In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fi...In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.展开更多
Software defect prediction is a critical component in maintaining software quality,enabling early identification and resolution of issues that could lead to system failures and significant financial losses.With the in...Software defect prediction is a critical component in maintaining software quality,enabling early identification and resolution of issues that could lead to system failures and significant financial losses.With the increasing reliance on user-generated content,social media reviews have emerged as a valuable source of real-time feedback,offering insights into potential software defects that traditional testing methods may overlook.However,existing models face challenges like handling imbalanced data,high computational complexity,and insufficient inte-gration of contextual information from these reviews.To overcome these limitations,this paper introduces the SESDP(Sentiment Analysis-Based Early Software Defect Prediction)model.SESDP employs a Transformer-Based Multi-Task Learning approach using Robustly Optimized Bidirectional Encoder Representations from Transformers Approach(RoBERTa)to simultaneously perform sentiment analysis and defect prediction.By integrating text embedding extraction,sentiment score computation,and feature fusion,the model effectively captures both the contextual nuances and sentiment expressed in user reviews.Experimental results show that SESDP achieves superior performance with an accuracy of 96.37%,precision of 94.7%,and recall of 95.4%,particularly excelling in handling imbalanced datasets compared to baseline models.This approach offers a scalable and efficient solution for early software defect detection,enhancing proactive software quality assurance.展开更多
Malware continues to pose a significant threat to cybersecurity,with new advanced infections that go beyond traditional detection.Limitations in existing systems include high false-positive rates,slow system response ...Malware continues to pose a significant threat to cybersecurity,with new advanced infections that go beyond traditional detection.Limitations in existing systems include high false-positive rates,slow system response times,and inability to respond quickly to new malware forms.To overcome these challenges,this paper proposes OMD-RAS:Implementing Malware Detection in an Optimized Way through Real-Time and Adaptive Security as an extensive approach,hoping to get good results towards better malware threat detection and remediation.The significant steps in the model are data collection followed by comprehensive preprocessing consisting of feature engineering and normalization.Static analysis,along with dynamic analysis,is done to capture the whole spectrum of malware behavior for the feature extraction process.The extracted processed features are given with a continuous learning mechanism to the Extreme Learning Machine model of real-time detection.This OMD-RAS trains quickly and has great accuracy,providing elite,advanced real-time detection capabilities.This approach uses continuous learning to adapt to new threats—ensuring the effectiveness of detection even as strategies used by malware may change over time.The experimental results showed that OMD-RAS performs better than the traditional approaches.For instance,the OMD-RAS model has been able to achieve an accuracy of 96.23%and massively reduce the rate of false positives across all datasets while eliciting a consistently high rate of precision and recall.The model’s adaptive learning reflected enhancements on other performance measures-for example,Matthews Correlation Coefficients and Log Loss.展开更多
Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to ...Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.展开更多
Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timec...Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timeconsuming,and include inaccuracies.Machine learning(ML)algorithms provide a more efficient alternative for this purpose,so after assessment with a statistical extraction method,ML algorithms including back-propagation neural network(BPNN),K-nearest neighbor(KNN),radial basis function(RBF),feed-forward neural networks(FFNN),and support vector regression(SVR)for predicting the uniaxial compressive strength(UCS)of soilcrete,were proposed in this study.The developed models in this study were optimized using an optimization technique,gradient descent(GD),throughout the analysis(direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters).After doing laboratory analysis,data pre-preprocessing,and data-processing analysis,a database including 600 soilcrete specimens was gathered,which includes two different soil types(clay and limestone)and metakaolin as a mineral additive.80%of the database was used for the training set and 20%for testing,considering eight input parameters,including metakaolin content,soil type,superplasticizer content,water-to-binder ratio,shrinkage,binder,density,and ultrasonic velocity.The analysis showed that most algorithms performed well in the prediction,with BPNN,KNN,and RBF having higher accuracy compared to others(R^(2)=0.95,0.95,0.92,respectively).Based on this evaluation,it was observed that all models show an acceptable accuracy rate in prediction(RMSE:BPNN=0.11,FFNN=0.24,KNN=0.05,SVR=0.06,RBF=0.05,MAD:BPNN=0.006,FFNN=0.012,KNN=0.008,SVR=0.006,RBF=0.009).The ML importance ranking-sensitivity analysis indicated that all input parameters influence theUCS of soilcrete,especially the water-to-binder ratio and density,which have themost impact.展开更多
The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging at...The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.展开更多
Mango farming significantly contributes to the economy,particularly in developing countries.However,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at...Mango farming significantly contributes to the economy,particularly in developing countries.However,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial losses.The development of deep learning models for detecting crop diseases is an active area of research in smart agriculture.This study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)architectures.Two datasets were used.The first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery mildew.The second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and Rot.Both datasets were obtained from publicly available sources.The proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS dataset.The results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more efficiently.The system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual diagnostics.Additionally,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.展开更多
Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the st...Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats.展开更多
Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic retinopathy.The traditional methods struggle with accu...Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic retinopathy.The traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic retinopathy.While the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data,dual-modal diabetic retinopathy grading methods offer superior performance.However,the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to multi-scale variations.This paper addresses these issues by focusing on multi-scale retinal vessel segmentation,dual feature fusion,data augmentation,and attention-based grading.The proposed model aims to improve comprehensive segmentation for retinal images with varying vessel thicknesses.It employs a dual-branch parallel architecture that integrates a transformer encoder with a convolutional neural network encoder to extract local and global information for synergistic saliency learning.Besides that,the model uses residual structures and attention modules to extract critical lesions,enhancing the accuracy and reliability of diabetic retinopathy grading.To evaluate the efficacy of the proposed approach,this study compared it with other pre-trained publicly open models,ResNet152V2,ConvNext,Efficient Net,DenseNet,and Swin Transform,with the same developmental parameters.All models achieved approximately 85%accuracy with the same image preparation method.However,the proposed approach outperforms and optimizes existing models by achieving an accuracy of 99.17%,99.04%,and 99.24%,on Kaggle APTOS19,IDRiD,and EyePACS datasets,respectively.These results support the model’s utility in helping ophthalmologists diagnose diabetic retinopathy more rapidly and accurately.展开更多
Deep neural networks provide accurate results for most applications.However,they need a big dataset to train properly.Providing a big dataset is a significant challenge in most applications.Image augmentation refers t...Deep neural networks provide accurate results for most applications.However,they need a big dataset to train properly.Providing a big dataset is a significant challenge in most applications.Image augmentation refers to techniques that increase the amount of image data.Common operations for image augmentation include changes in illumination,rotation,contrast,size,viewing angle,and others.Recently,Generative Adversarial Networks(GANs)have been employed for image generation.However,like image augmentation methods,GAN approaches can only generate images that are similar to the original images.Therefore,they also cannot generate new classes of data.Texture images presentmore challenges than general images,and generating textures is more complex than creating other types of images.This study proposes a gradient-based deep neural network method that generates a new class of texture.It is possible to rapidly generate new classes of textures using different kernels from pre-trained deep networks.After generating new textures for each class,the number of textures increases through image augmentation.During this process,several techniques are proposed to automatically remove incomplete and similar textures that are created.The proposed method is faster than some well-known generative networks by around 4 to 10 times.In addition,the quality of the generated textures surpasses that of these networks.The proposed method can generate textures that surpass those of someGANs and parametric models in certain image qualitymetrics.It can provide a big texture dataset to train deep networks.A new big texture dataset is created artificially using the proposed method.This dataset is approximately 2 GB in size and comprises 30,000 textures,each 150×150 pixels in size,organized into 600 classes.It is uploaded to the Kaggle site and Google Drive.This dataset is called BigTex.Compared to other texture datasets,the proposed dataset is the largest and can serve as a comprehensive texture dataset for training more powerful deep neural networks and mitigating overfitting.展开更多
The fracture energy of fiber-reinforced concrete(FRC)affects the durability and structural performance of concrete elements.Advancements in experimental studies have yet to overcome the challenges of estimating fractu...The fracture energy of fiber-reinforced concrete(FRC)affects the durability and structural performance of concrete elements.Advancements in experimental studies have yet to overcome the challenges of estimating fracture energy,as the process remains time-intensive and costly.Therefore,machine learning techniques have emerged as powerful alternatives.This study aims to investigate the performance of machine learning techniques to predict the fracture energy of FRC.For this purpose,500 data points,including 8 input parameters that affect the fracture energy of FRC,are collected fromthree-point bending tests and employed to train and evaluate themachine learning techniques.The findings showed that Gaussian process regression(GPR)outperforms all other models in terms of predictive accuracy,achieving the highest R2 of 0.93 and the lowest RMSE of 13.91 during holdout cross-validation.It is then followed by support vector regression(SVR)and extreme gradient boosting regression(XGBR),whereas K-nearest neighbours(KNN)and random forest regression(RFR)show the weakest predictions.The superiority of GPR is further reinforced in a 5-fold cross-validation,where it consistently delivers an average R2 above 0.96 and ranks highest in overall predictive performance.Empirical testing with additional sample sets validates GPR’s model on the key mix parameter’s impact on fracture energy,cementing its claim.The Fly-Ash cement exhibits the greatest fracture energy due to superior fiber-matrix interaction,whereas the glass fiber dominates energy absorption amongst the other types of fibers.In addition,increasing the water-to-cement(W/C)ratio from 0.30 to 0.50 yields a significant improvement in fracture energy,which aligns well with the machine learning predictions.Similarly,loading rate positively correlates with fracture energy,highlighting the strain-rate sensitivity of FRC.This work is the missing link to integrate experimental fracture mechanics and computational intelligence,optimally and reasonably predicting and refining the fracture energy of FRC.展开更多
