Long Short-term Memory Architecture Research Articles

Exposing Deep Fake Face Detection using LSTM and CNN

The rapid advancement of deep learning techniques, creating realistic multimedia content has become increasingly accessible, leading to the proliferation of DeepFake technology. DeepFake utilizes generative deep learning algorithms to produce or modify face features in a highly realistic manner, often making it challenging to differentiate between real and manipulated media. This technology, while beneficial in fields such as entertainment and education, also poses significant threats, including misinformation and identity theft. Consequently, detecting DeepFakes has become a critical area of research. In this paper, we propose a novel approach to DeepFake face detection by integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. Our method leverages the strengths of CNNs in spatial feature extraction and LSTMs in temporal sequence modeling to enhance detection accuracy. The CNN component captures intricate facial features, while the LSTM analyzes the temporal dynamics of video frames. We evaluate our model on several benchmark datasets, including Celeb-DF (v2), DeepFake Detection Challenge Preview, and FaceForensics++. Experimental results demonstrate that our hybrid CNN-LSTM model achieves state-of-the-art performance, surpassing existing methods in both accuracy and robustness. This study highlights the potential of combining CNN and LSTM architectures for effective DeepFake detection, contributing to the ongoing efforts to safeguard against digital media manipulation

Harvesting Earth's heat: A deep learning Odyssey for reservoir characterization and sustainable geothermal energy management

Characterizing and predicting reservoir behavior pose significant challenges in geothermal reservoir engineering. For sustainable geothermal field management, the potential occurrence of thermal breakthroughs in producers during the injection of cold water necessitates a profound understanding of how production is influenced by the injection philosophy. The fractured nature of geothermal reservoirs adds complexity and nonlinearity to the relationship between production and injection wells. In this study, we explore alternative models to simulate reservoir behavior as substitutes for full reservoir simulations. Utilizing deep learning algorithms, we investigate two different architectures, namely the standard Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). These models map injection flow rates at the injectors to tracer concentration data at the producers through layers, nodes, and activation functions. Training these models on a synthetic geothermal reservoir, Notably, the GRU architecture consistently demonstrates superior predictive capabilities across various scenarios for all three producers. Specifically, in configurations involving a target feed, the GRU model yields higher R2 values compared to its LSTM counterpart, indicating its effectiveness in capturing inter-well relationships. Furthermore, when considering all producers collectively, the GRU model exhibits a trend of lower test errors and higher R2 values, reaffirming its proficiency in modeling complex reservoir dynamics. These findings underscore the significance of GRU as a preferred choice for accurate reservoir behavior prediction. In addition to the aforementioned insights, our study contributes novel approaches to reservoir behavior prediction in the field of geothermal reservoir engineering. By leveraging deep learning algorithms, specifically the GRU and LSTM architectures, we introduce innovative methodologies for simulating reservoir behavior as alternatives to full reservoir simulations. Through comprehensive analysis, we demonstrate the superior predictive capabilities of the GRU model, particularly in capturing inter-well relationships and modeling complex reservoir dynamics.

Forecasting Indian Trade Trends through LSTM- based Predictive Modeling

The efficacy of Long Short-Term Memory (LSTM) neural networks and attention-based models in predicting next-day closing prices of the MSFT 500index is meticulously examined. A comprehensive suite of nine carefully chosen predictors spanning fundamental market data, macroeconomic indicators, and technical metrics is amalgamated, fostering a holistic comprehension of market behavior. Through rigorous analysis, the research evaluates single-layer and multilayer LSTM architectures alongside attention- based LSTM variants, juxtaposed against traditional ARIMA models. Surprisingly, the single-layer LSTM consistently outperforms its multilayer counterpart, demonstrating superior accuracy and model fit. The integration of corporate accounting statistics augments predictive capabilities, enriching the models' efficacy. Notably, attention-based LSTM models, particularly the Attention-LSTM variant, exhibit markedly lower prediction errors and higherreturns in trading strategies compared to other methodologies. However, the heightened complexity of stacked-LSTM structures fails to surpass the predictive acumen of simpler LSTM architectures.This inquiry underscores the paramount importance of leveraging advanced AI techniques and comprehensive datasets in navigating the intricate nuances of modern financialmarkets, offering invaluable insights for both researchers and practitioners engaged in stock priceforecasting endeavors.

