In certain emergencies, patients must be continuously monitored and cared for. However, visiting the hospital to do such activities is difficult because of time constraints. To modernize the healthcare sector, the stu...
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A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCMA regards both global evolution and local search as equivalent elemental metaheuristics, and search...
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A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCMA regards both global evolution and local search as equivalent elemental metaheuristics, and searches with a chain of metaheuristics performed alternatively on the target problem. A hidden Markov model based scheduling mechanism is employed in MCMA for the selection of metaheuristics. By using MCMA for optimizing the linkage weight vector, a feature weighting algorithm for metabolomics data is formed to identify relevant metabolite features and reveal their exact relationships with the given physiological states. An extreme learning machine based classifier is utilized in predicting the physiological states according to the weighted metabolite features. Experimental results on real metabolomics data of clinical liver transplantation demonstrate that the proposed feature weighting and classification method obtains better performance than the other compared algorithms.
With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep *** Deep Learning(FDL)is a novel distributed machine learning tec...
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With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep *** Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data ***,FDL can only guarantee data security and privacy among multiple clients during data *** the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively *** this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered ***,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and *** addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data ***,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear *** theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.
This paper focuses on the optimization and improvement of the line-of-sight tracking algorithm based on monocular vision, and aims to achieve a series of complex functions through image analysis based on line-of-sight...
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This paper investigates the suitability of advanced deep learning models for precise diagnosis of lung cancer from MRI images. Recurrent neural networks (RNN), K-Nearest Neighbors (KNN), ResNet50, and convolutional ne...
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Objective: This study aims to explore the application of Chain of Thought (CoT) reasoning in automating ICD coding, specifically focusing on lymphoma cases. By leveraging large language models (LLMs) and CoT...
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Internet of things is progressing very rapidly and involving multiple domains of everyday life including environment, governance, healthcare system, transportation system, energy management system, etc. smart city is ...
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In the field of autonomous vehicles(AVs),accurately discerning commander intent and executing linguistic commands within a visual context presents a significant *** paper introduces a sophisticated encoder-decoder fra...
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In the field of autonomous vehicles(AVs),accurately discerning commander intent and executing linguistic commands within a visual context presents a significant *** paper introduces a sophisticated encoder-decoder framework,developed to address visual grounding in *** Context-Aware Visual Grounding(CAVG)model is an advanced system that integrates five core encoders—Text,Emotion,Image,Context,and Cross-Modal—with a multimodal *** integration enables the CAVG model to adeptly capture contextual semantics and to learn human emotional features,augmented by state-of-the-art Large Language Models(LLMs)including *** architecture of CAVG is reinforced by the implementation of multi-head cross-modal attention mechanisms and a Region-Specific Dynamic(RSD)layer for attention *** architectural design enables the model to efficiently process and interpret a range of cross-modal inputs,yielding a comprehensive understanding of the correlation between verbal commands and corresponding visual *** evaluations on the Talk2Car dataset,a real-world benchmark,demonstrate that CAVG establishes new standards in prediction accuracy and operational ***,the model exhibits exceptional performance even with limited training data,ranging from 50%to 75%of the full *** feature highlights its effectiveness and potential for deployment in practical AV ***,CAVG has shown remarkable robustness and adaptability in challenging scenarios,including long-text command interpretation,low-light conditions,ambiguous command contexts,inclement weather conditions,and densely populated urban environments.
Visual perception plays an important role in autonomous driving technology. The two key factors in visual perception tasks are monocular object detection and structured data analysis. In this paper, a structured data ...
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