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1.
Hussah Nasser AlEisa El-Sayed M. El-kenawy Amel Ali Alhussan Mohamed Saber Abdelaziz A. Abdelhamid Doaa Sami Khafaga 《计算机、材料和连续体（英文）》2022,73(2):2371-2387
Most children and elderly people worldwide die from pneumonia, which is a contagious illness that causes lung ulcers. For diagnosing pneumonia from chest X-ray images, many deep learning models have been put forth. The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases. By varying the deep learning approach, three pre-trained models, GoogLeNet, ResNet18, and DenseNet121, are employed in this research to extract the main features of pneumonia and normal cases. In addition, the binary dipper throated optimization (DTO) algorithm is utilized to select the most significant features, which are then fed to the K-nearest neighbor (KNN) classifier for getting the final classification decision. To guarantee the best performance of KNN, its main parameter (K) is optimized using the continuous DTO algorithm. To test the proposed approach, six evaluation metrics were employed namely, positive and negative predictive values, accuracy, specificity, sensitivity, and F1-score. Moreover, the proposed approach is compared with other traditional approaches, and the findings confirmed the superiority of the proposed approach in terms of all the evaluation metrics. The minimum accuracy achieved by the proposed approach is (98.5%), and the maximum accuracy is (99.8%) when different test cases are included in the evaluation experiments. 相似文献
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Reem Alkanhel El-Sayed M. El-kenawy Abdelaziz A. Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga 《计算机、材料和连续体（英文）》2023,74(2):2677-2693
Applications of internet-of-things (IoT) are increasingly being used in many facets of our daily life, which results in an enormous volume of data. Cloud computing and fog computing, two of the most common technologies used in IoT applications, have led to major security concerns. Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient. Several artificial intelligence (AI) based security solutions, such as intrusion detection systems (IDS), have been proposed in recent years. Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection (FS) techniques to increase classification accuracy by minimizing the number of features selected. On the other hand, metaheuristic optimization algorithms have been widely used in feature selection in recent decades. In this paper, we proposed a hybrid optimization algorithm for feature selection in IDS. The proposed algorithm is based on grey wolf (GW), and dipper throated optimization (DTO) algorithms and is referred to as GWDTO. The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance. On the employed IoT-IDS dataset, the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in the literature to validate its superiority. In addition, a statistical analysis is performed to assess the stability and effectiveness of the proposed approach. Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks. 相似文献
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Doaa Sami Khafaga El-Sayed M. El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A. Abdelhamid D. L. Elsheweikh 《计算机、材料和连续体（英文）》2023,74(2):2379-2395
Electrocardiogram (ECG) signal is a measure of the heart’s electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were conducted to compare the results achieved by the proposed approach and other competing approaches. Moreover, statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests. Experimental results confirmed the superiority and effectiveness of the proposed approach. The classification accuracy achieved by the proposed approach is (99.98%). 相似文献
4.
Zijun Sha Lin Hu Babak Daneshvar Rouyendegh 《International journal of imaging systems and technology》2020,30(2):495-506
Breast cancer is caused by the abnormal and rapid growth of breast cells. An early diagnosis can ensure an easier and effective treatment. A mass in the breast is a significant early sign of breast cancer, even though differentiating the cancerous mass's tissue from normal tissue for diagnosis is a difficult task for radiologists. The development of computer-aided detection systems in recent years has led to nondestructive and efficient cancer diagnostic techniques. This paper proposes a comprehensive method to locate the cancerous region in the mammogram image. This method employs image noise reduction, optimal image segmentation based on the convolutional neural network, a grasshopper optimization algorithm, and optimized feature extraction and feature selection based on the grasshopper optimization algorithm, thereby improving precision and decreasing the computational cost. This method was applied to the Mammographic Image Analysis Society Digital Mammogram Database and Digital Database for Screening Mammography breast cancer databases and the simulation results were compared with 10 different state-of-the-art methods to analyze the proposed system's efficiency. Final results showed that the proposed method had 96% Sensitivity, 93% Specificity, 85% PPV, 97% NPV, 92% accuracy, and better efficiency than other traditional methods in terms of Sensitivity, Specificity, PPV, NPV, and Accuracy. 相似文献
5.
Reem Alkanhel Doaa Sami Khafaga El-Sayed M. El-kenawy Abdelaziz A. Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B. M. El-den 《计算机、材料和连续体（英文）》2023,74(2):2695-2709
The Internet of Things (IoT) is a modern approach that enables connection with a wide variety of devices remotely. Due to the resource constraints and open nature of IoT nodes, the routing protocol for low power and lossy (RPL) networks may be vulnerable to several routing attacks. That’s why a network intrusion detection system (NIDS) is needed to guard against routing assaults on RPL-based IoT networks. The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks. Therefore, we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique (LSH-SMOTE). The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms. To prove the effectiveness of the proposed approach, a set of experiments were conducted to evaluate the performance of NIDS for three cases, namely, detection without dataset balancing, detection with SMOTE balancing, and detection with the proposed optimized LSH-SOMTE balancing. Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy. In addition, a statistical analysis is performed to study the significance and stability of the proposed approach. The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset. Based on the proposed approach, the achieved accuracy is (98.1%), sensitivity is (97.8%), and specificity is (98.8%). 相似文献
6.
