Respiratory diseases are among the leading causes of morbidity worldwide, making timely and accurate diagnosis essential. However, interpreting chest X-rays is challenging due to the variability of pathological manifestations and the subjectivity of human analysis. In this study, we propose a multimodal approach that integrates automated image analysis with textual clinical data, leveraging a Visual Question Answering (VQA)based architecture and a text generation model for diagnostic report production. The use of Grad-CAM enhances the interpretability of the system by highlighting the most relevant image regions for diagnosis. The model was trained on a balanced dataset obtained by merging three sources-Lung X-ray Data, NIH Chest X-rays, and Chest X-Ray Images-ensuring fair classification across Normal and Pneumonia categories. The pipeline includes visual feature extraction using a Vision Transformer (ViT), automatic pathology classification, and diagnostic report generation with an advanced language model. Results indicate a significant improvement in diagnostic accuracy compared to traditional methods, supported by key performance metrics such as accuracy 95.3%, sensitivity, specificity, and F1-score. Furthermore, integrating the system into an interactive web app facilitates clinical adoption, enhancing diagnostic efficiency and supporting personalized management of pulmonary diseases.

Marine litter detection is crucial for environmental monitoring, yet the imbalance in existing datasets limits model performance in identifying various types of waste accurately. This paper presents an efficient data augmentation pipeline that combines generative diffusion models (e.g., Stable Diffusion) and Large Language Models (LLMs) to expand the G-Litter dataset, a marine litter dataset designed for autonomous detection in heterogeneous environments. Leveraging scalable diffusion models for image generation and Alpaca LLMs for diverse prompt generation, our approach augments underrepresented classes by generating over 200 additional images per class, significantly improving the dataset’s balance. Training G-Litter augmented dataset using YOLOv8 for object detection demonstrated an increase in detection performance, improving recall by 7.82% and mAP50 by 3.87% (compared with baseline results). This study emphasizes the potential for combining generative AI with HPC resources to automate data augmentation on large-scale, unstructured datasets, particularly in edge computing contexts for real-time marine monitoring. The models were tested on real videos captured during simulated missions, demonstrating a superior ability to detect submerged objects in dynamic scenarios. These results highlight the potential of generative AI techniques to improve dataset quality and detection model performance, laying the foundation for further expansion in real-time marine monitoring.

In the era of advanced meteorological data platforms such as Copernicus and Climate Data Store, the frontier of weather forecasting has evolved. The primary challenge is no longer the acquisition of accurate and high-resolution data, but rather the effective integration and utilization of diverse observational datasets to enhance localized weather predictions. Crowd sensed weather data through a network of low-cost, widely distributed weather stations can provide the granular data needed for precise local forecasts. However, this approach introduces challenges such as data integration, consistency, and privacy concerns. Federated Learning (FL) addresses these issues by enabling decentralized data processing while maintaining data privacy.This paper introduces an innovative implementation of a federated learning framework integrated with a cluster of Automated Weather Stations (AWS). The primary objective of this study is to leverage federated learning to enhance the predictive accuracy of the Weather Research and Forecasting (WRF) model by using each weather station not only as a data acquisition point but also as a computational node. This decentralized approach maintains data privacy and security while enabling local training of models, such as Crossformer, Autoformer, and DLinear. These models’ locally trained weights are periodically aggregated on the central server, which updates and redistributes the global model.Based on data collected over two years from two automated weather stations, the experimental results analyze the possibility of improving WRF model predictions for temperature and humidity. This research highlights the potential of Federated Learning in meteorological applications, offering a robust solution for enhancing weather forecast accuracy while ensuring data privacy and efficient resource utilization.

In the current era, vast amounts of data are readily available, particularly in the form of multi-structured data such as sequences, trees, and graphs. Analyzing these diverse data types requires specialized approaches. However, standard machine learning algorithms are not always suitable due to their lack of adaptability to the inherent nature of structured data. To address this limitation, our work uses a novel learning framework based on a general recursive scheme. This framework effectively embeds structured data into vector representations and leverages Principal Component Analysis to create a clustering technique. We conduct comparisons and experiments with established algorithms to evaluate performance across synthetic and real-world datasets.

