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The application of cloud accounting in enterprise financial decision making in the era of big data

APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年

作者： Zhang, Wenting Hunan Polytech Environm & Biol Coll Business Hengyang 421005 Hunan Peoples R China

In order to be able to better understand the financial situation of enterprises and ensure the maximum economic benefits, the analysis of enterprise financial decisions based on the background of big data cloud accounting is proposed. Establish an enterprise cloud accounting financial decision support module that relies on big data to optimize data collection and meet the data requirements for management decision making and operation of cloud accounting financial decision support system. We provide objective and rigorous financial analysis and implement the financial decisions proposed by the management based on the most satisfying results plan in line with the development strategy of the company. The optimal classification hyperplane is constructed in the vector space using support vector machines, and the Lagrange function is introduced to solve the constraint maximization, which changes the original space mapping to seek the optimal classification surface in the vector space of higher dimensions. The SVM classifier is trained by introducing relaxation variables that solve linearly indistinguishable problems and building labeled training samples to ensure that the risk analysis requirements are met. Combined with the decision tree algorithm to predict the number of information bits, calculate the information entropy to obtain the information gain value to compare one by one, and finally complete the financial decision analysis. The analysis results show that the financial decision model is constructed in the context of big data cloud accounting, and the algorithm of this paper is used to select the best enterprise decision solution, which has an economic growth value of 22,000,000 RMB and ensures the maximum economic benefits for the enterprise.

关键词： Big data era Cloud accounting Enterprise financial decision making Support vector machine decision tree algorithm

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Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Methodological Framework

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BUILDINGS 2023年第2期13卷 497-497页

作者： Almobarek, Malek Mendibil, Kepa Alrashdan, Abdalla Univ Strathclyde Fac Engn Dept Design Mfg & Engn Management Glasgow G1 1XQ Scotland Alfaisal Univ Coll Engn Ind Engn Dept Riyadh 50927 Saudi Arabia

Predictive maintenance is considered as one of the most important strategies for managing the utility systems of commercial buildings. This research focused on chilled water system (CWS) components and proposed a methodological framework to build a comprehensive predictive maintenance program in line with Industry 4.0/Quality 4.0 (PdM 4.0). This research followed a systematic literature review (SLR) study that addressed two research questions about the mechanism for handling CWS faults, as well as fault prediction methods. This research rectified the associated research gaps found in the SLR study, which were related to three points;namely fault handling, fault frequencies, and fault solutions. A framework was built based on the outcome of an industry survey study and contained three parts: setup, machine learning, and quality control. The first part explained the three arrangements required for preparing the framework. The second part proposed a decision tree (DT) model to predict CWS faults and listed the steps for building and training the model. In this part, two DT algorithms were proposed, C4.5 and CART. The last part, quality control, suggested managerial steps for controlling the maintenance program. The framework was implemented in a university, with encouraging outcomes, as the prediction accuracy of the presented prediction model was more than 98% for each CWS component. The DT model improved the fault prediction by more than 20% in all CWS components when compared to the existing control system at the university.

关键词： predictive maintenance Industry 4 0 Quality 4 decision tree algorithm chilled water system HVAC commercial buildings industrial engineering engineering management

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Estimation of wavy honeycombs' compression performance via a machine learning algorithm

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LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2021年第8期18卷

作者： Solak, Alparslan Temiztas, Birgul Ascioglu Bolat, Berna Yildiz Tech Univ Dept Mech Engn TR-34349 Istanbul Turkey

In this study, the wavy honeycomb's initial peak crushing force (IPCF) and energy absorption (EA) were estimated using the decision tree algorithm. First, using experimental results, Ls-Dyna models of honeycombs were verified. In this way, the stress-strain curves and shapes were compatible. Secondly, the effect of parameters was examined. Waves contribute significantly to values. In particular, for honeycombs with the same geometric properties, when the wavenumber is 3, the IPCF and specific energy absorption (SEA) values increase by 121.59% and 75.08%, respectively. In addition, when the wave amplitude is 0.15mm, IPCF and SEA increase by 60.89% and 71.3%, respectively. Afterward, using the full factorial, a data set with various parameter values was prepared. The parameters (inputs) and values (outputs) in the data set were used to train and verify the decision tree algorithm using Python. Finally, new data was introduced into the algorithm, and values were estimated. Errors ranged from 0.17% to 14.65% between Ls-Dyna and the algorithm results. These findings show that machine learning is suitable for wavy honeycombs.

