When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults.To protect IoMT devices and networks ...When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults.To protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT networks.This study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data complexity.Following rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning models.Augmentation using the BAT algorithm enhances dataset variability.Three deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data aspects.Their predictions form a meta-feature set for a subsequent meta-learner,which combines model strengths.Conventional classifiers validate meta-learner features for broad algorithm suitability.This comprehensive method demonstrates high accuracy and robustness in IoMT intrusion detection.Evaluations were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen categories.Experimental results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.展开更多
Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Altho...Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Although the Generative Adversarial Network(GAN)method can generate new samples by learning the feature distribution of the original samples,it is confronted with the problems of unstable training andmode collapse.To this end,a novel data augmenting approach called Graph CWGAN-GP is proposed in this paper.The traffic data is first converted into grayscale images as the input for the proposed model.Then,the minority class data is augmented with our proposed model,which is built by introducing conditional constraints and a new distance metric in typical GAN.Finally,the classical deep learning model is adopted as a classifier to classify datasets augmented by the Condition GAN(CGAN),Wasserstein GAN-Gradient Penalty(WGAN-GP)and Graph CWGAN-GP,respectively.Compared with the state-of-the-art GAN methods,the Graph CWGAN-GP cannot only control the modes of the data to be generated,but also overcome the problem of unstable training and generate more realistic and diverse samples.The experimental results show that the classification precision,recall and F1-Score of theminority class in the balanced dataset augmented in this paper have improved by more than 2.37%,3.39% and 4.57%,respectively.展开更多
Outcomes following peripheral nerve injury remain frustratingly poor. The reasons for this are multifactorial, although maintaining a growth permissive environment in the distal nerve stump following repair is arguabl...Outcomes following peripheral nerve injury remain frustratingly poor. The reasons for this are multifactorial, although maintaining a growth permissive environment in the distal nerve stump following repair is arguably the most important. The optimal environment for axonal regeneration relies on the synthesis and release of many biochemical mediators that are temporally and spatially regulated with a high level of incompletely understood complexity. The Schwann cell(SC) has emerged as a key player in this process. Prolonged periods of distal nerve stump denervation, characteristic of large gaps and proximal injuries, have been associated with a reduction in SC number and ability to support regenerating axons. Cell based therapy offers a potential therapy for the improvement of outcomes following peripheral nerve reconstruction. Stem cells have the potential to increase the number of SCs and prolong their ability to support regeneration. They may also have the ability to rescue and replenish populations of chromatolytic and apoptotic neurons following axotomy. Finally, they can be used in non-physiologic ways to preserve injured tissues such as denervated muscle while neuronal ingrowth has not yet occurred. Aside from stem cell type, careful consideration must be given to differentiation status, how stem cells are supported following transplantation and how they will be delivered to the site of injury. It is the aim of this article to review current opinions on the strategies of stem cell based therapy for the augmentation of peripheral nerve regeneration.展开更多
Android malware has evolved in various forms such as adware that continuously exposes advertisements,banking malware designed to access users’online banking accounts,and Short Message Service(SMS)malware that uses a ...Android malware has evolved in various forms such as adware that continuously exposes advertisements,banking malware designed to access users’online banking accounts,and Short Message Service(SMS)malware that uses a Command&Control(C&C)server to send malicious SMS,intercept SMS,and steal data.By using many malicious strategies,the number of malware is steadily increasing.Increasing Android malware threats numerous users,and thus,it is necessary to detect malware quickly and accurately.Each malware has distinguishable characteristics based on its actions.Therefore,security researchers have tried to categorize malware based on their behaviors by conducting the familial analysis which can help analysists to reduce the time and cost for analyzing malware.However,those studies algorithms typically used imbalanced,well-labeled open-source dataset,and thus,it is very difficult