A multi-objective optimization approach for the synthesis of granular computing-based classification systems in the graph domain

The synthesis of a pattern recognition system usually aims at the optimization of a given performance index. However, in many real-world scenarios, there exist other desired facets to take into account. In this regard, multi-objective optimization acts as the main tool for the optimization of different (and possibly conflicting) objective functions in order to seek for potential trade-offs among them. In this paper, we propose a three-objective optimization problem for the synthesis of a granular computing-based pattern recognition system in the graph domain. The core pattern recognition engine searches for suitable information granules (i.e., recurrent and/or meaningful subgraphs from the training data) on the top of which the graph embedding procedure towards the Euclidean space is performed. In the latter, any classification system can be employed. The optimization problem aims at jointly optimizing the performance of the classifier, the number of information granules and the structural complexity of the classification model. Furthermore, we address the problem of selecting a suitable number of solutions from the resulting Pareto Fronts in order to compose an ensemble of classifiers to be tested on previously unseen data. To perform such selection, we employed a multi-criteria decision making routine by analyzing different case studies that differ on how much each objective function weights in the ranking process. Results on five open-access datasets of fully labeled graphs show that exploiting the ensemble is effective (especially when the structural complexity of the model plays a minor role in the decision making process) if compared against the baseline solution that solely aims at maximizing the performances

Explainable clinical decision support system: opening black-box meta-learner algorithm expert's based

Mathematical optimization methods are the basic mathematical tools of all artificial intelligence theory. In the field of machine learning and deep learning the examples with which algorithms learn (training data) are used by sophisticated cost functions which can have solutions in closed form or through approximations. The interpretability of the models used and the relative transparency, opposed to the opacity of the black-boxes, is related to how the algorithm learns and this occurs through the optimization and minimization of the errors that the machine makes in the learning process. In particular in the present work is introduced a new method for the determination of the weights in an ensemble model, supervised and unsupervised, based on the well known Analytic Hierarchy Process method (AHP). This method is based on the concept that behind the choice of different and possible algorithms to be used in a machine learning problem, there is an expert who controls the decisionmaking process. The expert assigns a complexity score to each algorithm (based on the concept of complexity-interpretability trade-off) through which the weight with which each model contributes to the training and prediction phase is determined. In addition, different methods are presented to evaluate the performance of these algorithms and explain how each feature in the model contributes to the prediction of the outputs. The interpretability techniques used in machine learning are also combined with the method introduced based on AHP in the context of clinical decision support systems in order to make the algorithms (black-box) and the results interpretable and explainable, so that clinical-decision-makers can take controlled decisions together with the concept of "right to explanation" introduced by the legislator, because the decision-makers have a civil and legal responsibility of their choices in the clinical field based on systems that make use of artificial intelligence. No less, the central point is the interaction between the expert who controls the algorithm construction process and the domain expert, in this case the clinical one. Three applications on real data are implemented with the methods known in the literature and with those proposed in this work: one application concerns cervical cancer, another the problem related to diabetes and the last one focuses on a specific pathology developed by HIV-infected individuals. All applications are supported by plots, tables and explanations of the results, implemented through Python libraries. The main case study of this thesis regarding HIV-infected individuals concerns an unsupervised ensemble-type problem, in which a series of clustering algorithms are used on a set of features and which in turn produce an output used again as a set of meta-features to provide a set of labels for each given cluster. The meta-features and labels obtained by choosing the best algorithm are used to train a Logistic regression meta-learner, which in turn is used through some explainability methods to provide the value of the contribution that each algorithm has had in the training phase. The use of Logistic regression as a meta-learner classifier is motivated by the fact that it provides appreciable results and also because of the easy explainability of the estimated coefficients