Generative artificial intelligence (AI) is poised to revolutionize the healthcare sector by enhancing research methodologies, diagnostic procedures, and treatment protocols. This paper investigates the application of key generative AI technologies, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Pre-trained Generative Transformer models, with a particular focus on their implementation in medical imaging, drug discovery, and electronic health record management. Utilizing the Haystack framework, this study integrates these technologies to optimize data access and retrieval. The research addresses the primary challenges in healthcare AI adoption, such as data quality, model interpretability, ethical considerations, and regulatory compliance. By leveraging the Haystack framework, we identify future research opportunities, emphasizing the integration of multimodal data, the personalization of treatments, and the development of transparent AI systems. The study underscores the critical role of interdisciplinary collaboration between AI researchers and healthcare professionals in maximizing the benefits of these technologies, managing their complexities, and ensuring their successful integration into healthcare systems. Our findings demonstrate the potential of generative AI to significantly improve clinical decision-making and patient care, while also highlighting the importance of ethical guidelines and robust data security measures.

The use of Super-Resolution SR algorithms applied to Magnetic Resonance Images (MRIs) is increasingly common in the medical field. Increasing the resolution of images allows physicians to more easily observe image details. Over the years, several SR approaches have been tried by researchers. Among the various approaches, Diffusion Models (DMs) have been shown to perform well in the SR task. In this work, we propose the use of a Latent Diffusion Model (LDM) for the SR of medical images. Different studies have shown that LDMs improve the performance of DMs in several SR tasks. To our knowledge, LDMs have not been tested for SR of medical images such as MRIs. We therefore perform fine-tuning of an LDM on medical datasets. To evaluate the SR images generated by the LDM, we compare them to the original high-resolution images using two similarity measurements. We show that the LDM achieves better similarity values than other SR models on the same medical dataset. We also show with visual examples the advantage of applying SR using an LDM.

In the evolving Artificial Intelligence (AI) era, the need for real-time algorithm processing in marine edge en-vironments has become a crucial challenge. Data acquisition, analysis, and processing in complex marine situations require sophisticated and highly efficient platforms. This study optimizes real-time operations on a containerized distributed processing platform designed for Autonomous Surface Vehicles (ASV) to help safeguard the marine environment. The primary objective is to improve the efficiency and speed of data processing by adopting a microservice management system called DataX. DataX leverages containerization to break down operations into modular units, and resource coordination is based on Kubernetes. This combination of technologies enables more efficient resource management and real-time operations optimization, contributing significantly to the success of marine missions. The platform was developed to address the unique challenges of managing data and running advanced algorithms in a marine context, which often involves limited connectivity, high latencies, and energy restrictions. Finally, as a proof of concept to justify this platform's evolution, experiments were carried out using a cluster of single-board computers equipped with GPUs, running an AI-based marine litter detection application and demonstrating the tangible benefits of this solution and its suitability for the needs of maritime missions.

Data crowdsourcing is an increasingly pervasive and lifestyle-changing technology due to the flywheel effect that results from the interaction between the Internet of Things and Cloud Computing. This paper presents the Citizen Science for the Sea with Information Technologies (C4Sea-IT) framework. It is an open platform for gathering marine data from leisure boat instruments. C4Sea-IT aims to provide a coastal marine data gathering, moving, processing, exchange, and sharing platform using the existing navigation instruments and sensors for today's leisure and professional vessels. In this work, a use case for the detection and tracking of marine litter is shown. The final goal is weather/ocean forecasts argumentation with Artificial Intelligence prediction models trained with crowdsourced data.