Using the fuzzy rule-based classification method, normalized difference vegetation index (NDVI) images acquired from 1982 to 1998 were classified into seventeen phases. Based on these classification images, a probabil...Using the fuzzy rule-based classification method, normalized difference vegetation index (NDVI) images acquired from 1982 to 1998 were classified into seventeen phases. Based on these classification images, a probabilistic cellular automata-Markov Chain model was developed and used to simulate a land cover scenario of China for the year 2014. Spatiotemporal dynamics of land use/cover in China from 1982 to 2014 were then analyzed and evaluated. The results showed that the change trends of land cover type from 1998 to 2014 would be contrary to those from 1982 to 1998. In particular, forestland and grassland areas decreased by 1.56% and 1.46%, respectively, from 1982 to 1998, and should increase by 1.5% and 2.3% from 1998 to 2014, respectively.展开更多
According to China’s second national survey of pollution sources, the contribution of agricultural non-point sources(ANS) to water pollution is still high. Risk prevention and control are the main means to control co...According to China’s second national survey of pollution sources, the contribution of agricultural non-point sources(ANS) to water pollution is still high. Risk prevention and control are the main means to control costs and improve the efficiency of ANS, but most studies directly take pollution load as the risk standard, leading to a considerable misjudgment of the actual pollution risk. To objectively reflect the risk of agricultural non-point source pollution(ANSP) in Chongqing, China, we investigated the influences of initial source input, intermediate transformation, and terminal absorption of pollutants via literature research and the Delphi method and built a PTA(pressure kinetic energy, transformation kinetic energy, and absorption kinetic energy) model that covers 12 factors, with the support of geographical information system(GIS) technology. The terrain factor calculation results and the calculation results of other factors were optimized by Python tools to reduce human error and workload. Via centroid migration analysis and Kernel density analysis, the risk level, spatial aggregation degree, and key prevention and control regions could be accurately determined. There was a positive correlation between the water quality of the rivers in Chongqing and the risk assessment results of different periods, indirectly reflecting the reliability of the assessment results by the proposed model. There was an obvious tendency for the low-risk regions transforming into high-risk regions. The proportion of high-risk regions and extremely high-risk regions increased from 17.82% and 16.63%in 2000 to 18.10% and 16.76% in 2015, respectively. And the risk level in the main urban areas was significantly higher than that in the southeastern and northeastern areas of Chongqing. The centroids of all grades of risky areas presented a successive distribution from west to east, and the centroids of high-risk and extremely high-risk regions shifted eastward. From 2000 to 2015, the centroids of highrisk and extremely high-risk regions moved 4.63 km(1.68°) and 4.48 km(12.08°) east by north, respectively. The kernel density analysis results showed that the high-risk regions were mainly concentrated in the main urban areas and that the distribution of agglomeration areas overall displayed a transition trend from contiguous distribution to decentralized concentration. The risk levels of the regions with a high proportion of cultivated land and artificial surface were significantly increased, and the occupation of cultivated land in the process of urbanization promoted the movement of the centroids of high-risk and extremely high-risk regions. The identification of key areas for risk prevention and control provides data scientific basis for the development of prevention and control strategies.展开更多
This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei.This is crucial for histopathological image analysis,as it involves segmenting cell nuclei.Howeve...This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei.This is crucial for histopathological image analysis,as it involves segmenting cell nuclei.However,challenges exist,such as determining the boundary region of normal and deformed nuclei and identifying small,irregular nuclei structures.Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification,but their complex features limit their practical use in clinical settings.The existing studies have limited accuracy,significant processing costs,and a lack of resilience and generalizability across diverse datasets.We proposed the densely convolutional Breast U-shaped Network(BU-NET)framework to overcome the mentioned issues.The study employs BU-NET’s spatial and channel attention methods to enhance segmentation processes.The inclusion of residual blocks and skip connections in the BU-NEt architecture enhances the process of extracting features and reconstructing the output.This enhances the robustness of training and convergence processes by reducing the occurrence of vanishing gradients.The primary objective of BU-NEt is to enhance the model’s capacity to acquire and analyze more intricate features,all the while preserving an efficient working representation.The BU-NET experiments demonstrate that the framework achieved 88.7%average accuracy,88.8%F1 score for Multi-Organ Nuclei Segmentation Challenge(MoNuSeg),and 91.2%average accuracy,91.8%average F1 for the triple-negative breast cancer(TNBC)dataset.The framework also achieved 93.92 Area under the ROC Curve(AUC)for TNBC.The results demonstrated that the technology surpasses existing techniques in terms of accuracy and effectiveness in segmentation.Furthermore,it showcases the ability to withstand and recover from different tissue types and diseases,indicating possible uses in medical treatments.The research evaluated the efficacy of the proposed method on diverse histopathological imaging datasets,including cancer cells from many organs.The densely connected U-NEt technology offers a promising approach for automating and precisely segmenting cancer cells on histopathology slides,hence assisting pathologists in improving cancer diagnosis and treatment outcomes.展开更多
