VMD based Modified Multi-View Neural Network for Long-Term Load Forecasting

In the realm of water resource management, meticulous monitoring and control methodologies are quintessential to the refinement of wastewater treatment processes. This research elucidates an avant-garde methodology for forecasting the Chemical Oxygen Demand (COD), an instrumental indicator of water quality, by harnessing the capabilities of long short-term memory (LSTM) neural networks in conjunction with Internet of Things (IoT) paradigms. The efficacy of the LSTM model is juxtaposed with that of an advanced Deep Belief Network (DBN), as well as contemporary models like a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) hybrid model and a Transformer-based model, employing data sourced from a wastewater treatment facility located in Changsha. The empirical findings show that notwithstanding the comparable training durations used, the LSTM model exhibits a preeminent error rate of merely 7%, thus surpassing the DBN model (which has an error rate of 35%), the CNN-LSTM model (registering a 22% error rate), and the Transformer-based model (with a 17% error rate) in its predictive precision. This research underscores the potential of integrating an astute wastewater control system with IoT and LSTM models, thereby hinting at prospective enhancements in the sustainability and operational efficacy of wastewater treatment installations.

Abstract. The present work proposes a prediction model of significant wave height (SWH) and average wave period (APD) based on variational mode decomposition (VMD), temporal convolutional networks (TCNs), and long short-term memory (LSTM) networks. The wave sequence features were obtained using VMD technology based on the wave data from the National Data Buoy Center. Then the SWH and APD prediction models were established using TCNs, LSTM, and Bayesian hyperparameter optimization. The VMD–TCN–LSTM model was compared with the VMD–LSTM (without TCN cells) and LSTM (without VMD and TCN cells) models. The VMD–TCN–LSTM model has significant superiority and shows robustness and generality in different buoy prediction experiments. In the 3 h wave forecasts, VMD primarily improved the model performance, while the TCN had less of an influence. In the 12, 24, and 48 h wave forecasts, both VMD and TCNs improved the model performance. The contribution of the TCN to the improvement of the prediction result determination coefficient gradually increased as the forecasting length increased. In the 48 h SWH forecasts, the VMD and TCN improved the determination coefficient by 132.5 % and 36.8 %, respectively. In the 48 h APD forecasts, the VMD and TCN improved the determination coefficient by 119.7 % and 40.9 %, respectively.

In this paper, a software comparative analysis of two neural network models is presented, namely, Convolutional Neural Network (CNN), and Long Short Term Memory (LSTM) neural network. The evaluation is performed using the famous deep learning database the “MNIST” to check the accuracy, model size, speed and complexity of the two models for future digital realization on reconfigurable hardware. In addition to that, we optimize the size of the two models by quantizing the weights width to 8-bits instead of 32-bits. The results show an extensive reduction in the size of each model (by 10X) with a slight drop in the accuracy. The results also show that the CNN is more accurate and much faster than LSTMs making it the best model to be implemented on reconfigurable hardware.

Since it is one of the world's most significant financial markets, the foreign exchange (Forex) market has attracted a large number of investors. Accurately anticipating the forex trend has remained a popular but difficult issue to aid Forex traders' trading decisions. It is always a question of how precise a Forex prediction can be because of the market's tremendous complexity. The fast advancement of machine learning in recent decades has allowed artificial neural networks to be effectively adapted to several areas, including the Forex market. As a result, a slew of research articles aimed at improving the accuracy of currency forecasting has been released. The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used. Due to its high learning capacity, the LSTM neural network is increasingly being utilized to predict advanced Forex trading based on previous data. This model, on the other hand, can be improved by stacking it. The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis. The Hurst exponent (h) was used to determine the predictability of the Australian Dollar and United States Dollar (AUD/USD) dataset. TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD). A correlation study was performed between the AUD/USD, the Euro and the Australian Dollar (EUR/AUD), and the Australian Dollar and the Japanese Yen (AUD/JPY) to see how AUD/USD movement affects EUR/AUD and AUD/JPY. The model was compared with Single-Layer Long Short-Term (SL-LSTM), Multilayer Perceptron (MLP), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Improved Firefly Algorithm Long Short-Term Memory. Based on the evaluation metrics Mean Square Error (MSE), Root Mean Square Error, and Mean Absolute Error, the suggested TLS-LSTM, whose data selection is based on the Hurst exponent (h) value of 0.6026, outperforms SL-LSTM, MLP, and CEEMDAN-IFALSTM. The correlation analysis conducted shows both positive and negative relations between AUD/USD, EUR/AUD, and AUD/JPY which means that a change in AUD/USD will affect EUR/AUD and AUD/JPY as recorded depending on the magnitude of the correlation coefficient (r).