to classify some malware families which only have a few number of malware.To overcome this challenge,previous data augmentation studies augmented data by visualizing malicious codes and used them for malware analysis.However,visualization of malware can result in misclassifications because the behavior information of the malware could be compromised.In this study,we propose an android malware familial analysis system based on a data augmentation method that preserves malware behaviors to create an effective multi-class classifier for malware family analysis.To this end,we analyze malware and use Application Programming Interface(APIs)and permissions that can reflect the behavior of malware as features.By using these features,we augment malware dataset to enable effective malware detection while preserving original malicious behaviors.Our evaluation results demonstrate that,when a model is created by using only the augmented data,a macro-F1 score of 0.65 and accuracy of 0.63%.On the other hand,when the augmented data and original malware are used together,the evaluation results show that a macro-F1 score of 0.91 and an accuracy of 0.99%.展开更多
Adversarial training has been widely considered the most effective defense against adversarial attacks.However,recent studies have demonstrated that a large discrepancy exists in the class-wise robustness of adversari...Adversarial training has been widely considered the most effective defense against adversarial attacks.However,recent studies have demonstrated that a large discrepancy exists in the class-wise robustness of adversarial training,leading to two potential issues:firstly,the overall robustness of a model is compromised due to the weakest class;and secondly,ethical concerns arising from unequal protection and biases,where certain societal demographic groups receive less robustness in defense mechanisms.Despite these issues,solutions to address the discrepancy remain largely underexplored.In this paper,we advance beyond existing methods that focus on class-level solutions.Our investigation reveals that hard examples,identified by higher cross-entropy values,can provide more fine-grained information about the discrepancy.Furthermore,we find that enhancing the diversity of hard examples can effectively reduce the robustness gap between classes.Motivated by these observations,we propose Fair Adversarial Training(FairAT)to mitigate the discrepancy of class-wise robustness.Extensive experiments on various benchmark datasets and adversarial attacks demonstrate that FairAT outperforms state-of-the-art methods in terms of both overall robustness and fairness.For a WRN-28-10 model trained on CIFAR10,FairAT improves the average and worst-class robustness by 2.13%and 4.50%,respectively.展开更多
Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While suc...Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.展开更多
Phantom limb pain(PLP)is not only a physical pain experience but also poses a significant challenge to mental health and quality of life.Currently,the mechanism of PLP treatment is still unclear,and there are many met...Phantom limb pain(PLP)is not only a physical pain experience but also poses a significant challenge to mental health and quality of life.Currently,the mechanism of PLP treatment is still unclear,and there are many methods with varying effects.This article starts with the application research of extended reality technology in PLP treatment,through describing the application of its branch technologies(virtual reality,augmented reality,and mixed reality technology),to lay the foundation for subsequent research,in the hope of finding advanced and effective treatment methods,and providing a basis for future product transformation.展开更多
Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginn...Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginners to grasp the intricate composition rules of formulas.To address this gap,we introduce Formula-S,a situated visualization method for TCM formula learning in augmented reality(AR)and evaluate its performance.This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.Methods Formula-S is an interactive AR tool designed for TCM formula learning,featuring three modes(3D,Web,and Table).The dataset included TCM formulas and herb properties extracted from authoritative references,including textbook and the SymMap database.In Formula-S,the hierarchical visualization of the formulas as herbal medicine compositions,is linked to the multidimensional herb attribute visualization and embedded in the real world,where real herb samples are presented.To evaluate its effectiveness,a controlled study(n=30)was conducted.Participants who had no formal TCM knowledge were tasked with herbal medicine identification,formula composition,and recognition.In the study,participants interacted with the AR tool through HoloLens 2.Data were collected on both task performance(accuracy and response time)and user experience,with a focus on task efficiency,accuracy,and user preference across the different learning modes.Results The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks.Regarding user experience,our new approach demonstrated the highest system usability and lowest task load,effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency.Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment,suggesting this approach offers usability advantages for TCM education.Conclusions The situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods.Additionally,it provides superior contextual understanding of TCM formulas,making it a promising new solution for TCM learning.展开更多
Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)t...Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.展开更多
Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of suc...Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.展开更多
Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in ed...Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited t...Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.展开更多
In our opinion,being associated with the art of painting,these shifts laid the groundwork for the exciting painting art trends coming in 2025.Immersive installations(沉浸式虚拟现实装置).Imagine stepping into a paintin...In our opinion,being associated with the art of painting,these shifts laid the groundwork for the exciting painting art trends coming in 2025.Immersive installations(沉浸式虚拟现实装置).Imagine stepping into a painting that moves,speaks,and reacts to your presence.With virtual reality(VR)and augmented reality(AR),artists are building sensory⁃filled spaces where you're not just a viewer—you're part of the art.Picture entering an exhibit where the walls ripple as you walk by,sounds shift with your movements,and you can alter the piece just by being there.These installations combine technology and creativity to offer unforgettable experiences that redefine art as a shared and living entity.展开更多
An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect s...An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect sample data for light guide plate(LGP) in production,as well as the problem of minor defects.Two optimizations are made to the generator of CycleGAN:fusion of low resolution features obtained from partial up-sampling and down-sampling with high-resolution features,combination of self attention mechanism with residual network structure to replace the original residual module.Qualitative and quantitative experiments were conducted to compare different data augmentation methods,and the results show that the defect images of the LGP generated by the improved network were more realistic,and the accuracy of the you only look once version 5(YOLOv5) detection network for the LGP was improved by 5.6%,proving the effectiveness and accuracy of the proposed method.展开更多
Cassava is the most widely distributed food crop in Central Africa. Chikwangue, also known as kwanga in the Republic of Congo, is a starchy fermented cassava product that is a staple food in the country. This work aim...Cassava is the most widely distributed food crop in Central Africa. Chikwangue, also known as kwanga in the Republic of Congo, is a starchy fermented cassava product that is a staple food in the country. This work aims to determine the composition of bioactive compounds in chikwangue, including biosurfactant-like molecules and proteins content. Antibacterial activities were investigated through the preliminary emulsification index of chikwangue and fermented paste. Antibacterial assay, 16S rRNA, cytK, hblD, nheB and entFM PCR amplifications, DNA sequence analysis, NCBI homology analysis, and phylogenic tree were performed using NGPhylogeny. fr and iTOL (interactive of live). Fermented cassava paste and chikwangue contain biosurfactants with an emulsification index of 50%. The total protein concentration in fermented cassava paste was 4 g/ml and the chikwangue was 2.5 g/mL Further sequence analysis showed that isolates shared a homology of up to 99.9% with Bacillus cereus PQ432941.1, B. licheniformis PQ432758.1, B. altitudinis PQ432754.1, B. subtilis PQ432759.1, B. mojavensis PQ432755.1, B. tequilensis MT994788.1, B. subtilis MT994789.1, Paenibacillus polymyxa PQ452544.1, B. velezensis PQ452545.1, B. thuringiensis PQ432763.1, B. pumilus PQ432762.1, B. subtilis MT994787.1, B. mycoides PQ432890.1, B. thuringiensis PQ432766.1, B. subtilis PQ432757.1 and B. amyloliquefaciens PQ432756.1. Importantly, the emulsification index (E24) ranged from 60 to 100% and the crude biosurfactant for the Bacillus strains mentioned above could easily inhibit the growth for pathogen Gram-negative bacteria (S. enterica, S. flexneri, E. coli, Klebsiella sp. and P. aeruginosa) with diameters ranging from 2.3 ± 0.1 cm to 5.5 ± 0.4 cm. On the other hand, the diameters of Gram-positive pathogenic bacteria (B. cereus and S. aureus) varied between 1.5 ± 0.5 cm and 4.0 ± 0.2 cm. These findings involve the promise purpose of Bacillus isolated from retted cassava, and this study systematically uncovered the biodiversity and distribution characteristics of retted paste cassava and chikwangue.展开更多
Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual image processing, and consider...Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual image processing, and considerable time investment. Fortunately, the integration of deep learning and transfer learning offers invaluable assistance to medical practitioners. This study introduces an ensemble classification framework to detect and grade diabetic retinopathy into 5 classes leveraging the concepts of transfer learning and data fusion. It utilizes three benchmark datasets on diabetic retinopathy: APTOS 2019, IDRiD, and Messidor-2. Initially, these datasets are merged, resulting in a total of 5922 fundus images. Then this fused dataset undergoes pre-processing. Firstly, the images are cropped to remove unwanted regions. Then, Contrast Limited Adaptive Histogram Equalization is applied to improve image quality and fine details. To tackle class imbalance issues, Synthetic Minority Over Sampling technique is employed. Additionally, data augmentation techniques such as flipping, rotation, and zooming are used to increase dataset diversity. The dataset is split into training, validation, and testing sets at a ratio of 70:10:20. For classification, three pre-trained CNN models, EfficientNetB2, DenseNet121, and ResNet50, are fine-tuned. After these models are trained, an ensemble model is constructed by averaging the predictions of each model. Results show that the ensemble model achieved the highest test accuracy of 96.96% in grading diabetic retinopathy into 5 classes outperforming the individual pre-trained models. Furthermore, the ensemble model’s performance is compared with previously published approaches where this model demonstrated superior result.展开更多
Breast cancer is one of the major causes of deaths in women.However,the early diagnosis is important for screening and control the mortality rate.Thus for the diagnosis of breast cancer at the early stage,a computer-a...Breast cancer is one of the major causes of deaths in women.However,the early diagnosis is important for screening and control the mortality rate.Thus for the diagnosis of breast cancer at the early stage,a computer-aided diagnosis system is highly required.Ultrasound is an important examination technique for breast cancer diagnosis due to its low cost.Recently,many learning-based techniques have been introduced to classify breast cancer using breast ultrasound imaging dataset(BUSI)datasets;however,the manual handling is not an easy process and time consuming.The authors propose an EfficientNet-integrated ResNet deep network and XAI-based framework for accurately classifying breast cancer(malignant and benign).In the initial step,data augmentation is performed to increase the number of training samples.For this purpose,three-pixel flip mathematical equations are introduced:horizontal,vertical,and 90°.Later,two pretrained deep learning models were employed,skipped some layers,and fine-tuned.Both fine-tuned models are later trained using a deep transfer learning process and extracted features from the deeper layer.Explainable artificial intelligence-based analysed the performance of trained models.After that,a new feature selection technique is proposed based on the cuckoo search algorithm called cuckoo search controlled standard error mean.This technique selects the best features and fuses using a new parallel zeropadding maximum correlated coefficient features.In the end,the selection algorithm is applied again to the fused feature vector and classified using machine learning algorithms.The experimental process of the proposed framework is conducted on a publicly available BUSI and obtained 98.4%and 98%accuracy in two different experiments.Comparing the proposed framework is also conducted with recent techniques and shows improved accuracy.In addition,the proposed framework was executed less than the original deep learning models.展开更多
Recently,ship detection technology has been applied extensively in the marine security monitoring field.However,achieving accurate marine ship detection still poses significant challenges due to factors such as varyin...Recently,ship detection technology has been applied extensively in the marine security monitoring field.However,achieving accurate marine ship detection still poses significant challenges due to factors such as varying scales,slightly occluded objects,uneven illumination,and sea clutter.To address these issues,we propose a novel ship detection approach,i.e.,the Twin Feature Pyramid Network and Data Augmentation(TFPN-DA),which mainly consists of three modules.First,to eliminate the negative effects of slightly occluded objects and uneven illumination,we propose the Spatial Attention within the Twin Feature Pyramid Network(SA-TFPN)method,which is based on spatial attention to reconstruct the feature pyramid.Second,the ROI Feature Module(ROIFM)is introduced into the SA-TFPN,which is used to enhance specific crucial details from multi-scale features for object regression and classification.Additionally,data augmentation strategies such as spatial affine transformation and noise processing,are developed to optimize the data sample distribution.A self-construct dataset is used to train the detection model,and the experiments conducted on the dataset demonstrate the effectiveness of our model.展开更多
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.DGSSR-2023-02-02116.
