scholarly journals Prediction of Enterprise Economic Activity Behavior Based on Neural Network and ARIMA Hybrid Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zeyu Lin ◽  
Shuai Li
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Economic Activity ◽  
Rapid Development ◽  
Total Production ◽  
Economic Activities ◽  
Production Scale ◽  
Artificial Intelligence Models ◽  
Activity Behavior ◽  
Behavior Based

Enterprise economy refers to the comprehensive situation reflected in the gross product, production scale, total production and efficiency, technological content, marketing means, and so on; under certain social conditions, enterprises use resources obtained by law to engage in economic activities. Under the guidance of consciousness or culture, enterprises use “legally obtained resources” to promote economic development. Enterprise economy is affected by manpower, capital, management, operation, policy, and other aspects. In the context of the rapid development of big data in the current era, this paper proposes a prediction model of enterprise economic activity behavior based on neural network and ARIMA by investigating a variety of artificial intelligence models and verifies its feasibility. Commodity circulation enterprises have a more urgent demand for the development of business audit due to their operation characteristics. Therefore, this paper takes commodity circulation enterprises as representatives and predicts business audit in the big data environment based on the model proposed in this paper.

scholarly journals Prospects for Using Big Data in Financial Analysis

2021 ◽  
Vol 110 ◽  
pp. 05004
Author(s):  
Igor Sadyrin ◽  
Olga Syrovatskay ◽  
Olga Leonova
Keyword(s):  
Big Data ◽  
Economic Activity ◽  
Financial Analysis ◽  
Digital Technologies ◽  
Digital Transformation ◽  
Maximum Effect ◽  
Economic Activities ◽  
Analysis System ◽  
Effective Use ◽  
Big Data Technology

The ongoing digital transformation of the national economy inevitably sets the task of introducing digital technologies and tools into the practice of economic activities of organizations and enterprises. One of the areas of digitalization is Big Data technology, which in many ways is already being used in the field of finance. However, for the effective use of big data in the practice of financial analysis of economic activity, it is necessary to solve a variety of significant problems. The article discusses the promising directions of using big data in financial analysis procedures, which, being integrated into the system of forming various management decisions, can significantly increase their efficiency. The necessary elements of the financial analysis system, which, first of all, should be focused on the use of big data, are considered, as well as aspects that, when using digital technologies, can provide the maximum effect.

scholarly journals Fuzzy Method and Neural Network Model Parallel Implementation of Multi-Layer Neural Network Based on Cloud Computing for Real Time Data Transmission in Large Offshore Platform

2017 ◽  
Vol 24 (s2) ◽  
pp. 39-44 ◽  
Author(s):  
Zhang Hu ◽  
Wei Qin
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Big Data ◽  
Parallel Implementation ◽  
Rapid Development ◽  
Optimal Number ◽  
Bp Algorithm ◽  
Map Reduce ◽  
Time Data ◽  
Speed Up

Abstract With the rapid development of electronic technology, network technology and cloud computing technology, the current data is increasing in the way of mass, has entered the era of big data. Based on cloud computing clusters, this paper proposes a novel method of parallel implementation of multilayered neural networks based on Map-Reduce. Namely in order to meet the requirements of big data processing, this paper presents an efficient mapping scheme for a fully connected multi-layered neural network, which is trained by using error back propagation (BP) algorithm based on Map-Reduce on cloud computing clusters (MRBP). The batch-training (or epoch-training) regimes are used by effective segmentation of samples on the clusters, and are adopted in the separated training method, weight summary to achieve convergence by iterating. For a parallel BP algorithm on the clusters and a serial BP algorithm on uniprocessor, the required time for implementing the algorithms is derived. The performance parameters, such as speed-up, optimal number and minimum of data nodes are evaluated for the parallel BP algorithm on the clusters. Experiment results demonstrate that the proposed parallel BP algorithm in this paper has better speed-up, faster convergence rate, less iterations than that of the existed algorithms.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

TRADITIONAL OUTBUILDINGS OF THE MORDOVIANS

2019 ◽  
Vol 15 (2) ◽  
pp. 154-165 ◽  
Author(s):  
Elena N. Mokshina ◽  
Mihail I. Svyatkin
Keyword(s):  
Ethnic Group ◽  
Economic Activity ◽  
Building Materials ◽  
Animal Feed ◽  
Economic Activities ◽  
Important Place ◽  
Main Building ◽  
Genetic Problem ◽  
Materials Used ◽  
Functional Purpose

