Reliable assessment of in-service lubricant condition is essential for predictive maintenance in heavy-duty and military vehicle fleets, where undetected oil degradation can lead to equipment damage and reduced operational readiness. This work investigates Kolmogorov–Arnold Networks (KANs), a class of neural architectures with learnable edge-wise univariate functions, for assessing military engine lubricant quality using real operational data from the Spanish Army. KAN-based models are evaluated under a unified experimental protocol and compared with multilayer perceptron (MLP), support vector machine (SVM), and gradient boosting (XGBoost) baselines, considering realistic constraints of class imbalance and limited data availability. Results indicate that while overall accuracy and F1-score are similar across architectures, several KAN variants achieve higher recall for non-conforming lubricant samples, reaching up to 0.971. This improved sensitivity to degradation events is particularly valuable in safety-critical maintenance scenarios, where missed detections are more costly than false alarms. KAN-based models also achieve competitive balanced accuracy and area under the Receiver Operating Characteristic (ROC) curve, demonstrating robustness under asymmetric error costs. Beyond predictive performance, the learned edge-wise functions provide an intrinsic glass-box representation of the diagnostic process, enabling direct inspection of feature-specific response regimes. These responses qualitatively align with American Society for Testing and Materials (ASTM) based viscosity and contamination thresholds defined in the O-1236 specification, supporting expert validation and auditability. Overall, the results position KANs as a transparent and operationally suitable decision-support tool for lubricant condition monitoring in high-assurance engineering environments.

In recent years, cross-frequency coupling (CFC) has emerged as a valuable tool in the study of a wide range of cognitive processes due to the strong evidence of its functional role in neural computation and communication. CFC computed from electroencephalography (EEG) signals provides powerful information for detecting certain neurological conditions associated with atypical cerebral lateralization. The use of deep learning (DL) in this context offers several advantages, including improved scalability and adaptability to individual variability. However, it presents several significant challenges related to the limited availability of labelled samples and the high-dimensional and noisy nature of EEG data, which can lead to overfitting, poor generalization, and temporal and spatial variability between subjects. In this work, we propose a novel deep learning approach to reveal lateralization patterns based on inter-hemispheric functional differences via CFC. To overcome the challenges associated to the use of DL in this context, we propose the use of synthetic signals for pre-training the neural network that computes a specific type of CFC, phase-amplitude coupling (PAC), and a symmetric architecture for evaluating inter-hemispheric differences. Finally, our model incorporates a custom attention layer designed to learn the most relevant information across different EEG channels and its relative importance, further enhancing its ability to detect subtle hemispheric differences and providing the necessary explainability for clinical applications. The results demonstrate a good classification performance (AUC up to 0.85) in assessing lateralization, providing explainable insights into the mechanisms of the disorder. This may aid in early detection and provide a better understanding of the neural basis associated with this condition.

Reading difficulties are often associated with altered brain connectivity, but detecting these differences reliably is challenging. We present a Bayesian phase-amplitude coupling (PAC) framework to measure cross-frequency brain interactions, addressing the limitations of traditional PAC methods in EEG. Unlike standard PAC approaches that may miss complex directional interactions between brain rhythms, our Bayesian model incorporates prior knowledge of significant coupling at each electrode to guide its estimations, yielding a robust measure of neural synchronization both within and across brain regions. We applied this model to EEG recordings from 48 children (15 with reading difficulties, 33 controls) during auditory steady-state stimulation at 4.8, 16, and 40 Hz. The Bayesian approach revealed clear cross-frequency coupling patterns: significant theta–gamma coupling was found in both groups, especially in occipital–parietal regions involved in phonological processing and attention. Importantly, the reading difficulties group showed stronger and more widespread frontoparietal coupling at 16 Hz than the controls, including a prominent connection from electrode CP6 to FC6-suggesting a possible compensatory mechanism or disrupted pathway. No significant coupling was detected at 40 Hz, though near-significant trends hint at a subtle role for gamma oscillations. Finally, using PAC features from our model, a simple classifier distinguished children with and without reading difficulties with balanced accuracies around 75–80 % (significantly above chance), demonstrating the method’s practical efficacy. These results highlight that the Bayesian PAC framework not only uncovers meaningful brain connectivity patterns in noisy EEG data but also serves as a promising tool for identifying biomarkers of reading disabilities and potentially other cognitive conditions.

