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.

Surface Roughness Prediction Model of Cylinder Grinding

2012 ◽  
Vol 157-158 ◽  
pp. 123-126 ◽  
Author(s):  
Ning Ding ◽  
Yi Chen Wang ◽  
Ding Tong Zhang ◽  
Yu Xiang Shi ◽  
Jian Shi
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Prediction Model ◽  
Fuzzy Neural Network ◽  
Vibration Signal ◽  
Feed Speed ◽  
Vibration Data ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Roughness Prediction

Based on the theory of roughness during cylinder grinding and the theory of fuzzy-neural network, a surface roughness intelligent prediction model is developed in this paper. The feed, speed, and the vibration data are the inputs for the model. An accelerometer is used to gather the vibration signal in real time. The model is used in the grinding experiment, and verifies the feasibility of the proposed model.

scholarly journals Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 596 ◽  
Author(s):  
Nur Ezlin Zamri ◽  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Alyaa Alway ◽  
Siti Zulaikha Mohd Jamaludin ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Science ◽  
Clonal Selection ◽  
Data Extraction ◽  
Hopfield Neural Network ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Proposed Model ◽  
Artificial Neural Network Ann ◽  
Logical Representation

Amazon.com Inc. seeks alternative ways to improve manual transactions system of granting employees resources access in the field of data science. The work constructs a modified Artificial Neural Network (ANN) by incorporating a Discrete Hopfield Neural Network (DHNN) and Clonal Selection Algorithm (CSA) with 3-Satisfiability (3-SAT) logic to initiate an Artificial Intelligence (AI) model that executes optimization tasks for industrial data. The selection of 3-SAT logic is vital in data mining to represent entries of Amazon Employees Resources Access (AERA) via information theory. The proposed model employs CSA to improve the learning phase of DHNN by capitalizing features of CSA such as hypermutation and cloning process. This resulting the formation of the proposed model, as an alternative machine learning model to identify factors that should be prioritized in the approval of employees resources applications. Subsequently, reverse analysis method (SATRA) is integrated into our proposed model to extract the relationship of AERA entries based on logical representation. The study will be presented by implementing simulated, benchmark and AERA data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperformed the other existing methods in AERA data extraction.

scholarly journals Neural Network Characterization of Reflectarray Antennas

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Angelo Freni ◽  
Marco Mussetta ◽  
Paola Pirinoli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Procedure ◽  
Antenna Design ◽  
Ann Model ◽  
Design And Optimization ◽  
Full Wave ◽  
Proposed Model ◽  
Artificial Neural Network Ann

An efficient artificial neural network (ANN) approach for the modeling of reflectarray elementary components is introduced to improve the numerical efficiency of the different phases of the antenna design and optimization procedure, without loss in accuracy. The comparison between the results of the analysis of the entire reflectarray designed using the simplified ANN model or adopting a full-wave characterization of the unit cell finally proves the effectiveness of the proposed model.

scholarly journals Pattern Recognition using a Neural Network on a Microcontroller with I2C Ultrasonic Sensors

2019 ◽  
Vol 3 (1) ◽  
pp. 9-19 ◽  
Author(s):  
Fazal Noor
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Integrated Circuit ◽  
High Accuracy ◽  
Ultrasonic Sensors ◽  
Motion Patterns ◽  
The Neural Network ◽  
Proposed Model ◽  
Neural Network Algorithm

Ultrasonic sensors have been used in a variety of applications to measure ranges to objects. Hand gestures via ultrasonic sensors form unique motion patterns for controls. In this research, patterns formed by placing a set of objects in a grid of cells are used for control purposes. A neural network algorithm is implemented on a microcontroller which takes in range signals as inputs read from ultrasonic sensors and classifies them in one of four classes. The neural network is then trained to classify patterns based on objects’ locations in real-time. The testing of the neural network for pattern recognition is performed on a testbed consisting of Inter-Integrated Circuit (I2C) ultrasonic sensors and a microcontroller. The performance of the proposed model is presented and it is observed the model is highly scalable, accurate, robust and reliable for applications requiring high accuracy such as in robotics and artificial intelligence.

scholarly journals Evaluation of the Motivation Status of Enterprises and Higher Vocational Schools Participating in Modern Apprenticeship and Its Key Influencing Factors Based on Artificial Neural Network

2021 ◽  
Vol 16 (08) ◽  
pp. 188
Author(s):  
Shu Ji ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Evaluation Model ◽  
Intelligent Manufacturing ◽  
Evaluation Index System ◽  
Vocational Schools ◽  
Participation Motivation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

During the reform of talent training mode, higher vocational schools must promote and apply modern apprenticeship to meet the needs of intelligent manufacturing. However, most enterprises and schools differ greatly in the participation enthusiasm and implementation motivation for modern apprenticeship. To enhance the participation motivation, it is critical to correctly evaluate the motivation status of enterprises and schools participating in modern apprenticeship, and analyze its key influencing factors. For this reason, this paper employs the Artificial Neural Network (ANN) to evaluate such motivation status. Firstly, a Modern Apprenticeship Motivation Status (MAMS) evaluation model was established, along with its evaluation index system (EIS). Then, differences in the motivation status were compared from seven aspects. After that, an improved backpropagation (BP) neural network was built to construct and optimize the MAMS prediction model. Finally, the constructed model was proved valid through experiments.