DEVELOPMENT AND TRAINING OF LSTM MODELS FOR CONTROL OF VIRTUAL DISTRIBUTED SYSTEMS USING TENSORFLOW AND KERAS

DEVELOPMENT AND TRAINING OF LSTM MODELS FOR CONTROL OF VIRTUAL DISTRIBUTED SYSTEMS USING TENSORFLOW AND KERAS

Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling

A deep learning architecture, denoted as CNNsLSTM, is proposed for hourly rainfall–runoff modeling in this study. The architecture involves a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory (LSTM) network. In the proposed framework, multiple layers of the CNN component process long-term hourly meteorological time series data, while the LSTM component handles short-term meteorological time series data and utilizes the extracted features from the 1D-CNN. In order to demonstrate the effectiveness of the proposed approach, it was implemented for hourly rainfall–runoff modeling in the Ishikari River watershed, Japan. A meteorological dataset, including precipitation, air temperature, evapotranspiration, longwave radiation, and shortwave radiation, was utilized as input. The results of the proposed approach (CNNsLSTM) were compared with those of previously proposed deep learning approaches used in hydrologic modeling, such as 1D-CNN, LSTM with only hourly inputs (LSTMwHour), a parallel architecture of 1D-CNN and LSTM (CNNpLSTM), and the LSTM architecture, which uses both daily and hourly input data (LSTMwDpH). Meteorological and runoff datasets were separated into training, validation, and test periods to train the deep learning model without overfitting, and evaluate the model with an independent dataset. The proposed approach clearly improved estimation accuracy compared to previously utilized deep learning approaches in rainfall = runoff modeling. In comparison with the observed flows, the median values of the Nash–Sutcliffe efficiency for the test period were 0.455–0.469 for 1D-CNN, 0.639–0.656 for CNNpLSTM, 0.745 for LSTMwHour, 0.831 for LSTMwDpH, and 0.865–0.873 for the proposed CNNsLSTM. Furthermore, the proposed CNNsLSTM reduced the median root mean square error (RMSE) of 1D-CNN by 50.2%–51.4%, CNNpLSTM by 37.4%–40.8%, LSTMwHour by 27.3%–29.5%, and LSTMwDpH by 10.6%–13.4%. Particularly, the proposed CNNsLSTM improved the estimations for high flows (≧75th percentile) and peak flows (≧95th percentile). The computational speed of LSTMwDpH is the fastest among the five architectures. Although the computation speed of CNNsLSTM is slower than LSTMwDpH's, it is still 6.9–7.9 times faster than that of LSTMwHour. Therefore, the proposed CNNsLSTM would be an effective approach for flood management and hydraulic structure design, mainly under climate change conditions that require estimating hourly river flows using meteorological datasets.

Prediction of coronary artery bypass graft outcomes using a single surgical note: An artificial intelligence-based prediction model study.

