共查询到20条相似文献，搜索用时 15 毫秒
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给出了大数据和机器学习的子领域——深度学习的概念,阐述了深度学习对获取大数据中的有价值信息的重要作用。描述了大数据下利用图像处理单元(GPU)进行并行运算的深度学习框架,对其中的大规模卷积神经网络(CNN)、大规模深度置信网络(DBN)和大规模递归神经网络(RNN)进行了重点论述。分析了大数据的容量、多样性、速率特征,介绍了大规模数据、多样性数据、高速率数据下的深度学习方法。展望了大数据背景下深度学习的发展前景,指出在不远的将来,大数据与深度学习融合的技术将会在计算机视觉、机器智能等多个领域获得突破性进展。 相似文献
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针对多种定位因素存在复杂关联且不易准确提取的问题,提出了以完整双耳声信号作为输入的、基于深度学习的双耳声源定位算法。首先,分别采用深层全连接后向传播神经网络(Deep Back Propagation Neural Network,D-BPNN)和卷积神经网络(Convolutional Neural Network, CNN)实现深度学习框架;然后,分别以水平面 15°、30°和 45°空间角度间隔的双耳声信号进行模型训练;最后,采用前后混乱率、定位准确率与训练时长等指标进行算法有效性分析。模型预测结果表明,CNN模型的前后混乱率远低于 D-BPNN;D-BPNN模型的定位准确率能够达到87%以上,而 CNN模型的定位准确率能够达到 98%左右;在相同实验条件下,CNN模型的训练时长大于 D-BPNN,且随着水平面角度间隔的减小,两者训练时长之间的差异愈发显著。 相似文献
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The healthcare industry has been significantly impacted by the widespread adoption of advanced technologies such as deep learning (DL) and artificial intelligence (AI). Among various applications, computer-aided diagnosis has become a critical tool to enhance medical practice. In this research, we introduce a hybrid approach that combines a deep neural model, data collection, and classification methods for CT scans. This approach aims to detect and classify the severity of pulmonary disease and the stages of lung cancer. Our proposed lung cancer detector and stage classifier (LCDSC) demonstrate greater performance, achieving higher accuracy, sensitivity, specificity, recall, and precision. We employ an active contour model for lung cancer segmentation and high-resolution net (HRNet) for stage classification. This methodology is validated using the industry-standard benchmark image dataset lung image database consortium and image database resource initiative (LIDC-IDRI). The results show a remarkable accuracy of 98.4% in classifying lung cancer stages. Our approach presents a promising solution for early lung cancer diagnosis, potentially leading to improved patient outcomes. 相似文献
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Sourav Kumar Tanwar;Prakash Choudhary; Priyanka;Tarun Agrawal; 《International journal of imaging systems and technology》2024,34(4):e23149
Classifying fetal ultrasound images into different anatomical categories, such as the abdomen, brain, femur, thorax, and so forth can contribute to the early identification of potential anomalies or dangers during prenatal care. Ignoring major abnormalities that might lead to fetal death or permanent disability. This article proposes a novel hybrid capsule network architecture-based method for identifying fetal ultrasound images. The proposed architecture increases the precision of fetal image categorization by combining the benefits of a capsule network with a convolutional neural network. The proposed hybrid model surpasses conventional convolutional network-based techniques with an overall accuracy of 0.989 when tested on a publicly accessible dataset of prenatal ultrasound images. The results indicate that the proposed hybrid architecture is a promising approach for precisely and consistently classifying fetal ultrasound images, with potential uses in clinical settings. 相似文献
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阀门作为天然气管线的关键部件,若发生内漏会带来经济损失及生产安全隐患。因此,阀门内漏的有效诊断及内漏速率的准确量化具有重大意义。针对复杂背景噪声下内漏诊断效率不高的问题,以内漏信号和非泄漏噪声信号的功率谱密度图作为输入,构建了阀门内漏卷积神经网络(convolutional neural network, CNN)识别模型;针对物理理论及浅层网络模型在多工况阀门内漏数据上存在量化误差大的问题,构建了阀门内漏速率深度信念网络(deep belief network, DBN)量化回归模型,并与支持向量回归机、BP神经网络等模型进行了对比研究。研究结果表明:所构建模型的内漏识别准确率及内漏速率量化平均绝对百分比误差分别为99%和9.101 2,证实了所构建模型的高效性,为阀门内漏诊断与评价开拓了新的研究方向。 相似文献
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阀门作为天然气管线的关键部件,若发生内漏会带来经济损失及生产安全隐患。因此,阀门内漏的有效诊断及内漏速率的准确量化具有重大意义。针对复杂背景噪声下内漏诊断效率不高的问题,以内漏信号和非泄漏噪声信号的功率谱密度图作为输入,构建了阀门内漏卷积神经网络(convolutional neural network, CNN)识别模型;针对物理理论及浅层网络模型在多工况阀门内漏数据上存在量化误差大的问题,构建了阀门内漏速率深度信念网络(deep belief network, DBN)量化回归模型,并与支持向量回归机、BP神经网络等模型进行了对比研究。研究结果表明:所构建模型的内漏识别准确率及内漏速率量化平均绝对百分比误差分别为99%和9.101 2,证实了所构建模型的高效性,为阀门内漏诊断与评价开拓了新的研究方向。 相似文献
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针对水下多目标方位估计问题,提出了一种利用卷积神经网络模型对目标声源进行方位估计的方法。该方法使用不等强度的声源数据进行训练并使用焦点损失函数作为训练损失函数。通过对阵列接收到的信号进行特征提取,使用焦点损失函数指导卷积神经网络训练,最终利用训练好的卷积神经网络模型进行目标方位估计。对不同模型参数的训练进行对比,结果表明所训练的卷积神经网络模型在较低信噪比条件下也能正确估计弱目标的方位。试验结果表明,与采用二元交叉熵损失函数的卷积神经网络模型相比,该方法对弱目标的方位估计能力更强,提高了方位估计的准确率。 相似文献