An ensemble deep learning model for the detection of Oral Squamous Cell Carcinoma (OSCC) using histopathological images. To improve the early detection and diagnosis of oral cancer through advanced image analysis techniques. The basic methods of the convolutional neural network (CNN) and long short-term memory (LSTM) neural network are combine the convolutional neural network (CNN) and long short-term memory (LSTM) neural network to design an open oral scoring model based on CNN + LSTM. An experimental environment is then built to preprocess the data, and finally the model built in this study is trained and simulated. Evaluation metrics demonstrate the effectiveness of the model in diagnosing OSCC with high precision, preprocess the data, offering a potential tool for clinical decision support. Final outcome an oral cancer data analysis based cancer detection for cancer or non-cancer classification.

Online leakage current classification using convolutional neural network long short-term memory for high voltage insulators on web-based service

Diabetes is a persistent health condition led by insufficient use or inappropriate use of insulin in the body. If left undetected, it can lead to further complications involving organ damage such as heart, lungs, and eyes. Timely detection of diabetes helps obtain the right medication, diet, and exercise plan to lead a healthy life. ML approach has been utilized to obtain rapid and reliable diabetes detection, however, existing approaches suffer from the use of limited datasets, lack of generalizability, and lower accuracy. This study proposes a novel feature extraction approach to overcome these limitations by using an ensemble of convolutional neural network (CNN) and long short-term memory (LSTM) models. Multiple datasets are combined to make a larger dataset for experiments and multiple features are utilized for investigating the efficacy of the proposed approach. Features from the extra tree classifier, CNN, and LSTM are also considered for comparison. Experimental results reveal the superb performance of CNN-LSTM-based features with random forest model obtaining a 0.99 accuracy score. This performance is further validated by comparison with existing approaches and k-fold cross-validation which shows the proposed approach provides robust results.

In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components in aviation as well as for costly equipment. With the development of deep learning techniques, many RUL prediction methods employ convolutional neural network (CNN) and long short-term memory (LSTM) networks and demonstrate superior performance. In this paper, a novel two-stream network based on a bidirectional long short-term memory neural network (BiLSTM) is proposed to establish a two-stage residual life prediction model for mechanical devices using CNN as the feature extractor and BiLSTM as the timing processor, and finally, a particle swarm optimization (PSO) algorithm is used to adjust and optimize the network structural parameters for the initial data. Under the condition of lack of professional knowledge, the adaptive extraction of the features of the data accumulated by the enterprise and the effective processing of a large amount of timing data are achieved. Comparing the prediction results with other models through examples, it shows that the model established in this paper significantly improves the accuracy and efficiency of equipment remaining life prediction.

Semiconductor optical amplifier (SOA) is a promising solution for future O-band optical amplification. However, the SOA-induced nonlinearity will affect the quality of the signals and cause bit errors. Nowadays, the neural network is a hot topic in communication networks and has been considered a practical algorithm for signals equalization. In this paper, we investigate the performance of the three types of neural networks: deep neural networks (DNN), convolutional neural network (CNN), and long short term memory (LSTM) neural networks for the compensation of the SOA-induced nonlinearity. We first compare the fitting ability of the three neural networks on the saturated response of SOA. Based on the simulation result, LSTM has the best nonlinear expressiveness for the SOA-induced nonlinearity. Subsequently, we do some experimental validations in an intensity modulation and direct-detection (IM/DD) system, where the 100 Gbit/s pulse-amplitude-modulation-4 (PAM4) signals are transmitted over 80 km at O-band using an 18 GHz electro-absorption modulated laser (EML). The bit-error-ratio (BER) results show that the cell states in LSTM play a crucial role in nonlinearity compensation. Furthermore, LSTM offers the best equalization performance and can achieve a receiver sensitivity of −18.0 dBm.

