Long Short-term Memory Layers Research Articles

IT Parkinson’s Disease Diagnostics Based on the Freezing of Gait Analysis Using Long Short Term Memory Neural Network

An analysis of methods for processing data from gait deceleration sensors for detecting Parkinson’s disease and a description of the development of a Parkinson’s recognition system based on neural networks with long short term memory (LSTM) are performed. The data used was a publicly available dataset of gait deceleration scores of patients with Parkinson’s disease, obtained using three wearable sensors to collect data from different parts of the body. The research was carried out using machine learning using an LSTM neural network. First, the DAPHNet datasets were segmented using a fixed sliding window algorithm. The wavelet algorithm was then used to extract features from the data set: wavelet entropy and energy, wavelet waveform length, variance and standard deviation of wavelet coefficient. Next, a data enhancement algorithm was used to balance the number of samples in the data sets. To train the model, an LSTM neural network was built with a six-layer network structure: input layer, LSTM layer, reLU layer, fully connected layer, Softmax layer and output layer. After training the model for 1000 iterations, the LSTM neural network algorithm achieved 96.3 % accuracy, 96.05 % precision, 96.5 % sensitivity, and 96.24 % average F1 score for recognizing Parkinson’s disease based on test datasets. Similar studies conducted by other scientific organizations achieved a maximum accuracy of 91.9 % for the same data sets.

ConvLSNet: A lightweight architecture based on ConvLSTM model for the classification of pulmonary conditions using multichannel lung sound recordings

Characterization of lung sounds (LS) is indispensable for diagnosing respiratory pathology. Although conventional neural networks (NNs) have been widely employed for the automatic diagnosis of lung sounds, deep neural networks can potentially be more useful than conventional NNs by allowing accurate classification without requiring preprocessing and feature extraction. Utilizing the long short-term memory (LSTM) layers to reveal the sequence-based properties of the LS time series, a novel architecture consisting of a cascade of convolutional long short-term memory (ConvLSTM) and LSTM layers, namely ConvLSNet is developed, which permits highly accurate diagnosis of pulmonary disease states. By modeling the multichannel lung sounds through the ConvLSTM layer, the proposed ConvLSNet architecture can concurrently deal with the spatial and temporal properties of the six-channel LS recordings without heavy preprocessing or data transformation. Notably, the proposed model achieves a classification accuracy of 97.4 % based on LS data corresponding to three pulmonary conditions, namely asthma, COPD, and the healthy state. Compared with architectures consisting exclusively of CNN or LSTM layers, as well as those employing a cascade integration of 2DCNN and LSTM layers, the proposed ConvLSNet architecture exhibited the highest classification accuracy, while imposing the lowest computational cost as quantified by the number of parameters, training time, and learning rate.

Empowering Communication: A Deep Learning Framework for Arabic Sign Language Recognition with an Attention Mechanism

This article emphasises the urgent need for appropriate communication tools for communities of people who are deaf or hard-of-hearing, with a specific emphasis on Arabic Sign Language (ArSL). In this study, we use long short-term memory (LSTM) models in conjunction with MediaPipe to reduce the barriers to effective communication and social integration for deaf communities. The model design incorporates LSTM units and an attention mechanism to handle the input sequences of extracted keypoints from recorded gestures. The attention layer selectively directs its focus toward relevant segments of the input sequence, whereas the LSTM layer handles temporal relationships and encodes the sequential data. A comprehensive dataset comprised of fifty frequently used words and numbers in ArSL was collected for developing the recognition model. This dataset comprises many instances of gestures recorded by five volunteers. The results of the experiment support the effectiveness of the proposed approach, as the model achieved accuracies of more than 85% (individual volunteers) and 83% (combined data). The high level of precision emphasises the potential of artificial intelligence-powered translation software to improve effective communication for people with hearing impairments and to enable them to interact with the larger community more easily.

