Miquel Sànchez-Marrè

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• Ph.D in Artificial Intelligence, B.Sc in Computer Science
• Retired at Polytechnic University of Catalonia

In this study we propose a deep learning model architecture using long short-term memory (LSTM) networks and additive attention mechanisms for vetting transiting exoplanet candidates. Our method is applied to two different data sets (Kepler and TESS) comprising light curves, representing periodic fluctuations in star brightness that are potentially...

One of the major problems when designing control and supervision systems for environmental systems is the need to be adapted to the particularities of each system. The use of Artificial Intelligence (AI) techniques instead of classical control approaches have been used in recent years in the design of Intelligent Decision Support Systems (IDSS). Th...

The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone,...

Gas turbines play a key role in generating power. It is really important that they work efficiently, safely, and reliably. However, their performance can be adversely affected by factors such as component wear, vibrations, and temperature fluctuations, often leading to abnormal patterns indicative of potential failures. As a result, anomaly detecti...

In this paper, we propose a novel metaheuristic algorithm called Forgetful Swarm Optimization (FSO) for Astronomical Observation Scheduling (AOS), a type of combinatorial optimization problem defined by the tasks and constraints assigned to the telescopes and other devices involved in astrophysical research. FSO combines local optimization, Destroy...

The book was inadvertently published without including the contributor’s name Franz Wotawa in Chapters 5 and 7 and Table of Contents.

Bacterial resistance to antibiotics has been rapidly increasing, resulting in low antibiotic effectiveness even treating common infections. The presence of resistant pathogens in environments such as a hospital Intensive Care Unit (ICU) exacerbates the critical admission-acquired infections. This work focuses on the prediction of antibiotic resista...

When working with Intelligent Decision Support Systems (IDSS), data quality could compromise decisions and therefore, an undesirable behaviour of the supported system. In this paper, a novel methodology for time-series online data imputation is proposed. A Case-Based Reasoning (CBR) system is used to provide such imputation approach. The CBR princi...

In this chapter, the main kind of Model-driven methods that can be used in Intelligent Decision Support Systems are described: Agent-based Simulation Models, Expert-based models, Model-Based Reasoning methods, and Qualitative Reasoning models. Before detailing Agent-based Simulation models, the fundamentals of multi-agent systems are described. Des...

In this chapter, the main characteristics of Intelligent Decision Support Systems (IDSS) are described. Starting with a definition of an IDSS, next an introduction to the Artificial Intelligence (AI) field is provided. Main AI paradigms or approaches to solve complex problems are presented. This way the readers can have a better understanding of th...

In this chapter, several real case studies of the development of an IDSS are detailed to illustrate the use of both data-driven and model-driven approaches presented in previous Chaps. 5 and 6.KeywordsThe use of Intelligent models in decision supportIDSS design and developmentIDSS architectureIDSS case studies

In this chapter, a general summary of the material provided in this book is presented. In Chap. 9, some advanced topics were analyzed, but those have been addressed with some approaches in a rather satisfactorily way. However, some open challenges, not definitively solved in the field of Intelligent Decision Support Systems are discussed here. Thes...

In this chapter, some advanced topics in IDSSs and a specific application in the IDSS field are presented. The advanced topics analyzed are the Uncertainty Management problem, the Temporal Reasoning aspects and the Spatial Reasoning management. The advanced application of Intelligent Decision Support Systems is the so-called Recommender Systems. Fi...

In this chapter, the main useful software tools for the development of IDSS are presented. First, several data-driven tools available in the scientific community are reviewed, with the special focus on the free open-source software. Next, several model-driven technique tools are analyzed, focussing again on the available free open-source software....

In this chapter, the main kind of Data-driven methods that can be used in Intelligent Decision Support Systems are described: Descriptive Models, Association Models, Discriminant or Classifier Models, Predictive Models, Optimization Models. First, the concepts of data mining, knowledge discovery, and data science are explained and basic terminology...

