To prove that the efficiency of the decision tree algorithm is high compared with SVM algorithm for novel classification of Brain Tumor MRI Images. Materials And Methods: A total of 274 Brain Tumor MRI images are coll...
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ISBN:
(纸本)9781665460712
To prove that the efficiency of the decision tree algorithm is high compared with SVM algorithm for novel classification of Brain Tumor MRI Images. Materials And Methods: A total of 274 Brain Tumor MRI images are collected and samples are divided into training dataset (n=191 [70%]) and test dataset (n=83[30%]). Novel classification of MRI Images is performed by decision tree algorithm and SVM algorithm. Results and Discussion : Brain Tumor MRI images are classified to extract the quantitative information by using decision tree algorithm and attained accuracy of 97 % and SVM algorithm got 89%. decision tree algorithm and SVM algorithm are statistically significant with the independent sample T-Test value (plt;0.05). The results proved that the decision tree algorithm has better efficiency over the SVM algorithm in classification of Brain Tumor MRI images.
With the liberalization of the comprehensive second-child policy, the impact of fertility issues on womens employment has reappeared, causing widespread concern. The article introduces the current employment status an...
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ISBN:
(纸本)9781665484763
With the liberalization of the comprehensive second-child policy, the impact of fertility issues on womens employment has reappeared, causing widespread concern. The article introduces the current employment status and problems of women:women may extend the time to find a job due to the second child, or the professional continuity is not guaranteed, which will affect their career development,and then it explains the importance of the decision tree algorithm and how the decision tree algorithm is combined with the female talent recruitment system to help second-child women find suitable jobs as soon as possible and ease the competition in the talent market. Finally, it is concluded that the decision tree algorithm can make second-child women better understand which jobs they are more suitable for,and help second-child women master professional skills more quickly, learn more new knowledge, get more work challenges at work, let employees combine their own values and realize their own meaning in life.
In order to solve the financial risk problem of small and medium-sized logistics enterprises, a log algorithm is used to measure the financial risk of logistics. Combined with the actual situation of logistics finance...
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In order to solve the financial risk problem of small and medium-sized logistics enterprises, a log algorithm is used to measure the financial risk of logistics. Combined with the actual situation of logistics finance, through the analysis of randomly selected logistics finance data, quantitatively extract important financial risk indicators, build a credit rating model based on decisiontree C4.5 algorithm, and conduct empirical research on the model. The empirical results show that the quantitative financial risk indicator extraction method combined with decisiontree C4.5 algorithm credit rating model has high accuracy in the evaluation of logistics finance risk. Therefore, based on the existing logistics financial risk assessment methods and models, the decisiontree analysis technology is applied to analyze and make decisions on the credit status of credit applicants, and select companies with good credit, so as to reduce the financial risk of logistics enterprises and improve the efficiency of financial risk management.
Aim: The aim of this study is to predict the temperature for the next three days using a machine learning algorithm and the objective of this research is to improve the accuracy of temperature prediction. Materials an...
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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 plannin...
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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 decisiontree 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%.
The optimization of multistage production processes is of significant importance in modern manufacturing. However, conventional flagship approaches often struggle in dealing with the uncertainty of defect rates in th...
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ISBN:
(纸本)9798400712647
The optimization of multistage production processes is of significant importance in modern manufacturing. However, conventional flagship approaches often struggle in dealing with the uncertainty of defect rates in the manufacturing stage. This paper proposes a new multistage production optimization model based on decision tree algorithms, which is intended to overcome the deficiencies of traditional methods in dealing with the uncertainty of defect rates. First, through accurate characterization of the parameters relevant to each stage, the construction of objective functions and the definition of constraints, a decisiontree-based optimization framework was constructed, and an optimal inspection and disassembly decision strategy was extracted. Then a decision-making model based on dynamic searching, sampling inspection and decisiontreealgorithms were created on the basis of these. By simulating the sampling inspection process, this model estimates the confidence interval of the defect rate, which makes the inspection and disassembly strategies more, refined. The computational results demonstrate that this novel decision-making method outperforms the traditional methods for maximizing profits and thus far, enterprises can have a more scientific and adjustable decision-making process. This research not only fills the gap in the existing methods methodologies for managing defect rate uncertainty but also provides practical and effective optimization solutions to industrial enterprises, thus has a significant academic and practical significance.
