264 results on '"Neural network classifier"'
Search Results
2. V-LTCS: Backbone exploration for Multimodal Misogynous Meme detection
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Sneha Chinivar, Roopa M.S., Arunalatha J.S., and Venugopal K.R.
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Misogynous Memes ,Neural network classifier ,Vision-language transformer model ,Computational linguistics. Natural language processing ,P98-98.5 - Abstract
Memes have become a fundamental part of online communication and humour, reflecting and shaping the culture of today’s digital age. The amplified Meme culture is inadvertently endorsing and propagating casual Misogyny. This study proposes V-LTCS (Vision- Language Transformer Combination Search), a framework that encompasses all possible combinations of the most fitting Text (i.e. BERT, ALBERT, and XLM-R) and Vision (i.e. Swin, ConvNeXt, and ViT) Transformer Models to determine the backbone architecture for identifying Memes that contains misogynistic contents. All feasible Vision-Language Transformer Model combinations obtained from the recognized optimal Text and Vision Transformer Models are evaluated on two (smaller and larger) datasets using varied standard metrics (viz. Accuracy, Precision, Recall, and F1-Score). The BERT-ViT combinational Transformer Model demonstrated its efficiency on both datasets, validating its ability to serve as a backbone architecture for all subsequent efforts to recognize Multimodal Misogynous Memes.
- Published
- 2024
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3. PREDICTION OF COMPLEX EVENT GRAPHS WITH NEURAL NETWORKS.
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KOVÁCS, László, BAKSÁNÉ VARGA, Erika, and MILEFF, Péter
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ROBOTIC process automation ,PROCESS mining - Abstract
A key problem domain inside Robotic Process Automation is the automatic discovery of workflow process schemes. Considering current process mining technologies, graph-based approaches dominate the industry. On the other hand, the conventional methods suffer from low time efficiency and varying accuracy. Machine learning-based methods can provide better efficiency, but they have significant limitations considering schema flexibility. The paper presents a novel neural network-based schema induction model for the discovery of event patterns containing parallel and optional sequences of different actors. This model can process more complex event graphs and situations than the conventional methods. The performed analysis and test results show the unique power of this approach in process schema mining. [ABSTRACT FROM AUTHOR]
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- 2024
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4. Detecting land use changes using hybrid machine learning methods in the Australian tropical regions.
- Author
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Sedighkia, Mahdi and Datta, Bithin
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MACHINE learning ,PARTICLE swarm optimization ,BACK propagation ,LAND use ,EVOLUTIONARY algorithms ,ELECTRONIC data processing ,EVOLUTIONARY computation - Abstract
The present study evaluates the application of the hybrid machine learning methods to detect changes of land use with a focus on agricultural lands through remote sensing data processing. Two spectral images by Landsat 8 were applied to train and test the machine learning model. Feed forward neural network classifier was utilized as the machine learning model in which two evolutionary algorithms including particle swarm optimization and invasive weed optimization were applied for the training process. Moreover, three conventional training methods including Levenberg–Marquardt back propagation (LM), Scaled conjugate gradient backpropagation (SCG) and BFGS quasi-Newton backpropagation (BFG) were used for comparing the robustness and reliability of the evolutionary algorithms. Based on the results in the case study, evolutionary algorithms are not a reliable method for detecting changes through the remote sensing analysis in terms of accuracy and computational complexities. Either BFG or LM is the best method to detect the agricultural lands in the present study. BFG is slightly more robust than the LM method. However, LM might be preferred for applying in the projects due to low computational complexities. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
5. RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification.
- Author
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Yang, Rui, Zhao, Yingbo, and Shi, Yuan
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RADAR ,RADAR targets ,BISTATIC radar ,ALGORITHMS - Abstract
When radar receives target echoes to form plots, it is inevitably affected by clutter, which brings a lot of imprecise and uncertain information to target recognition. Traditional radar plot recognition algorithms often have poor performance in dealing with imprecise and uncertain information. To solve this problem, a radar plot recognition algorithm based on adaptive evidence classification (RPREC) is proposed in this paper. The RPREC can be considered as the evidence classification version under the belief functions. First, the recognition framework based on the belief functions for target, clutter, and uncertainty is created, and a deep neural network model classifier that can give the class of radar plots is also designed. Secondly, according to the classification results of each iteration round, the decision pieces of evidence are constructed and fused. Before being fused, evidence will be corrected based on the distribution of radar plots. Finally, based on the global fusion results, the class labels of all radar plots are updated, and the classifier is retrained and updated so as to iterate until all the class labels of radar plots are no longer changed. The performance of the RPREC is verified and analyzed based on the real radar plot datasets by comparison with other related methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
6. Neuroimaging feature extraction using a neural network classifier for imaging genetics
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Cédric Beaulac, Sidi Wu, Erin Gibson, Michelle F. Miranda, Jiguo Cao, Leno Rocha, Mirza Faisal Beg, and Farouk S. Nathoo
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Dimensionality reduction ,Feature extraction ,Neural Network Classifier ,Bayesian Hierarchical Modelling ,Imaging genetics ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer’s Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes. Results We find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features. Conclusions The pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone.
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- 2023
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7. Extracting relations from texts using vector language models and a neural network classifier.
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Shishaev, Maksim, Dikovitsky, Vladimir, Pimeshkov, Vadim, Kuprikov, Nikita, Kuprikov, Mikhail, and Shkodyrev, Viacheslav
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LANGUAGE models ,NATURAL languages - Abstract
The article investigates the possibility of identifying the presence of SKOS (Simple Knowledge Organization System) relations between concepts represented by terms on the base of their vector representation in general natural language models. Several language models of the Word2Vec and GloVe families are considered, on the basis of which an artificial neural network (ANN) classifier of SKOS relations is formed. To train and test the efficiency of the classifier, datasets formed on the basis of the DBPedia and EuroVoc thesauri are used. The experiments performed have shown the high efficiency of the classifier trained using GloVe family models, while training it with use of Word2Vec models looks impossible in the bounds of considered ANN-based classifier architecture. Based on the results, a conclusion is made about the key role of taking into account the global context of the use of terms in the text for the possibility of identifying SKOS relations. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
8. On classifier performance for remote sensing images compressed by different coders
- Author
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Galina Proskura, Oleksiy Rubel, Sergii Kryvenko, and Vladimir Lukin
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lossy compression ,three-channel images ,neural network classifier ,training data ,Motor vehicles. Aeronautics. Astronautics ,TL1-4050 - Abstract
Remote sensing data are widely used in numerous applications. A conventional task solved using remote sensing images is their classification. The classification maps are commonly produced by some pre-trained classifiers applied either to uncompressed or compressed images where lossy compression is often needed and employed in practice due to the necessity to reduce data volume at stages of image transfer and storage. Then, the classification accuracy depends on the characteristics of an image, a classifier, and a coder used. The main subject of this paper is the factors that determine classification accuracy. One of them is compressed image quality. We fix the quality of compressed image quality characterized by the peak signal-to-noise ratio for several coders and rely on the same training approach. Our goal is twofold. First, we would like to consider classification accuracy for two approaches to classifier training: based on undistorted data and images with simulated distortions. Second, our desire is to compare the performance of different techniques of image compression. The task of this paper is to obtain an idea is it worth training the neural network classifier for uncompressed images or images of similar quality to the quality of compressed data to be classified. The coder’s influence on classification results is of special interest as well. The main results are the following. First, the classification accuracy is almost the same for classifiers trained for uncompressed and simulated compressed data for the general distortion model. Second, there is a certain difference in the classification results for different compression techniques studied. Lightly better classification results are observed for data produced by more sophisticated (modern) coders. Experiments have been carried out for two real-life three-channel Sentinel-2 images of Kharkiv and the Kharkiv region having different complexity. Four typical classes have been considered. As a conclusion, it is possible to state that either the general model of distortions must be modified or the classifier training should be performed for data produced by the corresponding compression technique.