In this chapter, the historical evolution of Management Information Systems is detailed. These kinds of systems, which process data computationally to extract information, started with the automatic Transaction Processing Systems in the late 60s and early 70s, and evolved into what are considered the first Decisions Support Systems (DSSs) in the la...

In this chapter, the fundamentals of decisions are presented. Basic terminology (alternatives, states, events, outcomes, etc.) is introduced and the different typologies of decisions are analyzed. After a very brief review of the history of Decision Theory, the decision process is analyzed, from the point of view of several authors. Finally, it is...

Operational modes of a process are described by a number of relevant features that are indicative of the state of the process. Hundreds of sensors continuously collect data in industrial systems, which shows how the relationship between different variables changes over time and identifies different modes of operation. Gas turbines’ operational mode...

One threatening medical problem for human beings is the increasing antimicrobial resistance of some microorganisms. This problem is especially difficult in Intensive Care Units (ICUs) of hospitals due to the vulnerable state of patients. Knowing in advance whether a concrete bacterium is resistant or susceptible to an antibiotic is a crux step for...

This paper proposes an Intelligent Decision Support (IDS) methodology based on the integration of a data-driven technique –Case Based Reasoning (CBR)– and model-driven technique –Rule Based Reasoning (RBR)– for control, supervision and decision support on environmental systems. Design stage of control and decision support tools for environmental sy...

Although widely used, the majority of current music recommender systems still focus on recommendations' accuracy, user preferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as...

Case Base Maintenance algorithms update the contents of a case base in order to improve case-based reasoner performance. In this paper, we introduce a new case base maintenance method called Reputation-Based Maintenance (RBM) with the aim of increasing the classification accuracy of a Case-Based Reasoning system while reducing the size of its case...

Recently, the need of improved resource trading has arisen due to resource limitations and energy optimization problems. Various platforms supporting resource exchange and waste reuse in industrial symbiotic networks are being developed. However, the actors participating in these networks still mainly act based on predefined patterns, without takin...

The use of decision support systems (DSS) allows integrating all the issues related with sustainable development in view of providing a useful support to solve multi-scenario problems. In this work an extensive review on the DSSs applied to wastewater treatment plants (WWTPs) is presented. The main aim of the work is to provide an updated compendiu...

In this paper, some new components that have been integrated in the Diet4You system for the generation of nutritional plans are introduced. Negative user preferences have been modelled and introduced in the system. Furthermore, the cultural eating styles originated from the location where the user lives have been taken into account dividing the ori...

Data Mining (DM) is a fundamental component of the Data Science process. Over recent years a huge library of DM algorithms has been developed to tackle a variety of problems in fields such as medical imaging and traffic analysis. Many DM techniques are far more flexible than more classical numerial simulation or statistical modelling approaches. Th...

One of the major problems to design and implement a control/supervision system for a process lies in the need to establish an ad-hoc system for each process installation. On the other side, an open challenge related to the deployment of Intelligent Decision Support Systems (IDSSs) is the interoperability of the different methods used, in order to a...

Environmental data stream mining is an open challenge for Data Science. Common methods used are static because they analyze a static set of data, and provide static data-driven models. Environmental systems are dynamic and generate a continuous data stream. Dynamic methods coping with the temporal nature of data must be provided in Data Science. Ou...

Question Answering (QA) has reappeared in research activities and in companies over the past years. We present an architecture of Semantic-based closed and open domain Question Answering System (ScoQAS) over ontology resources (not free text) with two different prototyping: Ontology-based closed domain and an open domain under Linked Open Data (LOD...

Environmental decision support systems (EDSSs) are attractive tools to cope with the complexity of environmental global challenges. Several thoughtful reviews have analyzed EDSSs to identify the key challenges and best practices for their development. One of the major criticisms is that a wide and generalized use of deployed EDSSs has not been obse...

