To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security,retrieval efficiency,and retrieval accuracy.This research suggests a searchable encryption and deep ha...To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security,retrieval efficiency,and retrieval accuracy.This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure,searchable encryption scheme.First,a deep learning framework based on residual network and transfer learn-ing model is designed to extract more representative image deep features.Secondly,the central similarity is used to quantify and construct the deep hash sequence of features.The Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and low-complexity searchable index.Finally,according to the additive homomorphic property of Paillier homomorphic encryption,a similarity measurement method suitable for com-puting in the retrieval system’s security is ensured by the encrypted domain.The experimental results,which were obtained on Web Image Database from the National University of Singapore(NUS-WIDE),Microsoft Common Objects in Context(MS COCO),and ImageNet data sets,demonstrate the system’s robust security and precise retrieval,the proposed scheme can achieve efficient image retrieval without revealing user privacy.The retrieval accuracy is improved by at least 37%compared to traditional hashing schemes.At the same time,the retrieval time is saved by at least 9.7%compared to the latest deep hashing schemes.展开更多
Coronavirus has infected more than 753 million people,ranging in severity from one person to another,where more than six million infected people died worldwide.Computer-aided diagnostic(CAD)with artificial intelligenc...Coronavirus has infected more than 753 million people,ranging in severity from one person to another,where more than six million infected people died worldwide.Computer-aided diagnostic(CAD)with artificial intelligence(AI)showed outstanding performance in effectively diagnosing this virus in real-time.Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients.This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs.We used the methodology of systematic reviews and meta-analyses(PRISMA)flow method.This research aims to systematically analyze the supervised deep learning methods,open resource datasets,data augmentation methods,and loss functions used for various segment shapes of COVID-19 infection from computerized tomography(CT)chest images.We have selected 56 primary studies relevant to the topic of the paper.We have compared different aspects of the algorithms used to segment infected areas in the CT images.Limitations to deep learning in the segmentation of infected areas still need to be developed to predict smaller regions of infection at the beginning of their appearance.展开更多
Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish long-distance inte...Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish long-distance interdependence, which limits the segmentation performance. Transformer has been successfully applied to various computer vision, using self-attention mechanism to simulate long-distance interaction, so as to capture global information. However, self-attention lacks spatial location and high-performance computing. In order to solve the above problems, we develop a new medical transformer, which has a multi-scale context fusion function and can be used for medical image segmentation. The proposed model combines convolution operation and attention mechanism to form a u-shaped framework, which can capture both local and global information. First, the traditional converter module is improved to an advanced converter module, which uses post-layer normalization to obtain mild activation values, and uses scaled cosine attention with a moving window to obtain accurate spatial information. Secondly, we also introduce a deep supervision strategy to guide the model to fuse multi-scale feature information. It further enables the proposed model to effectively propagate feature information across layers, Thanks to this, it can achieve better segmentation performance while being more robust and efficient. The proposed model is evaluated on multiple medical image segmentation datasets. Experimental results demonstrate that the proposed model achieves better performance on a challenging dataset (ETIS) compared to existing methods that rely only on convolutional neural networks, transformers, or a combination of both. The mDice and mIou indicators increased by 2.74% and 3.3% respectively.展开更多
With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of cyberattacks has become more challenging due to the high dimensionality of...With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points.Recently,researchers have suggested deep learning(DL)algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks.However,due to the high dynamics and imbalanced nature of the data,the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks.Therefore,it is important to design a self-adaptive model for an intrusion detection system(IDS)to improve the detection of attacks.Consequently,in this paper,a novel hybrid weighted deep belief network(HW-DBN)algorithm is proposed for building an efficient and reliable IDS(DeepIoT.IDS)model to detect existing and novel cyberattacks.The HW-DBN algorithm integrates an improved Gaussian–Bernoulli restricted Boltzmann machine(Deep GB-RBM)feature learning operator with a weighted deep neural networks(WDNN)classifier.The CICIDS2017 dataset is selected to evaluate the DeepIoT.IDS model as it contains multiple types of attacks,complex data patterns,noise values,and imbalanced classes.We have compared the performance of the DeepIoT.IDS model with three recent models.The results show the DeepIoT.IDS model outperforms the three other models by achieving a higher detection accuracy of 99.38%and 99.99%for web attack and bot attack scenarios,respectively.Furthermore,it can detect the occurrence of low-frequency attacks that are undetectable by other models.展开更多
Liver cancer has the second highest incidence rate among all types of malignant tumors,and currently,its diagnosis heavily depends on doctors’manual labeling of CT scan images,a process that is time-consuming and sus...Liver cancer has the second highest incidence rate among all types of malignant tumors,and currently,its diagnosis heavily depends on doctors’manual labeling of CT scan images,a process that is time-consuming and susceptible to subjective errors.To address the aforementioned issues,we propose an automatic segmentation model for liver and tumors called Res2Swin Unet,which is based on the Unet architecture.The model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation,respectively.Attention-Res2 merges multiple feature map parts with an Attention gate via skip connections,while Swin Transformer captures long-range dependencies and models the input globally.And the model uses deep supervision and a hybrid loss function for faster convergence.On the LiTS2017 dataset,it achieves better segmentation performance than other models,with an average Dice coefficient of 97.0%for liver segmentation and 81.2%for tumor segmentation.展开更多
