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作者： De Castro, Ana Karoline Araujo Pinheiro, Placido Rogerio Pinheiro, Mirian Caliope Dantas Graduate Program in Applied Informatics University of Fortaleza Av. Washington Soares 1321 - Bloco J CEP: 60811-905 Fortaleza Ceara Brazil

ISBN: (纸本)9783642047565

Dementias are syndromes described by a decline in memory and other neuropsychological changes especially occurring in the elderly and increasing exponentially in function of age. Due to this fact and the therapeutical limitations in the most advanced stage of the disease, diagnosis of Alzheimer's disease is extremely important and it can provide better life conditions to patients and their families. This work presents a hybrid model, combining Influence Diagrams and the Multicriteria Method, for aiding to discover, from a battery of tests, which are the most attractive questions, in relation to the stages of CDR (Clinical Dementia Rating) in decision making for the diagnosis of Alzheimer's disease. This disease is the most common dementia. Influence Diagram is implemented using GeNie tool. Next, the judgment matrixes are constructed to obtain cardinal value scales which are implemented through MACBETH Multicriteria Methodology. The modeling and evaluation processes were carried out through a battery of standardized assessments for the evaluation of cases with Alzheimer's disease developed by Consortium to Establish a Registry for Alzheimer's disease (CERAD). © 2009 Springer Berlin Heidelberg.

关键词： Diagnosis

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Drought propagation in an Andean basin in South America: a spatio-temporal analysis approach with monitoring indices

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Arabian Journal of Geosciences 2025年第3期18卷 1-21页

作者： Ocampo-Marulanda, Camilo Mera-Franco, Julián David Lizcano, Víctor Augusto Carvajal-Escobar, Yesid Canchala, Teresita Zorrilla-Millan, Daniel Felipe Caicedo-Bravo, Eduardo Water Resources Engineering and Soil Research Group (IREHISA) School of Natural Resources and Environmental Engineering Universidad del Valle Cali Colombia Graduate Program in Biometrics and Applied Statistics Department of Statistics and Informatics Federal Rural University of Pernambuco Recife Brazil School of Exact Sciences and Engineering Universidad Sergio Arboleda Bogotá Colombia Programa de Ingeniería Ambiental Facultad de Ingeniería Universidad Mariana Pasto Colombia School of Electrical and Electronic Engineering Universidad del Valle Cali Colombia

Drought is a climatic phenomenon that has increased in severity over time. This study analyzed drought spread in the Alto Patía basin (13,047 km2) in South America, applying the Standardized Precipitation Index (SPI), Vegetation Health Index (VHI), and Streamflow Drought Index (SDI) to assess meteorological, agricultural, and hydrological drought, respectively. Synchronous and asynchronous correlations were estimated, and Wavelet analysis was conducted to verify relationships among drought types. Results show that the lag between drought types varies based on the intensity, temporal continuity, and spatial extent of meteorological drought. A lag of zero months (1–2 months) was observed between meteorological and hydrological drought when moderate (severe) meteorological drought partially (fully) covers the basin. Agricultural drought, however, does not always correspond to meteorological drought during isolated rainfall events. As the first study in Colombia to systematically connect these drought types, this research addresses a gap in understanding how drought impacts progress across systems in regions with complex rainfall patterns. These findings offer critical insights into the interdependencies among drought types, supporting enhanced drought monitoring and early warning systems in similar climatic regions. By identifying specific lags between meteorological and hydrological droughts, this study provides practical guidance for land management, water conservation, and crop planning, offering a foundation for future research on drought resilience strategies.

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An Approach for the Neuropsychological Diagnosis of Alzheimer's Disease: A Hybrid Model in Decision Making

An Approach for the Neuropsychological Diagnosis of Alzheime...

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4th International Conference on Rough Sets and Knowledge Technology, RSKT 2009

ISBN: (纸本)3642029612

This work presents a hybrid model, combining Influence Diagrams and the Multicriteria Method, for aiding to discover, from a battery of tests, which are the most attractive questions, in relation to the stages of Clinical Dementia Rating in decision making for the diagnosis of Alzheimer's disease. This disease is the most common dementia. Because of this and due to limitations in treatment at late stages of the disease early diagnosis is fundamental because it improves quality of life for patients and their families. Influence Diagram is implemented using GeNie tool. Next, the judgment matrixes are constructed to obtain cardinal value scales which are implemented through MACBETH Multicriteria Methodology. The modeling and evaluation processes were carried out through a battery of standardized assessments for the evaluation of cases with Alzheimer's disease developed by Consortium to Establish a Registry for Alzheimer's disease (CERAD). © 2009 Springer Berlin Heidelberg.