This paper characterizes part of an interdisciplinary research effort on Artificial Intelligence (AI) techniques and tools applied to Environmental Decision-Support Systems (EDSS). WaWO+ the ontology we present here, provides a set of concepts that are queried, advertised and used to support reasoning about and the management of urban water resourc...

The main goal of this work is to develop a methodology for finding nutritional patterns from a variety of individual characteristics which can contribute to better understand the interactions between nutrition and health, provided that the complexity of the phenomenon gives poor performance using classical approaches. An innovative methodology base...

Classical Pre-Post Intervention Studies are often analyzed using traditional statistics. Nevertheless, the nutritional interventions have small effects on the metabolism and traditional statistics are not enough to detect these subtle nutrient effects. Generally, this kind of studies assumes that the participants are adhered to the assigned dietary...

One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality. The more complex the reality to be analyzed is, the higher the risk of getting low quality data. Unfortunately real data often contain noise, un...

Cluster validation in Clustering is an open problem. The most exploited possibility is the validation through cluster validity indexes (CVIs). However, there are many indexes available, and they perform inconsistently scoring different partitions over a given dataset. The aim of the study carried out is the analysis of seventeen CVIs to get a commo...

The aim of this work is to analyze water and sanitation supply data from Nicaragua and Honduras by using different data mining tools. The data has been provided by SIASAR (Rural Water and Sanitation Information System), which is a water and sanitation management and information platform created through the joint effort of different Central American...

This paper presents a dynamic adaptive framework for building a case library being able to cope with a data stream in the field of Case-Based Reasoning. The framework provides a three-layer architecture formed by a set of case libraries dynamically built. This Dynamic and Adaptive Case Library (DACL), can process in an incremental way a data stream...

One of the main problems enterprises face today is the bulk of data derived from various resources. Furthermore, the growth of technology and sciences has greatly influenced the area of management and decision-making procedures, and has dramatically changed the decision-making processes in different levels, both quantitatively and qualitatively. Kn...

Cluster interpretation is an important step for a proper understanding of a set of classes, independently of whether they have been automatically discovered or expert-based. An understanding of classes is crucial for the further use of classes as the basis of a decision-making process. The abundant work on cluster validity found in the literature i...

In recent years, recommender systems have become an important part of various applications, supporting both customers and providers in their decision-making processes. However, these systems still must overcome limitations that reduce their performance, like recommendations' overspecialization, cold start, and difficulties when items with unequal p...

The textile industry in Europe is facing a new challenge in order to stay competitive into the textile market. They need to be flexible, cost efficient and produce with high quality. The setting of the machinery parameters is therefore an important aspect that combines implicit knowledge of workers and engineers with explicit knowledge. This makes...

Information diffusion in large-scale networks has been studied to identify the users influence. The influence has been targeted as a key feature either to reach large populations or influencing public opinion. Through the use of micro-blogs, such as Twitter, global influencers have been identified and ranked based on message propagation (retweets)....

Case retrieval is one important step in the case-based reasoning cycle. Up to now, several algorithms have been proposed for the indexing of cases, since the original indexing approach of k-d trees came up in literature. Main approaches propose the use a precomputed binary search tree to get an average logarithmic time effort in searching. The prop...

One of the main problems enterprises face today is the bulk of data derived from various resources. Furthermore, the growth of technology and sciences has greatly influenced the area of management and decision-making procedures, and has dramatically changed the decision-making processes in different levels, both quantitatively and qualitatively. Kn...

A profiling methodology is introduced for automatic interpretation of clusters in this paper. This methodology contributes to the characterization of the resulting classes from a clustering process. Our research aims to find a concordance between the proposed methodology and the experts' description of these classes. In this work the resulting clas...

This paper describes an innovative usage of Case-Based Reasoning to reduce the high cost derived from correctly setting the textile machinery within the framework of European MODSIMTex project. Furthermore, this system is capable of dealing with flexible queries, allowing to relax or restrict the searches in the case base. The paper discusses the i...

