共查询到20条相似文献，搜索用时 0 毫秒
1.
2.
3.
4.
Syed Muhammad Saqlain Shah Tahir Afzal Malik Robina khatoon Syed Saqlain Hassan Faiz Ali Shah 《计算机、材料和连续体（英文）》2019,61(2):535-553
Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers. In this paper, we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance. Research have mostly focused the problem of human detection in thin crowd, overall behavior of the crowd and actions of individuals in video sequences. Vision based Human behavior modeling is a complex task as it involves human detection, tracking, classifying normal and abnormal behavior. The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e., fill hole inside objects algorithm. Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm. The classification task is achieved using binary and multi class support vector machines. The proposed technique is validated through accuracy, precision, recall and F-measure metrics. 相似文献
5.
Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi 《计算机、材料和连续体（英文）》2023,74(2):2607-2623
The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet. The interconnectivity of networks has brought various complexities in maintaining network availability, consistency, and discretion. Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities. An intrusion detection system controls the flow of network traffic with the help of computer systems. Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic. For this purpose, when the network traffic encounters known or unknown intrusions in the network, a machine-learning framework is needed to identify and/or verify network intrusion. The Intrusion detection scheme empowered with a fused machine learning technique (IDS-FMLT) is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks. The proposed IDS-FMLT system model obtained 95.18% validation accuracy and a 4.82% miss rate in intrusion detection. 相似文献
6.
This paper investigates performance improvement via the incorporation of the support vector machine (SVM) into the vector tracking loop (VTL) for the Global Positioning System (GPS) in limited satellite visibility. Unlike the traditional scalar tracking loop (STL), the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the user. The method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error growth due to signal outage. Similar to the neural network, the SVM is motivated by its ability to approximate an unknown nonlinear input-output mapping through supervised training. The SVM is employed for predicting adequate numerical control oscillator (NCO) inputs, i.e., providing better prediction of residuals for the Doppler frequency and code phase in order to maintain regular operation of the navigation system. When the navigation processing is in good condition, the SVM is at the training stage, and the output information from the discriminator and navigation filter is adopted as the inputs. Other machine learning (ML) algorithms such as the radial basis function neural network (RBFNN) and the Adaptive Network-Based Fuzzy Inference System (ANFIS) are employed for comparison. Performance evaluation for the SVM assisted architecture as compared to the RBFNN- and ANFIS-assisted methods and the un-assisted VTL will be carried out and the performance evaluation during GPS signal outage will be presented. The proposed design is very useful for navigation during the environment of limited satellite visibility to effectively overcome the problem in the environment of GPS outage. 相似文献
7.
Temporal segmentation of actions has been under intensive focus in the field of computer vision for a prolonged period. The present study proposed a template-based framework to resolve the issues concerning timeliness and real-time performance in the temporal segmentation in a continuous video. A complete action can be detected, based on the previous frames, and the action can be segmented immediately without waiting for the follow-up frames. Herein, characteristic frames are selected by a martingale-based method, followed by the formation of the corresponding motion history through backtracking along the characteristic frames, and the final segmentation is determined according to the recognition model trained by the extreme learning machine. In the experiment on the IXMAS database, the average rate of the detection of action reached 91%, and the accuracy in the frame level reached 83.5%. In the experiment on the 3D skeleton data based on Kinect, the detection rate reached 94%. 相似文献
8.
基于机器视觉的药品包装生产线自动检测系统 总被引：1，自引：5，他引：1
目的提高包装药品效率,保证包装过程的正确率和安全性。方法在充分研究药品包装生产线现状的基础上,将机器视觉应用于药品包装生产线药品的自动检测,采用基于最大熵阈值,设计一种图像分割方法;同时采用自适应高斯引导图像滤波算法,设计一种图像去噪算法。结果通过实验验证,该系统可以实现药品包装生产线的自动检测,并能自动剔除不合格药品,保证生产安全。结论研究的药品包装生产线自动检测系统具有自动化程度高、效率高的优点,具有广阔的市场应用前景。 相似文献
9.
