies reported in the literature, this proposal makes use of [Formula: see text] cross-validation scheme, in order to evaluate the generalization capabilities of the models. Event-based error measurements are proposed instead of classification-error measurements, to evaluate the generalization capabilities of the model, and Fuzzy Systems are proposed as the generalization modeling technique. Using this method, the experimentation compares the most common solutions in the literature, such as Support Vector Machines, [Formula: see text]-Nearest Neighbors, Decision Trees and Fuzzy Systems. The event-based error measurement system records the results, penalizing those models that raise false alarms. The results showed the good generalization capabilities of Fuzzy Systems.

Wearable devices have promoted the application of Human Activity Recognition to the development of techniques for the assessment or diagnosing of illnesses and seizures, among other applications. For instance, the use of tri-axial accelerometry (3DACM) to detect abnormal and sudden movements has been introduced in the epileptic seizure recognition. In a previous research, Fuzzy Rule Based Classifiers (FRBC) have been found valid for the detection of epileptic convulsions; however, Ant Colony Systems learned FRBC performed with a high variability depending on the training data. In this study, we cope with this problem by the selection of a suitable partitioning method that has been extended to generate Fuzzy partitions. The comparison with the previous obtained results shows the fuzzy partitioning does not improve the overall performance in terms of error but highly reduces the variability in the performance of the obtained models, which allows us to obtain general models.

The development of efficient stroke-detection methods is of significant importance in today's society due to the effects and impact of stroke on health and economy worldwide. This study focuses on Human Activity Recognition (HAR), which is a key component in developing an early stroke-diagnosis tool. An overview of the proposed global approach able to discriminate normal resting from stroke-related paralysis is detailed. The main contributions include an extension of the Genetic Fuzzy Finite State Machine (GFFSM) method and a new hybrid feature selection (FS) algorithm involving Principal Component Analysis (PCA) and a voting scheme putting the cross-validation results together. Experimental results show that the proposed approach is a well-performing HAR tool that can be successfully embedded in devices.

A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

Abstract This interdisciplinary research is based on the application of unsupervized connectionist architectures in conjunction with modelling systems and on the determining of the optimal operating conditions of a new high precision industrial process known as laser milling. Laser milling is a relatively new micro‐manufacturing technique in the production of high‐value industrial components. The industrial problem is defined by a data set relayed through standard sensors situated on a laser‐milling centre, which is a machine tool for manufacturing high‐value micro‐moulds, micro‐dies and micro‐tools. The new three‐phase industrial system presented in this study is capable of identifying a model for the laser‐milling process based on low‐order models. The first two steps are based on the use of unsupervized connectionist models. The first step involves the analysis of the data sets that define each case study to identify if they are informative enough or if the experiments have to be performed again. In the second step, a feature selection phase is performed to determine the main variables to be processed in the third step. In this last step, the results of the study provide a model for a laser‐milling procedure based on low‐order models, such as black‐box, in order to approximate the optimal form of the laser‐milling process. The three‐step model has been tested with real data obtained for three different materials: aluminium, cooper and hardened steel. These three materials are used in the manufacture of micro‐moulds, micro‐coolers and micro‐dies, high‐value tools for the medical and automotive industries among others. As the model inputs are standard data provided by the laser‐milling centre, the industrial implementation of the model is immediate. Thus, this study demonstrates how a high precision industrial process can be improved using a combination of artificial intelligence and identification techniques.

The real-world problem of Urban Bicycles Renting Systems (UBRS) in a city requires the optimization of vehicle routes connecting several bicycle base stations and storage centers. This problem can be modeled as a capacitated Vehicle Routing Problem (VRP) with multiple depots and the simultaneaous need for pickup and delivery at each base station location. Based on the VRP model specification, an evolutionary approach is proposed to address the UBRS problem. Individuals are encoded as permutations of base stations and then translated to a set of routes subject to the constraints related to vehicle capacity and node demands. Better-fitted offspring generated via order crossover or swap mutation are asynchronously inserted in the population. The proposed evolutionary algorithm is engaged for the UBRS problem using data from the city of Barcelona with promising results. Some relevant parameters that can enhance the proposed approach have been identified and analysed via the computational experiments.

Algorithms for preprocessing databases with incomplete and imprecise data are seldom studied, partly because we lack numerical tools to quantify the interdependency between fuzzy random variables. In particular, many filter-type feature selection algorithms rely on crisp discretizations for estimating the mutual information between continuous variables, effectively preventing the use of vague data. Fuzzy rule based systems pass continuous input variables, in turn, through their own fuzzification interface. In the context of feature selection, should we rank the relevance of the inputs by means of their mutual information, it might happen that an apparently informative variable is useless after having been codified as a fuzzy subset of our catalog of linguistic terms. In this paper we propose to address both problems by estimating the mutual information with the same set of fuzzy partitions that will be used to codify the antecedents of the fuzzy rules. That is to say, we introduce a numerical algorithm for estimating the mutual information between two fuzzified continuous variables. This algorithm can be included in certain feature selection algorithms, and can also be used to obtain the most informative fuzzy partition for the data. The use of our definition will be exemplified with the help of some benchmark problems.