Convolutional Neural Network Long Short-term Memory Research Articles

Analysis of athletes’ technical action based on deep learning

In order to raise athletes’ technical proficiency and overall performance, this paper uses deep learning technology to precisely assess table tennis technical activities. The paper builds a hybrid neural network model for technical action analysis of table tennis players, based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in deep learning. First, CNN extracts the spatial characteristics from the video frames. After that, an LSTM processes these data in a time series to accurately recognize and classify the movements of athletes. Lastly, through trials, the model is trained and evaluated using the Table Tennis Tracking Network (TTNET) dataset. The experimental findings demonstrate that: (1) In table tennis technical action analysis, the suggested deep learning model performs better than other comparative models. The efficacy of combining convolution with sequence learning is fully demonstrated by the CNN-LSTM hybrid model, which performs best in all indicators when compared to the CNN, LSTM, Multi-Objective Function (MOF), and Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) combined models. Its accuracy rate, precision rate, recall rate, and F1 score are 0.923, 0.918, 0.925, and 0.921, respectively. In contrast, the performances of LSTM and CNN are also excellent, but the performance of MOF model is relatively low; (2) In the classification of technical actions, the model has the highest classification accuracy of service, reaching 0.930, and the classification accuracy of other technical actions such as forehand stroke, backhand stroke and spike is also above 0.9, and the time sequence consistency index also maintains a high level, indicating that the model can effectively identify and analyze table tennis technical actions. In addition, the performance evaluation of real-time feedback shows that the model can achieve low processing time and feedback delay in video data processing with different lengths, which ensures the real-time and reliability in practical applications. These results show that the proposed model can not only provide accurate technical action recognition, but also provide timely and effective feedback in practical application, which has high practical value. The results of this paper prove the potential of deep learning technology in the analysis of athletes’ technical actions, and provide scientific basis and effective tools for the technical training and optimization of table tennis players.

AI-Driven Prediction of Symptom Trajectories in Cancer Care: A Deep Learning Approach for Chemotherapy Management.

This study presents an advanced method for predicting symptom escalation in chemotherapy patients using Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). The accurate prediction of symptom escalation is critical in cancer care to enable timely interventions and improve symptom management to enhance patients' quality of life during treatment. The analytical dataset consists of daily self-reported symptom logs from chemotherapy patients, including a wide range of symptoms, such as nausea, fatigue, and pain. The original dataset was highly imbalanced, with approximately 84% of the data containing no symptom escalation. The data were resampled into varying interval lengths to address this imbalance and improve the model's ability to detect symptom escalation (n = 3 to n = 7 days). This allowed the model to predict significant changes in symptom severity across these intervals. The results indicate that shorter intervals (n = 3 days) yielded the highest overall performance, with the CNN model achieving an accuracy of 81%, precision of 87%, recall of 80%, and an F1 score of 83%. This was an improvement over the LSTM model, which had an accuracy of 79%, precision of 85%, recall of 79%, and an F1 score of 82%. The model's accuracy and recall declined as the interval length increased, though precision remained relatively stable. The findings demonstrate that both CNN's temporospatial feature extraction and LSTM's temporal modeling effectively capture escalation patterns in symptom progression. By integrating these predictive models into digital health systems, healthcare providers can offer more personalized and proactive care, enabling earlier interventions that may reduce symptom burden and improve treatment adherence. Ultimately, this approach has the potential to significantly enhance the overall quality of life for chemotherapy patients by providing real-time insights into symptom trajectories and guiding clinical decision making.

Predicting Monthly Runoff: A BO-CNN-LSTM Approach for Enhanced Water Resource Management

