scholarly journals Measurement of Accounting Information Accessibility and Understandability based on Distance of Information-state Transition Theory

2021 ◽  
Vol 13 ◽  
pp. 301-308
Author(s):  
Ping Guan ◽  
Jiayi Gu
Keyword(s):  
Information Acquisition ◽  
Recommendation System ◽  
State Transition ◽  
Transition Probability ◽  
Accounting Information ◽  
Network Environment ◽  
Information State ◽  
Information Distance ◽  
Information Navigation ◽  
Information Accessibility

With the advent of the era of big data, the problem of information selection and decision efficiency becomes more and more important under the network environment. Accounting information is an important economic information resource, but it has been facing the dilemma between the usefulness and useable. Based on the theory of Distance of Information-state Transition, this paper describes several concepts related to information distance, discusses key issues such as target state information, state chain, transition probability, measurement rules and determine standards, analyzes the influence of information retrieval, social tagging, recommendation system and information navigation on information distance. Based on the measurement of accounting information acquisition and knowledge acquisition, this paper studies the optimization of information distance in the network environment, which provides theoretical support and practical reference for the realization of effective accounting information utilization and information architecture

scholarly journals Grey theory–based BP-NN co-training for dense sequence long-term tendency prediction

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuling Hong ◽  
Yingjie Yang ◽  
Qishan Zhang
Keyword(s):  
State Transition ◽  
Transition Probability ◽  
Training Model ◽  
Transition Probability Matrix ◽  
Content Type ◽  
Fine Grained ◽  
State Transition Probability ◽  
Dense Sequence ◽  
Popularity Prediction

PurposeThe purpose of this paper is to solve the problems existing in topic popularity prediction in online social networks and advance a fine-grained and long-term prediction model for lack of sufficient data.Design/methodology/approachBased on GM(1,1) and neural networks, a co-training model for topic tendency prediction is proposed in this paper. The interpolation based on GM(1,1) is employed to generate fine-grained prediction values of topic popularity time series and two neural network models are considered to achieve convergence by transmitting training parameters via their loss functions.FindingsThe experiment results indicate that the integrated model can effectively predict dense sequence with higher performance than other algorithms, such as NN and RBF_LSSVM. Furthermore, the Markov chain state transition probability matrix model is used to improve the prediction results.Practical implicationsFine-grained and long-term topic popularity prediction, further improvement could be made by predicting any interpolation in the time interval of popularity data points.Originality/valueThe paper succeeds in constructing a co-training model with GM(1,1) and neural networks. Markov chain state transition probability matrix is deployed for further improvement of popularity tendency prediction.

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