Abstract:Air pollution consists of harmful gases and fine Particulate Matter (PM2.5) which affect the quality of air. This has not only become the key issues in scientific research but also turned to be an important social issues of the public’s life. Therefore, many experts and scholars at different R&Ds, universities, and abroad are involved in lot of research on PM2.5 pollutant predictions. In this scenario, the authors proposed various machine learning models such as linear regression, random forest, KNN, ridge… Show more
“…Meanwhile, the forecasted results contain numerous uncertainties in predicting spatial interpolation because of non-uniform distribution sampling points and ground monitoring stations. 3,4 So, in recent times, machine learning (ML) based air quality prediction techniques have gained more interest among research communities for accurate forecasts of air quality. An automated learning model with incremental update ability is more suitable for forecasting nonlinear, irregular, and non-stationary sequences of fine air particulate matter.…”
Section: Introductionmentioning
confidence: 99%
“…In general, the ground air quality monitoring stations erected with multiple sensors continuously monitor the PM2.5 level of atmosphere. Meanwhile, the forecasted results contain numerous uncertainties in predicting spatial interpolation because of non‐uniform distribution sampling points and ground monitoring stations 3,4 . So, in recent times, machine learning (ML) based air quality prediction techniques have gained more interest among research communities for accurate forecasts of air quality.…”
In recent times, air quality prediction is turned out to be one of the important research topics among research communities to prevent lives from negative health impacts. Random fluctuations of PM2.5 level brought about by frequent variations in meteorological factors create difficulties air pollution management. Forecasting the quality of air using time series data serves as a defense mechanism against threatening hazards by providing immense support to take preventive measures. Besides, handling dynamic real time workloads, forecasted by the prediction model requires appropriate computing resources to distribute workloads based on demands. To achieve this goal, this paper proposes a new Air Quality Prediction‐enabled Resource Allocation scheme for cloud‐based software services, which offers dynamic adjustment of resources based on workload demands with high energy efficiency. The proposed system is a two phase system that executes both air quality prediction and resource allocation processes consecutively. A new weighted average ensemble classifier is designed by combining support vector machine (SVM), artificial neural network (ANN), and gradient boosting machine (GBM) techniques to measure PM2.5 level on time series information of Beijing PM2.5 dataset. The system then dynamically allocates appropriate computing resources using crossover particle swarm optimization (CPSO) algorithm based on the forecasted results of PM2.5 level. This system has the potential to contribute significantly to reducing energy consumption and improving air quality in cities worldwide. The experimental results conducted to determine the efficiency of the proposed system in terms of different metrics proves that it achieves greater performance with less error functions for PM2.5 level prediction as well as minimizes energy consumption for resource allocation when compared with existing methods.
“…Meanwhile, the forecasted results contain numerous uncertainties in predicting spatial interpolation because of non-uniform distribution sampling points and ground monitoring stations. 3,4 So, in recent times, machine learning (ML) based air quality prediction techniques have gained more interest among research communities for accurate forecasts of air quality. An automated learning model with incremental update ability is more suitable for forecasting nonlinear, irregular, and non-stationary sequences of fine air particulate matter.…”
Section: Introductionmentioning
confidence: 99%
“…In general, the ground air quality monitoring stations erected with multiple sensors continuously monitor the PM2.5 level of atmosphere. Meanwhile, the forecasted results contain numerous uncertainties in predicting spatial interpolation because of non‐uniform distribution sampling points and ground monitoring stations 3,4 . So, in recent times, machine learning (ML) based air quality prediction techniques have gained more interest among research communities for accurate forecasts of air quality.…”
In recent times, air quality prediction is turned out to be one of the important research topics among research communities to prevent lives from negative health impacts. Random fluctuations of PM2.5 level brought about by frequent variations in meteorological factors create difficulties air pollution management. Forecasting the quality of air using time series data serves as a defense mechanism against threatening hazards by providing immense support to take preventive measures. Besides, handling dynamic real time workloads, forecasted by the prediction model requires appropriate computing resources to distribute workloads based on demands. To achieve this goal, this paper proposes a new Air Quality Prediction‐enabled Resource Allocation scheme for cloud‐based software services, which offers dynamic adjustment of resources based on workload demands with high energy efficiency. The proposed system is a two phase system that executes both air quality prediction and resource allocation processes consecutively. A new weighted average ensemble classifier is designed by combining support vector machine (SVM), artificial neural network (ANN), and gradient boosting machine (GBM) techniques to measure PM2.5 level on time series information of Beijing PM2.5 dataset. The system then dynamically allocates appropriate computing resources using crossover particle swarm optimization (CPSO) algorithm based on the forecasted results of PM2.5 level. This system has the potential to contribute significantly to reducing energy consumption and improving air quality in cities worldwide. The experimental results conducted to determine the efficiency of the proposed system in terms of different metrics proves that it achieves greater performance with less error functions for PM2.5 level prediction as well as minimizes energy consumption for resource allocation when compared with existing methods.
“…The rapid advancement of artificial intelligence (AI) and deep learning has led to significant improvements in the performance of various machine learning models across a wide range of applications, such as computer vision, natural language processing, and medical diagnosis [1,4,9,15]. However, as these models become more complex and sophisticated, their decision-making processes become increasingly opaque, often referred to as "black-box" models [7,8].…”
Section: Introductionmentioning
confidence: 99%
“…The growing awareness of the importance of interpretability and trustworthiness in AI has motivated researchers to develop methods and techniques that aim to explain and understand the predictions made by complex machine learning models. This field of research is known as Explainable AI (XAI) [2,9,11]. XAI aims to provide human users with insights into the decision-making process of AI systems, enabling them to trust, validate, and potentially challenge the outcomes produced by these models [1,6,12].…”
Explainable AI (XAI) aims to address the opacity of deep learning models, which can limit their adoption in critical decision-making applications. This paper presents a novel framework that integrates interpretable components and visualization techniques to enhance the transparency and trustworthiness of deep learning models. We propose a hybrid explanation method combining saliency maps, feature attribution, and local interpretable model-agnostic explanations (LIME) to provide comprehensive insights into the model's decision-making process.
Our experiments with convolutional neural networks (CNNs) and transformers demonstrate that our approach improves interpretability without compromising performance. User studies with domain experts indicate that our visualization dashboard facilitates better understanding and trust in AI systems. This research contributes to developing more transparent and trustworthy deep learning models, paving the way for broader adoption in sensitive applications where human users need to understand and trust AI decisions.
“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: The presence of these indicators undermines our confidence in the integrity of the article's content and we cannot, therefore, vouch for its reliability.…”
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