A Novel Financial Risk Early Warning Strategy Based on Decision Tree Algorithm

This paper requires a lot of assumptions for financial risk, which cannot use all of the data and is often limited to financial data; and in the past, most early warning models for financial crises did not, so they could not track the fluctuation and change trend of financial indicators. A decision tree algorithm model is used to propose a financial risk early warning method. Enterprises have suffered as a result of the financial crisis, and some have even gone bankrupt. Any financial crisis, on the other hand, has a gradual and deteriorating course. As a result, it is critical to track and monitor the company's financial operations so that early warning signs of a financial crisis can be identified and effective measures taken to mitigate the company’s business risk. This paper establishes a financial early warning system to predict financial operations using the decision tree algorithm in big data. Operators can take measures to improve their enterprise’s operation and prevent the failure of the embryonic stage of the financial crisis, to avoid greater losses after discovering the bud of the enterprise’s financial crisis, and to avoid greater losses after discovering the bud of the enterprise’s financial crisis. This prediction can be used by banks and other financial institutions to help them make loan decisions and keep track of their loans. Relevant businesses can use this signal to make credit decisions and effectively manage accounts receivable; CPAs can use this early warning information to determine their audit procedures, assess the enterprise's prospects, and reduce audit risk. As a result, the principle of steady operation should guide modern enterprise management. Prepare emergency plans in advance of a business risk or financial crisis to resolve the financial crisis and reduce the financial risk.

Research on Evaluation Model of Hospital Informatization Level Based on Decision Tree Algorithm

In order to improve the weight calculation accuracy of hospital informatization level evaluation and shorten the evaluation time, a research method of hospital informatization level evaluation model based on the decision tree algorithm is proposed. Using the decision tree algorithm combining fuzzy theory and ID3, the decision tree is constructed to analyze the hospital information data. By means of questionnaire survey, expert experience, mathematical statistics, and in-depth interview, information facilities construction, information resources construction, information scientific research application, management information, and information guarantee are selected as the nodes of the decision tree to evaluate the hospital information level. Construct the structural equation model, standardize the data, extract the weight of each evaluation index, and complete the evaluation of hospital informatization level. The experimental results show that the weight calculation results of this method are basically consistent with the actual results, and the evaluation efficiency is improved.

OMNeT++ Framework for Simulation of Centralized and Distributed Algorithms in Multi-Hop Networks

Theoretical applications and practical network algorithms are not very cost-effective, and most of the algorithms in the commercial market are implemented in the cutting-edge devices. Open-source network simulators have gained importance in recent years due to the necessity to implement network algorithms in more realistic scenarios with reasonable costs, especially for educational purposes and scientific researches. Although there have been various simulation tools, NS2 and NS3, OMNeT++ is more suitable to demonstrate network algorithms because it is convenient for the model establishment, modularization, expandability, etc. OMNeT++ network simulator is selected as a testbed in order to verify the correctness of the network algorithms. The study focuses on the algorithms based on centralized and distributed approaches for multi-hop networks in OMNeT++. Two network algorithms, the shortest path algorithm and flooding-based asynchronous spanning tree algorithm, were examined in OMNeT++. The implementation, analysis, and visualization of these algorithms have also been addressed.

Breast cancer diagnosis using feature extraction and boosted C5.0 decision tree algorithm with penalty factor

<abstract><p>To overcome the two class imbalance problem among breast cancer diagnosis, a hybrid method by combining principal component analysis (PCA) and boosted C5.0 decision tree algorithm with penalty factor is proposed to address this issue. PCA is used to reduce the dimension of feature subset. The boosted C5.0 decision tree algorithm is utilized as an ensemble classifier for classification. Penalty factor is used to optimize the classification result. To demonstrate the efficiency of the proposed method, it is implemented on biased-representative breast cancer datasets from the University of California Irvine(UCI) machine learning repository. Given the experimental results and further analysis, our proposal is a promising method for breast cancer and can be used as an alternative method in class imbalance learning. Indeed, we observe that the feature extraction process has helped us improve diagnostic accuracy. We also demonstrate that the extracted features considering breast cancer issues are essential to high diagnostic accuracy.</p></abstract>

Optimization of Management Mode of Small- and Medium-Sized Enterprises Based on Decision Tree Model

Decision tree algorithm is a common classification algorithm in data mining technology, and its results are usually expressed in the form of if-then rules. The C4.5 algorithm is one of the decision tree algorithms, which has the advantages of easy to understand and high accuracy, and the concept of information gain rate is added compared with its predecessor ID3 algorithm. After theoretical analysis, C4.5 algorithm is chosen to analyze the performance appraisal results, and the decision tree for performance appraisal is generated by collecting data, data preprocessing, calculating information gain rate, determining splitting attributes, and postpruning. The system is developed in B/S architecture, and an R&D project management system and platform that can realize performance assessment analysis are built by means of visualization tools, decision tree algorithm, and dynamic web pages. The system includes information storage, task management, report generation, role authority control, information visualization, and other management information system functional modules. They can realize the project management functions such as project establishment and management, task flow, employee information filling and management, performance assessment system establishment, report generation of various dimensions, management cockpit construction. With decision tree algorithm as the core technology, the system obtains scientific and reliable project management information with high accuracy and realizes data visualization, which can assist enterprises to establish a good management system in the era of big data.

Safety performance prediction and modification strategies for construction projects via machine learning techniques

PurposeUnsafe behavior accounts for a major part of high accident rates in construction projects. The awareness of unsafe circumstances can help modify unsafe behaviors. To improve awareness in project teams, the present study proposes a framework for predicting safety performance before the implementation of projects.Design/methodology/approachThe machine learning approach was adopted in this work. The proposed framework consists of two major phases: (1) data collection and (2) model development. The first phase involved several steps, including the identification of safety performance criteria, using a questionnaire to collect data, and converting the data into useful information. The second phase, on the other hand, included the use of the decision tree algorithm coupled with the k-Nearest Neighbors algorithm as the predictive tool along with the proposing modification strategies.FindingsA total of nine safety performance criteria were identified. The results showed that safety employees, training, rule adherence and management commitment were key criteria for safety performance prediction. It was also found that the decision tree algorithm is capable of predicting safety performance.Originality/valueThe main novelty of the present study is developing an integrated model to propose strategies for the safety enhancement of projects in the case of incorrect predictions.