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This research proposed an improved transfer-learning bird classification framework to achieve a more precise classification of Protected Indonesia Birds (PIB) which have been identified as the endangered bird species. The framework takes advantage of using the proposed sequence of Batch Normalization Dropout Fully-Connected (BNDFC) layers to enhance the baseline model of transfer learning. The main contribution of this work is the proposed sequence of BNDFC that can be applied to any Convolutional Neural Network (CNN) based model to improve the classification accuracy, especially for image-based species classification problems. The experiment results show that the proposed sequence of BNDFC layers outperform other combination of BNDFC. The addition of BNDFC can improve the model’s performance across ten different CNN-based models. On average, BNDFC can improve by approximately 19.88% in Accuracy, 24.43% in F-measure, 17.93% in G-mean, 23.41% in Sensitivity, and 18.76% in Precision. Moreover, applying fine-tuning (FT) is able to enhance the accuracy by 0.85% with a smaller validation loss of 18.33% improvement. In addition, MobileNetV2 was observed to be the best baseline model with the lightest size of 35.9 MB and the highest accuracy of 88.07% in the validation set. 相似文献
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Shui-Hua Wang Muhammad Attique Khan Vishnuvarthanan Govindaraj Steven L. Fernandes Ziquan Zhu Yu-Dong Zhang 《计算机、材料和连续体（英文）》2022,70(2):2797-2810
(Aim) To make a more accurate and precise COVID-19 diagnosis system, this study proposed a novel deep rank-based average pooling network (DRAPNet) model, i.e., deep rank-based average pooling network, for COVID-19 recognition. (Methods) 521 subjects yield 1164 slice images via the slice level selection method. All the 1164 slice images comprise four categories: COVID-19 positive; community-acquired pneumonia; second pulmonary tuberculosis; and healthy control. Our method firstly introduced an improved multiple-way data augmentation. Secondly, an n-conv rank-based average pooling module (NRAPM) was proposed in which rank-based pooling—particularly, rank-based average pooling (RAP)—was employed to avoid overfitting. Third, a novel DRAPNet was proposed based on NRAPM and inspired by the VGG network. Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis. (Results) Our DRAPNet achieved a micro-averaged F1 score of 95.49% by 10 runs over the test set. The sensitivities of the four classes were 95.44%, 96.07%, 94.41%, and 96.07%, respectively. The precisions of four classes were 96.45%, 95.22%, 95.05%, and 95.28%, respectively. The F1 scores of the four classes were 95.94%, 95.64%, 94.73%, and 95.67%, respectively. Besides, the confusion matrix was given. (Conclusions) The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases. The RAP gives better results than four other methods: strided convolution, l2-norm pooling, average pooling, and max pooling. 相似文献
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Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019. Due to the similarity in initial symptoms with viral fever, it is challenging to identify this virus initially. Non-detection of this virus at the early stage results in the death of the patient. Developing and densely populated countries face a scarcity of resources like hospitals, ventilators, oxygen, and healthcare workers. Technologies like the Internet of Things (IoT) and artificial intelligence can play a vital role in diagnosing the COVID-19 virus at an early stage. To minimize the spread of the pandemic, IoT-enabled devices can be used to collect patient’s data remotely in a secure manner. Collected data can be analyzed through a deep learning model to detect the presence of the COVID-19 virus. In this work, the authors have proposed a three-phase model to diagnose covid-19 by incorporating a chatbot, IoT, and deep learning technology. In phase one, an artificially assisted chatbot can guide an individual by asking about some common symptoms. In case of detection of even a single sign, the second phase of diagnosis can be considered, consisting of using a thermal scanner and pulse oximeter. In case of high temperature and low oxygen saturation levels, the third phase of diagnosis will be recommended, where chest radiography images can be analyzed through an AI-based model to diagnose the presence of the COVID-19 virus in the human body. The proposed model reduces human intervention through chatbot-based initial screening, sensor-based IoT devices, and deep learning-based X-ray analysis. It also helps in reducing the mortality rate by detecting the presence of the COVID-19 virus at an early stage. 相似文献
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刘忠;周泽华;邹淑云;刘圳;乔帅程 《发电技术》2025,46(1):161-170
目的 当水轮机发生空化故障时;机组效率下降、部件侵蚀加速;严重时甚至引发安全事故。因此;准确且快速识别水轮机空化状态;对水电站高效、安全运行至关重要。针对目前复杂卷积神经网络(convolutional neural network;CNN)模型存在的识别速度慢与简单CNN模型存在的识别准确率低等问题;提出一种基于知识蒸馏(knowledge distillation;KD)-CNN的水轮机空化状态识别方法。 方法 首先;引入知识蒸馏理论中教师模型与学生模型相互作用机理;定义3层CNN网络作为教师模型;定义单层CNN网络作为学生模型;然后;利用试验获取的空化声发射信号数据对教师模型进行训练;最后;将代表空化状态类型的数据标签替换成教师模型的输出;通过学生模型对替换标签后的新数据集进行学习;使交叉熵达到最小值。训练完成后的模型即为KD-CNN模型;利用该模型对各工况数据进行空化状态识别试验。 结果 KD-CNN模型在2 s内即可完成水轮机空化状态识别;且各工况的识别准确率均高于97%。 结论 KD-CNN模型结构简单;同时具有学生模型的识别速度与教师模型的识别准确率;为水轮机空化实时监测提供了新思路。 相似文献