Monthly runoff prediction is critical for sustainable water resource management, flood control, and drought preparedness. Accurate forecasts assist decision-making and management of water resources, which are critical for human and environmental demands. Previous prediction algorithms frequently lack accuracy due to human-defined hyperparameters. Autonomous hyperparameter tuning is required for improved predictions. Objectives: To improve the accuracy of monthly runoff predictions, this research proposes a BO-CNN-LSTM model that integrates Bayesian optimization (BO) with a convolutional neural network-long short-term memory neural network (CNN-LSTM). Methods/Analysis: The BO algorithm is utilized to enhance the CNN-LSTM model's hyperparameters, which helps to overcome the limits of human-set parameters. Model training and validation were carried out using monthly runoff data from 1956 to 2019 from Lanxi Station in the Hulan River Basin. The model's prediction performance was assessed using root mean square error (RMSE), mean absolute error (MAE), fitting coefficient (R²), and mean relative error (MAPE), and compared with other previous models. Findings: The results show that the BO-CNN-LSTM model outperforms conventional models in prediction accuracy and reduction of errors. The BO method efficiently enhances hyperparameters, resulting in higher performance metrics. This model gives more precise and dependable monthly runoff forecasts, which are critical for handling the Hulan River Basin's water resources. Novelty/Improvement: By combining BO and CNN-LSTM, the proposed model solves the constraints of previous models, improving prediction accuracy and reducing errors. This innovative technique presents a promising novel approach for monthly runoff prediction, with possible applications in larger hydrological prediction.

How Sophisticated Are Neural Networks Needed to Predict Long-Term Nonadiabatic Dynamics?

Nonadiabatic dynamics is key for understanding solar energy conversion and photochemical processes in condensed phases. This often involves the non-Markovian dynamics of the reduced density matrix in open quantum systems, where knowledge of the system's prior states is necessary to predict its future behavior. In this study, we explore time-series machine learning methods for predicting long-time nonadiabatic dynamics based on short-time input data, comparing these methods with the physics-based transfer tensor method (TTM). To understand the impact of memory time on these approaches, we demonstrate that non-Markovian dynamics can be represented as a linear map within the Nakajima-Zwanzig generalized quantum master equation framework. We further propose a practical method to estimate the effective memory time, within a given tolerance, for reduced density matrix propagation. Our predictive models are applied to various physical systems, including spin-boson models, multistate harmonic (MSH) models with Ohmic spectral densities and for a realistic organic photovoltaic system composed of a carotenoid-porphyrin-fullerene triad dissolved in tetrahydrofuran. Results indicate that the simple linear-mapping fully connected neural network (FCN) outperforms the more complicated nonlinear-mapping networks including the gated recurrent unit (GRU) and the convolutional neural network/long short-term memory (CNN-LSTM) in systems with short memory times, such as spin-boson and MSH models. Conversely, the nonlinear CNN-LSTM and GRU models yield higher accuracy in the triad MSH systems characterized by long memory times. These findings offer valuable insights into the role of effective memory time in non-Markovian quantum dynamics, providing practical guidance for the application of time-series machine learning models to complex chemical systems.

Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach

Pipeline leakage represents a critical challenge in smart cities and various industries, leading to severe economic, environmental, and safety consequences. Early detection of leaks is essential for overcoming these risks and ensuring the safe operation of pipeline systems. In this study, a hybrid convolutional neural network–long short-term memory (CNN-LSTM) model for pipeline leak detection that uses acoustic emission signals was designed. In this model, acoustic emission signals are initially preprocessed using a Savitzky–Golay filter to reduce noise. The filtered signals are input into the hybrid model, where spatial features are extracted using a CNN. The features are then passed to an LSTM network, which extracts temporal features from the signals. Based on these features, the presence or absence of a leakage is determined. The performance of the proposed model was compared with two alternative approaches: a method that employs combined features from the time domain and LSTM and a bidirectional gated recurrent unit model. The proposed approach demonstrated superior performance, as evidenced by lower validation loss, higher validation accuracy, enhanced confusion matrices, and improved t-distributed stochastic neighbor embedding plots compared to the other models when tested on industrial data. The findings indicate that the proposed model is more effective in accurately detecting pipeline leaks, offering a promising solution for enhancing smart cities and industrial safety.

