Autism, a neurological disorder, manifests uniquely in areas such as verbal and nonverbal communication, social interactions, behavioral adaptability, and specific interests. The results collected indicate that health...
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Dear Editor,This letter presents a multi-automated guided vehicles(AGV) routing planning method based on deep reinforcement learning(DRL)and recurrent neural network(RNN), specifically utilizing proximal policy optimi...
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Dear Editor,This letter presents a multi-automated guided vehicles(AGV) routing planning method based on deep reinforcement learning(DRL)and recurrent neural network(RNN), specifically utilizing proximal policy optimization(PPO) and long short-term memory(LSTM).
In order to improve the recognition level of abnormal high signal disease, a recognition method of abnormal high signal disease based on machine learning technology is proposed. The abnormal high signal feature extrac...
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Cloud computing and blockchain technology are the two transformative technologies that have created significantly impact in various industries. Cloud computing has revolutionized the way resources and services are del...
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State-of-the-art methods for knee OsteoArthritis (OA) disease grade classification using Deep learning (DL) based techniques are reviewed in the current work. Early detection-cum-classification of the knee is of subst...
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As space exploration advances, an increasing number of planets are becoming targets for landing missions. Before officially launching a lander, it is essential to conduct landing tests on Earth to verify the functiona...
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Retrofitting projects play a critical role in enhancing the sustainability of existing structures, yet balancing time, cost, and environmental impact remains a significant challenge for decision-makers. This study int...
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In the past few years, image processing has been widely adopted for symptom diagnosis of medical application. To achieve accurate analysis, the medical applications require high quality image for applying to the sympt...
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Multivariate Time Series (MTS) data imputation plays a pivotal role in enhancing the robustness of temporal data analyses across diverse domains. In this paper, we propose a novel hybrid model that combines the genera...
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In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basi...
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In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.
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