Autonomous Surface Vehicles and their management represent one of the significant challenges in coastal and offshore surveying. Although the development of this kind of data acquisition device has skyrocketed in the last few years, line guides and technological solutions still need to come. On the other hand, this kind of robotic vessel's true potential has yet to be explored. This paper presents ArgonautAI, a containerized distributed processing platform for autonomous surface vehicles. The proposed ArgonautAI architecture leverage a cluster of single-board computers with diverse and different characteristics (computing power, CUDA GPUs, FPGAs, GPIOs, PWMs, specialized I/O) orchestrated using Kubernetes and a customized programming interface. Furthermore, the proposed solution introduces two different types of containers: 1) the platform containers hosting the software life support for the platform and 2) the mission containers defined to support the survey mission-specific scopes. The firsts manage the vehicle's instruments (e.g. position, attitude, environment, depth), the data storage, the vessel-to-shore communication, and so on; the latter host mission-specific software components. Finally, as proof of concept of the proposed platform, we present an AI-based marine litter detection application using a hierarchical computer vision approach on heterogenic onboard computing resources.

On behalf of the Organizing Committee, we welcome you to the 31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP2023), organized by the Department of Science and Technology of the University of Naples “Parthenope”. The conference was hosted in Naples in the prestigious Villa Doria d'Angri from the 1st to the 3rd of March 2023.

Welcome to the 31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP2023).

Monitoring the impact of the pollutants on the sea is a crucial issue for coastal human activities, such as aquaculture. However, leveraging a continuous microbiological laboratory analysis is unfeasible for costs and practical reasons. Here we present a novel methodology finalized to predict water quality as categorized indexes leveraging an integrated approach between computational components and artificial intelligence techniques. As a paradigm demonstrator, we couple WaComM++ with AIQUAM. The use case presented is an application of AIQUAM in the Bay of Naples (Campania Region, Italy) for predicting bacteria contaminants in mussel farms. The results are encouraging as the model reached a correct prediction rate of 93%.

Fuzzy Cognitive Maps (FCMs) represent graphically the main concepts of a given domain and their relationships as a directed and weighted graph. As part of a growing need for intelligent systems that produce explanations for the decisions they make (the so-called XAI – eXplainable Artificial Intelligence), due to their intuitive yet formal nature, FCMs are invaluable tools for modeling complex real world scenarios, but are traditionally created through the analysis of direct interviews with a number of domain experts, hence requiring a largely manual, expensive, and cumbersome effort. The aim of this work is to design, develop and test a method for the automatic generation of FCMs from raw data in form of Twitter conversations. In order to improve the recognized entities and to cope with brevity, ambiguity and jargon, messages in tweets are first enriched with both domain-specific and general corpora, then analyzed and transformed into meaningful maps. As the data come from a population of common users instead of domain experts, the obtained FCMs are highly variable and should be read more as a snapshot of the beliefs of these users on a specific topic than an objective representation of what experts think on that topic. From clerical review, reported test cases confirm the viability and effectiveness of the proposed method.

BACKGROUND: Recent studies have indicated that a special class of long non-coding RNAs (lncRNAs), namely Transcribed-Ultraconservative Regions are transcribed from specific DNA regions (T-UCRs), 100[Formula: see text] conserved in human, mouse, and rat genomes. This is noticeable, as lncRNAs are usually poorly conserved. Despite their peculiarities, T-UCRs remain very understudied in many diseases, including cancer and, yet, it is known that dysregulation of T-UCRs is associated with cancer as well as with human neurological, cardiovascular, and developmental pathologies. We have recently reported the T-UCR uc.8+ as a potential prognostic biomarker in bladder cancer. RESULTS: The aim of this work is to develop a methodology, based on machine learning techniques, for the selection of a predictive signature panel for bladder cancer onset. To this end, we analyzed the expression profiles of T-UCRs from surgically removed normal and bladder cancer tissues, by using custom expression microarray. Bladder tissue samples from 24 bladder cancer patients (12 Low Grade and 12 High Grade), with complete clinical data, and 17 control samples from normal bladder epithelium were analysed. After the selection of preferentially expressed and statistically significant T-UCRs, we adopted an ensemble of statistical and machine learning based approaches (i.e., logistic regression, Random Forest, XGBoost and LASSO) for ranking the most important diagnostic molecules. We identified a signature panel of 13 selected T-UCRs with altered expression profiles in cancer, able to efficiently discriminate between normal and bladder cancer patient samples. Also, using this signature panel, we classified bladder cancer patients in four groups, each characterized by a different survival extent. As expected, the group including only Low Grade bladder cancer patients had greater overall survival than patients with the majority of High Grade bladder cancer. However, a specific signature of deregulated T-UCRs identifies sub-types of bladder cancer patients with different prognosis regardless of the bladder cancer Grade. CONCLUSIONS: Here we present the results for the classification of bladder cancer (Low and High Grade) patient samples and normal bladder epithelium controls by using a machine learning application. The T-UCR's panel can be used for learning an eXplainable Artificial Intelligent model and develop a robust decision support system for bladder cancer early diagnosis providing urinary T-UCRs data of new patients. The use of this system instead of the current methodology will result in a non-invasive approach, reducing uncomfortable procedures (such as cystoscopy) for the patients. Overall, these results raise the possibility of new automatic systems, which could help the RNA-based prognosis and/or the cancer therapy in bladder cancer patients, and demonstrate the successful application of Artificial Intelligence to the definition of an independent prognostic biomarker panel.

