Dataset Information

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects.

ABSTRACT: To determine the influence of the weight of the economic effectiveness evaluation criteria of the major investments of listed enterprises, and provide new management ideas for the development of the follow-up enterprises, firstly, the financial benefit evaluation system of investment projects is analyzed and constructed, and the specific evaluation process is analyzed. Then, on this basis, the evaluation index is refined; the basic structure of BP neural network (BPNN) is introduced, and genetic algorithm is used to improve BP neural network. The cost-benefit analysis model is constructed based on the improved BPNN. The listed company A is taken as an example to analyze its development data in recent years, and then the data of 10 listed companies are taken as the research object. Matlab simulation software is used to train and verify the improved BPNN model, analyze and predict the weight value of the financial benefit index of the investment projects of these 10 companies, and then determine the index to improve the financial benefit of the investment projects. Under the analysis of the development data of listed company A in the past 10 years, it is found that the indicators of the listed company's profitability per share, debt risk operation ability, development, and growth ability in the past 10 years are in relatively stable state. The principal component analysis of its 20 secondary sub-indexes is conducted based on the four primary indicators: profitability, debt risk, operational capacity, and development and growth. A total of eight principal components including return on equity (ROE), return on assets (ROA), (total asset turnover) TATO, turnover of account receivable (AR), asset-liability ratio, interest protection multiple, income growth rate, and year-on-year rate of increase for complete assets are extracted. The average error between the final output value, the actual value, and the expected value is 0.0304 and 0.0169, respectively. The weight coefficient of the monetary benefits evaluation indicator of investment items is calculated, and the computed results show that year-on-year rate of increase for complete assets, TOTA, ROA, turnover of total capital, and ROE are important indexes in the financial benefit evaluation of investment projects. It indicates that to improve the financial benefit of investment projects of listed enterprises, it is necessary to enhance the year-over-year growth degree of total properties and ROA.

SUBMITTER: Zhong Y

PROVIDER: S-EPMC7058345 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects.

Zhong Youwen Y Wu Xiaoling X

PloS one 20200305 3

To determine the influence of the weight of the economic effectiveness evaluation criteria of the major investments of listed enterprises, and provide new management ideas for the development of the follow-up enterprises, firstly, the financial benefit evaluation system of investment projects is analyzed and constructed, and the specific evaluation process is analyzed. Then, on this basis, the evaluation index is refined; the basic structure of BP neural network (BPNN) is introduced, and genetic ...[more]

PMID: 32134951

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Project description:ObjectiveTo reduce the danger for firefighters and ensure the safety of firefighters as much as possible, based on the back propagation neural network (BPNN) the fire sensor multi-sensor information fusion detection system is investigated.MethodAccording to previous studies, the information sources and information processing methods for the design of this study are first explained. Then, the basic structure and flowchart of the research object in this study are designed. Based on the structure diagram and flowchart, the BPNN is selected to fuse the feature layers in this study, and the fuzzy control is selected to fuse the decision layers in this study. The multi-sensor information fusion detection system collects information for the sensors first, processes the collected information, and sends it to the processor of the robot. The processor analyzes and processes the received signal, and transmits the obtained information to the control terminal through the wireless communication system.ResultsThrough the tests in this study, it is found that when the number of hidden layer nodes of the BPNN is 7, the optimal training result is obtained. On this basis, the test of BPNN in this study is performed. The test results show that after 127 iterations, the error of the BPNN reaches the lowest target value, indicating that the BPNN achieves an excellent level of accuracy. The trained BPNN has a running time of 0.0276 s and a mean square error of 0.0013. The smaller the mean square error value is, the higher the accuracy of the BPNN is, which shows that the BPNN meets the high precision requirements of this study.ConclusionThe research on the multi-sensor information fusion detection system of fire robots in this study can provide theoretical support for the research on forest fire detection in China. Since the proposed BPNN-based robot is applied to the inspection and processing of forest remaining fire, the results are applicable to the forests of various countries, with a wide range of applications.

Project description:Background The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group. Methods Adults aged ≥ 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid (99mTc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR). Results The study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 ± 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m2, which was smaller than that of LMR (4.59 ml/min/1.73 m2; p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m2; p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m2; p = 0.31), EKFC (-1.41 ml/min/1.73 m2; p = 0.26), BIS1 (0.64 ml/min/1.73 m2; p = 0.99), and MDRD (1.11 ml/min/1.73 m2; p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m2) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m2, the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m2). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55−2.78] and 0.24 [-2.58−1.61], respectively), smaller than any other equation. Conclusions The novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use. Supplementary Information The online version contains supplementary material available at 10.1186/s12877-023-04027-5. Highlights Accurate estimation of kidney function in older adults is an important step to management of chronic kidney disease. BPNN tool highly improved precision and accuracy, although bias remains suboptimal. BPNN tool can be recommended for clinical use routinely, especially for patients with GFR < 45 ml/min/1.73 m2. Supplementary Information The online version contains supplementary material available at 10.1186/s12877-023-04027-5.

Dataset Information

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects.

Publications

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects.

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