Healthcare providers currently calculate risk of the composite outcome of morbidity or mortality associated with a coronary artery bypass grafting (CABG) surgery through manual input of variables into a logistic regression-based risk calculator. This study indicates that automated artificial intelligence (AI)-based techniques can instead calculate risk. Specifically, we present novel numerical embedding techniques that enable NLP (natural language processing) models to achieve higher performance than the risk calculator using a single preoperative surgical note. The most recent preoperative surgical consult notes of 1,738 patients who received an isolated CABG from July 1, 2014 to November 1, 2022 at a single institution were analyzed. The primary outcome was the Society of Thoracic Surgeons defined composite outcome of morbidity or mortality (MM). We tested three numerical-embedding techniques on the widely used TextCNN classification model: 1a) Basic embedding, treat numbers as word tokens; 1b) Basic embedding with a dataloader that Replaces out-of-context (ROOC) numbers with a tag, where context is defined as within a number of tokens of specified keywords; 2) ScaleNum, an embedding technique that scales in-context numbers via a learned sigmoid-linear-log function; and 3) AttnToNum, a ScaleNum-derivative that updates the ScaleNum embeddings via multi-headed attention applied to local context. Predictive performance was measured via area under the receiver operating characteristic curve (AUC) on holdout sets from 10 random-split experiments. For eXplainable-AI (X-AI), we calculate SHapley Additive exPlanation (SHAP) values at an ngram resolution (SHAP-N). While the analyses focus on TextCNN, we execute an analogous performance pipeline with a long short-term memory (LSTM) model to test if the numerical embedding advantage is robust to model architecture. A total of 567 (32.6%) patients had MM following CABG. The embedding performances are as follows with the TextCNN architecture: 1a) Basic, mean AUC 0.788 [95% CI (confidence interval): 0.768-0.809]; 1b) ROOC, 0.801 [CI: 0.788-0.815]; 2) ScaleNum, 0.808 [CI: 0.785-0.821]; and 3) AttnToNum, 0.821 [CI: 0.806-0.834]. The LSTM architecture produced a similar trend. Permutation tests indicate that AttnToNum outperforms the other embedding techniques, though not statistically significant verse ScaleNum (p-value of .07). SHAP-N analyses indicate that the model learns to associate low blood urine nitrate (BUN) and creatinine values with survival. A correlation analysis of the attention-updated numerical embeddings indicates that AttnToNum learns to incorporate both number magnitude and local context to derive semantic similarities. This research presents both quantitative and clinical novel contributions. Quantitatively, we contribute two new embedding techniques: AttnToNum and ScaleNum. Both can embed strictly positive and bounded numerical values, and both surpass basic embeddings in predictive performance. The results suggest AttnToNum outperforms ScaleNum. With regards to clinical research, we show that AI methods can predict outcomes after CABG using a single preoperative note at a performance that matches or surpasses the current risk calculator. These findings reveal the potential role of NLP in automated registry reporting and quality improvement.

Unveiling Emotional Stress: Analyzing EEG Signals With Deep Learning Insights

This study focuses on a comprehensive exploration of EEG-based emotion recognition, with a particular focus on leveraging recurrent neural network (RNN) architectures, including long short-term memory (LSTM) and gated recurrent unit (GRU). The investigation is rooted in a publicly available EEG Brainwave Dataset, meticulously curated to capture human responses to emotional stimuli. The dataset features signals obtained from frontal and temporal brain lobes, categorized into distinct emotional states encompassing positive, neutral, and negative emotions. The analysis spans four distinct scenarios, each representing different combinations of features and preprocessing strategies. Notably primary emphasis is on LSTM and GRU architectures due to their proven effectiveness in sequential data processing tasks. In addition to evaluating the performance of LSTM and GRU models a thorough examination of all the four cases to assess their efficacy in accurately predicting emotional states from EEG signals. The study also emphasis on the importance of meticulous feature selection and preprocessing in optimizing model accuracy and robustness, highlighting the intricate interplay between data representation, model architecture, and task-specific requirements. Ultimately, the research findings contribute to advanced understanding of EEG-based emotion recognition and pave the way for further research in this exciting interdisciplinary field. Key Words: Electroencephalography(EEG), Long short-term memory (LSTM), Gated recurrent unit (GRU), Feature extraction, Preprocessing techniques, Statistical metrics, Frequency domain analysis.

