Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm

Chiroma, Haruna; Kareem, Sameem Abdul; Khan, Abdullah; Nawi, Nazri Mohd; Gital, Abdulsalam Ya’u; Shuib, Liyana; Abubakar, Adamu; Rahman, Muhammad Zubair; Herawan, Tutut

doi:10.1371/journal.pone.0136140

Cited by 41 publications

(14 citation statements)

References 52 publications

(64 reference statements)

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“…Since the convergence rate of BPNN is very low, the network easily becomes unstable and not suitable for large problems data sets. Furthermore, the convergence behaviour of BPNN also depends on the choice of initial values of connection weights and other parameters used in the algorithm such as the learning rate and the momentum term [10]. Thus, BPNN needs improvement to perform well and overcome those drawbacks.…”

Section: Methodsmentioning

confidence: 99%

“…The derivation of the proposed procedure for calculating the learning rate together with gain value is based on the gradient descent algorithm. The error function as defined in Equation 1 It should be noted that, the iterative formula as described in Equation (8) to calculate s 1 δ is as the same as used in the standard back propagation algorithms [10] except for the appearance of the gain value in the expression. The learning rule for calculating weight values as given in Equation 5is derived by combining (7) and (8).…”

Section: Randomlymentioning

confidence: 99%

See 1 more Smart Citation

An Optimized Back Propagation Learning Algorithm with Adaptive Learning Rate

Nawi¹,

Hamzah²,

Hamid³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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Back Propagation (BP) is commonly used algorithm that optimize the performance of network for training multilayer feed-forward artificial neural networks. However, BP is inherently slow in learning and it sometimes gets trapped at local minima. These problems occur mailnly due to a constant and non-optimum learning rate (a fixed step size) in which the fixed value of learning rate is set to an initial starting value before training patterns for an input layer and an output layer. This fixed learning rate often leads the BP network towrds failure during steepest descent. Therefore to overcome the limitations of BP, this paper introduces an improvement to back propagation gradient descent with adapative learning rate (BPGD-AL) by changing the values of learning rate locally during the learning process. The simulation results on selected benchmark datasets show that the adaptive learning rate significantly improves the learning efficiency of the Back Propagation Algorithm.

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Section: Methodsmentioning

confidence: 99%

Section: Randomlymentioning

confidence: 99%

An Optimized Back Propagation Learning Algorithm with Adaptive Learning Rate

Nawi¹,

Hamzah²,

Hamid³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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“…The application of evolutionary algorithms in solving problems has been successful in many domains [98]. Despite the fact that the training of ANN using evolutionary algorithms is more effective and efficient than backpropagation algorithms [99]- [101], their application to train ANN for exploring big datasets does not received tremendous attention, only limited number were found. The review shows that evolutionary algorithms have not been applied by previous researchers for the determination of weights, biases, structures, the adaptation of learning rules and internal parameters of the DNN, ESN, FLN, large-scale RNN and ConvNet within big data analytics.…”

Section: ) Swarm and Evolutionary Neural Networkmentioning

confidence: 99%

Progress on Artificial Neural Networks for Big Data Analytics: A Survey

et al. 2019

Self Cite

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Approximately 2.5 quintillion bytes of data are emitted on a daily basis, and this has brought the world into the era of ''big data.'' Artificial neural networks (ANNs) are known for their effectiveness and efficiency for small datasets, and this era of big data has posed a challenge to the big data analytics using ANN. Recently, much research effort has been devoted to the application of the ANN in big data analytics and is still ongoing, although it is in it is early stages. The purpose of this paper is to summarize recent progress, challenges, and opportunities for future research. This paper presents a concise view of the state of the art, challenges, and future research opportunities regarding the applications of the ANN in big data analytics and reveals that progress has been made in this area. Our review points out the limitations of the previous approaches, the challenges in the ANN approaches in terms of their applications in big data analytics, and several ANN architecture that have not yet been explored in big data analytics and opportunities for future research. We believe that this paper can serve as a yardstick for future progress on the applications of the ANN in big data analytics as well as a starting point for new researchers with an interest in the exploration of the ANN in big data analytics. INDEX TERMS Big data analytics, artificial neural networks, evolutionary neural network, convolutional neural network, dataset. The associate editor coordinating the review of this manuscript and approving it for publication was Shirui Pan. many organizations on an ongoing basis. These datasets are being collected from various sources, including but not limited to the World Wide Web (WWW), social networks and sensor networks [3]. The discovery of knowledge from unstructured data accumulated from the WWW remains a difficult task because the content is suitable for human consumption rather than for machines [4]. Experimental evidence has shown that if big datasets are exploited and managed properly, it can give rise to critical intelligence that can motivate informed decisions and

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“…Nawi et al [ 17 ] used the PSO algorithm to optimize the weights of recurrent neural networks and conduct data classification. Chiroma et al [ 18 ] applied an artificial neural network optimized by the PSO algorithm to predict OPEC CO 2 emissions. Chiroma H. et al [ 19 , 20 ] predicted crude oil prices using a neural network optimized by a genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce

et al. 2016

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A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.

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Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm

Cited by 41 publications

References 52 publications

An Optimized Back Propagation Learning Algorithm with Adaptive Learning Rate

An Optimized Back Propagation Learning Algorithm with Adaptive Learning Rate

Progress on Artificial Neural Networks for Big Data Analytics: A Survey

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce

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