Neuroimaging techniques have had a major impact on medical science, allowing advances in the research of many neurological diseases and improving their diagnosis. In this context, multimodal neuroimaging approaches, based on the neurovascular coupling phenomenon, exploit their individual strengths to provide complementary information on the neural activity of the brain cortex. This work proposes a novel method for combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to explore the functional activity of the brain processes related to low-level language processing of skilled and dyslexic seven-year-old readers. We have transformed EEG signals into image sequences considering the interaction between different frequency bands by means of cross-frequency coupling (CFC), and applied an activation mask sequence obtained from the local functional brain activity inferred from simultaneously recorded fNIRS signals. Thus, the resulting image sequences preserve spatial and temporal information of the communication and interaction between different neural processes and provide discriminative information that allows differentiation between controls and dyslexic subjects with an AUC of 77.1%. Finally, explainability is improved by introducing an easily comprehensible representation of the SHAP values obtained for the classification method in the brainSHAP maps.

Developmental dyslexia (DD) affects approximately 5-12% of learners, posing persistent challenges in reading and writing. This study presents a novel electroencephalography (EEG)-based methodology for identifying DD using two auditory stimuli modulated at 4.8[Formula: see text]Hz (prosodic) and 40[Formula: see text]Hz (phonemic). EEG signals were processed to estimate one-to-one Granger causality, yielding directed and weighted connectivity matrices. A novel Mutually Informed Correlation Coefficient (MICC) feature selection method was employed to identify the most relevant causal links, which were visualized using connectograms. Under the 4.8[Formula: see text]Hz stimulus, altered theta-band connectivity between frontal and occipital regions indicated compensatory frontal activation for prosodic processing and visual-auditory integration difficulties, while gamma-band anomalies between occipital and temporal regions suggested impaired visual-prosodic integration. Classification analysis under the 4.8[Formula: see text]Hz stimulus yielded area under the ROC curve (AUC) values of 0.92 (theta) and 0.91 (gamma band). Under the 40[Formula: see text]Hz stimulus, theta abnormalities reflected dysfunctions in integrating auditory phoneme signals with executive and motor regions, and gamma alterations indicated difficulties coordinating visual and auditory inputs for phonological decoding, with AUC values of 0.84 (theta) and 0.89 (gamma). These results support both the Temporal Sampling Framework and the Phonological Core Deficit Hypothesis. Future research should extend the range of stimuli frequencies and include more diverse cohorts to further validate these potential biomarkers.

Children with dyslexia face significant learning difficulties that require personalized and intensive interventions. Although computer-based support programs exist, they often fail to adapt to the unique needs of each child, representing a major challenge in the field of educational intervention. This article presents a new adaptive sequential guidance system for personalized dyslexia intervention that addresses these limitations. The proposed methodology incorporates several key innovations: (1) a dynamic word generator that creates phonetically modified words and pseudowords from seed words, (2) a three-dimensional matrix structure ( , ,and ) to effectively manage word difficulty and user performance, and (3) a recommendation algorithm based on matrix factorization. To mitigate cold-start problems, the system implements a heuristic initiation process and uses an extension technique to detect difficulties in specific derived words. Additionally, the concept of “virtual children” generated from real data and based on Bayesian Knowledge Tracking is introduced, allowing thorough testing and optimization of the system prior to its actual implementation. The evaluation of the system demonstrates three main results: (1) the use of heat maps and 3D visualization of the matrix allows identifying specific areas of difficulty for each user, facilitating more targeted interventions; (2) extensive testing confirms the robustness of the system to reduce error rates in multiple trials; and (3) a parametric study evidences the ability of the system to adapt through adjustable parameters, keeping each child in his or her optimal learning zone.

