From Generalized Linear Models to Neural Networks, and Back

2019 ◽  
Author(s):  
Mario V. Wuthrich
Keyword(s):  
Neural Networks ◽  
Generalized Linear Models ◽  
Linear Models

scholarly journals Brief Review of Machine Learning in Neurosurgery

2018 ◽  
Vol 15 (3) ◽  
pp. 3-7
Author(s):  
Yam Bahadur Roka
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Generalized Linear Models ◽  
Probabilistic Reasoning ◽  
Linear Models ◽  
The Body ◽  
Learning From Experience ◽  
Computer Gaming ◽  
Parkinsons Disease

Learning from experience is inherent to animals and humans and when used in computer models it is termed as Machine learning (ML) which was coined by Arthur Samuel the pioneer of computer gaming and artificial intelligence in 1959. This field grew out during the search for artificial intelligence and initially was developed using neural networks, perceptrons, probabilistic reasoning and generalized linear models of statistics. ML works by either of the two methods, supervised learning or unsupervised learning. Search for “ML in neurosurgery” in Pubmed showed 308 results. There were 298 studies with abstracts, 5 clinical trials, 20 review articles and 168 articles in human studies. Of these around 113 articles were either studies of ML in other parts of the body like liver, stroke, EEG, pathology and Parkinsons disease or not involving ML and hence were excluded. Of the 55 remaining cases the majority were studies done in glioma followed by medical imaging in neurosurgery, radiotherapy, language and learning studies. ML will definitely replace many of the cumbersome physical data collection to infer and formulate ways to treat patients in the future. It can make the process of research accumulation, filter, find correlations between variables and help to make algorithms to manage and predict, that can save, time, money and speedup the recovery of the patient

scholarly journals MODELAGEM DA ALTURA E DO INCREMENTO EM ÁREA TRANSVERSAL DE LOURO PARDO

Nativa ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 191
Author(s):  
Ana Claudia da Silveira ◽  
Luis Paulo Baldissera Schorr ◽  
Elisabete Vuaden ◽  
Jéssica Talheimer Aguiar ◽  
Tarik Cuchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Generalized Linear Models ◽  
Multilayer Perceptron ◽  
Linear Models ◽  
Annual Increment ◽  
Graphic Analysis ◽  
Artificial Neural ◽  
Pasture Area ◽  
Logarithmic Functions

O estudo teve como objetivo verificar a melhor técnica para a modelagem da altura e do incremento periódico anual em área transversal para Cordia trichotoma (Vell.) Arrab. ex Steud. Para isso, foram identificados e mensurados os diâmetros à altura do peito e as alturas totais de 35 indivíduos localizados em área de preservação permanente e de pastagem, com aproximadamente 4 ha, no município de Salto do Lontra, estado do Paraná. Posteriormente, foi realizada a análise de tronco pelo método não destrutivo verificando o incremento dos últimos 5 anos. Para a estimativa da altura e do incremento periódico anual em área transversal utilizou-se a técnica dos Modelos Lineares Generalizados (MLG) nas distribuições Gamma, Normal e Poisson nas funções de ligação identidade e logarítmica e Redes Neurais Artificiais (RNA) do tipo Multilayer Perceptron. Para comparação e escolha da melhor técnica, utilizou-se a correlação entre os valores observados e estimados, a raiz quadrada do erro médio e a análise gráfica dos resíduos. Os resultados mostraram que dentre os modelos de MLG, a distribuição Gamma função logarítmica foi indicada para modelagem da altura, ao passo que a distribuição Gamma função identidade foi a recomendada para a modelagem do incremento periódico em área transversal. Quando comparadas as duas técnicas evidenciou-se melhores resultados com a utilização das RNAs, as quais estimaram as variáveis estudadas com maior precisão.Palavra-chave: Cordia trichotoma, modelos lineares generalizados, redes neurais artificiais. MODELING HEIGHT AND TRANVERSAL AREA INCREMENT OF LOURO PARDO ABSTRACT:The present study aimed to verify the best technique for modeling height and annual periodic increment in transversal area for Cordia trichotoma (Vell.) Arrab. Ex Steud. For this purpose, we identify and measured the diameter at breast height and the total height of 35 individuals of this species which located in a permanent preservation and pasture area with approximately 4 hectares, in the municipality of Salto do Lontra, Paraná State, Brazil. Subsequently, the trunk analysis was performed by the non-destructive method, verifying the increment of the last 5 years. For the estimation of height and periodic annual increment in the transversal area, the Generalized Linear Models (MLG) technique was used in the Gamma, Normal and Poisson distributions in the identity and logarithmic functions and Artificial Neural Networks (RNA) of the Multilayer Perceptron type. For comparison and choice of the best technique, the correlation between the observed and estimated values, the square root of the mean error and the graphic analysis of the residues were used. The results showed that among the MLG models, the Gamma distribution with the logarithmic function was indicated for modeling height, whereas the Gamma with identity function was recommended for modeling periodic increment in transversal area. When we compared the two techniques, better results were obtained with the use of RNAs, which estimated the variables studied with greater accuracy.Keywords: Cordia trichotoma, generalized linear models, artificial neural networks. DOI:

scholarly journals On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections

2021 ◽  
Author(s):  
Jorge Baño-Medina ◽  
Rodrigo Manzanas ◽  
José Manuel Gutiérrez
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Statistical Model ◽  
Convolutional Neural Networks ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Global Climate Model ◽  
Future Climate Change ◽  
Deep Convolutional Neural Networks ◽  
Climate Change Projections

AbstractIn a recent paper, Baño-Medina et al. (Configuration and Intercomparison of deep learning neural models for statistical downscaling. preprint, 2019) assessed the suitability of deep convolutional neural networks (CNNs) for downscaling of temperature and precipitation over Europe using large-scale ‘perfect’ reanalysis predictors. They compared the results provided by CNNs with those obtained from a set of standard methods which have been traditionally used for downscaling purposes (linear and generalized linear models), concluding that CNNs are well suited for continental-wide applications. That analysis is extended here by assessing the suitability of CNNs for downscaling future climate change projections using Global Climate Model (GCM) outputs as predictors. This is particularly relevant for this type of “black-box” models, whose results cannot be easily explained based on physical reasons and could potentially lead to implausible downscaled projections due to uncontrolled extrapolation artifacts. Based on this premise, we analyze in this work the two key assumptions that are made in perfect prognosis downscaling: (1) the predictors chosen to build the statistical model should be well reproduced by GCMs and (2) the statistical model should be able to reliably extrapolate out of sample (climate change) conditions. As a first step to test the suitability of these models, the latter assumption is assessed here by analyzing how the CNNs affect the raw GCM climate change signal (defined as the difference, or delta, between future and historical climate). Our results show that, as compared to well-established generalized linear models (GLMs), CNNs yield smaller departures from the raw GCM outputs for the end of century, resulting in more plausible downscaling results for climate change applications. Moreover, as a consequence of the automatic treatment of spatial features, CNNs are also found to provide more spatially homogeneous downscaled patterns than GLMs.

Effect of Different Dentifrices, Bleaching with 35% Hydrogen Peroxide, and Red Wine on Surface Color and Roughness of Bovine Enamel

2020 ◽  
Vol 02 ◽  
Author(s):  
RM Garcia ◽  
WF Vieira-Junior ◽  
JD Theobaldo ◽  
NIP Pini ◽  
GM Ambrosano ◽  
...  
Keyword(s):  
Hydrogen Peroxide ◽  
Generalized Linear Models ◽  
Repeated Measures ◽  
Red Wine ◽  
Linear Models ◽  
Artificial Saliva ◽  
Surface Color ◽  
Dental Bleaching ◽  
Surface Alteration ◽  
Bovine Enamel

Objective: To evaluate color and roughness of bovine enamel exposed to dentifrices, dental bleaching with 35% hydrogen peroxide (HP), and erosion/staining by red wine. Methods: Bovine enamel blocks were exposed to: artificial saliva (control), Oral-B Pro-Health (stannous fluoride with sodium fluoride, SF), Sensodyne Repair & Protect (bioactive glass, BG), Colgate Pro-Relief (arginine and calcium carbonate, AR), or Chitodent (chitosan, CHI). After toothpaste exposure, half (n=12) of the samples were bleached (35% HP), and the other half were not (n=12). The color (CIE L*a* b*, ΔE), surface roughness (Ra), and scanning electron microscopy were evaluated. Color and roughness were assessed at baseline, post-dentifrice and/or -dental bleaching, and after red wine. The data were subjected to analysis of variance (ANOVA) (ΔE) for repeated measures (Ra), followed by Tukey ́s test. The L*, a*, and b* values were analyzed by generalized linear models (a=0.05). Results: The HP promoted an increase in Ra values; however, the SF, BG, and AR did not enable this alteration. After red wine, all groups apart from SF (unbleached) showed increases in Ra values; SF and AR promoted decreases in L* values; AR demonstrated higher ΔE values, differing from the control; and CHI decreased the L* variation in the unbleached group. Conclusion: Dentifrices did not interfere with bleaching efficacy of 35% HP. However, dentifrices acted as a preventive agent against surface alteration from dental bleaching (BG, SF, and AR) or red wine (SF). Dentifrices can decrease (CHI) or increase (AR and SF) staining by red wine.

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