This research employed feature engineering techniques to preprocess an original stock dataset, followed by the introduction of a decision tree prediction model for forecasting the dataset. Experimental results demonstrate an enhancement in predictive performance, offering a more effective analytical tool for forecasting stock market trends. This approach also serves as an inspiration in fields such as optoelectronic signal processing, optical image recognition, and optical computation and processing.
In this research, machine learning algorithms such as decision tree, random forest, and BP neural network are used to predict a certain dataset, and then a voting prediction model is built based on the above three machine learning algorithms. To verify the performance of this voting model, we introduced confusion matrix and F1 score to evaluate the effectiveness of machine learning. The experimental results show that the performance of the machine learning strategy based on the voting model outperforms that of a single machine learning algorithm and that adjusting the voting weights of a single algorithm can also affect the performance of the whole model. This result is well worth further study.
In complex environments, UAV wireless sensing networks suffer from path loss, incomplete channels, swarm access security and other problems, and the signal fading and packet loss rates are very obvious. In order to solve the problem of user control data security, this paper gives full play to the characteristics of blockchain technology and builds a blockchain-based UAV cluster anti-interference communication network in UAV cluster applications to achieve antiinterference performance. Meanwhile, Markov decision process is used to optimize and realize the research of UAV airground integrated radio wave anti-interference.
Blockchain technology represented by cryptocurrencies has increasingly become the focus of social attention. The consensus protocol is the foundation for how the blockchain works. PoW, as the most widely used protocol, received more attention from researchers. This paper analyzes the defects of PoW, the most popular public chain consensus protocol in Blockchain, from five perspectives and points out the natural defects of PoW in high energy consumption, electronic waste, carbon footprint, expensive transaction fees, and centralization. This paper encourages the use of PoS and DPoS protocols instead of PoW protocols as they reduce the intensity of competition and may address the root cause of the aforementioned issues.
KEYWORDS: Neural networks, Data modeling, Machine learning, Data processing, Neurons, Performance modeling, Process engineering, Evolutionary algorithms, Feature selection, Data conversion
This research used the machine learning algorithm of the BP neural network to predict a data set. In order to verify the performance of the prediction model, we introduce the confusion matrix and F1 score to evaluate the effect of machine learning. In order to optimize the BP neural network model, we use feature engineering to process the data set and apply the BP neural network model to this new data set. The experimental results show that the machine learning performance of the BP neural network model based on feature engineering is improved.
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