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The increasing capabilities of Artificial Intelligence (AI), has led researchers and visionaries to think in the direction of machines outperforming humans by gaining intelligence equal to or greater than humans, which may not always have a positive impact on the society. AI gone rogue, and Technological Singularity are major concerns in academia as well as the industry. It is necessary to identify the limitations of machines and analyze their incompetence, which could draw a line between human and machine intelligence. Internet memes are an amalgam of pictures, videos, underlying messages, ideas, sentiments, humor, and experiences, hence the way an internet meme is perceived by a human may not be entirely how a machine comprehends it. In this paper, we present experimental evidence on how comprehending Internet Memes is a challenge for AI. We use a combination of Optical Character Recognition techniques like Tesseract, Pixel Link, and East Detector to extract text from the memes, and machine learning algorithms like Convolutional Neural Networks (CNN), Region-based Convolutional Neural Networks (RCNN), and Transfer Learning with pre-trained denseNet for assessing the textual and facial emotions combined. We evaluate the performance using Sensitivity and Specificity. Our results show that comprehending memes is indeed a challenging task, and hence a major limitation of AI. This research would be of utmost interest to researchers working in the areas of Artificial General Intelligence and Technological Singularity.
相似文献16.
In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy (DR), which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy (DR) can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying the lesions are high. Similarly, training for Convolutional Neural Networks (CNN) is more time-consuming. This proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model (EDLM) for timely DR identification that is potentially more accurate than existing CNN-based models. The proposed model will detect various lesions from retinal images in the early stages. First, characteristics are retrieved from the retinal fundus picture and put into the EDLM for classification. For dimensionality reduction, EDLM is used. Additionally, the classification and feature extraction processes are optimized using the stochastic gradient descent (SGD) optimizer. The EDLM’s effectiveness is assessed on the KAGGLE dataset with 3459 retinal images, and results are compared over VGG16, VGG19, RESNET18, RESNET34, and RESNET50. Experimental results show that the EDLM achieves higher average sensitivity by 8.28% for VGG16, by 7.03% for VGG19, by 5.58% for ResNet18, by 4.26% for ResNet 34, and by 2.04% for ResNet 50, respectively. 相似文献
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Shahan Yamin Siddiqui Sagheer Abbas Muhammad Adnan Khan Iftikhar Naseer Tehreem Masood Khalid Masood Khan Mohammed A. Al Ghamdi Sultan H. Almotiri 《计算机、材料和连续体（英文）》2021,66(2):1719-1732
The prompt spread of Coronavirus (COVID-19) subsequently adorns a big threat to the people around the globe. The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector. Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected. Lately, the testing kits for COVID-19 are not available to deal it with required proficiency, along with-it countries have been widely hit by the COVID-19 disruption. To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19. It would be a feather in the cap if the early diagnosis of COVID-19 could reveal that how it has been affecting the masses immensely. According to the apparent clinical research, it has unleashed that most of the COVID-19 cases are more likely to fall for a lung infection. The abrupt changes do require a solution so the technology is out there to pace up, Chest X-ray and Computer tomography (CT) scan images could significantly identify the preliminaries of COVID-19 like lungs infection. CT scan and X-ray images could flourish the cause of detecting at an early stage and it has proved to be helpful to radiologists and the medical practitioners. The unbearable circumstances compel us to flatten the curve of the sufferers so a need to develop is obvious, a quick and highly responsive automatic system based on Artificial Intelligence (AI) is always there to aid against the masses to be prone to COVID-19. The proposed Intelligent decision support system for COVID-19 empowered with deep learning (ID2S-COVID19-DL) study suggests Deep learning (DL) based Convolutional neural network (CNN) approaches for effective and accurate detection to the maximum extent it could