A new method for the rapid identification of external water types in rainwater pipeline networks using UV-Vis absorption spectroscopy.

Ultraviolet-visible (UV-Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV-Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks.

Machine learning-based modeling of surface water temperature dynamics in arctic lakes.

Lake surface-water temperature (LSWT) regulates physical and biochemical processes in lakes. Therefore, understanding the LSWT dynamics is important, especially in Arctic zone since the region is experiencing a warming rate that is greater than the Earth's average. However, regular measurements of LSWT in the remote Arctic lakes always face difficulties or cannot be done by satellites accurately due to the cloud cover and their limited spatiotemporal resolution. Here, we used a historically rich data (1960-2023) to develop four machine learning-based algorithms for the daily LSWT modeling in Lake Inari, situated in Arctic zone, using the air-temperature data. Our results showed that both air-temperature (0.030 °C/yr) and LSWT (0.023m °C/yr) were warming with a rate faster than those in the globe. The long-short-term memory model, with the coefficients of determination varied from 0.96 to 0.98, outperformed other algorithms in modeling of the daily LSWT dynamics in Lake Inari, followed by both support vector regression and neural network tools, and random forest model. As the air-temperature data are widely accessible through synoptic stations and remote sensing techniques, our suggested models can be simply adopted for other Arctic lakes, where the local water-temperature data are often lacking or contain large windows of missing data due to harsh atmospheric conditions and equipment failure.

[Predicting Ozone Concentration in Hangzhou with the Fusion Class Stacking Algorithm].

Aiming at the problem that the single machine learning model has low prediction accuracy of daily average ozone concentration, an ozone concentration prediction method based on the fusion class Stacking algorithm （FSOP） was proposed, which combined the statistical method ordinary least squares （OLS） with machine learning algorithms and improved the prediction accuracy of the ozone concentration prediction model by integrating the advantages of different learners. Based on the principle of the Stacking algorithm, the observation data of the daily maximum 8h ozone average concentration and meteorological reanalysis data in Hangzhou from January 2017 to December 2022 were used. Firstly, the specific ozone concentration prediction models based on the light gradient boosting machine （LightGBM） algorithm, long short-term memory model （LSTM）, and Informer model were established, respectively. Then, the prediction results of the above models were used as meta-features, and the OLS algorithm was used to obtain the prediction expression of ozone concentration to fit the observed ozone concentration. The results showed that the prediction accuracy of the model combined with the class Stacking algorithm was improved, and the fitting effect of ozone concentration was better. Among them, R2, RMSE, and MAE were 0.84, 19.65 µg·m-3, and 15.50 µg·m-3, respectively, which improved the prediction accuracy by approximately 8% compared with that of the single machine learning model.

GEPAF: A non-monotonic generalized activation function in neural network for improving prediction with diverse data distributions characteristics.

The world today has made prescriptive analytics that uses data-driven insights to guide future actions. The distribution of data, however, differs depending on the scenario, making it difficult to interpret and comprehend the data efficiently. Different neural network models are used to solve this, taking inspiration from the complex network architecture in the human brain. The activation function is crucial in introducing non-linearity to process data gradients effectively. Although popular activation functions such as ReLU, Sigmoid, Swish, and Tanh have advantages and disadvantages, they may struggle to adapt to diverse data characteristics. A generalized activation function named the Generalized Exponential Parametric Activation Function (GEPAF) is proposed to address this issue. This function consists of three parameters expressed: α, which stands for a differencing factor similar to the mean; σ, which stands for a variance to control distribution spread; and p, which is a power factor that improves flexibility; all these parameters are present in the exponent. When p=2, the activation function resembles a Gaussian function. Initially, this paper describes the mathematical derivation and validation of the properties of this function mathematically and graphically. After this, the GEPAF function is practically implemented in real-world supply chain datasets. One dataset features a small sample size but exhibits high variance, while the other shows significant variance with a moderate amount of data. An LSTM network processes the dataset for sales and profit prediction. The suggested function performs better than popular activation functions when a comparative analysis of the activation function is performed, showing at least 30% improvement in regression evaluation metrics and better loss decay characteristics.

Developing effective optimized machine learning approaches for settlement prediction of shallow foundation.

