Weight Optimization in Recurrent Neural Networks with Hybrid Metaheuristic Cuckoo Search Techniques for Data Classification

Nawi, Nazri Mohd; Khan, Abdullah; Rehman, Muhammad Zubair; Chiroma, Haruna; Herawan, Tutut

doi:10.1155/2015/868375

Cited by 37 publications

(22 citation statements)

References 25 publications

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“…In the event of failure, this guarantees prompt and efficient performance of real-time work. Since backup significantly increases overhead storage, alternative methods are required that produce high resource utilization [4]. Although the cloud features are appealing and the uninterrupted performance of cloud services requires an inaccurate tolerance mechanism [5].…”

Section: Introductionmentioning

confidence: 99%

Secured Data Transmission with Integrated Fault Reduction Scheduling in Cloud Computing

Sheetal¹,

Lalitha²,

Gopi³

et al. 2021

ISI

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Cloud computing offers end users a scalable and cost-effective way to access multi-platform data. While the Cloud Storage features endorse it, resource loss is also likely. A fault-tolerant mechanism is therefore required to achieve uninterrupted cloud service performances. The two widely used defect-tolerant mechanisms are task relocation and replication. But the replication approach leads to enormous overhead storage and computing as the number of tasks gradually increases. When a large number of defects occur, it creates more overhead storage and time complexity depending on task criticalities. An Integrated Fault Reduction Scheduling (IFRS) cloud computing model is used to resolve these problems. The probability of failure of a VM is calculated by finding the previous failures and active executions in this model. Then a fault-related adaptive recovery timer is retained, modified depending on the fault type. Experimental findings showed that IFRS reached 67% lower storage costs and 24% less response time when comparing with the current technique for sensitive tasks.

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Section: Introductionmentioning

confidence: 99%

Secured Data Transmission with Integrated Fault Reduction Scheduling in Cloud Computing

Sheetal¹,

Lalitha²,

Gopi³

et al. 2021

ISI

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“…To overcome this limitation, a hybrid approach has been used. Mohd Nawi et al [22] investigated the data classifier problem by employing weight optimization on RNN using cuckoo search hybrid techniques. e convergence rate and local minima problem are addressed as the cuckoo search algorithm.…”

Section: Introductionmentioning

confidence: 99%

Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India

Supreetha

Shenoy

Nayak

2020

Applied Computational Intelligence and Soft Computing

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Groundwater is a precious natural resource. Groundwater level (GWL) forecasting is crucial in the field of water resource management. Measurement of GWL from observation-wells is the principle source of information about the aquifer and is critical to its evaluation. Most part of the Udupi district of Karnataka State in India consists of geological formations: lateritic terrain and gneissic complex. Due to the topographical ruggedness and inconsistency in rainfall, the GWL in Udupi region is declining continually and most of the open wells are drying-up during the summer. Hence, the current research aimed at developing a groundwater level forecasting model by using hybrid long short-term memory-lion algorithm (LSTM-LA). e historical GWL and rainfall data from an observation well from Udupi district, located in Karnataka state, India, were used to develop the model. e prediction accuracy of the hybrid LSTM-LA model was better than that of the feedforward neural network (FFNN) and the isolated LSTM models. e hybrid LSTM-LA-based forecasting model is promising for a larger dataset.

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“…They were feature values of form, texture, and the color obtaining its value from digital image processing. The value of the features obtained is then used as an input on algorithm learning of artificial neural network backpropagation [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

The Classification of Papaya using Digital Image Processing and Artificial Neural Networks

Wibowo¹,

Harjoko²,

Mustafidah³

et al. 2018

IJAST

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The quality identification process using a visual human eyes has its limitations, it requires more power to sort out. Furthermore, it has different levels of human perception in identifying the quality. This research develops digital image processing programs and artificial neural network for the quality classification of papaya Calina IPB-9 into three quality classes: Super, A, and B classes. Shape feature is extracted consistsing of compactness and roundness. Texture feature which is extracted consisting of the value of energy, entropy, contras, homogeneity, inverse difference moment, variance, and dissimilarity obtained by GLCM, another texture feature sought a feature LBP. Color feature is extracted consisting of the mean red, greeen, blue, hue, saturation, and value. These features serve as input to the training backpropagation neural network. The recognition process results shows the features of energy and entropy can distinguish quality classes of the papaya with the best accuracy rate that is equal to 87%.

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Weight Optimization in Recurrent Neural Networks with Hybrid Metaheuristic Cuckoo Search Techniques for Data Classification

Cited by 37 publications

References 25 publications

Secured Data Transmission with Integrated Fault Reduction Scheduling in Cloud Computing

Secured Data Transmission with Integrated Fault Reduction Scheduling in Cloud Computing

Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India

The Classification of Papaya using Digital Image Processing and Artificial Neural Networks

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