Amir Haider Muhammad Adnan Khan Abdur Rehman Muhib Ur Rahman Hyung Seok Kim 《计算机、材料和连续体（英文）》2021,66(2):1785-1805
In recent years, cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things (IoT) and the widespread development of computer infrastructure and systems. It is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intrusion detection framework that is integral to security. Researchers have worked on developing intrusion detection models that depend on machine learning (ML) methods to address these security problems. An intelligent intrusion detection device powered by data can exploit artificial intelligence (AI), and especially ML, techniques. Accordingly, we propose in this article an intrusion detection model based on a Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System (RTS-DELM-CSIDS) security model. The proposed model initially determines the rating of security aspects contributing to their significance and then develops a comprehensive intrusion detection framework focused on the essential characteristics. Furthermore, we investigated the feasibility of our proposed RTS-DELM-CSIDS framework by performing dataset evaluations and calculating accuracy parameters to validate. The experimental findings demonstrate that the RTS-DELM-CSIDS framework outperforms conventional algorithms. Furthermore, the proposed approach has not only research significance but also practical significance. 相似文献
10.
Amani Abdulrahman Albraikan Nadhem NEMRI Mimouna Abdullah Alkhonaini Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel 《计算机、材料和连续体（英文）》2023,74(2):2443-2459
Melanoma remains a serious illness which is a common form of skin cancer. Since the earlier detection of melanoma reduces the mortality rate, it is essential to design reliable and automated disease diagnosis model using dermoscopic images. The recent advances in deep learning (DL) models find useful to examine the medical image and make proper decisions. In this study, an automated deep learning based melanoma detection and classification (ADL-MDC) model is presented. The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma. The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage. Besides, the k-means clustering technique is applied for the image segmentation process. In addition, Adagrad optimizer based Capsule Network (CapsNet) model is derived for effective feature extraction process. Lastly, crow search optimization (CSO) algorithm with sparse autoencoder (SAE) model is utilized for the melanoma classification process. The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance. A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects. The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches. 相似文献
11.
Karen M. Feigh Matthew J. Miller Raunak P. Bhattacharyya Minyue Ma Samantha Krening Yosef Razin 《Theoretical Issues in Ergonomics Science》2018,19(4):389-405
Human factors practitioners (HFPs) play many different roles in the design, creation, operation and maintenance of engineered systems. Less well known are the methods which are aimed at helping with the early stages of design, which are more systems-oriented and often involve questions of the concept of operation in which the engineered system will be fielded. Emerging from the field of cognitive engineering, these methods, including simulation, cognitive work analysis, cognitive task analyses and hierarchical task analysis, will be important as autonomous systems become increasingly capable. Even the most capable systems will continue to interact with humans, and it is at these interfaces between humans and engineered systems that HFP will continue to be needed. This paper describes recent work to leverage these methods to inform concepts of operation in aviation and space, machine learning algorithms and goal-oriented human–machine collaboration. 相似文献
12.
目的 解决现有工业线束导线排序检测方法中存在的效率低、混色导线检测效果差等问题。方法 基于机器视觉技术设计一种线束导线排序检测装置,并结合图像处理技术和深度学习原理提出一种混色导线排序检测方法。首先根据线束图像中选择的感兴趣区域,分割出线束连接器图像和导线图像,并采用模板匹配和颜色定位方法完成连接器正反面的识别和单色导线的识别定位;然后采集并制作PE混色导线数据集,研究Faster R−CNN、SSD、YOLOv3和YOLOv5m等4种不同目标检测算法对PE混色导线的检测效果。结果 实验结果表明,YOLOv5m检测模型的检测速度和准确率兼顾性最好;改进系统后,检测时间减少了18.55%,平均识别准确率为98.83%。结论 改进后检测系统具有良好的检测效率和可靠性,适用于种类丰富的工业线束导线排序检测。 相似文献
13.
Khalid Mahmood Aamir Muhammad Ramzan Saima Skinadar Hikmat Ullah Khan Usman Tariq Hyunsoo Lee Yunyoung Nam Muhammad Attique Khan 《计算机、材料和连续体（英文）》2022,71(1):17-33
This paper focuses on detecting diseased signals and arrhythmias classification into two classes: ventricular tachycardia and premature ventricular contraction. The sole purpose of the signal detection is used to determine if a signal has been collected from a healthy or sick person. The proposed research approach presents a mathematical model for the signal detector based on calculating the instantaneous frequency (IF). Once a signal taken from a patient is detected, then the classifier takes that signal as input and classifies the target disease by predicting the class label. While applying the classifier, templates are designed separately for ventricular tachycardia and premature ventricular contraction. Similarities of a given signal with both the templates are computed in the spectral domain. The empirical analysis reveals precisions for the detector and the applied classifier are 100% and 77.27%, respectively. Moreover, instantaneous frequency analysis provides a benchmark that IF of a normal signal ranges from 0.8 to 1.1 Hz whereas IF range for ventricular tachycardia and premature ventricular contraction is 0.08–0.6 Hz. This indicates a serious loss of high-frequency contents in the spectrum, implying that the heart’s overall activity is slowed down. This study may help medical practitioners in detecting the heart disease type based on signal analysis. 相似文献
14.
