Capability of a Recurrent Deep Neural Network Optimized by Swarm Intelligence Techniques to Predict Exceedances of Airborne Pollution (PMx) in Largely Populated Areas

Kuri-Monge, Gerardo Javier; Aceves-Fernández, Marco Antonio; Ramirez-Montanez, Julio Alberto; Pedraza-Ortega, Jesús Carlos

doi:10.1109/icit52682.2021.9491649

Cited by 4 publications

(15 citation statements)

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“…In [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], and [65], swarm intelligence algorithms have been employed for optimizing different types of recurrent neural networks. In [53], the Nonlinear Auto-Regressive with Exogenous Input (NARX) recurrent neural network is used.…”

Section: Optimizing Different Types Of Artificial Neural Networkmentioning

confidence: 99%

“…In [58] and [59], deep recurrent neural networks with more than one hidden layer are used. In [60], [61], [62], [63], and [64], the Long Short Term Memory (LSTM) network is used. LSTM is a recurrent neural network with an additional unit called the memory cell which stores information for a long amount of time.…”

Section: Optimizing Different Types Of Artificial Neural Networkmentioning

confidence: 99%

“…In [56], [57], [58], [59], [63], [65], and [69], both the connection weights and the biases of recurrent neural networks are optimized during the training. In [60], [61], [62], and [64], the optimal structure of recurrent neural networks is determined using swarm intelligence algorithms prior to their training. In [61], the number of hidden layers, time lags, batch size, number of neurons in the hidden layers, and number of epochs of a recurrent neural network are optimized by a swarm intelligence algorithm.…”

Section: Optimizing Artificial Neural Network Weights Biases and Stru...mentioning

confidence: 99%

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A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

2023

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Artificial Neural Networks (ANNs) are becoming increasingly useful in numerous areas as they have a myriad of applications. Prior to using ANNs, the network structure needs to be determined and the ANN needs to be trained. The network structure is usually chosen based on trial and error. The training, which consists of finding the optimal connection weights and biases of the ANN, is usually done using gradient-descent algorithms. It has been found that swarm intelligence algorithms are favorable for both determining the network structure and for the training of ANNs. This is because they are able to determine the network structure in an intelligent way, and they are better at finding the most optimal connection weights and biases during the training as opposed to conventional algorithms. Recently, a number of swarm intelligence algorithms have been employed for optimizing different types of neural networks. However, there is no comprehensive survey on the swarm intelligence algorithms used for optimizing ANNs. In this paper, we present a review of the different types of ANNs optimized using swarm intelligence algorithms, the way the ANNs are optimized, the different swarm intelligence algorithms used, and the applications of the ANNs optimized by swarm intelligence algorithms.INDEX TERMS Artificial neural network, swarm intelligence, optimization. I. INTRODUCTIONArtificial Neural Networks (ANNs) are computational models that simulate the biological neural network that constitutes the human brain to generate inferences based on certain given information. They are suitable for both supervised and unsupervised learning for solving a myriad of classification, regression, clustering, and association problems in a multitude of areas. Notably, ANN has been a prominent algorithm in the domain of machine learning, and has paved the way for the advancement in multiple areas such as natural language processing, fraud detection, computational biology, computer vision, unassisted control of vehicles, speech recognition, medical diagnosis and recommendation systems [1]. Recently, ANNs have been applied for making decisions in healthcare organizations [2], for forecasting the energy use in buildings [3], for the development of greenhouse technol-The associate editor coordinating the review of this manuscript and approving it for publication was Yeliz Karaca .

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Section: Optimizing Different Types Of Artificial Neural Networkmentioning

confidence: 99%

Section: Optimizing Different Types Of Artificial Neural Networkmentioning

confidence: 99%

Section: Optimizing Artificial Neural Network Weights Biases and Stru...mentioning

confidence: 99%

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A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

2023

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“…Additionally, clarify that LSTM is an algorithm that belongs to recurrent neural networks or RNN [8]. The RNN's refer to neural networks that take their previous state as input, this means that the neural network will have two inputs, the new information entered into the network and its previous state, which is shown in Figure 2.…”

Section: Inputgatementioning

confidence: 99%

Perspective Chapter: Airborne Pollution (PM2.5) Forecasting Using Long Short-Term Memory Deep Recurrent Neural Network Optimized by Gaussian Process

Olguin-Sanchez¹,

Aceves-Fernández²,

Pedraza-Ortega³

et al. 2023

Swarm Intelligence - Recent Advances and Current Applications

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Forecasting air pollution is a challenging problem today that requires special attention in large cities since they are home to millions of people who are at risk of respiratory diseases every day. At the same time, there has been exponential growth in the research and application of deep learning, which is useful to treat temporary data such as pollution levels, leaving aside the physical and chemical characteristics of the particles and only focusing on predicting the next levels of contamination. This work seeks to contribute to society by presenting a useful way to optimize recurrent neural networks of the short and long-term memory type through a statistical process (Gaussian processes) for the correct optimization of the processes.

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“…AI techniques have been applied to environmental management problems for a long period with good results. AI tools like casebased reasoning, artificial neural networks, genetic algorithms, swarm intelligence, and decision trees were employed to develop various knowledge-based systems, expert systems, and fuzzy inference systems for this purpose (Ahmed et al, 2003;Riga et al, 2009;Dutta & Chaudhuri, 2014;Ong et al, 2015;Corominas et al, 2018;Qi et al, 2018;Abdul-Wahab et al, 2019;Zhao et al, 2019;Liu et al, 2019;Kuri-Monge et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Z-number-based AQI in rough set theoretic framework for interpretation of air quality for different thresholds of PM2.5 and PM10

Dutta

Pal

2022

Environ Monit Assess

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Kolkata has a reputation for being one of the world’s most polluted cities, particularly in the post-monsoon months of October, November, and December. Diwali, a Hindu festival, coincides with these months where a large number of firecrackers are set off followed by high emissions of air pollutants. As a result, the air quality index (AQI) deteriorates to “very poor” (301 ≤ AQI ≤ 400) and “poor” (201 ≤ AQI ≤ 300) categories. This situation stays for several days to a month. The present study aims to identify the thresholds for PM 2.5 and PM 10 that cause the AQI of Kolkata to deteriorate to “very poor” and “poor.” For this purpose, we have used a rough set theory-based condition-decision support system to predict the aforementioned categories of AQI. We have developed a Z-number-based novel quantification measure of semantic information of AQI to assess the reliability of the outcomes, as generated from the condition-decision-based decision rules, during post-monsoon season. The result reveals the best possible forecast of AQI with linguistic summarization of the reliability or confidence for different threshold ranges of PM 10 and PM 2.5 . Inverse-decision rules based on rough set theory are utilized to justify and validate the forecasts. The explainability of the condition-decision support system is demonstrated/visualized using a flow graph that maps rough-rule-based different decision paths between input and output with strength, certainty, and coverage. The investigation resulted in an advanced intelligent environmental decision support system (IEDSS) for air-quality prediction.

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Capability of a Recurrent Deep Neural Network Optimized by Swarm Intelligence Techniques to Predict Exceedances of Airborne Pollution (PMx) in Largely Populated Areas

Cited by 4 publications

References 8 publications

A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

Perspective Chapter: Airborne Pollution (PM2.5) Forecasting Using Long Short-Term Memory Deep Recurrent Neural Network Optimized by Gaussian Process

Z-number-based AQI in rough set theoretic framework for interpretation of air quality for different thresholds of PM2.5 and PM10

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