This work proposes a real-time deep learning-based model for predicting the small-signal stability of an electrical network. The trained models equip power system operators with an accurate and fast ...monitoring tool which can be used during online operation. To achieve this objective, three different model architectures are employed in this research; stacked long short-term memory (LSTM), convolutional neural network (CNN)-LSTM and Convectional LSTM (Conv-LSTM). These models are trained using datasets which contain the oscillatory parameters (frequency and damping ratio) of both local and inter-area modes of oscillations. In addition, the voltage phasors at different buses are taken as the model input where the output comprises the real-time oscillatory patterns of the modes. Furthermore, the overall performance of proposed models is shown for the New-England 10-machine, 39-bus, IEEE 16-machine, 68-bus, 5-area, and IEEE 50-machine, 145-bus benchmark test cases. The main findings show that training CNN-LSTM and Conv-LSTM models provide better performance compared with the stacked-LSTM model. The former models have less number of parameters and thus shorter training time. In addition, CNN_LSTM and Conv-LSTM models are less prone to overfitting problems in the network and have a better ability in capturing spatial and temporal features inherent in input data.
In order to improve the accuracy of the stock market prices forecasting, two hybrid forecasting models are proposed in this paper which combine the two kinds of empirical mode decomposition (EMD) ...with the long short-term memory (LSTM). The financial time series is a kind of non-linear and non-stationary random signal, which can be decomposed into several intrinsic mode functions of different time scales by the original EMD and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). To ensure the effect of historical data onto the prediction result, the LSTM prediction models are established for all each characteristic series from EMD and CEEMDAN deposition. The final prediction results are obtained by reconstructing each prediction series. The forecasting performance of the proposed models is verified by linear regression analysis of the major global stock market indices. Compared with single LSTM model, support vector machine (SVM), multi-layer perceptron (MLP) and other hybrid models, the experimental results show that the proposed models display a better performance in one-step-ahead forecasting of financial time series.
•A new hybrid time series forecasting method is established by combining EMD and CEEMDAN algorithm with LSTM neural network.•The forecasting efficiency of financial time series is improved by the model.•The forecasting results of the proposed model are more accurate than other similar models.
This article aims to tackle the problem of group activity recognition in the multiple-person scene. To model the group activity with multiple persons, most long short-term memory (LSTM)-based methods ...first learn the person-level action representations by several LSTMs and then integrate all the person-level action representations into the following LSTM to learn the group-level activity representation. This type of solution is a two-stage strategy, which neglects the "host-parasite" relationship between the group-level activity ("host") and person-level actions ("parasite") in spatiotemporal space. To this end, we propose a novel graph LSTM-in-LSTM (GLIL) for group activity recognition by modeling the person-level actions and the group-level activity simultaneously. GLIL is a "host-parasite" architecture, which can be seen as several person LSTMs (P-LSTMs) in the local view or a graph LSTM (G-LSTM) in the global view. Specifically, P-LSTMs model the person-level actions based on the interactions among persons. Meanwhile, G-LSTM models the group-level activity, where the person-level motion information in multiple P-LSTMs is selectively integrated and stored into G-LSTM based on their contributions to the inference of the group activity class. Furthermore, to use the person-level temporal features instead of the person-level static features as the input of GLIL, we introduce a residual LSTM with the residual connection to learn the person-level residual features, consisting of temporal features and static features. Experimental results on two public data sets illustrate the effectiveness of the proposed GLIL compared with state-of-the-art methods.
Emotion recognition can be achieved by speech recognition, the judgment of limb movements, analysis of Electrooculogram (EOG) or capturing of facial expressions. However, those types of emotion ...recognition methods cannot detect human emotion well, because humankind can use fake body movement and words to hide real emotions. In this paper, we proposed an EEG-based emotion classification method based on Bidirectional Long Short-Term Memory Network (BiLSTM). Electroencephalogram (EEG) signal can detect human emotion correctly because human represent their real emotions in their mind and cannot hide emotions there. Meanwhile, EEG is a time sequence signal which needs a model which can deal with this type of data. Therefore, we chose Long Short-term Memory Network to process the EEG signal. In particular, we used an improvement version of LSTM model BiLSTM to manage the signals. BiLSTM can processes input data from front to back and back to front. Meanwhile, BiLSTM can store important information and forget unnecessary information; therefore, this process increases the accuracy of the model. Our method classifies four discrete classifications (happy, sad, fear, and neutral) for emotion classification, which achieves competitive performance compared with other conventional emotion classification methods. The final experimental results show that we can achieve an accuracy of 84.21% for four emotional states classification by using our method.