This paper describes Traffic Lights Panel (TLP) as a useful interpretation-oriented tool for clustering results, suitable for helping the domain experts to induce a conceptualization of the resulting profiles. Till now, the TLP is manually derived from the clustering results, but it has been well accepted by the domain experts of several real appli...

The Data Mining for Environmental Sciences workshop series started inside iEMSs in 2006 and provides a valuable opportunity for close contact between KDD and Environmental community. After several editions of the workshop, possibilities of KDD for solving very complex environmental problems seems to be better understood by environmental scientists,...

This paper introduces the shared autonomy concept on the context of Assistive Technologies (AT), in particular using an Intelligent Tutoring System (ITS) to support the performance of Activities of Daily Living (ADLs) while maintaining the intrinsic abilities of cognitively impaired users and relieving their respective caregivers from full time ass...

In this paper we present a flexible CBR shell for Data-Intensive Case-Based Reasoning Systems which is fully integrated in an Intelligent Data Analysis Tool entitled GESCONDA. The main subgoal of the developed tool is to create a CBR Shell where no fixed domain exists and where letting the expert/user creates (models) his/her own domain. From an ab...

In this work, the GESCONDA system is presented. Initially it was conceived as a system for knowledge discovery and Data Mining, but currently, the system supports two new functionalities. A case-based reasoning engine and a rule-based reasoning shell are provided. These new skills of GESCONDA makes it a suitable prototype tool for the deployment of...

Agent-based systems have become an important area of research since the 1990s. They have been applied to a range of domains that are intrinsically complex. Among these, environmental problems are of special concern, given their ample affectation to our societies and everyday quality of life. This report provides a review of agent-based systems appl...

Continuous domains are domains where cases are generated from a continuous data stream. In these domains, a lot of cases are continuously solved and learned by a CBR system. This means that many cases could be stored in the case library. Thus the efficiency of the CBR system both in size and time could be deeply worsened. In this research work a dy...

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques su...

The goal of this chapter is to analyse major challenges in the development of Intelligent Environmental Decision Support Systems (IEDSS), to review some possible approaches and techniques to cope with them, and to study new trends for future research in the IEDSS field. IEDSS are the envisioned new tools to cope with the inherent complexity of envi...

A holistic model embeds water resources and economic components into a consistent mathematical programming model, with the objective of maximizing economic profits from water uses in various sectors. Such a model can be used to address combined environmental-economic ...

Nowadays a computer is used as a main tool for many tasks. Usually a session using a computer in a computer center takes from 50 up to 150 minutes, getting the user to be tired. With this research, the dangerous positions adopted by the students in a computer center are analyzed and recommendations of exercises are listed. The exercises avoid the p...

We present an important improvement related to the computation and use of Mutual Information index in Pseudobagging, a technique that adapts “bagging” to unsupervised context. The Mutual Information index plays a key role in this technique, assessing the quality of a partition. We propose the use of such an index to improve the Pseudobagging voting...

This paper proposes a knowledge discovery methodology for generating new hypothesis to a particular knowledge theory. A combination of AI and Statistical techniques is proposed to increase the corpus of current knowledge of the target domain. In this paper a particular application to analyze comorbid mental disorders with intellectual disability is...

This paper presents a new sonar based purely reactive navigation technique for mobile platforms. The method relies on Case-Based Reasoning to adapt itself to any robot and environment through learning, both by observation and self experience. Thus, unlike in other reactive techniques, kinematics or dynamics do not need to be explicitly taken into a...

The potential of Case-Based Reasoning to use the knowledge gained from past experiences to solve problematic situations has made this Artificial Intelligence technique a useful decision support tool in different environmental domains such as wastewater treatment. Case-Based Reasoning tools automatically identify similarities between present and pre...

Classical control has serious limitations when faced with solids separation problems in the activated sludge process. Lack of knowledge about the mechanisms involved in the imbalance within the different microbiological communities implies that a general solution to these undesirable situations has not yet been provided. However, operators have to...