be, detection of coronavirus is assisted by using X-ray and CT-scan images. The primary experimental results here have depicted the maximum accuracy for training and is around 98.11 percent and for validation it comes out to be approximately 95.5 percent while statistical parameters like sensitivity and specificity for training is 98.03 percent and 98.20 percent respectively, and for validation 94.38 percent and 97.06 percent respectively. The suggested Deep Learning-based CNN model unleashed here opts for a comparable performance with medical experts and it is helpful to enhance the working productivity of radiologists. It could take the curve down with the downright contribution of radiologists, rapid detection of COVID-19, and to overcome this current pandemic with the proven efficacy. 相似文献
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Mohammad Yamin Adnan Ahmed Abi Sen Zenah Mahmoud AlKubaisy Rahaf Almarzouki 《计算机、材料和连续体（英文）》2021,68(2):2283-2298
COVID-19 is a global pandemic disease, which results from a dangerous coronavirus attack, and spreads aggressively through close contacts with infected people and artifacts. So far, there is not any prescribed line of treatment for COVID-19 patients. Measures to control the disease are very limited, partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease. Early detection of positive cases is critical in preventing further spread, achieving the herd immunity, and saving lives. Unfortunately, so far we do not have effective toolkits to diagnose very early detection of the disease. Recent research findings have suggested that radiology images, such as X-rays, contain significant information to detect the presence of COVID-19 virus in early stages. However, to detect the presence of the disease in in very early stages from the X-ray images by the naked eye is not possible. Artificial Intelligence (AI) techniques, machine learning in particular, are known to be very helpful in accurately diagnosing many diseases from radiology images. This paper proposes an automatic technique to classify COVID-19 patients from their computerized tomography (CT) scan images. The technique is known as Advanced Inception based Recurrent Residual Convolution Neural Network (AIRRCNN), which uses machine learning techniques for classifying data. We focus on the Advanced Inception based Recurrent Residual Convolution Neural Network, because we do not find it being used in the literature. Also, we conduct principal component analysis, which is used for dimensional deduction. Experimental results of our method have demonstrated an accuracy of about 99%, which is regarded to be very efficient. 相似文献
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Nowadays, the amount of wed data is increasing at a rapid speed, which presents a serious challenge to the web monitoring. Text sentiment analysis, an important research topic in the area of natural language processing, is a crucial task in the web monitoring area. The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data. Deep learning is a hot research topic of the artificial intelligence in the recent years. By now, several research groups have studied the sentiment analysis of English texts using deep learning methods. In contrary, relatively few works have so far considered the Chinese text sentiment analysis toward this direction. In this paper, a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network (CNN) in deep learning in order to improve the analysis accuracy. The feature values of the CNN after the training process are nonuniformly distributed. In order to overcome this problem, a method for normalizing the feature values is proposed. Moreover, the dimensions of the text features are optimized through simulations. Finally, a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances. Experiment results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods, e.g., the support vector machine method. 相似文献
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Hicham Lamtougui Hicham El Moubtahij Hassan Fouadi Khalid Satori 《计算机、材料和连续体（英文）》2023,74(2):2871-2888
In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term Memory (BLSTM) followed by a Connectionist Temporal Classification layer (CTC). Moreover, during the training phase of the model, we introduce an algorithm of data augmentation to increase the quality of data. Our proposed approach can recognize Arabic handwritten texts without the need to segment the characters, thus overcoming several problems related to this point. To train and test (evaluate) our approach, we used two Arabic handwritten text recognition databases, which are IFN/ENIT and KHATT. The Experimental results show that our new approach, compared to other methods in the literature, gives better results. 相似文献