文摘When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults.To protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT networks.This study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data complexity.Following rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning models.Augmentation using the BAT algorithm enhances dataset variability.Three deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data aspects.Their predictions form a meta-feature set for a subsequent meta-learner,which combines model strengths.Conventional classifiers validate meta-learner features for broad algorithm suitability.This comprehensive method demonstrates high accuracy and robustness in IoMT intrusion detection.Evaluations were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen categories.Experimental results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.
基金supported by the National Natural Science Foundation of China (Grants Nos.61931004,62072250)the Talent Launch Fund of Nanjing University of Information Science and Technology (2020r061).
文摘Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Although the Generative Adversarial Network(GAN)method can generate new samples by learning the feature distribution of the original samples,it is confronted with the problems of unstable training andmode collapse.To this end,a novel data augmenting approach called Graph CWGAN-GP is proposed in this paper.The traffic data is first converted into grayscale images as the input for the proposed model.Then,the minority class data is augmented with our proposed model,which is built by introducing conditional constraints and a new distance metric in typical GAN.Finally,the classical deep learning model is adopted as a classifier to classify datasets augmented by the Condition GAN(CGAN),Wasserstein GAN-Gradient Penalty(WGAN-GP)and Graph CWGAN-GP,respectively.Compared with the state-of-the-art GAN methods,the Graph CWGAN-GP cannot only control the modes of the data to be generated,but also overcome the problem of unstable training and generate more realistic and diverse samples.The experimental results show that the classification precision,recall and F1-Score of theminority class in the balanced dataset augmented in this paper have improved by more than 2.37%,3.39% and 4.57%,respectively.
文摘Outcomes following peripheral nerve injury remain frustratingly poor. The reasons for this are multifactorial, although maintaining a growth permissive environment in the distal nerve stump following repair is arguably the most important. The optimal environment for axonal regeneration relies on the synthesis and release of many biochemical mediators that are temporally and spatially regulated with a high level of incompletely understood complexity. The Schwann cell(SC) has emerged as a key player in this process. Prolonged periods of distal nerve stump denervation, characteristic of large gaps and proximal injuries, have been associated with a reduction in SC number and ability to support regenerating axons. Cell based therapy offers a potential therapy for the improvement of outcomes following peripheral nerve reconstruction. Stem cells have the potential to increase the number of SCs and prolong their ability to support regeneration. They may also have the ability to rescue and replenish populations of chromatolytic and apoptotic neurons following axotomy. Finally, they can be used in non-physiologic ways to preserve injured tissues such as denervated muscle while neuronal ingrowth has not yet occurred. Aside from stem cell type, careful consideration must be given to differentiation status, how stem cells are supported following transplantation and how they will be delivered to the site of injury. It is the aim of this article to review current opinions on the strategies of stem cell based therapy for the augmentation of peripheral nerve regeneration.
基金This work was supported in part by the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(MSIT)Future Planning under Grant NRF-2020R1A2C2014336 and Grant NRF-2021R1A4A1029650.