MOTIVATION: The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&D halving every year or less. The selection and prioritization of targets is one the most influential decisions in drug discovery. Here we present a Gaussian Process model for the prioritization of drug targets cast as a problem of learning with only positive and unlabeled examples. RESULTS: Since the absence of negative samples does not allow standard methods for automatic selection of hyperparameters, we propose a novel approach for hyperparameter selection of the kernel in One Class Gaussian Processes. We compare our methods with state-of-the-art approaches on benchmark datasets and then show its application to druggability prediction of oncology drugs. Our score reaches an AUC 0.90 on a set of clinical trial targets starting from a small training set of 102 validated oncology targets. Our score recovers the majority of known drug targets and can be used to identify novel set of proteins as drug target candidates. AVAILABILITY AND IMPLEMENTATION: The matrix of features for each protein is available at: https://bit.ly/3iLgZTa. Source code implemented in Python is freely available for download at https://github.com/AntonioDeFalco/Adaptive-OCGP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This supplement contains seven revised and extended papers selected from CIBB 2018 and CIBB 2019, the 15th and 16th editions of the international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB is a venue that embraces researchers with different backgrounds, ranging from mathematics to computer science, from materials science to medicine, and from engineering to biology, all interested in the investigation and application of computational intelligence methods to open problems in bioinformatics, biostatistics, systems biology, synthetic biology, and medical informatics.

This work aims to introduce a methodology for resting-state brain activities detection by a consumer-grade EEG. From one hand, an adaptive noise reduction methodology based on non-linear Principal Component Analysis Neural Network is adopted. On the other hand, a Neuro-Fuzzy model (i.e., Fuzzy Relational Neural Network) is considered for brain activities detection since a combination of neural networks and fuzzy technology enhances the performance of control, decision-making and data analysis systems. Experiments are made on a corpus containing the activation strength of the fourteen electrodes of an EEG headset for eye state detection. We proved that by using the noised signals, the proposed methodology permits to obtain a high rate of classification accuracy.

BACKGROUND: High throughput methods, in biological and biomedical fields, acquire a large number of molecular parameters or omics data by a single experiment. Combining these omics data can significantly increase the capability for recovering fine-tuned structures or reducing the effects of experimental and biological noise in data. RESULTS: In this work we propose a multi-view integration methodology (named FH-Clust) for identifying patient subgroups from different omics information (e.g., Gene Expression, Mirna Expression, Methylation). In particular, hierarchical structures of patient data are obtained in each omic (or view) and finally their topologies are merged by consensus matrix. One of the main aspects of this methodology, is the use of a measure of dissimilarity between sets of observations, by using an appropriate metric. For each view, a dendrogram is obtained by using a hierarchical clustering based on a fuzzy equivalence relation with Łukasiewicz valued fuzzy similarity. Finally, a consensus matrix, that is a representative information of all dendrograms, is formed by combining multiple hierarchical agglomerations by an approach based on transitive consensus matrix construction. Several experiments and comparisons are made on real data (e.g., Glioblastoma, Prostate Cancer) to assess the proposed approach. CONCLUSIONS: Fuzzy logic allows us to introduce more flexible data agglomeration techniques. From the analysis of scientific literature, it appears to be the first time that a model based on fuzzy logic is used for the agglomeration of multi-omic data. The results suggest that FH-Clust provides better prognostic value and clinical significance compared to the analysis of single-omic data alone and it is very competitive with respect to other techniques from literature.

