Precipitation prediction by integrating Rough Set on Fuzzy Approximation Space with Deep Learning techniques

Manna, Tishya; Anitha, A.

doi:10.1016/j.asoc.2023.110253

Cited by 21 publications

(5 citation statements)

References 21 publications

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“…MCI is a diverse disease with a number of underlying causes, including AD. The EEG signal offers numerous advantages over other techniques in the early-stage investigation of AD, but it also has significant drawbacks, such as limited data sizes make it challenging in clinical data acquisition and the presence of too many overlapping signals [34]. Previous research typically did not take this variability into account, which has implications for the MCI prediction's efficiency.…”

Section: Previous Work Limitationsmentioning

confidence: 99%

Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Alahmadi,

Rahman,

Alhababi

et al. 2024

Mach. Learn.: Sci. Technol.

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Mild Cognitive Impairment (MCI) is a cognitive disease that primarily affects elderly persons. Patients with MCI have impairments in one or more cognitive areas, such as memory, attention, language, and problem-solving. The risk of Alzheimer's disease (AD) development is 10 times higher among individuals who meet the MCI diagnosis than in those who do not have such a diagnosis. Identifying the primary neurophysiological variations between those who are suffering from cognitive impairment and those who are ageing normally may provide helpful techniques to assess the effectiveness of therapies. Event-related Potentials (ERPs) are utilized to investigate the processing of sensory, cognitive, and motor information in the brain. ERPs enable excellent temporal resolution of underlying brain activity. ERP data is complex due to the temporal variation occurs in time domain. It is actually a type of electroencephalography (EEG) signal that is time-locked to a specific event or behavior. To remove artifacts from the data, this work utilizes Independent component analysis (ICA), finite impulse response (FIR) filter, and Fast Fourier Transformation (FFT) as preprocessing techniques. The Bidirectional Long Short-Term Memory (BiLSTM) network is utilized to retains the spatial relationships between the ERP data while learning changes in temporal information for a long time. This network performed well both in modeling and information extraction from the signals. To validate the model performance, the proposed framework is tested on two benchmark datasets. The proposed framework achieved state-of-the-art accuracy of 96.03% on SEED dataset and 97.31% on CAUEEG dataset for the classification tasks.

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Section: Previous Work Limitationsmentioning

confidence: 99%

Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Alahmadi,

Rahman,

Alhababi

et al. 2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

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“…According to the structural characteristics of the gravity dam and the actual condition of the project, under the effect of the upstream reservoir, the horizontal displacement on the gravity dam is linearly related to the water depth H, H 2 , H 3 , which can be expressed by Equation (14). The temperature at any point within the concrete of the dam can be approximated by a periodic function, assuming that the temperature displacement is linearly related to the concrete temperature, and the harmonic sine function can be chosen as a factor, i.e., the displacement caused by the temperature effect for a gravity dam can be expressed by Equation (15). The aging component of the dam reflects the creep, plastic deformation of the concrete and bedrock, as well as the compression deformation of the bedrock geological structure.…”

Section: Ssa Principlesmentioning

confidence: 99%

“…Tishya Manna et al [14] compared multiple models and found that their proposed RSFAS-bi-directional LSTM model had a shorter execution time, which proved beneficial in determining parameters for different seasonal decision processes. Li Xin et al [15] constructed a new model based on iterative amplitude adjusted Fourier transform (IAAFT) and bi-directional long short-term memory (BiLSTM) for classifying mild cognitive impairment (MCI) and a healthy control (HC) group. They discovered that BiLSTM was better suited for EEG classification tasks, and the proposed model achieved higher accuracy standards.…”

Section: Introductionmentioning

confidence: 99%

Concrete Dam Deformation Prediction Model Research Based on SSA–LSTM

Madiniyeti

Chao

et al. 2023

Applied Sciences

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In the context of dam deformation monitoring, the prediction task is essentially a time series prediction problem that involves non-stationarity and complex influencing factors. To enhance the accuracy of predictions and address the challenges posed by high randomness and parameter selection in LSTM models, a novel approach called sparrow search algorithm–long short-term memory (SSA–LSTM) has been proposed for predicting the deformation of concrete dams. SSA–LSTM combines the SSA optimization algorithm with LSTM to automatically optimize the model’s parameters, thereby enhancing the prediction performance. Firstly, a concrete dam was used as an example to preprocess the historical monitoring data by cleaning, normalizing, and denoising, and due to the specificity of the data structure, multi-level denoising of abnormal data was performed. Second, some of the data were used to train the model, and the hyperparameters of the long and short-term memory neural network model (LSTM) were optimized by the SSA algorithm to better match the input data with the network structure. Finally, high-precision prediction of concrete dam deformation was carried out. The proposed model in this study significantly improves the prediction accuracy in dam deformation forecasting and demonstrates effectiveness in long-term time series deformation prediction. The model provides a reliable and efficient approach for evaluating the long-term stability of dam structures, offering valuable insights for engineering practices and decision-making.

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“…To handle the presence of uncertainties and find the key features that contribute to the precipitation forecasting process. Manna et al [37] incorporated a rough ensemble on fuzzy approximation space (RSFAS) into the LSTM method.…”

Section: Related Workmentioning

confidence: 99%

An Explainable Artificial Intelligence Approach for Remaining Useful Life Prediction

Youness,

Aalah

2023

Aerospace

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Prognosis and health management depend on sufficient prior knowledge of the degradation process of critical components to predict the remaining useful life. This task is composed of two phases: learning and prediction. The first phase uses the available information to learn the system’s behavior. The second phase predicts future behavior based on the available information of the system and estimates its remaining lifetime. Deep learning approaches achieve good prognostic performance but usually suffer from a high computational load and a lack of interpretability. Complex feature extraction models do not solve this problem, as they lose information in the learning phase and thus have a poor prognosis for the remaining lifetime. A new prepossessing approach is used with feature clustering to address this issue. It allows for restructuring the data into homogeneous groups strongly related to each other using a simple architecture of the LSTM model. It is advantageous in terms of learning time and the possibility of using limited computational capabilities. Then, we focus on the interpretability of deep learning prognosis using Explainable AI to achieve interpretable RUL prediction. The proposed approach offers model improvement and enhanced interpretability, enabling a better understanding of feature contributions. Experimental results on the available NASA C-MAPSS dataset show the performance of the proposed model compared to other common methods.

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Precipitation prediction by integrating Rough Set on Fuzzy Approximation Space with Deep Learning techniques

Cited by 21 publications

References 21 publications

Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Concrete Dam Deformation Prediction Model Research Based on SSA–LSTM

An Explainable Artificial Intelligence Approach for Remaining Useful Life Prediction

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