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1.
Since the web service is essential in daily lives, cyber security becomes moreand more important in this digital world. Malicious Uniform Resource Locator (URL) isa common and serious threat to cybersecurity. It hosts unsolicited content and lureunsuspecting users to become victim of scams, such as theft of private information,monetary loss, and malware installation. Thus, it is imperative to detect such threats.However, traditional approaches for malicious URLs detection that based on theblacklists are easy to be bypassed and lack the ability to detect newly generated maliciousURLs. In this paper, we propose a novel malicious URL detection method based on deeplearning model to protect against web attacks. Specifically, we firstly use auto-encoder torepresent URLs. Then, the represented URLs will be input into a proposed compositeneural network for detection. In order to evaluate the proposed system, we madeextensive experiments on HTTP CSIC2010 dataset and a dataset we collected, and theexperimental results show the effectiveness of the proposed approach. 相似文献
2.
Rahul Sharma Amar Singh Kavita N. Z. Jhanjhi Mehedi Masud Emad Sami Jaha Sahil Verma 《计算机、材料和连续体（英文）》2022,71(2):2125-2140
Indian agriculture is striving to achieve sustainable intensification, the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem. Modern farming employs technology to improve productivity. Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity. Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost, approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert's opinion. Deep learning-based computer vision techniques like Convolutional Neural Network (CNN) and traditional machine learning-based image classification approaches are being applied to identify plant diseases. In this paper, the CNN model is proposed for the classification of rice and potato plant leaf diseases. Rice leaves are diagnosed with bacterial blight, blast, brown spot and tungro diseases. Potato leaf images are classified into three classes: healthy leaves, early blight and late blight diseases. Rice leaf dataset with 5932 images and 1500 potato leaf images are used in the study. The proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58% accuracy and potato leaves with 97.66% accuracy. The results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree and Random Forest. 相似文献
3.
Pallabi Sharma Dipankar Das Anmol Gautam Bunil Kumar Balabantaray 《International journal of imaging systems and technology》2023,33(2):495-510
The traditional process of disease diagnosis from medical images follows a manual process, which is tedious and arduous. A computer-aided diagnosis (CADs) system can work as an assistive tool to improve the diagnosis process. In this pursuit, this article introduces a unique architecture LPNet for classifying colon polyps from the colonoscopy video frames. Colon polyps are abnormal growth of cells in the colon wall. Over time, untreated colon polyps may cause colorectal cancer. Different convolutional neural networks (CNNs) based systems have been developed in recent years. However, CNN uses pooling to reduce the number of parameters and expand the receptive field. On the other hand, pooling results in data loss and is deleterious to subsequent processes. Pooling strategies based on discrete wavelet operations have been proposed in our architecture as a solution to this problem, with the promise of achieving a better trade-off between receptive field size and computing efficiency. The overall performance of this model is superior to the others, according to experimental results on a colonoscopy dataset. LPNet with bio-orthogonal wavelet achieved the highest performance with an accuracy of 93.55%. It outperforms the other state-of-the-art (SOTA) CNN models for the polyps classification task, and it is lightweight in terms of the number of learnable parameters compared with them, making the model easily deployable in edge devices. 相似文献
4.
Muhammed Yildirim;Hurşit Burak Mutlu; 《International journal of imaging systems and technology》2024,34(2):e23057
Osteoarthritis (OA) means that the slippery cartilage tissue that covers the bone surfaces in the joints and allows the joint to move easily loses its properties and wears out. Knee OA is the wear and tear of the cartilage in the knee joint. Knee OA is a disease whose incidence increases especially after a certain age. Knee OA is difficult and costly to be detected by specialists using traditional methods and may lead to misdiagnosis. In this study, computer-aided systems were used to prevent errors in traditional methods of detecting knee OA, shorten the diagnosis time, and accelerate the treatment process. In this study, a hybrid model was developed by using Darknet53, Histogram of Directional Gradients (HOG), Local Binary Model (LBP) methods for feature extraction, and Neighborhood Component Analysis (NCA) for feature selection. Our dataset used in experiments contains 1650 knee joint images and consists of five classes: Normal, Doubtful, Mild, Moderate, and Severe. In the experimental studies performed, the performance of the proposed method was compared with eight different Convolutional Neural Networks (CNN) Models. The developed model achieved better performance metrics than the eight different models used in the study and similar studies in the literature. The accuracy value of the developed model is 83.6%. 相似文献
5.