Credit risk management of property investments through multi-criteria indicators

The economic crisis of 2008 has highlighted the ineffectiveness of the banks in their disbursement of mortgages which caused the spread of Non-Performing Loans (NPLs) with underlying real estate. With the methods stated by the Basel III agreements, aimed at improving the capital requirements of banks and determining an adequate regulatory capital, the banks without the skills required have difficulties in applying the rigid weighting coefficients structures. The aim of the work is to identify a synthetic risk index through the participatory process, in order to support the restructuring debt operations to benefit smaller banks and small and medium-sized enterprises (SME), by analyzing the real estate credit risk. The proposed synthetic risk index aims at overcoming the complexity of Basel III methodologies through the implementation of three different multi-criteria techniques. In particular, the integration of objective financial variables with subjective expert judgments into a participatory process is not that common in the reference literature and brings its benefits for reaching more approved and shared results in the debt restructuring operations procedure. Moreover, the main findings derived by the application to a real case study have demonstrated how important it is for the credit manager to have an adequate synthetic index that could lead to the avoidance of risky scenarios where several modalities to repair the credit debt occur

Dynamics under Uncertainty: Modeling Simulation and Complexity

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc

Matching Possible Mitigations to Cyber Threats: A Document-Driven Decision Support Systems Approach

Cyber systems are ubiquitous in all aspects of society. At the same time, breaches to cyber systems continue to be front-page news (Calfas, 2018; Equifax, 2017) and, despite more than a decade of heightened focus on cybersecurity, the threat continues to evolve and grow, costing globally up to $575 billion annually (Center for Strategic and International Studies, 2014; Gosler & Von Thaer, 2013; Microsoft, 2016; Verizon, 2017). To address possible impacts due to cyber threats, information system (IS) stakeholders must assess the risks they face. Following a risk assessment, the next step is to determine mitigations to counter the threats that pose unacceptably high risks. The literature contains a robust collection of studies on optimizing mitigation selections, but they universally assume that the starting list of appropriate mitigations for specific threats exists from which to down-select. In current practice, producing this starting list is largely a manual process and it is challenging because it requires detailed cybersecurity knowledge from highly decentralized sources, is often deeply technical in nature, and is primarily described in textual form, leading to dependence on human experts to interpret the knowledge for each specific context. At the same time cybersecurity experts remain in short supply relative to the demand, while the delta between supply and demand continues to grow (Center for Cyber Safety and Education, 2017; Kauflin, 2017; Libicki, Senty, & Pollak, 2014). Thus, an approach is needed to help cybersecurity experts (CSE) cut through the volume of available mitigations to select those which are potentially viable to offset specific threats. This dissertation explores the application of machine learning and text retrieval techniques to automate matching of relevant mitigations to cyber threats, where both are expressed as unstructured or semi-structured English language text. Using the Design Science Research Methodology (Hevner & March, 2004; Peffers, Tuunanen, Rothenberger, & Chatterjee, 2007), we consider a number of possible designs for the matcher, ultimately selecting a supervised machine learning approach that combines two techniques: support vector machine classification and latent semantic analysis. The selected approach demonstrates high recall for mitigation documents in the relevant class, bolstering confidence that potentially viable mitigations will not be overlooked. It also has a strong ability to discern documents in the non-relevant class, allowing approximately 97% of non-relevant mitigations to be excluded automatically, greatly reducing the CSE’s workload over purely manual matching. A false v positive rate of up to 3% prevents totally automated mitigation selection and requires the CSE to reject a few false positives. This research contributes to theory a method for automatically mapping mitigations to threats when both are expressed as English language text documents. This artifact represents a novel machine learning approach to threat-mitigation mapping. The research also contributes an instantiation of the artifact for demonstration and evaluation. From a practical perspective the artifact benefits all threat-informed cyber risk assessment approaches, whether formal or ad hoc, by aiding decision-making for cybersecurity experts whose job it is to mitigate the identified cyber threats. In addition, an automated approach makes mitigation selection more repeatable, facilitates knowledge reuse, extends the reach of cybersecurity experts, and is extensible to accommodate the continued evolution of both cyber threats and mitigations. Moreover, the selection of mitigations applicable to each threat can serve as inputs into multifactor analyses of alternatives, both automated and manual, thereby bridging the gap between cyber risk assessment and final mitigation selection

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Food industry site selection using geospatial technology approach