关键词： decision tree algorithm Compression behavior Ls-Dyna Python Wavy honeycomb

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Research on sensitivity and strategy service of customer electricity charge based on big data

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Procedia CIRP 2019年 83卷 636-641页

作者： Xiaofeng Chen Yadi Zhao Feng Zhu Xiaoming Qi BEIJING SGITG ACCENTURE INFORMATION TECHNOLOGY CENTER CO. LTD. Beijing 100032 China

Taking the sensitivity of customer tariff as the starting point, we proposed an algorithm for evaluating the sensitivity of customer tariff based on decision tree, and constructed a tariff sensitive model for the selected value customers. By using entropy method and principal component analysis, the value customers were screened from three dimensions: economic contribution, development potential and credit contribution. decision tree algorithm was used to determine whether electricity is sensitive to customers, at the same time output indicators importance ranking. This method can support the development of a product service system based on data and strategy support for the relevant departments of power supply enterprises, in order to carry out accurate marketing, reduce the risk of electricity recovery and differentiated services, improve the overall customer satisfaction, and enhance the perception of customers.

关键词： Value customer electricity charge entropy method decision tree algorithm precision marketing service system

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A Capacity Assessment Method on Urban Expressway after Traffic Incident

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Procedia - Social and Behavioral Sciences 2013年 96卷 1921-1928页

作者： Chen Hong You Yue-sheng Zhang Bin School of Highway Chang’an University Xi’an 710064 China

To explore the urban expressway's capacity after a traffic incident, a capacity assessment approach based on VISSIM has been proposed, in which we disassemble the evolutionary processes of local traffic state after traffic incident and analyzed influence factors. Firstly, the ID algorithm of the decision tree was introduced, and accident delay time was solved by using information gain value. Then, according to the Traffic Flow Wave Theory, we determine the congestion dissipation time. Secondly, taking accident delay time as observation period, the maximum vehicle volume through the accident roads was considered as its capacity. Finally, it was simulated and calculated by VISSIM. The results show that, the accident delay time of vehicle anchor and the crowded dissipation time are the minimum, but the residual capacity of the accident roads was the maximum, the portion was 69.926%; the accident delay time of vehicle collision and the crowded dissipation time were the maximum, but the residual capacity of the accident roads was the minimum, the proportion was 49.963%; the residual capacity proportion of the vehicle roll-over, fire and rear-end were 56.87%, 61.611%, 59.796% respectively.

关键词： urban expressway decision tree algorithm accident delay time crowded dissipate time VISSIM

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Machine Learning for Return-to-Work Outcomes in Lower Back Conditions

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Procedia Computer Science 2024年 239卷 1887-1894页

作者： Emna Ammar Elhajamor Sonia Ghannouchi Slaheddine Ghannouchi RIADI Laboratory-ENSI Manouba University Campus Universitaire de la Manouba Manouba Tunisia Faculty of Medicine of Sousse University of Sousse Sousse Tunisia

This study investigates the impact of workplace accidents or illnesses leading to lower back conditions on an individual’s ability to return to work. Using medical data analysis, it aims to identify predictive factors, including rest justification and invalidity. The research utilizes a decision tree algorithm to predict return-to-work ability. Significant predictors include age, height, general health status, civil status, scarring, number of children, body mass index, weight, toe walking, limping, respiratory issues, marriage, and professional sector. The decision tree model demonstrates high efficiency and accuracy, providing valuable insights for evidence-based strategies to improve return-to-work outcomes in lower back condition cases.

关键词： Lower back conditions Return to work medical data predictive factors decision tree algorithm Lower back conditions decision tree algorithm

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An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis

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BMC MEDICAL INFORMATICS AND decision MAKING 2020年第1期20卷 308-308页

作者： Hu, Yusong Zhao, Yantao Liu, Jihong Pang, Jin Zhang, Chen Li, Peizhe Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China

Background Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods This paper proposed a high distinguishable frequency feature-the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features-the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity

关键词： Atrial fibrillation Frequency-domain feature Time– frequency analysis ECG decision tree algorithm

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Design and research of MOOC teaching system based on TG-C4.5 algorithm

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SYSTEMS AND SOFT COMPUTING 2023年 5卷

作者： Chen, Xinxin Qiqihar Med Univ Foreign Language Dept Qiqihar 161000 Peoples R China

