Intrusion detection is a prominent factor in the cybersecurity domain that prevents the network from malicious attacks. Cloud security is not satisfactory for securing the user’s information because it is based on st...
详细信息
Research on panicle detection is one of the most important aspects of paddy phenotypic analysis.A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based ***,it entails man...
详细信息
Research on panicle detection is one of the most important aspects of paddy phenotypic analysis.A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based ***,it entails many other challenges,including different illuminations,panicle sizes,shape distortions,partial occlusions,and complex *** detection algorithms are directly affected by these *** work proposes a model for detecting panicles called Border Sensitive Knowledge Distillation(BSKD).It is designed to prioritize the preservation of knowledge in border areas through the use of feature *** feature-based knowledge distillation method allows us to compress the model without sacrificing its *** imitation mask is used to distinguish panicle-related foreground features from irrelevant background features.A significant improvement in Unmanned Aerial Vehicle(UAV)images is achieved when students imitate the teacher’s *** the UAV rice imagery dataset,the proposed BSKD model shows superior performance with 76.3%mAP,88.3%precision,90.1%recall and 92.6%F1 score.
Robotic arms are widely used in the automation industry to package and deliver classified objects. When the products are small objects with very similar shapes, such as screwdriver bits with slightly different threads...
详细信息
Timely estimation of earthquake magnitude plays a crucial role in the early warning systems for earthquakes. Despite the inherent danger associated with earthquake energy, earthquake research necessitates extensive pa...
详细信息
Voice-based user interfaces (VUIs) represent a promising avenue for enhancing accessibility in humancomputer interaction (HCI). This research paper investigates the effectiveness of VUIs in addressing accessibility ch...
详细信息
The classification of breast cancer has emerged as a significant concern in the healthcare sector in recent times. This is primarily due to its status as the second leading cause of cancer-related fatalities among wom...
详细信息
The requirement for effective data classification on the Dark Web has increased following the rising sophistication and spread of illicit operations over this secretive internet segment. This paper systematically revi...
详细信息
ISBN:
(纸本)9798350389128
The requirement for effective data classification on the Dark Web has increased following the rising sophistication and spread of illicit operations over this secretive internet segment. This paper systematically reviews and compares the state-of-the-art Dark Web data classification methods that fall into text-based, image-based, and hybrid approaches. In the review process, this research outlines their strengths, weaknesses, and common challenges while also identifying gaps. The text-based approaches made extensive use of NLP, machine learning algorithms (such as SVM), and deep learning models, most notably, RNN and CNN. These techniques are good at information processing and understanding textual content, but high variability and obfuscation tactics make the communications hard to understand most of the time. Image-based classification leverages state-of-the-art computer vision techniques, for instance, CNN and GAN. These models are effective at detecting and classifying illicit images, such as goods or services that are illegal. However, it faces problems with the low quality or morphed images that are common on the Dark Web. Hybrid approaches incorporate both text and image in the analysis so that the information provided on the Dark Web is considered in an integrated form. These models use multimodal deep learning, which involves the use of CNN for image data and RNN for textual data samples. Hybrid methods combine text and image analysis to provide a more comprehensive understanding of Dark Web data samples. The approaches integrate multimodal deep learning models where CNNs are used for image data and RNNs for text data samples. Hybrid models show promise in improving classification accuracy by capturing the contextual information from both text and image samples. The present work outlines the necessity for developing highly adaptive and robust classification methods for Dark Web data samples. By assessing the strengths and limitations of existing methods, th
The fast increase of network traffic in recent times causes significant detection of intrusions in Internet of Things (IoT) environments. Currently, Deep Learning (DL) models play a crucial role in cyber security for ...
详细信息
While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the ...
详细信息
While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.
Anemia detection using multimodal approaches leverages the integration of multiple data sources, such as imaging, clinical records, and hematological parameters, to improve diagnostic accuracy. Such methods can captur...
详细信息
暂无评论