Food security has been an ongoing concern of governments and international organizations. One of the main issues in food security in Developing and Sanctioned Countries (DSCs) is establishment of food industries and related distributions in appropriate places. In this respect, geospatial technology offers the most up-to-date Land Cover (LC) information to improve site selection for assisting food security in the study area. Currently food security issues are not comprehensively addressed, especially in DSCs. In this research, ASTER L1B and LANDSAT satellite data were used to derive various LC biophysical parameters including build-up area, water body, forest, citrus, and rice fields in Qaemshahr city, Iran using different satellite-derived indices. A Product Level Fusion (PLF) approach was implemented to merge the outputs of the indices to prepare an improved LC map. The suitability of the proposed approach for LC mapping was evaluated in comparison with Support Vector Machine (SVM) and Artificial Neural Network (ANN) classification techniques. For implementing site selection, the outcomes of satellite-derived indices, as well as the city, village, road, railway, river, aqueduct, fault, casting, abattoir, cemetery, waste accumulation, wastewater treatment, educational centre, medical centre, military centre, asphalt factory, cement factory, and slope layers were obtained using Global Positioning System (GPS), on-screen digitizing, and image processing were used as input data. The Fuzzy Overlay and Weighted Linear Combination (WLC) methods were adopted to perform site selection process. The outcomes were then classified and analyzed based on the accessibility to main roads, cities and raw food materials. Finally, the existing industrial zones in the study area were evaluated for establishing food industries based on site selection results of this study. The results indicated higher performance of PLF method to provide up-to-date LC information with an overall accuracy and Kappa coefficient values of 95.95% and 0.95, respectively. The site selection result obtained using WLC method with the accuracy of 90% was superior, thus it was selected for further analyses. Based on the achieved results, the study has proven the applicability of current satellite data and geospatial technology for food industry site selection to resolve food security issues. In conclusion, site selection using geospatial technology provides a great potential for a reliable decision-making in food industry planning, as a significant issue in agro-based food security, especially in sanctioned countries

Advances in application of machine learning to life cycle assessment: a literature review

Purpose Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making. Methods In this study, previous research on ML for LCA were considered, and a literature review was undertaken. Results The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases. Conclusions The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature

Evaluation of optimal solutions in multicriteria models for intelligent decision support

La memoria se enmarca dentro de la optimización y su uso para la toma de decisiones. La secuencia lógica ha sido la modelación, implementación, resolución y validación que conducen a una decisión. Para esto, hemos utilizado herramientas del análisis multicrerio, optimización multiobjetivo y técnicas de inteligencia artificial. El trabajo se ha estructurado en dos partes (divididas en tres capítulos cada una) que se corresponden con la parte teórica y con la parte experimental. En la primera parte se analiza el contexto del campo de estudio con un análisis del marco histórico y posteriormente se dedica un capítulo a la optimización multicriterio en el se recogen modelos conocidos, junto con aportaciones originales de este trabajo. En el tercer capítulo, dedicado a la inteligencia artificial, se presentan los fundamentos del aprendizaje estadístico , las técnicas de aprendizaje automático y de aprendizaje profundo necesarias para las aportaciones en la segunda parte. La segunda parte contiene siete casos reales a los que se han aplicado las técnicas descritas. En el primer capítulo se estudian dos casos: el rendimiento académico de los estudiantes de la Universidad Industrial de Santander (Colombia) y un sistema objetivo para la asignación del premio MVP en la NBA. En el siguiente capítulo se utilizan técnicas de inteligencia artificial a la similitud musical (detección de plagios en Youtube), la predicción del precio de cierre de una empresa en el mercado bursátil de Nueva York y la clasificación automática de señales espaciales acústicas en entornos envolventes. En el último capítulo a la potencia de la inteligencia artificial se le incorporan técnicas de análisis multicriterio para detectar el fracaso escolar universitario de manera precoz (en la Universidad Industrial de Santander) y, para establecer un ranking de modelos de inteligencia artificial de se recurre a métodos multicriterio. Para acabar la memoria, a pesar de que cada capítulo contiene una conclusión parcial, en el capítulo 8 se recogen las principales conclusiones de toda la memoria y una bibliografía bastante exhaustiva de los temas tratados. Además, el trabajo concluye con tres apéndices que contienen los programas y herramientas, que a pesar de ser útiles para la comprensión de la memoria, se ha preferido poner por separado para que los capítulos resulten más fluidos