A Data-driven Deep Learning Approach for Remaining Useful Life in the ion mill etching Process

Prognostics and Health Management (PHM) is regarded as an essential element in the scope of intelligent manufacturing. Precise forecasting of the remaining useful life (RUL) of an ion mill is crucial in order to enhance the overall efficiency of the ion mill etching (IME) procedure. This paper proposed a Data-driven Deep Learning (DL) framework that integrates a Temporal Convolution Network (TCN), Long Short-Term Memory (LSTM), and self-attention mechanism to improve the accuracy of RUL prediction in the ion mill etching Process. Initially, sensor input data is divided into two parallel paths - one with TCN blocks for capturing long-range dependencies, and the other with LSTM layers for extracting temporal patterns. The outputs from both paths are then merged and input into an LSTM layer for enhanced learning, followed by a self-attention mechanism to highlight important features then fully connected layer for predicting RUL. The efficacy of this suggested model was assessed through the utilization of the 2018 PHM Data Challenge Dataset and juxtaposed against various Deep Learning models to demonstrate its efficacy. The results from the experiments indicate that ATCN-LSTM serves as a robust option for estimating the RUL in the ion mill etching Process as it outperformed all other models that were compared. The source code is publicly accessible at https://github.com/ion-mill-etching-Process.

Evaluasi Model Jaringan Saraf Tiruan Berbasis LSTM dalam Memprediksi Fluktuasi Harga Bitcoin

Amid the highly volatile fluctuations in the cryptocurrency market, the ability to accurately predict Bitcoin prices becomes crucial for investors and financial analysts. This study aims to develop a predictive model using Long Short-Term Memory (LSTM) Neural Networks, a specific form of recurrent neural network, to predict Bitcoin prices. Historical data on daily closing prices of Bitcoin from 2015 to 2023 was used to train and test the model. Following data preprocessing, which included normalization and the creation of a time series dataset, the LSTM model was constructed with two LSTM layers and two dense layers to enhance the predictive analysis. The model was trained with the data split into 80% for training and 20% for testing. Results show that the LSTM model was able to produce fairly accurate predictions with a low loss value on the test data. Further evaluation through comparison with baseline models showed significant improvements in predictive accuracy. This research demonstrates the potential application of advanced machine learning techniques in financial analysis, particularly in predicting the prices of highly volatile assets like Bitcoin. With continuous improvements to the model architecture and parameter optimization, Bitcoin price predictions could become more reliable, helping stakeholders make more informed investment decisions.

Federated Learning in the Detection of Fake News Using Deep Learning as a Basic Method.

This article explores the possibilities for federated learning with a deep learning method as a basic approach to train detection models for fake news recognition. Federated learning is the key issue in this research because this kind of learning makes machine learning more secure by training models on decentralized data at decentralized places, for example, at different IoT edges. The data are not transformed between decentralized places, which means that personally identifiable data are not shared. This could increase the security of data from sensors in intelligent houses and medical devices or data from various resources in online spaces. Each station edge could train a model separately on data obtained from its sensors and on data extracted from different sources. Consequently, the models trained on local data on local clients are aggregated at the central ending point. We have designed three different architectures for deep learning as a basis for use within federated learning. The detection models were based on embeddings, CNNs (convolutional neural networks), and LSTM (long short-term memory). The best results were achieved using more LSTM layers (F1 = 0.92). On the other hand, all three architectures achieved similar results. We also analyzed results obtained using federated learning and without it. As a result of the analysis, it was found that the use of federated learning, in which data were decomposed and divided into smaller local datasets, does not significantly reduce the accuracy of the models.

Intelligent Dust Monitoring System Based on IoT

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system train and test part was applied to dust phenomena historical data. Its data has been collected through the Iraqi Meteorological Organization and Seismology (IMOS) raw dataset with 170237 of 17023 rows and 10 columns. The LSTM model achieved small time, computationally complexity of, and layers number while being effective and accurate for dust prediction. The simulation results reveal that the model's mean square error test reaches 0.12877 and Mean Absolute Error (MAE) test is 0.07411 at the same rates of learning and exact features values of vector in the dense layer, representing a neural network layer deeply is connected to the LSTM training proposed model. Finally, the model suggested enhances monitoring performance.