The appearance of Artificial Intelligence (IA) has improved our ability to process large amount of data. These tools are particularly interesting in medical contexts, in order to evaluate the variables from patients’ screening analysis and disentangle the information that they contain. We propose in this work a novel method for evaluating the role of electrocardiogram (ECG) signals in the human cognitive decline. This framework offers a complete solution for all the steps in the classification pipeline, from the preprocessing of the raw signals to the final classification stage. Numerous metrics are computed from the original data in terms of different domains (time, frequency, etc.), and dimensionality is reduced through a Principal Component Analysis (PCA). The resulting characteristics are used as inputs of different classifiers (linear/non-linear Support Vector Machines, Random Forest, etc.) to determine the amount of information that they contain. Our system yielded an area under the Receiver Operating Characteristic (ROC) curve of 0.80 identifying Mild Cognitive Impairment (MCI) patients, showing that ECG contain crucial information for predicting the appearance of this pathology. These results are specially relevant given the fact that ECG acquisition is much more affordable and less invasive than brain imaging used in most of these intelligent systems, allowing our method to be used in environments of any socioeconomic range.

The combination of different sources of information is currently one of the most relevant aspects in the diagnostic process of several diseases. In the field of neurological disorders, different imaging modalities providing structural and functional information are frequently available. Those modalities are usually analyzed separately, although a joint of the features extracted from both sources can improve the classification performance of Computer-Aided Diagnosis (CAD) tools. Previous studies have computed independent models from each individual modality and combined them in a subsequent stage, which is not an optimum solution. In this work, we propose a method based on the principles of siamese neural networks to fuse information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). This framework quantifies the similarities between both modalities and relates them with the diagnostic label during the training process. The resulting latent space at the output of this network is then entered into an attention module in order to evaluate the relevance of each brain region at different stages of the development of Alzheimer's disease. The excellent results obtained and the high flexibility of the method proposed allow fusing more than two modalities, leading to a scalable methodology that can be used in a wide range of contexts.

Developmental dyslexia is characterized by a deficit of phonological awareness whose origin is related to atypical neural processing of speech streams. This can lead to differences in the neural networks that encode audio information for dyslexics. In this work, we investigate whether such differences exist using functional near-infrared spectroscopy (fNIRS) and complex network analysis. We have explored functional brain networks derived from low-level auditory processing of nonspeech stimuli related to speech units such as stress, syllables or phonemes of skilled and dyslexic seven-year-old readers. A complex network analysis was performed to examine the properties of functional brain networks and their temporal evolution. We characterized aspects of brain connectivity such as functional segregation, functional integration or small-worldness. These properties are used as features to extract differential patterns in controls and dyslexic subjects. The results corroborate the presence of discrepancies in the topological organizations of functional brain networks and their dynamics that differentiate between control and dyslexic subjects, reaching an Area Under ROC Curve (AUC) up to 0.89 in classification experiments.

The prevalence of dementia is currently increasing worldwide. This syndrome produces a deterioration in cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing its progress. The Clock Drawing Test (CDT) is a widely used paper-and-pencil test for cognitive assessment in which an individual has to manually draw a clock on a paper. There are a lot of scoring systems for this test and most of them depend on the subjective assessment of the expert. This study proposes a computer-aided diagnosis (CAD) system based on artificial intelligence (AI) methods to analyze the CDT and obtain an automatic diagnosis of cognitive impairment (CI). This system employs a preprocessing pipeline in which the clock is detected, centered and binarized to decrease the computational burden. Then, the resulting image is fed into a Convolutional Neural Network (CNN) to identify the informative patterns within the CDT drawings that are relevant for the assessment of the patient's cognitive status. Performance is evaluated in a real context where patients with CI and controls have been classified by clinical experts in a balanced sample size of [Formula: see text] drawings. The proposed method provides an accuracy of [Formula: see text] in the binary case-control classification task, with an AUC of [Formula: see text]. These results are indeed relevant considering the use of the classic version of the CDT. The large size of the sample suggests that the method proposed has a high reliability to be used in clinical contexts and demonstrates the suitability of CAD systems in the CDT assessment process. Explainable artificial intelligence (XAI) methods are applied to identify the most relevant regions during classification. Finding these patterns is extremely helpful to understand the brain damage caused by CI. A validation method using resubstitution with upper bound correction in a machine learning approach is also discussed.

