Prediction Using A Neural Network Algorithm Approach (A Review)

Harumy, T. Henny Febriana; Zarlis, Muhammad; Effendi, Syahril; Lidya, Maya Silvi

doi:10.1109/icsecs52883.2021.00066

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…Several previous studies [14] have focused on the development of protis in artificial neural networks. These studies draw inspiration from how amoebas divide themselves to survive and become stronger, aiming to improve the performance and accuracy of neural network models.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…To address the problem of determining the appropriate number of hidden layers for achieving a well-structured neural network architecture, a deeper exploration and exploitation of the protis way of life are considered necessary. This exploration involves studying the four phases: prophase, metaphase, and anaphase/telophase, and attempting to integrate them into developing hidden layers in neural networks [14].…”

Section: Deepening and Understanding The Concept Of Protis Theory And...mentioning

confidence: 99%

“…Step 8: test whether the stop condition is met. This stop condition is met if the resulting error value is less than the error value [14]. The following pseudocode is derived from the following algorithm design.…”

Section: Fig 2 Protis Neural Network Architecturementioning

confidence: 99%

“…Furthermore, the optimization of the dataset is used in this approach [13]. The protis approach, part of the metaheuristic intelligence and swarm intelligence algorithms, is currently being developed to optimize the architecture of artificial neural networks [14]. Previous literature on the development of the theory of protists in artificial neural networks [14,15] highlights the potential of the protist amoeba adaptive lifestyle to enhance performance.…”

Section: Introductionmentioning

confidence: 99%

“…The protis approach, part of the metaheuristic intelligence and swarm intelligence algorithms, is currently being developed to optimize the architecture of artificial neural networks [14]. Previous literature on the development of the theory of protists in artificial neural networks [14,15] highlights the potential of the protist amoeba adaptive lifestyle to enhance performance. Further exploration and exploitation of this approach are required to adapt it specifically to optimize the number of hidden layers in a neural network model [16].…”

Section: Introductionmentioning

confidence: 99%

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A novel approach to the development of neural network architecture based on metaheuristic protis approach

Harumy,

Zarlis,

Lydia

et al. 2023

EEJET

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Determining the best model for the neural network architecture and how to optimize the architecture with the metaheuristic Protis Approach is a subject of the study. A comprehensive investigation and utilization of metaheuristic methods are necessary. These methods aim to solve problems and adapt from the lifestyle of the amoeba protis. In this study, the proposed method modifies the life cycle of the amoeba, which consists of four phases: prophase, metaphase, anaphase, and telophase. These four phases are modified in the neural network architecture to optimize the appropriate number of hidden layers and produce an efficient architecture model. The results show that the protis approach optimized the neural network architecture, especially in generating hidden layers to improve the neural network model. Distinctive features of the results obtained are that the average range of degenerate neurons in the hidden layer is 0 to 35 neurons in each layer. The standard number of neurons makes it possible to solve the problem of determining the best model on the neural network architecture. The protis algorithm embedded in the protis recurrent neural network for categorical data measurements produces an average RMSE value, representing the difference between actual measurements and predictions, equal to 0.066. Consequently, the developed model surpasses the current classical neural network model in terms of performance. Regarding accuracy, the protis algorithm embedded in the neural network for categorical and time series data achieves an average precision of 0.952 and a recall of 0.950. The protis convolutional neural network achieves an accuracy of 95.9 %. Therefore, from the three tested datasets, the protis convolutional neural network exhibits the highest accuracy value

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Deepening and Understanding The Concept Of Protis Theory And...mentioning

confidence: 99%

Section: Fig 2 Protis Neural Network Architecturementioning

confidence: 99%