Automatic extraction of tailing ponds from Very High-Resolution(VHR)remotely sensed images is vital for mineral resource management.This study proposes a Pseudo-Siamese Visual Geometry Group Encoder-Decoder network(PS...Automatic extraction of tailing ponds from Very High-Resolution(VHR)remotely sensed images is vital for mineral resource management.This study proposes a Pseudo-Siamese Visual Geometry Group Encoder-Decoder network(PSVED)to achieve high accuracy tailing ponds extraction from VHR images.First,handcrafted feature(HCF)images are calculated from VHR images based on the index calculation algorithm,highlighting the tailing ponds'signals.Second,considering the information gap between VHR images and HCF images,the Pseudo-Siamese Visual Geometry Group(Pseudo-Siamese VGG)is utilized to extract independent and representative deep semantic features from VHR images and HCF images,respectively.Third,the deep supervision mechanism is attached to handle the optimization problem of gradients vanishing or exploding.A self-made tailing ponds extraction dataset(TPSet)produced with the Gaofen-6 images of part of Hebei province,China,was employed to conduct experiments.The results show that the proposed'method_achieves the best visual performance and accuracy for tailing ponds extraction in all the tested methods,whereas the running time of the proposed method maintains at the same level as other methods.This study has practical significance in automatically extracting tailing ponds from VHR images which is beneficial to tailing ponds management and monitoring.展开更多
Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and contro...Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.展开更多
Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks today.In MANET,the Intrusion Detection System(IDS)is crucial because it a...Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks today.In MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular operation.Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET.However,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of misclassification.Therefore,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models.Here,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of classifier.Then,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier.Moreover,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations.During evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.展开更多
基金supported by the National Natural Science Foundation of China(No.61862041).
文摘To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security,retrieval efficiency,and retrieval accuracy.This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure,searchable encryption scheme.First,a deep learning framework based on residual network and transfer learn-ing model is designed to extract more representative image deep features.Secondly,the central similarity is used to quantify and construct the deep hash sequence of features.The Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and low-complexity searchable index.Finally,according to the additive homomorphic property of Paillier homomorphic encryption,a similarity measurement method suitable for com-puting in the retrieval system’s security is ensured by the encrypted domain.The experimental results,which were obtained on Web Image Database from the National University of Singapore(NUS-WIDE),Microsoft Common Objects in Context(MS COCO),and ImageNet data sets,demonstrate the system’s robust security and precise retrieval,the proposed scheme can achieve efficient image retrieval without revealing user privacy.The retrieval accuracy is improved by at least 37%compared to traditional hashing schemes.At the same time,the retrieval time is saved by at least 9.7%compared to the latest deep hashing schemes.
文摘Coronavirus has infected more than 753 million people,ranging in severity from one person to another,where more than six million infected people died worldwide.Computer-aided diagnostic(CAD)with artificial intelligence(AI)showed outstanding performance in effectively diagnosing this virus in real-time.Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients.This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs.We used the methodology of systematic reviews and meta-analyses(PRISMA)flow method.This research aims to systematically analyze the supervised deep learning methods,open resource datasets,data augmentation methods,and loss functions used for various segment shapes of COVID-19 infection from computerized tomography(CT)chest images.We have selected 56 primary studies relevant to the topic of the paper.We have compared different aspects of the algorithms used to segment infected areas in the CT images.Limitations to deep learning in the segmentation of infected areas still need to be developed to predict smaller regions of infection at the beginning of their appearance.
文摘Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish long-distance interdependence, which limits the segmentation performance. Transformer has been successfully applied to various computer vision, using self-attention mechanism to simulate long-distance interaction, so as to capture global information. However, self-attention lacks spatial location and high-performance computing. In order to solve the above problems, we develop a new medical transformer, which has a multi-scale context fusion function and can be used for medical image segmentation. The proposed model combines convolution operation and attention mechanism to form a u-shaped framework, which can capture both local and global information. First, the traditional converter module is improved to an advanced converter module, which uses post-layer normalization to obtain mild activation values, and uses scaled cosine attention with a moving window to obtain accurate spatial information. Secondly, we also introduce a deep supervision strategy to guide the model to fuse multi-scale feature information. It further enables the proposed model to effectively propagate feature information across layers, Thanks to this, it can achieve better segmentation performance while being more robust and efficient. The proposed model is evaluated on multiple medical image segmentation datasets. Experimental results demonstrate that the proposed model achieves better performance on a challenging dataset (ETIS) compared to existing methods that rely only on convolutional neural networks, transformers, or a combination of both. The mDice and mIou indicators increased by 2.74% and 3.3% respectively.
