Analysing similarity assessment in feature-vector case representations

Case-Based Reasoning (CBR) is a good technique to solve new problems based in previous experience. Main assumption in CBR relies in the hypothesis that similar problems should have similar solutions. CBR systems retrieve the most similar cases or experiences among those stored in the Case Base. Then, previous solutions given to these most similar past-solved cases can be adapted to fit new solutions for new cases or problems in a particular domain, instead of derive them from scratch. Thus, similarity measures are key elements in obtaining reliable similar cases, which will be used to derive solutions for new cases. This paper describes a comparative analysis of several commonly used similarity measures, including a measure previously developed by the authors, and a study on its performance in the CBR retrieval step for feature-vector case representations. The testing has been done using six-teen data sets from the UCI Machine Learning Database Repository, plus two complex environmental databases.Postprint (published version

Classificació automàtica amb KLASS de les dades de procés d'una EDAR

In this study an automatic cluster is done on data froma a Waster Water Treatment Plant to obtain specific knowledge of an ill-structured domain like biological wastewater treatment process. The whole process requires the supervision of the expert of the process. This clustering is done using KLASS, which allows to deal simultaneously with numerical and symbolic variables in the description of objects. From this automatic classification, we obtain a set of clusters that can be labelled as typical operational states. All the knowledge and information acquired will be constitute the initial library of a case base reasoning system that together with expert system constitute a decision support system for this WWTP.Postprint (published version

Participatory detection of language barriers towards multilingual sustainability(ies) in Africa

CITATION: Litre, G. et al. 2022. Participatory detection of language barriers towards multilingual sustainability(ies) in Africa. Sustainability, 14(13):8133, doi:10.3390/su14138133.The original publication is available at https://www.mdpi.comAfter decades of political, economic, and scientific efforts, humanity has not gotten any closer to global sustainability. With less than a decade to reach the UN Sustainable Development Goals (SDGs) deadline of the 2030 Agenda, we show that global development agendas may be getting lost in translation, from their initial formulation to their final implementation. Sustainability science does not “speak” most of the 2000 languages from Africa, where the lack of indigenous terminology hinders global efforts such as the COVID-19 pandemic fight. Sociolinguistics, social psychology, cognitive ergonomics, geography, environmental sciences, and artificial intelligence (AI) are all relevant disciplinary fields to uncover the “foreign language effect” that hinders the implementation of the SDGs in Africa. We make the case for detecting and addressing language barriers towards multilingual sustainability in Africa by (1) exploring the ”foreign language effect” among African decision-makers and recognising their alternative social representations about sustainability; and (2) detecting Western language stereotypes about sustainability. We propose rethinking SDG-related scientific notions through participatory natural language processing (NLP) and the study of African social representations of sustainability, thus enabling a more inclusive and efficient approach to “sustainability(ies)”.Publisher's versio

A Detailed Analysis of Air Pollution Monitoring System and Prediction Using Machine Learning Methods

Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in the time and space of pollutants and particulates.Due to the presence of governing factors, varying land uses, and many sources for the elaboration of pollution, the forecast and analysis of air pollution is a difficult procedure. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens’ health and the environment. In this paper, various air pollution monitoring and prediction models with respect to hardware interfacing modules and various classification approaches. The Air Quality Index (AQI) parameter is used in this paper to monitor the quality of air pollution in various regions of the world. The drawbacks of the conventional air pollution monitoring and prediction models have been stated in this paper with the methodologies used for air pollution prediction

Caracterización e interpretación automática de descripciones conceptuales en dominios poco estructurados usando variables numéricas

La investigación que se presenta en este proyecto, tiene como objetivo fundamental: establecer una metodología formal para la generación automática de descripciones conceptuales de clases construidas en dominios de naturaleza continua, reales y complejos, llamados Dominios poco Estructurados. Si bien, la metodología tiene como punto de partida el estudio del boxplot múltiple, la formalización del procedimiento de interpretación visual pasa por determinar los valores de cada variable donde se producen cambios en la distribución y construir la tabla de frecuencias condicionadas a dichos intervalos. Ello da lugar a una representación difusa de los grados de pertenencia de los valores de la variable a las distintas clases; lo que constituye un cómodo soporte para caracterizar e interpretar automáticamente las descripciones conceptuales de las clases. La metodología aporta un sistema de caracterización de clases, desde un punto de vista semántico, en comparación con otros métodos de cluster, cuando se aplica sobre datos provenientes de un Dominio poco Estructurado; además, de una nueva aproximación para discretizar el espacio de atributos cuantitativos en términos de intervalos de longitud variable como base de la metodología, y contribuciones a la validación de una clasificación, en cuanto a su representación y calidad, en el sentido de que una clasificación es válida si probamos que las clases obtenidas tienen sentido o utilidad y a la generación automática de clases resultantes como base del proceso predicción y/o diagnóstico. La metodología representa una nueva forma para extraer conocimiento útil y comprensible por el usuario usando una combinación de herramientas estadísticas (boxplot múltiple, análisis de datos), inteligencia artificial (aprendizaje automático, sistemas basados en el conocimiento) y lógica difusa (modelos y razonamiento difusos). Como caso de estudio se ha aplicado a una base de datos de una depuradora de aguas residuales que se describe en el capítulo 4 usando atributos cuantitativos, los resultados que se han obtenidos son prometedores, constituyendo un primer paso para establecer una metodología formal en la obtención automática de interpretaciones conceptuales de clases, sobre la base de atributos cuantitativos para describir los objetos (días en este caso de estudio). Finalmente, nuestro trabajo cumple todas las fases del proceso KDD (Knowledge Discovery in Databases) descritas por Fayyad et al., enfatizando la fase de generación automática de interpretación, en nuestro caso, de las clases resultantes de una partición de referencia.Postprint (published version

Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support

<p>Abstract</p> <p>Background</p> <p>Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved.</p> <p>Method</p> <p>This paper introduces a new hybrid methodology <it>Expert-based Cooperative Analysis </it>(EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by <it>EbCA-Data Envelopment Analysis (EbCA-DEA)</it>, and 2) Case-mix of schizophrenia based on functional dependency using <it>Clustering Based on Rules (ClBR)</it>. In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases.</p> <p>Results</p> <p>EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here <it>Implicit Knowledg </it>-IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases.</p> <p>Discussion</p> <p>This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK.. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research.</p

Improving Self-Care of Patients with Chronic Disease using Online Personal Health Record

Background Effective management of chronic diseases such as prostate cancer is important. Research suggests a tendency to use self-care treatment options such as over-the-counter (OTC) complementary medications among prostate cancer patients. The current trend in patient-driven recording of health data in an online Personal Health Record (PHR) presents an opportunity to develop new data-driven approaches for improving prostate cancer patient care. However, the ability of current online solutions to share patients’ data for better decision support is limited. An informatics approach may improve online sharing of self-care interventions among these patients. It can also provide better evidence to support decisions made during their self-managed care. Aims To identify requirements for an online system and describe a new case-based reasoning (CBR) method for improving self-care of advanced prostate cancer patients in an online PHR environment. Method A non-identifying online survey was conducted to understand self-care patterns among prostate cancer patients and to identify requirements for an online information system. The pilot study was carried out between August 2010 and December 2010. A case-base of 52 patients was developed. Results The data analysis showed self-care patterns among the prostate cancer patients. Selenium (55%) was the common complementary supplement used by the patients. Paracetamol (about 45%) was the commonly used OTC by the patients. Conclusion The results of this study specified requirements for an online case-based reasoning information system. The outcomes of this study are being incorporated in design of the proposed Artificial Intelligence (AI) driven patient journey browser system. A basic version of the proposed system is currently being considered for implementation

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Intelligent airborne monitoring of irregularly shaped man-made objects in the maritime ecosystem using statistical Machine Learning techniques

The marine economy has historically been highly diversified and prolific due to the fact that the Earth’s oceans comprise two-thirds of its total surface area. As technology advances, leading enterprises and ecological organisations are building and mobilising new devices supported by cutting-edge marine mechatronics solutions to explore and harness this challenging environment. Automated tracking of these types of industries and the marine life around them can help us figure out what’s causing the current changes in species numbers, predict what could happen in the future, and create the right policies to help reduce the environmental impact and make the planet more sustainable. The objective of this study is to create a new platform for the automated detection of irregularly shaped man-made marine objects (ISMMMOs) in large datasets derived from marine aerial survey imagery. In this context, a novel nonparametric methodology, which harbours several hybrid statistical Machine Learning (ML) methods, was developed to automatically segment ISMMMOs on the sea surface in large surveys. This methodology was validated on a wide range of marine domains, providing robust empirical proof of concept. This approach enables the detection of ISMMMOs automatically, without any prior training, with accuracy (ACC), Matthews correlation coefficient (MCC), negative predictive value (NPV), positive predictive value (PPV), specificity (Sp) and sensitivity (Se) over 0.95. The outlined methodology can be utilised for a variety of purposes, but it’s especially useful for researchers and policymakers who want to keep an eye on how the maritime industry is deploying and make sure the right policies are in place to meet regulatory and legal requirements to promote maritime tech innovation and shape what the future looks like for the marine ecosystem. For the first time in the literature, a method, the so-called ISMMMOD, has been developed to automate the detection of all types of ISMMMOs by statistical ML techniques that require no prior training, which will pioneer the monitoring of human footprint in the marine ecosystem