Stock Market Prediction

The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its performance against traditional models such as Random Forest and Linear Regression. Data preprocessing involved handling missing values, normalizing the dataset, and transforming it into sequences suitable for LSTM training. The model's architecture includes multiple LSTM layers designed to capture temporal dependencies in the data. The study evaluates the model's performance using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and prediction accuracy. Comparative analysis shows that the LSTM model outperforms both Random Forest and Linear Regression models, with lower MSE and RMSE values and higher accuracy in predicting stock prices. This research discovered that LSTM's ability to retain long-term dependencies makes it particularly effective for stock market prediction, where historical trends and patterns significantly influence future prices. The results indicate that the LSTM model provides more reliable and precise predictions, which can enhance decision-making in trading and investment. This research highlights the potential of advanced neural network architectures in financial forecasting, offering a valuable tool for investors aiming to optimize their strategies and mitigate risks. The significance of this study lies in its practical application in the financial industry, demonstrating that machine learning models, particularly LSTM, can substantially improve the accuracy of stock market predictions. Future research could explore the integration of additional features, such as macroeconomic indicators and sentiment analysis, to further enhance model performance. This study underscores the importance of continuous innovation and the adoption of sophisticated algorithms to navigate the complexities of financial markets.

#2784 Deep learning algorithm predicts individual ultrafiltration trajectories for patients on automated peritoneal dialysis 12 weeks ahead

Abstract Background and Aims Adequate small solute clearance and ultrafiltration (UF) are the main requirements for fluid and salt homeostasis on peritoneal dialysis (PD). New automated peritoneal dialysis (APD) systems that allow remote patient monitoring in terms of PD treatment analysis have the potential to improve clinical outcomes. UF on APD although dependent on PD prescription, i.e. fill volumes and glucose concentration, exhibits substantial intra- and interindividual variation, even day-by-day. As conventional statistical methods are not able to assess these complex non-linear relationships adequately, we aimed to build a deep learning algorithm to predict ultrafiltration trajectories of individual patients 12 weeks ahead. Method Data on daily UF and PD prescriptions (i.e., glucose concentration, fill volumes) was extracted from the APD cycler management software (PD Link, Baxter, IL, USA) and patient charts at the Medical University of Vienna, for a secondary analysis of this cohort. The Keras high-level API for TensorFlow in R software (R Core Team 2023) was used to create a Long Short-Term Memory (LSTM) recurrent neural network based on historic prescription and gcUF data of 26 weeks (after the first 90 days of APD) to predict individual UF trajectories for the following 12 weeks. Results APD machine readouts from a total of 123 patients, with a mean time on PD of 117 (±73) days, were retrieved. The final recurrent neural network consisted of four LSTM layers with 64 cells each, sigmoid activation. The final model was fit using 30 epochs, 49 steps per epoch, and recurrent dropout to avoid overfitting. A 40-40-20% data split was used to train, validate, and test the model adequately. This deep learning algorithm was able to predict individual UF values, for each day of the upcoming 12 weeks, with an accuracy of at mean 201ml. Upon visual inspection, predicted individual UF trajectories smoothly resemble the overall day-by-day trajectory of the patient but fail to account for extreme spikes in either direction. Conclusion In conclusion, historic daily APD cycler and prescription data in coalescence with respective daily measurements of membrane function (i.e., gcUF) can be used to predict individual daily ultrafiltration values and trajectories of APD patients 12 weeks ahead using a deep learning algorithm. Therefore, continuous collection of PD prescription data and UF measurements may be used to monitor peritoneal membrane function of patients on APD and alert clinical teams upon detection of relevant changes of UF trajectories already 12 weeks ahead to facilitate early intervention. In contrast to current “screening” methods on PD, i.e., Peritoneal Equilibrium Test, these methods could reduce treatment burden.

Optimising air quality prediction in smart cities with hybrid particle swarm optimization‐long‐short term memory‐recurrent neural network model

AbstractIn smart cities, air pollution is a critical issue that affects individual health and harms the environment. The air pollution prediction can supply important information to all relevant parties to take appropriate initiatives. Air quality prediction is a hot area of research. The existing research encounters several challenges that is, poor accuracy and incorrect real‐time updates. This research presents a hybrid model based on long‐short term memory (LSTM), recurrent neural network (RNN), and Curiosity‐based Motivation method. The proposed model extracts a feature set from the training dataset using an RNN layer and achieves sequencing learning by applying an LSTM layer. Also, to deal with the overfitting issues in LSTM, the proposed model utilises a dropout strategy. In the proposed model, input and recurrent connections can be dropped from activation and weight updates using the dropout regularisation approach, and it utilises a Curiosity‐based Motivation model to construct a novel motivational model, which helps in the reconstruction of long short‐term memory recurrent neural network. To minimise the prediction error, particle swarm optimisation is implemented to optimise the LSTM neural network's weights. The authors utilise an online Air Pollution Monitoring dataset from Salt Lake City, USA with five air quality indicators for comparison, that is, SO2, CO, O3, and NO2, to predict air quality. The proposed model is compared with existing Gradient Boosted Tree Regression, Existing LSTM, and Support Vector Machine based Regression Model. Experimental analysis shows that the proposed method has 0.0184 (Root Mean Square Error (RMSE)), 0.0082 (Mean Absolute Error), 2002*109 (Mean Absolute Percentage Error), and 0.122 (R2‐Score). The experimental findings demonstrate that the proposed LSTM model had RMSE performance in the prescribed dataset and statistically significant superior outcomes compared to existing methods.