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the assumption of the temporal sampling framework of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. These alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are first partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. Then, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. Our system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.

There remains an open question about the usefulness and the interpretation of machine learning (ML) approaches for discrimination of spatial patterns of brain images between samples or activation states. In the last few decades, these approaches have limited their operation to feature extraction and linear classification tasks for between-group inference. In this context, statistical inference is assessed by randomly permuting image labels or by the use of random effect models that consider between-subject variability. These multivariate ML-based statistical pipelines, whilst potentially more effective for detecting activations than hypotheses-driven methods, have lost their mathematical elegance, ease of interpretation, and spatial localization of the ubiquitous General linear Model (GLM). Recently, the estimation of the conventional GLM parameters has been demonstrated to be connected to an univariate classification task when the design matrix in the GLM is expressed as a binary indicator matrix. In this paper we explore the complete connection between the univariate GLM and ML-based regressions. To this purpose we derive a refined statistical test with the GLM based on the parameters obtained by a linear Support Vector Regression (SVR) in the inverse problem (SVR-iGLM). Subsequently, random field theory (RFT) is employed for assessing statistical significance following a conventional GLM benchmark. Experimental results demonstrate how parameter estimations derived from each model (mainly GLM and SVR) result in different experimental design estimates that are significantly related to the predefined functional task. Moreover, using real data from a multisite initiative the proposed ML-based inference demonstrates statistical power and the control of false positives, outperforming the regular GLM.

Complex network analysis has an increasing relevance in the study of neurological disorders, enhancing the knowledge of brain’s structural and functional organization. Network structure and efficiency reveal different brain states along with different ways of processing the information. This work is structured around the exploratory analysis of the brain processes involved in low-level auditory processing. A complex network analysis was performed on the basis of brain coupling obtained from electroencephalography (EEG) data, while different auditory stimuli were presented to the subjects. This coupling is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes to explore differences between control and dyslexic subjects. Coupling data allows the construction of a graph, and then, graph theory is used to study the characteristics of the complex networks throughout time for control and dyslexic subjects. This results in a set of metrics including clustering coefficient, path length and small-worldness. From this, different characteristics linked to the temporal evolution of networks and coupling are pointed out for dyslexics. Our study revealed patterns related to Dyslexia as losing the small-world topology. Finally, these graph-based features are used to classify between control and dyslexic subjects by means of a Support Vector Machine (SVM).

Finding new biomarkers to model Parkinson's Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients with potential PD but also as a way to measure quantitatively the loss of dopaminergic neurons mainly concentrated at substantia nigra. Within this context, this work presented here tries to provide a set of imaging features based on morphological characteristics extracted from I[Formula: see text]-Ioflupane SPECT scans to discern between HC and PD participants in a balanced set of [Formula: see text] scans from Parkinson's Progression Markers Initiative (PPMI) database. These features, obtained from isosurfaces of each scan at different intensity levels, have been classified through the use of classical Machine Learning classifiers such as Support-Vector-Machines (SVM) or Naïve Bayesian and compared with the results obtained using a Multi-Layer Perceptron (MLP). The proposed system, based on a Mann-Whitney-Wilcoxon U-Test for feature selection and the SVM approach, yielded a [Formula: see text] balanced accuracy when the performance was evaluated using a [Formula: see text]-fold cross-validation. This proves the reliability of these biomarkers, especially those related to sphericity, center of mass, number of vertices, 2D-projected perimeter or the 2D-projected eccentricity, among others, but including both internal and external isosurfaces.

