In order to identify the right or wrong wiring of the smart meter box, a hybrid model composed of the object detection model and the image classification model was built to detect the key parts of the smart meter box ...
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The chest X-ray (CXR) is a widely used and easily accessible medical test for diagnosing common chest diseases. Recently, there have been numerous advancements in deep learning-based methods capable of effectively cla...
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ISBN:
(数字)9783031438981
ISBN:
(纸本)9783031438974;9783031438981
The chest X-ray (CXR) is a widely used and easily accessible medical test for diagnosing common chest diseases. Recently, there have been numerous advancements in deep learning-based methods capable of effectively classifying CXR. However, assessing whether these algorithms truly capture the cause-and-effect relationship between diseases and their underlying causes, or merely learn to map labels to images, remains a challenge. In this paper, we propose a causal approach to address the CXR classification problem, which involves constructing a structural causal model (SCM) and utilizing backdoor adjustment to select relevant visual information for CXR classification. Specifically, we design various probability optimization functions to eliminate the influence of confounding factors on the learning of genuine causality. Experimental results demonstrate that our proposed method surpasses the performance of two open-source datasets in terms of classification performance. To access the source code for our approach, please visit: https://***/zc2024/Causal_CXR.
Humans express emotions verbally and non-verbally through their voice, facial expressions, and body language. Facial expression recognition systems can identify the emotional state of any person by using different int...
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ISBN:
(纸本)9798350387896;9798350387889
Humans express emotions verbally and non-verbally through their voice, facial expressions, and body language. Facial expression recognition systems can identify the emotional state of any person by using different intelligent algorithms, such as Support Vector Machines, Hidden Markov Models, and Convolutional Neural Networks, among others. This study focuses on facial expression recognition using eye and mouth regions of images from the FER-2013 dataset by training convolutional neural network (CNN) models. Seven emotional states - happy, sad, fear, anger, disgust, surprise and neutral - were identified. The methodology included segmenting and concatenating the images to form three CNN models. The best-performing model, a four-layer CNN with 8, 16, 32, and 64 filters, achieved remarkable results: 99.05% accuracy, 100.00% precision, 93.75% recall, 96.77% F1-score, 95.95% validation accuracy, and a 0.15 validation loss with a processing time of 3.03 minutes. It was possible to develop a CNN model capable of identifying seven emotional states from only the data of the eye and mouth region using concatenated images.
In the lighting conditions such as snowing, hazing, raining, and weak lighting condition, the accuracy of traffic sign recognition is not very high. It is important to develop an algorithms for real-time fast detectio...
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Through computers, artists have found a way to enhance their production, discovering new ways for communicating their productions and devising new forms of expression. Being able to make the most of these facilities r...
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The goal of this work is to apply a denoising image transformer to remove the distortion from underwater images and compare it with other similar approaches. Automatic restoration of underwater images plays an importa...
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ISBN:
(纸本)9783031064333;9783031064326
The goal of this work is to apply a denoising image transformer to remove the distortion from underwater images and compare it with other similar approaches. Automatic restoration of underwater images plays an important role since it allows to increase the quality of the images, without the need for more expensive equipment. This is a critical example of the important role of the machine learning algorithms to support marine exploration and monitoring, reducing the need for human intervention like the manual processing of the images, thus saving time, effort, and cost. This paper is the first application of the image transformer-based approach called "Pre-Trained imageprocessing Transformer" to underwater images. This approach is tested on the UFO-120 dataset, containing 1500 images with the corresponding clean images.
Intestinal parasitic infections in animals can cause a range of symptoms, including diarrhea, weight loss, anemia, and malnutrition. This project aims to classify parasitic eggs belonging to the Monezia and Strongyles...
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The results of studies of the morphological analysis of the text are demonstrated. Application of the technology of automatic processing of Russian-language texts to determine the parts of speech presented in the digi...
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Preventing unintentional leakage of information about the training set has high relevance for many machine learning tasks, such as medical image segmentation. While differential privacy (DP) offers mathematically rigo...
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ISBN:
(纸本)9781665405409
Preventing unintentional leakage of information about the training set has high relevance for many machine learning tasks, such as medical image segmentation. While differential privacy (DP) offers mathematically rigorous protection, the high output dimensionality of segmentation tasks prevents the direct application of state-of-the-art algorithms such as Private Aggregation of Teacher Ensembles (PATE). In order to alleviate this problem, we propose to learn dimensionality-reducing transformations to map the prediction target into a bounded lower-dimensional space to reduce the required noise level during the aggregation stage. To this end, we assess the suitability of principal component analysis (PCA) and autoencoders. We conclude that autoencoders are an effective means to reduce the noise in the target variables.
Agriculture plays a foremost role in countries growth. The physical recognition of disease in the plant is more timeconsuming and necessity of expert labor is high. One of the most vital aspect in agriculture field is...
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