Short-term Memory Network Research Articles

LSTM-GCN based multidimensional parameter relationship analysis and prediction framework for system level experimental bench

In nuclear power plants (NPPs) operations, the prediction of multi-dimensional parameters is found to help operators to grasp the condition of the system. However, majority of existing studies are focused on single-dimensional parameter prediction. In this study, a multi-dimensional parameter prediction framework of NPPs based on Long Short-Term Memory Network and Graph Convolution Network (LSTM-GCN) and a multi-model integrated parameter correlation analysis framework (PCAF) are proposed, in which PCAF is used to build a parameter correlation network for GCN, and LSTM-GCN is used to predict multi-dimensional parameter of NPPs. To verify the feasibility of the LSTM-GCN framework, multi-dimensional parameter prediction researches are conducted using data from a thermohydraulic experimental bench that simulates the operation of NPPs. Results indicate that compared to traditional prediction models, LSTM-GCN framework enhances the prediction accuracy of multi-dimensional parameter, which benefits from the ability of LSTM-GCN to utilize the temporal dependencies and spatial correlations of parameters.

Artificial Intelligence in Advance Angiogenesis and Inflammation Research: A Breakthrough in Disease Prediction and Therapy

Background: Angiogenesis and inflammation are fundamental biological processes, crucial to human health. Dysregulation of these processes is implicated in diseases such as cancer, cardiovascular disorders, and autoimmune diseases. Despite advances in research, the complexity of these interactions has been challenging to understand. Recent developments in artificial intelligence (AI) offer a promising approach for overcoming these challenges, especially in big data analysis. This study explores AI applications in quantifying angiogenesis and inflammatory markers and predicting disease progression. Methods: AI algorithms, including machine learning (ML) and deep learning (DL), were employed to analyze high-throughput biological data. The study applied Lasso regression for biomarker discovery, Long Short-Term Memory (LSTM) networks for predicting disease progression, and Gaussian Mixture Models (GMM) for patient subgroup identification. Image analysis using DeepLabv3+ was conducted to assess angiogenesis and inflammatory markers in histological images. Model performance was evaluated using R-squared (R²), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) metrics. Results: The AI framework demonstrated high accuracy in predicting disease progression, with notable R² values and low MSE and RMSE values. The application of AI led to the successful identification of angiogenesis-related genes and biomarkers in various diseases, including diabetic foot ulcers and chronic obstructive pulmonary disease. AI-based image analysis also provided precise quantification of angiogenesis and inflammation, enhancing the understanding of disease mechanisms. Conclusion: AI-driven approaches significantly improve the analysis of complex biological processes, offering new insights into angiogenesis and inflammation. The high predictive accuracy of the AI models underscores their potential in clinical applications, such as personalized treatment strategies and disease monitoring. As AI continues to evolve, its integration into biomedical research will likely yield further advancements in disease prediction, diagnosis, and treatment.

An aircraft engine remaining useful life prediction method based on predictive vector angle minimization and feature fusion gate improved transformer model

Remaining useful life (RUL) prediction is crucial for achieving intelligent and predictive maintenance of aircraft engines. In practical applications, advance prediction values smaller than the true values can prevent serious deferred maintenance accidents. Using asymmetric loss functions directly leads to noticeable accuracy degradation, and existing methods often fail to satisfy accuracy and advance prediction requirements. To address this problem, this paper proposes a novel RUL prediction method based on the Prediction Vector Angle (PVA) minimization and Feature Fusion Gate (FFG) improved Transformer network. Specifically, the FFG is proposed to enhance Transformer prediction accuracy by dynamically fusing global and local features. The concept of PVA is first introduced based on the tilting properties of the RUL descent process. The target of the prediction model is cleverly transformed from error minimization to PVA minimization through the cosine similarity loss function. Various experiments on the CMAPSS dataset demonstrate the effectiveness of the proposed method in achieving high accuracy and advanced prediction. Compared to the state-of-the-art method, RMSE is reduced by at least 2.94 % and Score by 7.00 %. Finally, the PVA minimization mechanism significantly improves long short-term memory and convolutional neural network performance. The proposed method is noteworthy for its superiority and applicability.