The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography (EEG) experiments on children listening to amplitude modulated (AM) noise with slow-rythmic prosodic (0.5-1[Formula: see text]Hz), syllabic (4-8[Formula: see text]Hz) or the phoneme (12-40[Formula: see text]Hz) rates, aimed at detecting differences in perception of oscillatory sampling that could be associated with dyslexia. The purpose of this work is to check whether these differences exist and how they are related to children's performance in different language and cognitive tasks commonly used to detect dyslexia. To this purpose, temporal and spectral inter-channel EEG connectivity was estimated, and a denoising autoencoder (DAE) was trained to learn a low-dimensional representation of the connectivity matrices. This representation was studied via correlation and classification analysis, which revealed ability in detecting dyslexic subjects with an accuracy higher than 0.8, and balanced accuracy around 0.7. Some features of the DAE representation were significantly correlated ([Formula: see text]) with children's performance in language and cognitive tasks of the phonological hypothesis category such as phonological awareness and rapid symbolic naming, as well as reading efficiency and reading comprehension. Finally, a deeper analysis of the adjacency matrix revealed a reduced bilateral connection between electrodes of the temporal lobe (roughly the primary auditory cortex) in DD subjects, as well as an increased connectivity of the F7 electrode, placed roughly on Broca's area. These results pave the way for a complementary assessment of dyslexia using more objective methodologies such as EEG.

Diagnosis of learning difficulties is a challenging goal. There are huge number of factors involved in the evaluation procedure that present high variance among the population with the same difficulty. Diagnosis is usually performed by scoring subjects according to results obtained in different neuropsychological (performance-based) tests specifically designed to this end. One of the most frequent disorders is developmental dyslexia (DD), a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling. Its prevalence is estimated between 5% and 12% of the population. Traditional tests for DD diagnosis aim to measure different behavioral variables involved in the reading process. In this paper, we propose a diagnostic method not based on behavioral variables but on involuntary neurophysiological responses to different auditory stimuli. The experiments performed use electroencephalography (EEG) signals to analyze the temporal behavior and the spectral content of the signal acquired from each electrode to extract relevant (temporal and spectral) features. Moreover, the relationship of the features extracted among electrodes allows to infer a connectivity-like model showing brain areas that process auditory stimuli in a synchronized way. Then an anomaly detection system based on the reconstruction residuals of an autoencoder using these features has been proposed. Hence, classification is performed by the proposed system based on the differences in the resulting connectivity models that have demonstrated to be a useful tool for differential diagnosis of DD as well as a method to step towards gaining a better knowledge of the brain processes involved in DD. The results corroborate that nonspeech stimulus modulated at specific frequencies related to the sampling processes developed in the brain to capture rhymes, syllables and phonemes produces effects in specific frequency bands that differentiate between controls and DD subjects. The proposed method showed relatively high sensitivity above 0.6, and up to 0.9 in some of the experiments.

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.

The papers in this special section examine important current topics on multimodal data fusion in the medical context. All clinical data, including genomic and proteomic, play a role in the diagnosis and in particular in the treatment planning and follow-up. This is true for all types of data analyses whether in classification, regression, retrieval, clustering, or other. The interaction between several types of information is not always well understood. Experienced clinicians automatically and even unconsciously add multiple sources of information into their decision process, but machine learning tools often concentrate on single information sources. This special issue presents five examples where several data sources are fused. The papers give several examples of fusion techniques and also the results obtained in quite different application scenarios.

Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the arrival of the deep learning paradigm, it has become possible to extract high-level abstract features directly from MRI images that internally describe the distribution of data in low-dimensional manifolds. In this work, we try a new exploratory data analysis of AD based on deep convolutional autoencoders. We aim at finding links between cognitive symptoms and the underlying neurodegeneration process by fusing the information of neuropsychological test outcomes, diagnoses, and other clinical data with the imaging features extracted solely via a data-driven decomposition of MRI. The distribution of the extracted features in different combinations is then analyzed and visualized using regression and classification analysis, and the influence of each coordinate of the autoencoder manifold over the brain is estimated. The imaging-derived markers could then predict clinical variables with correlations above 0.6 in the case of neuropsychological evaluation variables such as the MMSE or the ADAS11 scores, achieving a classification accuracy over 80% for the diagnosis of AD.