A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms

The prevalence of extreme weather events gives rise to a significant degree of prediction bias in the forecasting of photovoltaic (PV) power. In order to enhance the precision of forecasting outcomes, this study examines the interrelationships between China’s 24 conventional solar terms and extreme meteorological events. Additionally, it proposes a methodology for estimating the short-term generation of PV power based on the division of solar term time series. Firstly, given that the meteorological data from the same festival is more representative of the climate state at the current prediction moment, the sample data are grouped according to the 24 festival time nodes. Secondly, a convolutional neural network–long short-term memory (CNN-LSTM) PV power prediction model based on an Attention mechanism is proposed. This model extracts temporal change information from nonlinear sample data through LSTM, and a CNN link is added at the front end of LSTM to address the issue of LSTM being unable to obtain the spatial linkage of multiple features. Additionally, an Attention mechanism is incorporated at the back end of the CNN to obtain the feature information of crucial time steps, further reducing the multi-step prediction error. Concurrently, a PV power error prediction model is constructed to rectify the outcomes of the aforementioned prediction model. The examination of the measured data from PV power stations and the comparison and analysis with other prediction models demonstrate that the model presented in this paper can effectively enhance the accuracy of PV power predictions.

A Deep Learning Approach for Optimizing Monoclonal Antibody Production Process Parameters

This study presents a new deep learning method for optimizing monoclonal antibody (mAb) production processes using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture. The model was developed and validated using industry data from 50 products over 18 months. The proposed design outperforms statistical models, machine learning algorithms, and other deep learning models, achieving a root mean squared error of 0.412 g/L and R^ 2 value of 0.947 for mAb titer prediction. Feature importance analysis identified temperature, dissolved oxygen, and pH as the most critical parameters affecting mAb production. In silico optimization, experiments demonstrated a 28.1% increase in mAb titer and a 27.9% improvement in volumetric productivity. The model's robustness and generalizability were validated across cell lines and bioreactor scales (50L to 2000L). A novel Dynamic Trajectory Similarity (DTS) score was introduced to quantify the model's ability to capture process dynamics, yielding a score of 0.923. This approach offers significant potential for enhancing process understanding, optimizing production efficiency, and facilitating scale-up in industrial mAb manufacturing. The study also discusses limitations, including interpretability challenges and the need for uncertainty quantification in future work.

Research on an aeroengine bearing fault diagnosis method based on WMSST-CNN-BKA-LSSVM

Abstract To effectively extract the fault feature from small sample datasets and improve the accuracy of the model, an aeroengine bearing fault diagnosis method is proposed in this research based on wavelet multi-synchrosqueezed transform-convolutional neural network-black-winged kite algorithm-least squares support vector machine (WMSST-CNN-BKA-LSSVM). WMSST was used to perform modal decomposition on the collected fault data to obtain the time-frequency images. The time-frequency images were input into CNN for fault data feature extraction. The results of the CNN fully connected layer were used as input to the LSSVM, and the BKA was used to optimize the key parameters of the LSSVM to classify aeroengine bearing faults. The method was verified by aeroengine bearing test data from the Harbin Institute of Technology. The research showed that the accuracy is 99.86% when the faulty test samples are 70%, and the accuracy is 93.90% when the faulty samples are 30%. The accuracy showed the applicability of this method in small sample fault diagnosis. By comparing the method with a one-dimensional convolutional neural network (1D-CNN) and the convolutional neural network - long short-term memory (CNN-LSTM), it is found that the accuracy has been dramatically improved.

Employing deep learning in crisis management and decision making through prediction using time series data in Mosul Dam Northern Iraq

