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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Xu, Wenda Shu, Lan Guangdong University of Science and Technology College of Finance and Economics Dongguan China

ISBN: (纸本)9798350315455

Blockchain technology is a very popular technology in the economic field today. Given the above background, this manuscript describes a decision tree model to deduce blockchain decision-making with digital asset management, and control the management decision-making time. This method can carry out digital asset management with constraints, formulate digital currency management, intellectual property management and virtual asset management, and improve the rationality of digital asset management. Finally, digital assets with blockchain technology can be managed and final decision-making results can be output. The outcomes descripted that proposed method can exactly make blockchain digital asset management decisions and improve the level of digital asset management of blockchain technology, the optimization rate is more than 95%, which is better than traditional asset management methods. Meanwhile, for short-term forecasting, the model still has a high validity when the amount of data is small since the algorithm proposed extracts the relatively optimal samples in advance under digital asset management of blockchain technology. © 2023 IEEE.

关键词： Attribution of digital assets blockchain technology decision tree algorithm digital asset management digital assets

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Simulation of Fault Analysis Model for High-Voltage Circuit Breaker Spring Mechanism based on decision tree algorithm 3

Simulation of Fault Analysis Model for High-Voltage Circuit ...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Chen, Shaoyu Feng, Yang Jiang, Yong Zheng, Yu Huang, Jian Yu, Lufeng State Grid Zhejiang Electric Power Co. Ltd. Quzhou Power Supply Company Zhejiang Quzhou China

ISBN: (纸本)9798350382426

During long-term use, the spring mechanism of high-voltage circuit breakers is prone to malfunction hazards, causing damage to the normal operation of high-voltage circuit breakers and thereby affecting the safe operation of the power system. Therefore, a simulation of fault analysis model for high-voltage circuit breaker spring mechanism based on decision tree algorithm. By preprocessing the fault data of the high-voltage circuit breaker spring mechanism, the vibration signal of the high-voltage circuit breaker spring mechanism fault was decomposed. The correlation coefficient method was used to extract the fault characteristics of the spring mechanism of high-voltage circuit breakers. On this basis, based on the decision tree algorithm, a fault analysis model for the spring mechanism of high-voltage circuit breakers is constructed to achieve fault analysis of the spring mechanism of high-voltage circuit breakers. The experimental results show that the proposed method has a shorter fault analysis time for the spring mechanism of high-voltage circuit breakers, and can effectively improve the accuracy of fault analysis for the spring mechanism of high-voltage circuit breakers. © 2023 IEEE.

关键词： decision tree algorithm Fault analysis model High voltage circuit breaker Spring mechanism

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Application of Computer Science and Technology Intelligence Based on Improved decision tree algorithm 3

Application of Computer Science and Technology Intelligence ...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Bai, Songlin School of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9798350317022

Data mining is the process of discovering large data sets and revealing hidden laws. This paper aims to study the intelligent application analysis of computer science and technology based on the improved decision tree algorithm. Taking the improvement of the ID3 algorithm as the research goal, on the basis of analyzing the tasks of data mining, decision tree algorithm and the application development of computer science and technology, it proposes improved scheme of ID3 algorithm is presented and realized. Finally, this paper uses the ID3 algorithm and the improved ID3 algorithm to conduct a decision tree construction experiment. The experimental results show that compared with the ID3 algorithm, the improved algorithm reduces a lot of training logarithms, and therefore shortens the construction of the same training data. The decision tree is time-consuming and greatly improves the computing performance. © 2023 IEEE.

