Hybrid Deep Learning-Based Model for Wind Speed Forecasting Based on DWPT and Bidirectional LSTM Network

This study evaluates the performance of traditional statistical methods, specifically the Seasonal Autoregressive Integrated Moving Average (SARIMA), in comparison to advanced deep learning models—Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Convolutional Neural Network (CNN)—for wind speed forecasting across various regions in Saudi Arabia: Al-Jouf, Abha, Al-Ahsa, and Al-Dawadami. The historical wind speed dataset covering the year 2018 was utilized, with data preprocessing conducted using the Exploratory Data Analysis (EDA) method to ensure consistency and quality. The models were assessed based on three primary error metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). Results show that CNN consistently outperforms SARIMA and the other deep learning models, particularly when forecasting stable wind speed patterns. LSTM demonstrates an ability to handle fluctuating wind speeds effectively, while BiLSTM offers advantages in capturing complex bidirectional temporal dependencies. On the other hand, SARIMA generally exhibits lower performance compared to deep learning approaches. The superior performance of CNN is likely attributed to its strength in local feature extraction and handling spatial patterns effectively, which are beneficial for short-term forecasting. These findings provide valuable insights into model selection for wind energy forecasting and can contribute to optimizing renewable energy integration and planning strategies. Future work may explore hybrid model approaches to further enhance forecasting accuracy.

Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data

Maglev is a developing transportation mode in the recent years, and its safety and stability are required to be ensured by structural health monitoring system. However, the data measured by some sensors may be partially or totally unavailable due to the external electromagnetic interference or sensor failure. While, in some cases, those missing data are important to identify and analyze damage of maglev bridges. Since the structural dynamic characteristics of maglev bridge are complicated, the data reconstruction of those sensors becomes challenging. Therefore, this study proposes a bidirectional long short-term memory (BiLSTM) network to accurately reconstruct the maglev bridge acceleration data. The design of long short-term memory helps to sufficiently exploit the time series data and learn the hidden features of maglev bridge acceleration data between sensors. The bidirectional architecture enables the network to simultaneously learn the time series data from past and future, which is beneficial to extract more hidden features. A dataset collected from an in-site experiment for maglev bridge is used to verify the feasibility of the proposed method. The lost acceleration data from abnormal sensors is predicted by the acceleration data recorded from normally operating sensors. The results shows that the difference between predicted and true acceleration data is at a very low magnitude. Consequently, the proposed method can be applied for the high-performance reconstruction of maglev bridge acceleration data.

Predicting remaining useful life (RUL) is crucial for tool condition monitoring (TCM) systems. Inaccurate predictions can lead to premature tool replacements or excessive usage, resulting in resource wastage and potential equipment failures. This study introduces a novel tool RUL prediction method that integrates the enhanced northern goshawk optimization (MSANGO) algorithm with a bidirectional long short-term memory (BiLSTM) network. Initially, key statistical features are extracted from collected signal data using multivariate variational mode decomposition. This is followed by effective feature reduction, facilitated by the uniform information coefficient and Mann–Kendall trend tests. The RUL predictions are subsequently refined through a BiLSTM network, with the MSANGO algorithm optimizing the network parameters. Comparative evaluations with BiLSTM, BiGRU, and NGO-BiLSTM models, as well as tests on real-world datasets, demonstrate this method’s superior accuracy and generalizability in RUL prediction, enhancing the efficacy of tool management systems.

Application of Deep Learning for Reservoir Porosity Prediction and Self Organizing Map for Lithofacies Prediction

A two-stage online remaining useful life prediction framework for supercapacitors based on the fusion of deep learning network and state estimation algorithm

Parts of Speech (pos) tagging corresponds to techniques of tagging accurate pos tags of each word of a sentence. It is a pivotal and indispensable part of sentiment analysis, synthesizing text to speech, analyzing textual data and many more Natural Language Processing (NLP) tasks. Though there are many researches done in the field of languages like English, Chinese etc., it is still comparatively unexplored for Bangla. An excellent pos tagger in Bangla language can accelerate NLP of Bangla textual data. In this study, we have developed a novel hybrid Deep Learning (DL) model with two parallel input networks with Convolutional Neural Network (CNN), and Bidirectional Long Short Term Memory (BiLSTM), and an output network of CNN. CNN works well regarding the local dependencies of words in sentences, and BiLSTM works well with the global dependencies of words regarding the context in a sentence. No other work has been done with local features of textual data along with long term word dependencies in Bangla pos tagging studies. The model can work with any external Word Embeddings. In this study, it is implemented with both Embedding Layer and Word2Vec layer. In the evaluation process, Mathews Correlation Coefficient (MCC), Precision-Recall curve are used along with accuracy and F1-score due to the dataset's imbalanced characteristic. Our proposed model accompanied by Word2Vec layer has outperformed Vanilla Recurrent Neural Network (RNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), BiLSTM networks, and also one recent study using the same dataset with the highest accuracy of 0.974, F1-score of 0.883, and MCC value of 0.936.

