In an era dominated by technological integration, Artificial Intelligence (AI) is pivotal across various sectors, driving significant advancements and demanding robust quality measures for its implementations. This paper introduces a novel AI maturity assessment framework designed in alignment with International Standards provided by ISO (International Organization for Standardization) and IEC (International Electrotechnical Commission), specifically with the ISO/IEC 33000 family of standards for process assessment. Our framework aims to provide AI system developers with a structured tool for continuous improvement of their development processes, thereby enhancing the reliability and efficacy of AI applications. To demonstrate the applicability of our proposed framework, we have validated it through a case study in the automotive sector. Specifically, the framework was employed to assess and enhance an AI project to develop a mechanism to determine vehicle behavior from sensor data within the constraints of onboard devices. Our findings identify key improvement points contributing to the iterative enhancement of AI system quality in engineering applications.

Bio-inspired algorithms have been successfully applied to solve complex optimization problems. They are also widely used to train and optimize machine learning and data-driven models, providing competitive results. This study presents a novel data-driven approach to identify and quantify the factors that characterize the excellent performance of handball players, depending on the specific position in which they play. This will give us the most important characteristics that differentiate the most excellent players in their positions. Based on bio-inspired algorithms, this research delves into the complex optimization problems inherent in sports analytics. The study utilizes data from the Women’s European Handball Championship, employing seven distinct algorithms, of which six are different bio-inspired algorithms - including a hybrid bio-inspired algorithm - and one Consensus-Based Aggregation algorithm to analyze and assign weights to each player’s actions during a match. This approach is further validated by comparing the findings against the top five players in each position as recognized by the European Handball Federation (EHF). Subsequently, the established model’s robustness and applicability are tested using data from the Women’s World Handball Championships. • A data-driven methodology to analyze the sporting performance of handball players. • Bio-inspired algorithms are employed to characterize excellent performance. • Real data from international women championships are used to test the methodology. • Explainable Artificial Intelligence characterizes the best players on the court. • Identify the most distinguishable characteristics of the best high-performance handball players.

Abstract Quality management techniques such as the quality function deployment model can help hospitals assess and improve the quality of their services by integrating the voice of customers. The different quality parameters of this model are usually determined and assessed by experts; nonetheless, obtaining such experts is not always easy or inexpensive. Moreover, in this method, patient opinions are not usually considered directly, although they are the real users of the services and those who can best assess those services. Nevertheless, these opinions are easily accessible today, owing to the development of medical social networks where patients directly convey their opinions about the different services and features of a hospital. Therefore, it is feasible to replace expert knowledge with the information provided by these opinions. Based on this idea, this study proposes a novel fuzzy recommendation model based on the quality function deployment method to rank hospitals depending on patient opinions and preferences regarding hospital services. This model integrates a topic modeling strategy for determining hospital requirements, customer needs, and the relationship between them as well as a sentiment analysis algorithm for assessing customer satisfaction regarding hospital services. To demonstrate the usefulness of the proposed method, several experiments were conducted using patient reviews from real hospitals, and the method was compared against other recommendation models. The results prove that this approach represents a step toward more personalized and effective health care system selection considering patient preferences and opinions.

Evaluating the quality of health care systems usually entails the examination of objective variables (waiting time, patients per doctor, etc.). Nonetheless, other subjective variables can be used thanks to new Internet tools. Many online medical services let users convey their opinions on the offered services. That information is an interesting tool to measure the quality of these services. It describes sentiments about different features using a wide range of adjectives, adverbs and nouns, many times, completely different for each feature. Therefore, it is interesting to assess every feature individually using different scales. This study presents an application of a multi-granular fuzzy linguistic model to represent the opinions about the different features of health care systems with the aim of recommending hospitals according to the user preferences. To test this approach, the opinions from real hospitals have been assessed using different user preferences. The obtained results outperform other state-of-the-art approaches.