Introduction. The article deals with the main traditional outbuildings of the Mordvinians, reported on their functional purpose in economic activity. The forms and types of outbuildings, as well as the main building materials used by Mordvinians are described in details. Their significance in the religious and ceremonial life of the ethnic group is shown. Materials and Methods. The research is based on traditional methods of ethnographic science, such as field observation, survey and interviews, and a comprehensive approach. Among the methods of historical science comparative-historical, historical-genetic, problem-chronological, structural-system were used. Among the general scientific methods of research logical, descriptive, narrative, generalization, classification and systematization were involved. To achieve the results of the study, the materials collected by the authors in the course of field surveys conducted in the Mordovian villages were mainly used. Results and Discussion. Traditional outbuildings were of great importance in the economic activity of the Mordovian ethnic group. According to their functional purpose, they can be divided into the following groups: for livestock and poultry (stable, chicken coop, stable, kalda), sanitary and hygienic (bath), warehouse buildings for storage of food, utensils, firewood, animal feed (barn, cellar, woodshed, hayloft), for processing of grain (sheep, riga, mill). Depending on the welfare and financial capacity of the family, the number of outbuildings was different. As a rule, the wealthier families had more outbuildings than the less wealthier ones. The main building material for the construction of these buildings was wood. Conclusion. Thus, the traditional outbuildings of the Mordvinians occupied an important place in its economic activities. At the same time, each of them had its own purpose and performed certain functions. Some buildings, such as a bath and a barn, had not only economic purpose, but also were the venue for a number of prayers and ceremonies. It is now ordinarily they have banya (bath-house), outdoor courtyard with standing in different places sheds, barn and cellar.

The specifics of shadow economic activity in Russia

2020 ◽  
Vol 19 (10) ◽  
pp. 1896-1915
Author(s):  
E.R. Ermakova ◽  
O.M. Lizina
Keyword(s):  
Economic Activity ◽  
Shadow Economy ◽  
Integrated Approach ◽  
Economic Activities ◽  
Reproductive Process ◽  
Scientific Methods ◽  
Current Period ◽  
Russian Economy ◽  
Analysis And Synthesis ◽  
The Impact

Subject. The article addresses the specifics of shadow economic activities in reformed Russia in the context of systemic transformations. Objectives. We focus on determining the role of shadow economy in the reproductive process, identifying and understanding the specifics of underground economic activity of the Russian economy. Methods. The study rests on general scientific methods (scientific abstraction, unity of historical and logical, analysis and synthesis, induction and deduction, comparison and analogy) and special methods of cognition (monetary methods). We employ the systems and integrated approach. The official statistics, regulations, works of leading researchers on shadow economy expansion, resources of reference and legal systems like Garant and ConsultantPlus serve as the study's information base. Results. We present a retrospective rapid analysis of the extent of shadow economic activity in the domestic economy, establishing the relationships with the processes that take place at different stages of the country's development. We also reveal the specifics of shadow economy relations in Russia, factors that play a key role in expansion for a particular period, a shift to another form of shadow economy. The study characterizes the current period of development, assesses the impact of external shocks on shadow economy expansion. Conclusions. The current period is characterized by the digitization of shadow relations, the shift of corruption to the upper echelons of power, the continued outflow of capital abroad, and increased penalties for underground activities.

Research on the Economic Organization Environmental Impact

2017 ◽  
Vol 68 (3) ◽  
pp. 599-601
Author(s):  
Dan Paul Stefanescu ◽  
Oana Roxana Chivu ◽  
Claudiu Babis ◽  
Augustin Semenescu ◽  
Alina Gligor
Keyword(s):  
Economic Activity ◽  
Economic Organization ◽  
Economic Activities ◽  
Environmental Implications ◽  
Minimal Impact ◽  
Wide Range ◽  
Material Environment ◽  
Negative Effect ◽  
Harmful Effects

Any economic activity carried out by an organization, can generate a wide range of environmental implications. Particularly important, must be considered the activities that have a significant negative effect on the environment, meaning those which pollute. Being known the harmful effects of pollution on the human health, the paper presents two models of utmost importance, one of the material environment-economy interactions balance and the other of the material flows between environmental factors and socio-economic activities. The study of these models enable specific conditions that must be satisfied for the economic processes friendly coexist to the environment for long term, meaning to have a minimal impact in that the residues resulting from the economic activity of the organization to be as less harmful to the environment.

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

scholarly journals Comparison of cephalometric measurements between conventional and automatic cephalometric analysis using convolutional neural network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sangmin Jeon ◽  
Kyungmin Clara Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Rapid Development ◽  
Cephalometric Analysis ◽  
Anatomical Landmarks ◽  
Limits Of Agreement ◽  
Mandibular Incisor ◽  
Lateral Cephalograms ◽  
Dental Measurements ◽  
Cephalometric Measurements

Abstract Objective The rapid development of artificial intelligence technologies for medical imaging has recently enabled automatic identification of anatomical landmarks on radiographs. The purpose of this study was to compare the results of an automatic cephalometric analysis using convolutional neural network with those obtained by a conventional cephalometric approach. Material and methods Cephalometric measurements of lateral cephalograms from 35 patients were obtained using an automatic program and a conventional program. Fifteen skeletal cephalometric measurements, nine dental cephalometric measurements, and two soft tissue cephalometric measurements obtained by the two methods were compared using paired t test and Bland-Altman plots. Results A comparison between the measurements from the automatic and conventional cephalometric analyses in terms of the paired t test confirmed that the saddle angle, linear measurements of maxillary incisor to NA line, and mandibular incisor to NB line showed statistically significant differences. All measurements were within the limits of agreement based on the Bland-Altman plots. The widths of limits of agreement were wider in dental measurements than those in the skeletal measurements. Conclusions Automatic cephalometric analyses based on convolutional neural network may offer clinically acceptable diagnostic performance. Careful consideration and additional manual adjustment are needed for dental measurements regarding tooth structures for higher accuracy and better performance.

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