Sqrt-Loglogish CNN and Markov model for 5G spectrum sensing application

The research presents innovative methods for spectrum sensing in 5G networks, using the Sqrt-Loglogish convolutional neural network (SL-CNN) and hidden orthogonal intuitionistic fuzzy Markov model (HOIFMM). The proposed methods aim to tackle issues related to detecting principal user signals accurately, mitigating interference, and efficiently utilizing the spectrum in wideband spectrum environments due to their diverse and ever changing characteristics. The Sqrt-Loglogish CNN improves spectrum sensing by addressing static threshold dependency and potential overfitting. The HOIFMM offers a complex framework for predicting sparsity levels and primary user patterns. The results highlight the effectiveness of the suggested techniques in differentiating primary user signals from noise and interference, resulting in enhanced interference management tactics and overall network performance. MATLAB simulation is performed and compared the proposed methods performance with existing state-of-the-art methods such as CNN, deep neural network (DNN), long short-term memory (LSTM) and artificial neural networks (ANN). The proposed method has outperformed existing methods in terms of sensitivity, accuracy, and precision. Future endeavors include improving these methods, investigating sophisticated machine learning algorithms, and doing real world validations to guarantee scalability and resilience in various 5G deployment situations. This research advances the spectrum sensing capabilities in 5G networks, potentially improving efficiency, reliability, and quality of service.

Deep learning performance prediction for solar-thermal-driven hydrogen production membrane reactor via bayesian optimized LSTM

The random fluctuation of solar energy poses significant challenges for direct solar utilization systems. Solar-driven membrane reactors (SMMR) are efficient devices for energy conversion and pure hydrogen production, featuring thermal inertia and complex physicochemical processes. To realize advance control of SMMR under solar variations to promote their practical application, accurate performance prediction is essential. This study develops a deep learning-assisted performance prediction model for SMMR using Bayesian optimized long short-term memory (BO-LSTM) network and weather classification. Time-series datasets with similar environmental features are generated through SMMR multi-physics modeling and trained by BO-LSTM. The BO-LSTM demonstrates high accuracy in both short-time (1-day) and long-time (40-day) predictions, with a correlation coefficient exceeding 0.997 and a deviation of ±0.0016 in long-term predictions. Weather classification ensures consistent prediction accuracy across different weather. Compared to RF, SVM, BPNN, and CNN models, BO-LSTM provides smoother prediction curves and significantly improves accuracy and stability, reducing RMSE by 44.6% on sunny days and 53.3% on cloudy days in short-time predictions, and by 56.7% and 68.0% in long-time predictions, respectively. The BO-LSTM proposed in this study accurately predicts SMMR performance under various weather, guiding practical applications and offering a reference for prediction and control in solar thermal utilization systems.

Machine learning based prediction of flyrock distance in rock blasting: A safe and sustainable mining approach

Flyrock is a significant environmental and safety concern in mining and construction. It arises from various geological and blast design factors, posing risks to workers, machinery, and nearby structures. This study examined how these factors affect the rate and distance of flyrock projections caused by blasts. To address this issue, advanced machine learning (ML) models were used to predict flyrock distances in the Akoko Edo dolomite quarries. The models examined included bidirectional recurrent neural networks (BRNNs), support vector regression (SVR) with different kernels (SVR-S, SVR-RBF, SVR-L, SVR-P), long short-term memory (LSTM) networks, and random forest (RF) algorithms. A case study was conducted using 258 blasting data samples to develop these models. Key factors influencing flyrock were identified: blast hole burden distance, maximum instantaneous charge, and rock brittleness index. Using these factors, a flyrock possibility assessment chart was created to enhance the safety of small-scale mining operations. The model’s prediction accuracy was evaluated using correlation coefficients and four performance metrics. The LSTM model stood out, achieving the highest coefficient of correlation (R2 = 0.99) for both training and testing datasets. This indicates that the LSTM model accurately predicts blast-induced flyrock distance. The study also revealed that the Gaussian-RBF kernel SVR has high prediction accuracy when compared to other SVR variants (SVR-S, SVR-L, and SVR-P). In conclusion, the study compared various ML models for flyrock reduction and found that the LSTM model was the most effective in estimating blast-induced flyrock distances.