Recently, the Erebus attack has proved to be a security threat to the blockchain network layer, and the existing research has faced challenges in detecting the Erebus attack on the blockchain network layer. The cloud-based active defense and one-sidedness detection strategies are the hindrances in detecting Erebus attacks. This study designs a detection approach by establishing a ReliefF_WMRmR-based two-stage feature selection algorithm and a deep learning-based multimodal classification detection model for Erebus attacks and responding to security threats to the blockchain network layer. The goal is to improve the performance of Erebus attack detection methods, by combining the traffic behavior with the routing status based on multimodal deep feature learning. The traffic behavior and routing status were first defined and used to describe the attack characteristics at diverse stages of s leak monitoring, hidden traffic overlay, and transaction identity forgery. The goal is to clarify how an Erebus attack affects the routing transfer and traffic state on the blockchain network layer. Consequently, detecting objects is expected to become more relevant and sensitive. A two-stage feature selection algorithm was designed based on ReliefF and weighted maximum relevance minimum redundancy (ReliefF_WMRmR) to alleviate the overfitting of the training model caused by redundant information and noise in multiple source features of the routing status and traffic behavior. The ReliefF algorithm was introduced to select strong correlations and highly informative features of the labeled data. According to WMRmR, a feature selection framework was defined to eliminate weakly correlated features, eliminate redundant information, and reduce the detection overhead of the model. A multimodal deep learning model was constructed based on the multilayer perceptron (MLP) to settle the high false alarm rates incurred by multisource data. Using this model, isolated inputs and deep learning were conducted on the selected routing status and traffic behavior. Redundant intermodal information was removed because of the complementarity of the multimodal network, which was followed by feature fusion and output feature representation to boost classification detection precision. The experimental results demonstrate that the proposed method can detect features, such as traffic data, at key link nodes and route messages in a real blockchain network environment. Additionally, the model can detect Erebus attacks effectively. This study provides novelty to the existing Erebus attack detection by increasing the accuracy detection by 1.05%, the recall rate by 2.01%, and the F1-score by 2.43%. 相似文献
15.
Muhammad Adnan Khan Abdur Rehman Khalid Masood Khan Mohammed A. Al Ghamdi Sultan H. Almotiri 《计算机、材料和连续体（英文）》2021,66(1):467-480
Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system(IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstandingadvancements of growth, current intrusion detection systems also experience dif-ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, severalresearchers concentrated on designing intrusion detection systems that rely onmachine learning approaches. Machine learning models will accurately identifythe underlying variations among regular information and irregular informationwith incredible efficiency. Artificial intelligence, particularly machine learningmethods can be used to develop an intelligent intrusion detection framework.There in this article in order to achieve this objective, we propose an intrusiondetection system focused on a Deep extreme learning machine (DELM) whichfirst establishes the assessment of safety features that lead to their prominenceand then constructs an adaptive intrusion detection system focusing on the important features. In the moment, we researched the viability of our suggested DELMbased intrusion detection system by conducting dataset assessments and evaluating the performance factors to validate the system reliability. The experimentalresults illustrate that the suggested framework outclasses traditional algorithms.In fact, the suggested framework is not only of interest to scientific researchbut also of functional importance. 相似文献
16.
Asma Daly Hedi Yazid Basel Solaiman Najoua Essoukri Ben Amara 《International journal of imaging systems and technology》2021,31(1):302-312
Atlas‐based segmentation is a high level segmentation technique which has become a standard paradigm for exploiting prior knowledge in image segmentation. Recent multiatlas‐based methods have provided greatly accurate segmentations of different parts of the human body by propagating manual delineations from multiple atlases in a data set to a query subject and fusing them. The female pelvic region is known to be of high variability which makes the segmentation task difficult. We propose, here, an approach for the segmentation of magnetic resonance imaging (MRI) called multiatlas‐based segmentation using online machine learning (OML). The proposed approach allows separating regions which may be affected by cervical cancer in a female pelvic MRI. The suggested approach is based on an online learning method for the construction of the dataset of atlases. The experiments demonstrate the higher accuracy of the suggested approach compared to a segmentation technique based on a fixed dataset of atlases and single‐atlas‐based segmentation technique. 相似文献
17.