This paper introduces a strategy for identifying design patterns in quantum circuits. The foundation of this approach relies on using the information procured from both the segmentation and the analysis of these circuits as primary data. The approach of the methodology is based on the novel interpretation of quantum circuit components through the lens of an automaton. Additionally, the method entails the generation of input symbols for this finite automaton. The symbols are derived from the matching process between design patterns and components of quantum circuits. Two tool prototypes, QPainter and QCDPDTool, have been developed to represent quantum circuits graphically and automatically detect quantum patterns. Using them, the primary reasoning process is carried out by an automaton that can manage representations of quantum circuit components. A suite of experiments on a set of quantum circuit sequences reveals promising results and offers empirical support for our approach. Furthermore, we explore how these experimental findings can be leveraged to improve the efficacy of design pattern identification in quantum circuits.

Abstract The automation of insect pest control activities implies the use of classifiers to monitor the temporal and spatial evolution of the population using computer vision algorithms. In this regard, the popularisation of supervised learning methods represents a breakthrough in this field. However, their claimed effectiveness is reduced regarding working in real-life conditions. In addition, the efficiency of the proposed models is usually measured in terms of their accuracy, without considering the actual context of the sensing platforms deployed at the edge, where image processing must occur. Hence, energy consumption is a key factor in embedded devices powered by renewable energy sources such as solar panels, particularly in energy harvesting platforms, which are increasingly popular in smart farming applications. In this work, we perform a two-fold performance analysis (accuracy and energy efficiency) of three commonly used methods in computer vision (e.g., HOG+SVM, LeNet-5 CNN, and PCA+Random Forest) for object classification, targeting the detection of the olive fly in chromatic traps. The training and testing of the models were carried out using pictures captured in various realistic conditions to obtain more reliable results. We conducted an exhaustive exploration of the solution space for each evaluated method, assessing the impact of the input dataset and configuration parameters on the learning process outcomes. To determine their suitability for deployment on edge embedded systems, we implemented a prototype on a Raspberry Pi 4 and measured the processing time, memory usage, and power consumption. The results show that the PCA-Random Forest method achieves the highest accuracy of 99%, with significantly lower processing time (approximately 6 and 48 times faster) and power consumption (approximately 10 and 44 times lower) compared with its competitors (LeNet-5-based CNN and HOG+SVM).

INTRODUCTION: Most hospital assessment systems are based on the study of objective statistical variables as well as patient opinions on their experiences with respect to the services offered by each hospital. Nevertheless, studies have indicated that most of these assessment systems fail to detect patient emotions when they are assessing their stays in a hospital. This information is vital to understanding most of the patient reviews, which are very complex and convey several emotions per review. Therefore, this study aimed to address the problem of detecting multiple emotions from patient reviews. METHODS: First, a large set of patient opinions was collected from a website that allowed patients to publish their experiences when visiting hospitals. Second, each opinion was labeled with the corresponding conveyed emotions. Third, a deep learning architecture based on a bidirectional gated recurrent unit with a multichannel convolutional neural network layer was proposed to detect multiple emotions from these reviews. Finally, the hyperparameters of this architecture were fine-tuned and different pretrained word embedding models were configured to test its performance. RESULTS: The results confirmed that our proposed method outperformed other deep learning and machine learning-based algorithms and achieved an average accuracy of 95.82%. Furthermore, the experiments show that clinical-domain word embedding slightly outperforms other general-domain word embeddings, although general-domain embeddings are larger in terms of dimensions. CONCLUSIONS: The combination of the gated recurrent unit and the multichannel convolutional neural network is able to exploit both semantic and syntactic characteristics of patient opinions. The findings of this study identify research gaps related to areas such as opinion-based hospital recommendations, thereby providing future research directions.

The process to assess a hospital performance usually needs the interaction of a lot of experts and patients and is very costly and time-consuming. Nevertheless, the availability of patient opinions on the Internet offers a great opportunity to develop systems that evaluate hospitals based on user feedback. The content of these opinions is very challenging, including information about the hospital services but also stories about their own patients, their families, and personal feelings or beliefs before or after leaving a hospital. Therefore, the task of recommending hospitals according to the quality of their services becomes really complicated. This study describes an application for ranking hospitals based on the user preferences about the different offered services as well as the opinions about them. First, it semiautomatically classifies all predefined hospital aspects, calculates the sentiment orientation, and represents their associated polarity by intuitionistic fuzzy sets. Second, by means of the user preferences towards the different aspects, an aggregation operator, and a multicriteria decision-making algorithm, all hospitals are ranked. To assess this methodology, a large set of reviews about hospitals have been collected. Further, considering all patient ratings about the different hospitals, an algorithm for ranking them is proposed, which develops baselines for comparison. In addition, an interval-valued Pythagorean fuzzy approach has been also implemented to compare the obtained results. These results confirm the soundness of the proposal.