- Published
- 2023
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- View/download PDF
9. Extracting relations from texts using vector language models and a neural network classifier
- Author
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Maksim Shishaev, Vladimir Dikovitsky, Vadim Pimeshkov, Nikita Kuprikov, Mikhail Kuprikov, and Viacheslav Shkodyrev
- Subjects
Relation extraction ,SKOS ,Neural network classifier ,Word2Vec ,GloVe ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
The article investigates the possibility of identifying the presence of SKOS (Simple Knowledge Organization System) relations between concepts represented by terms on the base of their vector representation in general natural language models. Several language models of the Word2Vec and GloVe families are considered, on the basis of which an artificial neural network (ANN) classifier of SKOS relations is formed. To train and test the efficiency of the classifier, datasets formed on the basis of the DBPedia and EuroVoc thesauri are used. The experiments performed have shown the high efficiency of the classifier trained using GloVe family models, while training it with use of Word2Vec models looks impossible in the bounds of considered ANN-based classifier architecture. Based on the results, a conclusion is made about the key role of taking into account the global context of the use of terms in the text for the possibility of identifying SKOS relations.
- Published
- 2023
- Full Text
- View/download PDF
10. Neuroimaging feature extraction using a neural network classifier for imaging genetics.
- Author
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Beaulac, Cédric, Wu, Sidi, Gibson, Erin, Miranda, Michelle F., Cao, Jiguo, Rocha, Leno, Beg, Mirza Faisal, and Nathoo, Farouk S.
- Subjects
- *
GENETICS , *FEATURE extraction , *GENETIC models , *SINGLE nucleotide polymorphisms , *STATISTICAL learning , *BRAIN imaging , *MEDICAL genetics - Abstract
Background: Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes. Results: We find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features. Conclusions: The pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
11. On classifier learning methodologies with application to compressed remote sensing images
- Author
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Galina Proskura, Oleksii Rubel, and Vladimir Lukin
- Subjects
lossy compression ,three-channel images ,classification ,neural network classifier ,training data ,Computer engineering. Computer hardware ,TK7885-7895 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Remote sensing images have found numerous applications nowadays. A traditional outcome or intermediate result of their processing is a classification map. Such maps are usually obtained from a pre-trained classifier and it is desired to have the produced classification maps as accurately as possible. The basic subject of this article is the factors determining this accuracy. The main among them are the quality of remote sensing data and classifier type, parameters and training approach. Image quality can be degraded due to several factors. One of them is distortions introduced by lossy compression that is widely used due to a huge volume of acquired data and the necessity to sufficiently decrease their size at transmission, storage and/or dissemination stages. Because of this, the main goal of this paper is to consider classification and lossy compression jointly. In particular, this means that the classifier learning can be performed for original (uncompressed, compressed in a lossless manner) images (if they are available) as well as for compressed data at hand (offered to a user for classification and further analysis). The task of this paper is to consider and compare these two options. The first one is the classifier learning for original images and further application to compressed data, where images can be compressed with different compression ratios while producing compressed data of different quality. The second option is the use of the classifier learning for compressed images, where compression parameters for training data can be approximately the same as for the images to which the classifier is applied. The main result is that the latter methodology can provide certain benefits compared to the classifier learning for original data if one has to classify compressed remote sensing data. Simulation data are obtained for a classifier based on a convolutional neural network. As images for training and verification, four real-life three-channel (visible range) Sentinel-2 remote sensing images of Kharkiv and Kharkiv region are employed that possess different complexity of the content and have four main classes. The practical recommendations are given. In conclusion, we can state that it is worth having classifiers trained for several degrees of compression and it is reasonable to compress complex structure images with special care.
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- 2022
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12. RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification
- Author
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Rui Yang, Yingbo Zhao, and Yuan Shi
- Subjects
radar plots ,belief functions ,neural network classifier ,evidence classification ,target recognition ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
When radar receives target echoes to form plots, it is inevitably affected by clutter, which brings a lot of imprecise and uncertain information to target recognition. Traditional radar plot recognition algorithms often have poor performance in dealing with imprecise and uncertain information. To solve this problem, a radar plot recognition algorithm based on adaptive evidence classification (RPREC) is proposed in this paper. The RPREC can be considered as the evidence classification version under the belief functions. First, the recognition framework based on the belief functions for target, clutter, and uncertainty is created, and a deep neural network model classifier that can give the class of radar plots is also designed. Secondly, according to the classification results of each iteration round, the decision pieces of evidence are constructed and fused. Before being fused, evidence will be corrected based on the distribution of radar plots. Finally, based on the global fusion results, the class labels of all radar plots are updated, and the classifier is retrained and updated so as to iterate until all the class labels of radar plots are no longer changed. The performance of the RPREC is verified and analyzed based on the real radar plot datasets by comparison with other related methods.
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- 2023
- Full Text
- View/download PDF
13. Neural network model of heteroassociative memory for the classification task
- Author
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Tatiana Martyniuk, Bohdan Krukivskyi, Leonid Kupershtein, and Vitaliy Lukichov
- Subjects
heteroassociative memory ,neural network classifier ,classification ,linear discriminant function ,Computer engineering. Computer hardware ,TK7885-7895 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
The subject of study in this article is the features of structural organization and functioning of the improved Hamming network as a model of neural network heteroassociative memory for classification by discriminant functions. The goal is to improve the neural network classifier based on the Hamming network, which implements the criterion of maximum similarity using discriminant functions and does not have restrictions on the representation of input data (not only binary data). The tasks: analyze the capabilities of associative memory models using neural networks as an example; analyze the features of classification on the principles of discriminant analysis; develop the structure of a neural network classifier as a model of neural network heteroassociative memory; perform simulation modeling of the classification process on the example of medical diagnosis. The methods used are a mathematical model of the functioning of a neural network as a classifier, and simulation in C#. The following results have been obtained: the structure of the neural network classifier has been improved through the formation connection matrix of a hidden layer from pre-calculated coefficients of linear discriminant functions, and the connection matrix of the output layer in the form symmetrical matrix with zeros on the main diagonal. This allows not only to simplify m connections, where m is the number of classes, in the structure of the output layer of the neural network classifier, but also to speed up the classification process, as well as to implement classification by the maximum of discriminant functions. Conclusions. The scientific novelty of the results obtained is as follows: the neural network classification method has been improved using pre-calculated elements of the connection matrices in the hidden and output layers of the classifier, which does not imply a long process of direct neural network learning with using discriminant functions; the structural organization of a neural network classifier is proposed, which is an improvement of the Hamming network as a model of heteroassociative memory, that allows using this classifier in a decision support system for medical diagnosis; the removal of positive feedback in neurons of the competitive (output) layer is implemented, which allows not only simplifies the structure of the neural network classifier but also speeds up the classification process almost 2 times, which is confirmed by the simulation results.
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- 2022
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14. A sequential deep learning algorithm for sampled mixed-integer optimisation problems.
