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Brazilian Conference on Intelligent Systems (BRACIS)

作者： Diego S.C. Nascimento Anne M.P. Canuto André L.V. Coelho Information System Group. Federal Institute of Rio Grande do Norte (IFRN) Ipanguaçu RN Brazil Informatics and Applied Mathematics Department Federal University of Rio Grande do Norte (UFRN) Natal RN Brazil University of Fortaleza (UNIFOR) Graduate Program in Applied Informatics Fortaleza CE Brazil

Studies with ensemble systems have gained attention recently and, most of them, propose new methods for the design (generation) of different components in these systems. In parallel, new contributions of meta-learning have been presented as an efficient alternative to automatic recommendation of algorithms. In this paper, we apply meta-learning in the process of recommendation of important parameters of ensemble systems, which are: architecture and individual classifiers. The main goal is to provide an efficient way to design ensemble systems. In order to validate the proposed approach, an empirical investigation is conducted, recommending three possible types of ensemble architectures (Bagging, Boosting and Multi-Boosting) and five possible types of learning algorithms to compose the ensemble systems (individual classifiers or components). An initial analysis of the results confirms that meta-learning can be a promising tool to be used in the automatic choice of important parameters in ensemble systems.

关键词： Barium Boosting Bagging Support vector machines Error analysis Algorithm design and analysis Niobium

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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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International Conference on Hybrid Intelligent Systems (HIS)

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

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.

关键词： Gallium Neurons Arrays Genetic algorithms Indexes Optimization Genetics

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Dynamic Model for Social Coalition Formation Based on Expertise, Temporal Reputation and Time Commitment

Dynamic Model for Social Coalition Formation Based on Expert...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Fabrício Enembreck Cristina Verçosa Pérez Barrios de Souza Graduate Program in Applied Informatics (PPGIa) Pontifical Catholic University of Parana (PUCPR) Curitiba Paraná Brazil

Existing approaches to coalition formation are generally gross simplifications of real problems of resource allocation where experience, reputation, and time optimization should be considered, although they are not usually studied together. To overcome this issue, this study proposes a dynamic and distributed social coalition formation model, that reproduces real-world environments where interactions are ruled by an underlying network that adapts itself based on the best updated reputation of local neighbors, in order to bring together individuals better suited for efficient cooperation. In this environment, agents possessing different levels of expertise must be organized to provide the most advantageous partnerships for the purpose of solving tasks, and an execution order of task's subtasks is defined to favor the use and release of agents' resources. To achieve this objective, we based our proposal on a coalitional skill game (CSG) approach, which organizes the use of resources by time commitment, and calculates and exploits the temporal reputation of heterogeneous agents to improve the utility of coalitions. Our experiments with different initial social networks allowed us to evaluate the effectiveness of this proposal and provided elements to exploit the advantages of an optimized social structure in a connected world.

关键词： Social network services Proposals Collaborative work Games Mathematical model Resource management Network topology

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Dimensioning Microwave Wireless Networks

Dimensioning Microwave Wireless Networks

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IEEE International Conference on Communications

作者： Alvinice Kodjo Brigitte Jaumard Napoleao Nepomuceno Mejdi Kaddour David Coudert Inria France & Univ. Nice Sophia Antipolis Department of Computer Science and Software Engineering Concordia University Montreal Graduate Program in Applied Informatics University of Fortaleza Department of Computer Science University of Oran

ISBN: (纸本)9781467364300

We aim at dimensioning fixed broadband microwave wireless networks under unreliable channel conditions. As the transport capacity of microwave links is prone to variations due to, e.g., weather conditions, such a dimensioning requires special attention. It can be formulated as the determination of the minimum cost bandwidth assignment of the links in the network for which traffic requirements can be met with high probability, while taking into account that transport link capacities vary depending on channel conditions. The proposed optimization model represents a major step forward since we consider dynamic routing. Experimental results show that the resulting solutions can save up to 45% of the bandwidth cost compared to the case where a bandwidth over-provisioning policy is uniformly applied to all links in the network planning. Comparisons with previous work also show that we can solve much larger instances in significantly shorter computing times, with a comparable level of reliability.