scholarly journals Fuzzy Neural Network (FNN) Pada Proses Identifikasi Penyakit ISPA

2021 ◽  
Vol 5 (3) ◽  
pp. 870
Author(s):  
Dhio Saputra ◽  
Musli Yanto ◽  
Wifra Safitri ◽  
Liga Mayola
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Fuzzy Neural Network ◽  
The Elderly ◽  
Diagnostic Process ◽  
Average Error ◽  
Identification Process ◽  
Runny Nose ◽  
Fuzzy Neural ◽  
Artificial Neural Network Ann

ISPA is a disease that can affect anyone from children, adolescents, adults, and even the elderly. The causes experienced by sufferers of this disease are quite simple, such as fever, runny nose, and cough. The discussion in this paper describes the process of ISPA disease identification by developing a Fuzzy Neural Network (FNN) model. The process will be optimized using Fuzzy Logic to form rules for the diagnostic process, then proceed with an Artificial Neural Network (ANN). This model can maximize the performance of ANN in the identification process so that the output given is quite precise and accurate. The results provided by Fuzzy Logic can describe the clarity of the rules in diagnosis by presenting several rules (rules) that are presented from the Fuzzyfication process to the Defuzzyfication process. The output obtained from the ANN process also shows quite perfect results with an average error value based on MSE of 0.00912 and accuracy value of 91.96%. With these results, it can be stated that the FNN model can be used in the ISPA diagnosis process so that the presentation of this paper aims to provide an alternative in the identification process

scholarly journals Space-Time Effect Prediction of Blasting Vibration Based on Intelligent Automatic Blasting Vibration Monitoring System

2021 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Fan Chen ◽  
Gengsheng He ◽  
Shun Dong ◽  
Shunjun Zhao ◽  
Lin Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Rough Set ◽  
Significant Influence ◽  
Vibration Frequency ◽  
Fuzzy Neural Network ◽  
Vibration Velocity ◽  
Blasting Vibration ◽  
High Rise ◽  
Fuzzy Neural

The vibration produced by blasting excavation in urban underground engineering has a significant influence on the surrounding environment, and the strength of vibration intensity involves many influencing factors. In order to predict the space-time effects of blasting vibration more accurately, an automatic intelligent monitoring system is constructed based on the rough set fuzzy neural network blasting vibration characteristic parameter prediction model and the network blasting vibrator (TC-6850). By setting up the regional monitoring network of monitoring points, the obtained monitoring data are analyzed. An artificial intelligence model is used to predict the influence of stratum condition, excavation hole, and high-rise building on blasting vibration velocity and frequency propagation. The results show that the artificial intelligence prediction model based on a rough set fuzzy neural network can accurately reflect the formation attenuation effect, hollow effect, and building amplification effect of blasting vibration by effectively fuzzing and standardizing the influencing factors. The propagation of blasting vibration in a soil–rock composite stratum is closely related to the surrounding rock conditions with a noticeable elastic modulus effect. The hollow effect is regional, which has a significant influence on the surrounding ground and buildings. Besides, the blasting vibration of the excavated area is stronger than that of the unexcavated area. The propagation of blasting vibration on high-rise buildings was complicated, of which the peak vibration velocity is maximum at the lower level of the building and decreased with the rise of the floor gradually. The whip sheath effect appears at the top floor, which is related to the blasting vibration frequency and the building’s natural vibration frequency.

scholarly journals An Evaluation Model for the Innovation and Entrepreneurship Thinking Ability of College Students Based on Neural Network

2021 ◽  
Vol 16 (02) ◽  
pp. 188
Author(s):  
Feng Zhang ◽  
Limin Xi
Keyword(s):  
Neural Network ◽  
College Students ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Analytic Hierarchy ◽  
Thinking Ability ◽  
Fuzzy Neural ◽  
Knowledge Rules ◽  
Innovation And Entrepreneurship

Mass innovation and entrepreneurship (I&E) is a national campaign in China. In this context, it is important to encourage college students to engage in I&E activities, and this calls for accurate and comprehensive evaluation of their I&E thinking ability. Therefore, this paper proposes an evaluation model for the I&E thinking ability of college students based on neural network (NN). Firstly, a reasonable evaluation index system was created for the I&E thinking ability of college students, and the evaluation indices were preprocessed through fuzzy analytic hierarchy process (AHP). Then, a fuzzy neural network (FNN) was constructed based on GA rule optimization and the specific steps of the algorithm were given. Moreover, a few representative rules were selected by GA based on uncertain fuzzy knowledge rules, a 4-layer NN model with fuzzy inputs and outputs was established, and the evaluation flow of the I&E thinking ability of college students was proposed. Finally, the effectiveness of the proposed model was verified through experiments. The research results of this paper provide a reference for the application of NN in the field of ability evaluation.

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.

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