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 ...
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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 decisiontree. 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 decisiontree in a distributed environment, supplementing the distributed decision tree algorithm, adding a modification rule and modifying the generated nonmonotonic decisiontree 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
In active distribution networks, fault location and recovery are pivotal functions. However, it's worth noting that there exists a discernible gap between the current state of domestic distribution networks in Chi...
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ISBN:
(纸本)9798400708299
In active distribution networks, fault location and recovery are pivotal functions. However, it's worth noting that there exists a discernible gap between the current state of domestic distribution networks in China and those in more developed countries. Consequently, the development of self-healing control technology for distribution networks has emerged as a crucial strategy to address the existing shortcomings and emerging challenges within China's distribution network infrastructure. In light of these considerations, this article focuses on diagnosing active faults within active distribution networks using decision tree algorithms, as well as developing a repair system. The decisiontree serves as a predictive model, establishing a mapping relationship between object attributes and their corresponding values. The research outcomes demonstrate that the time consumption associated with the first two stages can be effectively disregarded, thereby enabling swift recovery for faults that solely necessitate the execution of these initial stages. The overall fault recovery process primarily relies on the third stage. For a system comprising 39 nodes, the average time consumption remains below 2 seconds, signifying a notably efficient recovery plan and execution speed. This study further emphasizes the real-time transmission of detection data from various positions within the power supply system to the central power control management organization. In the event of a fault occurrence, the decision tree algorithm plays a pivotal role in achieving precise fault localization. This, in turn, significantly enhances the efficiency of fault identification and positioning processes.
With the continuous expansion of the enrollment scale of colleges and universities, the management of colleges and universities is becoming more and more difficult, and the corresponding data accumulation is also more...
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ISBN:
(纸本)9781728186160
With the continuous expansion of the enrollment scale of colleges and universities, the management of colleges and universities is becoming more and more difficult, and the corresponding data accumulation is also more and more. The key to the development of colleges and universities is to analyze the factors affecting the cultivation of students and improve the methods of students' training. According to the teaching data of colleges and universities, we can use the data mining technology to transform the existing teacher management data into useful information, and through data analysis to explore the internal factors affecting students' academic performance, so as to provide theoretical guidance for improving teaching methods. The results show that in order to improve the level of education and training in Colleges and universities, it is necessary to analyze the teaching quality of financial management course according to the needs of teaching. In order to optimize the course testing link, improve teaching methods and do a good job in teaching evaluation. We can guide the adjustment of students' cultural theory and practice courses through the analysis of students' performance data, and determine a reasonable student training program. This paper proposes to use decision tree algorithm to generate effective information to help managers to make decisions and financial budget management, so as to carry out experiments.
Background and purpose: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm ...
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Background and purpose: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm with various functional and environmental variables to identify stroke patients with a high possibility of home discharge. The present study aimed to identify the factors, including functional and environmental factors, affecting home discharge after stroke inpatient rehabilitation using the machine learning method. Method: This was a cohort study on data from the maintained database of all patients with stroke who were admitted to the convalescence rehabilitation ward of our facility. In total, 1125 stroke patients were investigated. We developed three classification and regression tree (CART) models to identify the possibility of home discharge after inpatient rehabilitation. Results: Among three models, CART model incorporating basic information, functional factor, and environmental factor variables achieved the highest accuracy for identification of home discharge. This model identified FIM dressing of the upper body (score of <2 or >2) as the first single discriminator for home discharge. Performing house renovation was associated with a high possibility of home discharge even in patients with stroke who had a poor FIM score in the ability to dress the upper body (<2) at admission into the convalescence rehabilitation ward. Interestingly, many patients who performed house renovation have achieved home discharge regardless of the degree of lower limb paralysis. Conclusion: We identified the influential factors for realizing home discharge using the decision tree algorithm, including environmental factors, in patients with convalescent stroke.
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