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1

SHUBHI SAXENA Prof (Mr.) Mohd. Arif

Abstract:
Air quality forecasting plays a crucial role in environmental management and public health. Traditional methods often struggle to accurately predict pollutant concentrations due to complex interactions of meteorological factors and emissions sources. Advanced 1D deep learning models, including convolutional and recurrent neural networks, have emerged as promising tools for improving forecasting accuracy. These models excel in capturing intricate temporal and spatial patterns in air quality data, offering advantages over conventional statistical approaches. This review explores recent advancements in 1D deep learning techniques applied to air quality forecasting, highlighting methodologies, case studies, and challenges. It aims to provide insights into the potential of deep learning models to transform air quality prediction, guiding future research and applications in environmental science and policy.


1-10
2

Sunita1, Dr. Vineeta Basotia2, Dr. Arun Kumar3

Abstract:
Bioactive compounds form different plant materials are used in a number of important pharmaceutical, food and chemical industries. Many conventional and unconventional methods are available to extract optimum yields of these bioactive compounds from various plant materials. This paper focuses on the extraction of bioactive compounds from Gardenia by modeling the problem as a nonlinear optimization problem with multiple objectives. The mathematical problems are then solved by Grey Wolf optimization and it is concluded that SCA is emerged as a winner in solving these problems. The convergence graphs also show the better performance of the algorithm in lesser iterations. Thus, it is concluded that SCA can be applied to these optimization problems for better performance.


11-18
Collaboration Partners

  • Indian Journals

  • Swedish Scientific
    Publications

  • The Universal
    Digital Library

  • Green Earth Research
    And Publishing House

  • Rashtriya Research Institute
    Of New Medical Sciences

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