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Pesquisa : G17.485 [Categoria DeCS]
Referências encontradas : 116 [refinar]
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Id: biblio-1285723
Autor: Rodrigues, Jonas Almeida; Krois, Joachim; Schwendicke, Falk.
Título: Demystifying artificial intelligence and deep learning in dentistry
Fonte: Braz. oral res. (Online);35:e094, 2021. graf.
Idioma: en.
Projeto: UFRGS.
Resumo: Abstract Artificial intelligence (AI) is a general term used to describe the development of computer systems which can perform tasks that normally require human cognition. Machine learning (ML) is one subfield of AI, where computers learn rules from data, capturing its intrinsic statistical patterns and structures. Neural networks (NNs) have been increasingly employed for ML complex data. The application of multilayered NN is referred to as "deep learning", which has been recently investigated in dentistry. Convolutional neural networks (CNNs) are mainly used for processing large and complex imagery data, as they are able to extract image features like edges, corners, shapes, and macroscopic patterns using layers of filters. CNN algorithms allow to perform tasks like image classification, object detection and segmentation. The literature involving AI in dentistry has increased rapidly, so a methodological guidance for designing, conducting and reporting studies must be rigorously followed, including the improvement of datasets. The limited interaction between the dental field and the technical disciplines, however, remains a hurdle for applicable dental AI. Similarly, dental users must understand why and how AI applications work and decide to appraise their decisions critically. Generalizable and robust AI applications will eventually prove helpful for clinicians and patients alike.
Descritores: Inteligência Artificial
Aprendizado Profundo
-Redes Neurais de Computação
Odontologia
Aprendizado de Máquina
Limites: Humanos
Responsável: BR1.1 - BIREME


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Id: biblio-1055380
Autor: Atrey, Kushangi; Sharma, Yogesh; Bodhey, Narendra K; Singh, Bikesh Kumar.
Título: Breast Cancer Prediction Using Dominance-based Feature Filtering Approach: A Comparative Investigation in Machine Learning Archetype
Fonte: Braz. arch. biol. technol;62:e19180486, 2019. tab, graf.
Idioma: en.
Resumo: Abstract Breast cancer is the most commonly witnessed cancer amongst women around the world. Computer aided diagnosis (CAD) have been playing a significant role in early detection of breast tumors hence to curb the overall mortality rate. This work presents an enhanced empirical study of impact of dominance-based filtering approach on performances of various state-of-the-art classifiers. The feature dominance level is proportional to the difference in means of benign and malignant tumors. The experiments were done on original Wisconsin Breast Cancer Dataset (WBCD) with total nine features. It is found that the classifiers' performances for top 4 and top 5 dominant-based features are almost equivalent to performances for all nine features. Artificial neural network (ANN) is come forth as the best performing classifier among all with accuracies of 98.9% and 99.6% for top 4 and top 5 dominant features respectively. The error rate of ANN between all nine and top 4 &5 dominant features is less than 2% for four performance evaluation parameters namely sensitivity, specificity, accuracy and AUC. Thus, it can be stated that the dominance-based filtering approach is appropriate for selecting a sound set of features from the feature pool, consequently, helps to reduce computation time with no deterioration in classifier's performance.
Descritores: Neoplasias da Mama/diagnóstico por imagem
Diagnóstico por Computador/instrumentação
Aprendizado de Máquina
-Redes Neurais de Computação
Responsável: BR1.1 - BIREME


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Id: biblio-1133761
Autor: Nistal-Nuño, Beatriz.
Título: A neural network for prediction of risk of nosocomial infection at intensive care units: a didactic preliminary model / Uma rede neural de previsão de riscos de infecção nosocomial em unidades de cuidado intensivo: um modelo preliminar didático
Fonte: Einstein (Säo Paulo);18:eAO5480, 2020. tab, graf.
Idioma: en.
Resumo: ABSTRACT Objective: To propose a preliminary artificial intelligence model, based on artificial neural networks, for predicting the risk of nosocomial infection at intensive care units. Methods: An artificial neural network is designed that employs supervised learning. The generation of the datasets was based on data derived from the Japanese Nosocomial Infection Surveillance system. It is studied how the Java Neural Network Simulator learns to categorize these patients to predict their risk of nosocomial infection. The simulations are performed with several backpropagation learning algorithms and with several groups of parameters, comparing their results through the sum of the squared errors and mean errors per pattern. Results: The backpropagation with momentum algorithm showed better performance than the backpropagation algorithm. The performance improved with the xor. README file parameter values compared to the default parameters. There were no failures in the categorization of the patients into their risk of nosocomial infection. Conclusion: While this model is still based on a synthetic dataset, the excellent performance observed with a small number of patterns suggests that using higher numbers of variables and network layers to analyze larger volumes of data can create powerful artificial neural networks, potentially capable of precisely anticipating nosocomial infection at intensive care units. Using a real database during the simulations has the potential to realize the predictive ability of this model.