基金This work was partially funded by the Industry Grant Scheme from Jaycorp Berhad in cooperation with UNITAR International University.The authors would like to thank INSFORNET,the Center for Advanced Computing Technology(C-ACT)at Universiti Teknikal Malaysia Melaka(UTeM),and the Center of Intelligent and Autonomous Systems(CIAS)at Universiti Tun Hussein Onn Malaysia(UTHM)for supporting this work.
文摘With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points.Recently,researchers have suggested deep learning(DL)algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks.However,due to the high dynamics and imbalanced nature of the data,the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks.Therefore,it is important to design a self-adaptive model for an intrusion detection system(IDS)to improve the detection of attacks.Consequently,in this paper,a novel hybrid weighted deep belief network(HW-DBN)algorithm is proposed for building an efficient and reliable IDS(DeepIoT.IDS)model to detect existing and novel cyberattacks.The HW-DBN algorithm integrates an improved Gaussian–Bernoulli restricted Boltzmann machine(Deep GB-RBM)feature learning operator with a weighted deep neural networks(WDNN)classifier.The CICIDS2017 dataset is selected to evaluate the DeepIoT.IDS model as it contains multiple types of attacks,complex data patterns,noise values,and imbalanced classes.We have compared the performance of the DeepIoT.IDS model with three recent models.The results show the DeepIoT.IDS model outperforms the three other models by achieving a higher detection accuracy of 99.38%and 99.99%for web attack and bot attack scenarios,respectively.Furthermore,it can detect the occurrence of low-frequency attacks that are undetectable by other models.
文摘Liver cancer has the second highest incidence rate among all types of malignant tumors,and currently,its diagnosis heavily depends on doctors’manual labeling of CT scan images,a process that is time-consuming and susceptible to subjective errors.To address the aforementioned issues,we propose an automatic segmentation model for liver and tumors called Res2Swin Unet,which is based on the Unet architecture.The model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation,respectively.Attention-Res2 merges multiple feature map parts with an Attention gate via skip connections,while Swin Transformer captures long-range dependencies and models the input globally.And the model uses deep supervision and a hybrid loss function for faster convergence.On the LiTS2017 dataset,it achieves better segmentation performance than other models,with an average Dice coefficient of 97.0%for liver segmentation and 81.2%for tumor segmentation.
基金supported by the National Key Research and Development Program[grant number:2022YFF1303301]The Open Foundation of the Key Laboratory of Coupling Process and Effect of Natural Resources Elements[grant number:2022KFKTC001]+1 种基金The National Natural Science Foundation of China[grant number:42271480]The Fundamental Research Funds for the Central Universities[grant number:2023ZKPYDC10,BBJ2023026].
文摘Automatic extraction of tailing ponds from Very High-Resolution(VHR)remotely sensed images is vital for mineral resource management.This study proposes a Pseudo-Siamese Visual Geometry Group Encoder-Decoder network(PSVED)to achieve high accuracy tailing ponds extraction from VHR images.First,handcrafted feature(HCF)images are calculated from VHR images based on the index calculation algorithm,highlighting the tailing ponds'signals.Second,considering the information gap between VHR images and HCF images,the Pseudo-Siamese Visual Geometry Group(Pseudo-Siamese VGG)is utilized to extract independent and representative deep semantic features from VHR images and HCF images,respectively.Third,the deep supervision mechanism is attached to handle the optimization problem of gradients vanishing or exploding.A self-made tailing ponds extraction dataset(TPSet)produced with the Gaofen-6 images of part of Hebei province,China,was employed to conduct experiments.The results show that the proposed'method_achieves the best visual performance and accuracy for tailing ponds extraction in all the tested methods,whereas the running time of the proposed method maintains at the same level as other methods.This study has practical significance in automatically extracting tailing ponds from VHR images which is beneficial to tailing ponds management and monitoring.
基金The research work presented in this paper is supported by the National Natural Science Foundation of China(Grant No.51675418).
文摘Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.
文摘Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks today.In MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular operation.Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET.However,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of misclassification.Therefore,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models.Here,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of classifier.Then,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier.Moreover,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations.During evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.