文摘Android malware has evolved in various forms such as adware that continuously exposes advertisements,banking malware designed to access users’online banking accounts,and Short Message Service(SMS)malware that uses a Command&Control(C&C)server to send malicious SMS,intercept SMS,and steal data.By using many malicious strategies,the number of malware is steadily increasing.Increasing Android malware threats numerous users,and thus,it is necessary to detect malware quickly and accurately.Each malware has distinguishable characteristics based on its actions.Therefore,security researchers have tried to categorize malware based on their behaviors by conducting the familial analysis which can help analysists to reduce the time and cost for analyzing malware.However,those studies algorithms typically used imbalanced,well-labeled open-source dataset,and thus,it is very difficult to classify some malware families which only have a few number of malware.To overcome this challenge,previous data augmentation studies augmented data by visualizing malicious codes and used them for malware analysis.However,visualization of malware can result in misclassifications because the behavior information of the malware could be compromised.In this study,we propose an android malware familial analysis system based on a data augmentation method that preserves malware behaviors to create an effective multi-class classifier for malware family analysis.To this end,we analyze malware and use Application Programming Interface(APIs)and permissions that can reflect the behavior of malware as features.By using these features,we augment malware dataset to enable effective malware detection while preserving original malicious behaviors.Our evaluation results demonstrate that,when a model is created by using only the augmented data,a macro-F1 score of 0.65 and accuracy of 0.63%.On the other hand,when the augmented data and original malware are used together,the evaluation results show that a macro-F1 score of 0.91 and an accuracy of 0.99%.
基金supported by the National Natural Science Foundation of China(Grant Nos.U20B2049,U21B2018 and 62302344).
文摘Adversarial training has been widely considered the most effective defense against adversarial attacks.However,recent studies have demonstrated that a large discrepancy exists in the class-wise robustness of adversarial training,leading to two potential issues:firstly,the overall robustness of a model is compromised due to the weakest class;and secondly,ethical concerns arising from unequal protection and biases,where certain societal demographic groups receive less robustness in defense mechanisms.Despite these issues,solutions to address the discrepancy remain largely underexplored.In this paper,we advance beyond existing methods that focus on class-level solutions.Our investigation reveals that hard examples,identified by higher cross-entropy values,can provide more fine-grained information about the discrepancy.Furthermore,we find that enhancing the diversity of hard examples can effectively reduce the robustness gap between classes.Motivated by these observations,we propose Fair Adversarial Training(FairAT)to mitigate the discrepancy of class-wise robustness.Extensive experiments on various benchmark datasets and adversarial attacks demonstrate that FairAT outperforms state-of-the-art methods in terms of both overall robustness and fairness.For a WRN-28-10 model trained on CIFAR10,FairAT improves the average and worst-class robustness by 2.13%and 4.50%,respectively.
基金National Natural Science Foundation of China(62171305,62405206,62004135,62001317,62111530301)Natural Science Foundation of Jiangsu Province(BK20240778,BK20241917)+3 种基金State Key Laboratory of Advanced Optical Communication Systems and Networks,China(2023GZKF08)China Postdoctoral Science Foundation(2024M752314)Postdoctoral Fellowship Program of CPSF(GZC20231883)Innovative and Entrepreneurial Talent Program of Jiangsu Province(JSSCRC2021527).
文摘Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.
文摘Phantom limb pain(PLP)is not only a physical pain experience but also poses a significant challenge to mental health and quality of life.Currently,the mechanism of PLP treatment is still unclear,and there are many methods with varying effects.This article starts with the application research of extended reality technology in PLP treatment,through describing the application of its branch technologies(virtual reality,augmented reality,and mixed reality technology),to lay the foundation for subsequent research,in the hope of finding advanced and effective treatment methods,and providing a basis for future product transformation.
文摘Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginners to grasp the intricate composition rules of formulas.To address this gap,we introduce Formula-S,a situated visualization method for TCM formula learning in augmented reality(AR)and evaluate its performance.This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.Methods Formula-S is an interactive AR tool designed for TCM formula learning,featuring three modes(3D,Web,and Table).The dataset included TCM formulas and herb properties extracted from authoritative references,including textbook and the SymMap database.In Formula-S,the hierarchical visualization of the formulas as herbal medicine compositions,is linked to the multidimensional herb attribute visualization and embedded in the real world,where real herb samples are presented.To evaluate its effectiveness,a controlled study(n=30)was conducted.Participants who had no formal TCM knowledge were tasked with herbal medicine identification,formula composition,and recognition.In the study,participants interacted with the AR tool through HoloLens 2.Data were collected on both task performance(accuracy and response time)and user experience,with a focus on task efficiency,accuracy,and user preference across the different learning modes.Results The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks.Regarding user experience,our new approach demonstrated the highest system usability and lowest task load,effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency.Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment,suggesting this approach offers usability advantages for TCM education.Conclusions The situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods.Additionally,it provides superior contextual understanding of TCM formulas,making it a promising new solution for TCM learning.