Nowadays, there has been much attention on computer vision regarding human-computer interaction, especially facial expression recognition (FER). Many researchers have explored and suggested systems ...for this field. In this paper, we propose the Deep Learning architecture to improve the performance of models from the previous work. Additionally, we propose the BiLSTM-CNN model, which combines our proposed CNN and BiLSTM model. Besides that, we also compare the model to our CNN and LSTM-CNN models. We conduct the experiments on the CK+ dataset and evaluate the accuracy rate of the built models. Data augmentation is used in the dataset to improve the model's performance and prevent overfitting. The results demonstrate that the BiLSTM-CNN method achieves a state-of-the-art accuracy rate compared to other methods from previous work. The highest accuracy of 99.43% is reached by the BiLSTM-CNN model with data augmentation.
•An hour ahead forecasting of power output for three different PV systems.•A hybrid deep learning algorithm (SSA-RNN-LSTM) is proposed.•The proposed model is better than RNN-LSTM, GA-RNN-LSTM and ...PSO-RNN-LSTM.•The model is robust for three different PV systems over four years data.
The integration of photovoltaic energy into a grid demands accurate power output forecasting. In this research, an hour ahead prediction of power output is performed on an annual basis over real data period (2016–2019) for three different PV systems based on polycrystalline, monocrystalline, and thin-film technologies. The solar radiation, ambient temperature, module temperature and wind speed are the considered input parameters, while the power output of each PV system is the output parameter. A hybrid deep learning (DL) method (SSA-RNN-LSTM) is proposed for an hour ahead prediction of output power for each PV system. The proposed technique is compared with GA-RNN-LSTM, PSO-RNN-LSTM and RNN-LSTM. The considered forecasting accuracy measurement parameters are RMSE, MSE, MAE and coefficient of determination (R2). The findings elaborate that SSA-RNN-LSTM has shown better forecasting accuracy with the lowest (RMSE and MSE), highest (R2) and highest convergence speed compared to other methods. The proposed model has shown testing (RMSE and MAE) of (19.14% and 21.57%), (15.4% and 10.81%) and (22.9% and 25.2%) lower than RNN-LSTM for polycrystalline, monocrystalline and thin-film PV systems respectively. Furthermore, the proposed model is found more robust in predicting the power output for three different PV systems over four years data period.
Enhancing Breast Cancer Diagnosis Sarangi, Archana; Mishra, Debahuti; Priyadarshani Behera, Mandakini
International journal of electrical and computer engineering systems,
06/2024, Letnik:
15, Številka:
6
Journal Article, Paper
Recenzirano
Odprti dostop
Breast cancer stands as a significant global health challenge, ranking as the second leading cause of mortality among women. The increasing complexity of timely and accurate remote diagnosis has ...spurred the need for advanced technological solutions. Breast cancer prediction involves utilizing risk assessment models to identify individuals at higher risk, enabling early detection and personalized treatment strategies. This research meticulously assesses the effectiveness of various long short-term memory (LSTM) classifiers, including simple LSTM, Vanilla LSTM, Stacked LSTM, and Bidirectional LSTM, utilizing a comprehensive breast cancer dataset. Among these, the Bidirectional LSTM emerges as the preferred choice based on a thorough evaluation of accuracy, precision, recall, and F1-Score metrics. In a strategic move to further enhance precision, the Bidirectional LSTM integrates with the variable step-size firefly algorithm (VSSFF). Renowned for dynamically adjusting its step size, VSSFF offers adaptive exploration and exploitation capabilities in optimization tasks. The resulting hybrid model, HVSSFFLSTM, showcases superior performance in breast cancer prediction, suggesting potential applicability across diverse health conditions. Comparative analyses with other models highlight the exceptional accuracy rates of HVSSFFLSTM, achieving 99.78% (training) and 97.37% (testing), precision rates of 99.56% (training) and 97.22% (testing), recall rates of 100% (training) and 98.59% (testing), F1 scores of 99.82% (training) and 97.9% (testing) and specificity of 99.81% (training) and 99.15% (testing). This study not only underscores the adaptability of VSSFF as a valuable optimization tool but also emphasizes the promising prospects of the proposed hybrid model in advancing automated disease analysis. The results indicate its potential beyond breast cancer, suggesting broader applications in various medical domains.
