Currently,edge Artificial Intelligence(AI)systems have significantly facilitated the functionalities of intelligent devices such as smartphones and smart cars,and supported diverse applications and *** fundamental sup...
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Currently,edge Artificial Intelligence(AI)systems have significantly facilitated the functionalities of intelligent devices such as smartphones and smart cars,and supported diverse applications and *** fundamental supports come from continuous data analysis and computation over these *** the resource constraints of terminal devices,multi-layer edge artificial intelligence systems improve the overall computing power of the system by scheduling computing tasks to edge and cloud servers for *** efforts tend to ignore the nature of strong pipelined characteristics of processing tasks in edge AI systems,such as the encryption,decryption and consensus algorithm supporting the implementation of Blockchain ***,this paper proposes a new pipelined task scheduling algorithm(referred to as PTS-RDQN),which utilizes the system representation ability of deep reinforcement learning and integrates multiple dimensional information to achieve global task ***,a co-optimization strategy based on Rainbow Deep Q-Learning(RainbowDQN)is proposed to allocate computation tasks for mobile devices,edge and cloud servers,which is able to comprehensively consider the balance of task turnaround time,link quality,and other factors,thus effectively improving system performance and user *** addition,a task scheduling strategy based on PTS-RDQN is proposed,which is capable of realizing dynamic task allocation according to device *** results based on many simulation experiments show that the proposed method can effectively improve the resource utilization,and provide an effective task scheduling strategy for the edge computing system with cloud-edge-end architecture.
In recent years, security has played a significant role in our daily lives. House security has become more popular with the enhancement of the Internet of Things (IoT) in intelligent home automation. This paper aims t...
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In light of increased constraints on healthcare systems, particularly as a result of the pandemic, the importance of directing patients to the appropriate healthcare departments for individualized treatment based on t...
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This paper presents the evolving role of artificial intelligence (AI) in improving internal control and management processes. AI-driven technologies, including Generative Adversarial Networks (GANs) and ontologies, in...
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Heart disease is a leading cause ofmortality ***(ECG)play a crucial role in diagnosing heart ***,interpreting ECGsignals necessitates specialized knowledge and *** development of automated methods for ECG analysis has...
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Heart disease is a leading cause ofmortality ***(ECG)play a crucial role in diagnosing heart ***,interpreting ECGsignals necessitates specialized knowledge and *** development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease *** research paper proposes a 3D Convolutional Long Short-Term Memory(Conv-LSTM)model for detecting heart disease using ECG *** proposed model combines the advantages of both convolutional neural networks(CNN)and long short-term memory(LSTM)*** considering both the spatial and temporal dependencies of ECG,the 3D Conv-LSTM model enables the detection of subtle changes in the signal over *** model is trained on a dataset of ECG recordings from patients with various heart conditions,including arrhythmia,myocardial infarction,and heart *** results show that the proposed 3D Conv-LSTM model outperforms traditional 2D CNN models in detecting heart disease,achieving an accuracy of 88%in the classification of five ***,themodel outperforms the other state-of-the-art deep learning models for ECG-based heart disease ***,the proposedConv-LSTMnetwork yields highly accurate outcomes in identifying abnormalities in specific ECG *** proposed 3D Conv-LSTM model holds promise as a valuable tool for automated heart disease detection and *** study underscores the significance of incorporating spatial and temporal dependencies in ECG-based heart disease *** highlights the potential of deep-learning models in enhancing the accuracy and efficiency of diagnosis.
The emergence of sophisticated variant botnet attacks like Okiru poses significant security challenges to the proliferating IoT devices. This study proposes a reinforcement learning (RL)-based defense system for IoT n...
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Web-based Geographical Information Systems (Web-GIS) aim to store, analyze, and disseminate geospatial information, enabling effective decision-making. However, their development requires professional expertise and in...
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Offline handwritten text recognition has been widely utilized in various fields including historical document analysis. Deep learning techniques have demonstrated their effectiveness in digitizing handwritten text as ...
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In recent years, the gaming industry has witnessed exponential growth, with an increasing focus on enhancing player experience and engagement. To achieve this, we propose a method that recognizes game experience trait...
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