Gabriella Casalino

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• Ph.D. in Computer Science
• Professor (Assistant) at University of Bari Aldo Moro

MicroRNAs (miRNAs) are a set of short non-coding RNAs that play significant regulatory roles in cells. The study of miRNA data produced by Next-Generation Sequencing techniques can be of valid help for the analysis of multifactorial diseases, such as Multiple Sclerosis (MS). Although extensive studies have been conducted on young adults affected by...

Smartphones enable to collect large data streams about phone calls that, once combined with Computational Intelligence techniques, bring great potential for improving the monitoring of patients with mental illnesses. However, the acoustic data streams recorded in uncontrolled environments are dynamically changing due to various sources of uncertain...

We introduce an approach called PLENARY (exPlaining bLack-box modEls in Natural lAnguage thRough fuzzY linguistic summaries), which is an explainable classifier based on a data-driven predictive model. Neural learning is exploited to derive a predictive model based on two levels of labels associated with the data. Then, model explanations are deriv...

We apply an evolving granular-computing modeling approach, called evolving Optimal Granular System (eOGS), to bipolar mood disorder (BD) diagnosis based on speech data streams. The eOGS online learning algorithm reveals information granules in the flow and design the structure and parameters of a granular rule-based model with a certain degree of i...

Contactless methods are widely used to measure vital signs from recorded or live videos using remote photoplethysmography (rPPG), which takes advantage of the slight skin color variation that occurs periodically on specific body regions with each blood pulse. However, existing rPPG-based solutions are typically expensive and not suitable for daily...

https://sites.google.com/view/explimed-2025/home-page **** IMPORTANT DATES **** Abstract submission deadline: May 10, 2025 Paper submission deadline: May 21, 2025 Notification of acceptance: July 21 2025 Final paper submission: September 12, 2025 Early registration deadline: TBA Workshop date: 25-26 October, 2025

With this Special Issue, we aim to advance research on Human-Centered Explainable AI (HC-XAI) as a crucial interdisciplinary approach to enhancing the transparency, effectiveness, and user alignment of AI explanations. By bridging the gap between technical XAI solutions and human cognitive needs, HC-XAI can foster greater trust, ensure regulatory c...

This special session aims to create an international forum specifically focusing on eXplainable Artificial Intelligence methods in healthcare. In recent years, Artificial Intelligence algorithms have become integral to our daily lives and applied in diverse contexts, including healthcare, economics, and law. The adoption of these systems conceals s...

The Second International Workshop on Artificial Intelligence Systems in Education (AIxEDU) marks its 2024 edition in conjunction with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA), held from 25-28 November 2024 in Bolzano, Italy. This workshop brings together Artificial Intelligence and Education e...

This research introduces a novel dataset developed for streaming learning analytics, derived from the Open University Learning Analytics Dataset (OULAD). The dataset incorporates essential temporal information that captures the timing of student interactions with the Virtual Learning Environment (VLE). By integrating these time-based interactions,...

The 2024 First Workshop on Explainable Artificial Intelligence for the Medical Domain (EXPLIMED) marks its inaugural edition in conjunction with the 27th European Conference on Artificial Intelligence (ECAI 2024) in Santiago de Compostela. This workshop brought together experts in Artificial Intelligence to deepen the latest innovations and best pr...

Recent developments in Artificial Intelligence are influencing people's everyday lives. The educational context is no exception, and AI is permeating the learning and teaching experiences. New Artificial Intelligence models offer possibilities to create new systems for enhancing the learning experience and supporting teachers in their roles. Applic...

Hypertension is a disease that stresses the arteries and can cause damage to vital organs. It is often asymptomatic, and timely diagnosis and management are crucial to prevent complications and mitigate the risks associated with the disease. Photoplethysmography has proven to be effective in capturing variations in blood volume within vessels and h...

The goal of this workshop is to explore and exhibit research, methodologies, and case studies that focus on the integration of Explainable Artificial Intelligence (XAI) in the medical domain. It will provide a platform for researchers, practitioners, and policymakers to share their insights and advancements in XAI. The purpose is to improve transpa...

This contribution briefly describes the research being carried out in the in AI-based e-health. Our research encompasses a wide array of methodologies and applications aimed at leveraging the capability of AI to empower the diagnosis, monitoring, and treatment of various health conditions. Through multifaceted research that covers neuroimaging anal...

