Error-output recurrent multi-layer Kernel Reservoir Network for electricity load time series forecasting

Liu, Zongying; Tahir, Ghalib Ahmed; Masuyama, Naoki; Kakudi, Habeebah Adamu; Fu, Zongwen; Pasupa, Kitsuchart

doi:10.1016/j.engappai.2022.105611

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…A neural network generally consists of three layers (i.e., input layer, hidden layer, and output layer). Based on these layers, ANN can be categorized into a single-layer feed-forward neural network (FFNN) [13], a multilayer feed-forward network [14], a single node with its feedback, and a recurrent multilayer network [15].…”

Section: Introductionmentioning

confidence: 99%

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

et al. 2023

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Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs’ adaptation for complex applications. Specifically, we investigate ANNs’ advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

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Section: Introductionmentioning

confidence: 99%

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

et al. 2023

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“…Their model could simulate the periodicity and instability of flow and make predictions for a longer time span. Electrical load time series prediction was performed using ESNs by Tahir et al [17]; their proposed algorithm has advantages in forecasting long-term power load. A new deep ESN model was proposed by Gao et al to learn the dynamic characteristics of effective wave height [18], and was tested on 12 datasets showing statistical superiority over other methods.…”

Section: Introductionmentioning

confidence: 99%

Sequence Prediction and Classification of Echo State Networks

Sun,

Li,

Peng

2023

Mathematics

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The echo state network is a unique form of recurrent neural network. Due to its feedback mechanism, it exhibits superior nonlinear behavior compared to traditional neural networks and is highly regarded for its simplicity and efficiency in computation. In recent years, as network development has progressed, the security threats faced by networks have increased. To detect and counter these threats, the analysis of network traffic has become a crucial research focus. The echo state network has demonstrated exceptional performance in sequence prediction. In this article, we delve into the impact of echo state networks on time series. We have enhanced the model by increasing the number of layers and adopting a different data input approach. We apply it to predict chaotic systems that appear ostensibly regular but are inherently irregular. Additionally, we utilize it for the classification of sound sequence data. Upon evaluating the model using root mean squared error and micro-F1, we have observed that our model exhibits commendable accuracy and stability.

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Weighted error-output recurrent Xavier echo state network for concept drift handling in water level prediction

Liu,

Zhang,

Pan

et al. 2024

Applied Soft Computing

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Error-output recurrent multi-layer Kernel Reservoir Network for electricity load time series forecasting

Cited by 5 publications

References 38 publications

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Sequence Prediction and Classification of Echo State Networks

Weighted error-output recurrent Xavier echo state network for concept drift handling in water level prediction

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