R. S. Chithra P. Jagatheeswari 《International journal of imaging systems and technology》2020,30(4):994-1011
Tuberculosis (TB) is a highly infectious disease and is one of the major health problems all over the world. The accurate detection of TB is a major challenge faced by most of the existing methods. This work addresses these issues and developed an effective mechanism for detecting TB using deep learning. Here, the color space transformation is applied for transforming the red green and blue image to LUV space, where L stands for luminance, U and V represent chromaticity values of color images. Then, adaptive thresholding is carried out for image segmentation and various features, like coverage, density, color histogram, area, length, and texture features, are extracted to enable effective classification. After the feature extraction, the size of the features is reduced using principal component analysis. The extracted features are subjected to fractional crow search-based deep convolutional neural network (FC-SVNN) for the classification. Then, the image level features, like bacilli count, bacilli area, scattering coefficients and skeleton features are considered to perform severity detection using proposed adaptive fractional crow (AFC)-deep CNN. Finally, the inflection level is determined using entropy, density and detection percentage. The proposed AFC-Deep CNN algorithm is designed by modifying FC algorithm using self-adaptive concept. The proposed AFC-Deep CNN shows better performance with maximum accuracy value as 0.935. 相似文献
6.
Suting Peng Wei Chen Jiawei Sun Boqiang Liu 《International journal of imaging systems and technology》2020,30(1):5-17
Gliomas segmentation is a critical and challenging task in surgery and treatment, and it is also the basis for subsequent evaluation of gliomas. Magnetic resonance imaging is extensively employed in diagnosing brain and nervous system abnormalities. However, brain tumor segmentation remains a challenging task, because differentiating brain tumors from normal tissues is difficult, tumor boundaries are often ambiguous and there is a high degree of variability in the shape, location, and extent of the patient. It is therefore desired to devise effective image segmentation architectures. In the past few decades, many algorithms for automatic segmentation of brain tumors have been proposed. Methods based on deep learning have achieved favorable performance for brain tumor segmentation. In this article, we propose a Multi-Scale 3D U-Nets architecture, which uses several U-net blocks to capture long-distance spatial information at different resolutions. We upsample feature maps at different resolutions to extract and utilize sufficient features, and we hypothesize that semantically similar features are easier to learn and process. In order to reduce the computational cost, we use 3D depthwise separable convolution instead of some standard 3D convolution. On BraTS 2015 testing set, we obtained dice scores of 0.85, 0.72, and 0.61 for the whole tumor, tumor core, and enhancing tumor, respectively. Our segmentation performance was competitive compared to other state-of-the-art methods. 相似文献
7.
Maoyang Zou Jinrong Hu Huan Zhang Xi Wu Jia He Zhijie Xu Yong Zhong 《计算机、材料和连续体（英文）》2019,60(2):511-525
8.
V. Karthikeyan;M. Navin Kishore;S. Sajin; 《International journal of imaging systems and technology》2024,34(1):e23022
Kidney disease is a major health problem that affects millions of people around the world. Human kidney problems can be diagnosed with the help of computed tomography (CT), which creates cross-sectional slices of the organ. A deep end-to-end convolutional neural network (CNN) model is proposed to help radiologists detect and characterize kidney problems in CT scans of patients. This has the potential to improve diagnostic accuracy and efficiency, which in turn benefits patient care. Our strategy involves teaching a suggested deep end-to-end CNN to distinguish between healthy and diseased kidneys. The recommended CNN is trained using a standard CT image library that has been annotated to show kidney stones, cysts, and tumors. The model can then be used to detect kidney abnormalities in fresh CT scans, which may enhance the effectiveness and speed with which diagnoses are made. A total of 1812 pictures were used, each one a unique cross-sectional CT scan of the patient. Our model has a detection rate of 99.17% in CT scan validation tests. We employed a different dataset with a total of 5077 normal samples, 3709 cyst samples, 1377 stone samples, and 2283 tumor samples. In tests, our model proved to be 99.68% accurate. The suggested framework has been validated by applying it to the clinical dataset, resulting in 99% accuracy in predictions. As low-cost and portable CT scanners become more commonplace, the described concept may soon be employed outside of a hospital environment, at the point of treatment, or even in the patient's own home. 相似文献
9.