A novel hybrid model, which integrates a stochastic diffusion process and artificial neural networks, is proposed for accurate modelling and forecasting of wind speed. A stochastic diffusion process model is deployed to capture the uncertainty of wind speed, while the drift and the diffusion of the stochastic diffusion process are approximated by artificial neural networks (ANNs). The proposed method takes advantages of both the stochastic diffusion process to simulate the evolution of wind speed and the universal approximation of ANNs to approximate the distribution of wind speed. The proposed method can easily be used to generate wind speed at any length. The effectiveness of the proposed method is validated through numerical studies on wind speed data collected from National Renewable Energy Laboratory, USA, by benchmarking against the persistence model and the improved Markov chain model in the literature. The numerical results illustrate that the proposed method achieved better modeling and forecasting in terms of mean absolute error and root mean square error than the benchmarks.

Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks

As the ubiquity of such wearable devices as smart-phones continues to deepen its presence in modern societies, it has become possible to analyze and visualize people who are moving as part of a trajectory of big data. In this study, we cluster human movement trajectories using time-series distributional representations. For the clustering, we calculated the distance of the representation vectors derived from neural network models. Previous work leveraged the Long short-term memory (LSTM) network to train the next mesh prediction. In this study, we propose using the Bi-directional LSTM (Bi-LSTM) network and the integrated additional geographical coordinates (latitude and longitude information) in models to accurately predict the next mesh and construct user clusters. As a result, we improved the accuracy of the next mesh prediction and obtained and visualized clusters of human movement trajectories.

Today, prevailing phishing scams have caused serious network security problems. By adopting a domain generation algorithm, the cyber attackers are able to disguise their malware in various forms, making it easier to steal personal identity and property. This paper proposed an innovative approach to detect malicious URLs by employing a Bidirectional Long Short-Term Memory (B-LSTM) network. The paper also compares the performance of the B-LSTM network with that of conventional machine learning methods and unidirectional LSTM. The results demonstrate that the B-LSTM network has superior performance than the other machine learning methods, producing a F1 score of 95.9%.

The rapid advancement of artificial intelligence and intelligent algorithms has led to the widespread adoption of natural language processing technology. Named entity recognition (NER), an important task in this field, takes a pivotal part in information extraction, machine translation, and knowledge graph construction. However, for a small language such as Lao, traditional methods have obvious deficiencies in recognition effectiveness due to its complex syllable structure, fuzzy word boundaries and scarce corpus resources. To address this problem, the study designs a named entity recognition model for Lao that incorporates a Bi-directional Long Short-Term Memory (BiLSTM) network, an attention mechanism and a conditional random field. The model first vectorizes multi-granularity features such as characters, syllables and lexemes through an embedding layer, then captures contextual dependencies by a BiLSTM network, introduces an Attention to highlight key information, and finally establishes a global label transfer relation through a conditional random field to ensure the consistency of the predicted sequence. The findings denote that the model achieves 91.5%, 91.0%, 90.7%, and 90.9% in accuracy rate, precision rate, recall rate, and F1 score, respectively, and the mean absolute error and mean square error are reduced to 0.062 and 0.011, which are significantly better than the comparison model. The results show that combining multi-level feature modeling and global constraint mechanism can effectively enhance the performance of named entity recognition in Lao, which provides a valuable reference and practical path for natural language processing in small languages.

Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.

Ultra-short-term wind speed forecasting based on EMD-VAR model and spatial correlation

A wind speed forecasting technique, using deep learning architectures based on long short-term memory (LSTM) model and bidirectional long short-term memory (BiLSTM) model is presented in this work. The coastal belts of the Indian peninsula are vulnerable to natural disasters like storm surges and inundations due to cyclones each year. The wind speed is a major parameter for analyzing extreme weather events. Prediction using numerical models is not efficient enough due to the irregular patterns in the data and, thus, deep neural network models involving many layers have been tested. The shallow feed-forward model has also been considered along with deep learning models to estimate future values from past data. The present work employs a comparison study of different models to forecast wind speed time series at two locations in the Bay of Bengal and the Arabian Sea, respectively, having different dynamics and randomness. For training the models, daily wind speed data are considered for the period 2006–2017 and an independent validation set is chosen comprising 2018 wind speed data to check the accuracy. To evaluate forecast efficiency among different network models fitted to given time series, mean square error (MSE) and root mean square error (RMSE) have been computed. Multiple experiments are conducted with different hidden unit values and epoch values to obtain the minimum error. Regression equations generated may be used for forecasting future time series. The BiLSTM model connecting hidden states of opposite directions proved to be most efficient for the wind speed forecasting in different regions.