关键词： Bandwidth Modulation Pricing Reliability Microwave communication Routing

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MILPdM: A Predictive Maintenance Architecture for the Military Domain 22

MILPdM: A Predictive Maintenance Architecture for the Milita...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Dalzochio, Jovani Kunst, Rafael Barbosa, Jorge Pignaton De Freitas, Edison Binotto, Alecio University of Vale Do Rio Dos Sinos Graduate Program in Applied Computing São Leopoldo Brazil Institute of Informatics Federal University of Rio Grande Do sui Porto Alegre Brazil Ibm Consulting Asset and Edge Optimization Munich Germany

ISBN: (纸本)9798350345346

Equipment monitoring for failure prediction is receiving attention from different sectors of society, such as industry, healthcare, and defense. In the defense domain, assets like military vehicles generate data that one can use to identify behavior changes and anticipate possible real-time failures, avoiding unnecessary maintenance interventions. Failure anticipation is crucial, as assets operated in the military domain perform critical tasks in which unexpected equipment failures result in high material and human costs. Approaches found in the literature typically deal with failure generation, aiming at analyzing the equipment's behavior. This paper proposes a broader approach called MILPdM. This proposal is a failure prediction architecture covering the whole failure prediction and predictive maintenance procedures. We evaluate MILPdM architecture by analyzing an engine-failure scenario where we train models to predict time series by collecting vibration data that describes the degradation of the vehicle's health. Considering the implementation of an LSTM-based neural network and Random Forest, the acquired results lead to a root mean square error of 0.15015 in the best case, which allows to predict the failure status two minutes in advance with only 3 hours of data history. This result shows that MILPdM is capable of anticipating failures with high assertiveness. © 2023 IEEE.

关键词： IoBT machine learning neural networks predictive maintenance random forest

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Towards an applied Multicriteria Model to the Diagnosis of Alzheimer’s Disease:A Neuroimaging Study Case

Towards an Applied Multicriteria Model to the Diagnosis of A...

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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Isabelle Tamanini Ana Karoline de Castro Plcido Rogrio Pinheiro Mirian Calíope Dantas Pinheiro University of Fortaleza （UNIFOR）-Graduate Program in Applied Computer Sciences Av.Washington Soares 1321-Bl J Sl 30-60.811-905-Fortaleza-Brazil

The Alzheimer’s disease has become the most fre-quent cause of dementia in the last few years,and it is responsible,alone or in association with other diseases,for 50% of the cases in western *** main focus of this work is to develop a multicriteria model for aiding in decision making on the diagnosis of Alzheimer’s disease by using the Aranau Tool,structured on the Verbal Decision *** this work,the modeling and evaluation processes were conducted with the aid of a medical expert,bibliographic sources and *** questionnaires taken into account were based mainly on patients’ neuroimaging tests,and we analyzed wheter or not there were problems in the patients’ brain that could be relevant to the diagnosis of Alzheimer’s disease.

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A task-oriented framework for generative AI in design

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Journal of Creativity 2024年第2期34卷

作者： Furtado, Lara Sucupira Soares, Jorge Barbosa Furtado, Vasco Department of Transportation Engineering Federal University of Ceará Brazil Applied Informatics Graduate Program University of Fortaleza Unifor Brazil ETICE State of Ceará Information Technology Company Brazil

The intersection of Artificial Intelligence and Design disciplines such as Architecture, Urban Planning, Engineering and Product Design has been a longstanding pursuit, with Generative AI (GAI) ushering in a new era of possibilities. The research presented here explores how GAI can enhance creativity and assist Design practitioners with tasks to create products such as, but not limited to, renderings, concepts, construction techniques, materials, data analytics or maps. We apply a framework of combinational, exploratory and transformational creativity to organize how recent advancements in GAI can support each creative category. We propose a conceptual framework of GAI towards transformational creativity, and identify real-world examples to demonstrate GAI's impact, such as transforming sketches into detailed renders, facilitating real-time 3D model generation, predicting trends through analytics and creating images or reports via text prompts. Our work envisions a future where GAI becomes a real-time collaborator to complete certain automated tasks while liberating Designers to focus on transformational innovation. © 2024

关键词： Creative computing Generative artificial intelligence Product Transformational Creativity

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An Expert System applied to the Diagnosis of Psychological Disorders

An Expert System Applied to the Diagnosis of Psychological D...