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.

Older adults are related to a reduction in the physical functionality, as a result of a musculoskeletal system degeneration. In that way, physical exercise has been stated as a suitable intervention to prevent such health problems. Therefore, an adequate assessment of the physical activity and functional fitness levels is needed to plan the individualized intervention. A broad test used to assess the functional fitness level is the 6-minutes walk test (6MWT). It has been previously measured using accelerometer sensors. In views of this background, the main aim of the present study is to use the Empirical Mode Decomposition (EMD) method to predict the physical activity and functional fitness levels of the older adults through the acceleration signals recorded by a smartphone during the 6MWT. A total of 17 participants were recruited. Anthropometric measurements (weight, height, and BMI), physical activity, and functional fitness levels from each participant were recorded. Consecutively, the EMD method was applied to determine the prediction. According to the results, the proposed method can predict the physical activity and functional fitness levels with high accuracy, even using only one cycle. Thus, the approach described in the present work could be implemented in future m-health systems to identify the physical activity profile of the older adults.

Spatial and intensity normalizations are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging modalities. Nuclear imaging modalities such as PET-FDG or FP-CIT SPECT, a common modality used in Parkinson's disease diagnosis, are especially dependent on intensity normalization. However, these steps are computationally expensive and furthermore, they may introduce deformations in the images, altering the information contained in them. Convolutional neural networks (CNNs), for their part, introduce position invariance to pattern recognition, and have been proven to classify objects regardless of their orientation, size, angle, etc. Therefore, a question arises: how well can CNNs account for spatial and intensity differences when analyzing nuclear brain imaging? Are spatial and intensity normalizations still needed? To answer this question, we have trained four different CNN models based on well-established architectures, using or not different spatial and intensity normalization preprocessings. The results show that a sufficiently complex model such as our three-dimensional version of the ALEXNET can effectively account for spatial differences, achieving a diagnosis accuracy of 94.1% with an area under the ROC curve of 0.984. The visualization of the differences via saliency maps shows that these models are correctly finding patterns that match those found in the literature, without the need of applying any complex spatial normalization procedure. However, the intensity normalization - and its type - is revealed as very influential in the results and accuracy of the trained model, and therefore must be well accounted.

In a recent publication in this journal, K. Buet al.(IEEE Trans. on Information Forensics and Security, vol. 8, no. 3, pp. 429–439, Mar. 2013) presented a protocol that detects cloned tags (replicas of genuine tags) in anonymous radio frequency identification (RFID) systems by leveraging unreconciled collisions in the medium access control layer. This does not require RFID readers to implement cryptographic mechanisms, or know the identities of tags. Improved versions have since been published that claim that detection can also be deterministic. In this paper, we analyze this protocol and show that the physical limitations of collision detection have not been adequately addressed and that, without any further assumptions, unreconcilable collisions cannot be detected. We then propose a solution for which the responses of tags are randomized and show that: 1) there is a tradeoff between the accuracy of detecting cloning attacks and execution time and 2) detection of cloned tags cannot be deterministic.

The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called computed aided diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on hidden Markov models (HMMs). The path is traced using information of intensity and spatial orientation in each node, adapting to the structure of the brain. Each path is itself a useful way to characterize the distribution of the tissue inside the magnetic resonance imaging (MRI) image by, for example, extracting the intensity levels at each node or generating statistical information of the tissue distribution. Additionally, a further processing consisting of a modification of the grey level co-occurrence matrix (GLCM) can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to Alzheimer's disease (AD), as well as providing a significant feature reduction. This methodology achieves moderate performance, up to 80.3% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's disease neuroimaging initiative (ADNI).

Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the construction of classification methods based on deep learning architectures applied on brain regions defined by the Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each brain area have been split into 3D patches according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks. An ensemble of deep belief networks is then composed where the final prediction is determined by a voting scheme. Two deep learning based structures and four different voting schemes are implemented and compared, giving as a result a potent classification architecture where discriminative features are computed in an unsupervised fashion. The resulting method has been evaluated using a large dataset from the Alzheimer's disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation prove that the proposed method is not only valid for differentiate between controls (NC) and AD images, but it also provides good performances when tested for the more challenging case of classifying Mild Cognitive Impairment (MCI) Subjects. In particular, the classification architecture provides accuracy values up to 0.90 and AUC of 0.95 for NC/AD classification, 0.84 and AUC of 0.91 for stable MCI/AD classification and 0.83 and AUC of 0.95 for NC/MCI converters classification.

Summary Bioinformatics applications frequently involve high‐dimensional model building or classification problems that require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed. Thus, feature selection has become an important issue on these applications. Moreover, several approaches for supervised and unsupervised feature selections as a multi‐objective optimization problem have been recently proposed to cope with issues on performance evaluation of classifiers and models. As parallel processing constitutes an important tool to reach efficient approaches that make it possible to tackle complex problems within reasonable computing times, in this paper, alternatives for the cooperation of subpopulations in multi‐objective evolutionary algorithms have been identified and classified, and several procedures have been implemented and evaluated on some synthetic and Brain–Computer Interface datasets. The results show different improvements achieved in the solution quality and speedups, depending on the cooperation alternative and dataset. We show alternatives that even provide superlinear speedups with only small reductions in the solution quality, besides another cooperation alternative that improves the quality of the solutions with speedups similar to, or only slightly higher than, the speedup obtained by the parallel fitness evaluation in a master‐worker implementation (the alternative used as reference that behaves as the corresponding sequential multi‐objective approach). Copyright © 2015 John Wiley & Sons, Ltd.

Feature selection is required in many applications that involve high dimensional model building or classification problems. Many bioinformatics applications belong to this type. Recently, some approaches for supervised and unsupervised feature selection as a multi-objective optimization problem have been proposed. As the performance of unsupervised classification is evaluated through the quality of the obtained groups or clusters in the data set to be classified, it is difficult to define a suitable objective function that drives the selection of the features. Thus, several evaluation measures, and thus multi-objective clustering characterization, could provide a suitable set of features for unsupervised classification. In this paper, we consider the parallel implementation of a multi-objective feature selection that makes it possible to apply it to complex classification problems such as those having many features to select, and specifically high-dimensional data sets with much more features than data items. In this paper, we propose master-worker implementations of two different parallel evolutionary models, the parallel computation of the cost functions for the individuals in the population, and the parallel execution of evolutionary multi-objective procedures on subpopulations. The experiments accomplished on different benchmarks, including some related with feature selection in classification of EEG (Electroencephalogram) signals for BCI (Brain Computer Interface) applications, show the benefits of parallel processing not only for decreasing the running time, but also for improving the solution quality.

Image segmentation aims to partition an image into its constituent parts. In Magnetic Resonance Imaging (MRI), image segmentation groups the voxels belonging to a specific tissue or fluids. Currently, this is an essential step in MRI analysis and provides a way to diagnose many neurological disorders such as the Alzheimer disease. In this paper we present a segmentation method for MRI based on the Growing Hierarchical Self-Organizing Map and multiobjective-based feature selection to optimize the performance of the segmentation process. Since the features extracted from the image result crucial for the final performance of the segmentation process, the optimized features which maximize the performance of the segmentation process on each plane are used. The experiments performed have been carried out using the Internet Brain Segmentation Repository (IBSR) as it has been widely used in previous works. Thus, the comparisons with other methods carried out using the IBSR database, show that our method outperforms other algorithms.