基金supported by the Natural Science Foundation of China(No.41804112,author:Chengyun Song).
文摘Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.
文摘Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.
文摘Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
文摘Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.
文摘In our opinion,being associated with the art of painting,these shifts laid the groundwork for the exciting painting art trends coming in 2025.Immersive installations(沉浸式虚拟现实装置).Imagine stepping into a painting that moves,speaks,and reacts to your presence.With virtual reality(VR)and augmented reality(AR),artists are building sensory⁃filled spaces where you're not just a viewer—you're part of the art.Picture entering an exhibit where the walls ripple as you walk by,sounds shift with your movements,and you can alter the piece just by being there.These installations combine technology and creativity to offer unforgettable experiences that redefine art as a shared and living entity.
基金supported by the Jiangsu Province IUR Cooperation Project (No.BY2021258)the Wuxi Science and Technology Development Fund Project (No.G20212028)。
文摘An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect sample data for light guide plate(LGP) in production,as well as the problem of minor defects.Two optimizations are made to the generator of CycleGAN:fusion of low resolution features obtained from partial up-sampling and down-sampling with high-resolution features,combination of self attention mechanism with residual network structure to replace the original residual module.Qualitative and quantitative experiments were conducted to compare different data augmentation methods,and the results show that the defect images of the LGP generated by the improved network were more realistic,and the accuracy of the you only look once version 5(YOLOv5) detection network for the LGP was improved by 5.6%,proving the effectiveness and accuracy of the proposed method.
文摘Cassava is the most widely distributed food crop in Central Africa. Chikwangue, also known as kwanga in the Republic of Congo, is a starchy fermented cassava product that is a staple food in the country. This work aims to determine the composition of bioactive compounds in chikwangue, including biosurfactant-like molecules and proteins content. Antibacterial activities were investigated through the preliminary emulsification index of chikwangue and fermented paste. Antibacterial assay, 16S rRNA, cytK, hblD, nheB and entFM PCR amplifications, DNA sequence analysis, NCBI homology analysis, and phylogenic tree were performed using NGPhylogeny. fr and iTOL (interactive of live). Fermented cassava paste and chikwangue contain biosurfactants with an emulsification index of 50%. The total protein concentration in fermented cassava paste was 4 g/ml and the chikwangue was 2.5 g/mL Further sequence analysis showed that isolates shared a homology of up to 99.9% with Bacillus cereus PQ432941.1, B. licheniformis PQ432758.1, B. altitudinis PQ432754.1, B. subtilis PQ432759.1, B. mojavensis PQ432755.1, B. tequilensis MT994788.1, B. subtilis MT994789.1, Paenibacillus polymyxa PQ452544.1, B. velezensis PQ452545.1, B. thuringiensis PQ432763.1, B. pumilus PQ432762.1, B. subtilis MT994787.1, B. mycoides PQ432890.1, B. thuringiensis PQ432766.1, B. subtilis PQ432757.1 and B. amyloliquefaciens PQ432756.1. Importantly, the emulsification index (E24) ranged from 60 to 100% and the crude biosurfactant for the Bacillus strains mentioned above could easily inhibit the growth for pathogen Gram-negative bacteria (S. enterica, S. flexneri, E. coli, Klebsiella sp. and P. aeruginosa) with diameters ranging from 2.3 ± 0.1 cm to 5.5 ± 0.4 cm. On the other hand, the diameters of Gram-positive pathogenic bacteria (B. cereus and S. aureus) varied between 1.5 ± 0.5 cm and 4.0 ± 0.2 cm. These findings involve the promise purpose of Bacillus isolated from retted cassava, and this study systematically uncovered the biodiversity and distribution characteristics of retted paste cassava and chikwangue.
