Vincenzo Loia

University of Salerno | UNISA · Management and Innovation Systems

Precision agriculture is evolving toward a contemporary approach that involves multiple sensing techniques to monitor and enhance crop quality while minimizing losses and waste of no longer considered inexhaustible resources, such as soil and water supplies. To understand crop status, it is necessary to integrate data from heterogeneous sensors and...

In an era where image manipulation is easily accessible, detecting digital image forgery has become more challenging. The manuscript delves into the essential issue of picture forgery and discusses the use of the standard Adaptive Neuro-Fuzzy Inference System (ANFIS) and some of its variants for identifying and analyzing altered digital image conte...

Fires represent a significant threat to the environment, infrastructure, and human safety, often spreading rapidly with wide-ranging consequences such as economic losses and life risks. Early detection and swift response to fire outbreaks are crucial to mitigating their impact. While satellite-based monitoring is effective, it may miss brief or ind...

The industrial sector faces significant cyberthreats due to the vast data exchange in Industry 4.0. Soft biometrics offer a suitable tradeoff by enhancing security through additional layers of recognition, mitigating privacy concerns through the collection of nonsensitive information, and improving identification accuracy.

The widespread utilization of social media for information consumption has significantly exacerbated the problem of information disorder. Recognizing the difficulty people face in discerning the truth, automated assistance is urgently needed. Current state-of-the-art approaches often involve fine-tuning existing models with contributions from domai...

The proliferation of fake news has raised concerns regarding its detection, posing a significant challenge. Motivated by the ongoing discussion on the sustainability of machine learning algorithms, this paper discusses the usefulness of data reduction for fake news detection. This is accomplished by using the fuzzy transform (or F-transform for sho...

The capacity to create ”fake” videos has recently raised concerns about the reliability of multimedia content. Identifying between true and false information is a critical step toward resolving this problem. On this issue, several algorithms utilizing deep learning and facial landmarks have yielded intriguing results. Facial landmarks are traits th...

This chapter deals with two Rough Set extensions: Neighborhood Rough Sets, used when facing continuous values, and Dominance-based Rough Sets, adopted in the case of ordinal values. The chapter includes Python code implementing Neighborhood Rough Sets.

The chapter provides an introduction of the fundamental concepts of Intelligence Analysis, explains the Intelligence as a process, presents analytic techniques to support the analyst’s work and proposes a discussion on some challenges of data-driven Intelligence Analysis.

This chapter, briefly describing the original vision of the authors, deals with the application of the Endley’s Model for Situation Awareness to support Intelligence Analysis and the Intelligence Cycle. Lastly, it is introduced the methodology behind the methods described in the next chapters.

This chapter deals with two different implementation strategies of probability-based rough set operators. Such implementations are compared in order to explain some good practices to develop real-world versions of such operations.

This chapter deals with the description of a method, for maritime surveillance, based on What-If Analysis that is mainly implemented by using probability-based rough sets and lattice structures. The chapter includes Python code implementing the different method steps.

This chapter deals with the implementation of structures of opposition, integrated to Three-Way Decisions and Rough Sets, to reason on situations that contains contradictions or, in general, elements of opposition. The proposed method can be adopted to analyse concept drifts in data. The chapter includes Python code implementing the different metho...

This chapter deals with the description of a method, for the analysis of critical nodes within a complex network, based on the integration of Graph Analysis and Evaluation-based Three-Way Decisions. The chapter also includes the discussion on reasoning approaches for critical functionality of a system and Python code implementing the different meth...

This chapter deals with the description of a method, based on Fuzzy Signature, for the analysis of terrorist group behavior. The chapter includes Python code implementing the different method steps.

The chapter aims at providing the foundational concepts and information to understand the computational techniques and the main paradigms adopted by the approaches described in the book. In particular, the chapter deals with Situation Awareness, Granular Computing and Three-Way Decisions. Lastly, Python is used to practise with the aforementioned a...

This chapter deals with the PySpark-based implementation of Three-Way Decisions In real-world scenarios, where it is needed to face huge volumes of streaming data. The map-and-reduce paradigm is adopted to define such implementation.

Nowadays, Massive Open Online Courses (MOOCs) are adopted by students worldwide. One of the main critical issues often associated to MOOCs is the dropout phenomenon. In other words, the percentage of students abandoning a MOOC-based study path is considered still too high. Therefore, an increasing number of scientific works, coming from several and...

