Hybrid Hunter–Prey Optimization with Deep Learning-Based Fintech for Predicting Financial Crises in the Economy and Society

Katib, Iyad; Assiri, Fatmah Yousef; Althaqafi, Turki; Hamed, Diaa; Ragab, Mahmoud

doi:10.3390/electronics12163429

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Nowadays, AI is being utilized in many different fields, with endless applications including prediction, Internet-of-Things (IoT), classification, detection, recognition, etc. [15][16][17][18][19][20]. The AI technique that we employed here is Deep Reinforcement Learning (DRL), a promising approach in handling advanced control problems [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

Younis,

Jambi,

Eassa

et al. 2024

Systems

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Intelligent systems are being proposed every day as advances in cloud systems are increasing. Mostly, the services offered by these cloud systems are dependent only on their providers, without the inclusion of services from other providers, specialized third parties, or individuals. This ‘vendor lock-in’ issue and the limitations related to offering tailored services could be resolved by allowing multiple providers or individuals to collaborate through intelligent task scheduling. To address such real-world systems’ limitations in provisioning and executing heterogeneous services, we employed Blockchain and Deep Reinforcement Learning here; the first is used for the token-based secured communication between parties, and the latter is to predict the appropriate task scheduling; hence, we guarantee the quality of not only the immediate decision but also the long-term. The empirical results show a high reward achieved, meaning that it accurately selected the candidates and adaptably assigned the tasks based on job nature and executors’ individual computing capabilities, with 95 s less than the baseline in job completion time to maintain the Quality of Service. The successful collaboration between parties in this tokenized system while securing transactions through Blockchain and predicting the right scheduling of tasks makes it a promising intelligent system for advanced use cases.

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Section: Introductionmentioning

confidence: 99%

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

Younis,

Jambi,

Eassa

et al. 2024

Systems

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show abstract

“…Smart FinTech is the new-generation FinTech, largely inspired and empowered by data science and artificial intelligence (DSAI) techniques (Katib et al, 2023). Smart FinTech synthesizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and personalized economic and financial businesses, services and system (Cao et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Reading Big Data by Machine Learning: The Used of Computer Science for Human Life

Subagio,

Sitepu

2023

jppipa, pendidikan ipa, fisika, biologi, kimia

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Machine learning (ML) models use big data to learn and improve predictability and performance automatically through experience and data, without being programmed to do so by humans. Artificial Intelligence (AI) techniques are being increasingly deployed in finance, in areas such as asset management, algorithmic trading, credit underwriting or blockchain-based finance, enabled by the abundance of available data and by affordable computing capacity. The purpose of this study is to describe in detail how the power of artificial intelligence with its complex system can help the needs of digital technology in the banking sector. The research method used is the elaboration of great thoughts and facts about artificial intelligence. Scientific data is interpreted with analytical power that is as precise as possible, so as to produce a description that meets the logic of structured thinking. The data is taken from relevant and up-to-date literature, the work of scientists who have been disseminated in various weighty scientific publications at the world level. The report can help policy makers to assess the implications of these new technologies and to identify the benefits and risks related to their use. It suggests policy responses that that are intended to support AI innovation in finance while ensuring that its use is consistent with promoting financial stability, market integrity and competition, while protecting financial consumers. Emerging risks from the deployment of AI techniques need to be identified and mitigated to support and promote the use of responsible AI. Existing regulatory and supervisory requirements may need to be clarified and sometimes adjusted, as appropriate, to address some of the perceived incompatibilities of existing arrangements with AI applications.

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Integrating spotted hyena optimization technique with generative artificial intelligence for time series forecasting

Salama

2024

Expert Systems

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Generative artificial intelligence (AI) has developed as an effective tool for time series predicting, revolutionizing the typical methods of prediction. Different classical approaches that depend on existing approaches and assumptions, generative AI controls advanced deep learning (DL) approaches like generative adversarial networks (GANs) and recurrent neural networks (RNNs), to identify designs and connections in time series data. DL has accomplished major success in optimizing performances connected with AI. In the financial area, it can be extremely utilized for the stock market predictive, trade implementation approaches, and set of optimizers. Stock market predictive is the most important use case in this field. GANs with advanced AI approaches have become more significant in recent times. However, it can be utilized in image‐image‐translation and other computer vision (CV) conditions. GANs could not utilized greatly for stock market prediction because of their effort to establish the proper set of hyperparameters. This study develops an integrated spotted hyena optimization algorithm with generative artificial intelligence for time series forecasting (SHOAGAI‐TSF) technique. The purpose of the SHOAGAI‐TSF technique is to accomplish a forecasting process for the utilization of stock price prediction. The SHOAGAI‐TSF technique uses probabilistic forecasting with a conditional GAN (CGAN) approach for the prediction of stock prices. The CGAN model learns the data generation distribution and determines the probabilistic prediction from it. To boost the prediction results of the CGAN approach, the hyperparameter tuning can be performed by the use of the SHOA. The simulation result analysis of the SHOAGAI‐TSF technique takes place on the stock market dataset. The experimental outcomes determine the significant solution of the SHOAGAI‐TSF algorithm with other compared methods in terms of distinct metrics.

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Hybrid Hunter–Prey Optimization with Deep Learning-Based Fintech for Predicting Financial Crises in the Economy and Society

Cited by 5 publications

References 18 publications

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

Reading Big Data by Machine Learning: The Used of Computer Science for Human Life

Integrating spotted hyena optimization technique with generative artificial intelligence for time series forecasting

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