scholarly journals MEASURING CLASSIFICATION ACCURACIES USING IMPROVED THERMAL INFRARED DATA

2014 ◽  
pp. 137-143
Author(s):  
Venkateswarulu Cheruku ◽  
Sumanth Yenduri ◽  
S. S. Iyengar
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Classification Accuracy ◽  
Digital Image Analysis ◽  
Remotely Sensed Data ◽  
Maximum Likelihood Approach ◽  
Final Note ◽  
Thermal Data ◽  
Likelihood Approach ◽  
Infrared Data

Image classification is one of the major aspects in digital image analysis of remotely sensed data. In this paper, we present the effects on classification accuracy if improved thermal data are used instead of raw thermal data. We use two methods, Artificial Neural Networks (ANN) and Maximum Likelihood Approach (MLH) to demonstrate our purpose. Using each method different combinations of raw and improved data are tested to classify in order to compare the accuracies. As a final note, the findings are discussed.

scholarly journals Synaptic Dynamics as Convolutional Units

2020 ◽  
Author(s):  
Julian Rossbroich ◽  
Daniel Trotter ◽  
Katalin Tóth ◽  
Richard Naud
Keyword(s):  
Neural Networks ◽  
Action Potentials ◽  
Computational Models ◽  
Mathematical Framework ◽  
Challenging Problem ◽  
Maximum Likelihood Approach ◽  
Different Types ◽  
Likelihood Approach ◽  
Synaptic Dynamics ◽  
Nonlinear Operation

AbstractSynaptic dynamics differ markedly across connections and strongly regulate how action potentials are being communicated. To model the range of synaptic dynamics observed in experiments, we develop a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.Author summaryUnderstanding how information is transmitted relies heavily on knowledge of the underlying regulatory synaptic dynamics. Existing computational models for capturing such dynamics are often either very complex or too restrictive. As a result, effectively capturing the different types of dynamics observed experimentally remains a challenging problem. Here, we propose a mathematically flexible linear-nonlinear model that is capable of efficiently characterizing synaptic dynamics. We demonstrate the ability of this model to capture different features of experimentally observed data.

Relationship between Surface Froth Features and Flotation Indexes in the Flotation of a Sulphide Copper Ore

2012 ◽  
Vol 503-504 ◽  
pp. 650-653
Author(s):  
Gui Chun He ◽  
Jin Ni Feng ◽  
Yi Peng Wu ◽  
Hua Mei Xiang ◽  
Mei Chao Qi
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Texture Features ◽  
Rbf Neural Networks ◽  
Copper Ore ◽  
Analysis Techniques ◽  
Relation Model ◽  
Image Analysis Techniques

Froth images are pre-processed, which are acquired at the flotation laboratory. Digital image analysis techniques are used to analysize these froth images and their grey histogram and to extract statistical texture features of those froth images. Finally, the relation model for statistical texture features of those froth images and flotation index is established by RBF neural networks. A simulation showed that the relation model is higher precise

The interpretation of flotation froth surfaces by using digital image analysis and neural networks

1995 ◽  
Vol 50 (22) ◽  
pp. 3501-3513 ◽  
Author(s):  
D.W. Moolman ◽  
C. Aldrich ◽  
J.S.J. Van Deventer ◽  
D.J. Bradshaw
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis

scholarly journals Artificial Intelligence-Based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection

2020 ◽  
Author(s):  
Sana Syed ◽  
Lubaina Ehsan ◽  
Aman Shrivastava ◽  
Saurav Sengupta ◽  
Marium Khan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Image Analysis ◽  
Deep Learning ◽  
Small Bowel ◽  
Classification Accuracy ◽  
Black Box ◽  
Decision Making Process ◽  
Analysis Platform

Objectives: Striking histopathological overlap between distinct but related conditions poses a significant disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; Environmental Enteropathy (EE) and Celiac Disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies. Methods: Data for secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using convolutional neural networks (CNNs: ResNet and custom Shallow CNN). Gradient-weighted Class Activation Mappings (Grad-CAMs) were used to visualize the decision making process of the models. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAM visualizations to confirm structural preservation and biological relevance, respectively. Results: 461 high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37.5 (19.0 to 121.5) months with a roughly equal sex distribution; 77 males (51.3%). ResNet50 and Shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98.3% with an ensemble. Grad-CAMs demonstrated ability of the models to learn distinct microscopic morphological features. Conclusion: Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features, emulating human pathologist decision making process, performing in the case of suboptimal computational environment, and being modified for improving disease classification accuracy. Grad-CAMs that were employed illuminated the otherwise black box of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.

Probability Density Estimation Using Entropy Maximization

1998 ◽  
Vol 10 (7) ◽  
pp. 1925-1938 ◽  
Author(s):  
Gad Miller ◽  
David Horn
Keyword(s):  
Neural Networks ◽  
Probability Density ◽  
Probability Density Functions ◽  
Conditional Density ◽  
Density Functions ◽  
Probability Density Estimation ◽  
Entropy Maximization ◽  
Maximum Likelihood Approach ◽  
Likelihood Approach ◽  
Stochastic Gradient Ascent

We propose a method for estimating probability density functions and conditional density functions by training on data produced by such distributions. The algorithm employs new stochastic variables that amount to coding of the input, using a principle of entropy maximization. It is shown to be closely related to the maximum likelihood approach. The encoding step of the algorithm provides an estimate of the probability distribution. The decoding step serves as a generative mode, producing an ensemble of data with the desired distribution. The algorithm is readily implemented by neural networks, using stochastic gradient ascent to achieve entropy maximization.

scholarly journals Linear-nonlinear cascades capture synaptic dynamics

2021 ◽  
Vol 17 (3) ◽  
pp. e1008013
Author(s):  
Julian Rossbroich ◽  
Daniel Trotter ◽  
John Beninger ◽  
Katalin Tóth ◽  
Richard Naud
Keyword(s):  
Neural Networks ◽  
Maximum Likelihood ◽  
Action Potentials ◽  
Mathematical Framework ◽  
Short Term ◽  
Maximum Likelihood Approach ◽  
Different Types ◽  
Likelihood Approach ◽  
Synaptic Dynamics ◽  
Nonlinear Operation

Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.

Morphometric analysis of cellular interactions with the endothelium during the development of arterial lesions using Scanning Electron Microscopy and digital image analysis

1987 ◽  
Vol 45 ◽  
pp. 918-919
Author(s):  
M.E. Rosenfeld ◽  
C. Karboski ◽  
M.F. Prescott ◽  
P. Goodwin ◽  
R. Ross
Keyword(s):  
Electron Microscopy ◽  
Image Analysis ◽  
Quantitative Data ◽  
Digital Image Analysis ◽  
Lower Cost ◽  
Matched Control ◽  
Digital Analysis ◽  
Cellular Interactions ◽  
Arterial Lesions ◽  
Scanning Electron

Previous research documenting the chronology of the cellular interactions that occur on or below the surface of the endothelium during the initiation and progression of arterial lesions, primarily consisted of descriptive studies. The recent development of lower cost image analysis hardware and software has facilitated the collection of high resolution quantitative data from microscopic images. In this report we present preliminary quantitative data on the sequence of cellular interactions that occur on the endothelium during the initiation of atherosclerosis or vasculitis utilizing digital analysis of images obtained directly from the scanning electron microscope. Segments of both atherosclerotic and normal arteries were obtained from either diet-induced or endogenously (WHHL) hypercholesterolemic rabbits following 1-4 months duration of hypercholesterolemia and age matched control rabbits. Vasculitis was induced in rats following placement of an endotoxin soaked thread adjacent to the adventitial surface of arteries.

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