scholarly journals Analysis of Financial Statement and Prewarning of Audit Risks Based on Artificial Neural Network

2021 ◽  
Vol 15 ◽  
pp. 1015-1024
Author(s):  
Yanxia Zhang
Keyword(s):  
Neural Network ◽  
Evaluation Index System ◽  
Financial Statement ◽  
Analysis Framework ◽  
Scientific Evaluation ◽  
Audit Risk ◽  
The Core ◽  
Income Statement ◽  
Proposed Model ◽  
Key Steps

The marketization of social capital has resulted in frequent audit failures, and financial statement frauds. One of the key steps of auditing is the identification of material misstatement risk of financial statement. However, there is no unified analysis framework or quantitative method for identifying this risk. Therefore, this paper aims to analyze financial statement and prewarn audit risks in an accurate manner. Firstly, the items of financial statement were analyzed in three aspects of the target enterprise: balance statement, income statement, and cash flow statement. Next, the authors probed deep into the core indices of the post audit risk verification and evaluation of the business process, constructed a scientific evaluation index system for audit risks of financial statement, and quantified the 89 tertiary indices, 21 secondary indices, and 3 primary indices. After that, an audit risk prediction model for financial statement was established based on neural network. Experimental results show the effectiveness of the proposed model for audit risk prewarning, and applicable to other tasks of financial auditing.

scholarly journals A Deep Neural Network Based Model for a Kind of Magnetorheological Dampers

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1333 ◽  
Author(s):  
Carlos Duchanoy ◽  
Marco Moreno-Armendáriz ◽  
Juan Moreno-Torres ◽  
Carlos Cruz-Villar
Keyword(s):  
Neural Network ◽  
3D Printing ◽  
Deep Neural Network ◽  
Small Scale ◽  
Magnetorheological Dampers ◽  
Experimental Platform ◽  
The Core ◽  
Mr Fluids ◽  
Proposed Model ◽  
Load Conditions

In this paper, a deep neural network based model for a set of small-scale magnetorheological dampers (MRD) is developed where relevant parameters that have a physical meaning are inputs to the model. An experimental platform and a 3D-printing rapid prototyping facility provided a set of different conditions including MRD filled with two different MR fluids, which were used to train a Deep Neural Network (DNN), which is the core of the proposed model. Testing results indicate the model could forecast the hysteretic response of magnetorheological dampers for different load conditions and various physical configurations.

scholarly journals Behavioral Analysis of Neural Network Using Various Visualization Strategies

2021 ◽  
pp. 180-188
Author(s):  
Asma Ansari ◽  
Adiba Kalaniya ◽  
Shaziya Memon
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architecture ◽  
Black Box ◽  
Neural Network Architecture ◽  
The Core ◽  
Saliency Maps ◽  
Image Capturing ◽  
Visualization Techniques ◽  
Key Steps

Currently extensive researches are focusing on understanding Machine Learning models mainly Deep Learning ones because of their black-box nature. Convolutional neural network (CNN) architectures used in Deep Learning have made their way into computer vision. Saliency maps or attribution maps are mainly used to find the most important features which in turn help us predicting results in the model. In this paper we have worked on different visualization techniques like Gradient Class Activation Map (Grad-CAM), Grad-CAM++, Score-CAM, and Faster Score-CAM on various architectures like VGG16, ResNet-based architectures, and pre-trained models and further investigated their results. Three different datasets (Plant village, Internet augmented, Real-world augmented) have been used and experimented on. The core processes comprise of image capturing, study and implementation of image pre-processing, testing on different neural network architecture, and assessment of data visualization. All of the key steps required to implement the model are detailed throughout the document.

scholarly journals Financial Crisis Warning of Financial Robot Based on Artificial Intelligence

2020 ◽  
Vol 34 (5) ◽  
pp. 553-561
Author(s):  
Wenxia Li
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Financial Crisis ◽  
Financial Management ◽  
Evaluation Index System ◽  
Actual Application ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Artificial Neural Network Ann ◽  
Hierarchical Evaluation

Robotic process automation (RPA) financial robot provides a modern and intelligent tool for financial management, and financial business processing. Currently, more than 32% of financial applications are implemented by RPA financial robot. As an alternative of human in operation and judgment, the financial robot faces some inevitable risks in actual application. So far, there is a severe lack of theoretical or practical research into the risks or operational guarantees of RPA financial robot. To bridge the gap, this paper proposes a financial crisis warning model for financial robot based on artificial neural network (ANN), drawing on the merits of artificial intelligence (AI) like self-learning, self-adaptation, and self-adjustment. Specifically, a hierarchical evaluation index system (EIS) and the corresponding warning strategy were prepared for the financial crisis of RPA financial robot. Next, the financial crisis of RPA financial robot was evaluated both statically and dynamically. Then, antecedent and subsequent networks were merged into a fuzzy neural network (FNN) for predicting financial crisis of RPA financial robot. The proposed model was proved effective and accurate through experiments.

Managing Pro Forma Stock Option Expense under SFAS No. 123

2003 ◽  
Vol 17 (1) ◽  
pp. 31-45 ◽  
Author(s):  
Steven Balsam ◽  
Haim A. Mozes ◽  
Harry A. Newman
Keyword(s):  
Small Firms ◽  
Stock Option ◽  
Financial Accounting ◽  
Financial Statement ◽  
First Year ◽  
Parameter Estimates ◽  
Option Value ◽  
Income Statement ◽  
Options Value ◽  
Pro Forma

We investigate whether footnote disclosures under Statement of Financial Accounting Standards (SFAS) No. 123 are managed in 1996, the first year that the disclosure was required. The 1996 phase-in of SFAS No. 123 provided firms with a unique opportunity to manipulate the pro forma disclosure in the initial years. SFAS No. 123 allows firms discretion in estimating the value of their stock option grants and in allocating that value across accounting periods. Although we find little evidence that firms manage the estimated value of their option grants, we find that firm-specific incentives affect how that value is allocated. Specifically, firms that provide high levels of either CEO compensation or stock option compensation relative to performance allocate a smaller proportion of the options' value to the 1996 pro forma expense, apparently to reduce criticism of that compensation. Small firms and firms that recently went public also allocate a smaller proportion of option value to the 1996 pro forma expense, apparently to increase perceptions of their profitability. We conjecture that firms were less likely to manage the value of the options granted than the allocation of that value in 1996 because the parameter estimates underlying the reported option value must be disclosed in the footnote, whereas the inputs to the allocation computation are not disclosed. These results, which suggest that firms manipulated pro forma stock option expense when their estimate choices cannot be observed, have implications for both standard setters and financial statement users. In particular, the FASB's current deliberations on the transition from footnote disclosure to income statement recognition for stock options should consider additional disclosures to minimize unobservable choices. More generally, the FASB may reduce potential manipulation by requiring expanded disclosures about the choices used in computing both pro forma and reported numbers.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

scholarly journals Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 920
Author(s):  
Liesle Caballero ◽  
Álvaro Perafan ◽  
Martha Rinaldy ◽  
Winston Percybrooks
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Energy Budget ◽  
Dynamic Models ◽  
Pearson Correlation ◽  
Experimental Conditions ◽  
Grid Map ◽  
Proposed Model ◽  
Exploration Task

This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.

scholarly journals Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 651
Author(s):  
Shengyi Zhao ◽  
Yun Peng ◽  
Jizhan Liu ◽  
Shuo Wu
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Model Comparison ◽  
Research Direction ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Identification Accuracy ◽  
Main Research ◽  
Proposed Model ◽  
Complex Features

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

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