In recent years, the field of Machine Learning is showing great interest towards the processing of structured data, such as sequences, trees and graphs. In this paper an unsupervised recursive learning schema for structured data clustering is introduced. The schema allows to process data organized in graphs for both graph-focused and node-focused applications. The approach uses the Fuzzy C-Means algorithm as building block. Some experiments are proposed to show its performances and to compare it with another approach known in literature.

The aim of this study is to introduce a new scheme, based on a compressive sampling technique, for the reconstruction of lost data in multimedia streaming. The audio streaming data are encapsulated in different packets, at the sender, by using an interleaving technique. The compressive sampling technique is used to recover audio information in case of lost packets, at the receiver. Experimental results are presented for speech and musical audio signals which illustrate the performances and the capabilities of the proposed methodology.

In this work a multi-step approach for clustering assessment, visualization and data validation is introduced. Three main approaches for data clustering are used and compared: K-means, self organizing maps and probabilistic principal surfaces. A model explorer approach with different similarity measures is used to obtain the best parameters of the methods. The approach is used to identify genes periodically expressed in tumors related to the human cell cycle. Finally, clusters are validated by using GO term information.

MOTIVATION: The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation. RESULTS: We present a new and comprehensive machine learning data mining framework consisting in a non-linear PCA neural network for feature extraction, and probabilistic principal surfaces combined with an agglomerative approach based on Negentropy aimed at clustering gene microarray data. The method, which provides a user-friendly visualization interface, can work on noisy data with missing points and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Cell-cycle dataset and a detailed analysis confirm the biological nature of the most significant clusters. AVAILABILITY: The software described here is a subpackage part of the ASTRONEURAL package and is available upon request from the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper deals with the access control problem. We assume that valuable resources need to be protected against unauthorized users and that, to this aim, a password-based access control scheme is employed. Such an abstract scenario captures many applicative settings. The issue we focus our attention on is the following: password-based schemes provide a certain level of security as long as users choose good passwords, i.e., passwords that are hard to guess in a reasonable amount of time. In order to force the users to make good choices, a proactive password checker can be implemented as a submodule of the access control scheme. Such a checker, any time the user chooses/changes his own password, decides on the fly whether to accept or refuse the new password, depending on its guessability. Hence, the question is: how can we get an effective and efficient proactive password checker? By means of neural networks and statistical techniques, we answer the above question, developing suitable proactive password checkers. Through a series of experiments, we show that these checkers have very good performance: error rates are comparable to those of the best existing checkers, implemented on different principles and by using other methodologies, and the memory requirements are better in several cases. It is the first time that neural network technology has been fully and successfully applied to designing proactive password checkers

In this paper a Toolbox to generate and to analyze linear, non-linear, delayed and convolved mixtures of real source signals is presented. From one hand, in fact, a simple interface based on a physical model has been implemented and stereo, dolby, delayed, and convolved mixtures can be generated. On the other hand a blind source separation analysis can be accomplished. In fact, a novel separation algorithm (i.e. APDP) is included in the Toolbox. Several experiments to separate delayed mixtures of real instruments are made. Three musical recordings of different instrumental scores are mixed and analyzed by using the Toolbox. Several comparisons with known methods are made.