Application of LSTM neural network in the research of cyclical industries: taking the automotive industry as an illustrative case

Anticipating future developmental trends within an industry stands as one of the paramount responsibilities for industry researchers. Nevertheless, owing to the intricate and subjective nature inherent in industry research, coupled with researchers usually having to rely on their own subjective judgments when making predictions, the forecasting outcomes frequently prove unsatisfactory. In order to address this issue, this study introduces deep learning technology into the industry research field and employs Long & Short-Term Memory (LSTM) networks to forecast a typical cyclical industry - the automotive industry. In the specific experiment, Adam optimization algorithm is employed to enhance model training speed, followed by utilization of the grid search algorithm for optimal parameter selection. Subsequently, the obtained optimal model is utilized for LSTM multivariate sequence prediction in order to forecast the future development trend of the automotive industry over the next six months. The ultimate outcome demonstrates that the LSTM model accurately anticipated the forthcoming U-shaped rebound trend in the automotive industry, albeit with some limitations in predicting precise numerical values and timing. Nevertheless, it aligns closely with the actual developmental trajectory. This investigation represents a constructive application of deep learning technology in industrial research, and its final findings hold significant implications for future industry research endeavors.

An improved convolutional recurrent neural network for stock price forecasting

<p>Stock price forecasting is a challenging area of research, particularly due to the complexity and unpredictability of financial markets. The accuracy of prediction models is influenced by various factors, including nonlinearity, seasonality, and economic shocks. Deep learning has demonstrated better forecasts of stock prices than traditional approaches. This study, therefore, proposed a new approach to improve forecasting system based on an end-to-end convolutional recurrent neural network (CRNN) with attention mechanism. Our approach first investigates local stock price features using 1D convolutional neural network, and then employs a bidirectional long short-term memory (Bi-LSTM) network for forecasting. This model stands out by effectively utilizing contextual data and representing the temporal character of data. The Bi-LSTM is helpful for understanding the history and future contextual information since it uncovers both past and future contexts of stock data. Furthermore, integrating attention mechanism within the CRNN represents a significant improvement. This allows our model to handle long input sequences more effectively and capture the inherent stochasticity in stock prices, which is often missed by traditional models. The effectiveness of our approach is investigated using data on 10 stock indexes from Yahoo Finance. The results show that our method outperforms ARIMA, LSTM, and conventional methods.</p><p> </p>

Advancing Heart Disease Forecasting: Integrating CNN-LSTM Models with Feature Enhancement for Improved Predictive Accuracy

Background: Heart failure (HF) is the leading cause of death worldwide, with accurate early diagnosis being challenging due to the disease's subtle symptoms and the requirement for extensive medical expertise. The development of predictive models is crucial for early intervention. Objective: This study proposes a method for Dynamic Forecasting (DF) of Heart Diseases (HD) using a Deep Learning (DL) model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The aim is to create an automatic, accurate, and cost-effective system for predicting cardiac valve failure. Methods: The study introduces a Hybrid DL model combining CNN and LSTM with Feature Enhancement (FE) and Comprehensible Artificial Intelligence (CAI) techniques. The CNN extracts features from clinical data, which are then processed by the LSTM model to handle temporal dependencies. The model's effectiveness was evaluated using a freely available cardiovascular disease dataset. Accuracy was assessed with and without FE. Results: The CNN-LSTM model achieved an accuracy of 83.5% with FE and 88.2% without FE. The model demonstrated superior performance in terms of sensitivity (80.04% with FE), specificity (87.11% with FE), and overall accuracy compared to other methods including Support Vector Machines (SVM), Decision Trees (DT), and Random Forests (RF). Conclusion: The proposed CNN-LSTM model offers a significant improvement in forecasting heart disease dynamics by effectively integrating CNN for feature extraction and LSTM for temporal analysis. This hybrid approach, combined with advanced feature enhancement and explainable AI techniques, provides a more accurate and comprehensible prediction tool for early heart disease detection.