RESUMO Objetivo: Propor um modelo preliminar de inteligência artificial, baseado em redes neurais artificiais, para previsão do risco de infecção hospitalar em unidades de cuidado intensivo. Métodos: Foi usada uma rede neural artificial, que utiliza aprendizagem supervisionada. A geração dos conjuntos de dados baseia-se em dados derivados do sistema Japanese Nosocomial Infection Surveillance . Estudamos como o Java Neural Network Simulator aprende a categorizar esses pacientes para prever o respectivo risco de infecção hospitalar. As simulações são realizadas com diferentes algoritmos de aprendizagem por retropropagação e diversos grupos de parâmetros, comparando-se os resultados com base na soma dos erros quadráticos e erros médios por padrão. Resultados: O algoritmo de retropropagação com momentum mostrou desempenho superior ao do algoritmo de retropropagação. O desempenho foi melhor com os valores de parâmetros do arquivo xor. README em comparação aos parâmetros default . Não houve falhas na categorização de pacientes quanto ao respectivo risco de infecção hospitalar. Conclusão: Embora esse modelo se baseie em um conjunto de dados sintéticos, o excelente desempenho observado com um pequeno número de padrões sugere que o uso de números maiores de variáveis e camadas de rede para analisar volumes maiores de dados pode criar redes neurais artificiais poderosas, possivelmente capazes de prever com precisão o risco de infecção hospitalar em unidades de cuidado intensivo. O uso de um banco de dados real durante as simulações torna possível a realização da capacidade preditiva desse modelo.
Descritores: Inteligência Artificial
Infecção Hospitalar
Redes Neurais de Computação
Medição de Risco/métodos
-Algoritmos
APACHE
Unidades de Terapia Intensiva
Limites: Humanos
Responsável: BR1.1 - BIREME


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Id: biblio-1285550
Autor: Rana, Poonam; Gupta, Pradeep Kumar; Sharma, Vineet.
Título: A Novel Deep Learning-based Whale Optimization Algorithm for Prediction of Breast Cancer
Fonte: Braz. arch. biol. technol;64:e21200221, 2021. tab, graf.
Idioma: en.
Resumo: HIGHLIGHTS Novel whale optimization algorithm is proposed for prediction of breast cancer. Deep learning-based WOA adjusts the CNN structure as per maximum detection accuracy. Proposed method achieves 92.4% accuracy in comparison to 90.3%. Validity of method is evaluated with magnifying factors like 40x, 100 x, 200x, 400x.

Abstract Breast cancer is one of the most common cancers among women that cause billions of deaths worldwide. Identification of breast cancer often depends on the examination of digital biomedical photography such as the histopathological images of various health professionals, and clinicians. Analyzing histopathological images is a unique task and always requires special knowledge to conclude investigating these types of images. In this paper, a novel efficient technique has been proposed for the detection and prediction of breast cancer at its early stage. Initially, the dataset of images is used to carry out the pre-processing phase, which helps to transform a human pictorial image into a computer photographic image and adjust the parameters appropriate to the Convolutional neural network (CNN) classifier. Afterward, all the transformed images are assigned to the CNN classifier for the training process. CNN classifies incoming breast cancer clinical images as malignant and benign without prior information about the occurrence of cancer. For parameter optimization of CNN, a deep learning-based whale optimization algorithm (WOA) has been proposed which proficiently and automatically adjusts the CNN network structure by maximizing the detection accuracy. We have also compared the obtained accuracy of the proposed algorithm with a standard CNN and other existing classifiers and it is found that the proposed algorithm supersedes the other existing algorithms.
Descritores: Neoplasias da Mama/prevenção & controle
Detecção Precoce de Câncer
-Baleias
Redes Neurais de Computação
Aprendizado Profundo
Limites: Humanos
Responsável: BR1.1 - BIREME