关键词： Computer Science and Technology decision tree algorithm ID3 algorithm Intelligent Application

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Forecasting Uncertainty Parameters of Virtual Power Plants Using decision tree algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第16期51卷 1756-1769页

作者： Krishna, Raji Sathish, Hemamalini Zhou, Ning Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India SUNY Binghamton ECE Dept New York NY USA

Renewable energy sources (RESs) are increasingly used to meet the world's growing electrical needs, especially for the economic benefits and environmental problems associated with fossil fuel use. Small-scale renewable energy sources, controllable loads, energy storage devices, and other nonrenewable sources are effectively integrated to form a virtual power plant (VPP). Uncertainty in forecasting renewable energy generation due to the intermittent nature of renewable energy sources is one of the biggest challenges in VPPs. Power generation by RESs changes with the day of the week, season, location, climate, and resource availability. Also, load demand and utility price vary with time and need to be forecasted for proper energy management of VPPs. However, the dispatching and planning of VPPs are significantly impacted by the volatile nature of RESs, load demand, and utility price. Predicting these uncertainties with high accuracy is essential to balance the electrical power generation and the load demand. In this article, a decision tree (DT) algorithm is proposed, to predict the uncertainty parameters, such as the day-ahead power from the RES, load demand, and utility prices of VPPs. The efficiency of the proposed model and the predicted results are compared with other complex models, such as the artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA) algorithms. Root-mean square error (RMSE), mean square error (MSE), coefficient of determination (R-2), and mean absolute error (MAE) are the statistical metrics used to evaluate the accuracy of the prediction. One-year meteorological data of the Chennai zone in India is considered for predicting the uncertainty parameters. IEEE 16-bus and 33-bus test systems are used to validate the forecasting model. It is evident from the results that the proposed DT algorithm can predict the uncertainty parameters more accurately and use lesser time than the ANN and ARIMA algorithms.

关键词： forecasting uncertainty parameters renewable energy sources distributed generators machine learning decision tree algorithm ANN algorithm ARIMA algorithm statistical metrics

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An improved performance of reversible data hiding in encrypted images using decision tree algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期137卷

作者： Konduru, Upendra Raju Nagarajan, Amutha Prabha Sai, Chandavolu Venkata Sri SV Coll Engn ECE Karakambadi Rd Tirupati AP India VIT Univ Sch Elect Engn Vellore India L&T Technol Serv Bangalore India

The advancements in increase of internet technology, transfer of data through insecure channel has become more popular. Now a day's most of the customers were turned to digital world and the channel is the main resource of communication. There is no guarantee that stored data will not be accessed by any hacker or cloud service provider. Privacy and Security is the main aspect of the rapid development in the digital storage. These conditions demand to make sure that the data is encrypted, stored and transmitted. Reversible Data Hiding in Encrypted images using machine learning is an emerging methodology used in the domain to secure the information for end-to-end secure data transmission. In this proposed method, all the data's can be Encrypted using Digital Keys, and these data can be embedded in cover images using decision tree algorithm. In the receiver side, the data can be retrieved back if receiver have right keys which is used while encryption. This system uses pseudo random number generation for generating multiple keys to encrypt and decrypt the secret data. This method is designed for encryption the Images and Text data. To increase further security and embedding capacity a machine learning algorithm is also implemented. The proposed decision tree method is tested on the standard image processing images and certain parameters are calculated for performance metrics and compare the results with existing schemes. The algorithm is implemented in MATLAB and used the Statistics and Machine Learning library in MATLAB.

关键词： Cryptography Steganography Reversible data hiding Image encryption decision tree algorithm Support vector machine Machine learning K -nearest neighbors

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Classification and Recognition of Building Appearance Based on Optimized Gradient-Boosted decision tree algorithm

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SENSORS 2023年第11期23卷 5353-5353页

作者： Hu, Mengting Guo, Lingxiang Liu, Jing Song, Yuxuan Xiamen Univ Technol Sch Civil Engn & Architecture Xiamen 361024 Peoples R China Fuzhou Univ Arts & Design Coll Xiamen Xiamen 361000 Peoples R China Jilin Agr Univ Coll Forestry & Grassland Changchun 130118 Peoples R China