In the digital era, online radicalization has emerged as a significant concern for governments, social media platforms, and researchers. Detecting and preventing online radicalization have become key priorities, leading to extensive research efforts. This study presents a comprehensive survey of existing works in this field, covering various techni...

Interconnectivity and smart automation of Internet of Things in recent times have led to the concept of Industry 4.0. Together with the improvement in productivity and new business models, employment conditions should take advantage of these new technologies. Safety in the workplace is one of the most sensitive topics on matters that needs targeted...

The paper presents and evaluates an approach based on Rough Set Theory, and some variants and extensions of this theory, to analyze phenomena related to Information Disorder. The main concepts and constructs of Rough Set Theory, such as lower and upper approximations of a target set, indiscernibility and neighborhood binary relations, are used to m...

By incorporating three-way decision model into formal concept analysis (FCA) methodology, an emerging novel data analysis methodology, termed three-way concept analysis (3WCA), has been widely used in both computer science and social science areas. However, the construction of three-way concept lattice is quite time-consuming and proved as an NP-co...

In this article, we introduce a variant of the adaptive network-based fuzzy inference system (ANFIS). The proposed variant does not use backpropagation and grid partitioning, but the least-squares method with fractional Tikhonov regularization. The fractional regularization is a generalization of the standard regularization and is applied here to t...

The spreading of machine learning (ML) and deep learning (DL) methods in different and critical application domains, like medicine and healthcare, introduces many opportunities but raises risks and opens ethical issues, mainly attaining to the lack of transparency. This contribution deals with the lack of transparency of ML and DL models focusing o...

In view of the ability of the three-way concept lattices to describe the correlations and hierarchical relationships among the knowledge points, this paper proposes a knowledge point navigation approach based on the three-way concept lattices for autonomous learning. First, this paper constructs the formal context of the exercises-knowledge points...

The extraction of valuable insights from unstructured content has attracted much attention in the last decades. Main results lie in the area of text mining, while the understanding of multimedia contents, thanks to the improvements in computer vision, mainly relies on adopting emerging deep learning models. About image understanding, people’s name...

This chapter is devoted to fuzzy relations. Fuzzy relations on classical and fuzzy sets are discussed. Composition of fuzzy relations are presented. Fuzzy relational equations are also introduced.

This chapter starts off by briefly recalling Artificial Neural Networks. A general Fuzzy Neural Netowrk scheme is discussed. The Adaptive Neuro-Fuzzy Inference System (ANFIS) and one of its variants are presented.

In this chapter, the direct and inverse fuzzy transform is presented. Its application to data compression is discussed. Its use as a reduction technique to preprocess data presented to machine learning algorithms is also discussed.

The general structure of fuzzy inference systems is introduced in this chapter. The Mamdani and Takagi-Sugeno-Kang systems are discussed.

In this chapter, the fuzzy logic basic connectives are introduced. Linguistic variables are also presented, along with linguistic modifiers. Fuzzy propositions, modus ponens and generalized modus ponens are discussed.

This chapter starts off with the Zadeh’s extension principle. The concept of fuzzy number is then introduced, by also presenting its parametric form. Different types of fuzzy numbers are discussed. Concepts from interval mathematics are briefly recalled. Methods for performing arithmetic operations with fuzzy numbers are illustrated.

In this chapter, the basic notions of granular computing are presented. The concept of information granule is briefly illustrated. Selected models of granular neural networks are discussed, by also providing an application example.

This chapter is devoted to introduce fuzzy sets and the main related notions. The operations on fuzzy sets are also presented. Different types of fuzzy sets are briefly recalled.

Uncertain coalitional game is a type of coalitional games where the transferable payoffs are assumed to be uncertain variables. As solutions of uncertain coalitional game, uncertain core, uncertain Shapley value, and uncertain stable set have been offered. This article further presents a new thought of uncertain nucleolus as another solution to the...

Massive Open Online Courses (MOOCs) allow accessing qualitative online educational resources for huge amounts of online students. In this context, the dropout phenomenon is known as a nasty problem faced by several existing studies proposing methods and techniques to make predictions on students who are at risk of dropping out. Although the majorit...

In complex environments, decision-making processes are more and more dependent on gathering, processing and analysis of huge amounts of data, often produced with different velocities and different formats by distributed sensors (human or automatic). Such streams of data also suffer of imprecision and uncertainty. On the other hand, Three-way Decisi...

