Basilis Boutsinas

• PhD
• Professor (Full) at University of Patras

Extreme values or outliers are patterns in the data, which do not conform to a well-defined concept of normal behavior and usually appear to be produced by a different mechanism from the rest of the data. Determining an extreme value is a challenge for both humans and computers. In this paper, we propose a new approach to detect extreme values in u...

Background/Objectives: This systematic review explores the integration of digital and AI-enhanced cognitive behavioral therapy (CBT) for insomnia, focusing on underlying neurocognitive mechanisms and associated clinical outcomes. Insomnia significantly impairs cognitive functioning, overall health, and quality of life. Although traditional CBT has...

Combining Rule-based reasoning and Case-based reasoning has been widely used, exhibiting quite successful results, since they have complementary capabilities. A system that can utilize both approaches could potentially take advantage of the positive aspects of both while minimizing their negative aspects. In this paper, we propose a hybrid reasoner...

Trip recommendation for groups of tourists (TRGT) is a challenging task in tourism since many tourists travel in groups, inducing social interaction and bringing various social benefits. However, TRGT must address various real-life constraints such as limited time for touring, cost, etc. TRGT aims to design personalized tours that meet the preferen...

Social Signal Processing is a new research field aiming at providing computers with the ability to understand human social signals, i.e., (dis)agreement, amusement, etc. Social signals are manifested through an array of nonverbal behavioral cues, such as gestures, postures. Neuro-tourism is an idea that combines the fields of Neuroscience and Touri...

Health tourism is a unique form of tourism that mixes vacations with the prevention and treatment of physical and mental conditions. It refers to all relationships and phenomena that result from a change in location and residence of individuals and aims to promote, stabilize, and restore, as needed, their physical, mental, and emotional health and...

Recommender systems aim to forecast users’ rank, interests, and preferences in specific products and recommend them to a user for purchase. Collaborative filtering is the most popular approach, where the user’s past purchase behavior consists of the user’s feedback. One of the most challenging problems in collaborative filtering is handling users w...

Mental trauma has become widely acknowledged as a significant element in developing psychopathology in children and adolescents in recent years. Health issues associated with mental trauma in children and adolescents have been researched in the worldwide literature. Simultaneously, the risks of exposing children and adolescents to traumatic experie...

There is a growing interest in the offering of novel alternative choices to users of recommender systems.

The interesting properties of scale-free and small-world networks recently observed have triggered the attention of the research community to the study of real growing complex networks. In scale-free networks, most vertices are sparsely connected, while a few vertices are intensively connected to many others, indicating a “preferential linking” dur...

The recommender systems process data for extracting information relevant to the user profile. In this study, we present an innovative recommender system aiming at matching health tourist preferences to health/tourism providers. It focuses on providing complete health tourism products, by matching the user profile to characteristics of both health a...

Last decades, tourism, which has grown rapidly worldwide, is highly interconnected with environment quality. Specifically, many activities in hospitality industry have adverse impacts on environment. Hotels are customer-oriented organizations, operating within a complex structure aiming to provide services to people that travel for a variety of rea...

In this paper, we discuss the multiple p-median problem (MPMP), an extension of the original p-median problem and present several potential applications. The objective of the well-known p-median problem is to locate p facilities in order to minimize the total distance between demand points and facilities. Each demand point should be covered by its...

There is a growing interest in the offering of novel alternative choices to users of recommender systems. These recommendations should match the target query while at the same time they should be diverse with each other in order to provide useful alternatives to the user, i.e., novel recommendations. In this paper, the problem of extracting novel r...

This chapter discussed on an ontology developed for a case‐based reasoning system that aims at supporting people facing autism spectrum disorders (ASD). PAVEFS is an intelligent information system designed for the personalized provision of services for the diagnosis and the care of individuals of various ages and types of autism. The objective of P...

This work deals with the issue of understanding a user’s behaviour as this is expressed via a gamified application. On a previous work we have introduced the notion of ontologies and the association of concepts in relevance to decisions that have to be made. The current work extends the previous, as it introduces a new process-based approach, based...