Specifically, Iraq is threatened in its second-largest northern city, Mosul, by the collapse of the Mosul Dam due to problems at the root of the dam, causing a wave of floods that will cause massive loss of life, resources, and public property. The objective of this study is to effectively monitor the level of dam water by predicting the level of water held by the dam In anticipation of achieving flood stage and breaking the dam, and supporting its behavior through formation 14-day time series data to predict seven days later. Used six deep learning models (deep neural network (DNN), convolutional neural network (CNN), convolutional neural network long short-term memory (CNN-LSTM), CNN-LSTM-Skip and CNN-LSTM Skip Attention) that models were trained to predict the water level in the dam; these levels of being under surveillance and prepared In case of danger, alert people to potential flood threats depending on the dam’s water level. These time series were created from the actual data sets of the dam; it’s a fundamental historical reading for 13 years (1993–2006) of the water level stored in the Mosul dam and was adopted in coordination with the Iraqi Ministry of Water Resources and the Centre for Research on Dams and Water Resources at Mosul University. The methodology applied in this study shows the model’s performance efficiency and the prediction results’ low error rate. It also compares those practical results to determine and adopt the performance-efficient model and a lower error rate. The comparison of these results proved the accuracy of its results, and superior to the CNN-LSTM model, it has the highest ability to perform through high accuracy with MAE = 0.087153 and time steps = 0 s 196 ms/step and loss = 0.00067. The current study demonstrated the ability to predict the water level in Mosul Dam, which suffers from foundation problems and may collapse in the future. Therefore, the water level in the dam must be monitored accurately. It also aims to test the effectiveness of the six models proposed in this study after evaluating their performance and applying the prediction process within a scenario to obtain predictive values after 14 days. The results showed the practical effectiveness of the hybrid CNN-LSTM model in correctly and accurately obtaining predictive values within the integrated framework of the required scenario. The study concluded that it is possible to enhance the ability to monitor and identify the potential risk of Mosul Dam at an early stage, and it also allows for proactive crisis management and sound decision-making, thus mitigating the adverse effects of crises on public safety and infrastructure and reducing human losses in areas along the Tigris River.

An efficient thermal comfort prediction method for indoor airflow environment using a CFD-based deep learning model

Thermal comfort in indoor environments significantly affects human health and productivity, while there remains room for improvement in enhancing thermal comfort around individuals. This study proposed an efficient thermal comfort prediction method based on the Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) model to rapidly and accurately assess indoor thermal comfort. As demonstrated with a high-speed train, the computational fluid dynamics (CFD) technology is combined to establish the dataset. Five design parameters (the ratio and angle of the upper inlets, supply air temperature and humidity, and external temperature) and four evaluation indices (air velocity, air temperature, Predicted Mean Vote, and Draft Rate) are considered in assessing the accuracy of the method on the test dataset. The results indicate that CNN-LSTM achieves consistent and accurate predictive performance, with average mean absolute error (MAE) close to 0.01 m/s, 0.2 °C, 0.1, and 1.0. On the generalization test set, the predictive performance of CNN-LSTM decreases slightly, but the average of the determination coefficients (R2) still approaches 0.89. The thermal comfort prediction method developed in this study demonstrates significant advantages in predictive performance, showing great potential for application in the construction of healthy and comfortable indoor environments in buildings, aircraft, subways, etc.

Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model.

Heart failure with preserved ejection fraction (HFpEF) requires an efficient screening method. We developed a deep learning model (DLM) to screen HFpEF risk using electrocardiograms (ECGs). A cohort study was conducted utilising data from Cohorts A and B. A convolutional neural network-long short-term memory (CNN-LSTM) DLM was employed. HFpEF risk was determined by left ventricular end-diastolic pressure (LVEDP) and clinical symptoms. The DLM was trained by ECGs. LVEDP for each patient was collected through invasive left ventricular catheterisation. Cohort A and B comprised data from individuals at high risk for HFpEF (LVEDP>12mmHg) and low risk for HFpEF (LVEDP≤12mmHg). The model was trained on Cohort A and prospectively validated on Cohort B. A total of 238 patients underwent ECG and left ventricular catheterisation for model training in Cohort A, and 117 patients for validation in Cohort B. The DLM achieved 78% accuracy in assessing HFpEF risk in Cohort A, while in Cohort B, it demonstrated 78% accuracy, 71.9% specificity, and 71.7% sensitivity. In the validation Cohort B, the DLM-identified high-risk HFpEF group exhibited significantly higher prevalence of diabetes (22.03%-11.86%, P<0.01), higher BMI indices (25.92-24.22kg/cm2, P<0.01), and lower usage history of calcium channel blockers (CCB) (11.76%-28.81%, P<0.01) compared with the DLM-identified low-risk HFpEF group. Traditional HFpEF indicators, including B-type natriuretic peptide (BNP) (22-20pg/mL, P=0.71) and E/E' (8.25-8.5, P=0.66), did not exhibit significant differences between the two groups. The DLM offers an accurate, cost-effective tool for HFpEF risk assessment, potentially facilitating early detection and improved clinical management.