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Chamanbaz, Mohammadreza and Bouffanais, Roland
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MACHINE learning , *DEEP learning , *ELECTRICAL load , *ALGORITHMS , *SEQUENTIAL learning - Abstract
Mixed-integer optimisation problems can be computationally challenging. Here, we introduce and analyse two efficient algorithms with a specific sequential design that are aimed at dealing with sampled problems within this class. At each iteration step of both algorithms, we first test the feasibility of a given test solution for each and every constraint associated with the sampled optimisation at hand, while also identifying those constraints that are violated. Subsequently, an optimisation problem is constructed with a constraint set consisting of the current basis—namely, the smallest set of constraints that fully specifies the current test solution—as well as constraints related to a limited number of the identified violating samples. We show that both algorithms exhibit finite-time convergence towards the optimal solution. Algorithm 2 features a neural network classifier that notably improves the computational performance compared to Algorithm 1. We quantitatively establish these algorithms' efficacy through three numerical tests: robust optimal power flow, robust unit commitment, and robust random mixed-integer linear program. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
15. Investigating Determinants and Evaluating Deep Learning Training Approaches for Visual Acuity in Foveal Hypoplasia
- Author
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Volha V. Malechka, MD, Dat Duong, PhD, Keyla D. Bordonada, MD, Amy Turriff, MS, Delphine Blain, MS, MBA, Elizabeth Murphy, PhD, Wendy J. Introne, MD, Bernadette R. Gochuico, MD, David R. Adams, MD, PhD, Wadih M. Zein, MD, Brian P. Brooks, MD, PhD, Laryssa A. Huryn, MD, Benjamin D. Solomon, MD, and Robert B. Hufnagel, MD, PhD
- Subjects
Generative adversarial network ,Foveal hypoplasia ,Neural network classifier ,Nystagmus ,OCT ,Ophthalmology ,RE1-994 - Abstract
Purpose: To describe the relationships between foveal structure and visual function in a cohort of individuals with foveal hypoplasia (FH) and to estimate FH grade and visual acuity using a deep learning classifier. Design: Retrospective cohort study and experimental study. Participants: A total of 201 patients with FH were evaluated at the National Eye Institute from 2004 to 2018. Methods: Structural components of foveal OCT scans and corresponding clinical data were analyzed to assess their contributions to visual acuity. To automate FH scoring and visual acuity correlations, we evaluated the following 3 inputs for training a neural network predictor: (1) OCT scans, (2) OCT scans and metadata, and (3) real OCT scans and fake OCT scans created from a generative adversarial network. Main Outcome Measures: The relationships between visual acuity outcomes and determinants, such as foveal morphology, nystagmus, and refractive error. Results: The mean subject age was 24.4 years (range, 1–73 years; standard deviation = 18.25 years) at the time of OCT imaging. The mean best-corrected visual acuity (n = 398 eyes) was equivalent to a logarithm of the minimal angle of resolution (LogMAR) value of 0.75 (Snellen 20/115). Spherical equivalent refractive error (SER) ranged from −20.25 diopters (D) to +13.63 D with a median of +0.50 D. The presence of nystagmus and a high-LogMAR value showed a statistically significant relationship (P < 0.0001). The participants whose SER values were farther from plano demonstrated higher LogMAR values (n = 382 eyes). The proportion of patients with nystagmus increased with a higher FH grade. Variability in SER with grade 4 (range, −20.25 D to +13.00 D) compared with grade 1 (range, −8.88 D to +8.50 D) was statistically significant (P < 0.0001). Our neural network predictors reliably estimated the FH grading and visual acuity (correlation to true value > 0.85 and > 0.70, respectively) for a test cohort of 37 individuals (98 OCT scans). Training the predictor on real OCT scans with metadata and fake OCT scans improved the accuracy over the model trained on real OCT scans alone. Conclusions: Nystagmus and foveal anatomy impact visual outcomes in patients with FH, and computational algorithms reliably estimate FH grading and visual acuity.
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- 2023
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- View/download PDF
16. Neural network classifier of hyperspectral images of skin pathologies
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V.O. Vinokurov, I.A. Matveeva, Y.A. Khristoforova, O.O. Myakinin, I.A. Bratchenko, L.A. Bratchenko, A.A. Moryatov, S.G. Kozlov, A.S. Machikhin, I. Abdulhalim, and V.P. Zakharov
- Subjects
hyperspectral imaging ,neural network classifier ,melanin ,hemoglobin ,oncopathology ,melanoma ,basal cell carcinoma ,vgg ,Information theory ,Q350-390 ,Optics. Light ,QC350-467 - Abstract
The paper presents results of using a neural network classifier to analyze images of malignant skin lesions obtained using a hyper-spectral camera. Using a three-block neural network of VGG architecture, we conducted the classification of a set of two-dimensional images of melanoma, papilloma and basal cell carcinoma, obtained in the range of 530 – 570 and 600 – 606 nm, characterized by the highest absorption of melanin and hemoglobin. The sufficiency of the inclusion in the training set of two-dimensional images of a limited spectral range is analyzed. The results obtained show significant prospects of using neural network algorithms for processing hyperspectral data for the classification of skin pathologies. With a relatively small set of training data used in the study, the classification accuracy for the three types of neoplasms was as high as 96 %.
- Published
- 2021
- Full Text
- View/download PDF
17. Shape and Texture Aware Facial Expression Recognition Using Spatial Pyramid Zernike Moments and Law’s Textures Feature Set
- Author
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Vijayalakshmi G. V. Mahesh, Chengji Chen, Vijayarajan Rajangam, Alex Noel Joseph Raj, and Palani Thanaraj Krishnan
- Subjects
Facial expressions ,emotions ,Zernike moments ,Law’s texture features ,neural network classifier ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Facial expression recognition (FER) requires better descriptors to represent the face patterns as the facial region changes due to the movement of the face muscles during an expression. In this paper, a method of concatenating spatial pyramid Zernike moments based shape features and Law’s texture features is proposed to uniquely capture the macro and micro details of each facial expression. The proposed method employs multilayer perceptron and radial basis function feed forward artificial neural networks for recognizing the facial expressions. The suitability of the features in recognizing the expressions is explored across the datasets independent of the subjects or persons. The experiments conducted on JAFFE and KDEF datasets demonstrate that the concatenated feature vectors are capable of representing the facial expressions with better accuracy and least errors. The radial basis function based classifier delivers a performance with an average recognition accuracy of 95.86% and 88.87% on the JAFFE and KDEF datasets respectively for subject dependent FER.
- Published
- 2021
- Full Text
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18. Snapshot-Based Human Action Recognition using OpenPose and Deep Learning.
- Author
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Emanuel, Andi W. R., Mudjihartono, Paulus, and Nugraha, Joanna A. M.
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HUMAN behavior ,DEEP learning ,ARTIFICIAL neural networks ,K-nearest neighbor classification ,RANDOM forest algorithms ,SUPPORT vector machines - Abstract
This research builds a human action recognition system based on a single image or video capture snapshot. The TensorFlow Deep Learning models are developed using human keypoints generated by OpenPose. Four classifiers are considered: Neural Network, Random Forest, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) Classifiers. The models’ input layer are 50 points from x and y coordinate of 25 keypoints from OpenPose, and the output layer is the numerical representation of 11 human action labels which are 'hand-wave', 'jump', 'leg-cross', 'plank', 'ride', 'run', 'sit', 'lay-down', 'squat', 'stand', 'walk’. A total of 2132 images dataset was used for model training and testing. The results show the two best classifier models: Neural Network Classifier with 512 hidden nodes with an accuracy of 0.7733, and Random Forest Classifier with 60 estimators with an accuracy of 0.7752. Both models are then used as inference engines to recognize human action from images and real-time video. [ABSTRACT FROM AUTHOR]
- Published
- 2021
19. Hidden Authentication of the User Based on Neural Network Analysis of the Dynamic Profile
- Author
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Anastasiya Sivova, Alexey Vulfin, Konstantin Mironov, and Anastasiya Kirillova
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keyboard handwriting ,hidden authentication ,neural network classifier ,Electronic computers. Computer science ,QA75.5-76.95 ,Technology - Abstract
The problem of continuous hidden user authentication based on the analysis of keyboard handwriting is considered. The main purpose of the analysis is to continuously verify the identity of the subject during his work on the keyboard. The aim of the work is to increase the efficiency of hidden user authentication algorithms based on a neural network analysis of a dynamic profile, formed by keyboard handwriting. The idea of user authentication using keyboard handwriting is based on measuring the time of keystrokes and the intervals between keystrokes, followed by comparing the resulting data set with the stored dynamic user profile. Studies have shown that analyzing the average value of the time each key is pressed is inefficient. It is proposed to analyze the holding time of a combination of several keys and the time between their presses. An approach in which not the times of pressing individual keys, but the parameters of pressing the most common letter combinations are analyzed, will increase the accuracy of recognition of dynamic images. An algorithm and software implementation for Russian keyboard layout have been developed, experiments conducted on field data allow us to conclude that the proposed method is effectively used to authenticate the user using keyboard handwriting.