In this work the GESCONDA software is presented. It is a tool for intelligent data analysis and implicit knowledge management of databases, with special focus on environmental databases. Differing from existing commercial systems, the more relevant aspects of this proposal are the incorporation of the statistical data filtering and pre-processing i...

Environmental problems tend to be very intricate, thus traditional software approaches cannot cope with this complexity when developing an environmental system. Multi-Agent systems (MAS) have the ability to deal with complex problems, thus we propose the development of a MAS for supporting the decision-making in a river basin system. With this prop...

In this paper we present a novel approach for combining,Case-Based Reasoning (CBR) and Argumentation. This approach involves 1) the use of CBR for evalu- ating the arguments submitted by agents in collaborative decision making dialogs, and 2) the use of Argument Schemes and Critical Questions to organize the CBR memory,space. The former involves us...

There are inherent open problems arising when developing and running Intelligent Environmental Decision Support Systems (IEDSS). During daily operation of IEDSS several open challenge problems appear. The uncertainty of data being processed is intrinsic to the environmental system, which is being monitored by several on-line sensors and off-line da...

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques su...

In recent years, several researchers have studied the suitability of CBR to cope with dynamic or continuous or temporal domains. In these domains, the current state depends on the past temporal states. This feature really makes difficult to cope with these domains. This means that classical individual case retrieval is not very accurate, as the dyn...

Clustering techniques have a great importance in knowledge discovery because they can find out new groups or clusters of objects within databases. Thus, they are unsupervised learning methods, very useful when facing unknown, unlabelled and ill-structured databases, as environmental databases are. In this paper, different clustering algorithms are...

The interaction of the atmosphere and the ocean has a profound effect on climate, while the uptake by the oceans of a major fraction of atmospheric CO 2 has a moderating influence. By improving accuracy in the quantification of the ocean's ...

The underlying idea of the methodology proposed in this paper is to provide a new methodology that could select the most appropriate feature weighting algorithm for a given database. The main idea is to implement a case-based system, where cases are formed by description of databases and feature weighting techniques optimising their generalisation...

The step of identifying to which class of operational situation belongs the current environmental system (ES) situation is a key element to build successful environmental decision support systems (EDSS). This diagnosis phase is especially difficult due to multiple features involved in most environmental systems. It is not an easy task for environme...

This paper characterizes part of an interdisciplinary research effort on AI techniques applied to environmental decision-support systems. The architectural design of the OntoWEDSS decision-support system for wastewater management is presented. This system augments classic rule-based reasoning and case-based reasoning with a domain ontology, which p...

The complexity of environmental problems makes necessary the development and application of new tools capable of processing not only numerical aspects, but also experience from experts and wide public participation, which are all needed in decision-making processes. Environmental decision support systems (EDSSs) are among the most promising approac...

A project based on the integration of new technologies and artificial intelligence to develop a device--e-tool--for disabled patients and elderly people is presented. A mobile platform in intelligent environments (skilled-care facilities and home-care), controlled and managed by a multi-level architecture, is proposed to support patients and caregi...

This paper presents an application of lazy learning algorithms in the domain of industrial processes. These processes are described by a set of variables, each corresponding a time series. Each variable plays a different role in the process and some mutual influences can be discovered. A methodology to study the different variables and their roles...

In this work, last results of the research project "Development of an Intelligent Data Analysis System for Knowledge Management in Environmental Data Bases (DB)" are presented. The project is focussed on the design and development of a prototype for Knowledge Discovery (KD) and intelligent data analysis, and specially oriented to environmental DB....

In this paper, a tool for Knowledge Discovery and Data Mining in environmental databases is presented. In the long term, the main goal of this research is to design and develop a tool, named GESCONDA, for intelligent data analysis and management of implicit knowledge from databases; it will provide support to Knowledge Discovery and Data Mining tas...