Investigation of Load, Solar and Wind Generation as Target Variables in LSTM Time Series Forecasting, Using Exogenous Weather Variables

Accurately forecasting energy metrics is essential for efficiently managing renewable energy generation. Given the high variability in load and renewable energy power output, this represents a crucial area of research in order to pave the way for increased adoption of low-carbon energy solutions. Whilst the impact of different neural network architectures and algorithmic approaches has been researched extensively, the impact of utilising additional weather variables in forecasts have received far less attention. This article demonstrates that weather variables can have a significant influence on energy forecasting and presents methodologies for using these variables within a long short-term memory (LSTM) architecture to achieve improvements in forecasting accuracy. Moreover, we introduce the use of the seasonal components of the target time series, as exogenous variables, that are also observed to increase accuracy. Load, solar and wind generation time series were forecast one hour ahead using an LSTM architecture. Time series data were collected in five Spanish cities and aggregated for analysis, alongside five exogenous weather variables, also recorded in Spain. A variety of LSTM architectures and hyperparameters were investigated. By tuning exogenous weather variables, a 33% decrease in mean squared error was observed for solar generation forecasting. A 22% decrease in mean absolute squared error (MASE), compared to 24-h ahead forecasts made by the Transmission Service Operator (TSO) in Spain, was also observed for solar generation. Compared to using the target variable in isolation, utilising exogenous weather variables decreased MASE by approximately 10%, 15% and 12% for load, solar and wind generation, respectively. By using the seasonal component of the target variables as an exogenous variable itself, we demonstrated decreases in MASE of 19%, 12% and 8% for load, solar and wind generation, respectively. These results emphasise the significant benefits of incorporating weather and seasonal components into energy-related time series forecasts.

Deepfake Detection Based on Temporal Analysis of Facial Dynamics Using LSTM and ResNeXt Architectures

The proliferation of deepfake technology presents a critical challenge to the authenticity and trustworthiness of digital media. To address this issue, we propose an innovative deepfake detection framework that combines the power of Long Short-Term Memory (LSTM) and ResNeXt architectures. By integrating spatial and temporal analysis methods, our approach aims to accurately identify manipulated videos amidst the vast sea of online content. Through rigorous experimentation and evaluation using diverse datasets, our framework demonstrates promising results in effectively distinguishing between genuine and fake videos. This research contributes to the ongoing efforts to combat deepfake misinformation and uphold the integrity of digital media platforms.

AI-Based Holistic Model for Decision-Support System in Inventory Optimization using deep Learning

Abstract: This study introduces an innovative artificial intelligence-driven comprehensive model designed for a decision-support system in inventory optimization. This model was developed using advanced deep learning techniques. The model aims to surpass the constraints of typical inventory management systems by effectively capturing temporal correlations and nuanced patterns in the data. This is achieved by integrating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. To navigate modern supply chains effectively, the primary focus should be on offering real-time decision support. The approach aims to enhance the decision-making processes in inventory management. This brief introduction offers insight into a distinctive method aimed at addressing the challenges of modern supply networks. The strategy focuses on providing realtime decision support and optimization strategies to enhance inventory management efficiency. The model's effectiveness is demonstrated by thorough performance evaluations, showing higher accuracy, precision, and recall compared to existing logistic regression models. The suggested LSTM model achieves an accuracy of 0.99, precision of 0.96, recall of 0.94, and an F1 Score of 0.97, surpassing existing models with higher scores in all metrics. The GRU model, albeit somewhat less effective than the LSTM, shows great overall performance, especially in recall with a score of 0.97. This investigation compares the effectiveness of different models, focusing on the potential advancements made possible by LSTM and GRU architectures in improving decision-support systems for inventory optimization. The AI-Based Holistic Model provided is at the forefront of technology advancements, offering a comprehensive solution for making educated decisions and optimizing inventories sustainably in today's highly competitive corporate climate.

