Integrated neurosymbolic decision support systems: problems and opportunities

Demidovskij, Alexander; Babkin, Eduard

doi:10.17323/2587-814x.2021.3.7.23

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…Firstly, the participation of power generation enterprises in carbon emissions trading will inevitably increase the cost of carbon allowance trading, technical transformation, and management costs. Secondly, it affects the production methods of enterprises [ 18 ]. Thermal power enterprises with difficulties in reducing emissions will inevitably consider adjusting their energy mix to avoid penalties or quota shortage situations and promote the low-carbon transformation of their energy mix.…”

Section: Analysis Of a Digital Neural Network Model For Carbon Assets...mentioning

confidence: 99%

A Neural Network Model for Digitizing Enterprise Carbon Assets Based on Multimodal Knowledge Mapping

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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In this paper, a multimodal knowledge mapping approach is used to digitize enterprise carbon assets, and a corresponding neural network model is designed for use in the practical process. Rich textual entity labels associated with images are obtained using an entity annotation system. A topology-based data fusion method is also designed based on the hierarchical relationship between WordNet and DBpedia to fuse the knowledge obtained from image visualization and text description mining. Existing neural network-based entity linking methods ignore the semantic gap between the context of sequential entity denotative items and the context of graph-structured entities, thus affecting the accuracy of entity linking. It is observed that the importance of words in the context of entity denotative items is different, and the importance of content in the entity context is also different. To solve the above problems, this paper proposes an entity linking method that combines a common attention mechanism with a graph convolutional neural network. Secondly, based on the basic theory of value assessment, the characteristics of classical asset valuation methods and their inapplicability to the valuation of carbon assets are analyzed, and thus the real option valuation method and its two classical models are introduced; after demonstrating the real option characteristics of carbon assets of power enterprise projects, a real option model-based carbon asset valuation model for power enterprise projects is constructed and its applicability is verified with case studies. Through analyzing the current situation and problems of carbon asset valuation work in power enterprises, targeted practical suggestions are put forward to further strengthen and enhance the carbon asset valuation work in power enterprises in the future.

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Section: Analysis Of a Digital Neural Network Model For Carbon Assets...mentioning

confidence: 99%

A Neural Network Model for Digitizing Enterprise Carbon Assets Based on Multimodal Knowledge Mapping

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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“…Opportunities to avoid this contradiction are provided by the active development of machine learning methods implemented on the basis of artificial neural networks (ANN), which are able to formalize a wide range of nonlinear problems with a given level of accuracy. ANN are increasingly being used in various fields of activity (Lecun et al, 2015;Panigrahi & Behera, 2017;Demidovskij & Babkin, 2021).…”

Section: Introductionmentioning

confidence: 99%

Applicability of machine learning models using a neural network for predicting the parameters of the development of food markets

Dubovitski¹,

Klimentova²,

ROGOV³

2022

J Process Man & New Technol

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Forecasting the parameters of the food market is a difficult task due to the volatility of demand, which depends on many factors. In this study, the authors attempted to implement a machine learning model based on multiple data on the food market. A boxed recurrent neural network was chosen as a prediction technique. The information basis was made up of data from 3,200 US cities for 2010-2012, reflecting characteristics that may be directly or indirectly related to the price of dairy products. The following models were used for data preprocessing, anomaly search, dimensionality reduction: AdaBoost, LogisticRegression, SVM. As a result of analytical actions, a neural network architecture has been formed for use in market forecasting: two competitive neural networks. First: 2 layers with Bidirectional GRU+Dropout. Second: 3 layers of LSTM+Dropout + Attention with skip-layers. Its use makes it possible to obtain a prediction model of the desired parameters with qualitative indicators of the validation sample - R^= 0.86. The applicability of the constructed machine learning model is considered on the example of classical agricultural production with the presentation of the stages of deployment of such a model at the enterprise level.

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“…the decision support system (DSS) focused on the processing of information for analysis and decision making. Currently, they are based on applications in the form of expert systems and artificial intelligence [1]. In addition, such canonical forms of analytical content as tables (formed according to certain rules) and graphs remain in demand.…”

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confidence: 99%

Influence of algorithmization and interface for the preparation of management decisions

Gutgarts

2023

Bus. Inform.

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In modern conditions, managerial decision-making is carried out using automated systems under the general name “Decision Support Systems” (DSS). When creating them, it is important to consider two key points. The first is the algorithmic component, which reflects the logic of the system as a whole and its individual parts. The second is the application interface through which the user interacts with it. The interface is a graphical interpretation of the algorithms that are implemented within the system. Therefore, it is very important to design and create such a relationship between the algorithm and the interface so that the user is as comfortable as possible using the DSS to solve current tasks (information input, its processing, presentation and analysis for decision making). Thus, there is a directly proportional relationship between the interface and the algorithm. Moreover, despite the fact that there are many studies on these aspects, both theoretical and practical, there are still questions to which one should pay attention to in terms of application. The purpose of this study is to formulate practical recommendations to prevent the entry of incorrect information into the DSS database and to present the results in a form convenient for its analysis. The main tasks of the work are to show by means of examples which errors can contribute to the entry of unreliable information into the database, as well as how best to present information on the monitor screen in accordance with the psychophysiological characteristics of a person in order to reduce the time for its analysis and decision-making.

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Integrated neurosymbolic decision support systems: problems and opportunities

Cited by 6 publications

References 52 publications

A Neural Network Model for Digitizing Enterprise Carbon Assets Based on Multimodal Knowledge Mapping

A Neural Network Model for Digitizing Enterprise Carbon Assets Based on Multimodal Knowledge Mapping

Applicability of machine learning models using a neural network for predicting the parameters of the development of food markets

Influence of algorithmization and interface for the preparation of management decisions

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