- Published
- 2020
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20. PERSON IDENTIFICATION BASED ON DIFFERENT COLOUR MODELS IRIS BIOMETRIC AND CONTOURLET TRANSFORM
- Author
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Dhuha Hussein Hameed and Maher Khudhiar Mahmood
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colored iris identification system ,color models ,contourlet transform ,iris classifier ,neural network classifier ,euclidean distance classifier ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Iris identification plays an important role in many applications like security, banking, access to buildings, and surveillance …. etc. Since the iris part of the eye image can be significantly affected by some factors, such as lighting conditions source, eyelids, eyelashes, pupil, sclera, and shadowing, therefore iris identification research is still wide and rich. The work proposed in this paper operates the iris identification system on the distorted colored images captured under visible light. The proposed idea minimizes the number of iris regions affected by distortion, by dividing the iris region into separable regions. Only the region without distortion part or region with distortion is less probable is used. The paper studies the effect of different color model such as HSV, YIQ, YCbCr, and RGB color models on iris identification. High quality feature extraction is introduced in this paper by using Contourlet Transform (CT). Euclidian Distance (ED) or Neural Network (NN) is used as classifiers. Simulation results show that the proposed method operating on non-distortion iris region outperforms the conventional method operating on the whole iris region for any selected color model and for standard databases (UPOL andUTIRIS) and a suggested one.
- Published
- 2020
- Full Text
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21. Prediction of WHO grade and methylation class of aggressive meningiomas: Extraction of diagnostic information from infrared spectroscopic data.
- Author
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Galli R, Lehner F, Richter S, Kirsche K, Meinhardt M, Juratli TA, Temme A, Kirsch M, Warta R, Herold-Mende C, Ricklefs FL, Lamszus K, Sievers P, Sahm F, Eyüpoglu IY, and Uckermann O
- Abstract
Background: Infrared (IR) spectroscopy allows intraoperative, optical brain tumor diagnosis. Here, we explored it as a translational technology for the identification of aggressive meningioma types according to both, the WHO CNS grading system and the methylation classes (MC)., Methods: Frozen sections of 47 meningioma were examined by IR spectroscopic imaging and different classification approaches were compared to discern samples according to WHO grade or MC., Results: IR spectroscopic differences were more pronounced between WHO grade 2 and 3 than between MC intermediate and MC malignant, although similar spectral ranges were affected. Aggressive types of meningioma exhibited reduced bands of carbohydrates (at 1024 cm
-1 ) and nucleic acids (at 1080 cm-1 ), along with increased bands of phospholipids (at 1240 and 1450 cm-1 ). While linear discriminant analysis was able to discern spectra of WHO grade 2 and 3 meningiomas (AUC 0.89), it failed for MC (AUC 0.66). However, neural network classifiers were effective for classification according to both WHO grade (AUC 0.91) and MC (AUC 0.83), resulting in the correct classification of 20/23 meningiomas of the test set., Conclusions: IR spectroscopy proved capable of extracting information about the malignancy of meningiomas, not only according to the WHO grade, but also for a diagnostic system based on molecular tumor characteristics. In future clinical use, physicians could assess the goodness of the classification by considering classification probabilities and cross-measurement validation. This might enhance the overall accuracy and clinical utility, reinforcing the potential of IR spectroscopy in advancing precision medicine for meningioma characterization., Competing Interests: None., (© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)- Published
- 2024
- Full Text
- View/download PDF
22. Improve Profiling Bank Customer’s Behavior Using Machine Learning
- Author
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Emad Abd Elaziz Dawood, Essamedean Elfakhrany, and Fahima A. Maghraby
- Subjects
Profiling ,banking ,machine learning ,k-mean ,fuzzy c-mean ,neural network classifier ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
In the banking industry, credit card evolution is a noticeable occurrence. Each banking system includes a huge dataset for customer's transactions of their credit cards. Therefore, banks would be in need of customer profiling. Profiling bank customer's cognize the issuer's decisions about whom to give banking facilities and what a credit limit to provide. It also helps the issuers get a better understanding of their potential and current customers. In previous research, Customer profiling mainly depends on transaction data or demographic data, but in this research, we merge both data in order to get a more accurate result and minimize the risk. By finding the best technique, it leads to improvement in accuracy and helps banks to get higher profitability by customer satisfaction through a focus on the valuable customer (companies) which consider as the main engine in the bank's profitability. This study aims at using k-mean, improved k-mean, fuzzy c-means and neural networks. The used dataset is labeled and creating a ýnew label as a target for neural network classification is the main aspect of this study, which helps to reduce the clustering execution time and get the best accuracy results. Finally, by comparing the accuracy ratio it shows that the neural network ýis the best clustering technique.
- Published
- 2019
- Full Text
- View/download PDF
23. Advanced Quantum Based Neural Network Classifier and Its Application for Objectionable Web Content Filtering
- Author
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Om Prakash Patel, Neha Bharill, Aruna Tiwari, Vikram Patel, Ojas Gupta, Jian Cao, Jun Li, and Mukesh Prasad
- Subjects
Quantum computing ,Web crawler ,neural network classifier ,objectionable Web content ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
In this paper, an Advanced Quantum-based Neural Network Classifier (AQNN) is proposed. The proposed AQNN is used to form an objectionable Web content filtering system (OWF). The aim is to design a neural network with a few numbers of hidden layer neurons with the optimal connection weights and the threshold of neurons. The proposed algorithm uses the concept of quantum computing and genetic concept to evolve connection weights and the threshold of neurons. Quantum computing uses qubit as a probabilistic representation which is the smallest unit of information in the quantum computing concept. In this algorithm, a threshold boundary parameter is also introduced to find the optimal value of the threshold of neurons. The proposed algorithm forms neural network architecture which is used to form an objectionable Web content filtering system which detects objectionable Web request by the user. To judge the performance of the proposed AQNN, a total of 2000 (1000 objectionable + 1000 non-objectionable) Website's contents have been used. The results of AQNN are also compared with QNN-F and well-known classifiers as backpropagation, support vector machine (SVM), multilayer perceptron, decision tree algorithm, and artificial neural network. The results show that the AQNN as classifier performs better than existing classifiers. The performance of the proposed objectionable Web content filtering system (OWF) is also compared with well-known objectionable Web filtering software and existing models. It is found that the proposed OWF performs better than existing solutions in terms of filtering objectionable content.
- Published
- 2019
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- View/download PDF
24. Oral Malignancy Detection Using Color Features from Digital True Color Images.
- Author
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B. R., Nandita, A., Geetha Kiran, H. S., Chandrashekar, M. S., Dinesh, and S., Murali
- Subjects
PRECANCEROUS conditions ,ORAL cancer - Abstract
One of the most prevalent forms of cancer worldwide is oral cancer which has a high rate of mortality. Diagnosis and treatment of oral premalignant lesions at an early stage reduces the death rate. The objective of this work is to detect malignancies by analyzing color features of digital true color oral images. A dataset of around 433 oral lesion images has been created that includes benign, premalignant and malignant lesions. The proposed method was experimented on this dataset. Different classifiers have been trained using various color features. The neural network classifier detects abnormalities with an accuracy of 94.82%. Results indicate that the color features have better potential in identifying benign and malignant oral lesions. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
25. Discretized Target Size Detection in Electrical Impedance Tomography Using Neural Network Classifier.
- Author
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Huang, Shu-Wei, Rohde, Gustavo K., Cheng, Hao-Min, and Lin, Shien-Fong
- Subjects
- *
ELECTRICAL impedance tomography , *FISHER discriminant analysis , *INVERSE problems - Abstract
Electrical impedance tomography (EIT) uses non-invasive and non-radiative imaging to detect inhomogeneous electrical properties in tissues. The inverse problem of EIT is a highly nonlinear, ill-posed problem, which causes inaccuracy in target size calculation. We propose a novel approach to discretize the target size and use a neural network (NN) classifier to classify the unknown size in discrete steps. The target size is discretized into distinct steps, and each step can be a unique class. The data is pre-processed with the cumulative distribution transform (CDT) to enhance distinguishability. First, the NN is trained with simulated datasets, divided into time difference (t-d) group and CDT group. After training, the NN classifier is tested by experimental data recorded in a phantom experiment. Linear discriminant analysis (LDA) is performed to assess the distinguishability of classes. There is a significant increase in distance between classes after the CDT pre-processing. The density of the classes has an upward trend with a higher degree of clustering after CDT pre-processing. The CDT data clustering into distinguishable classes is essential to excellent NN classification results. Such an approach is a significant paradigm shift by turning the cumbersome inverse calculation with uncertain accuracy into a classification problem with predetermined step errors. The accuracy and resolution can be further extended by increasing the discretization steps. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
26. SARCASM DETECTION IN ONLINE REVIEW TEXT
- Author
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Srishti Sharma and Shampa Chakraverty
- Subjects
Sarcasm ,Machine Learning ,Support Vector Machines ,Neural Network Classifier ,Amazon ,Twitter ,Computer engineering. Computer hardware ,TK7885-7895 - Abstract
Sarcasm is a type of sentiment where people express negative sentiment using positive connotation words in text and vice-versa. In this work, we propose a cross-domain sarcasm detection framework that allows acquisition, storage and processing of tweets for detecting sarcastic content in online reviews. We conduct our experiments on Amazon product review dataset namely the Sarcasm Corpus Version1 having about 2000 reviews. We use Support Vector Machines (SVM) and Neural Networks (NN) for detecting sarcasm using lexical, pragmatic, linguistic incongruity and context incongruity features. We report the results and present a comparative evaluation of SVM and NN classifiers for single domain sarcasm detection indicating their suitability for the task. Then, we use these models for cross-domain sarcasm detection. The experimental results indicate the reliability of our approach.
