Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree a...Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD.Signal datasets were obtained by numerical experiments and vibration experiments,respectively.Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage.Results indicated a 5%to 10%improvement in detection accuracy compared to using original datasets without feature selection,demonstrating the feasibility of this method.The proposed method,based on tree model and classification,addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.展开更多
The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software w...The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.展开更多
Object detection plays a critical role in drone imagery analysis,especially in remote sensing applications where accurate and efficient detection of small objects is essential.Despite significant advancements in drone...Object detection plays a critical role in drone imagery analysis,especially in remote sensing applications where accurate and efficient detection of small objects is essential.Despite significant advancements in drone imagery detection,most models still struggle with small object detection due to challenges such as object size,complex backgrounds.To address these issues,we propose a robust detection model based on You Only Look Once(YOLO)that balances accuracy and efficiency.The model mainly contains several major innovation:feature selection pyramid network,Inner-Shape Intersection over Union(ISIoU)loss function and small object detection head.To overcome the limitations of traditional fusion methods in handling multi-level features,we introduce a Feature Selection Pyramid Network integrated into the Neck component,which preserves shallow feature details critical for detecting small objects.Additionally,recognizing that deep network structures often neglect or degrade small object features,we design a specialized small object detection head in the shallow layers to enhance detection accuracy for these challenging targets.To effectively model both local and global dependencies,we introduce a Conv-Former module that simulates Transformer mechanisms using a convolutional structure,thereby improving feature enhancement.Furthermore,we employ ISIoU to address object imbalance and scale variation This approach accelerates model conver-gence and improves regression accuracy.Experimental results show that,compared to the baseline model,the proposed method significantly improves small object detection performance on the VisDrone2019 dataset,with mAP@50 increasing by 4.9%and mAP@50-95 rising by 6.7%.This model also outperforms other state-of-the-art algorithms,demonstrating its reliability and effectiveness in both small object detection and remote sensing image fusion tasks.展开更多
In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted...In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition.展开更多
Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features.Several cloud-based IoT health providers have been described in the literature prev...Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features.Several cloud-based IoT health providers have been described in the literature previously.Furthermore,there are a number of issues related to time consumed and overall network performance when it comes to big data information.In the existing method,less performed optimization algorithms were used for optimizing the data.In the proposed method,the Chaotic Cuckoo Optimization algorithm was used for feature selection,and Convolutional Support Vector Machine(CSVM)was used.The research presents a method for analyzing healthcare information that uses in future prediction.The major goal is to take a variety of data while improving efficiency and minimizing process time.The suggested method employs a hybrid method that is divided into two stages.In the first stage,it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight,opposition-based learning,and distributor operator.In the second stage,CSVM is used which combines the benefits of convolutional neural network(CNN)and SVM.The CSVM modifies CNN’s convolution product to learn hidden deep inside data sources.For improved economic flexibility,greater protection,greater analytics with confidentiality,and lower operating cost,the suggested approach is built on fog computing.Overall results of the experiments show that the suggested method can minimize the number of features in the datasets,enhances the accuracy by 82%,and decrease the time of the process.展开更多
With the rapid development of the Internet of Things(IoT),all kinds of data are increasing exponentially.Data storage and computing on cloud servers are increasingly restricted by hardware.This has prompted the develo...With the rapid development of the Internet of Things(IoT),all kinds of data are increasing exponentially.Data storage and computing on cloud servers are increasingly restricted by hardware.This has prompted the development of fog computing.Fog computing is to place the calculation and storage of data at the edge of the network,so that the entire Internet of Things system can run more efficiently.The main function of fog computing is to reduce the burden of cloud servers.By placing fog nodes in the IoT network,the data in the IoT devices can be transferred to the fog nodes for storage and calculation.Many of the information collected by IoT devices are malicious traffic,which contains a large number of malicious attacks.Because IoT devices do not have strong computing power and the ability to detect malicious traffic,we need to deploy a system to detect malicious attacks on the fog node.In response to this situation,we propose an intrusion detection system based on distributed ensemble design.The system mainly uses Convolutional Neural Network(CNN)as the first-level learner.In the second level,the random forest will finally classify the prediction results obtained in the first level.This paper uses the UNSW-NB15 dataset to evaluate the performance of the model.Experimental results show that the model has good detection performance for most attacks.展开更多
物联网(Internet of Things,IoT)技术的发展给工业界和日常生活带来便利的同时,海量易受到各种攻击和破坏的IoT设备也降低了分布式拒绝服务(Distributed Denial of Service,DDoS)攻击发起的成本,使被攻击方无法响应正常用户访问.为了在...物联网(Internet of Things,IoT)技术的发展给工业界和日常生活带来便利的同时,海量易受到各种攻击和破坏的IoT设备也降低了分布式拒绝服务(Distributed Denial of Service,DDoS)攻击发起的成本,使被攻击方无法响应正常用户访问.为了在物联网边缘中快速、准确地完成DDoS攻击检测,弥补现有方法资源开销大、不精确的缺陷,本文提出了一种基于轻量化卷积神经网络(Lightweight Convolutional Neural Networks,LCNN)的DDoS检测方法.面向物联网流量特性,方法首先提取包级特征和经冗余分析筛选得到的流级特征.之后设计了低参数和运算量的卷积神经网络LCNN,最后基于变维后的特征,快速检测定位攻击.实验结果表明,方法检测准确率达99.4%.同时LCNN在FPGA中能够以较少的资源消耗,保证在1ms时间内完成对一条流的推理判断.展开更多
基金supported by the Project of Guangdong Province High Level University Construction for Guangdong University of Technology(Grant No.262519003)the College Student Innovation Training Program of Guangdong University of Technology(Grant Nos.S202211845154 and xj2023118450384).
