Electrocardiography (ECG) signals provides assistance to the cardiologists for identification of various cardiovascular diseases (CVD). ECG machine records the electrical activity of the heart with the assistance of e...
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Electrocardiography (ECG) signals provides assistance to the cardiologists for identification of various cardiovascular diseases (CVD). ECG machine records the electrical activity of the heart with the assistance of electrodes placed on the patient's body. Qualitative characterization of ECG signal reflects its sensitiveness towards distinct artifacts that resulted in low diagnostic accuracy and may lead to incorrect decision of the clinician. The artifacts are removed utilizing a robust noise estimator employing DTCWT using various threshold values and functions. The segments and intervals of ECG signals are calculated using the peak detection algorithm followed by particle swarm optimization (PSO) and the proposed optimization technique to select the best features from a considerable pool of features. Out of the 12 features, the best four features are selected using PSO and the proposed optimization technique. Comparative analysis with other feature selection methods and state-of-the-art techniques demonstrated that the proposed algorithm precisely selects principle features for handling the ECG signal and attains better classification utilizing distinctive machine learning algorithms. The obtained accuracy using our proposed optimization technique is 95.71% employing k-NN and neural networks. Also, 4% and 10% improvements have been observed while using k-NN over ANN and SVM, respectively, when the PSO technique is executed. Similarly, a 14.16% improvement is achieved while using k-NN and ANN over the SVM machine learning technique for the proposed optimization technique. Heart rate is calculated using the proposed estimator and optimization technique, which is in consensus with the gold standard.
Quality assessment of agricultural products is one of the most important factors in promoting their marketability and waste control management. imageprocessing systems are new and non-destructive methods that have va...
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Quality assessment of agricultural products is one of the most important factors in promoting their marketability and waste control management. imageprocessing systems are new and non-destructive methods that have various applications in the agriculture sector, including product grading. The purpose of this study is to use an improved CNN algorithm to detect the apparent defects of sour lemon fruit, grade them and provide an efficient system to do so. In order to identify and categorize defects, sour lemon images were prepared and placed in two groups of healthy and damaged ones. After pre-processing, the images were categorized based on an improved algorithm (CNN). From the data augmentation and the stochastic pooling mechanism were used to improve CNN results. In addition, to compare the proposed model with other methods, feature extraction algorithms (histogram of oriented gradients (HOG) and local binary patterns (LBP)) and k-nearest neighbour (KNN), artifical neural network (ANN), Fuzzy, support vector machine (SVM) and decision tree (DT) classification algorithms were used. The results showed that the accuracy of the convolutional neural network (CNN) was 100 %. Therefore, it can be said that the CNN method and imageprocessing are effective in managing waste and promoting the traditional method of sour lemon grading.
t-distributed stochastic neighbor embedding (t-SNE) is a well-established visualization method for complex high-dimensional data. However, the original t-SNE method is nonparametric, stochastic, and often cannot well ...
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While achieving remarkable success in remote sensing (RS) scene classification for the past few years, convolutional neural network (CNN) based methods suffer from the demand for large amounts of training data. The bo...
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While achieving remarkable success in remote sensing (RS) scene classification for the past few years, convolutional neural network (CNN) based methods suffer from the demand for large amounts of training data. The bottleneck in prediction accuracy has shifted from data processing limits toward a lack of ground truth samples, usually collected manually by experienced experts. In this work, we provide a metalearning framework for few-shot classification of RS scene. Under the umbrella of meta-learning, we show it is possible to learn much information about a new category from only 1 or 5 samples. The proposed method is based on Prototypical Networks with a pre-trained stage and a learnable similarity metric. The experimental results show that our method outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN, on two challenging datasets: NWPU-RESISC45 and RSD46-WHU.
As early intervention is highly effective for young children with autism spectrum disorder (ASD), it is imperative to make accurate diagnosis as early as possible. ASD has often been associated with atypical visual at...
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As early intervention is highly effective for young children with autism spectrum disorder (ASD), it is imperative to make accurate diagnosis as early as possible. ASD has often been associated with atypical visual attention and eye gaze data can be collected at a very early age. An automatic screening tool based on eye gaze data that could identify ASD risk offers the opportunity for intervention before the full set of symptoms is present. In this paper, we propose two machine learning methods, synthetic saccade approach and image based approach, to automatically classify ASD given children?s eye gaze data collected from free-viewing tasks of natural images. The first approach uses a generative model of synthetic saccade patterns to represent the baseline scan-path from a typical non-ASD individual and combines it with the real scan-path as well as other auxiliary data as inputs to a deep learning classifier. The second approach adopts a more holistic image based approach by feeding the input image and a sequence of fixation maps into a convolutional or recurrent neural network. Using a publicly-accessible collection of children?s gaze data, our experiments indicate that the ASD prediction accuracy reaches 67.23% accuracy on the validation dataset and 62.13% accuracy on the test dataset.
image deblurring is an essential task in computer vision, focusing on restoring sharp and clear images from blurred inputs caused by camera motion, object movement, or defocus. Improving image clarity not only enhance...
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The anti-counterfeiting information is implanted into the QR Code with tiny pixels as single halftone dot. This method to realize information hiding and anti-counterfeiting is one of the research hotspots in this fiel...
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stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models. However, the majority of ...
ISBN:
(数字)9781509066315
ISBN:
(纸本)9781509066322
stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models. However, the majority of existing methods have limited expressive power due to the Gaussian assumption of latent variables. In this paper, we advocate learning implicit latent representations using semi-implicit variational inference to further increase model flexibility. Semi-implicit stochastic recurrent neural network (SIS-RNN) is developed to enrich inferred model posteriors that may have no analytic density functions, as long as independent random samples can be generated via reparameterization. Extensive experiments in different tasks on real-world datasets show that SIS-RNN outperforms the existing methods.
Fake speech consists on voice recordings created even by artificial intelligence or signalprocessing techniques. Among the methods for generating false voice recordings are Deep Voice and Imitation. In Deep voice, th...
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Fake speech consists on voice recordings created even by artificial intelligence or signalprocessing techniques. Among the methods for generating false voice recordings are Deep Voice and Imitation. In Deep voice, the recordings sound slightly synthesized, whereas in Imitation, they sound natural. On the other hand, the task of detecting fake content is not trivial considering the large number of voice recordings that are transmitted over the Internet. In order to detect fake voice recordings obtained by Deep Voice and Imitation, we propose a solution based on a Convolutional neural Network (CNN), using image augmentation and dropout. The proposed architecture was trained with 2092 histograms of both original and fake voice recordings and cross-validated with 864 histograms. 476 new histograms were used for external validation, and Precision (P) and Recall (R) were calculated. Detection of fake audios reached P = 0.997, R = 0.997 for Imitation-based recordings, and P = 0.985, R = 0.944 for Deep Voice-based recordings. The global accuracy was 0.985. According to the results, the proposed system is successful in detecting fake voice content.
Mango (Mangifera Indica L) is part of a fruit plant species that have different color and texture characteristics to indicate its type. The identification of the types of mangoes uses the manual method through direct ...
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