Artificial Intelligence (AI) has become an integral part of our lives, and Explainable Artificial Intelligence (XAI) is becoming more essential to ensure trustworthiness and comply with regulations. XAI methodologies help to explain the automatic processing behind data analysis. This paper provides an overview of the use of XAI in the educational d...

https://lnkd.in/dpNfpbSv Important Dates: Abstract submission (200-300 words): September 11, 2024 Submission Deadline (flexible): September 19, 2024 Notification of Acceptance: October 15, 2024 Camera-Ready Submission: tbd Workshop Date: tbd within November 25-28, 2024 Accepted papers will be published in the CEUR proceedings of the AI*IA Series,...

This study focuses on how artificial intelligence (AI) can be used in education while emphasizing the importance of adhering to European regulations requiring explanations of automatic methods. The study uses a prototype-based dynamic incremental classification algorithm called Dynamic Incremental Semi-Supervised Fuzzy C-Means-DISSFCM, based on Fuz...

2024 Explainable Online Learning for Uncertain Data Streams (OLUD 2024) within the IEEE International Conference on Evolving and Adaptive Intelligent Systems 2024 (IEEE EAIS 2024)Madrid, Spain, 23-24 May, 2024 https://sites.google.com/view/olud/home _______________________________________________________________________________ The key research qu...

We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robustness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to capt...

We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robust-ness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to cap...

This paper presents AMAdam, an innovative adaptive modifier gradient descent optimization algorithm that aims to overcome the challenges faced by traditional optimization methods in the field of artificial intelligence. The core of AMAdam’s contribution is its capacity to dynamically adjust the learning rate according to subtle gradient variations,...

This special session aims to explore and discuss various explainable AI methods that enhance the transparency and interpretability of machine learning models, ensuring trust and usability in healthcare applications.

We propose a method to perform regression on partially labeled data, which is based on SSFCM (Semi-Supervised Fuzzy C-Means), an algorithm for semi-supervised classification based on fuzzy clustering. The proposed method, called SSFCM-R, precedes the application of SSFCM with a relabeling module based on target discretization. After the application...

The goal of knowledge tracing (KT) is to track a students' progress over time by analyzing their historical data, so as to predict their future performance on tests related to the topics they have covered. The rise of online platforms for education, where the learning process is embedded, unlocked the potential of customized teaching such as in int...

Article available at the following link: https://www.amcs.uz.zgora.pl/?action=paper&paper=1714 ______________________________________________________ Acoustic features of speech are promising as objective markers for mental health monitoring. Specialized smartphone apps can gather such acoustic data without disrupting the daily activities of patien...

This special session aims at creating an international forum with specific focus on Computational Intelligence methods related to the use of pervasive computing for healthcare and wellbeing. Traditional healthcare environments are extremely complex and challenging to manage. Pervasive and ubiquitous technologies seek to overcome these limits by suc...

Machine learning algorithms are useful in assisting medical judgments, but the resulting models are frequently challenging for doctors to comprehend. Conversely, IF-THEN rules articulated in natural language are successful at describing interpretable models that are returned by Fuzzy Inference Systems. However, the construction of the fuzzy rule ba...

The current advances in wearable sensors show the shining future of socially implemented Internet-of-Medical-Things (IoMT) devices (e.g., smartwatches). However, the recent machine learning approaches cannot be applied well in these devices, because almost all the processing in the IoMT devices is now being performed in classic forms (mainly as cen...

Learning Analytics has been widely used to enhance the educational field employing Artificial Intelligence. However, explanations of the data processing have become mandatory. In order to do this, we suggest using Neuro-Fuzzy Systems (NFSs), which can provide both precise forecasts and descriptions of the processes that produced the outcomes. The b...

Artificial Intelligence-based methods have been thoroughly applied in various fields over the years and the educational scenario is not an exception. However, the usage of the so-called explainable Artificial Intelligence, even if desirable, is still limited, especially whenever we consider educational datasets. Moreover, the time dimension is not...

This editorial note provides an overview of the papers accepted to the First Workshop on Online Learning from Uncertain Data Streams (OLUD 2022) and related sub-areas. The OLUD workshop was intended to facilitate interdisciplinary discussion on recent advancements of state-of-the-art online machine learning and incremental pattern recognition metho...

Virtual Learning Environments (VLEs) are online educational platforms that combine static educational content with interactive tools to support the learning process. Click-based data, reporting the students' interactions with the VLE, are continuously collected, so automated methods able to manage big, non-stationary, and changing data are necessar...

In this paper, we present a new gradient descent optimization method that overcomes some weaknesses of existing gradient descentoptimization strategies. The proposed optimization technique is basedon the variation of simple gradients and regulates the learning rate inresponse to the need for an efficient learning adjustment mechanism.The memory and...

The overwhelming popularity of technology-based solutions and innovations to address day-to-day processes has significantly contributed to the emergence of smart cities. where millions of interconnected devices and sensors generate and share huge volumes of data. The easy and high availability of rich personal and public data generated in these dig...