There are high concentrations of urban spaces and increasingly complex land use types. Providing an efficient and scientific identification of building types has become a major challenge in urban architectural planning. This study used an optimized gradient-boosted decision tree algorithm to enhance a decision tree model for building classification. Through supervised classification learning, machine learning training was conducted using a business-type weighted database. We innovatively established a form database to store input items. During parameter optimization, parameters such as the number of nodes, maximum depth, and learning rate were gradually adjusted based on the performance of the verification set to achieve optimal performance on the verification set under the same conditions. Simultaneously, a k-fold cross-validation method was used to avoid overfitting. The model clusters trained in the machine learning training corresponded to various city sizes. By setting the parameters to determine the size of the area of land for a target city, the corresponding classification model could be invoked. The experimental results show that this algorithm has high accuracy in building recognition. Especially in R, S, and U-class buildings, the overall accuracy rate of recognition reaches over 94%.

关键词： decision tree algorithm building classification k-fold cross-validation method model cluster

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Taguchi approach and decision tree algorithm for prediction of wear rate in zinc oxide-filled AA7075 matrix composites

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SURFACE TOPOGRAPHY-METROLOGY AND PROPERTIES 2021年第3期9卷 035005-035005页

作者： Alagarsamy, S., V Balasundaram, R. Ravichandran, M. Mohanavel, V Karthick, Alagar Devi, S. Sathiya Mahath Amma Inst Engn & Technol Dept Mech Engn Pudukkottai 622101 Tamil Nadu India K Ramakrishnan Coll Engn Dept Mech Engn Tiruchirappalli 621112 Tamil Nadu India Bharath Inst Higher Educ & Res Ctr Mat Engn & Regenerat Med Chennai 600073 Tamil Nadu India KPR Inst Engn & Technol Dept Elect & Elect Engn Coimbatore 641407 Tamil Nadu India Univ Coll Engn Dept Comp Sci Engn Bit Campus Tiruchirappalli 620024 Tamil Nadu India

This work aims to apply data-driven approach [decision tree (DT) algorithm] to analyse the wear rate (WR) of ZnO-filled AA7075 composites. The results of model-based analysis was compared with Taguchi analysis. Stir casting was used to produce the composite samples. Characterization studies were conducted to analyse the composition and morphology. The scanning electron microscopy results indicated the even dispersion of ZnO in the AA7075. The energy-dispersive x-ray spectroscopy pattern ensures the presence of matrix elements and the inclusion of reinforcement particles into the proposed composites. To minimize the number of experimentation, L27 Orthogonal array is used for finding WR. The 'DuCom' Pin-on-Disc apparatus were used to prepare WR data for the set of the proposed composites. Taguchi technique reveals the optimum level factors for obtaining the minimum 'WR' is reinforcement content of 10 wt.%, applied load (P) at 10 N, sliding velocity (V) at 1 m s(-1) and sliding distance (D) of 1000 m. The experiments results from DT algorithm, and analysis of variance and signal-to-noise ratio analysis from Taguchi-based approach confirmed that reinforcement is the primary element for affecting wear of the composites. The reason for applying DT algorithm is that, the low-level knowledge could be converted into high-level knowledge (If-then-else rules), which can be effortlessly explicable by semiskilled personnel.

关键词： composites aluminium alloy zinc oxide wear rate optimization taguchi method decision tree algorithm

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EFFECT OF MACHINING PARAMETERS ON SURFACE ROUGHNESS FOR ALUMINIUM MATRIX COMPOSITE BY USING TAGUCHI METHOD WITH decision tree algorithm

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SURFACE REVIEW AND LETTERS 2021年第4期28卷 2150021-2150021页

作者： Raveendran, P. Alagarsamy, S. V. Ravichandran, M. Meignanamoorthy, M. Mahath Amma Inst Engn & Technol Dept Mech Engn Pudukkottai 622101 Tamil Nadu India K Ramakrishnan Coll Engn Dept Mech Engn Tiruchirappalli 621112 Tamil Nadu India

