Current automatic segment extraction techniques for identifying target characters in videos have several limitations, including low accuracy, slow processing speeds, and poor adaptability to diverse scenes. This paper...
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Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological *** terms of performance,fly ash and slag are preferredmaterials for precursors ...
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Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological *** terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part ***,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely ***,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part *** database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing *** performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed *** and parametric analysis were also performed to identify the significant contributor to *** to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 *** water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.
Electroencephalography (EEG) based emotion recognition shows promise in human-computer interaction and mental health monitoring, but faces challenges in cross-dataset generalization. This study introduces the Unified ...
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With the continuous advancement of satellite technology, remote sensing images has been increasingly applied in fields such as urban planning, environmental monitoring, and disaster response. However, remote sensing i...
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With the continuous advancement of satellite technology, remote sensing images has been increasingly applied in fields such as urban planning, environmental monitoring, and disaster response. However, remote sensing images often feature small target sizes and complex backgrounds, posing significant computational challenges for object detection tasks. To address this issue, this paper proposes a lightweight remote sensing images object detection algorithm based on YOLOv9. The proposed algorithm incorporates the SimRMB module, which effectively reduces computational complexity while improving the efficiency and accuracy of feature extraction. Through a dynamic attention mechanism, SimRMB is capable of focusing on important regions while minimizing background interference, and by integrating residual learning and skip connections, it ensures the stability of deep networks. To further enhance detection performance, the FasterRepNCSPELAN4 module is introduced, which employs PConv operations to reduce computational load and memory usage. It also utilizes dilated convolutions and DFC attention mechanisms to strengthen feature extraction, thereby increasing the efficiency and accuracy of object detection. Additionally, this study integrates the GhostModuleV2 module, which generates core feature maps and employs lightweight operations to create redundant features, greatly reducing the computational complexity of *** results show that on the SIMD dataset, the improved YOLOv9 model has a parameter size of 167.88 MB and GFLOPs of 208.6. Compared to the baseline YOLOv9 model (parameter size: 194.57 MB, GFLOPs: 239.0), the parameter size is reduced by 13.71%, GFLOPs are reduced by 12.72%, and detection accuracy is improved by 1.4%. These results demonstrate that the proposed lightweight YOLOv9 model effectively reduces computational overhead while maintaining excellent detection performance, providing an efficient solution for object detection tasks in resou
The use of foam,as the most economical soil conditioning technique,in earth pressure balance tunnel boring machine(EPB-TBM)tunneling projects has significant effects on operation efficiency,excavation cost,and operati...
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The use of foam,as the most economical soil conditioning technique,in earth pressure balance tunnel boring machine(EPB-TBM)tunneling projects has significant effects on operation efficiency,excavation cost,and operation *** study mainly focuses on developing models to predict the foam(surfac-tant)*** this purpose,five empirical models are developed based on a database containing 11048 datasets of real-time foam consumption from three EPB-TBM tunneling projects in *** database includes the most effective machine operational parameters and soil geomechanical properties on the foam *** linear regression analysis,multiple non-linear regression analysis,M5Prime decision tree,artificial neural network,and least squares support vector machine techniques are used to construct the *** evaluate the performance of developed models,three performance evaluation criteria(including normalized root mean square error,variance account for,and coefficient of determination)are used based on the training and testing *** results show that the developed models have high performance and their validity is guaranteed according to the testing ***,the M5Prime model,which demonstrates the best performance compared to other models,is applied to predict the foam consumption in 19 excavation rings of Kohandezh station in Isfahan metro,*** conducting an excavation operation in this station and comparing the results,it was found that the M5Prime model accurately predicts foam consumption with an average error of less than 13％.Therefore,the developed models,particularly M5Prime model,can be confidently applied in EPB-TBM tunneling projects for predicting foam consumption with a low error rate.
Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial...
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Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.
Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application t...
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Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge *** MCM-CNN is designed by adopting a memristor crossbar composed of a pair of ***-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and *** of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection *** results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.
X-ray security inspection for detecting prohibited items is widely used to maintain social order and ensure the safety of people’s lives and property. Due to the large number of parameters and high computational comp...
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The network security analyzers use intrusion detection systems(IDSes)to distinguish malicious traffic from benign *** deep learning-based(DL-based)IDSes are proposed to auto-extract high-level features and eliminate t...
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The network security analyzers use intrusion detection systems(IDSes)to distinguish malicious traffic from benign *** deep learning-based(DL-based)IDSes are proposed to auto-extract high-level features and eliminate the time-consuming and costly signature extraction ***,this new generation of IDSes still needs to overcome a number of challenges to be employed in practical *** of the main issues of an applicable IDS is facing traffic concept drift,which manifests itself as new(i.e.,zero-day)attacks,in addition to the changing behavior of benign users/***,a practical DL-based IDS needs to be conformed to a distributed(i.e.,multi-sensor)architecture in order to yield more accurate detections,create a collective attack knowledge based on the observations of different sensors,and also handle big data challenges for supporting high throughput *** paper proposes a novel multi-agent network intrusion detection framework to address the above shortcomings,considering a more practical scenario(i.e.,online adaptable IDSes).This framework employs continual deep anomaly detectors for adapting each agent to the changing attack/benign patterns in its local *** addition,a federated learning approach is proposed for sharing and exchanging local knowledge between different ***,the proposed framework implements sequential packet labeling for each flow,which provides an attack probability score for the flow by gradually observing each flow packet and updating its *** evaluate the proposed framework by employing different deep models(including CNN-based and LSTM-based)over the CICIDS2017 and CSE-CIC-IDS2018 *** extensive evaluations and experiments,we show that the proposed distributed framework is well adapted to the traffic concept *** precisely,our results indicate that the CNNbased models are well suited for continually adapting to the traffic concept drift(i.e.,achieving
In the data stream, the data has non-stationary quality because of continual and inconsistent change. This change is represented as the concept drift in the classifying process of the streaming data. Representing this...
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