Intelligent systems for the medical domain often require processing data streams that evolve over time and are only partially labeled. At the same time, the need for explanations is of utmost importance not only due to various regulations, but also to increase trust among systems’ users. In this work, an online data-driven learning method with focu...

Call for papers

The healthcare domain has undergone a huge transformation thanks to the availability of new technologies. In particular, health monitoring systems have entered everyday life without interfering with the daily routine. Mobile phones are increasingly used as health monitoring systems by means of ad-hoc applications. In this work, we propose a mobile...

Call for papers https://helmeto2022.unipa.it/

Nowadays, applications in various domains (computer science, engineering, medicine, economy, etc.) are based on sensor data or depend on data transmission in the cloud. Effective modeling approaches to address such a massive amount of dynamically-changing data in a feasible period of time are of utmost importance. Traditional modeling approaches fo...

With the rapid development of deep learning techniques, new innovative license plate recognition systems have gained considerable attention from researchers all over the world. These systems have numerous applications, such as law enforcement, parking lot management, toll terminals, traffic regulation, etc. At present, most of these systems rely he...

We study the impact of fuzziness on the behavior of Fuzzy Rule-Based Classifiers (FRBCs) defined by trapezoidal fuzzy sets forming Strong Fuzzy Partitions. In particular, if an FRBC selects the class related to the rule with the highest activation (so-called Winner-Takes-All approach), then fuzziness, as quantified by the slope of the membership fu...

The 2022 IEEE Conference on Evolving and Adaptive Intelligent Systems (IEEE EAIS 2022) will be held in Larnaca (Cyprus), a picturesque sea side town. Larnaca is a small town combining old colonial architecture with modern buildings. The area has been inhabited for at least 3000 years and is framed in the east by the Larnaca Salt Lake and to the sou...

Learning Analytics techniques are widely used to improve students’ performance. Data collected from students’ assessments are helpful to predict their success and questionnaires are extensively adopted to assess students’ knowledge. Several mathematical models studying the correlation between students’ hidden skills and their performance to questio...

Learning Analytics techniques are widely used to improve students’ performance. Data collected from Virtual Learning Environments (VLEs) are helpful to predict students’ outcomes through the analysis of their interactions with the platform. In this work, we propose the use of hybrid models which are able to return accurate predictions together with...

In this work, neuro-fuzzy systems are compared to standard machine learning algorithms to predict the hypertension risk level. Hypertension is a cardiovascular disease, which should be continuously monitored to avoid the worsening of its symptoms. Automatic techniques are useful to support the clinicians in this task, however, most of the machine l...

MHealth technologies play a fundamental role in epidemiological situations such as the ongoing outbreak of COVID-19 because they allow people to self-monitor their health status (e.g. vital parameters) at any time and place, without necessarily having to physically go to a medical clinic. Among vital parameters, special care should be given to moni...

The assessment of students' performances is one of the essential components of teaching activities, and it poses different challenges to teachers and instructors, especially when considering the grading of responses to open-ended questions (i.e., short-answers or essays). Open-ended tasks allow a more in-depth assessment of students' learning level...

A large section of the population around the globe is migrating towards urban settlements. Nations are working toward smart city projects to provide a better wellbeing for the inhabitants. Cyber-physical systems are at the core of the smart city setups. They are used in almost every system component within a smart city ecosystem. This paper attempt...

Although several vaccination campaigns have been launched to combat the ongoing COVID-19 pandemic, the primary treatment of suspected infected people is still symptomatic. In particular, the analysis of images derived from computed tomography (CT) appears to be useful for retrospectively analyzing the novel coronavirus and the chest injuries it cau...

Satire is a way of criticizing people (or ideas) by ridiculing them on political, social, and morals topics often used to denounce political and societal problems, leveraging comedic devices such as parody exaggeration, incongruity, etc.etera. Detecting satire is one of the most challenging computational linguistics tasks, natural language processi...

The number of patient health data has been estimated to have reached 2314 exabytes by 2020 [...]

Voice features from everyday phone conversations are regarded as a sensitive digital marker of mood phases in bipolar disorder. At the same time, although acoustic data collected from smartphones are relatively large, their psychiatric labelling is usually very limited, and there is still a need for intelligent and interpretable approaches to proce...

Annual Meeting of the Bioinformatics Italian Society (BITS 2021)

As the number of online financial transactions increases, the problem of credit card fraud detection has become quite urgent. Machine learning methods, including supervised and unsupervised approaches, have been proven to be effective to detect fraudulent activities. In our previous work presented at EUSFLAT2019 we proposed the use of an incrementa...

