A Modified Deep Learning Enthused Adversarial Network Model to Predict Financial Fluctuations in Stock Market

Sabeena*, Jasmine; Reddy, Poli Venkata Subba

doi:10.35940/ijeat.f9011.088619

Cited by 9 publications

(8 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To increase CNN's accuracy, some researchers integrated CNN with other models and proposed new hybrid models. For example, by integrating gated recurrent unit (GRU) and CNN, Sabeena et al [44] introduced a hybrid DL model to predict financial fluctuations in the real-time stock market that is able to process the real-time data from online financial sites. GRUs are developed based on the RNN architectures.…”

Section: Cnnmentioning

confidence: 99%

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

et al. 2020

View full text Add to dashboard Cite

This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

show abstract

Section: Cnnmentioning

confidence: 99%

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

et al. 2020

View full text Add to dashboard Cite

show abstract

“…is figure illustrates the suggested method's performance accuracy to other researchers. According to this figure, the accuracy of recommended, reference [3], reference [5], and reference [6] in 100 trials is 0.899, 0.823, 0.823, and 0.723, respectively. e accuracy of recommended, reference [3], reference [5], and reference [6] for 200 tests is 0.932, 0.885, 0.856, and 0.797, respectively.…”

Section: Simulation and Results Analysismentioning

confidence: 92%

“…erefore, it can be said that it has become a precious resource in the overall development of enterprises. If enterprises gain the upper hand from human resources, they will gain a competitive advantage [3]. e most difficult problem for corporate managers these days is figuring out how to improve enterprise human resource management.…”

Section: Introductionmentioning

confidence: 99%

The Performance Evaluation Model of Hotel Green Human Resources Based on Internet of Things and Fuzzy Theory

Shen

2022

Mobile Information Systems

View full text Add to dashboard Cite

The Internet of Things (IoT) is influencing national centers of excellence as well as businesses’ approaches to development and how they generate and exploit opportunities in day-to-day hotel activities. The IoT is revolutionary and will transform how human resources are created and managed, necessitating a fresh and adaptable strategy for human resource development. The promise of the IoT is that each device will have a unique means of identifying itself and will be addressable, allowing any object to be connected. This will present a new difficulty to how green human resource development in hotels is conducted. Besides, environmental protection has emerged as the most pressing problem in world politics. Green problems are widely acknowledged as a fundamental source of development. Due to increased customer awareness, the hotel service sector has advanced in terms of quality of services as green marketing has evolved into a crucial instrument for sustainable business approaches. Green marketing strategies are being implemented by businesses to improve company success. In order to improve the adaptive performance assessment capacity of hotel green human resources, this research proposes a performance evaluation model of hotel green human resources based on the Internet of Things and fuzzy theory. It develops a large data analysis model of hotel green human resource performance by statistically quantitatively assessing the constraint parameters of hotel green human resource performance assessment. It extracts the entropy characteristic amount of hotel green human resource performance big data to make the best choice and assessment of hotel green human resource performance by green hotel maximum economic benefit evaluation and fuzzy decision technique. Furthermore, it builds an expert system analysis model for evaluating the hotel green human resource performance. Adaptive optimization of hotel green human resource performance evaluation is carried out by the fuzzy theory adaptive learning algorithm, and the optimization strategy of the hotel green human supply performance evaluation model is realized by similarity characteristic parameter estimation and fuzzy multidimensional parameter constraint. The simulation results show that the performance evaluation of hotel green human resources with this method has high accuracy and good confidence level, which improves the performance evaluation efficiency of hotel green human resources.

show abstract

“…To increase the accuracy of CNN, some researchers integrated CNN with other models and proposed new hybrid models. For example, by integrating gated recurrent unit (GRU) and CNN, Sabeena et al [44] introduced a hybrid DL model to predict financial fluctuations in the real-time stock market that is able to process the real-time data from online financial sites. GRUs are developed based on the RNN architectures.…”

Section: Cnnmentioning

confidence: 99%

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Nosratabadi

Mosavi²,

Duan

et al. 2020

SSRN Journal

View full text Add to dashboard Cite

A Modified Deep Learning Enthused Adversarial Network Model to Predict Financial Fluctuations in Stock Market

Cited by 9 publications

References 3 publications

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

The Performance Evaluation Model of Hotel Green Human Resources Based on Internet of Things and Fuzzy Theory

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Contact Info

Product

Resources

About