Ekta Shivhare Vineeta Saxena 《International journal of imaging systems and technology》2021,31(1):253-269
Breast cancer is one of the deadly diseases in women that have raised the mortality rate of women. An accurate and early detection of breast cancer using mammogram images is still a complex task. Hence, this article proposes a novel breast cancer detection model, which included five major phases: (a) preprocessing, (b) segmentation, (c) feature extraction, (d) feature selection, and (e) classification. The input mammogram image is initially preprocessed using contrast limited adaptive histogram equalization (CLAHE) and median filtering. The preprocessed image is then subjected to segmentation via the region growing algorithm. Subsequently, geometric features, texture features and gradient features are extracted from the segmented image. Since the length of the feature vector is large, it is essential to select the optimal features. Here, the selection of optimal features is done by a hybrid optimization algorithm. Once the optimal features are selected, they are subjected to the classification process involving the neural network (NN) classifier. As a novelty, the weight of NN is selected optimally to enhance the accuracy of diagnosis (benign and malignant). The optimal feature selection as well as the weight optimization of NN is accomplished by merging the Lion algorithm (LA) and particle swarm optimization (PSO), named as velocity updated lion algorithm (VU‐LA). Finally, a performance‐based evaluation is carried out between VU‐LA and the existing models like, whale optimization algorithm (WOA), gray wolf optimization (GWO), firefly (FF), PSO, and LA. 相似文献
7.
Fangming Bi Xuanyi Fu Wei Chen Weidong Fang Xuzhi Miao Biruk Assefa 《计算机、材料和连续体（英文）》2020,62(1):199-216
Aiming at the defects of the traditional fire detection methods, which arecaused by false positives and false negatives in large space buildings, a fire identificationdetection method based on video images is proposed. The algorithm first uses the hybridGaussian background modeling method and the RGB color model to perform fireprejudgment on the video image, which can eliminate most non-fire interferences.Secondly, the traditional regional growth algorithm is improved and the fire imagesegmentation effect is effectively improved. Then, based on the segmented image, thedynamic and static features of the fire flame are further analyzed and extracted in the areaof the suspected fire flame. Finally, the dynamic features of the extracted fire flameimages were fused and classified by improved fruit fly optimization support vectormachine, and the recognition results were obtained. The video-based fire detectionmethod proposed in this paper greatly improves the accuracy of fire detection and issuitable for fire detection and identification in large space scenarios. 相似文献
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图像分割是计算机图像识别和理解的基础，本文提出一种基于色彩特征的彩色多普勒图像分割和基于频域双线性插值的图像旋转与用户交互式剪切相结合的图像分析方法，通过计算彩色超声医学图像的三基色R，G，B的色彩特征，提取出感兴趣的区域并实现了图像的分割，实验证明这是快速可行的彩色分割方法。 相似文献
10.
基于特征匹配的地图图像自动配准技术研究 总被引：3，自引：1，他引：2
本文针对地图中的特征点,提出了一种基于广义特征点的图像自动配准方法,将特征点从单纯的点拓展到特征区域。以Moravec算子结合其他特征约束条件来自动搜索广义特征点。分别对两幅图像提取广义特征点后,利用基于根均方误差和交叉相关的两级匹配算法完成同名控制点的建立。并以局部加权直线拟合方法来校正图像的几何畸变。最后建立两幅图像之间的函数映射关系,完成图像的配准。实验结果证明了该方法的有效性。该方法可用于校正近景面地图影像的几何畸变和遥感图像的局部几何畸变。 相似文献
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特征提取是水下目标识别研究中最为关键的技术之一,特征参数的优劣将直接决定分类识别系统的性能。将声信号的听觉与视觉感知特征结合,应用于水下目标识别,通过实验得出如下结论,相比于单独应用听觉特征,融合特征的平均识别率能提高4%~6%以上,特别是将听觉特征与声谱图的Gabor小波变换特征、灰度-梯度共生特征进行融合后,分类性能较好,平均达到87%以上。 相似文献
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The content-based image retrieval (CBIR) in dermatological diagnosis context, the information matching is the major concern in terms of feature vector-based classification. The discrimination of the feature vector leads to better classification as well as retrieval rate. Better retrieval results help the dermatologist to improve the diagnosis. In this paper, we proposed a support vector machine weight map (SVM W-Map)-based feature selection along with multi-class particle swarm optimization (PSO) presented for multi-class dermatological imaging dataset. The performance of the system was tested on a dataset including 1450 images and obtained 99.7% for specificity and 95.89% for sensitivity. The analysis and evaluations of results show that the proposed system has higher diagnosis ability when compared with other works. 相似文献