The emergence of Internet information technology has led to the development of MOOC-based online teaching methods. The study uses the traditional C4.5 algorithm for data mining to improve teaching quality and simplifies and quantifies it with the Taylor series and GINI index. The study also considers the uncertainty of data changes and the characteristics of MOOC teaching to design a parallel processing system of the HD-TG-C4.5 algorithm under the framework of the Hadoop platform. The experimental results show that the minimum data classification error of the algorithm is 2%, and the maximum recommendation accuracy of teaching resources is 92.6%. Moreover, the response time and resource search time of this algorithm system are significantly better than traditional algorithms in terms of system debugging. The average login response time is less than 0.87 s, and the success rate of system debugging reaches 90%. The probability value of students mastering teaching resource knowledge points is also above 0.7. The MOOC teaching system based on TG-C4.5 algorithm can effectively mine learner behavior data and reduce the complexity and consumption of C4.5 algorithm. The MOOC teaching system based on TG algorithm can provide technical support for the decision-making information of teaching participants and provide early warning information for predicting learning behavior.

关键词： decision tree algorithm Large scale open online courses Teaching system Taylor series Gini coefficient Distributed computing framework

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Grammatical evolution decision trees for detecting gene-gene interactions

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BIODATA MINING 2010年第1期3卷 8-8页

作者： Motsinger-Reif, Alison A. Deodhar, Sushamna Winham, Stacey J. Hardison, Nicholas E. N Carolina State Univ Bioinformat Res Ctr Raleigh NC 27695 USA N Carolina State Univ Dept Stat Raleigh NC 27695 USA N Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA

Background: A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such epistatic models present an important analytical challenge, requiring that methods perform not only statistical modeling, but also variable selection to generate testable genetic model hypotheses. This challenge is amplified by recent advances in genotyping technology, as the number of potential predictor variables is rapidly increasing. Methods: decision trees are a highly successful, easily interpretable data-mining method that are typically optimized with a hierarchical model building approach, which limits their potential to identify interacting effects. To overcome this limitation, we utilize evolutionary computation, specifically grammatical evolution, to build decision trees to detect and model gene-gene interactions. In the current study, we introduce the Grammatical Evolution decision trees (GEDT) method and software and evaluate this approach on simulated data representing gene-gene interaction models of a range of effect sizes. We compare the performance of the method to a traditional decision tree algorithm and a random search approach and demonstrate the improved performance of the method to detect purely epistatic interactions. Results: The results of our simulations demonstrate that GEDT has high power to detect even very moderate genetic risk models. GEDT has high power to detect interactions with and without main effects. Conclusions: GEDT, while still in its initial stages of development, is a promising new approach for identifying gene-gene interactions in genetic association studies.

关键词： Genetic Association Study Multifactor Dimensionality Reduction decision tree algorithm Grammatical Evolution Epistatic Model

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Application of artificial intelligence for tree risk assessment optimization in Itanhaem-Sao Paulo, Brazil

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URBAN FORESTRY & URBAN GREENING 2023年 81卷

作者： Maria, Tamara Ribeiro Botelho de Carvalho Biondi, Daniela Behling, Alexandre Reis, Allan Rodrigo Nunho dos Zamproni, Kendra Ho, Tatiane Lima Univ Fed Parana 58 Benedito Souza St apt 1 BR-11740000 Itanhaem Sao Paulo Brazil Univ Fed Parana Dept Forestry & Wood Sci 632Prefeito Lothario Meissner Ave BR-80210170 Curitiba PR Brazil Univ Fed Parana 632 Prefeito Lothario Meissner Ave BR-80210170 Curitiba PR Brazil

tree risk assessment consists of the identification of a set of defects that may affect the stability of the tree, leading to a possible collapse or failure, either of the whole plant or a part of it;and also, the identification of the targets to be reached and the side effects caused by any eventual failure. Although this is a necessary practice, the large number of variables involved in this evaluation makes the analysis time-consuming. Thus, this research aimed to optimize the tree risk assessment by creating a new protocol with the three visual assessment methods common variables and generating a new protocol applied to trees of species frequently used in urban affores-tation worldwide: Terminalia catappa, Ficus Benjamin and Delonix regia. Altogether 36 variables were used for tree risk visual assessment applied in the evaluation of 230 trees located in the urban forest in Itanhae ' m -Sa similar to o Paulo -Brazil. The data collection was carried out using a smartphone and a data spreadsheet created in ODK collect app, facilitating data storage and processing. To identify the variables with the greatest possibility to determine the risk of falling, artificial intelligence was used through the decision tree algorithm (C4.5) in the WEKA software. The results showed that, from the 36 variables evaluated, 14 were enough to determine the risk of tree falling, with 73% hit rate in the tree risk classification. It is concluded that the use of artificial intelligence was essential in detecting tree problems in order to redirect management practices.

关键词： decision tree algorithm tree risk management Urban forests Urban trees

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