The Use of Recurrent Neural Networks in the Optimization of Computer Science Algorithms
A new era of computational efficacy and problem-solving abilities has begun with the combination of Recurrent Neural Networks (RNNs) and computer science methods. It is crucial in modern computing to combine RNNs with methods from the field of computer science. RNNs bring a dynamic learning paradigm...
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| Vydáno v: | 2023 International Conference on Emerging Research in Computational Science (ICERCS) s. 1 - 6 |
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IEEE
07.12.2023
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| Abstract | A new era of computational efficacy and problem-solving abilities has begun with the combination of Recurrent Neural Networks (RNNs) and computer science methods. It is crucial in modern computing to combine RNNs with methods from the field of computer science. RNNs bring a dynamic learning paradigm, making algorithms more efficient and flexible by allowing them to process sequential input and complicated dependencies. Their usefulness goes beyond one specific area of study, and into others such as optimization issues, recommendation systems, image processing, and natural language processing. RNNs serve a vital role in addressing the severe challenges that algorithms confront, including processing sequential input, identifying temporal connections, and optimizing in high-dimensional environments. By providing dynamic and adaptive modeling, RNNs aid algorithms in navigating these obstacles. Recurrent Neural Network-Based Optimization Algorithm (RNN-OA) is an innovative novel approach presented in this research. RNN-OA is an approach to algorithmic optimization that makes use of RNNs' dynamic learning capability. Mechanisms for selective data processing with attention, regularization methods for model stability, and interpretability improvements for openness are all a part of it. Quickening the process of optimization in many different types of problems is transfer learning and fine-tuning approaches. There are a wide variety of computer science fields where RNN-OA can be useful. Time-series analysis, language modeling, picture recognition, and recommendation systems all benefit greatly from its ability to handle sequential data. The efficacy, scalability, and robustness of the method are thoroughly evaluated by simulating various problem scenarios and data streams. The results of these simulations provide illumination for the benefits and drawbacks of RNN-OA, paving the way for its development toward actual application in algorithmic optimization. |
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| AbstractList | A new era of computational efficacy and problem-solving abilities has begun with the combination of Recurrent Neural Networks (RNNs) and computer science methods. It is crucial in modern computing to combine RNNs with methods from the field of computer science. RNNs bring a dynamic learning paradigm, making algorithms more efficient and flexible by allowing them to process sequential input and complicated dependencies. Their usefulness goes beyond one specific area of study, and into others such as optimization issues, recommendation systems, image processing, and natural language processing. RNNs serve a vital role in addressing the severe challenges that algorithms confront, including processing sequential input, identifying temporal connections, and optimizing in high-dimensional environments. By providing dynamic and adaptive modeling, RNNs aid algorithms in navigating these obstacles. Recurrent Neural Network-Based Optimization Algorithm (RNN-OA) is an innovative novel approach presented in this research. RNN-OA is an approach to algorithmic optimization that makes use of RNNs' dynamic learning capability. Mechanisms for selective data processing with attention, regularization methods for model stability, and interpretability improvements for openness are all a part of it. Quickening the process of optimization in many different types of problems is transfer learning and fine-tuning approaches. There are a wide variety of computer science fields where RNN-OA can be useful. Time-series analysis, language modeling, picture recognition, and recommendation systems all benefit greatly from its ability to handle sequential data. The efficacy, scalability, and robustness of the method are thoroughly evaluated by simulating various problem scenarios and data streams. The results of these simulations provide illumination for the benefits and drawbacks of RNN-OA, paving the way for its development toward actual application in algorithmic optimization. |
| Author | Sabasti Prabu, S. Igni Ronald Doni, A. Franklin, Ramya G Poornima, D. |
| Author_xml | – sequence: 1 givenname: Ramya G surname: Franklin fullname: Franklin, Ramya G email: ramyagfranklin123@gmail.com organization: Sathyabama Institute of Science & Technology,Department of Computer Science and Engineering,Chennai,India – sequence: 2 givenname: A. surname: Ronald Doni fullname: Ronald Doni, A. email: ronaldtony.a@gmail.com organization: Sathyabama Institute of Science & Technology,Department of Computer Science and Engineering,Chennai,India – sequence: 3 givenname: D. surname: Poornima fullname: Poornima, D. email: poorniramesh2011@gmail.com organization: Sathyabama Institute of Science & Technology,Department of Computer Science and Engineering,Chennai,India – sequence: 4 givenname: S. Igni surname: Sabasti Prabu fullname: Sabasti Prabu, S. Igni email: igni.prabu@sathyabama.ac.in organization: Sathyabama Institute of Science & Technology,Department of Computer Science and Engineering,Chennai,India |
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| SubjectTerms | Computational modeling Computer science Data models Heuristic algorithms High-Dimensional Environments Optimization Recommender systems Recurrent neural networks |
| Title | The Use of Recurrent Neural Networks in the Optimization of Computer Science Algorithms |
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