Wear fault diagnosis in hydro-turbine via the incorporation of the IWSO algorithm optimized CNN-LSTM neural network

When a hydropower unit operates in a sediment-laden river, the sediment accelerates hydro-turbine wear, leading to efficiency loss or even shutdown. Therefore, wear fault diagnosis is crucial for its safe and stable operation. A hydro-turbine wear fault diagnosis method based on improved WT (wavelet threshold algorithm) preprocessing combined with IWSO (improved white shark optimizer) optimized CNN-LSTM (convolutional neural network-long-short term memory) is proposed. The improved WT algorithm is utilized to denoise the preprocessing of the original signals. Chaotic mapping, bird flock search, and cosine elite variation strategies are introduced to enhance the WSO algorithm's robust performance, and the CNN-LSTM model's hyperparameters are optimized using the IWSO algorithm to improve the diagnostic performance. The experimental results show that the accuracy of the proposed method reaches 96.2%, which is 8.9% higher than that of the IWSO-CNN-LSTM model without denoising. The study also found that the diagnostic accuracy of hydro-turbine wear faults increased with increasing sediment concentration in the water. This study can supplement the existing hydro-turbine condition monitoring and fault diagnosis system. Meanwhile, diagnosing wear faults in hydro-turbines can improve power generation efficiency and quality and minimize resource consumption.

A cloud 15kV-HDPE insulator leakage current classification based improved particle swarm optimization and LSTM-CNN deep learning approach

Real-time insulator leakage current classification is crucial in preventing the pollution flashover phenomenon and providing appropriate maintenance schedules in high-voltage transmission towers. However, current methodologies only utilize traditional artificial neural networks, which have limitations when performing big data analysis. This research developed a novel cloud 15kV-HDPE insulator leakage current classified framework, utilizing a long short-term memory convolutional neural network (LSTM-CNN). The hybrid model structure is optimized through hyperparameter fine-tuning based on improved particle swarm optimization (IPSO), which reduces human effort and considerable time compared with PSO and random search (RS) techniques. The IPSO-LSTM-CNN model can productively identify correlations between selected weather features and target leakage current levels of 15kV-HDPE insulators. LSTM efficiently captures long-term patterns in sequential data, while CNN layers competently extract high-level dependency in time-invariant information. Four 15kV-HDPE insulators’ datasets, collected in high-voltage transmission lines in the coastal area of Taiwan for more than one year, are deployed for analyzing and comparing classified performance. Other conventional models are developed to evaluate and compare classified performance with the proposed IPSO-LSTM-CNN approach, which acquires the most significant enhancement of 48.08 % loss, 45.91 % validating loss, 52.57 % MAE, 35.47 % validating MAE, 47.34 % MSE, 27.02 % validating MSE, 9.15 % PRE, 3.40 % validating PRE, 4.76 % REC, and 6.17 % validating REC. The experiment outcomes demonstrate that the developed IPSO-LSTM-CNN model acquires improved robustness and accuracy in the leakage current classified capability of 15kV-HDPE insulators.

Research on input parameter optimization for NOx deep learning prediction model

Optimizing the input parameters for NOx prediction model is instrumental in enhancing the precision and efficacy of the prediction model. This paper focuses on the input variables of the data-driven diesel engine NOx prediction model. According to the diesel engine NOx generation mechanism, at first relevant variables are selected, then the similarity method and parameter sensitivity method are compared to optimize the input variables. Firstly, this paper conducted a real-road emission test of a heavy-duty diesel vehicle, and proposed a data-driven deep learning model for predicting diesel engine NOx emissions based on the experimental data. The experimental data were processed using an Attention-Mechanism based Convolutional Neural Networks-Long Short-Term Memory (AM-CNN-LSTM) model. Subsequently, input parameters were selected according to the sensitivity weights assigned to each parameter in the preliminary model. This approach refined the modeling data and excluded variables not pertinent to NOx emission prediction. The AM-CNN-LSTM model prediction performance has been improved with 2% increase of model R2 as well as the model training time is reduced by 23%, and the types of parameters required to train the model are reduced by 50%. It accomplished this by effectively reducing data dimensions, discarding non-essential variables for NOx prediction, and diminishing computational complexity. Six input parameters were identified as pivotal in reconstructing the modeling data for accurate NOx prediction: engine speed, torque, EGR valve position, exhaust flow rate, air mass flow rate, and oil temperature. In optimizing the NOx prediction model, it is crucial to determine an optimal number of input parameters. Excessive parameters do not enhance model accuracy, while an insufficient number impairs the model’s ability to precisely emulate the NOx generation process, leading to diminished predictive accuracy.

