Қазақ ұлттық қыздар педагогикалық университеті / Казахский национальный женский педагогический университет
Постоянный URI для этого сообществаhttps://cris.qyzpu.kz/handle/123456789/1
Просмотр
2 результаты
Результаты поиска
- Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Development of a Framework for Classification of Impulsive Urban Sounds using BiLSTM Network(Science and Information Organization, 2023-01-01); ;Aigerim Bakatkaliyevna Altayeva ;Bayan Abduraimova; Zh. MadibaiulyUrban environments are awash with myriad sounds, among which impulsive noises stand distinct due to their brief and often disruptive nature. As cities evolve and expand, the accurate classification and management of these impulsive sounds become paramount for urban planners, environmental scientists, and public health advocates. This paper introduces a novel framework leveraging the Bidirectional Long Short-Term Memory (BiLSTM) Network for the systematic categorization of impulsive urban sounds. Traditional methodologies often falter in recognizing the nuanced intricacies of such noises. In contrast, the presented BiLSTM-based approach adapts to the temporal variability intrinsic to these sounds, thereby enhancing classification accuracy. The research harnesses an expansive dataset, curated from various urban settings, to train and validate the model. Preliminary findings suggest that our BiLSTM framework outperforms existing models, with a marked increase in both specificity and sensitivity metrics. The outcome of this study holds profound implications for city acoustics management, noise pollution control, and urban health interventions. Moreover, the framework's adaptability paves the way for its application across diverse acoustic landscapes beyond the urban realm. Future endeavors should seek to further optimize the model by integrating more diverse soundscapes and addressing potential biases in data collection.Цитирования (OpenAlex) 3Цитирования (OpenCitations) 3 - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Research of the Finite Difference Numerical Method Using Artificial Intelligence for Solving Problems of Incompressible Fluid Flow in a Rectangular Domain with Initial and Boundary Conditions(2025-01-01) ;Lyailya Tukenova; ;S. Sapakova ;Bayan AbduraimovaЖ. Р. УалиевThis study explores the enhancement of the finite difference method using artificial intelligence (AI) to model incompressible fluid flow within a rectangular domain.Accurate and efficient modeling of incompressible flows is crucial in hydrodynamics and aerodynamics, where traditional numerical methods like finite difference often face limitations in accuracy and computational speed, especially for complex problems with varied initial and boundary conditions.By integrating AI techniques such as neural networks and machine learning, this approach enables adaptive parameter selection, optimizing the calculation process, improving accuracy, and reducing computational demands.The combined finite difference and AI approach demonstrated significantly reduced computational complexity and improved accuracy, as verified across multiple test cases with different boundary conditions.Results indicate that AI-enhanced numerical methods improve the efficiency and reliability of solutions for complex hydrodynamic problems, offering faster, more precise modeling of fluid flow behaviors.The study highlights the potential of AI to enhance classical numerical methods, enabling the development of more accurate fluid flow models even with limited computational resources.The novelty of this work lies in the specific integration of machine learning with the finite difference method, presenting an adaptive approach that extends beyond traditional techniques.This integration opens up new possibilities for handling complex fluid dynamics scenarios, expanding the applications of numerical methods in scientific and engineering calculations.