Abstract The efficiency of handball goalkeepers is a good predictor of team ranking in tournaments, but despite this, very few studies have been carried out into the performance characteristics of elite goalkeepers. This paper provides the criteria for evaluating a handball goalkeeper and applies a variety of soft-computing methodologies for estimating their weights. More specifically, a fuzzy multi-criteria decision-making method, a metaheuristic optimisation algorithm, and statistical and domain-knowledge-based methods were used to evaluate the actions of goalkeepers during the game. Computer experiments were performed for all the proposed methodologies, using data from the 2020 European Men’s Handball Championship, in order to estimate the weights of the indicators. Then, these weights were used to identify the best goalkeeper and identify and rank the top five goalkeepers as determined by the tournament organisers. The results obtained show that using the metaheuristic-based method is extremely helpful in quantifying the expert assessments, which are often challenging to express in a disaggregated form. The other two techniques offer a less optimal but more easily interpretable result for coaches and fans.

Understanding opinions about products offered by big online providers, for instance, TripAdvisor, is relatively easy because the features assessed by the user about hotels are well-known (food, room, desk, sleep quality, etc.). Nonetheless, in the health domain, many times the user provides free-text reviews which are not clearly focused on a few specific features. The present study proposes a methodology for recommending hospitals according to textual opinions which describe the quality of the offered services. First, it detects hospital aspects, which represent the hospital services, by a Latent Dirichlet Allocation-based approach following the criteria of a quality model called SERVQUAL. The polarity of those aspects is computed and modelled by intuitionistic fuzzy sets. Depending on the user preferences or his/her attitude, the aspects are aggregated to finally rank the alternative hospitals following a Multicriteria Decision Making algorithm (PROMETHEE II). The methodology has been tested using a large collection of free-text reviews on hospitals, which contain information about their offered services, obtaining interesting results.

We have witnessed a flourish of review websites where users can buy many products/services and share their opinions about them. Most of those opinions may be broken down into different sub-opinions on the different aspects describing said products/services. This fact makes more complicated the task of computing the overall polarity about the product/service studied.We are presenting a fuzzy aggregation mechanism to compute the overall sentiment conveyed in a opinion/review taking into account the individual ratings for the different aspects commented by the opinion holder. This proposal has been tested using real data from Yelp dataset obtaining promising results.

The automatic keyphrases extraction is a useful task for many computational applications in the natural language processing and text mining fields. Several solutions to this problem have been reported, but the obtained results still show low rates of accuracy and performance. In this paper, a new unsupervised method for keyphrase extraction from text documents is proposed. The use of lexical-syntactic patterns is combined with a graph-based topic modeling in this approach. The topic modeling is supported by a semantic analysis process carried out from the fuzzy logic perspective. The method was evaluated with the SemEval-2010 and Inspec datasets and compared with other state-of-the-art proposals.

Within the framework of the Intelligent Data Suite (IDS) that is being developed by the company Prometeus Global Solutions, there is a Sentiment Analysis and Opinion Mining module focused on detecting `dangerous' (to the tool user company) messages on Social Media. This can be useful for sending `early warnings' to alert tool user company analysts to take preventive measures against potentially harmful messages. In this paper, a brief description of IDS features, regarding tweets filtering and classification is firstly presented. Affective Norms for English Words (ANEW) provides a set of normative emotional ratings for a large number of words in English and three emotions (valence, arousal and dominance) measures for each term. It is used as a basis for describing a fuzzy model containing five categories for representing the opinion of a microblogging text (very negative, negative, neutral, positive and very positive). The proposal is implemented and tested on the IDS framework.

