共查询到20条相似文献，搜索用时 15 毫秒
1.
极限学习机是一种针对单隐含层前馈神经网络的新算法,具有训练速度快,泛化性能高等优点.将其应用于软测量技术,避免了传统神经网络高计算复杂度的缺点,可以实现难以直接测量参数的快速获取,在计量测量技术领域有着广阔的应用前景. 相似文献
2.
Fouzia Nawaz Muhammad Ramzan Khalid Mehmood Hikmat Ullah Khan Saleem Hayat Khan Muhammad Raheel Bhutta 《计算机、材料和连续体（英文）》2021,66(2):1631-1652
Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness. DR occurs due to the high blood sugar level of the patient, and it is clumsy to be detected at an early stage as no early symptoms appear at the initial level. To prevent blindness, early detection and regular treatment are needed. Automated detection based on machine intelligence may assist the ophthalmologist in examining the patients’ condition more accurately and efficiently. The purpose of this study is to produce an automated screening system for recognition and grading of diabetic retinopathy using machine learning through deep transfer and representational learning. The artificial intelligence technique used is transfer learning on the deep neural network, Inception-v4. Two configuration variants of transfer learning are applied on Inception-v4: Fine-tune mode and fixed feature extractor mode. Both configuration modes have achieved decent accuracy values, but the fine-tuning method outperforms the fixed feature extractor configuration mode. Fine-tune configuration mode has gained 96.6% accuracy in early detection of DR and 97.7% accuracy in grading the disease and has outperformed the state of the art methods in the relevant literature. 相似文献
3.
Gulzar Ahmed Tahir Alyas Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A. Bahaddad Khalid Ali Almarhabi 《计算机、材料和连续体（英文）》2022,73(2):2967-2984
Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for extracting the alphabets, while diagonal-based extraction methods are implemented in online systems. Moreover, our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text recognition. To this end, we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline mediums. Subsequently, we carried out detailed experiments for character recognition, as detailed in the results. The proposed CNN model outperformed as compared to previously published approaches. 相似文献
4.
Software defect prediction plays an important role in software quality assurance. However, the performance of the prediction model is susceptible to the irrelevant and redundant features. In addition, previous studies mostly regard software defect prediction as a single objective optimization problem, and multi-objective software defect prediction has not been thoroughly investigated. For the above two reasons, we propose the following solutions in this paper: (1) we leverage an advanced deep neural network—StackedContractive AutoEncoder (SCAE) to extract the robust deep semantic features from the original defect features, which has stronger discrimination capacity for different classes (defective or non-defective). (2) we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimizethe advanced neural network—Extreme learning machine (ELM) based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE. We mainly consider two objectives. One objective is to maximize the performance of ELM, which refers to the benefit of the SMONGE model. Another objective is to minimize the output weight normof ELM, which is related to the cost of the SMONGE model. We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE modelwithout SCAE across 20 open source software projects. The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics. 相似文献
5.
Sleep stage classification can provide important information regarding neonatal brain development and maturation. Visual annotation, using polysomnography (PSG), is considered as a gold standard for neonatal sleep stage classification. However, visual annotation is time consuming and needs professional neurologists. For this reason, an internet of things and ensemble-based automatic sleep stage classification has been proposed in this study. 12 EEG features, from 9 bipolar channels, were used to train and test the base classifiers including convolutional neural network, support vector machine, and multilayer perceptron. Bagging and stacking ensembles are then used to combine the outputs for final classification. The proposed algorithm can reach a mean kappa of 0.73 and 0.66 for 2-stage and 3-stage (wake, active sleep, and quiet sleep) classification, respectively. The proposed network works as a semi-real time application because a smoothing filter is used to hold the sleep stage for 3 min. The high-performance parameters and its ability to work in semi real-time makes it a promising candidate for use in hospitalized newborn infants. 相似文献
6.
深度卷积神经网络在目标检测与识别等方面表现出了优异性能,但将其用于SAR目标识别时,较少的训练样本和深度模型的优化设计是必须解决的两个问题。本文设计了一种结合二维随机卷积特征和集成超限学习机的SAR目标识别算法。首先,随机生成具有不同宽度的二维卷积核,对输入图像进行卷积与池化操作,提取随机卷积特征向量。其次,为提高分类器的泛化能力,并降低训练时间,基于集成学习思想对提取的卷积特征进行随机采样,然后采用超限学习机训练基分类器。最后,通过投票表决法对基分类器的分类结果进行集成。采用MSTAR数据集进行了SAR目标识别实验,实验结果表明,由于采用的超限学习机具有快速训练能力,训练时间降低了数十倍,在无需进行数据增强的情况下,分类精度与采用数据增强和多层卷积神经网络的深度学习算法相当。提出的算法具有实现简单、需要调整参数少等优点,采用集成学习思想提高了分类器的泛化能力。
相似文献7.