Under the inflammable or explosive environment, the direct measurement methods by opening up the explo- sion-proof shell of electrical installations were not adopted. So, it＇s impossible to have a quantitative analys...Under the inflammable or explosive environment, the direct measurement methods by opening up the explo- sion-proof shell of electrical installations were not adopted. So, it＇s impossible to have a quantitative analysis on the limit of conducted disturbance for electrical fast transient burst （EFT/B） in such dangerous environments. Transient conducted coupling model, which using EFT/B as its excitation source, can be built based on circuit and electromagnetic field theory. Furthermore, numerical analysis was performed. The results indicate that the capacitive coupling voltage is the same polarity as EFT/B, and is the main disturbance form of conducted coupling in mines. The inductive coupling voltage is reversed polarity with the ca- pacitive coupling voltage, and both peaks appear only in the rising time of EFT/B, which increase with the rising of load resistance. Moreover, the cable coupling voltage on the side of disturbance source is higher than the one on the other side in tunnel. To reduce the common resistance can suppress the resistive coupling disturbance.展开更多
Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the hig...Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.展开更多
Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and avail...Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud services.However,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm.Furthermore,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud services.Moreover,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches.In this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and password.The underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers.The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.展开更多
Acute Lymphoblastic Leukemia(ALL)is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow.Early prognosis of ALL is indispensable for the effectual remediation of...Acute Lymphoblastic Leukemia(ALL)is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow.Early prognosis of ALL is indispensable for the effectual remediation of this disease.Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images,a process which is time-consuming and prone to errors.Therefore,many deep learning-based computer-aided diagnosis(CAD)systems have been established to automatically diagnose ALL.This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images.The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks(CNNs)to identify the existence of ALL in blood smears.An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images.A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set.The latent features are used to perform image classification using Support Vector Machine(SVM)classifier.The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features.Moreover,the classification performance of the system with various sizes of the latent feature set is evaluated.The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.展开更多
Social media forums have emerged as the most popular form of communication in the modern technology era,allowing people to discuss and express their opinions.This increases the amount of material being shared on socia...Social media forums have emerged as the most popular form of communication in the modern technology era,allowing people to discuss and express their opinions.This increases the amount of material being shared on social media sites.There is a wealth of information about the threat that may be found in such open data sources.The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information.Despite the fact that several models for detecting cybersecurity events have been presented,it remains challenging to extract security events from the vast amounts of unstructured text present in public data sources.The majority of the currently available methods concentrate on detecting events that have a high number of dimensions.This is because the unstructured text in open data sources typically contains a large number of dimensions.However,to react to attacks quicker than they can be launched,security analysts and information technology operators need to be aware of critical security events as soon as possible,regardless of how often they are reported.This research provides a unique event detection method that can swiftly identify significant security events from open forums such as Twitter.The proposed work identified new threats and the revival of an attack or related event,independent of the volume of mentions relating to those events on Twitter.In this research work,deep learning has been used to extract predictive features from open-source text.The proposed model is composed of data collection,data transformation,feature extraction using deep learning,Latent Dirichlet Allocation(LDA)based medium-level cyber-event detection and final Google Trends-based high-level cyber-event detection.The proposed technique has been evaluated on numerous datasets.Experiment results show that the proposed method outperforms existing methods in detecting cyber events by giving 95.96% accuracy.展开更多
In recent years,the growth of female employees in the commercial market and industries has increased.As a result,some people think travelling to distant and isolated locations during odd hours generates new threats to...In recent years,the growth of female employees in the commercial market and industries has increased.As a result,some people think travelling to distant and isolated locations during odd hours generates new threats to women’s safety.The exponential increase in assaults and attacks on women,on the other hand,is posing a threat to women’s growth,development,and security.At the time of the attack,it appears the women were immobilized and needed immediate support.Only self-defense isn’t sufficient against abuse;a new technological solution is desired and can be used as quickly as hitting a switch or button.The proposed Women Safety Gadget(WSG)aims to design a wearable safety device model based on Internet-of-Things(IoT)and Cloud Technology.It is designed in three layers,namely layer-1,having an android app;layer-2,with messaging and location tracking system;and layer-3,which updates information in the cloud database.WSG can detect an unsafe condition by the pressure sensor of the finger on the artificial nail,consequently diffuses a pepper spray,and automatically notifies the saved closest contacts and police station through messaging and location settings.WSG has a response time of 1000 ms once the nail is pressed;the average time for pulse rate measure is 0.475 s,and diffusing the pepper spray is 0.2–0.5 s.The average activation time is 2.079 s.展开更多
文摘In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.
基金funded by a grant from the Center of Excellence in Information Assurance(CoEIA),King Saud University(KSU).