A feature extractor for temporal data of electronic nose based on parallel long short-term memory network in flavor discrimination of Chinese vinegars

Volatile flavor is a key indicator of food quality which can directly affect consumer preference and purchase intention. Electronic nose is considered as a promising intelligent sensory analysis tool for food flavor assessment, however, extracting effective features from the gas sensor array is still a major challenge, which largely determines the performance of subsequent classifiers. Here, a parallel long short-term memory (LSTM) network is proposed as a feature extractor for automatically extracting features from the whole time series of sensor responses in flavor discrimination of five Chinese vinegars. The network was trained by the temporal data from the sensor array and yielded different feature patterns corresponding to different vinegars, which were then fed to other conventional classifiers for pattern recognition. We also evaluated the influence of the extracted feature dimension that is related to the dimension of the hidden state of the LSTM layer on the classification performance. The results indicate that a larger dimension of extracted feature is unnecessary for promoting classification accuracy, instead, the optimum dimension 4 of the hidden state gives the highest accuracy of 95.8% in this application under the softmax evaluator. Moreover, much higher accuracies were obtained when combined with other sophisticated classifiers such as support vector machine. The results demonstrate that the proposed network is competent to extract features directly and automatically from the temporal data of the electronic nose.

Predicting Transcription Factor Binding Sites with Deep Learning.

Prediction of binding sites for transcription factors is important to understand how the latter regulate gene expression and how this regulation can be modulated for therapeutic purposes. A consistent number of references address this issue with different approaches, Machine Learning being one of the most successful. Nevertheless, we note that many such approaches fail to propose a robust and meaningful method to embed the genetic data under analysis. We try to overcome this problem by proposing a bidirectional transformer-based encoder, empowered by bidirectional long-short term memory layers and with a capsule layer responsible for the final prediction. To evaluate the efficiency of the proposed approach, we use benchmark ChIP-seq datasets of five cell lines available in the ENCODE repository (A549, GM12878, Hep-G2, H1-hESC, and Hela). The results show that the proposed method can predict TFBS within the five different cell lines very well; moreover, cross-cell predictions provide satisfactory results as well. Experiments conducted across cell lines are reinforced by the analysis of five additional lines used only to test the model trained using the others. The results confirm that prediction across cell lines remains very high, allowing an extensive cross-transcription factor analysis to be performed from which several indications of interest for molecular biology may be drawn.

Instrument Detection and Descriptive Gesture Segmentation on a Robotic Surgical Maneuvers Dataset

Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and methodologies, thereby assessing their effectiveness and performance. The ROSMA (Robotics Surgical Maneuvers) dataset provides 206 trials of common surgical training tasks performed with the da Vinci Research Kit (dVRK). In this work, we extend the ROSMA dataset with two annotated subsets: ROSMAT24, which contains bounding box annotations for instrument detection, and ROSMAG40, which contains high and low-level gesture annotations. We propose an annotation method that provides independent labels for the right-handed tools and the left-handed tools. For instrument identification, we validate our proposal with a YOLOv4 model in two experimental scenarios. We demonstrate the generalization capabilities of the network to detect instruments in unseen scenarios. On the other hand, for gesture segmentation, we propose two label categories: high-level annotations that describe gestures at a maneuvers level, and low-level annotations that describe gestures at a fine-grain level. To validate this proposal, we have designed a recurrent neural network based on a bidirectional long-short term memory layer. We present results for four cross-validation experimental setups, reaching up to a 77.35% mAP.