文摘Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD.Signal datasets were obtained by numerical experiments and vibration experiments,respectively.Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage.Results indicated a 5%to 10%improvement in detection accuracy compared to using original datasets without feature selection,demonstrating the feasibility of this method.The proposed method,based on tree model and classification,addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.
文摘The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.
文摘Object detection plays a critical role in drone imagery analysis,especially in remote sensing applications where accurate and efficient detection of small objects is essential.Despite significant advancements in drone imagery detection,most models still struggle with small object detection due to challenges such as object size,complex backgrounds.To address these issues,we propose a robust detection model based on You Only Look Once(YOLO)that balances accuracy and efficiency.The model mainly contains several major innovation:feature selection pyramid network,Inner-Shape Intersection over Union(ISIoU)loss function and small object detection head.To overcome the limitations of traditional fusion methods in handling multi-level features,we introduce a Feature Selection Pyramid Network integrated into the Neck component,which preserves shallow feature details critical for detecting small objects.Additionally,recognizing that deep network structures often neglect or degrade small object features,we design a specialized small object detection head in the shallow layers to enhance detection accuracy for these challenging targets.To effectively model both local and global dependencies,we introduce a Conv-Former module that simulates Transformer mechanisms using a convolutional structure,thereby improving feature enhancement.Furthermore,we employ ISIoU to address object imbalance and scale variation This approach accelerates model conver-gence and improves regression accuracy.Experimental results show that,compared to the baseline model,the proposed method significantly improves small object detection performance on the VisDrone2019 dataset,with mAP@50 increasing by 4.9%and mAP@50-95 rising by 6.7%.This model also outperforms other state-of-the-art algorithms,demonstrating its reliability and effectiveness in both small object detection and remote sensing image fusion tasks.
基金Supported by the National Natural Science Foundation of China(61701029)Basic Research Foundation of Beijing Institute of Technology(20170542008)Industry-University Research Innovation Foundation of the Science and Technology Development Center of the Ministry of Education(2018A02012)。
文摘In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features.Several cloud-based IoT health providers have been described in the literature previously.Furthermore,there are a number of issues related to time consumed and overall network performance when it comes to big data information.In the existing method,less performed optimization algorithms were used for optimizing the data.In the proposed method,the Chaotic Cuckoo Optimization algorithm was used for feature selection,and Convolutional Support Vector Machine(CSVM)was used.The research presents a method for analyzing healthcare information that uses in future prediction.The major goal is to take a variety of data while improving efficiency and minimizing process time.The suggested method employs a hybrid method that is divided into two stages.In the first stage,it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight,opposition-based learning,and distributor operator.In the second stage,CSVM is used which combines the benefits of convolutional neural network(CNN)and SVM.The CSVM modifies CNN’s convolution product to learn hidden deep inside data sources.For improved economic flexibility,greater protection,greater analytics with confidentiality,and lower operating cost,the suggested approach is built on fog computing.Overall results of the experiments show that the suggested method can minimize the number of features in the datasets,enhances the accuracy by 82%,and decrease the time of the process.
基金supported in part by the Beijing Natural Science Foundation (No.4212015)Natural Science Foundation of China (No.61801008)+2 种基金China Ministry of Education China Mobile Scientific Research Foundation (No.MCM20200102)China Postdoctoral Science Foundation (No.2020M670074)Beijing Municipal Commission of Education Foundation (No.KM201910005025).
文摘With the rapid development of the Internet of Things(IoT),all kinds of data are increasing exponentially.Data storage and computing on cloud servers are increasingly restricted by hardware.This has prompted the development of fog computing.Fog computing is to place the calculation and storage of data at the edge of the network,so that the entire Internet of Things system can run more efficiently.The main function of fog computing is to reduce the burden of cloud servers.By placing fog nodes in the IoT network,the data in the IoT devices can be transferred to the fog nodes for storage and calculation.Many of the information collected by IoT devices are malicious traffic,which contains a large number of malicious attacks.Because IoT devices do not have strong computing power and the ability to detect malicious traffic,we need to deploy a system to detect malicious attacks on the fog node.In response to this situation,we propose an intrusion detection system based on distributed ensemble design.The system mainly uses Convolutional Neural Network(CNN)as the first-level learner.In the second level,the random forest will finally classify the prediction results obtained in the first level.This paper uses the UNSW-NB15 dataset to evaluate the performance of the model.Experimental results show that the model has good detection performance for most attacks.
文摘物联网(Internet of Things,IoT)技术的发展给工业界和日常生活带来便利的同时,海量易受到各种攻击和破坏的IoT设备也降低了分布式拒绝服务(Distributed Denial of Service,DDoS)攻击发起的成本,使被攻击方无法响应正常用户访问.为了在物联网边缘中快速、准确地完成DDoS攻击检测,弥补现有方法资源开销大、不精确的缺陷,本文提出了一种基于轻量化卷积神经网络(Lightweight Convolutional Neural Networks,LCNN)的DDoS检测方法.面向物联网流量特性,方法首先提取包级特征和经冗余分析筛选得到的流级特征.之后设计了低参数和运算量的卷积神经网络LCNN,最后基于变维后的特征,快速检测定位攻击.实验结果表明,方法检测准确率达99.4%.同时LCNN在FPGA中能够以较少的资源消耗,保证在1ms时间内完成对一条流的推理判断.