文摘Software defect prediction is a critical component in maintaining software quality,enabling early identification and resolution of issues that could lead to system failures and significant financial losses.With the increasing reliance on user-generated content,social media reviews have emerged as a valuable source of real-time feedback,offering insights into potential software defects that traditional testing methods may overlook.However,existing models face challenges like handling imbalanced data,high computational complexity,and insufficient inte-gration of contextual information from these reviews.To overcome these limitations,this paper introduces the SESDP(Sentiment Analysis-Based Early Software Defect Prediction)model.SESDP employs a Transformer-Based Multi-Task Learning approach using Robustly Optimized Bidirectional Encoder Representations from Transformers Approach(RoBERTa)to simultaneously perform sentiment analysis and defect prediction.By integrating text embedding extraction,sentiment score computation,and feature fusion,the model effectively captures both the contextual nuances and sentiment expressed in user reviews.Experimental results show that SESDP achieves superior performance with an accuracy of 96.37%,precision of 94.7%,and recall of 95.4%,particularly excelling in handling imbalanced datasets compared to baseline models.This approach offers a scalable and efficient solution for early software defect detection,enhancing proactive software quality assurance.
基金supported by a grant from the Center of Excellence in Information Assurance(CoEIA),King Saud University(KSU).
文摘Malware continues to pose a significant threat to cybersecurity,with new advanced infections that go beyond traditional detection.Limitations in existing systems include high false-positive rates,slow system response times,and inability to respond quickly to new malware forms.To overcome these challenges,this paper proposes OMD-RAS:Implementing Malware Detection in an Optimized Way through Real-Time and Adaptive Security as an extensive approach,hoping to get good results towards better malware threat detection and remediation.The significant steps in the model are data collection followed by comprehensive preprocessing consisting of feature engineering and normalization.Static analysis,along with dynamic analysis,is done to capture the whole spectrum of malware behavior for the feature extraction process.The extracted processed features are given with a continuous learning mechanism to the Extreme Learning Machine model of real-time detection.This OMD-RAS trains quickly and has great accuracy,providing elite,advanced real-time detection capabilities.This approach uses continuous learning to adapt to new threats—ensuring the effectiveness of detection even as strategies used by malware may change over time.The experimental results showed that OMD-RAS performs better than the traditional approaches.For instance,the OMD-RAS model has been able to achieve an accuracy of 96.23%and massively reduce the rate of false positives across all datasets while eliciting a consistently high rate of precision and recall.The model’s adaptive learning reflected enhancements on other performance measures-for example,Matthews Correlation Coefficients and Log Loss.
文摘Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.
基金The support of Prince Sultan University for paying the Article Processing Charge(APC)of this publication and their support.Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R300).
文摘Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timeconsuming,and include inaccuracies.Machine learning(ML)algorithms provide a more efficient alternative for this purpose,so after assessment with a statistical extraction method,ML algorithms including back-propagation neural network(BPNN),K-nearest neighbor(KNN),radial basis function(RBF),feed-forward neural networks(FFNN),and support vector regression(SVR)for predicting the uniaxial compressive strength(UCS)of soilcrete,were proposed in this study.The developed models in this study were optimized using an optimization technique,gradient descent(GD),throughout the analysis(direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters).After doing laboratory analysis,data pre-preprocessing,and data-processing analysis,a database including 600 soilcrete specimens was gathered,which includes two different soil types(clay and limestone)and metakaolin as a mineral additive.80%of the database was used for the training set and 20%for testing,considering eight input parameters,including metakaolin content,soil type,superplasticizer content,water-to-binder ratio,shrinkage,binder,density,and ultrasonic velocity.The analysis showed that most algorithms performed well in the prediction,with BPNN,KNN,and RBF having higher accuracy compared to others(R^(2)=0.95,0.95,0.92,respectively).Based on this evaluation,it was observed that all models show an acceptable accuracy rate in prediction(RMSE:BPNN=0.11,FFNN=0.24,KNN=0.05,SVR=0.06,RBF=0.05,MAD:BPNN=0.006,FFNN=0.012,KNN=0.008,SVR=0.006,RBF=0.009).The ML importance ranking-sensitivity analysis indicated that all input parameters influence theUCS of soilcrete,especially the water-to-binder ratio and density,which have themost impact.
基金Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2025R319)Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publication.Special acknowledgement to Automated Systems&Soft Computing Lab(ASSCL),Prince Sultan University,Riyadh,Saudi Arabia.
文摘The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.
基金funded by Princess Nourah bint Abdulrahman University and Researchers Supporting Project number(PNURSP2025R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Mango farming significantly contributes to the economy,particularly in developing countries.However,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial losses.The development of deep learning models for detecting crop diseases is an active area of research in smart agriculture.This study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)architectures.Two datasets were used.The first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery mildew.The second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and Rot.Both datasets were obtained from publicly available sources.The proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS dataset.The results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more efficiently.The system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual diagnostics.Additionally,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R319),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publicationResearchers Supporting Project Number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats.