An Attention-Based Deep Learning Approach for Lithium-ion Battery Lifespan Prediction: Analysis and Experimental Validation

The potential for lithium-ion batteries to become unstable can lead to operational malfunctions within the system and result in safety incidents. Therefore, accurately forecasting the remaining useful life (RUL) is beneficial in mitigating the likelihood of battery failure and prolonging its operational lifespan. Hence, precise estimation of RUL can help prevent numerous safety incidents and minimize resource wastage, presenting a significant and complex issue. This paper introduces a Deep Learning (DL) model that utilizes Long Short-Term Memory (LSTM) and attention mechanism to improve the accuracy of predicting the RUL of lithium-ion batteries. Initially, the battery capacity regeneration phenomenon is captured by applying four LSTM layers, followed by implementing an attention mechanism to align input and output sequences based on the content or semantics of the input sequence. Finally, the final prediction outcomes are generated via a Fully Connected (FC) layer. The efficacy of the proposed model is assessed through the utilization of the NASA dataset, and its performance is contrasted with various deep learning models to highlight its efficacy. Results from the experiments demonstrate that the suggested At-LSTM presents a robust option for forecasting the RUL of lithium-ion batteries, as it delivers superior results compared to all other models examined.

State-of-Health Estimation for Sustainable Electric Vehicle Batteries Using Temporal-Enhanced Self-Attention Graph Neural Networks

Abstract Electric vehicles (EVs) have emerged as an environmentally friendly alternative to conventional fuel vehicles. Lithium-ion batteries are the major energy source for EVs, but they degrade under dynamic operating conditions. Accurate estimation of battery state of health is important for sustainability as it quantifies battery condition, influences reuse possibilities, and helps alleviate capacity degradation, which finally impacts battery lifespan and energy efficiency. In this paper, a self-attention graph neural network combined with long short-term memory (LSTM) is introduced by focusing on using temporal and spatial dependencies in battery data. The LSTM layer utilizes a sliding window to extract temporal dependencies in the battery health factors. Two different approaches to the graph construction layer are subsequently developed: health factor-based and window-based graphs. Each approach emphasizes the interconnections between individual health factors and exploits temporal features in a deeper way, respectively. The self-attention mechanism is used to compute the adjacent weight matrix, which measures the strength of interactions between nodes in the graph. The impact of the two graph structures on the model performance is discussed. The model accuracy and computational cost of the proposed model are compared with the individual LSTM and gated recurrent unit (GRU) models.

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

In order to efficiently and accurately monitor blood glucose concentration (BGC) synthetically influenced by various factors, quantitative blood glucose in vitro detection was studied using photoacoustic temporal spectroscopy (PTS) combined with a fusion deep neural network (fDNN). Meanwhile, a photoacoustic detection system influenced by five factors was set up, and 625 time-resolved photoacoustic signals of rabbit blood were collected under different influencing factors.In view of the sequence property for temporal signals, a dimension convolutional neural network (1DCNN) was established to extract features containing BGC. Through the parameters optimization and adjusting, the mean square error (MSE) of BGC was 0.51001 mmol/L for 125 testing sets. Then, due to the long-term dependence on temporal signals, a long short-term memory (LSTM) module was connected to enhance the prediction accuracy of BGC. With the optimal LSTM layers, the MSE of BGC decreased to 0.32104 mmol/L. To further improve prediction accuracy, a self-attention mechanism (SAM) module was coupled into and formed an fDNN model, i.e., 1DCNN-SAM-LSTM. The fDNN model not only combines the advantages of temporal expansion of 1DCNN and data long-term memory of LSTM, but also focuses on the learning of more important features of BGC. Comparison results show that the fDNN model outperforms the other six models. The determination coefficient of BGC for the testing set was 0.990, and the MSE reached 0.1432 mmol/L. Results demonstrate that PTS combined with 1DCNN-SAM-LSTM ensures higher accuracy of BGC under the synthetical influence of various factors, as well as greatly enhances the detection efficiency.