Bioinformatics systems benefit from the use of data mining strategies to locate interesting and pertinent relationships within massive information. For example, data mining methods can ascertain and summarize the set of genes responding to a certain level of stress in an organism. Even a cursory glance through the literature in journals, reveals the persistent role of data mining in experimental biology. Integrating data mining within the context of experimental investigations is central to bioinformatics software. In this paper we describe the framework of probabilistic principal surfaces, a latent variable model which offers a large variety of appealing visualization capabilities and which can be successfully integrated in the context of microarray analysis. A preprocessing phase consisting of a nonlinear PCA neural network which seems to be very useful to deal with noisy and time dependent nature of microarray data has been added to this framework.

A novel approach based on the ordinal sums and genetic algorithms is introduced. The main characteristic of an ordinal sum lies in the use of different t-norms (t-conorms) defined over disjoint subintervals of the unit interval. In this approach, a genetic optimisation environment to construct the ordinal sums and to optimise subintervals, and to allocate the individual local t-norms is introduced. Different parametric and non-parametric t-norms (t-conorms) are used. Several results to demonstrate the properties of the approach are proposed. The application of the genetically designed ordinal sums in case of the Zimmermann-Zysno logic operator data is also shown.

The recent technological advances are producing huge data sets in almost all fields of scientific research, from astronomy to genetics. Although each research field often requires ad-hoc, fine tuned, procedures to properly exploit all the available information inherently present in the data, there is an urgent need for a new generation of general computational theories and tools capable to boost most human activities of data analysis. Here, we propose probabilistic principal surfaces (PPS) as an effective high-D data visualization and clustering tool for data mining applications, emphasizing its flexibility and generality of use in data-rich field. In order to better illustrate the potentialities of the method, we also provide a real world case-study by discussing the use of PPS for the analysis of yeast gene expression levels from microarray chips.

Acoustic signals emitted by organ pipes in a variety of experimental frameworks have been recorded and analyzed by using independent component analysis. Starting from this analysis, relevant features of the signals related to single tones of the chords have been extracted. Three Landau modes are extracted with three well defined frequencies. Following the dynamical systems approach, a simple and suitable analogical model, able to reproduce the registered waveform and sound in listening, have been constructed. The conclusion is that, in first approximation, the low dimensional dynamical system representing on average the fluid-dynamical equations modelling organ pipe is constituted by three linearly coupled nonlinear oscillators in limit cycle regime.

In this paper a novel approach to solve the permutation indeterminacy in the separation of convolved mixtures in frequency domain is proposed. A fixed-point algorithm in complex domain to perform the separation of the signals for each frequency domain is used. To obtain the frequency bins a short time Fourier transform on a set of fixed frames, is considered. To solve the ambiguity of the amplitude dilation a simple method is proposed. The permutation indeterminacy is solved using an approach based on the Hungarian algorithm that solves an assignment problem and an algorithm of dynamic programming. To obtain the distances in the assignment problem, a Kullback-Leibler divergence is adopted. We shall see that this approach presents a good performance and permits to obtain a clear separation of the signals.

Independent component analysis (ICA) is used to analyze the seismic signals produced by explosions of the Stromboli volcano. It has been experimentally proved that it is possible to extract the most significant components from seismometer recorders. In particular, the signal, eventually thought as generated by the source, is corresponding to the higher power spectrum, isolated by our analysis. Furthermore, the amplitude of the source signals has been found by using a simple trick and so overcoming, for this specific case, the classical problem of ICA regarding the amplitude loss of the separated signals.

In this paper, a neuro-fuzzy model is introduced. The model describes a fuzzy relational "IF-THEN" reasoning scheme using an adaptive structure based on fuzzy relations. Two training schemes for the learning of the parameters based respectively on the backpropagation algorithm and pseudo-inverse matrix technique are illustrated. The model qualities are investigated by a series of simulation examples: function approximation, classification and rule extraction. These preliminary and promising results show that the model has a good performance and that it could be used for complex systems in real world applications.

We present a hybrid system composed of a neural network based estimator system and genetic algorithms. It uses an unsupervised Hebbian nonlinear neural algorithm to extract the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies. We generalize this method to avoid an interpolation preprocessing step and to improve the performance by using a new stop criterion to avoid over fitting. Furthermore, genetic algorithms are used to optimize the neural net weight initialization.