基金funded by Princess Nourah bint Abdulrahman University and Researchers Supporting Project number(PNURSP2025R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic retinopathy.The traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic retinopathy.While the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data,dual-modal diabetic retinopathy grading methods offer superior performance.However,the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to multi-scale variations.This paper addresses these issues by focusing on multi-scale retinal vessel segmentation,dual feature fusion,data augmentation,and attention-based grading.The proposed model aims to improve comprehensive segmentation for retinal images with varying vessel thicknesses.It employs a dual-branch parallel architecture that integrates a transformer encoder with a convolutional neural network encoder to extract local and global information for synergistic saliency learning.Besides that,the model uses residual structures and attention modules to extract critical lesions,enhancing the accuracy and reliability of diabetic retinopathy grading.To evaluate the efficacy of the proposed approach,this study compared it with other pre-trained publicly open models,ResNet152V2,ConvNext,Efficient Net,DenseNet,and Swin Transform,with the same developmental parameters.All models achieved approximately 85%accuracy with the same image preparation method.However,the proposed approach outperforms and optimizes existing models by achieving an accuracy of 99.17%,99.04%,and 99.24%,on Kaggle APTOS19,IDRiD,and EyePACS datasets,respectively.These results support the model’s utility in helping ophthalmologists diagnose diabetic retinopathy more rapidly and accurately.
基金supported via funding from Prince Sattam bin Abdulaziz University(PSAU/2025/R/1446)Princess Nourah bint Abdulrahman University(PNURSP2025R300)Prince Sultan University.
文摘Deep neural networks provide accurate results for most applications.However,they need a big dataset to train properly.Providing a big dataset is a significant challenge in most applications.Image augmentation refers to techniques that increase the amount of image data.Common operations for image augmentation include changes in illumination,rotation,contrast,size,viewing angle,and others.Recently,Generative Adversarial Networks(GANs)have been employed for image generation.However,like image augmentation methods,GAN approaches can only generate images that are similar to the original images.Therefore,they also cannot generate new classes of data.Texture images presentmore challenges than general images,and generating textures is more complex than creating other types of images.This study proposes a gradient-based deep neural network method that generates a new class of texture.It is possible to rapidly generate new classes of textures using different kernels from pre-trained deep networks.After generating new textures for each class,the number of textures increases through image augmentation.During this process,several techniques are proposed to automatically remove incomplete and similar textures that are created.The proposed method is faster than some well-known generative networks by around 4 to 10 times.In addition,the quality of the generated textures surpasses that of these networks.The proposed method can generate textures that surpass those of someGANs and parametric models in certain image qualitymetrics.It can provide a big texture dataset to train deep networks.A new big texture dataset is created artificially using the proposed method.This dataset is approximately 2 GB in size and comprises 30,000 textures,each 150×150 pixels in size,organized into 600 classes.It is uploaded to the Kaggle site and Google Drive.This dataset is called BigTex.Compared to other texture datasets,the proposed dataset is the largest and can serve as a comprehensive texture dataset for training more powerful deep neural networks and mitigating overfitting.
基金Prince Sultan University for their supportPrincess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R300)supported via funding from Prince Sattam Bin Abdulaziz University project number(PSAU/2025/R/1447).
文摘The fracture energy of fiber-reinforced concrete(FRC)affects the durability and structural performance of concrete elements.Advancements in experimental studies have yet to overcome the challenges of estimating fracture energy,as the process remains time-intensive and costly.Therefore,machine learning techniques have emerged as powerful alternatives.This study aims to investigate the performance of machine learning techniques to predict the fracture energy of FRC.For this purpose,500 data points,including 8 input parameters that affect the fracture energy of FRC,are collected fromthree-point bending tests and employed to train and evaluate themachine learning techniques.The findings showed that Gaussian process regression(GPR)outperforms all other models in terms of predictive accuracy,achieving the highest R2 of 0.93 and the lowest RMSE of 13.91 during holdout cross-validation.It is then followed by support vector regression(SVR)and extreme gradient boosting regression(XGBR),whereas K-nearest neighbours(KNN)and random forest regression(RFR)show the weakest predictions.The superiority of GPR is further reinforced in a 5-fold cross-validation,where it consistently delivers an average R2 above 0.96 and ranks highest in overall predictive performance.Empirical testing with additional sample sets validates GPR’s model on the key mix parameter’s impact on fracture energy,cementing its claim.The Fly-Ash cement exhibits the greatest fracture energy due to superior fiber-matrix interaction,whereas the glass fiber dominates energy absorption amongst the other types of fibers.In addition,increasing the water-to-cement(W/C)ratio from 0.30 to 0.50 yields a significant improvement in fracture energy,which aligns well with the machine learning predictions.Similarly,loading rate positively correlates with fracture energy,highlighting the strain-rate sensitivity of FRC.This work is the missing link to integrate experimental fracture mechanics and computational intelligence,optimally and reasonably predicting and refining the fracture energy of FRC.
