Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,in...
详细信息
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
CAPTCHAs (Completely Automated Public Turing Test to Tell computers and Humans Apart) have become universal in web security systems to differentiate between automated bots and human users. This research presents a nov...
详细信息
The expansion of deep learning techniques, as well as the availability of large audio/sound datasets, have fueled tremendous breakthroughs in audio/sound classification during the last several years. The transfer lear...
详细信息
The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have...
详细信息
The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have made progress,a common challenge is the low accuracy of existing detection *** models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource *** proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and *** leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial *** advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the *** results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these *** CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.
Identifying drug–target interactions (DTIs) is a critical step in both drug repositioning. The labor-intensive, time-consuming, and costly nature of classic DTI laboratory studies makes it imperative to create effici...
详细信息
The Computational Visual Media(CVM)conference series is intended to provide a prominent international forum for exchanging innovative research ideas and significant computational methodologies that either underpin or ...
详细信息
The Computational Visual Media(CVM)conference series is intended to provide a prominent international forum for exchanging innovative research ideas and significant computational methodologies that either underpin or apply visual media.
The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE service...
详细信息
The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.
Traffic congestion in cities poses a significant challenge, impacting public health, the environment, and the economy. Effective management strategies are critical for mitigating traffic congestion and enhancing flow ...
详细信息
Traffic congestion in cities poses a significant challenge, impacting public health, the environment, and the economy. Effective management strategies are critical for mitigating traffic congestion and enhancing flow efficiency. Since supervised machine learning (ML) and unsupervised techniques have been widely applied to traffic flow and congestion prediction, they face notable limitations, such as high computational costs, dependency on high-quality data, limited adaptability to dynamic traffic patterns, and scalability challenges in handling large datasets. These constraints highlight the need for hybrid approaches that integrate clustering and supervised learning to overcome some of these issues. The proposed hybrid approach integrates the concept of K-means clustering with some supervised machine learning techniques, such as Linear Regression (LR), Decision Tree (DT), Gradient Boosting (GB), Random Forest (RF), and Support Vector Regression (SVR) to identify distinct traffic patterns based on features such as road length, traffic volume, and time of day, thereby improving traffic congestion prediction. Our proposed hybrid approach significantly improves predictions compared to individual machine learning techniques, as evidenced by better Root Mean Square Error (RMSE), Coefficient of determination or R-squared (R2), and Mean Absolute Error (MAE) values than those achieved using only supervised machine learning methods. The RMSE for GB decreases from 2180.85 (without clustering) to 620.84 (with clustering), and for RF, it drops from 2186.32 to 632.07. Similarly, in case of DT the RMSE value decreases from 2188.05(without clustering) to 640.80(with clustering). The R2 scores improved significantly when K-means clustering integrated with Gradient Boosting, Random Forest, and Decision Tree, reaching 0.92 compared to lower values without using clustering. SVR also shows some improvements with clustering as well. Its RMSE decreases significantly
Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially cata...
详细信息
作者：
Sophatsathit, Peraphon
Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand
This research proposes a Biological-like Architecture for Software Systems (BASS) that make up of software components. The design principle is to mimic the simplicity of uni-cellular life form as fixed-sized blocks li...
详细信息
暂无评论