A novel hybrid cooperative scatter search (SS) algorithm with an elite learning mechanism (HSSA) is proposed for addressing complex continuous global optimization problems. the HSSA integrates the evolutionary mechani...
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ISBN:
(纸本)9798350373141;9798350373158
A novel hybrid cooperative scatter search (SS) algorithm with an elite learning mechanism (HSSA) is proposed for addressing complex continuous global optimization problems. the HSSA integrates the evolutionary mechanism of the backtracking search algorithm (BSA) into the reference set update process of SS. In addition, an elite individual-guided learning mechanism is proposed to guide the evolutionary direction of the population through the information provided by the current optimal individual. An opposition-based learning mechanism is adopted to avoid the population from falling into stagnation. Experimental results conducted on the CEC2017 benchmark test set demonstrate the effectiveness of the HSSA algorithm.
In this work, two different deep learning architectures Residual Network (ResNet) and VGG Network are implemented for the MNIST digit recognition challenge. With a changed architecture, the ResNet which was initially ...
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Almond is a nut rich in essential nutrients. In addition to being a food, it is also used in cosmetics and the pharmaceutical industry. the market value of almonds is determined according to the quality of the almonds...
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Stroke is a leading cause of long-term disability, significantly affecting patients' motor skills and daily activities. Traditional rehabilitation methods, while beneficial, often lack the precision and adaptabili...
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ISBN:
(纸本)9798350367782;9798350367775
Stroke is a leading cause of long-term disability, significantly affecting patients' motor skills and daily activities. Traditional rehabilitation methods, while beneficial, often lack the precision and adaptability required for optimal recovery. this paper explores the integration of deep learning models optimized with Particle Swarm optimization (PSO) to enhance stroke rehabilitation outcomes using brain-computer interface (BCI) technology. We employed a dataset from the BCI Competition IV, which includes EEG data from multiple participants engaged in motor imagery tasks. Various deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), EEGNet, and Multi-layer Perceptrons (MLP), were optimized using PSO to improve classification accuracy. the results demonstrate that PSO significantly enhances the performance of these models, providing a robust framework for developing advanced rehabilitation systems. this approach not only improves the accuracy of motor imagery classification but also offers a personalized rehabilitation experience for stroke patients.
the smart grid is an advanced power system network that integrates cutting-edge technologies to enable efficient, reliable, and sustainable energy generation, distribution, and utilization. this paper provides a compr...
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this paper proposes a radar high-resolution range profile (HRRP) recognition algorithm based on ResNet, combined withthe SE (Squeeze-and-Excitation) channel attention mechanism. In most current HRRP target recognitio...
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Withthe advancement of artificial intelligence technology, object detection technology in the field of computer vision has played a key role. this article aims to address the accuracy and speed of existing methods in...
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ISBN:
(纸本)9798350375084;9798350375077
Withthe advancement of artificial intelligence technology, object detection technology in the field of computer vision has played a key role. this article aims to address the accuracy and speed of existing methods in processing live video streams. Withthe development of deep learning technology, we propose a novel framework of convolutional neural network (CNN) architecture, which optimizes the process of feature extraction and object classification, and significantly improves the detection accuracy. In addition, we have integrated recurrent neural networks (RNNs) to improve the tracking continuity of targets in video sequences. through the fusion of these technologies, our model not only performs well in multi-target detection, but also reliably tracks targets in the case of occlusion and fast movement. Experimental results show that compared withthe existing deep learning methods, the performance of our model on the standard dataset is significantly improved, with a 20% increase in detection speed and a 15% increase in accuracy.
In today's society, because of the increase in the frequency of people's use of electronic products, it leads to the phenomenon of irregular writing of Chinese characters and forgetting the characters when put...
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ISBN:
(纸本)9798350375084;9798350375077
In today's society, because of the increase in the frequency of people's use of electronic products, it leads to the phenomenon of irregular writing of Chinese characters and forgetting the characters when putting pen to paper, thus affecting the inheritance of the excellent traditional Chinese culture to a certain extent, so this paper proposes a handwritten Chinese character writing specification evaluation system based on deep learning. this system adopts two deep learning multi-classification models based on ViT to reason about the handwritten Chinese character images in order to obtain the calligraphic evaluation factors, and then calculate the overall evaluation of this handwritten Chinese character through the AHP mathematical evaluation model, and at the same time generate comments for the user through the individual evaluation values.
the proceedings contain 265 papers. the topics discussed include: optimizing renewable energy integration: a hybrid solar and wind-powered smart grid enhanced with IoT monitoring;forecasting India’s air quality: a ma...
ISBN:
(纸本)9798350349900
the proceedings contain 265 papers. the topics discussed include: optimizing renewable energy integration: a hybrid solar and wind-powered smart grid enhanced with IoT monitoring;forecasting India’s air quality: a machine learning approach for comprehensive analysis and prediction;job candidate eligibility prediction using convolutional neural network;comparison of machine learning models for chronic kidney disease prediction using UCI dataset;simulation-based evaluation of IDMT characteristics for overcurrent relay protection;blockchain-enhanced federated learning: a new paradigm for secure distributed machine learning;machine learning based smart crop recommendation system;and transcranial acoustoelectric of functional brain activity extraction based on Internet of things.
the energy industry's shift towards renewable sources and larger power grids has increased the complexity of electricity generation and consumption. Efficient grid management is essential for stability, performanc...
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ISBN:
(纸本)9798350367782;9798350367775
the energy industry's shift towards renewable sources and larger power grids has increased the complexity of electricity generation and consumption. Efficient grid management is essential for stability, performance, and cost reduction. Traditional methods for forecasting electricity transfer metrics often fall short due to their reliance on static models and historical data. this paper explores the use of neural networks to improve predictions of electricity transfer efficiency and duration. By leveraging deep learning, we aim to enhance forecast accuracy and reliability, thus optimizing grid operations and integrating renewable energy sources more effectively. We present a methodology for developing and evaluating neural network models, including dataset preparation and model design. Our analysis demonstrates the advantages of deep learning over traditional methods, contributing to the modernization of power grid management and the promotion of sustainable energy practices.
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