The study of graph neural networks has revealed that they can unleash new applications in a variety of disciplines using such a basic process that we cannot imagine in the context of other deep learning designs. Many ...
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Maternal health during pregnancy is influenced by various factors that significantly impact pregnancy outcomes. This paper aims to highlight these critical factors, promote awareness, and advocate proactive self-care ...
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Diabetic retinopathy is a severe eye condition that can lead to vision loss at severe stages necessitating the early detection. Automating the detection reduces the labor and facilitates timely intervention. Deep lear...
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Mental health support is crucial for students, who are vulnerable to challenges due to academic pressures and social expectations. University culture often fosters misconceptions about mental well-being, leading to st...
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In contemporary times, there has been a notable shift among youth and young adults towards prioritizing their health, encompassing both physical and mental well-being. Recognizing this trend, innovative solutions have...
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Climate change poses significant challenges worldwide, with urban areas particularly susceptible to its impacts. Understanding local climate trends is essential for informed decision-making and proactive measures towa...
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Runge Kutta Optimization(RUN)is a widely utilized metaheuristic ***,it suffers from these issues:the imbalance between exploration and exploitation and the tendency to fall into local optima when it solves real-world ...
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Runge Kutta Optimization(RUN)is a widely utilized metaheuristic ***,it suffers from these issues:the imbalance between exploration and exploitation and the tendency to fall into local optima when it solves real-world opti-mization *** address these challenges,this study aims to endow each individual in the population with a certain level of intelligence,allowing them to make autonomous decisions about their next optimization *** incorporating Reinforcement Learning(RL)and the Composite Mutation Strategy(CMS),each individual in the population goes through additional self-improvement steps after completing the original algorithmic phases,referred to as *** is,each individual in the RUN population is trained intelligently using RL to independently choose three different differentiation strategies in CMS when solving different *** validate the competitiveness of RLRUN,comprehensive empirical tests were conducted using the IEEE CEC 2017 benchmark *** comparative experiments with 13 conventional algorithms and 10 advanced algorithms were *** experimental results demonstrated that RLRUN excels in convergence accuracy and speed,surpassing even some champion ***,this study introduced a binary version of RLRUN,named bRLRUN,which was employed for the feature selection *** 24 high-dimensional datasets encompassing UCI datasets and SBCB machine learning library microarray datasets,bRLRUN occupies the top position in classification accuracy and the number of selected feature subsets compared to some *** conclusion,the proposed algorithm demonstrated that it exhibits a strong competitive advantage in high-dimensional feature selection for complex datasets.
Visual Feature Learning (VFL) is a critical area of research in computer vision that involves the automatic extraction of features and patterns from images and videos. The applications of VFL are vast, including objec...
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The present research investigates the implementation of various methods of machine learning to analyze fluid information from executable files for the purpose identify contamination. We extracted feature vectors from ...
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This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to im...
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This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to improve recognition accuracy and computational *** method in this paper adopts the event sequence segmentation technique,combines location awareness with time interval reasoning,and improves human activity recognition through ontology *** with the existing methods,the framework performs better when dealing with uncertain data and complex scenes,and the experimental results show that its recognition accuracy is improved by 15.6%and processing time is reduced by 22.4%.In addition,it is found that with the increase of context complexity,the traditional ontology inferencemodel has limitations in abnormal behavior recognition,especially in the case of high data redundancy,which tends to lead to a decrease in recognition *** study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.
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