An Explainable Deep Learning Approach for Classifying Monkeypox Disease by Leveraging Skin Lesion Image Data

According to the World Health Organization's (WHO) external situation report on the multi-country outbreak of Monkeypox in 2023, from 11 countries in Southeast Asia Regions, Thailand recorded the highest reported cases, totaling 461. The ongoing Monkeypox outbreak has raised significant public health concerns due to its rapid spread across several nations. Early detection and diagnosis are imperative for effectively treating and controlling Monkeypox. Given this context, this study aimed to determine the most efficient model for detecting Monkeypox by employing interpretable deep learning techniques. This study utilizes deep learning techniques to diagnose Monkeypox based on images of skin lesions. We evaluate based on four models—convolutional neural network (CNN), gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional long short term memory (BiLSTM)—using a publicly available dataset. Additionally, we incorporate Local Interpretable Model-Agnostic Explanations (LIME) and techniques for explainable AI, facilitating visual interpretation of model predictions for healthcare practitioners. The CNN model's performance and LSTM model's performance have an accuracy of 100%, while the GRU model's performance and BiLSTM model's performance have an accuracy of 99.88% and 99.45%. Our findings demonstrate the effectiveness of deep learning models, including the suggested CNN model leveraging the pre-trained MobileNetV2 and LSTM. These models can play a pivotal role in combating the Monkeypox virus. Doi: 10.28991/ESJ-2024-08-05-013 Full Text: PDF

Hybrid Long Short-Term Memory Wavelet Transform Models for Short-Term Electricity Load Forecasting

To ensure the constant availability of electrical energy, power companies must consistently maintain a balance between supply and demand. However, electrical load is influenced by a variety of factors, necessitating the development of robust forecasting models. This study seeks to enhance electricity load forecasting by proposing a hybrid model that combines Sorted Coefficient Wavelet Decomposition with Long Short-Term Memory (LSTM) networks. This approach offers significant advantages in reducing algorithmic complexity and effectively processing patterns within the same class of data. Various models, including Stacked LSTM, Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM), were compared and optimized using grid search with cross-validation on consumption data from Lome, a city in Togo. The results indicate that the ConvLSTM model outperforms its counterparts based on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and correlation coefficient (R2) metrics. The ConvLSTM model was further refined using wavelet decomposition with coefficient sorting, resulting in the WT+ConvLSTM model. This proposed approach significantly narrows the gap between actual and predicted loads, reducing discrepancies from 10–50 MW to 0.5–3 MW. In comparison, the WT+ConvLSTM model surpasses Autoregressive Integrated Moving Average (ARIMA) models and Multilayer Perceptron (MLP) type artificial neural networks, achieving a MAPE of 0.485%, an RMSE of 0.61 MW, and an R2 of 0.99. This approach demonstrates substantial robustness in electricity load forecasting, aiding stakeholders in the energy sector to make more informed decisions.