This paper presents and evaluates a method to combine time-based granulation and three-way decisions to support decision makers in understanding and reasoning on the learned granular structures conceptualising spatio-temporal events. The method uses an existing approach to discover periodic events in the data, such as periods of intense traffic in...

The technologies of Industry 4.0 provide an opportunity to improve the effectiveness of Visual Management in manufacturing. The opportunity of improvement is twofold. From one side, Visual Management theory and practice can inspire the design of new software tools suitable for Industry 4.0; on the other side, the technology of Industry 4.0 can be u...

This paper presents a comprehensive model for representing and reasoning on situations to support decision makers in Intelligence analysis activities. The main result presented in the paper stems from a work of refinement and abstraction of previous results of the authors related to the use of Situation Awareness and Granular Computing for the deve...

We present a computing scheme as a variant of a recently proposed granular recurrent neural network. Being deduced from a generic system of partial differential equations, this variant is able to capture the spatiotemporal variability of some datasets and problems. The convergence of the computing scheme has been formally discussed. Some preliminar...

The work proposes a new method to detect influential news in online communities. Influential news are articles that induce shifts in users’ opinions or, in general, lead to a polarization of opinions or change like-mindedness of users. The method aims at supporting online platform managers and editors in understanding the impact that social content...

Three-way concept analysis (3WCA), a combination of three-way decision and formal concept analysis, is widely used in the field of knowledge discovery. Generally, constructing three-way concept lattices requires the original formal context and its complement context simultaneously. Additionally, the existing three-way concept lattice construction a...

Three-way concept analysis (3WCA) has been an emerging and important methodology for knowledge discovery and data analysis. Particularly, 3WCA can efficiently characterize the information of “jointly possessed” and “jointly not possessed” compared to the classical formal concept only can describe common attributes owned by objects. This property, t...

The paper reports the results of an analysis of COVID-19 diffusion in Italy. The analysis was carried out with a new method based on the combined use of a 3 Way Decisions model and graph theory. Specifically, the data about infected people in the Italian regions is assessed by means of an evaluation function which allows the tri-partitioning of Ita...

Performance of Machine Learning models heavily depends on the quality of the training dataset. Among others, the quality of training data relies on the consistency of the labels assigned to similar items. Indeed, the labels should be coherently assigned (or collected) by avoiding inconsistencies for increasing the performance of the machine learnin...

In online learning, the dropout phenomenon is a relevant issue to address with practical solutions. Several data sets stimulate original, and resolutive data analysis approaches, demonstrating the importance of the dropout phenomenon. This study proposes a novel approach to predicting massive online open course (MOOC) students at risk of dropout st...

This work describes the decision-making solution provided by the CONSENSUS project. Such solution is based on contextualising, applying and experimenting both Fuzzy Consensus Model and Average Rating Values algorithm to the Consensus Conference, i.e., a decision-making method widely used to achieve an agreement among different opinions on controver...

In the era of Industry 4.0, cognitive computing and its enabling technologies (Artificial Intelligence, Machine Learning, etc.) allow to define systems able to support maintenance by providing relevant information, at the right time, retrieved from structured companies' databases, and unstructured documents, like technical manuals, intervention rep...

In the current industrial practices, the exponential growth in terms of availability and affordability of sensors, data acquisition systems, and computer networks forces factories to move toward implementing high integrating Cyber-Physical Systems (CPS) with production, logistics, and services. This transforms today's factories into Industry 4.0 fa...

Cyber-Physical Systems (CPS) play a crucial role in the era of the 4thIndustrial Revolution. Recently, the application of the CPS to industrial manufacturing leads to a specialization of them referred as Cyber-Physical Production Systems (CPPS). Among other challenges, CPS and CPPS should be able to address interoperability issues, since one of the...

Emergency Department (ED) overcrowding is a worldwide problem (Morley et al. 2018). Always, people who present to EDs are required to wait too much. This problem compromises the health management efficacy, the satisfaction of the patients, the quality of the public offered service, also impacting on the indicators of the quality of life. The reason...

In pharmacovigilance, post-marketing surveillance is mainly supported by spontaneous reporting systems (SRS) collecting adverse drug events explicitly submitted by physicians alerted by their patients. Nowadays, this activity could leverage on mining opinions and experiences of individuals from social media by monitoring users’ posts citing symptom...