- Published
- 2018
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27. Operating performance assessment based on multi-source heterogeneous information with deep learning for smelting process of electro-fused magnesium furnace
- Author
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Fuli Wang, Kaiqing Bu, and Yan Liu
- Subjects
Computer science ,business.industry ,Applied Mathematics ,Deep learning ,Process (computing) ,Smelting process ,computer.software_genre ,Neural network classifier ,Convolutional neural network ,Computer Science Applications ,Control and Systems Engineering ,Softmax function ,Heterogeneous information ,Artificial intelligence ,Data mining ,Electrical and Electronic Engineering ,business ,Instrumentation ,computer ,Multi-source - Abstract
The process operating performance assessment is critical for the smelting process of electro-fused magnesium furnaces to improve quality of the magnesia product and pursue optimal comprehensive economic benefit. This paper proposes a new method of multi-source heterogeneous information deep feature fusion (MSHIDFF) to achieve higher accuracy operating performance assessment in the electro-fused magnesium smelting process. Firstly, we utilize convolutional neural network, bidirectional long short-term memory network and stacked auto-encoder to extract deep features from raw image, sound and current of different performance grades. Furthermore, those multi-source deep features are fused and the softmax regression with attention mechanism is employed to train a neural network classifier for the fused deep features of different performance grades. The simulation results show that the proposed MSHIDFF method obtains the superior assessment accuracy.
- Published
- 2022
28. ECG based Atrial Fibrillation detection using Sequency Ordered Complex Hadamard Transform and Hybrid Firefly Algorithm
- Author
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Padmavathi Kora, Ambika Annavarapu, Priyanka Yadlapalli, K. Sri Rama Krishna, and Viswanadharaju Somalaraju
- Subjects
Atrial Fibrillation ,ECG ,Fast CS-SCHT ,Neural Network Classifier ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Electrocardiogram (ECG), a non-invasive diagnostic technique, used for detecting cardiac arrhythmia. From last decade industry dealing with biomedical instrumentation and research, demanding an advancement in its ability to distinguish different cardiac arrhythmia. Atrial Fibrillation (AF) is an irregular rhythm of the human heart. During AF, the atrial moments are quicker than the normal rate. As blood is not completely ejected out of atria, chances for the formation of blood clots in atrium. These abnormalities in the heart can be identified by the changes in the morphology of the ECG. The first step in the detection of AF is preprocessing of ECG, which removes noise using filters. Feature extraction is the next key process in this research. Recent feature extraction methods, such as Auto Regressive (AR) modeling, Magnitude Squared Coherence (MSC) and Wavelet Coherence (WTC) using standard database (MIT-BIH), yielded a lot of features. Many of these features might be insignificant containing some redundant and non-discriminatory features that introduce computational burden and loss of performance. This paper presents fast Conjugate Symmetric Sequency Ordered Complex Hadamard Transform (CS-SCHT) for extracting relevant features from the ECG signal. The sparse matrix factorization method is used for developing fast and efficient CS-SCHT algorithm and its computational performance is examined and compared to that of the HT and NCHT. The applications of the CS-SCHT in the ECG-based AF detection is also discussed. These fast CS-SCHT features are optimized using Hybrid Firefly and Particle Swarm Optimization (FFPSO) to increase the performance of the classifier.
- Published
- 2017
- Full Text
- View/download PDF
29. Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data.
- Author
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Nambiar A, S H, and S S
- Abstract
Introduction: The COVID-19 pandemic had a global impact and created an unprecedented emergency in healthcare and other related frontline sectors. Various Artificial-Intelligence-based models were developed to effectively manage medical resources and identify patients at high risk. However, many of these AI models were limited in their practical high-risk applicability due to their "black-box" nature, i.e., lack of interpretability of the model. To tackle this problem, Explainable Artificial Intelligence (XAI) was introduced, aiming to explore the "black box" behavior of machine learning models and offer definitive and interpretable evidence. XAI provides interpretable analysis in a human-compliant way, thus boosting our confidence in the successful implementation of AI systems in the wild., Methods: In this regard, this study explores the use of model-agnostic XAI models, such as SHapley Additive exPlanations values (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), for COVID-19 symptom analysis in Indian patients toward a COVID severity prediction task. Various machine learning models such as Decision Tree Classifier, XGBoost Classifier, and Neural Network Classifier are leveraged to develop Machine Learning models., Results and Discussion: The proposed XAI tools are found to augment the high performance of AI systems with human interpretable evidence and reasoning, as shown through the interpretation of various explainability plots. Our comparative analysis illustrates the significance of XAI tools and their impact within a healthcare context. The study suggests that SHAP and LIME analysis are promising methods for incorporating explainability in model development and can lead to better and more trustworthy ML models in the future., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Nambiar, S and S.)
- Published
- 2023
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30. Acquisition and analysis of heart sound data
- Author
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Hebden, John Edward
- Subjects
610.28 ,Phonocardiography ,Neural network classifier - Published
- 1997
31. Neural Network Classifier-Based OPC With Imbalanced Training Data.
- Author
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Choi, Suhyeong, Shim, Seongbo, and Shin, Youngsoo
- Subjects
- *
MACHINE learning , *IMAGE segmentation , *ARTIFICIAL neural networks , *INSTRUCTIONAL systems , *FOURIER transforms - Abstract
Machine learning-guided optical proximity correction, called ML-OPC in this paper, has recently been proposed to alleviate long runtime of model-based OPC. ML-OPC using regression methods has been presented but with limited prediction accuracy. We propose neural network classifier-based OPC (NNC-OPC), in which a neural network classifier serves as a mask bias model. A few techniques are applied to enhance basic NNC-OPC: parameterization of layout segments using polar Fourier transform signals, dimensionality reduction through weighted principal component analysis, and sampling of training layout segments. Training segments are typically imbalanced over the range of mask biases, which may cause large prediction error for segments that appear less frequently. This is resolved by three techniques: 1) synthetic data generation; 2) class reorganization; and 3) an adaptive learning rate. Experiments with NNC-OPC with all techniques applied indicate that prediction error of mask bias and training time are reduced by 29% and 80%, respectively, compared to state-of-the-art ML-OPC with regression methods. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
32. Automatic Text Recognition in Natural Scene Using Neural Network Classifier with Dynamic-group-based Hybrid Particle Swarm Optimization.