Sentiment Analysis on the Quality of Public Services with User Satisfaction Prediction of YuhSinau Application Managed by BKPSDM Kabupaten Kebumen Using LSTM Method

The quality of public services is critical in providing effective and responsive governance in an increasingly digital society. The development of the YuhSinau application by the Personnel and Human Resource Development Agency (BKPSDM) of Kebumen Regency offers an innovative response to the growing need for e-learning solutions for local government civil servants (Pegawai Negeri Sipil or PNS). However, determining the app’s effectiveness and user satisfaction is critical. This demands a thorough sentiment analysis in order to acquire insights into users’ thoughts and opinions about the quality of public services supplied by YuhSinau. The Long Short-Term Memory (LSTM) approach is used in this article to examine feelings and forecast customer pleasure. Data collection from multiple sources, initial data preprocessing, LSTM model construction, training, validation, and prediction are all part of the process. The results show that the model has some drawbacks, most notably its failure to appropriately explain variation in target data due to a negative R-squared value. Enhancements to the LSTM architecture, hyperparameter adjustment, and the use of more diverse and representative training data are proposed to improve the model. Continuous review and responsiveness to user comments are critical for improving the quality of public services via the YuhSinau application.
 Keywords: sentiment analysis, public service quality, user satisfaction, YuhSinau application,LSTM method

Winds of Change: Enhancing Wind Power Generation Forecasting with LSTM Models and Advanced Techniques in a Single Turbine Wind Farm

This article presents a comprehensive analysis of wind power generation using a Long Short-Term Memory (LSTM) model in the context of a wind farm with a single turbine. The novelty of the research lies in the integration of a technical model, predictive model, heat map analysis, and LSTM architecture to enhance forecasting accuracy. The performance of the forecasted model is evaluated using standard metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). In the estimation phase, wind power generation is predicted based on current weather conditions and historical trends. Leveraging multivariate time series forecasting with LSTM through the Kera library, different look-back values are explored to optimise prediction accuracy. In particular, the study achieves favourable performance metrics, including MSE of 27.781, RMSE of 5.271, MAE of 3.281, and variance of 0.886 with a 70% training and 30% test split. Furthermore, a 60% training and 40% test split yield improved accuracy with MSE of 28.791, RMSE of 5.366, MAE of 3.376, and variance of 0.888. In the prediction phase, the LSTM model is employed to forecast power generation without relying on future weather information. Through various experiments, the optimal look-back period and number of neurones for the LSTM model are determined. Notably, the achieved mean absolute percent errors (MAPE) of 11.433% and 11.158% for 24 and 48 hours of data respectively showcase the impact of varying neuron counts on prediction accuracy. To further enhance predictive capabilities, optimisation techniques are implemented, involving adjustments to the LSTM model, including increased input batch size, batch normalisation, and dense layers. This optimisation leads to a substantial decrease in MAPE from 92.53% to 87.14% in a monthly prediction experiment. Furthermore, forward predictions are made for multiple days in the future, where LSTM successfully captures patterns and fluctuations, resulting in MAPEs of 70.74% for 24 hours, 39.26% for 2 days, and 51.48% for 1 week. Additionally, a comparison of two LSTM-based models, Autoencoder LSTM and FFT-Encoder-Decoder-LSTM, demonstrates the superior performance of the latter in terms of RMSE. This study emphasizes the potential of LSTM models in wind power generation prediction while introducing novel insights into optimizing model performance for accurate forecasting. The integration of technical and predictive models, along with heat map analysis and LSTM architecture, sets the groundwork for advancing wind power prediction methodologies.