Three-dimensional wake transition of rectangular cylinders and temporal prediction of flow patterns based on a machine learning algorithm

This study investigates the three-dimensional (3D) wake transition in unconfined flows over rectangular cylinders using direct numerical simulation (DNS). Two different cross-sectional aspect ratios (AR) and Reynolds numbers (Re) are scrutinized: AR = 0.5 at Re = 200 and AR = 3 at Re = 600. The investigation focuses on characterizing the flow patterns and forecasting their temporal evolution utilizing the proper orthogonal decomposition (POD) technique coupled with a long short-term memory (LSTM) network. The DNS results reveal the emergence of an ordered mode A for AR = 3, attributed to the stabilizing effect of the elongated AR. On the other hand, the case with a smaller AR (= 0.5) exhibits a mode-swapping regime characterized by modes A and B's distinct and simultaneous manifestation. The spanwise wavelengths of mode A and mode B are approximately 4.7 and 1.2 D for AR = 0.5, while the spanwise wavelength of mode A is 3.5 D for AR = 3. The POD serves as a dimensionality reduction technique, and LSTM facilitates temporal prediction. This algorithm demonstrates satisfactory performance in predicting the flow patterns, including the instabilities of modes A and B, across both transverse and spanwise directions. The employed algorithm adeptly predicts the pressure time series surrounding the cylinders. The duration for training the algorithm is only about 0.5% of the time required for DNS computations. This research, for the first time, demonstrates the effectiveness of the POD–LSTM algorithm in predicting complex 3D instantaneous wake transition patterns for flow past rectangular cylinders.

Based on CNN-LSTM for photovoltaic output prediction of multi-scale method

Abstract To maximize power generating efficiency, accurate forecast of generation power is the very important job for the photovoltaic generation system. In this paper, photovoltaic production is forecasted using a understanding way called multi-scale based on convolutional neural network-long short-term memory network (CNN-LSTM) for photovoltaic output prediction of multi-scale method. However, existing methods are often challenged by the multi-scale nature of the data when dealing with PV output prediction, so a more efficient method is needed to overcome difficulties. Therefore, this paper put forward an idea of a multi-scale method based on CNN-LSTM, which combines CNN and LSTM to accurately predict photovoltaic output at different time scales. The CNN-LSTM model can achieve the precision of 0.9097 for 15 minutes, the prediction accuracy of 0.8750 for 1 hour, and the prediction accuracy of 0.7987 for 4 hours, which can prove that the model can meet the scheduling rules.

Unraveling Alzheimer’s Disease Classification with Deep Learning: A Comprehensive Review

Abstract: Alzheimer's disease (AD) is a neurological disease that progresses over time and is the primary global cause of dementia. Timely and accurate diagnosis is essential for therapeutic and interventional success. Conventional approaches, like clinical evaluations and neuroimaging procedures, are not always able to detect the illness at an early stage. This paper provides an extensive overview of current developments in using deep learning methods for AD early diagnosis and Identification. Numerous approaches have been put up, utilizing diverse forms of data such as MRI scans, eye-tracking actions, and text data that is clinically significant. Convolutional neural networks (CNNs), deep neural networks (DNNs), and long short-term memory networks (LSTMs) are just a few of the deep learning designs used by these models, which highlight how flexible and adaptive these methods are. Using cutting-edge methods to extract discriminative features from the data, feature extraction and selection have become essential components of these models. Additionally, a number of studies have concentrated on improving model performance through the use of methods such as depth-wise convolutional procedures, ensemble learning approaches, and particle swarm optimization for hyperparameter tweaking. Even with high accuracy rates attained, there is still opportunity for development, particularly with regard to managing data scarcity and model interpretability. This survey indicates possible routes for future research and offers insightful information about the state of deep learning applications in AD categorization.

