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| 1 | 用人工神经网络混合元启发式优化技术分析不可压缩黏性流体在收敛和发散通道中的流动:一种智能方法显示文摘本文采用人工神经网络(ANN)与进化算法(特别是阿基米德优化算法(AOA)和水循环算法(WCA)相结合的方法)对非线性磁流体动力学(MHD)的Jeffery-Hamel问题,特别是收敛和发散通道中的拉伸/收缩问题进行了数值研究。这种组合技术被称为ANN-AOA-WCA。将基于复杂非线性磁流体动力学Jeffery-Hamel问题的偏微分方程转化为速度和温度的非线性常微分方程系统,我们建立了基于人工神经网络的适应度函数来求解非线性微分问题。随后,采用了一种新的AOA和WCA结合方法(AOAWCA)来优化基于神经网络的适应度函数,并确定了神经网络的最优权值和偏差。为了证明提出混合方法的有效性和多功能性,探索了一系列雷诺数、通道角和可拉伸边界值的MHD模型,从而开发了两种不同的情况。ANN-AOA-WCA的数值结果与参考解(NDSOLVE)非常接近,NDSOLVE与ANNAOA-WCA的绝对误差约为3.35×10^(−8),对可拉伸收敛和发散通道的理解特别关键。此外,为了验证ANN-AOA-WCA技术,我们对150多个独立运行进行了统计分析,以获得适应度值。 | ASLAM Muhammad Naeem RIAZ Arshad SHAUKAT Nadeem ALI Shahzad AKRAM Safia BHATTI M.M. | 2023 | Journal of Central South University2023，30，12： | 0 |
| 2 | HybridHR-Net:Action Recognition in Video Sequences Using Optimal Deep Learning Fusion Assisted Framework显示文摘The combination of spatiotemporal videos and essential features can improve the performance of human action recognition(HAR);however,the individual type of features usually degrades the performance due to similar actions and complex backgrounds.The deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial information.This article proposes a new framework called for video surveillance human action recognition dubbed HybridHR-Net.On a few selected datasets,deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning model.Bayesian optimization is employed for the tuning of hyperparameters of the fine-tuned deep model.Instead of fully connected layer features,we considered the average pooling layer features and performed two feature selection techniques-an improved artificial bee colony and an entropy-based approach.Using a serial nature technique,the features that were selected are combined into a single vector,and then the results are categorized by machine learning classifiers.Five publically accessible datasets have been utilized for the experimental approach and obtained notable accuracy of 97%,98.7%,100%,99.7%,and 96.8%,respectively.Additionally,a comparison of the proposed framework with contemporarymethods is done to demonstrate the increase in accuracy. | Muhammad Naeem Akbar Seemab Khan Muhammad Umar Farooq Majed Alhaisoni Usman Tariq Muhammad Usman Akram | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
| 3 | A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification显示文摘This study presents a deep learning model for efficient intracranial hemorrhage(ICH)detection and subtype classification on non-contrast head computed tomography(CT)images.ICH refers to bleeding in the skull,leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis.It is classified as intra-axial hemorrhage(intraventricular,intraparenchymal)and extra-axial hemorrhage(subdural,epidural,subarachnoid)based on the bleeding location inside the skull.Many computer-aided diagnoses(CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels.However,these approaches performonly binary classification and suffer from a large number of parameters,which increase storage costs.Further,the accuracy of brain hemorrhage detection in existing models is significantly low for medically critical applications.To overcome these problems,a fast and efficient system for the automatic detection of ICH is needed.We designed a double-branch model based on xception architecture that extracts spatial and instant features,concatenates them,and creates the 3D spatial context(common feature vectors)fed to a decision tree classifier for final predictions.The data employed for the experimentation was gathered during the 2019 Radiologist Society of North America(RSNA)brain hemorrhage detection challenge.Our model outperformed benchmark models and achieved better accuracy in intraventricular(99.49%),subarachnoid(99.49%),intraparenchymal(99.10%),and subdural(98.09%)categories,thereby justifying the performance of the proposed double-branch xception architecture for ICH detection and classification. | Muhammad Naeem Akram Muhammad Usman Yaseen Muhammad Waqar Muhammad Imran Aftab Hussain | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
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