Image Captioning: A Comprehensive Review

Abstract: The Image Caption Generator is an intriguing project that bridges the domains of computer vision and natural language processing, aiming to automatically generate descriptive text for images. In the last decade, considerable progress has been made, yet key challenges in optimising Convolutional Neural Networks (CNNs) for precise image feature extraction and refining Long Short-Term Memory (LSTM) networks for coherent text generation persist. Challenges include overfitting, limited context understanding, and difficulties in modelling long-range dependencies. Solutions have emerged in the form of attention mechanisms, which help focus on relevant image regions, and advanced LSTM architectures like the use of Gated Recurrent Units (GRUs) to improve sequential modelling. These innovations have significantly enhanced the overall performance, leading to more accurate and contextually relevant image captions, ultimately advancing the synergy between computer vision and natural language processing in the realm of image captioning

Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions

ABSTRACT Streamflow information is crucial for effectively managing water resources. The declining number of active gauging stations in many rivers is a global concern, necessitating the need for reliable streamflow estimates. Deep learning techniques offer potential solutions, but their application in southern Africa remains largely underexplored. To fill this gap, this study evaluated the predictive performance of gated recurrent unit (GRU) and long short-term memory (LSTM) networks using two headwater catchments of the Steelpoort River, South Africa, as case studies. The model inputs included rainfall, maximum, and minimum temperature, as well as past streamflow, which was utilized in an autoregressive sense. The inclusion of streamflow in this way allowed for the incorporation of simulated streamflow values into the look-back window for predicting the streamflow of the testing set. Two modifications were required to the GRU and LSTM architectures to ensure physically consistent predictions, including a change in the activation function of the GRU/LSTM cells in the final hidden layer, and a non-negative constraint that was used in the dense layer. Models trained using commercial weather station data produced reliable streamflow estimates, while moderately accurate predictions were obtained using freely available gridded weather data.

Toward interpretable LSTM-based modeling of hydrological systems

Abstract. Several studies have demonstrated the ability of long short-term memory (LSTM) machine-learning-based modeling to outperform traditional spatially lumped process-based modeling approaches for streamflow prediction. However, due mainly to the structural complexity of the LSTM network (which includes gating operations and sequential processing of the data), difficulties can arise when interpreting the internal processes and weights in the model. Here, we propose and test a modification of LSTM architecture that is calibrated in a manner that is analogous to a hydrological system. Our architecture, called “HydroLSTM”, simulates the sequential updating of the Markovian storage while the gating operation has access to historical information. Specifically, we modify how data are fed to the new representation to facilitate simultaneous access to past lagged inputs and consolidated information, which explicitly acknowledges the importance of trends and patterns in the data. We compare the performance of the HydroLSTM and LSTM architectures using data from 10 hydro-climatically varied catchments. We further examine how the new architecture exploits the information in lagged inputs, for 588 catchments across the USA. The HydroLSTM-based models require fewer cell states to obtain similar performance to their LSTM-based counterparts. Further, the weight patterns associated with lagged input variables are interpretable and consistent with regional hydroclimatic characteristics (snowmelt-dominated, recent rainfall-dominated, and historical rainfall-dominated). These findings illustrate how the hydrological interpretability of LSTM-based models can be enhanced by appropriate architectural modifications that are physically and conceptually consistent with our understanding of the system.

Using Machine Learning and Finite Element Analysis to Extract Traction-Separation Relations at Bonding Wire Interfaces of Insulated Gate Bipolar Transistor Modules.

For insulated gate bipolar transistor (IGBT) modules using wire bonding as the interconnection method, the main failure mechanism is cracking of the bonded interface. Studying the mechanical properties of the bonded interface is crucial for assessing the reliability of IGBT modules. In this paper, first, shear tests are conducted on the bonded interface to test the bonded interface's strength. Then, finite element-cohesive zone modeling (FE-CZM) is established to describe the mechanical behavior of the bonded interface. A novel machine learning (ML) architecture integrating a convolutional neural network (CNN) and a long short-term memory (LSTM) network is used to identify the shape and parameters of the traction separation law (TSL) of the FE-CZM model accurately and efficiently. The CNN-LSTM architecture not only has excellent feature extraction and sequence-data-processing abilities but can also effectively address the long-term dependency problem. A total of 1800 sets of datasets are obtained based on numerical computations, and the CNN-LSTM architecture is trained with load-displacement (F-δ) curves as input parameters and TSL shapes and parameters as output parameters. The results show that the error rate of the model for TSL shape prediction is only 0.186%. The performance metric's mean absolute percentage error (MAPE) is less than 3.5044% for all the predictions of the TSL parameters. Compared with separate CNN and LSTM architectures, the proposed CNN-LSTM-architecture approach exhibits obvious advantages in recognizing TSL shapes and parameters. A combination of the FE-CZM and ML methods in this paper provides a promising and effective solution for identifying the mechanical parameters of the bonded interfaces of IGBT modules.