MLP, CNN, LSTM and Hybrid SVM for Stock Index Forecasting Task to INDU and FTSE100

Text classification is a classic task in the field of natural language processing. however, the existing methods of text classification tasks still need to be improved because of the complex abstraction of text semantic information and the strong relecvance of context. In this paper, we combine the advantages of two traditional neural network model, Long Short-Term Memory(LSTM) and Convolutional Neural Network(CNN). LSTM can effectively preserve the characteristics of historical information in long text sequences, and extract local features of text by using the structure of CNN. We proposes a hybrid model of LSTM and CNN, construct CNN model on the top of LSTM, the text feature vector output from LSTM is further extracted by CNN structure. The performance of the hybrid model is compared with that of other models in the experiment. The experimental results show that the hybrid model can effectively improve the accuracy of text classification.

Speech spectrograms exhibit strong contextual dependencies along both time and frequency dimensions. In this paper, a novel composite model integrating a long short-term memory (LSTM) and convolutional neural network (CNN) to exploit temporal and spectral contextual speech information, respectively, is proposed for speech separation. LSTM and CNN operate in a parallel fashion to speed up the process and independently extract a complementary set of speech features. A fully-connected network then maps these features to the real and imaginary components of a ratio mask to enhance the magnitude and phase of the corrupted speech simultaneously. In the CNN path, a new delicately designed CNN with frequency dilated one-dimension (1D) convolutional layers is employed to expand the receptive field of CNN kernels without increasing the complexity. Furthermore, this CNN benefits from residual learning and skip connections to facilitate training and accelerate convergence. In spite of different neural networks included in the composite model, the proposed separation system not only has a low computational complexity, but also significantly outperforms some other deep learning-based methods.

Artificial intelligence models have been widely applied for natural gas consumption forecasting over the past decades, especially for short-term consumption forecasting. This paper proposes a three-layer neural network forecasting model that can extract key information from input factors and improve the weight optimization mechanism of long short-term memory (LSTM) neural network to effectively forecast short-term consumption. In the proposed model, a convolutional neural network (CNN) layer is adopted to extract the features among various factors affecting natural gas consumption and improve computing efficiency. The LSTM layer is able to learn and save the long-distance state through the gating mechanism and overcomes the defects of gradient disappearance and explosion in the recurrent neural network. To solve the problem of encoding input sequences as fixed-length vectors, the layer of attention (ATT) is used to optimize the assignment of weights and highlight the key sequences. Apart from the comparisons with other popular forecasting models, the performance and robustness of the proposed model are validated on datasets with different fluctuations and complexities. Compared with traditional two-layer models (CNN-LSTM and LSTM-ATT), the mean absolute range normalized errors (MARNE) of the proposed model in Athens and Spata are improved by more than 16% and 11%, respectively. In comparison with single LSTM, back propagation neural network, support vector regression, and multiple linear regression methods, the improvement in MARNE exceeds 42% in Athens. The coefficient of determination is improved by more than 25%, even in the high-complexity dataset, Spata.

Gait phase is an important state variable that helps exoskeletons provide assistance to users. Exoskeleton systems need to determine the assistance time and calculate the reference assistance force based on the gait phase. A large number of studies have used Finite State Machines (FSMs) or Adaptive Oscillators (AOs) to estimate gait phase. However, FSMs require manual calibration of gait events, and AOs can only estimate the phase of stable gait in real time. Both are difficult to adapt to irregular changes in gait. Deep learning algorithms can learn the nonlinear characteristics of kinematic data to estimate joint torque, gait phase, etc. The control algorithm based on Long Short-Term Memory (LSTM) Neural Network can automatically detect gait events according to the joint angle collected by Inertial Measurement Unit (IMU), effectively responding to dynamic changes in gait. This paper designed a Light-Weight Long Short-Term Memory (LWLSTM) Neural Network to estimate gait phase precisely and reduce training time of LSTM model. The experimental results showed that the Root Mean Square Error (RMSE) of LWLSTM was 8. 0 0 3 3%, quite lower than Convolutional Neural Network (CNN) and AO.

A novel hybrid algorithm based on Empirical Fourier decomposition and deep learning for wind speed forecasting