Το ΠΑΥΕΥΣ είναι ένα «Βασισμένο σε Γνώση Ευφυές Πληροφοριακό Σύστημα» (Knowledge based Intelligent Information System) για την υποστήριξη, με τη μορφή αυτόματης παροχής πληροφοριών και συμβουλών, προς όλους όσους συνδέονται με άτομα με Διαταραχές του Αυτιστικού Φάσματος (γονείς, ειδικούς, παρόχους υπηρεσιών υγείας, κλπ). Αυτές οι πληροφορίες και συμ...

In this paper we present a clustering heuristic for solving demand covering models where the objective is to determine locations for servers that optimally cover a given set of demand points. This heuristic is based on the concept of biclusters and processes the set of demand points as well as the set of potential servers and determines biclusters...

The 10 tables in the Tourism Satellite Account (TSA) framework store a significant amount of data. These data when broken down are able to support significantly more statistical analysis. However, sophisticated software is needed, not only for the efficient processing of and access to the data but also for the effective extraction of the informatio...

Cellular manufacturing is the cornerstone of many modern flexible manufacturing techniques, taking advantage of the similarities between parts in order to decrease the complexity of the design and manufacturing life cycle. Part-Machine Grouping (PMG) problem is the key step in cellular manufacturing aiming at grouping parts with similar processing...

Ο αυτισμός είναι μία σύνθετη νευρο‐βιολογική διαταραχή που διαρκεί καθ’όλη τη διάρκεια ζωής του ατόμου. Ο πληθυσμός των ατόμων που θεωρείται ότι έχουν αυτισμό είναι πολύ ετερογενής, με κάθε άτομο να παρουσιάζει ένα μοναδικό προφίλ από ικανότητες, αδυναμίες και ανάγκες. Είναι ανάγκη λοιπόν, να αναπτυχθούν υπηρεσίες για την εξειδικευμένη παροχή υποστ...

Data Mining is an emerging knowledge discovery process of extracting previously unknown, actionable information from very large scientific and commercial databases. Usually, a data mining process extracts rules by processing high dimensional categorical and/or numerical data. However, in the data mining context the user often has to analyze hundred...

Clustering has been applied in a wide variety of disciplines and has also been utilized in many scientific areas. Usually, clustering algorithms construct either clusters of rows or clusters of columns of the input data matrix. Biclustering is a methodology where biclusters are formed by both a subset of rows and a subset of columns, such that obje...

An area of focus in music improvization is interactive improvization between a human and a computer system in real time. In this paper, we present a musical interactive system acting as a melody continuator. For each musical pattern given by the user, a new one is returned by the system which is built by using general patterns for both pitch and du...

Due to the decentralization in the Semantic Web, ontologies can be designed and developed by different communities, using different vocabularies and overlapping content. In this paper, we present a system for ontology exchanging between communities. More specifically, the system updates parts of ontologies, which are considered to be interesting by...

The essence of demand covering models is to determine locations for servers that optimally cover a given demand. Two main classes of problems may be defined in this context, namely mandatory models where the whole of the demand must be covered with the minimum number of servers, or maximal covering models where the maximum proportion of the demand...

Purpose The paper aims to present an approach for services in the domain of tourism based on a software application in the area of ontology engineering, showing a methodology for intelligent knowledge‐based P2P networks creation, in the tourism knowledge domain, given that, potential tourists share and organize their experiences, interests and know...

In this paper we introduce a method called CL.E.D.M. (CLassification through ELECTRE and Data Mining), that employs aspects of the methodological framework of the ELECTRE I outranking method, and aims at increasing the accuracy of existing data mining classification algorithms. In particular, the method chooses the best decision rules extracted fro...

In this paper we present a new method for clustering categorical data sets named CL.E.KMODES. The proposed method is a modified k-modes algorithm that incorporates a new four-step dissimilarity measure, which is based on elements of the methodological framework of the ELECTRE I multicriteria method. The four-step dissimilarity measure introduces an...

