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...
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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.
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...
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The Intelligent Internet of Things(IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approache...
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The Intelligent Internet of Things(IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence(AI) and machine learning, to make accurate decisions. Data science is the science of dealing with data and its relationships through intelligent approaches. Most state-of-the-art research focuses independently on either data science or IIoT, rather than exploring their integration. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions.
Mental health disorders profoundly affect intellectual ability, emotional resilience, and social stability, leading to lasting effects on both individuals and society. Increasing global concern over mental health call...
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This study investigates a safe reinforcement learning algorithm for grid-forming(GFM)inverter based frequency *** guarantee the stability of the inverter-based resource(IBR)system under the learned control policy,a mo...
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This study investigates a safe reinforcement learning algorithm for grid-forming(GFM)inverter based frequency *** guarantee the stability of the inverter-based resource(IBR)system under the learned control policy,a modelbased reinforcement learning(MBRL)algorithm is combined with Lyapunov approach,which determines the safe region of states and *** obtain near optimal control policy,the control performance is safely improved by approximate dynamic programming(ADP)using data sampled from the region of attraction(ROA).Moreover,to enhance the control robustness against parameter uncertainty in the inverter,a Gaussian process(GP)model is adopted by the proposed algorithm to effectively learn system dynamics from *** simulations validate the effectiveness of the proposed algorithm.
The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for ...
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Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can...
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The disappearance of Indigenous languages results in a decrease in cultural diversity, hence making the preservation of these languages extremely important. Conventional methods of documentation are lengthy, and the p...
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Disastrous situations pose a formidable challenge, testing our resilience against nature's fury and the race against time to prevent the loss of human life. It is noted that in such situations that Microblogging p...
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Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective *** Deep Learning(DL)approaches have shown promise in AD di...
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Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective *** Deep Learning(DL)approaches have shown promise in AD diagnosis,existing methods often struggle with the issues of precision,interpretability,and class *** study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence(XAI)techniques,in particular attention mechanisms,Gradient-Weighted Class Activation Mapping(Grad-CAM),and Local Interpretable Model-Agnostic Explanations(LIME),to improve bothmodel interpretability and feature *** study evaluates four different DL architectures(ResMLP,VGG16,Xception,and Convolutional Neural Network(CNN)with attention mechanism)on a balanced dataset of 3714 MRI brain scans from patients aged 70 and *** proposed CNN with attention model achieved superior performance,demonstrating 99.18%accuracy on the primary dataset and 96.64% accuracy on the ADNI dataset,significantly advancing the state-of-the-art in AD *** ability of the framework to provide comprehensive,interpretable results through multiple visualization techniques while maintaining high classification accuracy represents a significant advancement in the computational diagnosis of AD,potentially enabling more accurate and earlier intervention in clinical settings.
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