文摘Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual image processing, and considerable time investment. Fortunately, the integration of deep learning and transfer learning offers invaluable assistance to medical practitioners. This study introduces an ensemble classification framework to detect and grade diabetic retinopathy into 5 classes leveraging the concepts of transfer learning and data fusion. It utilizes three benchmark datasets on diabetic retinopathy: APTOS 2019, IDRiD, and Messidor-2. Initially, these datasets are merged, resulting in a total of 5922 fundus images. Then this fused dataset undergoes pre-processing. Firstly, the images are cropped to remove unwanted regions. Then, Contrast Limited Adaptive Histogram Equalization is applied to improve image quality and fine details. To tackle class imbalance issues, Synthetic Minority Over Sampling technique is employed. Additionally, data augmentation techniques such as flipping, rotation, and zooming are used to increase dataset diversity. The dataset is split into training, validation, and testing sets at a ratio of 70:10:20. For classification, three pre-trained CNN models, EfficientNetB2, DenseNet121, and ResNet50, are fine-tuned. After these models are trained, an ensemble model is constructed by averaging the predictions of each model. Results show that the ensemble model achieved the highest test accuracy of 96.96% in grading diabetic retinopathy into 5 classes outperforming the individual pre-trained models. Furthermore, the ensemble model’s performance is compared with previously published approaches where this model demonstrated superior result.
文摘Breast cancer is one of the major causes of deaths in women.However,the early diagnosis is important for screening and control the mortality rate.Thus for the diagnosis of breast cancer at the early stage,a computer-aided diagnosis system is highly required.Ultrasound is an important examination technique for breast cancer diagnosis due to its low cost.Recently,many learning-based techniques have been introduced to classify breast cancer using breast ultrasound imaging dataset(BUSI)datasets;however,the manual handling is not an easy process and time consuming.The authors propose an EfficientNet-integrated ResNet deep network and XAI-based framework for accurately classifying breast cancer(malignant and benign).In the initial step,data augmentation is performed to increase the number of training samples.For this purpose,three-pixel flip mathematical equations are introduced:horizontal,vertical,and 90°.Later,two pretrained deep learning models were employed,skipped some layers,and fine-tuned.Both fine-tuned models are later trained using a deep transfer learning process and extracted features from the deeper layer.Explainable artificial intelligence-based analysed the performance of trained models.After that,a new feature selection technique is proposed based on the cuckoo search algorithm called cuckoo search controlled standard error mean.This technique selects the best features and fuses using a new parallel zeropadding maximum correlated coefficient features.In the end,the selection algorithm is applied again to the fused feature vector and classified using machine learning algorithms.The experimental process of the proposed framework is conducted on a publicly available BUSI and obtained 98.4%and 98%accuracy in two different experiments.Comparing the proposed framework is also conducted with recent techniques and shows improved accuracy.In addition,the proposed framework was executed less than the original deep learning models.
文摘Recently,ship detection technology has been applied extensively in the marine security monitoring field.However,achieving accurate marine ship detection still poses significant challenges due to factors such as varying scales,slightly occluded objects,uneven illumination,and sea clutter.To address these issues,we propose a novel ship detection approach,i.e.,the Twin Feature Pyramid Network and Data Augmentation(TFPN-DA),which mainly consists of three modules.First,to eliminate the negative effects of slightly occluded objects and uneven illumination,we propose the Spatial Attention within the Twin Feature Pyramid Network(SA-TFPN)method,which is based on spatial attention to reconstruct the feature pyramid.Second,the ROI Feature Module(ROIFM)is introduced into the SA-TFPN,which is used to enhance specific crucial details from multi-scale features for object regression and classification.Additionally,data augmentation strategies such as spatial affine transformation and noise processing,are developed to optimize the data sample distribution.A self-construct dataset is used to train the detection model,and the experiments conducted on the dataset demonstrate the effectiveness of our model.