The increasing complexity and dynamical property in stock markets are key challenges of the financial industry, in which inflexible trading strategies designed by experienced financial practitioners fail to achieve satisfactory performance in all market conditions. To meet this challenge, adaptive stock trading strategies with deep reinforcement le...

In the current industrial practices, the exponential growth in terms of availability and affordability of sensors, data acquisition systems, and computer networks forces factories to move toward implementing high integrating Cyber-Physical Systems (CPS) with production, logistics, and services. This transforms today’s factories into Industry 4.0 fa...

Cyber-Physical Systems (CPS) play a crucial role in the era of the 4th Industrial Revolution. Recently, the application of the CPS to industrial manufacturing leads to a specialization of them referred as Cyber-Physical Production Systems (CPPS). Among other challenges, CPS and CPPS should be able to address interoperability issues, since one of th...

In this paper, we formally discuss a computational scheme, which combines a local weighted regression model with fuzzy transform (or F-transform for short). The latter acts as a reduction technique on the cardinality of the learning problem, resulting in a more efficient algorithm. We tested the proposed approach first through two typical benchmark...

In this paper, we present a new granular classifier in two versions (iterative and non–iterative), by adopting some ideas originating from a kind of Functional Link Artificial Neural Network and the Functional Network schemes. These two architectures are substantially the same: they both use a function basis instead of the usual activation function...

The paper presents a new interactive method to analyse and assess hypotheses, and its application to terrorism events. The method combines probability, fuzzy and rough set theories and supports decision makers and analysts of counterterrorism in the analysis of intelligence information by using behavioural models of known terrorist groups. Starting...

Purpose The purpose of this paper is to propose a trusted security zone architecture that uses a blockchain technology to provide secure sharing of data in the security zone while maintaining the integrity, confidentiality and availability of data. The blockchain uses a distributed network to ensure data availability and uses public ledgers to ensu...

Lack of situation awareness when dealing with complex dynamic environments is recognized as one of the main causes of human errors that may lead to serious incidents, poor performance, etc. Thus, there is a need to define systems able to support operators in focusing their attention on active goals and, when really needed, switching them on more su...

The unpredictability and intermittency introduced by Renewable Energy Sources (RESs) in power systems may lead to unforeseen peaks of energy production, which might differ from energy demand. To manage these mismatches, a proper communication between prosumers (i.e., users with RESs that can either inject or absorb energy) and active users (i.e., u...

The article proposes flatness‐based control for stabilization of a stock‐loan valuation process that is described by a partial differential equation. By applying semi‐discretization and the finite differences method, the state‐space model of the stock loan has been obtained. It has been proven that the individual rows of this state‐space model are...

Video scene understanding is leading to increased research investment in developing Artificial Intelligence technologies, Pattern Recognition, and Computer Vision, especially with the advance in sensor technologies. Developing Autonomous Unmanned Vehicles, able to recognize not just targets appearing in a scene, but a complete scene the targets are...

In recent years, Unmanned Vehicles (UVs) have been largely employed in many applications. They, enhanced with Computer Vision and Artificial Intelligence, can autonomously recognize targets in an environment and detect events occurring in a real-world scenario. The employment of cooperative UVs can provide multiple interpretations supporting a mult...

Recently, the development of smart home technologies has played a crucial role in enhancing several real-life smart applications. They help improve the quality of life through systems designed to enhance convenience, comfort, entertainment, health of the householders, and security. Note, however, that malware attacks on smart home devices are incre...

Nowadays, many resources for counter-terrorism operations are available for researchers belonging to different areas. In particular, the START project provides the Global Terrorism Database (GTD) that can be analyzed in order to provide, for instance, prediction models. The main idea underlying this work is using the historical data provided by GTD...

In this paper, we formally deduce a new computational model, with a recurrent structure, by means of data granulation. The proposed scheme can be regarded as an Echo State Network (ESN), with an additional granular layer. ESNs have been recently revisited in the context of deep learning. In view of such a state-of-the-art, and coherently with the c...

In Video Surveillance age, the monitoring activity, especially from unmanned vehicles, needs some degree of autonomy in the scenario interpretation. Video Analysis tasks are crucial for the target tracking and recognition; anyway, it would be desirable if a further level of understanding could provide a comprehensive, high-level scene description,...