The intend of this research work is to explore the effect of various parameters in a CNC turning process like cutting speed (V), feed (F), and depth of cut (D) on surface roughness (Ra) of turning AA7075 filled with 10wt.% of TiO2 composite fabricated through stir casting method. Taguchi method and decision tree (DT) algorithm were utilized to foresee the surface roughness (Ra) of the proposed composite. The microstructure of composite was ensured with the presence of TiO2 particles dispersed in a homogeneous manner within the matrix material. The machining of composite was carried out by using the CNC turning center and tungsten carbide insert as tool material. This experimental work was designed on L27 (3(3)) orthogonal array using Taguchi's design of experiments. From its signal-to-noise (S/N) ratio study, the minimum surface roughness (Ra) was obtained at the optimum level of parameters with the cutting speed at 1500rpm, feed at 0.15mm/rev and depth of cut at 0.3mm. Analysis of variance (ANOVA) and decision tree (DT) algorithm were used to identify the significant effect of parameters. The experimental result shows that depth of cut was the major significant parameter on surface roughness (Ra) when compared to cutting speed and feed.

关键词： AA7075 TiO2 CNC turning surface roughness taguchi method ANOVA decision tree algorithm

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Information-based massive data retrieval method based on distributed decision tree algorithm

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INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING 2023年第1期14卷

作者： Chen, Bin Chen, Qingming Ye, Peishan China Southern Power Grid Co Ltd Guangzhou 510663 Guangdong Peoples R China China Southern Power Grid Digital Media Technol C Guangzhou 510060 Guangdong Peoples R China

Based on the distributed decision tree algorithm, this paper first proposes a method of vertically partitioning datasets and synchronously updating the hash table to establish an information-based mass data retrieval method in a heterogeneous distributed environment, as well as using interval segmentation and interval filtering technologies for improved algorithm of distributed decision tree. The distributed decision tree algorithm uses the attribute histogram data structure to merge the category list into each attribute list, reducing the amount of data that needs to reside in the memory. Second, we adopt the strategy of vertically dividing the dataset and synchronously updating the hash table, select the hash table entries that can be used to update according to the minimum Gini value, modify the corresponding entries and use the hash table to record and control each sub-site. In the case of node splitting, it has a high accuracy rate. In addition, for classification problems that meet monotonic constraints in a distributed environment, this paper will extend the idea of building a monotonic decision tree in a distributed environment, supplementing the distributed decision tree algorithm, adding a modification rule and modifying the generated nonmonotonic decision tree to monotonicity. In order to solve the high load problem of the privacy-protected data stream classification mining algorithm under a single node, a Storm platform for the parallel algorithm PPFDT_P based on the distributed decision tree algorithm is designed and implemented. At the same time, considering that the word vector model improves the deep representation of features and solves the problem of feature high-dimensional sparseness, and the iterative decision tree algorithm GBDT model is more suitable for non-high-dimensional dense features, the iterative decision tree algorithm will be integrated into the word vector model (GBDT) in the data retrieval application, using the distributed represe

关键词： Distributed structure decision tree algorithm informationized data retrieval method

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The Promotion of Women's Leisure Sports Behavior Based on Improved decision tree algorithm

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INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2024年第1期20卷 1页

作者： Luo, Huaping Hunan Coll Chem Technol Zhuzhou Peoples R China

In women's daily leisure choices, sports is an important content that cannot be ignored. In this context, this paper studies the promotion of women's leisure sports behavior based on improved decision tree algorithm. Based on the simple analysis of the research progress of leisure sports and decision tree algorithm, a female leisure sports behavior model based on decision tree is constructed. Based on the decision tree algorithm, the calculation method of information gain rate is optimized to avoid logarithmic operation, and the continuous attributes are discretized. Simulation results show that in terms of classification accuracy, the improved decision tree algorithm is significantly higher than the classical decision tree algorithm, and can significantly shorten the running time, which has high application value in the realization of accurate classification analysis of female leisure sports behavior.

关键词： decision tree algorithm Female Leisure Sports Behavior Information Gain Rate Taylor Formula

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