Applications of game theory in deep learning: a survey

Hazra, Tanmoy; Anjaria, Kushal

doi:10.1007/s11042-022-12153-2

Cited by 33 publications

(20 citation statements)

References 93 publications

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“…is objective function is also known as the "model" of this numerical optimization problem. Different problems correspond to different objective functions [11]. In other words, different mathematical models are established.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning Algorithm in Web Page Advertising Design Style

Deng¹,

Yang

2022

Security and Communication Networks

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With the development of deep learning algorithm in Internet web pages and customers’ demand for web advertising design, different businesses begin to pay attention to web advertising design style. In order to predict and promote the development of web advertising design style, this paper studies the establishment process of web design model based on deep learning algorithm in detail. With the application of this model to the web advertising design of different businesses, this paper compares and analyzes the differences of web advertising design styles of different businesses and puts forward corresponding suggestions on web advertising design styles, which promotes the diversified development of web advertising design styles to a certain extent and has a good application effect.

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Section: Introductionmentioning

confidence: 99%

Application of Deep Learning Algorithm in Web Page Advertising Design Style

Deng¹,

Yang

2022

Security and Communication Networks

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“…Asynchronous mediation is a mediation in which parties do not see each other, for example, online mediation on the internet (Bollen et al, 2012). We plan to associate game‐theoretic analysis (Hazra & Anjaria, 2021; Hazra & Anjaria, 2022) with group conflict handling and resolution methods as a part of future research. Last but not least, in the future, it would be interesting to study the linkage between procedural justice perceptions and perceptions of mediation effectiveness.…”

Section: Degree Of Truth and Entropy‐based Methodology: A Discussion ...mentioning

confidence: 99%

Group conflict handling under moderate evaluative partial mediation using entropic decision framework

Anjaria

2022

Conflict Resolution Qtrly

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The present paper aims to present entropy and fuzzy degrees of the truth‐based framework to handle the group conflict under moderate evaluative, partial mediation. For group conflict handling, the present work considers Thomas and Kilmann's five conflict‐handling modes: competing, collaborating, avoiding, accommodating, and compromising. A methodology proposed in the current work systemically formalizes decision‐making on deploying a conflict‐handling method with mediators' opinions. The formalization of decision‐making elucidated in the present work involves measurements based on entropy and fuzzy degrees of truth. The current work applies the developed formal quantitative framework to a well‐known case of group conflict that involves conflict among a group of engineering students. The case supports the proposed formal framework. Behavioral, psychological, and cognitive facts about engineers come in handy for validation of the proposed framework. The case shows that the proposed approach can be highly generalized, interpretable, and replicable.

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“…Face recognition technology has made great progress after the deep learning theory was put forward. Hazra used a layer-by-layer greedy algorithm to make up for the difficult problem of multi-layer neural network training, improve the computing ability of the computer, and quickly carry out deep neural network training through GPU, which has attracted extensive attention of many scholars [ 3 ]. Yang designed a Deep Face Recognition System (DeepFace) as a deep neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of an Artificial Intelligence System Recognition Based on the Deep Neural Network Algorithm

Zhang

Yang

2022

Computational Intelligence and Neuroscience

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With the development of artificial intelligence and computer technology, the deep neural network algorithm is applied to the intelligentization of various fields of production and life. However, from the current application status, the application of artificial intelligence technology has many shortcomings. Based on this, this paper starts with the deep neural network algorithm, takes face recognition as the research tool, and deeply studies how to use the deep neural network algorithm to demonstrate the application of intelligent face recognition in complex environments. A face recognition neural network algorithm is proposed, and the accuracy of the algorithm is checked by testing. The results show that the average accuracy of a single sample in the LFW dataset is 99.17%, and the efficiency of using a single sample is close to that of many smelting models, which can be applied to various intelligent recognition scenarios.

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Applications of game theory in deep learning: a survey

Cited by 33 publications

References 93 publications

Application of Deep Learning Algorithm in Web Page Advertising Design Style

Application of Deep Learning Algorithm in Web Page Advertising Design Style

Group conflict handling under moderate evaluative partial mediation using entropic decision framework

Application of an Artificial Intelligence System Recognition Based on the Deep Neural Network Algorithm

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