- Author
-
KUANG-HUI TANG, CHUAN-KUEI HUANG, and CHENG-JIAN LIN
- Subjects
PARTICLE swarm optimization ,ARTIFICIAL neural networks ,SEARCH algorithms ,PATTERN recognition systems ,TEXT recognition - Abstract
This paper presents a two-stage algorithm for automatic text detection and recognition. In the first stage, using a stroke width transform and an improved connected component, an edge analysis method detects a candidate character region. Subsequently, a text region is located by filtering and linking characters with similar font sizes and colors. For the second stage, a histogram of oriented gradient is employed as a feature descriptor, and a neural network classifier is built with dynamic-group-based hybrid particle swarm optimization (DGHPSO) for character recognition. In DGHPSO, each group's threshold value of similarity depends on the threshold values of fitness and distance. In addition, a local search algorithm is used to improve the search for a global optimum. The proposed algorithm was experimentally validated; it outperformed a number of recently published studies in terms of the text recognition rate when tested on the ICDAR 2003 database and the Street View Text database. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
33. Methodology for robust multi-parametric control in linear continuous-time systems.
- Author
-
Sun, Muxin, Villanueva, Mario E., Pistikopoulos, Efstratios N., and Chachuat, Benoît
- Subjects
- *
CATALYTIC cracking , *CHEMICAL reactors , *DISCRETIZATION methods , *CONTINUOUS time systems , *ROBUST control - Abstract
Highlights • Robust multi-parametric NCO-tracking controllers do not entail a discretization of the continuous-time dynamics. • The robust-counterpart multi-parametric dynamic optimization (mp-DO) problem retains the same complexity as nominal mp-DO. • Data classifiers based on deep learning can accurately describe the critical regions in (nominal or robust) mp-DO. • The methodology is demonstrated for a fluid catalytic cracking (FCC) unit and a chemical reactor cascade. Abstract This paper presents an extension of the recent multi-parametric (mp-)NCO-tracking methodology by Sun et al. [Comput. Chem. Eng. 92 (2016) 64–77] for the design of robust multi-parametric controllers for constrained continuous-time linear systems in the presence of uncertainty. We propose a robust-counterpart formulation and solution of multi-parametric dynamic optimization (mp-DO), whereby the constraints are backed-off based on a worst-case propagation of the uncertainty using either interval analysis or ellipsoidal calculus and an ancillary linear state feedback. We address the case of additive uncertainty, and we discuss approaches to dealing with multiplicative uncertainty that retain tractability of the mp-NCO-tracking design problem, subject to extra conservativeness. In order to assist with the implementation of these controllers, we also investigate the use of data classifiers based on deep learning for approximating the critical regions in continuous-time mp-DO problems, and subsequently searching for a critical region during on-line execution. We illustrate these developments with the case studies of a fluid catalytic cracking (FCC) unit and a chemical reactor cascade. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
34. NEURAL NETWORK CLASSIFIER OF ENERGY FACILITIES OPERATING MODES AND ITS RECOGNITION ABILITY ASSESSMENT AT DIFFERENT NUMBER OF PRECEDENTS
- Author
-
Yuri A. Dementiy and Aleksandr N. Maslov
- Subjects
Computer science ,business.industry ,Energy facilities ,Artificial intelligence ,Machine learning ,computer.software_genre ,business ,Neural network classifier ,computer - Abstract
Classical algorithms of relay protection construction do not use all available information base and therefore cannot provide the highest possible sensitivity with guaranteed selectivity. These algorithms, as a rule, concentrate different information, as a result of which it is partially lost. For example, the resistance relay operates with complex resistance, that is, two real parameters, although two complex variables – voltage and current – are used to calculate the complex resistance. This paper shows the solution to the problem of classification of power line operating modes using a neural network algorithm. The simplest neural network, a perceptron, is a universal classifier, since a convergence theorem has been proved for it, showing that if a classification exists, a perceptron of sufficient complexity is able to describe it. The statistical and geometrical interpretations of various algorithms are discussed. The dependence of the quality of the classifier’s work on the distribution of precedents in the training sample, on which the training is based, as well as on the structure and parameters of the neural network, is shown. The recognition ability of the neural network classifier, i.e. the ability to distinguish short circuits within the protected zone from short circuits outside the protected zone at different number of precedents in the training sample, is evaluated. The limits of applicability of such algorithms to the task of classification of object operation modes in electric power industry are shown and recommendations for their practical application are formulated. The results obtained indicate the need to develop methods for training classifiers that are based on a source of informative precedents in the form of a simulation model of the object.
- Published
- 2021
35. A deep neural-network classifier for photograph-based estimation of hearing protection attenuation and fit
- Author
-
Gregory Ciccarelli, Aaron Rodriguez, William J. Murphy, and Christoper J Smalt
- Subjects
Acoustics and Ultrasonics ,Artificial neural network ,Computer science ,Hearing loss ,Attenuation ,Speech recognition ,Neural network classifier ,Article ,Compliance Monitoring ,Hearing protection ,Noise ,Hearing ,Hearing Loss, Noise-Induced ,Arts and Humanities (miscellaneous) ,QUIET ,Noise, Occupational ,Quality of Life ,otorhinolaryngologic diseases ,medicine ,Humans ,Ear Protective Devices ,Neural Networks, Computer ,medicine.symptom - Abstract
Occupational and recreational acoustic noise exposure is known to cause permanent hearing damage and reduced quality of life, which indicates the importance of noise controls including hearing protection devices (HPDs) in situations where high noise levels exist. While HPDs can provide adequate protection for many noise exposures, it is often a challenge to properly train HPD users and maintain compliance with usage guidelines. HPD fit-testing systems are commercially available to ensure proper attenuation is achieved, but they often require specific facilities designed for hearing testing (e.g., a quiet room or an audiometric booth) or special equipment (e.g., modified HPDs designed specifically for fit testing). In this study, we explored using visual information from a photograph of an HPD inserted into the ear to estimate hearing protector attenuation. Our dataset consists of 960 unique photographs from four types of hearing protectors across 160 individuals. We achieved 73% classification accuracy in predicting if the fit was greater or less than the median measured attenuation (29 dB at 1 kHz) using a deep neural network. Ultimately, the fit-test technique developed in this research could be used for training as well as for automated compliance monitoring in noisy environments to prevent hearing loss.
- Published
- 2021
36. Synthetic aperture radar automatic target classification processing concept.
- Author
-
Woollard, M., Bannon, A., Ritchie, M., and Griffiths, H.
- Abstract
A new simulation and processing methodology based on open source tools to produce high fidelity synthetic aperture radar (SAR) simulations of ground vehicles of varying types, as well as analysis of an applied automatic target recognition (ATR) technique is presented in this Letter. This work is based around the RaySAR open‐source model and the outputs have been configured for both monostatic and bistatic geometries. Input CAD models of various military and civilian vehicles are used to produce the SAR imagery. This output imagery was then used to train a tiny you only look once convolutional neural network (CNN) classifier. The classification success of the CNN applied was showed to produce significantly accurate results and the whole pipeline of processing enabled rapid evaluation of potential ATR methods against targets of choice. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
37. Accelerating nearest neighbor partitioning neural network classifier based on CUDA.
- Author
-
Wang, Lin, Zhu, Xuehui, Yang, Bo, Guo, Jifeng, Liu, Shuangrong, Li, Meihui, Zhu, Jian, and Abraham, Ajith
- Subjects
- *
ARTIFICIAL neural networks , *NEAREST neighbor analysis (Statistics) , *CUDA (Computer architecture) , *PERFORMANCE evaluation , *GRAPHICS processing units - Abstract
The nearest neighbor partitioning (NNP) method is a high performance approach which is used for improving traditional neural network classifiers. However, the construction process of NNP model is very time-consuming, particularly for large data sets, thus limiting its range of application. In this study, a parallel NNP method is proposed to accelerate NNP based on Compute Unified Device Architecture(CUDA). In this method, blocks and threads are used to evaluate potential neural networks and to perform parallel subtasks, respectively. Experimental results manifest that the proposed parallel method improves performance of NNP neural network classifier. Furthermore, the application of parallel NNP in performance evaluation of cement microstructure indicates that the proposed approach has favorable performance. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
38. Classification of the Surface Technological Defects in Rolled Metal Products with the Help of a Deep Neural Network
- Author
-
I. V. Konovalenko and Pavlo Maruschak
- Subjects
Surface (mathematics) ,Materials science ,Artificial neural network ,Basis (linear algebra) ,business.industry ,Mechanical Engineering ,Pattern recognition ,Condensed Matter Physics ,Neural network classifier ,Mechanics of Materials ,Nondestructive testing ,General Materials Science ,Artificial intelligence ,business ,Classifier (UML) ,Automated method - Abstract
We develop and study an automated method for the detection and classification of three types of technological defects in rolled metal products. The method is based on the ResNet50 neural network classifier, which makes it possible to recognize three classes of defects in the images of flat surfaces with a total accuracy of 95.8% on the basis of the analysis of experimental data with unbalanced numbers of images of different types. To train the classifier, we used about 88,000 images. It is shown that the application of the model developed on the basis of the ResNet50 neural network guarantees its excellent productivity, high quality of recognition, high speed, and high accuracy, which turns the proposed classifier into an effective tool for the solution of problems of engineering diagnostics and nondestructive testing aimed at the classification of defects on the surfaces of rolled metal products.