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Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision tree, and the random forest were used to create the classifier models that will help to predict breast cancer. Two different experiments were conducted using three datasets: Gene expression (GE), deoxyribonucleic acid (DNA) methylation, and a combination of the two. Six measures were used to evaluate the performance of the proposed algorithm, which include the following: Accuracy, precision, recall, specificity, area under the curve (AUC), and execution time. The effectiveness of the proposed classifiers was evaluated through comprehensive experiments. The results demonstrated that our approach outperformed the conventional classifiers as expected in terms of accuracy and execution time. High accuracy values of 99.77%, 99.45%, and 99.45% have been achieved by SA-SVM for GE, DNA methylation, and the combined datasets, respectively. The execution time of the proposed approach was significantly reduced, in comparison to that of the traditional classifiers and the best execution time has been reached by SA-SVM, which was 0.02, 0.03, and 0.02 on GE, DNA methylation, and the combined datasets respectively. In regard to precision and specificity, SA-RF obtained the best result of 100 on GE dataset. While SA-SVM attained the best recall result of 100 on GE dataset. 相似文献
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为了研制可以和现场机型斗齿座配套的挖掘机新型复合材料斗齿,首先需知斗齿结构的全部设计数据.斗齿是一种外形不规则、内腔有配合要求的形状复杂的功能零件,要获取其全部结构尺寸参数,使用一般的测量器具极其费时,测量精度也无法保证,尤其是对精度要求相对最高的斗齿内腔形状和销孔位置等部分参数更是难以检测.为此,采用实物反求工程的技术路线,研究了斗齿结构参数的获取方法.针对实测到的斗齿点云数据,提出以实体模型的建立为目的首先提取关键形状的特征线,再将提取结果用于三维模型的重建.介绍了借助Imageware软件完成特征线提取以及Pro/E软件创建实体模型的策略和步骤.按反求CAD模型生成的工程图样试制的斗齿,经过了现场试用,结果表明包括斗齿内腔表面和销孔位置等重要参数在内的尺寸和形状反求精度符合要求,说明了所采用的技术路线可行、有效. 相似文献
17.
Hong Fang Hongyu Fan Shan Lin Zhang Qing Fatima Rashid Sheykhahmad 《International journal of imaging systems and technology》2021,31(1):425-438
Breast cancer is the second deadliest type of cancer. Early detection of breast cancer can considerably improve the effectiveness of treatment. A significant early sign of breast cancer is the mass. However, separating the cancerous masses from the normal portions of the breast tissue is usually a challenge for radiologists. Recently, because of the availability of high‐accuracy computing, computer‐aided detection systems based on image processing have become capable of accurately diagnosing the various types of cancers. The main purpose of this study is to utilize a powerful image segmentation method for the diagnosis of cancerous regions through mammography, based on a new configuration of the multilayer perceptron (MLP) neural network. The most popular method for minimizing the errors in an MLP neural network is backpropagation. However, this method has certain drawbacks, such as a low convergence speed and becoming trapped at the local minimum. In this study, a new training algorithm based on the whale optimization algorithm is proposed for the MLP network. This algorithm is capable of solving various problems toward the current algorithms for the analyzed systems. The proposed method is validated on the Mammographic Image Analysis Society database, which contains 322 digitized mammography images, and the Digital Database for Screening Mammography, which contains approximately 2500 digitized mammography images. To assess the detection performance of the proposed system, the correct detection rate, percentage of identification with false acceptance, and percentage of identification with false rejection were evaluated and compared using various methods. The results indicate that the proposed method is highly efficient and yields significantly better accuracy compared with other methods. 相似文献
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Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation quality. Finally, we determine several skin features based on the results of wrinkle and cell segmentation. Our proposed segmentation scheme achieved a mean accuracy of 0.854, mean of intersection over union of 0.749, and mean boundary F1 score of 0.852, which achieved 1.1%, 6.7%, and 14.8% improvement over the panoptic-based semantic segmentation method, respectively. 相似文献
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针对摩擦振动特征难以有效提取的问题,提出利用连续小波变换（ＣＷＴ）时频谱图像和图像处理技术提取摩擦振动特征参数的方法。运用ＣＷＴ变换绘制６Ｓ５０ＭＣ船用柴油机缸套－活塞环试样摩擦振动信号的时频谱图,再用图像分割技术提取振动特征体及相应的特征参数,探讨特征参数与不同润滑剂润滑条件下缸套－活塞环摩擦振动特性的内在联系。结果表明,随着摩擦磨损过程的进行,摩擦振动信号特征参数出现规律性变化;不同润滑工况下的特征参数呈现出明显的差异,反映了摩擦磨损的状态。振动特征体特征参数能定量刻画摩擦振动信号的特征。 相似文献
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The generalized Hough transform is a common technique for feature detection in image processing. In this paper, we develop a size invariant Hough framework for the detection of arbitrary shapes in three dimensional digital microstructure datasets. The Hough transform is efficiently implemented via kernel convolution with complex Hough filters, where shape is captured in the magnitude of the filter and scale in the complex phase. In this paper, we further generalize the concept of a Hough filter by encoding other parameters of interest (e.g. orientation of plate or fiber constituents) in the complex phase, broadening the applicability of Hough transform techniques. We demonstrate the application of these techniques to feature detection in micrographs (2-D) and three-dimensional (3-D) microstructure datasets, and explore their utility to the closely related applications of feature based image segmentation and calculation of 3-D microstructure metrics. 相似文献