关键词： Bayesian networks

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On self-organizing feature map (SOFM) formation by direct optimization through a genetic algorithm

On self-organizing feature map (SOFM) formation by direct op...

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8th International Conference on Hybrid Intelligent Systems, HIS 2008

作者： Maia, José Everardo B. Barreto, Guilherme A. Coelho, André L. V. State Univ. of Ceará Department of Statistics and Computing Fortaleza Ceará Brazil Federal Univ. of Ceará Department of Teleinformatics Engineering Fortaleza Ceará Brazil University of Fortaleza Graduate Program in Applied Informatics Fortaleza Ceará Brazil

ISBN: (纸本)9780769533261

This paper examines the formation of self-organizing feature maps (SOFM) by the direct optimization of a cost function through a genetic algorithm (GA). The resulting SOFM is expected to produce simultaneously a topologically correct mapping between input and output spaces and a low quantization error. The proposed approach adopts a cost (fitness) function which is a weighted combination of indices that measure these two aspects of the map quality, specifically, the quantization error and the Pearson correlation coefficient between the corresponding distances in input and output spaces. The resulting maps are compared with those generated by the Kohonen's self-organizing map (SOM) algorithm in terms of the Quantization Error (QE), the Weighted Topological Error (WTE) and the Pearson correlation coefficient (PCC) indices. The experiments show the proposed approach produces better values of the quality indices as well as is more robust to outliers. © 2008 IEEE.

关键词： Self organizing maps

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Tackling the Irregular Strip Packing problem by hybridizing genetic algorithm and bottom-left heuristic

Tackling the Irregular Strip Packing problem by hybridizing ...

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Congress on Evolutionary Computation

作者： Bonfim A. Junior Plácido R. Pinheiro Rommel D. Saraiva Graduate Program in Applied Informatics University of Fortaleza Fortaleza Brazil

ISBN: (纸本)9781479904532

This paper addresses the Irregular Strip Packing problem, a particular case of Cutting and Packing problems in which a set of polygons has to be packed within a rectangular object. To identify good quality solutions, we propose a hybrid methodology based on a meta-heuristic engine (i.e., Genetic Algorithm) and a well known placement heuristic called Bottom-Left. In addition, differently from several approaches presented in the literature, we investigate the application of the No-fit Polygon as a placement tool for obtaining local optima. The results are further improved by a shrinking algorithm that works within the meta-heuristic component. To assess the potentials of the proposed methodology, computational experiments performed on a set of difficult benchmark instances of the Irregular Strip Packing problem are discussed here for evaluation purposes.

关键词： Genetic algorithms Strips Layout Biological cells Shape Benchmark testing Search methods

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Task assignment to distributed teams based on a qualitative multi-criteria approach

Task assignment to distributed teams based on a qualitative ...

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Iberian Conference on Information Systems and Technologies (CISTI)

作者： Marum Simão Filho Plácido Rogério Pinheiro Adriano Bessa Albuquerque Graduate Program in Applied Informatics University of Fortaleza Fortaleza Brazil

ISBN: (纸本)9781509050475

The high growth of companies in recent years has caused the transformation of local markets into global ones. The software industry is not stranger to this transformation and has been looking for effective ways to work in a distributed scenario. A critical activity in distributed projects concerns the assignment of tasks to remote teams. This is a multicriteria decision-making problem and can be supported by Verbal Decision Analysis approaches. This paper describes an approach based on Verbal Decision Analysis methods to aid decision on factors that influence the assignment of tasks to distrubuted teams. The approach is based on the ORCLASS and ZAPROS III-i methods. The first results showed that factors such as "low turnover rate" and "team maturity" are among those considered more important, and the factors "personal trust", "low labor cost", "cultural similarities" and "time zone" were considered less relevant in DSD context.