Accurate short-time traffic flow prediction has gained gradually increasing importance for traffic plan and management with the deployment of intelligent transportation systems (ITSs). However, the ...existing approaches for short-term traffic flow prediction are unable to efficiently capture the complex nonlinearity of traffic flow, which provide unsatisfactory prediction accuracy. In this paper, we propose a deep learning based model which uses hybrid and multiple-layer architectures to automatically extract inherent features of traffic flow data. Firstly, built on the convolutional neural network (CNN) and the long short-term memory (LSTM) network, we develop an attention-based Conv-LSTM module to extract the spatial and short-term temporal features. The attention mechanism is properly designed to distinguish the importance of flow sequences at different times by automatically assigning different weights. Secondly, to further explore long-term temporal features, we propose a bidirectional LSTM (Bi-LSTM) module to extract daily and weekly periodic features so as to capture variance tendency of the traffic flow from both previous and posterior directions. Finally, extensive experimental results are presented to show that the proposed model combining the attention Conv-LSTM and Bi-LSTM achieves better prediction performance compared with other existing approaches.
•Frequency response analysis (FRA) is used to fault identification considering different fault impedance, type and location.•Deep learning applications CNN, LSTM, and C-LSTM are utilized for ...interpretation of the results of FRA.
Timely and accurate detection of transmission line faults is one of the most important issues in the reliability of the power systems. In this paper, in order to assess the effects of impedance and location of the fault in identifying and classifying it, the frequency response analysis (FRA) method is utilized. This method clearly shows the smallest effects of the faults on voltage and current signals in the frequency domain. Interpretation of the results associated with the FRA procedure is considered a weakness of this method. To overcome this issue and accurately categorize types and locations of various transmission lines faults such as asymmetric faults and symmetric faults, machine learning, and deep learning applications called support vector machine (SVM), decision tree (DT), k-Nearest Neighbors (k-NN), convolutional neural network (CNN), long short term memory (LSTM), and a hybrid model of convolutional LSTM (C-LSTM) are utilized. Introduced faults are applied with various impedances in 6 segments of an IEEE standard transmission line system. Then, the frequency response curves (FRCs) for them are computed and selected as input datasets for the suggested networks. After categorizing the types and locations of faults, the results for each network are analyzed via different statistical performance evaluation metrics. Finally, in order to early detection of faults, the new high impedance faults (7000 and 9000 O) are applied based on the previous routine in the transmission line. At this stage, evaluations demonstrate the capability of the C-LSTM followed by SVM, DT, k-NN, CNN, and LSTM in categorizing the type and location of transmission line faults.
Blood pressure monitoring is one avenue to monitor people's health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with ...cardiovascular diseases. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for blood pressure changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) and photoplethysmogram (PPG) for the real-time estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We use a bidirectional layer of long short-term memory (LSTM) as the first layer and add a residual connection inside each of the following layers of the LSTMs. We also perform experiments to compare the performance between the traditional machine learning methods, another existing deep learning model, and the proposed deep learning models using the dataset of Physionet's multiparameter intelligent monitoring in intensive care II (MIMIC II) as the source of ECG and PPG signals as well as the arterial blood pressure (ABP) signal. The results show that the proposed model outperforms the existing methods and is able to achieve accurate estimation which is promising in order to be applied in clinical practice effectively.
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