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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Luciano Comin Nunes Plácido Rogério Pinheiro Tarcísio Cavalcante Pequeno Graduate Program in Applied Computer Sciences University of Fortaleza Av.Washington Soares 1321-Bloco Jsala 30CEP:60811-905FortalezaCearáBrazil

Psychological disorders have kept away and incapacitated professionals in different sectors of *** most serious problems may be associated with various types of pathologies,however,it appears,more often,as psychotic disorders,mood disorders,anxiety disorders,antisocial personality,multiple personality and addiction,causing a micro level damage to the individual and his/her family and in a macro level to the production system and the country *** lack of early diagnosis has provided reactive measures,and sometimes very late,when the professional is already showing psychological signs of incapacity to *** study aims to help the early diagnosis of psychological disorders with a hybrid proposal of an expert system that is integrated to structured methodologies in decision support (Multi-Criteria Decision Analysis-MCDA) and knowledge structured representations into production rules and probabilities (Artificial Intelligence-AI).

关键词： Early diagnosis,Expert SINTA,expert systems,psychological disorders,health,multicriteria

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Realistic river image synthesis using deep generative adversarial networks

arXiv

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arXiv 2020年

作者： Gautam, Akshat Sit, Muhammed Demir, Ibrahim School of Engineering and Applied Sciences Columbia University Interdisciplinary Graduate Program in Informatics University of Iowa Department of Civil and Environmental Engineering University of Iowa

In this paper, we demonstrated a practical application of realistic river image generation using deep learning. Specifically, we explored a generative adversarial network (GAN) model capable of generating high-resolution and realistic river images that can be used to support modeling and analysis in surface water estimation, river meandering, wetland loss, and other hydrological research studies. First, we have created an extensive repository of overhead river images to be used in training. Second, we incorporated the Progressive Growing GAN (PGGAN), a network architecture that iteratively trains smaller-resolution GANs to gradually build up to a very high resolution to generate high quality (i.e., 1024x1024) synthetic river imagery. With simpler GAN architectures, difficulties arose in terms of exponential increase of training time and vanishing/exploding gradient issues, which the PGGAN implementation seemed to significantly reduce. The results presented in this study show great promise in generating high-quality images and capturing the details of river structure and flow to support hydrological research, which often requires extensive imagery for model performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Rivers

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Inter-comparison of multiple Global Climate Model (GCM) data based on spatial pattern of rainfall over Indonesia 5

Inter-comparison of multiple Global Climate Model (GCM) data...

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5th International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring 2018, LISAT-FSEM 2018

作者： Aflahah, Elania Latifah, Arnida Lailatul Hidayat, Rahmat Hidayati, Rini Ihwan, Andi Applied Climatology Study Program Graduate School Bogor Agricultural University Bogor16680 Indonesia Laboratorium of High-Performance Computing Research Center for Informatics Indonesian Institute of Sciences West Java Bogor16916 Indonesia Department of Geophysics and Meteorology Bogor Agricultural University Bogor16680 Indonesia

The Coupled Model Inter-comparison Project Phase 5 (CMIP5) is the output of many coupled atmosphere-ocean of global climate models (GCMs) and widely used for climate research, especially for driving regional climate model. There are more than 40 CMIP5 GCMs data available, but no single model can be considered as the best for every region. The use of CMIP5 GCMs data for rainfall projection in Indonesia is important to improve the accuracy of the monthly and seasonal rainfall forecast. Then, this study evaluates the capability of the CMIP5 GCMs data for Indonesia region by quantitatively comparing the spatial pattern of the precipitation mean and standard deviation of the CMIP5 data against GPCP, GPCC, and CRU data in the period 1980-2005. Furthermore, the composite analysis is conducted to observe the model performance in reproducing the precipitation characteristic over some areas in Indonesia. In conclusion, the models NorESM1-M, NorESM1-ME, GFDL-ESM2M, CSIRO-MK3-6-0 perform the rainfall mean better than others, while the standard deviation of the rainfall show that the models NorESM1-M, BNU-ESM, CMCC-CMS are superior in which NorESM1-M gives the best performance. The annual precipitation pattern of the model NorESM1-M over various areas in Indonesia is also highly correlated with the observations. Thus, the most suitable model for Indonesia region is NorESM1-M. © Published under licence by IOP Publishing Ltd.

关键词： Climate models

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