关键词： Software Resource management Object oriented modeling Companies Decision analysis Project management

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Towards the applied hybrid model in decision making: Support the early diagnosis of type 2 diabetes

Towards the applied hybrid model in decision making: Support...

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3rd International Conference on Information Computing and Applications, ICICA 2012

作者： Menezes, Andrea Carvalho Pinheiro, Placido Rogerio Pinheiro, Mirian Caliope Dantas Cavalcante, Tarcísio Pequeno Graduate Program in Applied Informatics Av. Washington Soares 1321 - Bl J Sl 30 60.811-905 Fortaleza Brazil

ISBN: (纸本)9783642340611

A hybrid model, combining Bayesian network and a multicriteria method, is presented in order to assist with the decision making process about which questions would be more attractive to the definition of the diagnosis of Diabetes type 2. We have proposed the application of an expert system structured in probability rules and structured representations of knowledge in production rules and probabilities (Artificial Intelligence - AI). The importance of the early diagnosis associated with the appropriate treatment is to decrease the chance of developing the complications of diabetes, reducing the impact on our society. Diabetes is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. © 2012 Springer-Verlag.

关键词： Expert systems

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Genetic and ranking-based selection of components for multilabel classifier ensembles

Genetic and ranking-based selection of components for multil...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： Nathanael Costa André L. V. Coelho Graduate Program in Applied Informatics University of Fortaleza-UNIFOR Fortaleza Brazil

This paper deals with the problem of selecting (aka pruning) components for multilabel classifier ensembles. Three methods, one based on a genetic algorithm (GA) and the others based on ranking, are applied for this purpose. In particular, we investigate the adoption of different multilabel classification measures to play the role of the objective function of these methods, paying special attention to how the choice of one measure affects the accuracy of the pruned ensembles as gauged by other measures. A preliminary empirical analysis is conducted on five problems and the results achieved indicate that the classification performance of pruned ensembles as estimated by a given multilabel measure may significantly vary according to the measure adopted for selecting the components. Moreover, although the pruning methods used can yield significant gains in terms of ensemble size reduction, the ensemble models they generate usually lag behind when contrasted to those equipped with the whole sets of components.

关键词： Accuracy Genetic algorithms Loss measurement Training Particle measurements Atmospheric measurements Hybrid intelligent systems

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Tuning the Size and Stiffness of Inflatable Particles

arXiv

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arXiv 2025年

作者： Pashine, Nidhi Wang, Dong Baines, Robert Goyal, Medha Shattuck, Mark D. O’Hern, Corey S. Kramer-Bottiglio, Rebecca Department of Mechanical Engineering Yale University New HavenCT06520 United States Department of Physics BioInspired Institute Syracuse University SyracuseNY13244 United States Department of Physics Benjamin Levich Institute City College of New York New York CityNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Biomedical Informatics Yale University New HavenCT06520 United States

We describe size-varying cylindrical particles made from silicone elastomers that can serve as building blocks for robotic granular materials. The particle size variation, which is achieved by inflation, gives rise to changes in stiffness under compression. We design and fabricate inflatable particles that can become stiffer or softer during inflation, depending on key parameters of the particle geometry, such as the ratio of the fillet radius to the wall thickness, r/t. We also conduct numerical simulations of the inflatable particles and show that they only soften during inflation when localization of large strains occurs in the regime r/t → 0. This work introduces novel particle systems with tunable size and stiffness that can be implemented in numerous soft robotic applications. © 2025, CC BY.

关键词： Stiffness

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Stroke Lesion Detection Using Convolutional Neural Networks

Stroke Lesion Detection Using Convolutional Neural Networks

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Pereira, Danillo Roberto Filho, Pedro P. Rebouças De Rosa, Gustavo Henrique Papa, João Paulo De Albuquerque, Victor Hugo C. Department of Computing São Paulo State University Bauru SP Brazil Federal Institute of Education Science and Technology of Ceará CE Brazil Graduate Program in Applied Informatics University of Fortaleza Fortaleza CE Brazil

ISBN: (纸本)9781509060146

Stroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density's map. The results evidenced that CNN's are suitable to deal with stroke detection, obtaining promising results. © 2018 IEEE.

关键词： Convolution

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