基金Supported by the National Natural Science Foundation of China(No.30730021)the Applied Basic Research Programs of Yunnan Province,China(Nos.2011FZ140 and 2010CD047)
文摘Using the fuzzy rule-based classification method, normalized difference vegetation index (NDVI) images acquired from 1982 to 1998 were classified into seventeen phases. Based on these classification images, a probabilistic cellular automata-Markov Chain model was developed and used to simulate a land cover scenario of China for the year 2014. Spatiotemporal dynamics of land use/cover in China from 1982 to 2014 were then analyzed and evaluated. The results showed that the change trends of land cover type from 1998 to 2014 would be contrary to those from 1982 to 1998. In particular, forestland and grassland areas decreased by 1.56% and 1.46%, respectively, from 1982 to 1998, and should increase by 1.5% and 2.3% from 1998 to 2014, respectively.
基金Under the auspices of the Chongqing Science and Technology Commission(No.cstc2018jxjl20012,cstc2018jszx-zdyfxm X0021,cstc2019jscx-gksb X0103)。
文摘According to China’s second national survey of pollution sources, the contribution of agricultural non-point sources(ANS) to water pollution is still high. Risk prevention and control are the main means to control costs and improve the efficiency of ANS, but most studies directly take pollution load as the risk standard, leading to a considerable misjudgment of the actual pollution risk. To objectively reflect the risk of agricultural non-point source pollution(ANSP) in Chongqing, China, we investigated the influences of initial source input, intermediate transformation, and terminal absorption of pollutants via literature research and the Delphi method and built a PTA(pressure kinetic energy, transformation kinetic energy, and absorption kinetic energy) model that covers 12 factors, with the support of geographical information system(GIS) technology. The terrain factor calculation results and the calculation results of other factors were optimized by Python tools to reduce human error and workload. Via centroid migration analysis and Kernel density analysis, the risk level, spatial aggregation degree, and key prevention and control regions could be accurately determined. There was a positive correlation between the water quality of the rivers in Chongqing and the risk assessment results of different periods, indirectly reflecting the reliability of the assessment results by the proposed model. There was an obvious tendency for the low-risk regions transforming into high-risk regions. The proportion of high-risk regions and extremely high-risk regions increased from 17.82% and 16.63%in 2000 to 18.10% and 16.76% in 2015, respectively. And the risk level in the main urban areas was significantly higher than that in the southeastern and northeastern areas of Chongqing. The centroids of all grades of risky areas presented a successive distribution from west to east, and the centroids of high-risk and extremely high-risk regions shifted eastward. From 2000 to 2015, the centroids of highrisk and extremely high-risk regions moved 4.63 km(1.68°) and 4.48 km(12.08°) east by north, respectively. The kernel density analysis results showed that the high-risk regions were mainly concentrated in the main urban areas and that the distribution of agglomeration areas overall displayed a transition trend from contiguous distribution to decentralized concentration. The risk levels of the regions with a high proportion of cultivated land and artificial surface were significantly increased, and the occupation of cultivated land in the process of urbanization promoted the movement of the centroids of high-risk and extremely high-risk regions. The identification of key areas for risk prevention and control provides data scientific basis for the development of prevention and control strategies.
基金funded by Princess Nourah bint Abdulrahman University and Researchers supporting Project number (PNURSP2024R346),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei.This is crucial for histopathological image analysis,as it involves segmenting cell nuclei.However,challenges exist,such as determining the boundary region of normal and deformed nuclei and identifying small,irregular nuclei structures.Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification,but their complex features limit their practical use in clinical settings.The existing studies have limited accuracy,significant processing costs,and a lack of resilience and generalizability across diverse datasets.We proposed the densely convolutional Breast U-shaped Network(BU-NET)framework to overcome the mentioned issues.The study employs BU-NET’s spatial and channel attention methods to enhance segmentation processes.The inclusion of residual blocks and skip connections in the BU-NEt architecture enhances the process of extracting features and reconstructing the output.This enhances the robustness of training and convergence processes by reducing the occurrence of vanishing gradients.The primary objective of BU-NEt is to enhance the model’s capacity to acquire and analyze more intricate features,all the while preserving an efficient working representation.The BU-NET experiments demonstrate that the framework achieved 88.7%average accuracy,88.8%F1 score for Multi-Organ Nuclei Segmentation Challenge(MoNuSeg),and 91.2%average accuracy,91.8%average F1 for the triple-negative breast cancer(TNBC)dataset.The framework also achieved 93.92 Area under the ROC Curve(AUC)for TNBC.The results demonstrated that the technology surpasses existing techniques in terms of accuracy and effectiveness in segmentation.Furthermore,it showcases the ability to withstand and recover from different tissue types and diseases,indicating possible uses in medical treatments.The research evaluated the efficacy of the proposed method on diverse histopathological imaging datasets,including cancer cells from many organs.The densely connected U-NEt technology offers a promising approach for automating and precisely segmenting cancer cells on histopathology slides,hence assisting pathologists in improving cancer diagnosis and treatment outcomes.