Bryan J. Heard Joshua M. Rosvold Marvin J. Fritzler Hani El-Gabalawy J. Preston Wiley Roman J. Krawetz 《Journal of the Royal Society Interface》2014,11(97)
The objective of this study was to develop a method for categorizing normal individuals (normal, n = 100) as well as patients with osteoarthritis (OA, n = 100), and rheumatoid arthritis (RA, n = 100) based on a panel of inflammatory cytokines expressed in serum samples. Two panels of inflammatory proteins were used as training sets in the construction of two separate artificial neural networks (ANNs). The first training set consisted of all proteins (38 in total) and the second consisted of only the significantly different proteins expressed (12 in total) between at least two patient groups. Both ANNs obtained high levels of sensitivity and specificity, with the first and second ANN each diagnosing 100% of test set patients correctly. These results were then verified by re-investigating the entire dataset using a decision tree algorithm. We show that ANNs can be used for the accurate differentiation between serum samples of patients with OA, a diagnosed RA patient comparator cohort and normal/control cohort. Using neural network and systems biology approaches to manage large datasets derived from high-throughput proteomics should be further explored and considered for diagnosing diseases with complex pathologies. 相似文献
8.
Sukhendra Singh Sur Singh Rawat Manoj Gupta B. K. Tripathi Faisal Alanazi Arnab Majumdar Pattaraporn Khuwuthyakorn Orawit Thinnukool 《计算机、材料和连续体（英文）》2023,74(2):3063-3083
Currently, breast cancer has been a major cause of deaths in women worldwide and the World Health Organization (WHO) has confirmed this. The severity of this disease can be minimized to the large extend, if it is diagnosed properly at an early stage of the disease. Therefore, the proper treatment of a patient having cancer can be processed in better way, if it can be diagnosed properly as early as possible using the better algorithms. Moreover, it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast tissues. To address the above said issues, this paper presents a hybrid model using the transfer learning to study the histopathological images, which help in detection and rectification of the disease at a low cost. Extensive dataset experiments were carried out to validate the suggested hybrid model in this paper. The experimental results show that the proposed model outperformed the baseline methods, with F-scores of 0.81 for DenseNet + Logistic Regression hybrid model, (F-score: 0.73) for Visual Geometry Group (VGG) + Logistic Regression hybrid model, (F-score: 0.74) for VGG + Random Forest, (F-score: 0.79) for DenseNet + Random Forest, and (F-score: 0.79) for VGG + Densenet + Logistic Regression hybrid model on the dataset of histopathological images. 相似文献
9.
Ashir Javeed;Peter Anderberg;Muhammad Asim Saleem;Ahmad Nauman Ghazi;Johan Sanmartin Berglund; 《International journal of imaging systems and technology》2024,34(6):e23221
Globally, cancer is the second-leading cause of death after cardiovascular disease. To improve survival rates, risk factors and cancer predictors must be identified early. From the literature, researchers have developed several kinds of machine learning-based diagnostic systems for early cancer prediction. This study presented a diagnostic system that can identify the risk factors linked to the onset of cancer in order to anticipate cancer early. The newly constructed diagnostic system consists of two modules: the first module relies on a statistical F-score method to rank the variables in the dataset, and the second module deploys the random forest (RF) model for classification. Using a genetic algorithm, the hyperparameters of the RF model were optimized for improved accuracy. A dataset including 10 765 samples with 74 variables per sample was gathered from the Swedish National Study on Aging and Care (SNAC). The acquired dataset has a bias issue due to the extreme imbalance between the classes. In order to address this issue and prevent bias in the newly constructed model, we balanced the classes using a random undersampling strategy. The model's components are integrated into a single unit called F-RUS-RF. With a sensitivity of 92.25% and a specificity of 85.14%, the F-RUS-RF model achieved the highest accuracy of 86.15%, utilizing only six highly ranked variables according to the statistical F-score approach. We can lower the incidence of cancer in the aging population by addressing the risk factors for cancer that the F-RUS-RF model found. 相似文献
10.