- Published
- 2021
39. Security and safety aspects of AI in industry applications
- Author
-
Doran, Hans Dermot and Doran, Hans Dermot
- Abstract
In this relatively informal discussion-paper we summarise issues in the domains of safety and security in machine learning that will affect industry sectors in the next five to ten years. Various products using neural network classification, most often in vision related applications but also in predictive maintenance, have been researched and applied in real-world applications in recent years. Nevertheless, reports of underlying problems in both safety and security related domains, for instance adversarial attacks have unsettled early adopters and are threatening to hinder wider scale adoption of this technology. The problem for real-world applicability lies in being able to assess the risk of applying these technologies. In this discussion-paper we describe the process of arriving at a machine-learnt neural network classifier pointing out safety and security vulnerabilities in that workflow, citing relevant research where appropriate.
- Published
- 2022
40. A deep neural network classifier for P300 BCI speller based on Cohen’s class time-frequency distribution
- Author
-
Hamed Ghazikhani and Modjtaba Rouhani
- Subjects
Time frequency distribution ,General Computer Science ,Computer science ,business.industry ,Pattern recognition ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Class (biology) ,Neural network classifier ,Brain–computer interface - Published
- 2021
41. Super resolution and recognition of long range captured multi‐frame iris images.
- Author
-
Deshpande, Anand and Patavardhan, Prashant P.
- Abstract
In this study, a framework to super resolve and recognise the long range captured iris polar images is proposed. The proposed framework consists of best frame selection algorithm, modified diamond search algorithm, Gaussian process regression (GPR) based and enhanced iterated back projection (EIBP)‐based super‐resolution approach, fuzzy entropy‐based feature selector and neural network (NN) classifier. The framework uses linear kernel co‐variance function in local patch‐based GPR and EIBP algorithms and it super resolves the iris images depending on the contents of the patches, without an external dataset. NN classifier classifies the iris images by using features extracted using discrete cosine transform domain based no‐reference image quality assessment model, Gray level co‐occurrence matrix, Hu seven moments and statistical features. The framework is tested using CASIA long range iris database by comparing and analysing the peak signal‐to‐noise ratio, structural similarity index matrix and visual information fidelity in pixel domain of proposed algorithms with Yang and Nguyen framework. The results demonstrate that the proposed framework is well suited for recognition of iris images captured at a long distance. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
42. Modeling Electrode Place Discrimination in Cochlear Implant Stimulation.
- Author
-
Gao, Xiao, Grayden, David B., and McDonnell, Mark D.
- Subjects
- *
ELECTRODES , *COCHLEAR implants , *BIOLOGICAL systems , *NEURAL circuitry , *ARTIFICIAL neural networks - Abstract
Objective: By modeling the cochlear implant (CI) electrode-to-nerve interface and quantifying electrode discriminability in the model, we address the questions of how many individual channels can be distinguished by CI recipients and the extent to which performance might be improved by inserting electrodes deeper into the cochlea. Method: We adapt an artificial neural network to model electrode discrimination as well as a commonly used psychophysical measure (four-interval forced-choice) in CI stimulation and predict how well the locations of the stimulating electrodes can be inferred from simulated auditory nerve spiking patterns. Results: We show that a longer electrode leads to better electrode place discrimination in our model. For a simulated four-interval forced-choice procedure, correct classification rates significantly reduce with decreasing distance between the test electrodes and the reference electrodes, and higher correct classification rates may be achieved by the basal electrodes than apical electrodes. Conclusion: Our results suggest that enhanced electrode discriminability results from a longer CI electrode array, and the locations where the errors occur along the electrode array are not only affected by the distance between electrodes but also the twirling angle between electrodes. Significance: Our models and simulations provide theoretical insights into several important clinically relevant problems that will inform future designs of CI electrode arrays and stimulation strategies. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
43. Gas Sensors Characterization and Multilayer Perceptron (MLP) Hardware Implementation for Gas Identification Using a Field Programmable Gate Array (FPGA)
- Author
-
Mokhtar Attari, Mounir Bouhedda, and Fayçal Benrekia
- Subjects
e-nose ,gas sensor array ,pattern recognition ,neural network classifier ,pic-microcontroller ,FPGA-implementation ,Chemical technology ,TP1-1185 - Abstract
This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO2 thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases.
- Published
- 2013
- Full Text
- View/download PDF
44. Animal Detection and its Disease Prediction by Neural Network Classifier
- Author
-
M. Bharaneedharan
- Subjects
Computer science ,business.industry ,Pattern recognition ,Disease ,Artificial intelligence ,business ,Neural network classifier - Published
- 2020
45. A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections
- Author
-
Michael B. Mayhew, Uros Midic, Ljubomir Buturovic, Andrew R. Moore, David Rawling, Roland Luethy, Jonasel Roque, James Wacker, Angela J. Rogers, Timothy E. Sweeney, Tola Asuni, Kirindi Choi, Purvesh Khatri, Melissa Remmel, and Brian D. Shaller
- Subjects
Male ,0301 basic medicine ,Support Vector Machine ,Antibiotics ,Datasets as Topic ,General Physics and Astronomy ,0302 clinical medicine ,Medicine ,Hospital Mortality ,030212 general & internal medicine ,Independent data ,lcsh:Science ,Aged, 80 and over ,Multidisciplinary ,Training set ,Bacterial Infections ,Middle Aged ,3. Good health ,Intensive Care Units ,Virus Diseases ,Acute Disease ,Host-Pathogen Interactions ,Hospital admission ,Cohort ,Female ,Adult ,medicine.medical_specialty ,medicine.drug_class ,Science ,education ,Neural network classifier ,Article ,General Biochemistry, Genetics and Molecular Biology ,Sepsis ,03 medical and health sciences ,Internal medicine ,Humans ,In patient ,RNA, Messenger ,Aged ,business.industry ,Gene Expression Profiling ,Diagnostic markers ,General Chemistry ,medicine.disease ,Computational biology and bioinformatics ,030104 developmental biology ,ROC Curve ,Viral infection ,lcsh:Q ,Neural Networks, Computer ,Bacterial infection ,business - Abstract
Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable host-gene-expression-based classifier for acute bacterial and viral infections. We use training data (N = 1069) from 18 retrospective transcriptomic studies. Using only 29 preselected host mRNAs, we train a neural-network classifier with a bacterial-vs-other area under the receiver-operating characteristic curve (AUROC) 0.92 (95% CI 0.90–0.93) and a viral-vs-other AUROC 0.92 (95% CI 0.90–0.93). We then apply this classifier, inflammatix-bacterial-viral-noninfected-version 1 (IMX-BVN-1), without retraining, to an independent cohort (N = 163). In this cohort, IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.86 (95% CI 0.77–0.93), and viral-vs.-other 0.85 (95% CI 0.76–0.93). In patients enrolled within 36 h of hospital admission (N = 70), IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.92 (95% CI 0.83–0.99), and viral-vs.-other 0.91 (95% CI 0.82–0.98). With further study, IMX-BVN-1 could provide a tool for assessing patients with suspected infection and sepsis at hospital admission., Diagnosing acute infections based on transcriptional host response shows promise, but generalizability is wanting. Here, the authors use a co-normalization framework to train a classifier to diagnose acute infections and apply it to independent data on a targeted diagnostic platform.
