RETRACTED ARTICLE: Integration of RNN with GARCH refined by whale optimization algorithm for yield forecasting: a hybrid machine learning approach

Murali, P.; Revathy, R.; Balamurali, S.; Tayade, A. S.

doi:10.1007/s12652-020-01922-2

Cited by 31 publications

(15 citation statements)

References 20 publications

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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%

“…The NARX neural network used has one hidden layer with seven neurons. In [54], [55], [56], and [57], Elman recurrent neural networks are optimized using swarm intelligence algorithms. Elman RNNs are recurrent neural networks with an additional unit called the context unit which is connected to the hidden layer.…”

Section: Optimizing Different Types Of Artificial Neural Networkmentioning

confidence: 99%

“…In [55], the Elman neural network used consists of one input layer with five neurons, two hidden layers with 15 and 20 neurons respectively, and one output layer with one neuron. In [56], the RNN used consists of three hidden layers with a variable number of neurons in each layer. In [57], the Elman RNN used consists of one hidden layer with five nodes.…”

Section: Optimizing Different Types Of Artificial Neural Networkmentioning

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 Different Types Of Artificial Neural Networkmentioning

confidence: 99%

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

2023

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“…stock price volatility. Recent works such as, e.g., Murali et al (2020), were more focused on numerical procedures, again based on hybrid models. Further, Anton (2012); Guo et al (2014); Hajizadeh et al (2012); Lu et al (2016) proposed a hybrid algorithm between an artificial neural network and a GARCH model to predict the volatility of the S&P 500 index return.…”

Section: Literature Reviewmentioning

confidence: 99%

Forward-Looking Volatility Estimation for Risk-Managed Investment Strategies during the COVID-19 Crisis

Persio

Garbelli

Wallbaum³

2021

Risks

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Under the impact of both increasing credit pressure and low economic returns characterizing developed countries, investment levels have decreased over recent years. Moreover, the recent turbulence caused by the COVID-19 crisis has accelerated the latter process. Within this scenario, we consider the so-called Volatility Target (VolTarget) strategy. In particular, we focus our attention on estimating volatility levels of a risky asset to perform a VolTarget simulation over two different time horizons. We first consider a 20 year period, from January 2000 to January 2020, then we analyse the last 12 months to emphasize the effects related to the COVID-19 virus’s diffusion. We propose a hybrid algorithm based on the composition of a GARCH model with a Neural Network (NN) approach. Let us underline that, as an alternative to standard allocation methods based on realized and backward oriented volatilities, we exploited an innovative forward-looking estimation process exploiting a Machine Learning (ML) solution. Our solution provides a more accurate volatility estimation, allowing us to derive an effective investor risk-return profile during market crisis periods. Moreover, we show that, via a forward-looking VolTarget strategy while using an ML-based prediction as the input, the average outcome for an investment in a drawdown plan is more sustainable while representing an efficient risk-control solution for long time period investments.

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“…The recent advancements in the field of AI have presented ample opportunities; AI applications in crop research and agriculture have so far primarily benefited large-scale industrial farming ( Carbonell, 2016 ), with research and development investment mainly focused on: commodity crops, such as wheat, rice, and maize; high-value horticulture crops, such as soft fruits; and the enhancement of large-scale orchards and vineyards. Yield forecasting in agricultural crops is a challenging task; to address this, Murali et al (2020 ) developed a ML hybrid model with available data for yield forecasting. Combining a statistical model, such as generalized autoregressive conditional heteroscedasticity (GARCH), with a recurrent neural network (RNN) refined using the whale optimization algorithm, was found to be more appropriate for sugarcane yield forecasting in the medium term.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Sugarcane Genomics, Physiology, and Phenomics for Superior Agronomic Traits

et al. 2022

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Advances in sugarcane breeding have contributed significantly to improvements in agronomic traits and crop yield. However, the growing global demand for sugar and biofuel in the context of climate change requires further improvements in cane and sugar yields. Attempts to achieve the desired rates of genetic gain in sugarcane by conventional breeding means are difficult as many agronomic traits are genetically complex and polygenic, with each gene exerting small effects. Unlike those of many other crops, the sugarcane genome is highly heterozygous due to its autopolyploid nature, which further hinders the development of a comprehensive genetic map. Despite these limitations, many superior agronomic traits/genes for higher cane yield, sugar production, and disease/pest resistance have been identified through the mapping of quantitative trait loci, genome-wide association studies, and transcriptome approaches. Improvements in traits controlled by one or two loci are relatively easy to achieve; however, this is not the case for traits governed by many genes. Many desirable phenotypic traits are controlled by quantitative trait nucleotides (QTNs) with small and variable effects. Assembling these desired QTNs by conventional breeding methods is time consuming and inefficient due to genetic drift. However, recent developments in genomics selection (GS) have allowed sugarcane researchers to select and accumulate desirable alleles imparting superior traits as GS is based on genomic estimated breeding values, which substantially increases the selection efficiency and genetic gain in sugarcane breeding programs. Next-generation sequencing techniques coupled with genome-editing technologies have provided new vistas in harnessing the sugarcane genome to look for desirable agronomic traits such as erect canopy, leaf angle, prolonged greening, high biomass, deep root system, and the non-flowering nature of the crop. Many desirable cane-yielding traits, such as single cane weight, numbers of tillers, numbers of millable canes, as well as cane quality traits, such as sucrose and sugar yield, have been explored using these recent biotechnological tools. This review will focus on the recent advances in sugarcane genomics related to genetic gain and the identification of favorable alleles for superior agronomic traits for further utilization in sugarcane breeding programs.

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RETRACTED ARTICLE: Integration of RNN with GARCH refined by whale optimization algorithm for yield forecasting: a hybrid machine learning approach

Cited by 31 publications

References 20 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

Forward-Looking Volatility Estimation for Risk-Managed Investment Strategies during the COVID-19 Crisis

Recent Advances in Sugarcane Genomics, Physiology, and Phenomics for Superior Agronomic Traits

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