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Id: biblio-1285563
Autor: Maciel, Joylan Nunes; Wentz, Victor Hugo; Ledesma, Jorge Javier Gimenez; Ando Junior, Oswaldo Hideo.
Título: Analysis of Artificial Neural Networks for Forecasting Photovoltaic Energy Generation with Solar Irradiance
Fonte: Braz. arch. biol. technol;64(spe):e21210131, 2021. tab, graf.
Idioma: en.
Projeto: Triple Agenda Institutional Program of the Federal University of Latin American Integration; . Araucária Foundation of Support to the Scientific and Technological Development of the State of Paraná; . Brazilian National Council for Scientific and Technological Development.
Resumo: Abstract The growth in the use of solar energy has encouraged the development of techniques for short-term prediction of solar photovoltaic energy generation (PSPEG). Machine learning with Artificial Neural Networks (ANNs) is the most widely used technique to solve this problem. However, comparative studies of these networks with distinct structural configurations, input parameters and prediction horizon, have not been observed in the literature. In this context, the aim of this study is to evaluate the prediction accuracy of the Global Horizontal Irradiance (GHI), which is often used in the PSPEG, generated by ANN models with different construction structures, sets of input meteorological variables and in three short-term prediction horizons, considering a unique database. The analyses were performed with controlled environment and experimental configuration. The results suggest that ANNs using the input GHI variable provide better accuracy (approximately 10%), and their absence increases error variability. No significant difference (p>0.05) was identified in the prediction error models trained with distinct meteorological input data sets. The prediction errors were similar for the same ANN model in the different prediction horizons, and ANNs with 30 and 60 neurons with one hidden layer demonstrated similar or higher accuracy than those with two hidden layers.
Descritores: Energia Solar
-Redes Neurais de Computação
Radiação Solar
Energia Fotovoltaica
Responsável: BR1.1 - BIREME


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Id: biblio-1278436
Autor: Martins, Thais Serra; Sousa, Thiago Sousa e; Sales, Victor Hugo Gomes; Bandeira, Maria da Gloria Almeida; Higuita, Diana Maria Cano; Vélez, Harvey Alexander Villa.
Título: Comparison between Thin-Layer Models and Non-Traditional Methods in the Modelling of Drying Kinetics of Crustacean Wastes
Fonte: Braz. arch. biol. technol;64:e21210130, 2021. tab, graf.
Idioma: en.
Projeto: Maranhão Foundation for the Protection of Research and Scientific and Technological Development.
Resumo: Abstract This research aims to compare the classical thin-layer models, stepwise fit regression method (SRG) and artificial neural networks (ANN) in the modelling of drying kinetics of shrimp shell and crab exoskeleton. Thus, drying curves were obtained using a convective dryer (3.0 m/s) at temperatures of 30.45 and 60oC. The results showed a decreasing tendency for the drying time as the temperature increased for both materials. Drying curves modelling of both materials showed fitted results with R 2 adj >0.998 and MRE<13.128% for some thin-layer models. On the other hand, by SRG a simple model could be obtained as a function of time and temperature, with the greatest accuracy being found in the modelling of experimental data of crab exoskeleton, with MRE<10.149%. Finally, the ANNs were employed successfully in the modelling of drying kinetics, showing high prediction quality with the trained recurrent ANN models.
Descritores: Crustáceos
Exoesqueleto
-Cinética
Redes Neurais de Computação
Modelos Anatômicos
Responsável: BR1.1 - BIREME


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Id: biblio-1278463
Autor: Torres, Norah Nadia Sánchez; Diaz, Valentin Nicolas Silvera; Ando Junior, Oswaldo Hideo; Ledesma, Jorge Javier Gimenez.
Título: Analysis of the technical feasibility of using artificial intelligence for smoothing active power in a photovoltaic system connected to the power system
Fonte: Braz. arch. biol. technol;64(spe):e21210196, 2021. tab, graf.
Idioma: en.
Projeto: Institutional Program Triple Agenda; . Federal University of Integration of Latin America; . CAPES; . CNPq; . ITAI; . P&D COPEL.
Resumo: Abstract Recent technological advances and increased participation of energy systems based on photovoltaic solar energy place this renewable energy source in a prominent position in the current scenario. With the increase in the share of solar photovoltaic systems, the impact of power fluctuations in these sources has worsened, which can affect the quality of electrical energy and the reliability of the electrical power system. Therefore, with the use of energy storage together with control algorithms based on artificial intelligence, it is possible to control and perform power smoothing. In this context, the study presents a technical feasibility study on the use of artificial neural network (ANN) to perform the power smoothing of the photovoltaic system connected to the network. Being studied the performance of a real photovoltaic system operating in conjunction with an ideal energy storage for comparative analysis of the performance of the artificial neural network when the numbers of neurons and layers are modified for different real operating conditions considered as temperature variation, humidity, irradiation, pressure and wind speed, which are considered to be ANN input data. The results obtained point to the feasibility of using ANN, with acceptable precision, for power smoothing. According to the analyzes carried out, it is clear that ANN's with few neurons, the smoothing profile tends to be more accurate when compared to larger amounts of neurons. In the current state of the study, it was not possible to determine a relationship between the variations in the number of neurons with the most accurate results, it is important to note that the development of the curve pointed by the neural network can be influenced by the database. It should be noted that, when ANN exceeds or does not reach the optimal smoothing curve, the storage system compensates for the lack or excess of power, and there is a need for other mechanisms to optimize power smoothing.
Descritores: Energia Solar
Redes Neurais de Computação
Fontes Geradoras de Energia
Sistemas Microeletromecânicos/métodos
-Inteligência Artificial
Estudos de Viabilidade
Responsável: BR1.1 - BIREME