针对人脸关键点检测(人脸对齐)在应用场景下的速度和精度需求,首先在SSD基础之上融合更多分布均匀的特征层,对人脸框坐标进行级联预测,形成对于多尺度人脸信息均具有更加鲁棒响应的深度学习检测器MR-SSD。其次在局部二值特征LBF的级联形状回归方法基础上,提出了基于面部像素差值的多角度初始化算法。采用端正人脸正负90°倾斜范围内的五组特征点形状进行初始化,求取每组回归后形状的眼部特征点像素均方差值并以最大者对应方案作为最终回归形状,从而实现对多角度倾斜人脸优异的拟合效果。本文所提出的最优架构可以实时获得极具鲁棒性的人脸框坐标并且可实现对于多角度倾斜人脸的关键点检测。 相似文献
18.
刘玫玲;姚馨博;孙昌昊;张朕朕;江凯;李宏楠;毛传林;梁珺成 《计量科学与技术》2024,68(9):61-67
为提升检定仪器的效率和准确性,降低α、β表面污染仪检定过程中人工操作仪器、人工读数和记录对检定结果的影响,基于自动化技术和机器视觉算法研制了一套α、β表面污染仪自动检定装置。该装置包括双层换源转盘、基于机器视觉技术的图像训练软件和基于C#语言编写的自动检定控制软件。对装置进行了检定流程、硬件结构和软件优化设计,并在软件中增加了识别异常结果检验算法,可根据目标识别区域的特点进行条件筛选和异常数据剔除,提高了光学字符识别（OCR）正确率。开展了识别率测试、本底影响测试、比对测试和自动检定流程测试,检验了该装置的性能。结果证明,该装置对原始数据的识别率达到100%,装置内平面源的集中放置没有产生额外的本底干扰,使用手动定位支架和自动检定装置的测量结果的最大相对偏差为−6.0%,两结果在不确定度范围内一致性较好。该装置在满足JJG 478-2016要求的基础上,优化了辐射防护和源的固有安全性,提高了检定工作效率。 相似文献
19.
Yibeltal Tamyalew Ayodeji Olalekan Salau Aleka Melese Ayalew 《International journal of imaging systems and technology》2023,33(1):158-174
Large bowel obstruction (LBO) occurs when there is a blockage or twisting in the large bowel that prevents wastes and gas from passing through. If left untreated, the blockage cuts off blood supply to the colon, causing sections of it to die which results in high rates of morbidity and fatality. The examination of clinical symptoms of LBO involves careful inspection of the cecum and colon. Radiologists use X-rays to inspect the clinical signs. Some research has been done to automate the detection of related abdominal and intestinal diseases. However, all these studies concentrate only on detecting Crohn's, ulcerative colitis, Acute Appendicitis, colorectal cancer, celiac diseases, liver diseases, and chronic kidney diseases. Automatic detection and classification of LBO has not been given due attention so far to the best of the authors knowledge. To address this challenge, we have designed a model for the detection and classification of LBO. The models development comprises of stages such as preprocessing, detection, segmentation, feature extraction, and classification. We used YOLOv3 for detection and used a gray scale level co-occurrence matrix (GLCM), and a convolutional neural network for feature extraction, while support vector machine (SVM) and softmax were used for classification. The proposed model achieved a diagnostic accuracy of 89% when feature extraction methods such as CNN and median filter with softmax classifier were used. CNN and Gaussian filter with soft max classifier achieved 91%, while CNN and anisotropic filter with soft max classifier achieved 92%. GLCM with threshold segmentation and Gaussian filter with SVM classifier achieved 87%, while CNN with watershed segmentation and Gaussian filter with SVM classifier achieved 97% and CNN-GLCM with watershed segmentation and anisotropic diffusion filter with SVM classifier achieved 98% for detection and classification of LBO. Finally, this paper presented a performance analysis of various machine learning approaches for detection and classification of LBO. Hence, our model is designed to assist human experts (Radiologists) in diagnosing LBO. 相似文献
20.
Pedestrians must use a variety of cues when making safe decisions, many of which require processing of auditory information. We examined detection and localization of approaching vehicles using auditory cues. 50 adults ages 18–49 were presented with actual sounds of vehicles approaching at 5, 12, 25, and 35 mph. Three indices were of interest: the distance at which vehicles were detected, participants’ decision regarding the direction from which vehicles were approaching, and their determination of the vehicles’ arrival at their location. Participants more easily detected vehicles moving at higher speeds and vehicles approaching from the right. Determination of the direction of approach reached 90% accuracy or better when vehicles were traveling at, or greater than, 12 mph, and were more approaching from the right. Determination of vehicle arrival deteriorated significantly as speeds increased. Implications of the use of auditory cues in pedestrian settings, and future directions, are discussed. 相似文献