Music analysis, i.e. using computers to analyze fully notated pieces of musical score, is one of the most important research issues in computer music. Machine learning has played a crucial role in the computer music almost since its beginning. Recently, research in the ¯eld has focused on music mining. Data Mining is an emerging knowledge discovery...

This paper investigates patterns in cardiovascular risk factors from a large population sample of cardiac patients and their matched controls. Various factors were taken into consideration and were used as inputs to effectively demonstrate online analytical process, OLAP methodology. OLAP is a new method that is used to explore the role of several...

Ontology mapping is one of the most important processes in ontology engineering. It is imposed by the decentralized nature of both the WWW and the Semantic Web, where heterogeneous and incompatible ontologies can be developed by different communities. Ontology mapping can be used to establish efficient information sharing by determining corresponde...

Ontology merging/alignment is one of the most important tasks in ontology engineering. It is imposed by the decentralized nature of both the WWW and the Semantic Web, where heterogeneous and incompatible ontologies can be developed by different communities. Usually, ontology merging/alignment is based on an ontology mapping that has been establishe...

One of the most important data mining problems is learning association rules of the form "90% of the customers that purchase product x also purchase product y". Discovering association rules from huge volumes of data requires substantial processing power. In this paper we present an efficient distributed algorithm for mining association rules that...

Clustering consists in partitioning a set of objects into disjoint and homogeneous clusters. For many years, clustering methods have been applied in a wide variety of disciplines and they also have been utilized in many scientific areas. Traditionally, clustering methods deal with numerical data, i.e. objects represented by a conjunction of numeric...

One of the main challenges of today's data mining systems is their ability to manage a huge volume of data generated possibly by different sources. On the other hand, inductive learning algorithms have been extensively researched in machine learning using small amounts of judiciously chosen laboratory examples. There is an increasing concern in cla...

Purpose This paper aims to present a methodology for activity‐based costing, which combines simulation modeling and association rule mining, one of the core data‐mining techniques. The objective of the proposed methodology is to deal with the problem of defining cost drivers. Design/methodology/approach Activity‐based costing uses the output produ...

Background: Classification algorithms are used in a variety of medical domains for rule induction, prediction and classification. We present a comparative study of two classification algorithms (one that builds decision trees and one that is based on clustering), as well as logistic regression analysis for the task of classifying cardiac patients i...

Clustering is the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups (clusters). A fundamental and unresolved issue in cluster analysis is to determine how many clusters are present in a given set of patterns. In this paper, we present the z-windows clustering algorithm, which aims to address this problem usin...

Association models aim at extracting dependencies between variables in scientific and biological databases. Nowadays, these databases store voluminous data. Obtaining associations in large biological databases is a difficult task, due to the multiple comparisons and the limitations that these comparisons involve.

Recently there has been increasing interest in On Line Analytical Processing (OLAP) to satisfy the organizational needs of high-level information delivery and advanced data analysis. The actual application of OLAP tools involves the use of various functions, such as the common drilling down and slicing and dicing. Usually each particular OLAP funct...

The knowledge that must be acquired by machine learning systems which try to mimic common sense, as exhibited by humans, is inherently incomplete, redundant or even con-tradictory. Thus, the main characteristics of common sense is nonmonotonicity, which is introduced by exceptions to general rules, redundancy, which is introduced by continuous beli...

Contrary to much of the research in machine learning where there is a concentration on problems with relatively small volume of data, one of the main challenges of the today's data mining systems is their ability to handle data that is substantially larger than available main memory on a single processor. In this paper, we present a distributed tec...

Multiple criteria decision making has been extensively used to solve classification problems. In this paper, a new methodology is proposed, that involves the combination of multiple criteria decision making and data mining techniques. In particular, the methodological framework of ELECTRE is extended, using a data mining classification algorithm. T...

Classification is a widely used technique in various fields, including data mining and statistical data analysis. Decision trees are one of the most frequently occurring knowledge representation schemes used in classification algorithms. Decision trees can offer a more practical way of capturing knowledge than coding rules in more conventional lang...