Advanced thermal prediction for green roofs: CNN-LSTM model with SSA optimization

Green roofs serve as an innovative strategy for urban architecture, playing a pivotal role in mitigating urban heat island effects and enhancing building energy efficiency. This study proposes a hybrid model based on the Sparrow Search Algorithm (SSA) optimized Convolutional Neural Network − Long Short-Term Memory Network (CNN-LSTM) for rapid and accurate prediction of the thermal performance of green roofs. The model integrates the spatial feature extraction of CNN with the temporal analysis capability of LSTM, enhancing the predictive accuracy of multi-feature time series through global optimization by SSA. Taking the green roof in Guangzhou China as a case study and utilizing hourly meteorological and thermal property data, this model has demonstrated superior predictive performance: the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-squared (R2), and Mean Absolute Percentage Error (MAPE) for the prediction of inner surface temperature are 0.718, 0.908 %, 0.922, and 0.025 %, respectively; for the outer surface temperature prediction, the corresponding values are 0.618, 0.886 %, 0.983, and 0.028 %. Comparative analysis with existing AI models and ablation experiments further confirm the superiority of this model. Additionally, through Spearman correlation analysis, this study reveals the correlation between key input parameters and surface temperatures, providing data support for the effective design and management of green roofs. The model presented in this study not only provides an efficient tool for predicting the thermal performance of green roofs but also holds significant importance for promoting green building and sustainable urban development.

Optimized differential evolution and hybrid deep learning for superior drug-target binding affinity prediction

Investigating Drug-Target Interactions (DTI) is crucial for drug repositioning and discovery tasks. However, discovering DTIs through experimental approaches is time-consuming and requires substantial financial resources. To address these challenges, machine learning-based methodologies have been adopted to reduce costs and save time. Unfortunately, the effectiveness of these methods has been limited due to the binary classification approach and the lack of empirically validated negative samples. The availability of abundant DTI datasets and protein structure data has enabled the development of new approaches, such as redefining the DTI problem as a regression task. Given this context, we propose an innovative deep-learning approach to predict binding affinities between drugs and targets. Our model, named the Convolution Self-Attention Network with Attention-based Bidirectional Long Short-Term Memory Network (CSAN-BiLSTM-Att), integrates convolutional neural network (CNN) blocks with self-attention mechanisms to create an attention-based bidirectional long short-term memory (BiLSTM) model, followed by fully connected layers. Due to the model's complexity, proper hyperparameter tuning is essential. To optimize this, we employ the Differential Evolution (DE) technique to select the most suitable hyperparameters. Experimental results demonstrate that the DE-based CSAN-BiLSTM-Att model outperforms previous approaches. Specifically, the model achieved a concordance index of 0.898 and a mean square error of 0.228 on the DAVIS dataset, and a concordance value of 0.971 with a mean square error of 0.014 on the KIBA dataset.

Discriminative feature learning based on multi-view attention network with diffusion joint loss for speech emotion recognition