Leaf species identification leads to multitude of societal applications. There is enormous research in the lines of plant identification using pattern recognition. With the help of robust algorithms for leaf identification, rural medicine has the potential to reappear as like the previous decades. This paper discusses CNN based approaches for Indian leaf species identification from white background using smartphones. Variations of CNN models over the features like traditional shape, texture, color and venation apart from the other miniature features of uniformity of edge patterns, leaf tip, margin and other statistical features are explored for efficient leaf classification. 相似文献
10.
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
11.
Harshal A. Sanghvi Riki H. Patel Ankur Agarwal Shailesh Gupta Vivek Sawhney Abhijit S. Pandya 《International journal of imaging systems and technology》2023,33(1):18-38
In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X-ray Images. The model is a framework consisting of the modeling software which assists in Health Insurance Portability and Accountability Act Compliance which protects and secures the Protected Health Information . The need of the proposed framework in medical facilities shall give the feedback to the radiologist for detecting COVID and pneumonia though the transfer learning methods. A Graphical User Interface tool allows the technician to upload the chest X-ray Image. The software then uploads chest X-ray radiograph (CXR) to the developed detection model for the detection. Once the radiographs are processed, the radiologist shall receive the Classification of the disease which further aids them to verify the similar CXR Images and draw the conclusion. Our model consists of the dataset from Kaggle and if we observe the results, we get an accuracy of 99.1%, sensitivity of 98.5%, and specificity of 98.95%. The proposed Bio-Medical Innovation is a user-ready framework which assists the medical providers in providing the patients with the best-suited medication regimen by looking into the previous CXR Images and confirming the results. There is a motivation to design more such applications for Medical Image Analysis in the future to serve the community and improve the patient care. 相似文献
12.
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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14.
Human action recognition under complex environment is a challenging work. Recently, sparse representation has achieved excellent results of dealing with human action recognition problem under different conditions. The main idea of sparse representation classification is to construct a general classification scheme where the training samples of each class can be considered as the dictionary to express the query class, and the minimal reconstruction error indicates its corresponding class. However, how to learn a discriminative dictionary is still a difficult work. In this work, we make two contributions. First, we build a new and robust human action recognition framework by combining one modified sparse classification model and deep convolutional neural network (CNN) features. Secondly, we construct a novel classification model which consists of the representation-constrained term and the coefficients incoherence term. Experimental results on benchmark datasets show that our modified model can obtain competitive results in comparison to other state-of-the-art models. 相似文献
15.
Sukanta Ghosh Amar Singh Kavita N. Z. Jhanjhi Mehedi Masud Sultan Aljahdali 《计算机、材料和连续体（英文）》2022,71(3):4257-4274
Automatic plant classification through plant leaf is a classical problem in Computer Vision. Plants classification is challenging due to the introduction of new species with a similar pattern and look-a-like. Many efforts are made to automate plant classification using plant leaf, plant flower, bark, or stem. After much effort, it has been proven that leaf is the most reliable source for plant classification. But it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations, like sizes, textures, shapes, and venation. Therefore, it is required to normalize all plant leaves into the same size to get better performance. Convolutional Neural Networks (CNN) provides a fair amount of accuracy when leaves are classified using this approach. But the performance can be improved by classifying using the traditional approach after applying CNN. In this paper, two approaches, namely CNN + Support Vector Machine (SVM) and CNN + K-Nearest Neighbors (kNN) used on 3 datasets, namely LeafSnap dataset, Flavia Dataset, and MalayaKew Dataset. The datasets are augmented to take care all the possibilities. The assessments and correlations of the predetermined feature extractor models are given. CNN + kNN managed to reach maximum accuracy of 99.5%, 97.4%, and 80.04%, respectively, in the three datasets. 相似文献
16.
