With the increasing complexity of business environment, the importance of data analysis in business decision-making has become increasingly prominent. As a powerful data analysis tool, machinelearning algorithm has b...
With the increasing complexity of business environment, the importance of data analysis in business decision-making has become increasingly prominent. As a powerful data analysis tool, machinelearning algorithm has been widely used in the field of business data analysis. This paper will introduce the application of machinelearning algorithm in business data analysis, including classification, clustering, prediction and association rule mining.
The model for developing the predictive climate resilience and disaster management in the most vulnerable regions is discussed in the current paper. At its base, the model enhances climate hazard forecasting by real-t...
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The term steganography represent the science of hiding secret information in a carrier medium like Image, Audio, Video, etc. This field has been participated in multidisciplinary applications and different sectors. Fo...
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In order to explore the ecological vulnerability of Helan Mountain in the context of the ecological protection project, this paper, based on the SRP model, selects 15 evaluation indicators to construct an ecological v...
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With the improvement of digitalization and the development of information technology, intelligent campus management has become an important research direction in the current education field. Based on digital twin tech...
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Traditional Indian textile designs are very rich and varied and reflect the culture of the area in which they are popular. Unfortunately, artisans for these forms of art are dwindling because of the onslaught of mecha...
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An automatic speech recognition software based on STM32FI03 is developed. The purpose is to solve the problems in the storage and processing of speech recognition. A language processing method based on quantitative de...
An automatic speech recognition software based on STM32FI03 is developed. The purpose is to solve the problems in the storage and processing of speech recognition. A language processing method based on quantitative deep learning is proposed based on the analysis of language. Firstly, the difference between the characteristics of speech and the background noise in the time domain is used to set the corresponding threshold to achieve the recognition of the target. A method of speech feature extraction based on Mayer transform is proposed, which cannot solve the complex speech features effectively. Meier cestrum and Mel-Meier cestrum coefficients are calculated for different types of speech signals, and mixed feature parameters based on Fisher criterion are constructed. This paper presents a speech recognition method based on convolutional neural network. Compared with common English language, it is found that this method has better accuracy and achieves better results in a shorter time. Because the system is small in size and easy to carry, it can easily operate language on various devices.
Challenges in target detection include significant variations in target size, insufficient detection accuracy on small, resource-constrained devices, and the high cost of fully annotating large datasets. To address th...
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ISBN:
(纸本)9798400713231
Challenges in target detection include significant variations in target size, insufficient detection accuracy on small, resource-constrained devices, and the high cost of fully annotating large datasets. To address these issues, a semi-supervised lightweight target detection method based on the improved YOLOv7-tiny algorithm is proposed. First, a new spatial pyramid pooling network, MSPP, is designed to address the problem of significant variations in target size. Then, a lightweight convolutional attention module, GC-SAM, is introduced, which generates stronger feature representations by combining channel and spatial attention mechanisms. Finally, a multi feature semi-supervised data filtering method is proposed, which enhances the model's ability to learn valuable information from unlabeled data, effectively improving detection accuracy. Experimental results show that the improved algorithm achieves a detection accuracy of 82.2% and 63.6% for mAP@50 on 30% of the KITTI and SODA1000 datasets, which is 5.4% and 2.7% higher than the original model, respectively. The model size is only 14.4 MB and the detection speed is 82.1 frames per second, confirming the effectiveness of the algorithm.
data analysis and mining play an important role in the research of intelligent information management system, but there is a problem of inaccurate information management. Traditional machinelearning cannot solve the ...
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ISBN:
(数字)9798350354171
ISBN:
(纸本)9798350354188
data analysis and mining play an important role in the research of intelligent information management system, but there is a problem of inaccurate information management. Traditional machinelearning cannot solve the information management problem in the research of intelligent information management system, and the effect is not satisfactory. Therefore, this paper proposes the application of intelligent information management system based on big data, and the application of intelligent information management system research an analysis was carried out. Firstly, The key is your guiding theory, es, to reduce the interference factors in data analysis and mining. Then, the dataset theory is used to form a big data analysis and mining scheme, and the data analysis and mining results are comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, big data is better than traditional machinelearning in terms of data analysis and mining accuracy, data analysis and mining influencing factor time.
State-of-the-art classification neural networks, used for images and various data types, are complex and require significant energy and computational resources relying on super-vised gradient back-propagation. In cont...
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ISBN:
(数字)9798350372977
ISBN:
(纸本)9798350372984
State-of-the-art classification neural networks, used for images and various data types, are complex and require significant energy and computational resources relying on super-vised gradient back-propagation. In contrast, the Hopfield Neural Network (HNN) is simpler, being a single-layer, fully connected network that mimics the human brain's associative memory network, making it easy to implement and computationally efficient. Its compatibility with oscillatory neural networks (ONNs) makes it ideal for lightweight machinelearning applications in the Internet of Things (IoT) era. Normally, HNN has been primarily used for associative memory aiding in image processing, patternrecognition, and more, but this paper introduces it as a classifier. The proposed HNN classifier is adaptable to various datasets, including images and tabular data, and requires zero training time, making it suitable for resource-limited environments. It represents a significant leap in classification, with the highest accuracy reported for HNN classifiers to the best of our knowl-edge, achieving 96% accuracy on the MNIST dataset, a 36% percentage improvement over previous models.
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