Present trend towards multiprocessor and multi-core architectures as well as programmable NICs (Network Interface Cards) provides new opportunities to exploit the available parallelism in the network interface design and implementation to cope with the high communication overhead required to take advantage of a multi-gigabit links. In this paper we have used dynamic web servers to evaluate several alternatives to distribute the network interface work among the different cores available in the node. These alternatives have been devised according to the affinity of the work to be done with the location (proximity to the memories where the different data structures are stored) and characteristics of the processing core. Improvements in the throughput (up to 110%), the response time (up to 500%) and the requests attended per second (up to 85%) have been observed in our experiments on dynamic web servers.

The availability of multi-core processors and programmable NICs (Network Interface Cards), such as TOEs (TCP/IP Offloading Engines), provides new opportunities for designing efficient network interfaces to cope with the gap between the improvement rates of link bandwidths and microprocessor performance. This gap poses important challenges related with the high computational requirements associated to the traffic volumes and wider functionality that the network interface has to support. An opportunity to reach these goals comes from the exploitation of the parallelism in the communication path by distributing the protocol processing work across processors available in the computer, i.e. multi-core microprocessors and programmable NICs. Thus, alternatives such as offloading and onloading try to release host CPU cycles by this approach. Nevertheless, whereas onloading uses another general-purpose processor, either included in a multi-core microprocessor or in a symmetric multiprocessor (SMP), offloading takes advantage of processors in programmable network interface cards (NICs). Some experimental results demonstrate that the relative improvement on peak throughput offered by offloading and onloading depends on the rate of application workload to communication overhead, the message sizes, and on the characteristics of system architecture, more specifically the bandwidth of the buses and the way the NIC is connected to the system processor and memory. Thus, in this paper we propose a network interface that takes the advantages of both offloading and onloading approaches while avoids their respective drawbacks. The performance analyses done by using a full-system simulator, shows that, in the benchmarks and application used for the experiments, our hybrid interface improves the latency and bandwidth behavior of the onloading and offloading approaches.

This paper compares the onloading and offloading alternatives for improving up communication. Both strategies try to release host CPU cycles by taking advantage of the execution of the communication workload in other processors present in the node. Nevertheless, whereas onloading uses another general-purpose processor, either included in a chip multiprocessor (CMP) or in a symmetric multiprocessor (SMP), offloading takes advantage of processors in programmable network interface cards (NICs). Here, it is shown that the relative improvement on peak throughput offered by offloading and onloading depends on the rate of application workload to communication overhead, the message sizes, and the characteristics of system architecture, more specifically the buses bandwidth and the way the NIC is connected to the system processor and memory. In our implementations, offloading provides lower latencies than onloading although the CPU utilization and interrupts are lower for onloading.

Due to the network technology advances, an order-of-magnitude jump has been produced in the network bandwidth. This fact has reawake the interest in protocol offloading (particularly in protocols such as TCP, that require a lot of CPU resources to process the stack), since network communication is one key factor for the system performance. Nevertheless, there are controversial studies on protocol offloading benefits, and much research work on offloading, particularly on TCP offloading as it has been the main transport protocol for many years, and in fact, it is the most used protocol in the networks. This paper describes a model to simulate by Simics the protocol offloading technique to keep low the communication overheads on the host CPU. The simulation platform provided by Simics can be used for a functional system simulation, including the application program, the operating system, the protocol stack and the device drivers, since network-oriented applications require a full-system simulation. As Simics does not provide a detailed network I/O model, in this paper we describe the way we have generated a model that allows the simulation of the offloading effects

This paper describes our work with Simics (Magnusson, 2002) in the evaluation of protocol offloading to keep low the communication overheads on the host CPU. Simics is a platform that can be used for a functional system simulation, including the application program, the operating system, the protocol stack and the device drivers. Nevertheless, network-oriented applications require a full-system simulation that includes not only the OS code, but also a model of the memory system and a detailed timing model of network DMA effects (Binkert, 2003). As Simics does not provide a detailed network I/O model, it could present some limitations that make it difficult an accurate network-oriented full-system simulation. Here we present the way we have overcome this problem