Fast recognition of elevator buttons is a key step for service robots to ride elevators automatically. Although there are some studies in this field, none of them can achieve real-time application due to problems such as recognition speed and algorithm complexity. Elevator button recognition is a comprehensive problem. Not only does it need to detect the position of multiple buttons at the same time, but also needs to accurately identify the characters on each button. The latest version 5 of you only look once algorithm (YOLOv5) has the fastest reasoning speed and can be used for detecting multiple objects in real-time. The advantages of YOLOv5 make it an ideal choice for detecting the position of multiple buttons in an elevator, but it’s not good at specific word recognition. Optical character recognition (OCR) is a well-known technique for character recognition. This paper innovatively improved the YOLOv5 network, integrated OCR technology, and applied them to the elevator button recognition process. First, we changed the detection scale in the YOLOv5 network and only maintained the detection scales of 40 * 40 and 80 * 80, thus improving the overall object detection speed. Then, we put a modified OCR branch after the YOLOv5 network to identify the numbers on the buttons. Finally, we verified this method on different datasets and compared it with other typical methods. The results show that the average recall and precision of this method are 81.2% and 92.4%. Compared with others, the accuracy of this method has reached a very high level, but the recognition speed has reached 0.056 s, which is far higher than other methods. 相似文献
17.
目的 针对人工分拣组成的零件包装盒常常会出现缺少部分零件的问题,开发一套集训练、识别、分选于一体的智能分拣系统.方法 在设计过程中,提出一种基于深度学习的改进Yolov3算法,针对工业现场光照、业零件形状和质地等实际因素,对Yolo算法的训练和检测进行改进,通过对包装盒产品的一次拍摄,检测出画面中出现的预设物体,并与标准设置相比对,从而判断出该盒内产品是否有缺料、多料的情况,以此分选出合格与否的包装盒.结果 在物体摆放相互重叠不超过20％的情况下,物体检测的准确率为98.2％,召回率为99.5％.结论 通过文中提出的改进算法,设计的检测系统能够在复杂的工业现场环境下正常工作,并能对包装的完整性进行准确的检测. 相似文献
18.
Walid El-Shafai Noha A. El-Hag Ahmed Sedik Ghada Elbanby Fathi E. Abd El-Samie Naglaa F. Soliman Hussah Nasser AlEisa Mohammed E. Abdel Samea 《计算机、材料和连续体（英文）》2023,74(2):2905-2919
Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis. 相似文献
19.
主流的目标检测网络在高质量RGB图像上的目标检测能力突出,但应用于分辨率低的红外图像上时目标检测性能则有比较明显的下降。为了提高复杂场景下的红外目标检测识别能力,本文采用了以下措施：第一、借鉴领域自适应的方法,采用合适的红外图像预处理手段,使红外图像更接近RGB图像,从而可以应用主流的目标检测网络进一步提高检测精度。第二、采用单阶段目标检测网络YOLOv3作为基础网络,并用GIOU损失函数代替原有的MSE损失函数。经实验验证,该算法在公开红外数据集FLIR上检测的准确率提升明显。第三、针对FLIR数据集存在的目标尺寸跨度大的问题,借鉴空间金字塔思想,加入SPP模块,丰富特征图的表达能力,扩大特征图的感受野。实验表明,所采用的方法可以进一步提高目标检测的精度。 相似文献
20.
目的 针对当前卷烟烟组端面缺陷检测技术无法准确识别具体缺陷类型,以及未兼顾识别滤嘴变形或夹沫的局限性,加之在检测精度上存在不足,本文引入一种基于轻量化YOLOv7模型的检测策略。方法 在GDX2包装机上设计并安装图像采集系统,通过集成轻量化网络结构ShuffleNetV2、DWConv、DSConv对YOLOv7架构进行定制化改造,有效减少模型参数量,加速训练与推理过程;结合SimAM注意力机制进一步增强模型对轻微缺陷特征的关注能力,显著提升轻微外观缺陷识别的准确性。结果 实验结果显示,所提算法参数量下降85%,计算量下降81%,mAP@0.5值达到0.973,缺陷类型中滤嘴变形和夹沫的平均精度分别达到0.978和0.945。在低算力(11.15 TFLOPS)环境下,该系统的推理时间低至1.98 ms。结论 该技术已经应用于GDX2型卷烟包装机烟组端面缺陷检测,能够有效提升缺陷检测的综合性能,为烟草行业提供兼具高效性与经济性的外观质量控制解决方案。 相似文献