Decision support systems, embedded in modern telemedicine applications, are a tool to improve the skills of general practitioners and patients in decision-making in medicine. Nowadays, one of the more challenging problems in this context is how to diagnose those diseases, whose early clinical signs are often subtle, and many of their common signs and symptoms are similar to other. These “fuzzy diseases,” even they can have distinctive features, are not diagnosable through a concrete clinical test or symptom, and, thus, they are difficult to recognize, especially in their initial phases when they might be mistaken for other similar ones. Then, the diagnosis of a fuzzy disease set is based on the exclusion of symptoms and tests results, due to the similarity between them. In the present article, it is proposed the development of a Clinical Decision Support System framework to diagnose a set of fuzzy diseases, concretely applied to Fibromyalgia and associated syndromes. For this purpose, in this paper a reasoning method that uses theories about conceptual categorization from the psychology, pattern recognition, and Zadeh's prototypes has been designed. Through the use of this model, satisfactory results in the evaluation of patients were obtained.

There are several types of business documents, ranging from brief accounting documents to complex legal agreements. Companies extensively use such documents to communicate, transact business and analyse their productivity. This results in the generation of a large number of documents daily, and small- and medium-sized enterprises are easily overwhelmed by this situation. Given this background, companies require software solutions which provide all of the features required by users for optimal document management, as well as optimising management processes and automating the extraction of relevant information from the documents. Open-source software provides these organizations with low-cost, high-quality software which incorporates an array of advanced features that extend beyond only storage solutions. In this study, we test several computational-intelligence open-source software tools in order to enhance the information-retrieval capabilities in small business document-management systems. We implement a prototype to test these Natural Language Processing (NLP) tools and Machine-Learning techniques in a business environment, with the aim of choosing the best alternative for each process.

This paper shows how some fuzzy logic tecniques applied to a recommender engine can be used in a Learning Object Repository. A Fuzzy Linguistic model based on three dimensions: structural, contextual, personal is proposed. The contextual and personal dimensions are modelled using domain ontologies and a automatically built fuzzy ontology, respectively. The experiment results indicate that the presented approach is useful and warrants further research in recommending and retrieval information.

Nowadays, the impact of e-Learning developments on improving educational activities is becoming more evident. The Portfolio approach has emerged as important alternative to increase the learning process. However, most of tools to support portfolio assessment are far from to come up to the expectations. In this work we propose a fuzzy ontology-based framework to support portfolio assessment. Our approach focuses on portfolio semantic representation and conceptual matching to generate a portfolio evaluation report, which helps teachers in portfolio assessment tasks. The initial experiments results indicate that the approach is useful and warrants further research.

Software development project management has a poor reputation in terms of avoiding cost and schedule overruns. The cause of this situation is based on the feature of the software development process that is characterized by quickly growing complexity and change. Therefore, there are many uncertainties to define exactly the necessary time to complete a tasks according to the person's performance. In this scenario Soft-Computing techniques may offer new approaches with the aim of helping the participants of the project to manage their time, give priority to their activities and readjust the work to complete satisfactorily the project tasks. This work presents an automatic features extraction process with the aim of defining the elements involved in a software project. This knowledge is represented by means fuzzy sets and fuzzy prototypes. The source of data is the Personal Software Project time recording logs. A preliminary experiment illustrates the feasibility of this approach.

Category-based information filtering is ground on the representation of user preferences according to a set of categories of similar items. The use of type 1 fuzzy sets provides a good method to represent categories when only one static interpretation of them is considered. This representation is not enough when documents do not have the same meaning for two different users because there are some degrees of subjectivity. On the other hand, type 2 fuzzy sets have been successfully applied to manage uncertainty more effectively than type-1 fuzzy sets in several environments. This paper presents a method to manage efficiently uncertainties in the filtering process in environments where there is a constant flow of new information (news, e-mail, etc.) and multiple users are involved. The proposed solution is based on the extension of the categories-based filtering method using interval type 2 fuzzy sets for representing each category and the user preferences. Experimental results, that illustrate the feasibility of this approach, are provided.

