Self-supervised Sentiment Classification based on Semantic Similarity Measures and Contextual Embedding using metaheuristic optimizer

Seilsepour, Azam; Alizadeh, Mousa; Ravanmehr, Reza; Beheshti, Mohammad Taghi Hamidi; Nassiri, Ramin

doi:10.1109/icspis56952.2022.10043914

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…The random search, on the contrary, can sample the critical dimension of search space, performing more efficiently than grid search 41,42 . Employing nature‐inspired meta‐heuristic algorithms is an alternative way to optimize hyper‐parameters of neural networks with promising results 43,44 . As presented in Reference 45, an LSTM‐CNN network forecasted energy consumption, where the PSO algorithm finds the optimal number of CNN kernels, the number of LSTM units, and the number of units in the fully connected layer.…”

Section: Related Workmentioning

confidence: 99%

“…41,42 Employing nature-inspired meta-heuristic algorithms is an alternative way to optimize hyper-parameters of neural networks with promising results. 43,44 As presented in Reference 45, an LSTM-CNN network forecasted energy consumption, where the PSO algorithm finds the optimal number of CNN kernels, the number of LSTM units, and the number of units in the fully connected layer. Predicting bitcoin price via an LSTM network was proposed in Reference 46, employing ABC to optimize hyper-parameters like sliding window, the number of LSTM units, dropout rate, regularizer, regularizer rate, optimizer, and learning rate.…”

Section: Hyper-parameter Optimizationmentioning

confidence: 99%

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An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Alizadeh,

Beheshti,

Ramezani

et al. 2023

Trans Emerging Tel Tech

Self Cite

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With the rapid development of telecommunication networks, the predictability of network traffic is of significant interest in network analysis and optimization, bandwidth allocation, and load balancing adjustment. Consequently, in recent years, significant research attention has been paid to forecasting telecommunication network traffic. Telecommunication traffic forecasting problems can be considered a time‐series problem, wherein periodic historical data is fed as the input to a model. Time‐series forecasting approaches are broadly categorized as statistical machine learning (ML) methods and their combinations. Statistical approaches forecast linear characteristics of time‐series data, unable to capture nonlinear and complex patterns. ML‐based approaches can model nonlinear characteristics of data. In recent years, hybrid approaches combining statistical and ML‐based approaches have been widely used to model linear and nonlinear data characteristics. However, the performance of these approaches highly depends on feature selection techniques and hyper‐parameter tuning of ML methods. A novel hybrid method is proposed for short‐term traffic forecasting based on feature selection and hyperparameter optimization to address this problem. It combines statistical and ML methods to model linear and nonlinear components of data. First, a novel feature selection technique, modified mutual information based on a linear combination of targets, is proposed to find the candidate input variables. Next, a combination of vector auto regressive moving average (VARMA), long short‐term memory (LSTM), and multilayer perceptron (MLP), called VARMA‐LSTM‐MLP forecaster, is suggested to forecast short‐term traffic. A hybrid metaheuristic algorithm, composed of firefly and BAT, is employed to find the optimal set of hyper‐parameter values. The proposed method is assessed by a real‐world dataset containing Tehran city's daily telecommunication data in IRAN. The evaluation results demonstrate that the proposed method outperforms the existing methods in terms of mean squared error and mean absolute error.

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Section: Related Workmentioning

confidence: 99%

Section: Hyper-parameter Optimizationmentioning

confidence: 99%

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Alizadeh,

Beheshti,

Ramezani

et al. 2023

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to provide important guiding principles for the protection and inheritance of ICH and ensure the sustainability and vitality of cultural heritage, this section discusses the theoretical content related to the protection of ICH [ 27 ].…”

Section: Research Modelmentioning

confidence: 99%

Application interface design of Chongqing intangible cultural heritage based on deep learning

Liu,

Cheng,

2023

Heliyon

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

Xu,

Chen,

Xiao

et al. 2024

Neural Comput & Applic

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Self-supervised Sentiment Classification based on Semantic Similarity Measures and Contextual Embedding using metaheuristic optimizer

Cited by 7 publications

References 22 publications

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Application interface design of Chongqing intangible cultural heritage based on deep learning

A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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