Data Mining Optimization Software and Its Application in Financial Audit Data Analysis

Shan, Rui; Xiao, Xianfei; Che, Junwen; Du, Jingfang; Li, Yuwu

doi:10.1155/2022/6851616

Cited by 4 publications

(5 citation statements)

References 26 publications

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“…Where w indicates a vector representing the diameter of the circle the waterwheel plant will search for possible locations, R is an exponential variable with values in the range [0, 1], and ( ) If after three iterations the solution doesn't change, Eqn (11) can be used to modify the waterwheel's location.…”

Section: Waterwheel Plant Algorithm (Wwpa)mentioning

confidence: 99%

“…The article's financial analysis technology, which has particular theoretical and practical relevance and complies with both the evolving body of accounting knowledge and the real-world business requirements of organizations, serves as the foundation for the data mining platform [11,12]. Errors and delays resulting from mergers plague traditional financial analysis.…”

Section: Introductionmentioning

confidence: 99%

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Construction and Optimization of Financial Transformation Decision Model Based on Data Mining

Ruifen Zhao

2024

jes

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Real-time data processing and analysis is crucial in the financial markets since transactions in real time are directly linked to profit. Understanding the integration of finance and business allows the organization to enhance its core competitiveness by improving management and enabling it to lead business expansion more effectively. This manuscript proposes a Sparse Oblique Trees Algorithm (SOTA) optimized with the Waterwheel Plant Algorithm (WWPA) for the construction of financial transformation. The data are collected from multi-national organization dataset. Afterward, the data’s are fed to pre-processing. In pre-processing segment; it removes the noise and enhances the input data’s utilizing Data-Adaptive Gaussian Average Filtering (DAGAF). The outcome from the pre-processing data is transferred to the SOTA. The investment and dividend are successfully classified by using SOTA. The WWPA is used to optimize the weight parameter of SOTA. The proposed SOTA-WWPA is applied in python working platform. The proposed technique was computed by examining performance indicators like precision, F1-score, accuracy, sensitivity and recall. The proposed SOTA-WWPA technique yields improved results in terms of accuracy (16.65%, 18.85%, and 17.89%), sensitivity (16.34, 12.23%, and 18.54%), and precision (14.89%, 16.89%, and 18.23%). The proposed FT-SOTA-WWPA method is contrasted with the existing methods like FT-CNN, FT-MBLSTMNN, and FT-SVM models respectively.

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Section: Waterwheel Plant Algorithm (Wwpa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Construction and Optimization of Financial Transformation Decision Model Based on Data Mining

Ruifen Zhao

2024

jes

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“…Equation ( 11) is used in LOA design to forecast the lyrebird's journey toward the closest acceptable hiding site, hence determining a new location for every LOA member. If, in accordance with Equation (12), this new position enhances the objective function's value, it takes over the connected member's previous role.…”

Section: Step 3: Fitness Functionmentioning

confidence: 99%

“…This proactive approach helps safeguard against unexpected events, market fluctuations, and economic uncertainties that could negatively impact the financial position of a business [11]. Different types of financial risk can appear, including operational risk, credit risk, systemic, market, and liquidity risks [12]. Examples of market risk include fluctuations in stock prices, commodities prices, interest rates, and exchange rates [13].…”

Section: Introductionmentioning

confidence: 99%

Variational Neural Network Optimized with Lyrebird Optimization Algorithm for Analysis and Empirical Evidence of Using Data Mining in Financial Risk Prevention Research

Mengrui Bao

2024

jes

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In the contemporary landscape of global finance, the effective prevention and mitigation of financial risks have become pivotal for the stability, resilience, and sustainable growth of financial institutions and markets. As financial instruments and markets continue to evolve in complexity, the need for a thorough analysis of risk prevention strategies has become increasingly apparent. In this manuscript, Variational Neural Network (VNN) optimized with lyrebird optimization algorithm (VNN-LOA) is proposed. Initially data is taken from machinehack-financial risk prediction. Afterward the data is fed to Adaptive Variational Bayesian Filter (VBF) based pre-processing process. The pre-processing output is provided to the Variational Neural Network (VNN) to effectively classify financial risk prevention as either involving risk or no risk. The learnable parameters of the VNN are optimized using lyrebird optimization algorithm (LOA). The proposed method is implemented in MATLAB and the efficiency of the proposed method DM-FRP-VNN-COA is estimated with the help of several performances evaluating metrics like, accuracy, precision, recall, f1-score, sensitivity, specificity, computational time, and ROC are analyzed. The proposed DM-FRP-VNN-LOA method attains 38.88%, 35.75%, and 33.16% higher accuracy for risk classification; 34.31%, 38.47% and 37.23% higher accuracy for no risk classification; 22.63%, 33.27% and 21.49% high precision for risk classification; 22.63%, 33.27% and 21.49% high precision for no risk; 35.136%, 39.04% and 38.81% lower computation Time compared with the existing method like data mining using financial risk prevention based back propagation neural network (DM-FRP-BPNN), data mining using financial risk prevention based machine learning (DM-FRP-ML), and data mining using financial risk prevention based convolutional neural network (DM-FRP-CNN) respectively.

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“…Decision trees represent a supervised learning method and, as a data-driven modeling technique, have found extensive use in fields such as artificial intelligence, gaming, and finance for classification and prediction [19][20][21]. In the realm of architecture, decision trees prove effective in addressing complex issues related to architectural design and management, with a particular strength in predicting building energy usage to enhance intelligent building performance.…”

Section: Introductionmentioning

confidence: 99%

Research on the Decision-Making Method for the Passive Design Parameters of Zero Energy Houses in Severe Cold Regions Based on Decision Trees

Yao,

Chen,

Han

et al. 2024

Energies

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As the field of zero energy building design and research continues to progress, the use of data analysis methods is on the rise. These methods are applied to create assessment criteria, compare performance, and aid in design decision making. Decision trees, as a data-driven approach, offer interpretability and predictability, assisting designers in summarizing their design experience and serving as a foundation for design references. However, the current application of decision tree methods in the zero energy house sector primarily focuses on HVAC systems, lacking a comprehensive exploration from an architectural design perspective. Therefore, this study presents an empirical method for building and applying models based on decision trees, using zero energy house cases in severely cold regions of China as samples. Through an analysis of the interactions among various passive design parameters and the use of EnergyPlus for performance simulations, a decision tree model is established. This model aids in determining the recommended combinations of passive design parameters that meet the criteria of low energy consumption. Moreover, feature weighting highlights the most influential passive design parameters on building energy consumption, including the length of the architectural gestalt plane, the roof shape, and the ground thermal resistance. This research provides valuable methods and guidance for the design and construction of zero energy houses in severely cold regions of China.

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Data Mining Optimization Software and Its Application in Financial Audit Data Analysis

Cited by 4 publications

References 26 publications

Construction and Optimization of Financial Transformation Decision Model Based on Data Mining

Construction and Optimization of Financial Transformation Decision Model Based on Data Mining

Variational Neural Network Optimized with Lyrebird Optimization Algorithm for Analysis and Empirical Evidence of Using Data Mining in Financial Risk Prevention Research

Research on the Decision-Making Method for the Passive Design Parameters of Zero Energy Houses in Severe Cold Regions Based on Decision Trees

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