Novel Custom Loss Functions and Metrics for Reinforced Forecasting of High and Low Day-Ahead Electricity Prices Using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and Ensemble Learning

Day-ahead electricity price forecasting (DAEPF) is vital for participants in energy markets, particularly in regions with high integration of renewable energy sources (RESs), where price volatility poses significant challenges. The accurate forecasting of high and low electricity prices is particularly essential, as market participants seek to optimize their strategies by selling electricity when prices are high and purchasing when prices are low to maximize profits and minimize costs. In Japan, the increasing integration of RES has caused day-ahead electricity prices to frequently fall to almost zero JPY/kWh during periods of high RES output, creating significant profitability challenges for electricity retailers. This paper introduces novel custom loss functions and metrics specifically designed to improve the forecasting accuracy of extreme prices (high and low prices) in DAEPF, with a focus on the Japanese wholesale electricity market, addressing the unique challenges posed by the volatility of RES. To implement this, we integrate these custom loss functions into a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, augmented by an ensemble learning approach and multimodal features. The proposed custom loss functions and metrics were rigorously validated, demonstrating their effectiveness in accurately predicting high and low electricity prices, thereby indicating their practical application in enhancing the economic strategies of market participants.

Displacement Interval Prediction Method for Arch Dam with Cracks: Integrated STL, MF-DFA and Bootstrap

Displacement prediction models based on measured data have been widely applied in structural health monitoring. However, most models neglect the particularity of displacement monitoring for arch dams with cracks, nor do they thoroughly analyze the non-stationarity and uncertainty of displacement. To address this issue, the influencing factors of displacement were first considered, with crack opening displacement being incorporated into them, leading to the construction of the HSCT model that accounts for the effects of cracks. Feature selection was performed on the factors of the HSCT model utilizing the max-relevance and min-redundancy (mRMR) algorithm, resulting in the screened subset of displacement influence factors. Next, displacement was decomposed into trend, seasonal, and remainder components applying the seasonal-trend decomposition using loess (STL) algorithm. The multifractal characteristics of these displacement components were then analyzed by multifractal detrended fluctuation analysis (MF-DFA). Subsequently, displacement components were predicted employing the convolutional neural network-long short-term memory (CNN-LSTM) model. Finally, the impact of uncertainty factors was quantified using prediction intervals based on the bootstrap method. The results indicate that the proposed methods and models are effective, yielding satisfactory prediction accuracy and providing scientific basis and technical support for the health diagnosis of hydraulic structures.

Research on Pricing and Dynamic Replenishment Planning Strategies for Perishable Vegetables Based on the RF-GWO Model

This paper addresses the challenges of automated pricing and replenishment strategies for perishable products with time-varying deterioration rates, aiming to assist wholesalers and retailers in optimizing their production, transportation, and sales processes to meet market demand while minimizing inventory backlog and losses. The study utilizes an improved convolutional neural network–long short-term memory (CNN-LSTM) hybrid model, autoregressive moving average (ARIMA) model, and random forest–grey wolf optimization (RF-GWO) algorithm. Using fresh vegetables as an example, the cost relationship is analyzed through linear regression, sales volume is predicted using the LSTM recurrent neural network, and pricing is forecasted with a time series analysis. The RF-GWO algorithm is then employed to solve the profit maximization problem, identifying the optimal replenishment quantity, type, and most effective pricing strategy, which involves dynamically adjusting prices based on predicted sales and market conditions. The experimental results indicate a 5.4% reduction in inventory losses and a 6.15% increase in sales profits, confirming the model’s effectiveness. The proposed mathematical model offers a novel approach to automated pricing and replenishment in managing perishable goods, providing valuable insights for dynamic inventory control and profit optimization.

Research on Depression Recognition Based on University Students’ Facial Expressions and Actions with the Assistance of Artificial Intelligence

As artificial intelligence (AI) technology advances, its application in the field of psychology has witnessed significant advancements. In this paper, with the assistance of AI, 80 university students with depression and 80 university students with normal psychology were selected as the subjects. The facial expression feature data were extracted through OpenFace, and the action feature data were extracted based on a Kinect camera. Then, the convolutional neural network-long short-term memory (CNN-LSTM) and temporal convolutional neural network (TCN) approaches were designed for recognition. Finally, a weighted fusion recognition method was proposed. The results showed that compared with the support vector machine, back-propagation neural network, and other approaches, the CNN-LSTM and TCN methods showed better performance in the recognition of single feature data, and the accuracy reached 0.781 and 0.769, respectively. After weighted fusion, the accuracy reached the highest at 0.875. The results verify that the methods designed in this paper are effective in identifying depressive emotions through facial expressions and actions among university students and have the potential for practical application.