Ontologies represent a method of sharing and reusing knowledge on the semantic web. A fuzzy ontology is an extension of domain ontologies for solving the problems of uncertainty. This paper shows how a Fuzzy Ontology based approach can represent user profiles in e-learning environments. The ontological representation of the user profile enhances the performance in tasks such as filtering and information retrieval. An algorithm that allows automatically creating the construction of the ontology is also introduced. This approach has been integrated into a management tool for Learning Objects, in which each user profile is built from Learning Objects published by the user himself. The initial experiments confirm that the automatically obtained fuzzy ontology is a good representation of the user's preferences. The experiment results also indicate that the approach is useful and warrants further research.

The goal of this paper is to present the architecture of a metasearch engine called Castalia, still under development, which includes several underlying Q&A systems. Usually metasearch engines manage typical search engines like Google or Yahoo, but in this case the encapsulation of Q&A systems proposes new challenges that can be modeled by fuzzy logic apart from the other existing challenges such as the fuzzy modeling of temporal or causal questions.

Existing search engines and question-answering (QA) systems have made possible processing large volumes of textual information. Current work on QA has mainly focused on answering two basic types of questions: factoid and definition questions. However, the capability to synthesize an answer to a query by drawing on bodies of information which reside in various parts of the knowledge base is not among the capabilities of those systems. In this paper, a system oriented to infer query answers from a collection of propositions expressed in natural language is introduced. By means of a specific example, it is outlined how the system proceeds to face those situations. This approach is based on the use of formal constraining relations modeling copulative and comparative sentences. Combining those propositions with others contained in different knowledge bases and applying deduction rules, the desired answer could be obtained.

In this poster, a tool named BUCEFALO is presented. This tool is specially designed to improve the information retrieval tasks in web-based Personal Health Records (PHR). This tool implements semantic and multilingual query expansion techniques and information filtering algorithms in order to help users find the most valuable information about a specific clinical case. The filtering model is based on fuzzy prototypes based filtering, data quality measures, user profiles and healthcare ontologies. The first experimental results illustrate the feasibility of this tool.

The need for an explicit common terminology within Empirical Software Engineering (an ESE-Glossary of terms) was highlighted in the ISERN 2007 meeting [2]. The goal was to define a glossary of terms related to ESE based on an initial glossary published in http://lens-ese.cos.ufrj.br/wikiese. This initial glossary was built manually, based on expert knowledge. However, owing to the dynamic nature of the research works in ESE, this glossary must be dynamically updated with information extracted from the relevant documents in the research domain. Automation is, therefore, mandatory. We propose a text mining technique for the automatic extraction of the most relevant terms used in ESE documents. Our technique also provides the relationships between terms, with the degree of affinity between them. Our approach could, therefore, be useful in the improvement of the initial glossary of terms and in discovering relationships between terms.

In this paper, a novel document filtering model in dynamic web repositories based on fuzzy deformable prototypes is presented. This model is based on fuzzy hierarchical categorization of documents. It defines an easy process to deal with the incoming documents and an efficient method to update their structure. The process is performed comparing the fuzzy prototypes of document cluster with the available information about documents contents. It exploits conceptual-based filtering criteria and category-based filtering techniques to deliver to the user an intelligent structure of the documents. Since filtering is a dynamic process, the knowledge base can update the hierarchy of existing documents. The clusters hierarchy can be easily and efficiently updated when new documents income on the repository by means of an inference method which is based on fuzzy deformable prototypes.

Abstract This article presents a semantic‐based Web retrieval system that is capable of retrieving the Web pages that are conceptually related to the implicit concepts of the query. The concept of “concept” is managed from a fuzzy point of view by means of semantic areas. In this context, the proposed system improves most search engines that are based on matching words. The key of the system is to use a new version of the Fuzzy Interrelations and Synonymy‐Based Concept Representation Model (FIS‐CRM) to extract and represent the concepts contained in both the Web pages and the user query. This model, which was integrated into other tools such as the Fuzzy Interrelations and Synonymy based Searcher (FISS) metasearcher and the fz‐mail system, considers the fuzzy synonymy and the fuzzy generality interrelations as a means of representing word interrelations (stored in a fuzzy synonymy dictionary and ontologies). The new version of the model, which is based on the study of the cooccurrences of synonyms, integrates a soft method for disambiguating word senses. This method also considers the context of the word to be disambiguated and the thematic ontologies and sets of synonyms stored in the dictionary.