Undoubtedly, mental health exerts a profound influence on various aspects of human existence, spanning personal, familial, educational, societal, and even national domains. Our objective lies in the identification and...
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This paper introduces an AI-driven fashion design system that uses Generative Adversarial Networks (GANs) and real-time fabric simulations to create optimized sportswear, particularly for gymnastics. By simulating fab...
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In recent years, the increasing need for crowdfunding has become a prominent phenomenon, driven by a variety of factors shaping the contemporary economic landscape. One of the primary catalysts is the surge in entrepr...
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The paper discusses an IoT-based railway management system for detecting cracks on railway tracks, monitoring platform availability, fire detection and suppression, train movement control, and automatic level crossing...
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The Railway Track Tracer technology for Creature Detection is a technology that detects fractures in railway tracks. This method will assist in preventing numerous train accidents. This device routinely checks railway...
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In online social networks(OSN),they generate several specific user activities daily,corresponding to the billions of data points ***,although users exhibit significant interest in social media,they are uninterested in t...
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In online social networks(OSN),they generate several specific user activities daily,corresponding to the billions of data points ***,although users exhibit significant interest in social media,they are uninterested in the content,discussions,or opinions available on certain ***,this study aims to identify influential communities and understand user behavior across networks in the information diffusion *** media platforms,such as Facebook and Twitter,extract data to analyze the information diffusion process,based on which they cascade information among the individuals in the ***,this study proposes an influential information diffusion model that identifies influential communities across these two social media ***-over,it addresses site migration by visualizing a set of overlapping communities using hyper-edge ***,the overlapping community structure is used to identify similar communities with identical user ***,the com-munity structure helps in determining the node activation and user influence from the information cascade ***,the Fraction of Intra/Inter-Layer(FIL)dif-fusion score is used to evaluate the efficiency of the influential information diffu-sion model by analyzing the trending influential communities in a multilayer ***,from the experimental result,it observes that the FIL diffusion score for the proposed method achieves better results in terms of accuracy,preci-sion,recall and efficiency of community detection than the existing methods.
Alzheimer impacts a patient's memory, leading to cognitive decline and the priority lies in risk reduction, providing early intervention and precise symptom diagnosis, rather than finding a cure for it. In the bra...
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With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engi...
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With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.
A complicated and subjective part of the human experience, emotion includes a wide range of sensations and responses to different stimuli. Emotion Recognition from Recorded Speech stands out as a commonly utilized app...
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Email security is still a major concern because of the rise in cyberthreats and vulnerabilities, despite the fact that email communication has become an indispensable tool for both personal and business interaction. M...
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