- Published
- 2020
46. MULTI-CLASS RECOGNITION FOR DETERMINATION THE TECHNICAL STATE OF THE WELDED JOINT TANK WITH DEFECTS BY USING NEURAL NETWORK CLASSIFIER
- Author
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Olga Lukianchenko and Serhii Rupich
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Probabilistic neural network ,Computer science ,business.industry ,Structural integrity ,Pattern recognition ,Monitoring system ,Artificial intelligence ,business ,Geometric modeling ,General Economics, Econometrics and Finance ,Classifier (UML) ,Neural network classifier - Abstract
The paper shows the efficiency of the multi-class recognition of technical states the welded joint tank by using the neural network classifier that based on Probabilistic Neural Network. Implementation of modern multichannel monitoring systems for determination the technical state of spatial objects requires researching of changes of a stress-strain state construction elements which function under the operation pressure and possible impact its structural integrity. Such researching are needed for prevention of cracks or damage determining and future prediction of object’s technical state. In the paper the tasks of a multi-class recognition of a state of multi-site damage the welded joint tank are determined. In the research was created the model of the object with probable location places cracks. One of them is a vertical crack and two are horizontal. Directions of their propagation are given. Cracks have the same value. The first researching included when cracks turn up one by one. The next one related with multi-focal defects when cracks were arised and they evolve in parallel and independently. The data of strain in the structural of the welded joint tank where sensors were attached is given. The research of the possibility of the error-free recognition was conducted by the developed classifier based on the stress-strain state of the geometric model of the tank structural elements with multi-site damage, where sensors are located. The development of the classifier was done by using Probabilistic Neural Network, which provides the best results of a multi-class recognition for determination technical state of spatial object with multidimensional vectors of diagnostic features. As a result, the probability of recognition from the network influence parameter, which shows the effectiveness of the neural network classifier for localization of single damage and localization of multiple cracks, was established
- Published
- 2019
47. Evaluation of a New Neural Network Classifier for Diabetic Retinopathy
- Author
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Naomi Kirshner, Liz Cohen, Yaacov Hoch, Aviel Hadad, Tsvi Lev, David R. Owens, Richard John Hewitt, Or Katz, Dan Presil, and Roi Nachmani
- Subjects
Diabetic Retinopathy ,Computer science ,business.industry ,Endocrinology, Diabetes and Metabolism ,Biomedical Engineering ,Bioengineering ,Image processing ,Pattern recognition ,Image segmentation ,Diabetic retinopathy ,Original Articles ,Diagnostic Techniques, Ophthalmological ,medicine.disease ,Neural network classifier ,Retinal screening ,Field (computer science) ,Macular Edema ,Internal Medicine ,medicine ,Photography ,Diabetes Mellitus ,Humans ,Artificial intelligence ,Neural Networks, Computer ,business - Abstract
Background: Medical image segmentation is a well-studied subject within the field of image processing. The goal of this research is to create an AI retinal screening grading system that is both accurate and fast. We introduce a new segmentation network which achieves state-of-the-art results on semantic segmentation of color fundus photographs. By applying the net-work to identify anatomical markers of diabetic retinopathy (DR) and diabetic macular edema (DME), we collect sufficient information to classify patients by grades R0 and R1 or above, M0 and M1. Methods: The AI grading system was trained on screening data to evaluate the presence of DR and DME. The core algorithm of the system is a deep learning network that segments relevant anatomical features in a retinal image. Patients were graded according to the standard NHS Diabetic Eye Screening Program feature-based grading protocol. Results: The algorithm performance was evaluated with a series of 6,981 patient retinal images from routine diabetic eye screenings. It correctly predicted 98.9% of retinopathy events and 95.5% of maculopathy events. Non-disease events prediction rate was 68.6% for retinopathy and 81.2% for maculopathy. Conclusion: This novel deep learning model was trained and tested on patient data from annual diabetic retinopathy screenings can classify with high accuracy the DR and DME status of a person with diabetes. The system can be easily reconfigured according to any grading protocol, without running a long AI training procedure. The incorporation of the AI grading system can increase the graders’ productivity and improve the final outcome accuracy of the screening process.
- Published
- 2021
48. Passive method for rescale detection using quadrature mirror filter based higher order statistical features.
- Author
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Birajdar, Gajanan K. and Mankar, Vijay H.
- Subjects
- *
QUADRATURE mirror filters , *HIGH resolution imaging , *DIGITAL cameras , *STATISTICAL models , *ROBUST control - Abstract
High resolution digital cameras and state-of-the-art image editing software tools has given rise to large amount of manipulated images leaving no traces of being subjected to any manipulation. Passive or blind forgery detection algorithms are used in order to determine its authenticity. In this paper, an algorithm is proposed that blindly detects global rescaling operation using the statistical models computed based on quadrature mirror filter (QMF) decomposition. Fuzzy entropy measure is employed to choose the relevant features and to remove non-important features whereas artificial neural network classifier is used for forgery detection. Experimental results are presented on grayscale and -component images of UCID database to prove the validity of the algorithm under different interpolation schemes. Results are provided for the detection of rescaled images with JPEG compression, arbitrary cropping and white Gaussian noise addition. Further, results are shown using USC-SIPI database to prove the robustness of the algorithm against the type of database. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
49. Colon Cancer Detection Using Hybrid Features and Genetically Optimized Neural Network Classifier
- Author
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Samrat Pundalik Khadilkar
- Subjects
Artificial neural network ,Computer science ,business.industry ,Colorectal cancer ,Gabor wavelet ,Pattern recognition ,medicine.disease ,Computer Graphics and Computer-Aided Design ,Neural network classifier ,Computer Science Applications ,Task (project management) ,Biological property ,medicine ,Computer Vision and Pattern Recognition ,Artificial intelligence ,business - Abstract
Computer-assisted colon cancer detection on the histopathological images has become a tedious task due to its shape characteristics and other biological properties. The images acquired through the histopathological microscope may vary in magnifications for better visibility. This may change the morphological properties and hence an automated magnification independent colon cancer detection system is essential. The manual diagnosis of colon biopsy images is subjective, sluggish, laborious leading to nonconformity between histopathologists due to visual evaluation at various microscopic magnifications. Automatic detection of colon across image magnifications is challenging due to many aspects like tailored segmentation and varying features. This demands techniques that take advantage of the textural, color, and geometric properties of colon tissue. This work exhibits a segmentation approach based on the morphological features derived from the segmented region. Gabor Wavelet, Harris Corner, and DWT-LBP coefficients are extracted as it should not be dependent on the spatial domain with respect to the magnification. These features are fed to the Genetically Optimized Neural Network classifier to classify them as normal and malignant ones. Here, the genetic algorithm is used to learn the best hyper-parameters for a neural network.
- Published
- 2021
50. Neural network classifiers for images of genetic conditions with cutaneous manifestations
- Author
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Ping Hu, Cedrik Tekendo-Ngongang, Rebekah L. Waikel, Dat Duong, and Benjamin D. Solomon
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Artificial neural network ,Pixel ,Computer science ,business.industry ,Deep learning ,Body segment ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,deep learning ,Pattern recognition ,QH426-470 ,artificial intelligence ,Neural network classifier ,Article ,genetic conditions ,machine learning ,genomics ,Molecular Medicine ,genetics ,medical genomics ,Artificial intelligence ,Skin lesion ,business ,Classifier (UML) ,Genetics (clinical) ,Skin Findings - Abstract
Summary Neural networks have shown strong potential in research and in healthcare. Mainly due to the need for large datasets, these applications have focused on common medical conditions, where more data are typically available. Leveraging publicly available data, we trained a neural network classifier on images of rare genetic conditions with skin findings. We used approximately 100 images per condition to classify 6 different genetic conditions. We analyzed both preprocessed images that were cropped to show only the skin lesions as well as more complex images showing features such as the entire body segment, the person, and/or the background. The classifier construction process included attribution methods to visualize which pixels were most important for computer-based classification. Our classifier was significantly more accurate than pediatricians or medical geneticists for both types of images and suggests steps for further research involving clinical scenarios and other applications., We built a neural network classifier for images of skin findings in selected genetic conditions. We compared the accuracy (using both cropped and larger images) of our classifier to that of pediatricians and medical geneticists. We show how attribution methods can help refine a classifier.
- Published
- 2021
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