Mehdi Hassan Iqbal Murtza Muhammad Aqdus Zafar Khan Syed Fahad Tahir Labiba Gillani Fahad 《International journal of imaging systems and technology》2019,29(4):633-644
Imaging based sensitive clinical diagnosis is critically depending on image quality. In this article, the problem of enhancing fundus images is addressed by a novel fusion technique. The proposed approach utilizes the representation capability of wavelet transform and the learning ability of artificial neural networks. In this approach, input images are decomposed into wavelet transform followed by appropriate feature extraction for training of neural networks to obtain fused image. To ensure homogeneity, it employs consistency verification for minimizing the fusion artifacts. The visual and quantitative performance of the proposed approach is assessed using a number of experiments performed on the standard datasets of DRIVE and DRION-DB. The experimental results demonstrate that the proposed fusion technique offers high average structural similarity of “0.99.” The proposed approach outperforms state-of-the-art techniques which validates its effectiveness. The developed approach might be applied by the clinical diagnosis system for fundus related diseases. 相似文献
11.
验证码是一种区分用户是计算机还是人的公共全自动程序.为了尽可能大批量地获取某网站的信息,就需要让机器可以全自动地识别该网站的验证码.为了破解验证码,对深度学习的验证码图像识别方法进行了研究.提出使用图像标注的方法来生成验证码图像中的字母序列.实验采用深度学习框架Caffe,将卷积神经网络与循环神经网络相结合进行训练.将卷积神经网络的输出用于训练循环神经网络,来不断地预测出序列中下一个最有可能出现的字母.训练的目标是将输出的词尽量和预期的词一致.测试结果表明,该模型能够对该网站的验证码图像做到97%的识别准确率.该方法比只采用卷积神经网络进行识别效果好. 相似文献
12.
Predicting the performance of thermoelectric generators (TEGs) is an essential part of designing high-performance TEGs. However, due to the complexity of the TEG system, the existing methods are either time-consuming or not precise enough, inconvenient for device optimization. In this paper, the deep learning (DL) method to fast and accurately get the performance of TEG devices is presented. First, the key features of a typical TEG device are captured and the training dataset is prepared based on the extracted features and finite element simulations. Next, a proper deep neural network architecture is acquired and the model is trained to converge at a low loss. Finally, the experimental data is used to validate the generalization ability of the presented model. Besides, the device optimization based on the DL solution is performed and an output power enhancement of up to 182% is achieved for the authors’ sample module. The presented DL solution thus can be well applied in designing or optimizing high-performance TEGs. Furthermore, the established framework also sheds considerable light on applying the DL approach to solve general engineering problems. 相似文献
13.
Ahsan Malik 《中国工程学刊》2016,39(1):74-78
Due to the heterogeneous and complex nature of clinical data, the need to use sophisticated diagnosis techniques has increased significantly in recent years. The proposed approach for diagnosis of breast cancer exploits the potential of an extreme learning machine (ELM) and analyzes its performance after classification into benign and malignant cases. To optimize the ELM network in terms of computation time and memory resources, weight pruning is used without performance compromise. Using real data sets, numerical experiments have been conducted. With an accuracy of 93%, the optimum numbers of node layers for breast cancer diagnosis has been found to be 20. Comparative results demonstrate over-performance of the proposed ELM approach. 相似文献
14.
This study presents a hybrid learning neural fuzzy system for accurately predicting system reliability. Neural fuzzy system learning with and without supervision has been successfully applied in control systems and pattern recognition problems. This investigation modifies the hybrid learning fuzzy systems to accept time series data and therefore examines the feasibility of reliability prediction. Two neural network systems are developed for solving different reliability prediction problems. Additionally, a scaled conjugate gradient learning method is applied to accelerate the training in the supervised learning phase. Several existing approaches, including feed‐forward multilayer perceptron (MLP) networks, radial basis function (RBF) neural networks and Box–Jenkins autoregressive integrated moving average (ARIMA) models, are used to compare the performance of the reliability prediction. The numerical results demonstrate that the neural fuzzy systems have higher prediction accuracy than the other methods. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
15.