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Id: biblio-892913
Autor: Seckiner, Ilker; Seckiner, Serap; Sen, Haluk; Bayrak, Omer; Dogan, Kazım; Erturhan, Sakip.
Título: A neural network - based algorithm for predicting stone -free status after ESWL therapy
Fonte: Int. braz. j. urol;43(6):1110-1114, Nov.-Dec. 2017. tab, graf.
Idioma: en.
Resumo: ABSTRACT Objective: The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones. Materials and Methods: Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables. Regression analysis and the ANN method were applied to predict treatment success using the same series of data. Results: Subsequently, patients were divided into three groups by neural network software, in order to implement the ANN: training group (n=139), validation group (n=32), and the test group (n=32). ANN analysis demonstrated that the prediction accuracy of the stone-free rate was 99.25% in the training group, 85.48% in the validation group, and 88.70% in the test group. Conclusions: Successful results were obtained to predict the stone-free rate, with the help of the ANN model designed by using a series of data collected from real patients in whom ESWL was implemented to help in planning treatment for kidney stones.
Descritores: Algoritmos
Litotripsia
Cálculos Renais/terapia
Redes Neurais de Computação
-Valor Preditivo dos Testes
Análise de Regressão
Pessoa de Meia-Idade
Limites: Humanos
Masculino
Feminino
Lactente
Pré-Escolar
Criança
Adolescente
Adulto
Idoso
Adulto Jovem
Responsável: BR1.1 - BIREME


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Cerri, Giovanni Guido
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Id: biblio-952841
Autor: Madi, Marisa Riscalla; Cerri, Giovanni Guido.
Título: Organization of the cancer network in SUS: evolution of the care model
Fonte: Clinics;73(supl.1):e430s, 2018. tab, graf.
Idioma: en.
Resumo: In the current context of epidemiological transition, demographic changes, changes in consumption and lifestyle habits, and pressure on care costs and organized health systems for acute conditions, the Integrated Care Model by Shortell has become a conceptual reference in the search for new methods to manage chronic conditions by focusing on the health conditions of a given population that must be addressed by a set of institutions organized into networks. Within the last 15 years, cancer has gone from the third- to the second-leading cause of death in the State of São Paulo and has shown a gradual increase in the number of new cases; it has thus become a relevant issue for public health and health management. The model adopted by the State for the organization of the cancer care network was the motivation for this study, which aimed to evaluate the evolution of the model of care for cancer patients within the Unified Health System (Sistema Único de Saúde) based on the integrated care model. Since 1993, the year that cancer was first considered highly complex in the Sistema Único de Saúde by the Ministry of Health, it has been possible to observe a progressive orientation towards the integral and integrated care of patients with cancer. In the State of São Paulo, the active participation of qualified service providers through a Technical Reference Committee showed that experts could contribute to the definition of public policies, thereby providing a technical base for decision making and contributing to the development of clinical management.
Descritores: Redes Neurais de Computação
Oncologia/organização & administração
Programas Nacionais de Saúde
Neoplasias/terapia
-Brasil/epidemiologia
Saúde Pública
Neoplasias/epidemiologia
Limites: Humanos
Tipo de Publ: Revisão
Responsável: BR1.1 - BIREME


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Id: biblio-950760
Autor: Ruz, Gonzalo A; Timmermann, Tania; Barrera, Javiera; Goles, Eric.
Título: Neutral space analysis for a Boolean network model of the fission yeast cell cycle network
Fonte: Biol. Res;47:1-12, 2014. ilus, graf, tab.
Idioma: en.
Projeto: CONICYT-Chile; . ANILLO.
Resumo: BACKGROUND: Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast) cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle. RESULTS: Through simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks. Significant differences were found between the functional networks in the connected component of the wildtype network and the rest of the network, not only at a topological level, but also at the state space level, where significant differences in the distribution of the basin of attraction for the G1 fixed point was found for deterministic updating schemes. CONCLUSIONS: In general, functional networks in the wildtype network connected component, can mutate up to no more than 3 times, then they reach a point of no return where the networks leave the connected component of the wildtype. The proposed method to construct a neutral graph is general and can be used to explore the neutral space of other biologically interesting networks, and also formulate new biological hypotheses studying the functional networks in the wildtype network connected component.
Descritores: Schizosaccharomyces/fisiologia
Ciclo Celular/fisiologia
Quinases Ciclina-Dependentes/metabolismo
Redes Reguladoras de Genes/fisiologia
Modelos Biológicos
-Schizosaccharomyces/genética
Gráficos por Computador
Simulação por Computador
Fase G1/fisiologia
Redes Neurais de Computação
Proteínas de Ciclo Celular/metabolismo
Biologia Computacional
Tipo de Publ: Research Support, Non-U.S. Gov't
Responsável: CL1.1 - Biblioteca Central



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