the agricultural sector continually seeks innovative solutions, and inexperienced farmers do not know how to choose the suitable seed for their agricultural land. this causes unstable crop harvesting and waste of mone...
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the proposed study evaluates the critical role of machine learning (ML) algorithms in predicting renal function decline in individuals with chronic kidney disease (CKD). the crucial need originates from the limitation...
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the proceedings contain 108 papers. the topics discussed include: fuzzy PID control based on genetic algorithm optimization inverted pendulum system;multi-task recognition of modulation types and arrival directions of...
the proceedings contain 108 papers. the topics discussed include: fuzzy PID control based on genetic algorithm optimization inverted pendulum system;multi-task recognition of modulation types and arrival directions of underwater acoustic signals based on convolutional neural networks;localization and detection of underwater acoustic communication signals using convolutional recursive neural networks;online trajectory anomaly detection model based on graph neural networks and variational autoencoder;an improved dynamical variational autoencoder framework for predicting aero-engine remaining useful life;construction of digital twin workshop integrated with edge computing and deep learning;research on electromagnetic pulse signal detection method based on intelligent compressed sensing technology;and path planning and collision avoidance approach for a multi-agent system in grid environments.
In recent years, the public has paid more and more attention to the problem of hair loss, which not only leads to a negative impact on personal image to a certain extent but also may cause patients to have psychologic...
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ISBN:
(纸本)9798400716645
In recent years, the public has paid more and more attention to the problem of hair loss, which not only leads to a negative impact on personal image to a certain extent but also may cause patients to have psychological inferiority feelings and even mental illness. Detecting the stages of hair loss in men is essential for treating hair loss. the Hamilton-Norwood classification is the most commonly used method to describe male hair loss. However, clinicians often estimate the stage of male hair loss by visual inspection combined withthe Hamilton-Norwood classification scale, which is subjective and time-consuming. In this paper, three deep learning methods, VGG16, ResNet-50, and RegNet-64, were used to detect the stage of male hair loss automatically. the experimental results show that RegNet-64 achieves 93.08% accuracy in classifying male baldness, indicating that deep learning is a good choice for assessing the severity of male hair loss.
In this paper, a multi-strategy enhanced slime mould algorithm (IMSMA) is proposed to address issues such as population initialization, convergence speed, and the tendency to fall into local optima in the slime mould ...
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Crop disease identification is crucial for its role of maintaining crop yield and quality in precision agriculture. Traditional disease detection methods are often inefficient and error-prone. To address this issue, w...
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ISBN:
(纸本)9798350375084;9798350375077
Crop disease identification is crucial for its role of maintaining crop yield and quality in precision agriculture. Traditional disease detection methods are often inefficient and error-prone. To address this issue, we propose the ECA-ViT model, which combines Efficient Channel Attention (ECA) and Vision Transformers (ViT) to identify crop leaf diseases in cultivation environments. the ECA-ViT model uses adaptive 1D convolution kernels for rapid cross-channel interaction of local disease features in crop images, compensating for the Vision Transformer's lack of global context understanding and optim-izing the model. this balanced approach enhances feature learning while maintaining computational efficiency without increasing model complexity. We evaluated the ECA-ViT model using rice leaf disease datasets from real field settings. the results demonstrated that ECA-ViT outperformed existing deep lear-ning methods, achieving an accuracy of 95.41%.
In this study, we present a comprehensive approach to enhancing submersible trajectory prediction in deepsea environments by integrating grey relational analysis and reinforcement learning techniques. the utilization ...
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ISBN:
(纸本)9798350362770;9798350362763
In this study, we present a comprehensive approach to enhancing submersible trajectory prediction in deepsea environments by integrating grey relational analysis and reinforcement learning techniques. the utilization of grey relational evaluation models and Multi-Agent Reinforcement learning withthe MADDPG method allows for the optimization of search and rescue equipment selection for deep-sea submersibles. By considering factors such as equipment availability, maintenance, preparation, and usage-related costs, the proposed methodology aims to improve the efficiency and effectiveness of search and rescue operations in challenging underwater conditions. Furthermore, the integration of grey relational analysis and reinforcement learning offers a novel and advanced strategy for predicting submersible trajectories with increased accuracy and reliability. By leveraging the capabilities of these analytical tools, this research contributes to the development of more robust and adaptive systems for deep-sea exploration and recovery missions. the findings of this study have significant implications for enhancing the safety and success of submersible operations in complex underwater environments, ultimately advancing the field of deep-sea exploration and rescue efforts.
Quadruped robots hold immense potential for navigating in unknown environments due to their ability to use individual footholds as well as their increased stability in uneven terrain. However, legged robots often expe...
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ISBN:
(数字)9783031774263
ISBN:
(纸本)9783031774256;9783031774263
Quadruped robots hold immense potential for navigating in unknown environments due to their ability to use individual footholds as well as their increased stability in uneven terrain. However, legged robots often experience limitations due to weight shifts during gait transitions. these weight shifts can cause torque peaks that exceed the capacity of the jointmotors (overdrive torque), which lead to an increased risk of mechanical failure. through the optimization of gait parameters, it is possible to reduce these risks while maximizing performance. this paper presents the use of multi-objective optimization algorithms for gait optimization in a simulated quadruped mammal robot within the Pybullet physics engine. the main focus of the study was to compare the performance of NSGA-II, NSGA-III and U-NSGA-III in minimizing overdrive torque while maximizing travel distance. the results showed that the three algorithms solve this problem, although the NSGA-III consistently yields better results in comparison to the other versions of the NSGA algorithm.
Smart grids have witnessed substantial developments in power systems and have shaped a new trajectory in power system research. this study identifies the major themes and research trends in the intelligent operation a...
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the proceedings contain 34 papers. the topics discussed include: a low-cost deployment case study for industrial IoT data collection and processing;ERSCHED: an efficient and reliable scheduler for workflows in multi-e...
ISBN:
(纸本)9798331506292
the proceedings contain 34 papers. the topics discussed include: a low-cost deployment case study for industrial IoT data collection and processing;ERSCHED: an efficient and reliable scheduler for workflows in multi-edge systems;a SAR image speckle suppression method based on transformer and frequency features;ground moving target detection using two high-resolution aerial images: theory and methods;joint optimization of service caching and task scheduling in energy-efficient mobile edge computing;economic dispatch of integrated energy systems based on improved data-driven adjustable robust model;an embedded system I/O isolation technology for Raspberry Pi;and hybrid energy storage capacity configuration based on empirical mode decomposition for industrial park.
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