该文提出了一个理论驱动的弹性结构体系图神经网络计算模型StructGNN-E,能够高保真数字化结构体系的拓扑连接关系与构件组成信息,无需外部标签数据即可实现对任意杆系结构体系的弹性内力分析,且计算结果具有理论正确性。总结了结构体系层次的特点,理论分析了常规神经网络的不可行性,进而采用了基于非欧图数据的图神经网络架构,能够有效刻画结构体系的非序列性与非平移不变性。考虑到体系层次数据严重匮乏以及常规智能计算方法忽视力学意义的问题,通过将三大力学方程与深度学习推理过程相结合,提出了适用于体系内力分析的理论驱动模式,实现了不依赖于外部标签数据的智能求解方案。数值试验表明：StructGNN-E模型能够高精度完成杆系结构体系的弹性内力分析,且在大规模框架结构计算中计算效率提升可达36%。通过具体的对比试验,证明了常规深度学习模型与数据驱动模式在体系层次的不适用性,进一步阐释了StructGNN-E模型的有效性与合理性。 相似文献
16.
轨道角动量(OAM)复用和编码技术可有效提高光通信系统信道容量。近些年研究者提出将机器学习(ML)技术用于OAM模式探测以提高OAM光通信系统性能。本文对基于机器学习的OAM模式探测方案进行了综述,包括误差反向传播(BP)神经网络、自组织神经网络(SOM)、支持向量机(SVM)、卷积神经网络(CNN)、光束变换辅助的识别技术以及全光衍射深度神经网络(D2NN),分析了各类机器学习OAM探测器在对抗大气、水下信道带来的干扰时展现出的性能差异以及各自优势。
相似文献17.
In this paper, we discuss the inextricable link between automating training environment adaptation and deep understanding of the context surrounding specific decisions and actions executed in the performance environment. To enable deep contextual understanding, psychological measurement strategies are needed to more accurately and rapidly model the psychologically meaningful details of the trainee's interactions with events, objects, and people in the training environment. As these interactions often entail complex, nonlinear cue-action relationships, the underlying models must effectively capture the nuance, complexity, and largely intuitive nature of human decision-making. This paper discusses the promise of an emerging field of machine learning – deep neural networks – for supporting this requirement. 相似文献
18.
现有的细粒度分类模型不仅利用图像的类别标签,还使用大量人工标注的额外信息。为解决该问题,本文提出一种深度迁移学习模型,将大规模有标签细粒度数据集上学习到的图像特征有效地迁移至微型细粒度数据集中。首先,通过衔接域定量计算域间任务的关联度。然后,根据关联度选择适合目标域的迁移特征。最后,使用细粒度数据集视图类标签进行辅助学习,通过联合学习所有属性来获取更多的特征表示。实验表明,本文方法不仅可以获得较高精度,而且能够有效减少模型训练时间,同时也验证了进行域间特征迁移可以加速网络学习与优化这一结论。 相似文献
19.
Muhammad Imran Hafeez Anwar Muhammad Tufail Abdullah Khan Murad Khan Dzati Athiar Ramli 《计算机、材料和连续体（英文）》2023,74(1):203-216
We propose to perform an image-based framework for electrical energy meter reading. Our aim is to extract the image region that depicts the digits and then recognize them to record the consumed units. Combining the readings of serial numbers and energy meter units, an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time setup. However, such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display, orientation and scale variations caused by camera positioning, and non-uniform illumination caused by shades. To this end, our work evaluates and compares the state-of-the art deep learning algorithm You Only Look Once (YOLO ) along with traditional handcrafted features for text extraction and recognition. Our image dataset contains 10,000 images of electrical energy meters and is further expanded by data augmentation such as in-plane rotation and scaling to make the deep learning algorithms robust to these image variations. For training and evaluation, the image dataset is annotated to produce the ground truth of all the images. Consequently, YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98% for all the digits. It proves to be robust against the mentioned image variations compared with the traditional handcrafted features. Our proposed method can be highly instrumental in reducing the time and effort involved in the current meter reading, where workers visit door to door, take images of meters and manually extract readings from these images. 相似文献
20.
针对传统的神经网络收敛判断以模型的拟合精度为指标造成训练时间过长和过拟合等缺点,提出了一种改进神经网络(M-ANN).M-ANN将样本分成训练样本和校验样本,并提出了过拟合判据参数.通过训练样本采用误差反传算法对网络进行训练,训练过程中以模型对校验样本的预测性能为指标,通过过拟合判据参数的计算自适应地在获得具有最佳预测性能模型时终止网络训练.同时,针对影响初馏塔塔顶石脑油干点的因素众多且呈高度非线性的特征,应用M-ANN建立初顶石脑油干点软测量模型,获得模型的预测相对误差平方和均值比传统神经网络模型降低了27.5%. 相似文献