Tool Wear Prediction Based on LSTM and Deep Residual Network

To improve the accuracy and efficiency of tool wear predictions, this study proposes a tool wear prediction model called LSTM_ResNet which is based on the long short-term memory (LSTM) network and the Residual Network (ResNet). The model utilizes LSTM layers for processing, where the first block and loop blocks serve as the core modules of the deep residual network. The model employs a series of methods including convolution, batch normalization (BN), and Rectified Linear Unit (ReLU) to enhance the model’s expression and prediction capabilities. The performance of the LSTM_ResNet model was evaluated using experimental data from the PHM2010 datasets and two different depths (64 and 128 layers), training both LSTM_ResNet models for 200 epochs. The 64-layer model’s root mean square error (RMSE) values are 3.36, 4.35, and 3.59, and the mean absolute error (MAE) values are 2.42, 2.85, and 2.21; using 128 layers, the RMSE values are 3.66, 3.99, and 3.77, and the MAE values are 2.49, 2.73, and 3.01. The results indicate that the 64-layer LSTM has smaller average errors, suggesting that compared to other common network structures, the LSTM_ResNet network has a higher performance. This research provides an effective solution for tool wear prediction and helps to improve the technical level of tool wear prediction in China.

An integrated deep neural network model combining 1D CNN and LSTM for structural health monitoring utilizing multisensor time-series data

Introducing deep learning algorithms into the field of structural health monitoring (SHM) has contributed to the automatic extraction of damage-sensitive features, but the type and architecture of these algorithms are still in dispute. This paper proposes a hybrid deep learning framework entitled time-distributed one-dimensional convolutional neural network (1D CNN) long short-term memory (LSTM) model, which utilizes raw multisensor time histories to detect structural damages. Using a sliding window that moves along the temporal dimension, the multisensor data are first segmented into subsequences. The 1D CNN layers are simultaneously applied to each subsequence to extract damage-sensitive features from row data samples. These features are then fed into the LSTM layers to extract temporal features between subsequences. As the final step, these extracted features are classified using fully connected layers. In order to assess the performance of this model, a numerical model of a high-rise frame with nonlinear members is used. This hybrid model is assumed to identify the location of damages to this frame. In order to assess the proposed model with a real-world structure, a well-known benchmark building is employed to identify damage patterns by this deep hybrid neural network. A set of metrics related to the performance of the model is measured and evaluated. It is found that the model has an average accuracy of above 96.6% in localizing damage in the numerical structure and above 99.6% in detecting each damage pattern in the experimental building. The results indicate that the proposed model can be applied effectively to the SHM of different structural systems with different damage patterns.

Prediction of instantaneous flow characteristics of hydrocyclone with long short-term memory network based on computational fluid dynamics data

Accelerating the prediction time of separation performance and flow field characteristics in industrial hydrocyclones holds paramount importance for real-time control. Machine learning methods exhibit significant advantages in this particular aspect. This study presents a network model that integrates a long short-term memory (LSTM) layer with a fully connected layer to accurately forecast the flow field and separation performance under various operational conditions, leveraging CFD data. The study evaluates the effect of factors such as the number of LSTM layers and neurons per layer on the model's performance, aiming to identify the optimal network structure. The results demonstrate that the predicted flow field and performance of the hydrocyclones using LSTM closely align with the outcomes from CFD simulations. The average absolute percentage error is constrained to within 6%, which significantly enhances the prediction efficiency. The findings contribute to a more efficient and automated separation process in industrial environments.

Optimizing Stock Price Prediction: Exploring LSTM Architectural Parameters in Financial Forecasting

Accurate stock price prediction is a crucial concern in the field of finance, impacting investors, traders, and the broader economic landscape. Traditional time series analysis and statistical models often fall short in capturing the dynamic and nonlinear nature of financial markets. Financial market forecasting has advanced significantly with the advent of deep learning technology, especially Long Short-Term Memory (LSTM) networks. This study conducts an in-depth exploration of LSTM networks' critical architectural parameters and their impact on stock price prediction. The performance of the LSTM-based model is assessed using historical stock price data from Amazon, with particular attention paid to hyperparameters like the number of neurons, dropout rates, and LSTM layers. The research reveals that a well-balanced model architecture with moderate units per layer (e.g., 64), a single layer, and a dropout rate of 0.7 is key to achieving accurate predictions in financial markets. The outcomes of this research hold practical significance for both investors and financial analysts. Furthermore, they emphasize the promising potential of LSTM networks in the realm of financial forecasting, opening avenues for future studies in finance. This investigation deepens the comprehension of LSTM network architecture and its applicability in predicting stock prices, providing valuable insights that extend beyond the finance sector.