In speech emotion recognition, existing models often struggle to accurately classify emotions with high similarity. In this paper, we propose a novel architecture that integrates a multi-view attention network (MVAN) and diffusion joint loss to alleviate confusion by placing a stronger focus on emotions that are challenging to classify accurately. First, we use logarithmic Mel-spectrograms (log-Mels), deltas, and delta-deltas of log-Mels as three-dimensional features to minimize external interference. Then, we design the MVAN to extract effective multi-time scale emotion features, where the channel and spatial attention are used to selectively localize the regions in the input features related to the target emotion. A Multi-time view bidirectional long and short-term memory network is used to extract the shallow edge features and deep semantic features, and multi-scale self-attention fuses these features through cross-scale attention fusion to obtain multi-time scale emotion features. Finally, a diffusion joint loss strategy is introduced to distinguish the emotional embeddings with high similarity by the generated complex emotion triplets in a diffusing fashion. We evaluated our proposed method on the Interactive Emotional Mood Binary Motion Capture (IEMOCAP), Chinese Academy of Sciences Automation Institute of Automation (CASIA), and Berlin German Emotion Speech Bank (EMODB) corpus. The results show significant improvements over existing methods, achieving 86.87% WA, 86.60% UA, and 86.82% WF1 on IEMOCAP; 70.74% WA, 70.74% UA, and 70.25% WF1 on CASIA; and 93.65% WA, 91.13% UA, and 92.26% WF1 on EMODB. These results confirm the superiority of our method. Our code and model are available at https://github.com/Littleznnz/MVAN-DiffSEG.

Securing the Internet of Health Things: Embedded Federated Learning-Driven Long Short-Term Memory for Cyberattack Detection

The proliferation of the Internet of Health Things (IoHT) introduces significant benefits for healthcare through enhanced connectivity and data-driven insights, but it also presents substantial cybersecurity challenges. Protecting sensitive health data from cyberattacks is critical. This paper proposes a novel approach for detecting cyberattacks in IoHT environments using a Federated Learning (FL) framework integrated with Long Short-Term Memory (LSTM) networks. The FL paradigm ensures data privacy by allowing individual IoHT devices to collaboratively train a global model without sharing local data, thereby maintaining patient confidentiality. LSTM networks, known for their effectiveness in handling time-series data, are employed to capture and analyze temporal patterns indicative of cyberthreats. Our proposed system uses an embedded feature selection technique that minimizes the computational complexity of the cyberattack detection model and leverages the decentralized nature of FL to create a robust and scalable cyberattack detection mechanism. We refer to the proposed approach as Embedded Federated Learning-Driven Long Short-Term Memory (EFL-LSTM). Extensive experiments using real-world ECU-IoHT data demonstrate that our proposed model outperforms traditional models regarding accuracy (97.16%) and data privacy. The outcomes highlight the feasibility and advantages of integrating Federated Learning with LSTM networks to enhance the cybersecurity posture of IoHT infrastructures. This research paves the way for future developments in secure and privacy-preserving IoHT systems, ensuring reliable protection against evolving cyberthreats.

Reservoir Operation based Machine Learning Models: Comprehensive Review for Limitations, Research Gap, and Possible Future Research Direction

The operation of dams and reservoirs is critical for water resource management, including flood control, irrigation, hydropower generation, and environmental conservation. Traditional optimization techniques like Dynamic Programming (DP), Linear Programming (LP), and Nonlinear Programming (NLP) have been foundational in managing these operations. However, they often fall short in addressing the complexities of modern water management challenges posed by climate variability and increasing water demands. Machine learning (ML) techniques have emerged as powerful tools to enhance the efficiency and accuracy of dam and reservoir operations. This paper provides a comprehensive review of various ML models, including Neural Networks, Genetic Algorithms, Decision Trees, and Ensemble Methods, highlighting their applications in predicting reservoir inflows, optimizing water release schedules, and improving flood risk management. Notably, ML models like Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) have shown significant improvements in forecasting accuracy and operational decision-making. Despite these advancements, several limitations and research gaps persist, including the need for real-time data integration, adaptive learning mechanisms, and models that consider socio-economic and climatic factors. This review underscores the importance of addressing these gaps to develop more robust and generalizable ML models. Future research directions are suggested to focus on hybrid models combining ML with traditional optimization techniques, comprehensive validation across diverse conditions, and the integration of ecological and economic considerations. By systematically identifying and addressing these limitations, this research aims to pave the way for more effective and sustainable dam and reservoir management practices. Leading towards suggesting that enhancing real-time data integration and developing adaptive learning mechanisms are in order to improve model responsiveness.