Dual-attention LSTM autoencoder for fault detection in industrial complex dynamic processes

Complex dynamic characteristics resulting from multi-system coupling and closed-loop control are ubiquitous in modern industrial process data, presenting significant challenges for process fault detection. However, conventional data-driven fault detection methods assume the data to be static or slightly dynamic. Addressing the complex dynamic characteristics and nonlinearity inherent in industrial processes, this paper proposes a dual-attention long short-term memory autoencoder (DALSTM-AE) for fault detection in dynamic processes. Long short-term memory (LSTM) and autoencoder (AE) are combined into a special encoder-decoder LSTM architecture to learn both dynamic features and deep representations of variables in an unsupervised manner. Then, a dual-attention module is embedded in the decoder to properly learn the temporal dependencies associated with long input sequences and retain the most critical information. In addition, based on the reconstruction results of the DALSTM-AE model, two monitoring statistics are designed for fault detection. Finally, the effectiveness and superiority of the proposed method are fully demonstrated through case studies on a numerical simulation example, the Tennessee Eastman (TE) benchmark process, and practical coal pulverizing systems in power plants.

REEGNet: A resource efficient EEGNet for EEG trail classification in healthcare

The architecture of REEGNet (Recurrent EEG Network) combines convolutional blocks from EEGNet with long short-term memory (LSTM) layers in order to improve temporal modeling capabilities. Additionally, the use of depthwise convolutions enables REEGNet to learn spatial filters specific to distinct frequency information. When evaluated on the exceptionally challenging IMAGENET configuration of the MindBigData Brain Dataset, consisting of 70,060 samples across 5 channels sampled at 128 Hz, REEGNet outperformed state-of-the-art models including EEGNet and LSTM architectures across all class configurations ranging from 2 to 40 classes. Specifically, REEGNet surpasses EEGNet by a margin of 10.6% in multi-class classification scenarios such as configurations with 10 classes, achieving an accuracy of 27.9%. These results highlight REEGNet’s potential to increase the accessibility of electroencephalography (EEG) analysis by allowing for reliable classification without extensive computational resources. This makes REEGNet uniquely suited for deployment in resource-constrained real-world environments and applications.

Bitcoin Price Prediction Using Hybrid LSTM-GRU Models

Cryptocurrency price prediction is a challenging task due to the inherent volatility and complexity of the market. In this research, we propose a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network model for predicting Bitcoin prices. The model is implemented using the TensorFlow and Keras libraries and is evaluated on historical Bitcoin price data obtained from Yahoo Finance. Our approach aims to leverage the strengths of both LSTM and GRU architectures to enhance the accuracy of price predictions. The results suggest that the proposed hybrid LSTM-GRU model holds promise for effectively capturing the complex dynamics of cryptocurrency markets, addressing the challenges associated with traditional time-series analysis techniques.

Deep learning-assisted Hubble parameter analysis

We turn our attention on evaluating the most recent Hubble parameter data measured via the differential evolution of cosmic-chronometers from a deep learning perspective. To achieve this goal, we start our investigation by introducing the selected theoretical setup and compiling the most recent statistical data obtained in cosmology experiments. Then we implement a tuned version of the long-short term memory (LSTM) architecture and run it to predict possible values of the Cosmic Hubble parameter for different red-shift states. Since we observe a good correlation between the observed and predicted datasets of the Hubble parameter, we conclude that the machine learning approaches can play important roles in the future cosmology investigations.