Business and scientific organizations, nowadays, own databases containing confidential information that needs to be analyzed, through data mining techniques, in order to support their planning activities. The need for privacy is imposed due to, either legal restrictions (for medical and socio-economic databases), or the unwillingness of business or...

Clustering can be defined as the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups (clusters) . There is a growing need for parallel algorithms in this field since databases of huge size are common nowadays. This paper presents a parallel version of a recently proposed algorithm that has the ability to scale...

Data mining is an emerging research area that develops techniques for knowledge discovery in huge volumes of data. Usually, data mining rules can be used either to classify data into predefined classes, or to partition a set of patterns into disjoint and homogeneous clusters, or to reveal frequent dependencies among data. The discovery of data mini...

Clustering algorithms require a large amount of computations of distances among patterns and centers of clusters. Hence, their complexity is dominated by the number of patterns. On the other hand, there is an explosive growth of business or scientific databases storing huge volumes of data. One of the main challenges of today's knowledge discovery...

The process of partitioning a large set of patterns into disjoint and homogeneous clusters is fundamental in knowledge acquisition. It is called Clustering in the literature and it is applied in various fields including data mining, statistical data analysis, compression and vector quantization. The k-means is a very popular algorithm and one of th...

We propose a novel method for the analysis of the magnetoencephalogram (MEG) of epileptic patients. The proposed method was based on the reconstruction of the phase space from the one-dimension signals to higher-dimension phase space. An especially developed clustering algorithm was applied on the reconstructed data in order to investigate the dist...

Clustering, that is the partitioning of a set of patterns into disjoint and homogeneous meaningful groups (clusters), is a fundamental process in the practice of science. k-windows is an efficient clustering algorithm that reduces the number of patterns that need to be examined for similarity, using a windowing technique. It exploits well known spa...

In this paper, we introduce Artificial Nonmonotonic Neural Networks (ANNNs), a kind of hybrid learning systems that are capable of nonmonotonic reasoning. Nonmonotonic reasoning plays an important role in the development of artificial intelligent systems that try to mimic common sense reasoning, as exhibited by humans. On the other hand, a hybrid l...

In the present paper we propose a novel technique for the analysis of the fetal MagnetoCardioGram (f-MCG) in normal and pathological pregnancies. The f-MCG is a measure of the magnetic component of the electromagnetic fields emitted from the fetal heart, reflecting the underlying dynamics. The extremely weak magnetic fields emitted from the fetal h...

INTRODUCTION Nonmonotonic reasoning plays an important role in the development of systems that try to mimic commonsense reasoning. Human beings are constantly forced to make decisions and reach conclusions in a fuzzy world. The knowledge that can be acquired by observation is inherently incomplete and may even contain conflicting information as wel...

An application of Nonmonotonic Connectionist Expert Systems (NCESs) in mining classification rules from large relational databases is presented. NCESs are hybrid learning systems that can acquire symbolic knowledge of a nonmonotonic domain, represented using nonmonotonic inheritance networks. This initial knowledge can be refined using connectionis...

Efficient representation of knowledge, under a multiple inheritance scheme with exceptions, plays an important role in artificial intelligence. Fast verification of the existence of a transitive relationship in such a hierarchy is of great importance. This paper presents an efficient algorithm for computing transitive relationships with exceptions....

Efficient representation of knowledge, under a multiple inheritance scheme with exceptions, plays an important role in Artificial Intelligence. Fast verification of the existence of a transitive relationship in such a hierarchy is of great importance. This paper presents an efficient algorithm for computing transitive relationships with exceptions....

Machine learning techniques suggest new approaches to the problems encountered in Systems Engineering. This paper presents a framework for the analysis and verification of a class of rule-based realtime decision making systems. This framework is based on the technique of Explanation-based generalization that is used to generalize rule-based program...

The main characteristic of formal education is teaching within an administrative framework. Therefore a lot of teaching and administrative activities must be carried out during different education processes, from library services evaluation to building cognitive student models. Efficient development of such education processes usually rest either o...