Enhanced Weight-Optimized Recurrent Neural Networks Based on Sine Cosine Algorithm for Wave Height Prediction

Alqushaibi, Alawi; Abdulkadir, Said Jadid; Rais, Helmi Md; Al-Tashi, Qasem; Ragab, Mohammed Gamal; Alhussian, Hitham

doi:10.3390/jmse9050524

Cited by 22 publications

(15 citation statements)

References 73 publications

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“…Specific qualities such as rapid convergence speed, great global searchability, excellent optimization precision, and minimal tuning parameters all contribute to the SCA's advantages when applied to an optimization issue. The SCA has been utilised successfully in the solving of optimization issues in a variety of fields, including image processing [34], power system engineering [35], machine learning [36], networking, and other fields [37]. SCA was also hybridized with arithmetic optimization algorithm (AOA) as presented in [38].…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Harris Hawks With Sine Cosine for Optimal Node Placement and Congestion Reduction in an Industrial Wireless Mesh Network

et al. 2023

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The optimal performance of a wireless mesh network (WMN) can be greatly improved by strategically placing wireless mesh routers. As a result, it is crucial to optimally locate the WMN routers for better coverage and connectivity. Besides the optimal placement, the network congestion due to overlaying routers has to be taken into consideration. These issues have become a motivation for researchers to identify a variety of approaches to optimize WMN performance. Multiple metaheuristic algorithms have been employed for identifying the trade-offs between coverage and connectivity in WMN. Consequently, a novel hybrid Harris Hawks optimization with the sine cosine algorithm (HHOSCA) is presented in this work to tackle the aforementioned WMN optimization problems. The proposed HHOSCA seeks optimal router placement that leads to significantly increased network coverage and achieves full connectivity between the mesh routers. In addition, the proposed HHOSCA produces a cost-effective WMN by reducing the congestion in the network to the minimum number of routers whilst ensuring maximum coverage and connectivity. The superiority of the proposed HHOSCA in comparison to the other algorithm was validated by using 33 benchmark functions. It was compared against four well-known algorithms including Sine Cosine Algorithm (SCA), Harris Hawks optimization (HHO), Gray Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). These algorithms are statistically analyzed and compared to the simulated results of the proposed method. In addition, the performance of HHOSCA is compared to the state-of-the-art to highlight the efficacy of the proposed algorithm. The statistical analyses and simulation findings confirm that the HHOSCA outperforms the other algorithms in terms of network connectivity, coverage, network reduction, and convergence. The experimental results reveal that the proposed HHOSCA method achieves favourable optimization results compared with other relevant methods.INDEX TERMS Optimal node placement, reliable wireless networks, network deployment optimization, particle swarm optimization, gray wolf optimization, Harris Hawks optimization, sine cosine optimization, industrial wireless mesh networks.The associate editor coordinating the review of this manuscript and approving it for publication was Xiaolong Li .

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid Harris Hawks With Sine Cosine for Optimal Node Placement and Congestion Reduction in an Industrial Wireless Mesh Network

et al. 2023

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“…SA tasks are perceived as sequential tasks that rely on a particular vector and cause text information loss if the vector length is too short. LSTM has been comprehensively studied in Alayba et al (2018) , Wang et al (2016a) , Chen & Wang, 2018 , Kaladevi & Thyagarajah (2019) , Wang et al (2020) , Alqushaibi et al (2021) for the extraordinary outputs of sentiment polarity classification with the combination of other neural networks, e.g ., CNN. However, the LSTM and CNN combination efficiency relies on the quantity and quality of datasets.…”

Section: Related Workmentioning

confidence: 99%

ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis

Kamyab

Rasool

et al. 2022

PeerJ Computer Science

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Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been successfully applied to Natural Language Processing (NLP), especially in sentiment analysis. NLP can execute numerous functions to achieve significant results through RNN and CNN. Likewise, previous research shows that RNN achieved meaningful results than CNN due to extracting long-term dependencies. Meanwhile, CNN has its advantage; it can extract high-level features using its local fixed-size context at the input level. However, integrating these advantages into one network is challenging because of overfitting in training. Another problem with such models is the consideration of all the features equally. To this end, we propose an attention-based sentiment analysis using CNN and two independent bidirectional RNN networks to address the problems mentioned above and improve sentiment knowledge. Firstly, we apply a preprocessor to enhance the data quality by correcting spelling mistakes and removing noisy content. Secondly, our model utilizes CNN with max-pooling to extract contextual features and reduce feature dimensionality. Thirdly, two independent bidirectional RNN, i.e., Long Short-Term Memory and Gated Recurrent Unit are used to capture long-term dependencies. We also applied the attention mechanism to the RNN layer output to emphasize each word’s attention level. Furthermore, Gaussian Noise and Dropout as regularization are applied to avoid the overfitting problem. Finally, we verify the model’s robustness on four standard datasets. Compared with existing improvements on the most recent neural network models, the experiment results show that our model significantly outperformed the state-of-the-art models.

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“…This issue is addressed by recurrent neural networks. They are networks that contain loops, which enable information to endure [37], [38].…”

Section: Deep Learning Neural Network (Dlnn)mentioning

confidence: 99%

Short term residential load forecasting using long short-term memory recurrent neural network

Muneer

Ali

Almaghthawi

et al. 2022

IJECE

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<span>Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.</span>

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Enhanced Weight-Optimized Recurrent Neural Networks Based on Sine Cosine Algorithm for Wave Height Prediction

Cited by 22 publications

References 73 publications

Hybrid Harris Hawks With Sine Cosine for Optimal Node Placement and Congestion Reduction in an Industrial Wireless Mesh Network

Hybrid Harris Hawks With Sine Cosine for Optimal Node Placement and Congestion Reduction in an Industrial Wireless Mesh Network

ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis

Short term residential load forecasting using long short-term memory recurrent neural network

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