基金Supported by the National Natural Science Foundation of China （50674093） the Project of Fujian Provincial Education Department （JA11098）
文摘Under the inflammable or explosive environment, the direct measurement methods by opening up the explo- sion-proof shell of electrical installations were not adopted. So, it＇s impossible to have a quantitative analysis on the limit of conducted disturbance for electrical fast transient burst （EFT/B） in such dangerous environments. Transient conducted coupling model, which using EFT/B as its excitation source, can be built based on circuit and electromagnetic field theory. Furthermore, numerical analysis was performed. The results indicate that the capacitive coupling voltage is the same polarity as EFT/B, and is the main disturbance form of conducted coupling in mines. The inductive coupling voltage is reversed polarity with the ca- pacitive coupling voltage, and both peaks appear only in the rising time of EFT/B, which increase with the rising of load resistance. Moreover, the cable coupling voltage on the side of disturbance source is higher than the one on the other side in tunnel. To reduce the common resistance can suppress the resistive coupling disturbance.
基金a grant from the Center of Excellence in Information Assurance(CoEIA),KSU.
文摘Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.
基金The research work was supported by UTP-Universitas Telkom,Indonesia International Collaborative Research Funding(ICRF)015ME0-153 and Center for Graduate Studies(CGS),Universiti Teknologi PETRONAS(UTP),Perak,Malaysia.
文摘Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud services.However,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm.Furthermore,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud services.Moreover,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches.In this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and password.The underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers.The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.
文摘Acute Lymphoblastic Leukemia(ALL)is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow.Early prognosis of ALL is indispensable for the effectual remediation of this disease.Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images,a process which is time-consuming and prone to errors.Therefore,many deep learning-based computer-aided diagnosis(CAD)systems have been established to automatically diagnose ALL.This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images.The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks(CNNs)to identify the existence of ALL in blood smears.An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images.A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set.The latent features are used to perform image classification using Support Vector Machine(SVM)classifier.The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features.Moreover,the classification performance of the system with various sizes of the latent feature set is evaluated.The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.
基金funded by a grant from the Center of Excellence in Information Assurance(CoEIA),KSU.
文摘Social media forums have emerged as the most popular form of communication in the modern technology era,allowing people to discuss and express their opinions.This increases the amount of material being shared on social media sites.There is a wealth of information about the threat that may be found in such open data sources.The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information.Despite the fact that several models for detecting cybersecurity events have been presented,it remains challenging to extract security events from the vast amounts of unstructured text present in public data sources.The majority of the currently available methods concentrate on detecting events that have a high number of dimensions.This is because the unstructured text in open data sources typically contains a large number of dimensions.However,to react to attacks quicker than they can be launched,security analysts and information technology operators need to be aware of critical security events as soon as possible,regardless of how often they are reported.This research provides a unique event detection method that can swiftly identify significant security events from open forums such as Twitter.The proposed work identified new threats and the revival of an attack or related event,independent of the volume of mentions relating to those events on Twitter.In this research work,deep learning has been used to extract predictive features from open-source text.The proposed model is composed of data collection,data transformation,feature extraction using deep learning,Latent Dirichlet Allocation(LDA)based medium-level cyber-event detection and final Google Trends-based high-level cyber-event detection.The proposed technique has been evaluated on numerous datasets.Experiment results show that the proposed method outperforms existing methods in detecting cyber events by giving 95.96% accuracy.
基金The authors extend their appreciation to the deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through project number(IFP-2020-19).
文摘In recent years,the growth of female employees in the commercial market and industries has increased.As a result,some people think travelling to distant and isolated locations during odd hours generates new threats to women’s safety.The exponential increase in assaults and attacks on women,on the other hand,is posing a threat to women’s growth,development,and security.At the time of the attack,it appears the women were immobilized and needed immediate support.Only self-defense isn’t sufficient against abuse;a new technological solution is desired and can be used as quickly as hitting a switch or button.The proposed Women Safety Gadget(WSG)aims to design a wearable safety device model based on Internet-of-Things(IoT)and Cloud Technology.It is designed in three layers,namely layer-1,having an android app;layer-2,with messaging and location tracking system;and layer-3,which updates information in the cloud database.WSG can detect an unsafe condition by the pressure sensor of the finger on the artificial nail,consequently diffuses a pepper spray,and automatically notifies the saved closest contacts and police station through messaging and location settings.WSG has a response time of 1000 ms once the nail is pressed;the average time for pulse rate measure is 0.475 s,and diffusing the pepper spray is 0.2–0.5 s.The average activation time is 2.079 s.