Cardiovascular diseases are the first cause of death in Italy. This has been worsened by the COVID-19 pandemic we are living in. Indeed, worldwide citizens are invited to stay at home to reduce the spreading of the virus, in the hospitals the priority is given to patients affected by COVID-19, and often patients affected by other diseases prefer to...

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, S...

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the...

Learning Analytics (LA) is a recent research branch that refers to methods for measuring, collecting, analyzing, and reporting learners' data, in order to better understand and optimize the processes and the environments. Knowledge Tracing (KT) deals with the modeling of the evolution, during the time, of the students' learning process. Particularl...

The analysis of gene expression data is a complex task, and many tools and pipelines are available to handle big sequencing datasets for case-control (bivariate) studies. In some cases, such as pilot or exploratory studies, the researcher needs to compare more than two groups of samples consisting of a few replicates. Both standard statistical bioi...

Virtual Learning Environments (VLEs) are web platforms where educational content is delivered, along with tools to support individual study. Logs that record how students interact with the platform are collected daily, so automated methods can be used to extract useful knowledge from these data. All stakeholders involved in the learning activities...

The last decade was about connectivity, and we describe that dynamic with the Internet of Things. This decade is really about adding intelligence to different devices, services, etc. We have been confronted with a new IoT - The intelligence of things. Meanwhile, the future of Intelligence of Things and AI encompasses advanced cognitive methods capa...

Abstract presented at the Third International Conference on Data Science & Social Research (DSSR 2020)

Student oriented subset of the Open University Learning Analytics dataset (https://orcid.org/0000-0003-0713-2260) Gabriella Casalino; (https://orcid.org/0000-0002-6489-8628) Giovanna Castellano; (https://orcid.org/0000-0002-0883-2691) Gennaro Vessio The Open University (OU) dataset is an open database containing student demographic and click-stream...

Bipolar Disorder (BD) is a chronic mental illness characterized by changing episodes from euthymia (healthy state) through depression and mania to the mixed states. In this context, data collected through the interaction of patients with smartphones enable the creation of predictive models to support the early prediction of a starting episode. Prev...

Dear Colleagues, Hope you are keeping well in this tough time of COVID-19 Pandemic. We are organizing the Special Session "Computational Intelligence for Web Economy” within the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020), organized by the Machine Intelligence Research Labs (MIRLabs), that will be held i...

Extended Abstract presented at the 2nd International Workshop on Higher Education Learning Methodologies and Technologies Online (HELMeTO 2020)

Bipolar Disorder (BD) is a chronic mental illness characterized by changing episodes from euthymia (healthy state) through depression and mania to the mixed states. In this context, data collected through the interaction of patients with smartphones enable the creation of predictive models to support the early prediction of a starting episode. Prev...

Mobile health (mHealth) technologies play a fundamental role in epidemiological situations such as the ongoing outbreak of COVID-19 because they allow citizen to self-monitor their health status while staying at home and being constantly in remote connection with the physicians despite the quarantine. Special care should be given to self-monitoring...

Bipolar disorder is a chronic mental illness characterized with changing episodes (depression, mania, mixed state, euthymia). In the recent years, smartphone becomes an increasingly important tool in the early prediction of a starting episode. Usually, the state of the art research applies supervised learning methods and first of all, limits the da...

Cardiovascular diseases are a group of heart and blood vessels disorders that are one of the main causes of dead and invalidity. Prevention is fundamental to diagnose the condition in its early stage. Machine learning techniques have been proven to be useful tools to support clinicians in their daily tasks. Particularly the big availability of digi...

Virtual Learning Environments (VLEs) are Web-based platforms where educational contents, together with study support tools, are provided. Logs recording the interactions between students and VLEs are collected on a daily basis, thus automatic techniques are needed to manage and analyze such huge quantities of data. Students, teachers, managers, and...

This study examined the perception of end-users regarding the monitoring process offered by an innovative cardiac self-care system. The main goal was to assess the efficacy of the process implemented by a smart device designed to support people for real-time monitoring of cardio-vascular parameters in everyday life, thereby encouraging patients to...

Multiple Sclerosis (MS) is a demyelinating autoimmune disease that usually affects young adults; however, recently some symptoms of cognitive impairment have been recognized as early signs of MS onset in pediatric patients (PedMS). The underlying relationships between these two conditions, as well as their molecular markers, have not been fully und...

Microarray data are a kind of numerical non-negative data used to collect gene expression profiles. Since the number of genes in DNA is huge, they are usually high dimensional, therefore they require dimensionality reduction and clustering techniques to extract useful information. In this paper we use NMF, non-negative matrix factorization, to anal...