Enhancing pressure transient analysis in reservoir characterization through deep learning neural networks

Deep learning neural networks exhibit remarkable efficiency in numerous tasks within machine learning applications across various disciplines, including reservoir engineering. Traditional pressure transient analysis (PTA) methodologies, however, are challenged by limitations such as human error in accurately estimating critical reservoir parameters like permeability and wellbore damage (skin). In this study, we evaluate the efficiency and accuracy of various machine learning models in pressure transient analysis (PTA) tasks. Our investigation initially focused on integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance the precision in estimating reservoir parameters and identifying reservoir models. However, through comprehensive testing and validation using simulated well-test data and out-of-distribution samples, we discovered that tree-based models, specifically XGBoost and Random Forest, exhibit superior performance, particularly in generalization tasks. These models outperform CNN-LSTM and 1D-CNN models, demonstrating enhanced robustness and accuracy in handling diverse and unseen data, thereby addressing traditional challenges in pressure transient analysis more effectively. Moreover, Savitzky–Golay Filters were used to handle the noise problems of data, and this method proved effective for data with noise levels up to a standard deviation of 2. Compared with other models, the XGBoost model has a better performance in reservoir model identification with an out-of-distribution accuracy of 92%. On the other hand, Random Forest showed a remarkable result in the task of reservoir parameter estimation, especially for the permeability and skin with out-of-distribution R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} of 98% and 97%, respectively. The findings of this study can help for a better understanding of the different deep learning models and their applications in pressure transient analysis. Specifically, the superior performance of the tree-based models in overcoming traditional pressure transient analysis challenges demonstrates their potential to enhance reservoir characterization predictions. Employing these advanced machine learning techniques can significantly reduce human error, increase the accuracy of parameter estimation, and improve the analysis process, thereby aiding strategic decision-making in reservoir management

Integrating Temporal and Feedforward Models for Solar Energy Prediction: LSTM and ANN Hybrid Approaches

The increasing reliance on renewable energy, particularly solar power, necessitates accurate models for predicting energy output to optimize storage and distribution systems. Traditional methods such as Long Short-Term Memory (LSTM) networks and Artificial Neural Networks (ANNs) offer unique strengths in forecasting photovoltaic (PV) system outputs. LSTM excels in capturing temporal dependencies in time-series data, while ANNs effectively model nonlinear relationships between variables. This study aims to develop and evaluate a hybrid LSTM-ANN model for improving the accuracy of PV energy output predictions, focusing on voltage, power, and irradiance. Using data collected from a solar-powered greenhouse in Talang Kemang, Indonesia, the model was trained and validated. The hybrid model demonstrated significant improvements in prediction accuracy. For voltage, the model achieved a Mean Absolute Error (MAE) of 0.1016 and a Root Mean Squared Error (RMSE) of 0.1417, while irradiance predictions resulted in an MAE of 0.0895 and RMSE of 0.1149. Power predictions also yielded strong results, with an MAE of 0.1506 and RMSE of 0.1954. These results highlight the hybrid LSTM-ANN model's effectiveness in combining temporal and nonlinear data processing capabilities, leading to superior accuracy in predicting PV system outputs. This approach can enhance the reliability of energy forecasting models, enabling better integration of solar power into electrical grids. The model holds promise for broader applications in renewable energy systems, improving their efficiency and sustainability

Stock Price Prediction in Indonesia's Mining Sector Using a Hybrid Conv1D-LSTM Model

This study presents a novel approach to forecasting stock prices in Indonesia's mining sector by leveraging a hybrid model combining Convolutional Neural Networks (Conv1D) and Long Short-Term Memory (LSTM) networks. Given the volatile nature of stock markets and the specific characteristics of the mining industry, accurate prediction models are essential for investors and analysts. The hybrid Conv1D-LSTM model integrates the feature extraction capabilities of Conv1D with the sequence learning strengths of LSTM, providing a robust framework for time series forecasting.