The precise assessment of shallow foundation settlement on cohesionless soils is a challenging geotechnical issue, primarily due to the significant uncertainties related to the factors influencing the settlement. This study aims to create an advanced hybrid machine learning methodology for accurately estimating shallow foundations' settlement (Sm). The initial contribution of the current research is developing and validating a robust hybrid optimization methodology based on an artificial electric field and single candidate optimizer (AEFSCO). This approach is thoroughly tested using various benchmark functions. AEFSCO will also be used to optimize three useful machine learning methods: long short-term memory (LSTM), support vector regression (SVR), and multilayer perceptron neural network (MLPNN) by adjusting their hyperparameters for predicting the settlement of shallow foundations. A database consisting of 189 individual case histories, conducted through various investigations, was used for training and testing the models. The database includes five input parameters and one output. These factors encompassed both the geometric characteristics of the foundation and the properties of the sandy soil. The results demonstrate that employing effective optimization strategies to adjust the ML models' hyperparameters can significantly improve the accuracy of predicted results. The AEFSCO has increased the coefficient of determination (R2) value of the MLPNN model by 9.3 %, the SVR model by 8 %, and the LSTM model by 22 %. Also, the LSTM-AEFSCO model is more accurate than the SVR-AEFSCO and MLPNN-AEFSCO models. This is shown by the fact that R2 went from 0.9494 to 0.9290 to 0.9903, which is an increase of 4.5 % and 6 %.

A critical review of RNN and LSTM variants in hydrological time series predictions.

The rapid advancement in Artificial Intelligence (AI) and big data has developed significance in the water sector, particularly in hydrological time-series predictions. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks have become research focal points due to their effectiveness in modeling non-linear, time-variant hydrological systems. This review explores the different architectures of RNNs, LSTMs, and Gated Recurrent Units (GRUs) and their efficacy in predicting hydrological time-series data.•RNNs are foundational but face limitations such as vanishing gradients, which impede their ability to model long-term dependencies. LSTMs and GRUs have been developed to overcome these limitations, with LSTMs using memory cells and gating mechanisms, while GRUs provide a more streamlined architecture with similar benefits.•The integration of attention mechanisms and hybrid models that combine RNNs, LSTMs, and GRUs with other Machine learning (ML) and Deep Learning (DL) has improved prediction accuracy by capturing both temporal and spatial dependencies.•Despite their effectiveness, practical implementations of these models in hydrological time series prediction require extensive datasets and substantial computational resources. Future research should develop interpretable architectures, enhance data quality, incorporate domain knowledge, and utilize transfer learning to improve model generalization and scalability across diverse hydrological contexts.

Hybrid attention-based deep neural networks for short-term wind power forecasting using meteorological data in desert regions.

This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R2: 0.93). The optimization clearly works for higher wind speeds, achieving a remarkable improvement of 22.9%. When we look at the monthly performance, all the months presented at least some level of consistent enhancement (RRMSE reductions from 1.6 to 10.2%). These findings highlight the potential of advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid method developed in this study presents a promising direction for improving renewable energy management. This allows for more efficient resource allocation and improves wind resource predictability.

Paternal preconception donepezil exposure enhances learning in offspring.

BACKGROUND: Recent research has indicated that parental use of central nervous system-targeting medications during periconceptional periods may affect offspring across various developmental and behavioral domains. The present study sought to investigate the potential influence of paternal use of donepezil, a specific reversible central acetylcholinesterase inhibitor that activates the cholinergic system to promote cognition, on offspring. RESULTS: In this study, male rats were bred after 21 days of oral donepezil administration at a dose of 4 mg/kg to generate F1 offspring. Both male and female F1 offspring displayed enhanced performance in learning and short-term memory tests, including novel object recognition, Y maze, and operant learning. Transcriptomic analysis revealed notable alterations in genes associated with the extracellular matrix in the hippocampal tissue of the F1 generation. Integration with genes related to intelligence identified potential core genes that may be involved in the observed behavioral enhancements. CONCLUSIONS: These findings indicate that prolonged paternal exposure to donepezil may enhance the learning and memory abilities of offspring, possibly by targeting nonneural, extracellular regions. Further research is required to fully elucidate any potential transgenerational effects.

Promoted production of Fe(IV)/Fe(V) intermediates in the calcium peroxide/ferrate(VI) process for low-damage removal of algal contaminants and membrane fouling control.

Ultrafiltration (UF) is widely employed for harmful algae rejection, whereas severe membrane fouling hampers its long-term operation. Herein, calcium peroxide (CaO2) and ferrate (Fe(VI)) were innovatively coupled for low-damage removal of algal contaminants and fouling control in the UF process. As a result, the terminal J/J0 increased from 0.13 to 0.66, with Rr and Rir respectively decreased by 96.74 % and 48.47 %. The cake layer filtration was significantly postponed, and pore blocking was reduced. The ζ-potential of algal foulants was weakened from -34.4 mV to -18.7 mV, and algal cells of 86.15 % were removed with flocs of 300 µm generated. The cell integrity was better remained in comparison to the Fe(VI) treatment, and Fe(IV)/Fe(V) was verified to be the dominant reactive species. The membrane fouling alleviation mechanisms could be attributed to the reduction of the fouling loads and the changes in the interfacial free energies. A membrane fouling prediction model was built based on a long short-term memory deep learning network, which predicted that the filtration volume at J/J0= 0.2 increased from 288 to 1400 mL. The results provide a new routine for controlling algal membrane fouling from the perspective of promoting the generation of Fe(IV)/Fe(V) intermediates.

Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long Short-Term Memory and Continuous Sensor Data.

Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient η using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast η values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter η, achieving an R2 of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast η with an R2 of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring.

A Hybrid Algorithm of LSTM and Factor Graph for Improving Combined GNSS/INS Positioning Accuracy during GNSS Interruptions.

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss.

Energy-Efficient Anomaly Detection and Chaoticity in Electric Vehicle Driving Behavior.

Detection of abnormal situations in mobile systems not only provides predictions about risky situations but also has the potential to increase energy efficiency. In this study, two real-world drives of a battery electric vehicle and unsupervised hybrid anomaly detection approaches were developed. The anomaly detection performances of hybrid models created with the combination of Long Short-Term Memory (LSTM)-Autoencoder, the Local Outlier Factor (LOF), and the Mahalanobis distance were evaluated with the silhouette score, Davies-Bouldin index, and Calinski-Harabasz index, and the potential energy recovery rates were also determined. Two driving datasets were evaluated in terms of chaotic aspects using the Lyapunov exponent, Kolmogorov-Sinai entropy, and fractal dimension metrics. The developed hybrid models are superior to the sub-methods in anomaly detection. Hybrid Model-2 had 2.92% more successful results in anomaly detection compared to Hybrid Model-1. In terms of potential energy saving, Hybrid Model-1 provided 31.26% superiority, while Hybrid Model-2 provided 31.48%. It was also observed that there is a close relationship between anomaly and chaoticity. In the literature where cyber security and visual sources dominate in anomaly detection, a strategy was developed that provides energy efficiency-based anomaly detection and chaotic analysis from data obtained without additional sensor data.

A Novel TCN-LSTM Hybrid Model for sEMG-Based Continuous Estimation of Wrist Joint Angles.

Surface electromyography (sEMG) offers a novel method in human-machine interactions (HMIs) since it is a distinct physiological electrical signal that conceals human movement intention and muscle information. Unfortunately, the nonlinear and non-smooth features of sEMG signals often make joint angle estimation difficult. This paper proposes a joint angle prediction model for the continuous estimation of wrist motion angle changes based on sEMG signals. The proposed model combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network, where the TCN can sense local information and mine the deeper information of the sEMG signals, while LSTM, with its excellent temporal memory capability, can make up for the lack of the ability of the TCN to capture the long-term dependence of the sEMG signals, resulting in a better prediction. We validated the proposed method in the publicly available Ninapro DB1 dataset by selecting the first eight subjects and picking three types of wrist-dependent movements: wrist flexion (WF), wrist ulnar deviation (WUD), and wrist extension and closed hand (WECH). Finally, the proposed TCN-LSTM model was compared with the TCN and LSTM models. The proposed TCN-LSTM outperformed the TCN and LSTM models in terms of the root mean square error (RMSE) and average coefficient of determination (R2). The TCN-LSTM model achieved an average RMSE of 0.064, representing a 41% reduction compared to the TCN model and a 52% reduction compared to the LSTM model. The TCN-LSTM also achieved an average R2 of 0.93, indicating an 11% improvement over the TCN model and an 18% improvement over the LSTM model.

Machine Learning-Guided Cycle Life Prediction for Electrochromic Devices Based on Deuterium and Water Mixing Solvent.

Electrochromic devices have demonstrated considerable potential in a range of applications, including smart windows and automotive rearview mirrors. However, traditional cycle life testing methods are time-consuming and require significant resources to process a substantial amount of generated data, which presents a significant challenge and remains an urgent issue to be addressed. To address this challenge, we proposed the use of Long Short-Term Memory (LSTM) networks to construct a prediction model of the cycle life of electrochromic devices and introduced an interpretable analysis method to further analyze the model's predictive capabilities. The original dataset used for modeling was derived from preliminary experiments conducted under 1000 cycles of six devices prepared with varying mixing ratios of heavy water (D2O). Furthermore, validation experiments confirmed the feasibility of the D2O mixing strategy, with 83% of the devices exhibiting a high initial transmittance modulation amplitude (ΔT = 43.95%), a rapid response time (tc = 7 s and tb = 8 s), and excellent cyclic stability (ΔT = 44.92% after 1000 cycles). This study is the first to use machine learning techniques to predict the cycle life of electrochromic devices while proposing performance enhancement and experimental time savings for inorganic all-liquid electrochromic devices.

Underwater Acoustic Orthogonal Frequency-Division Multiplexing Communication Using Deep Neural Network-Based Receiver: River Trial Results.

In this article, a deep neural network (DNN)-based underwater acoustic (UA) communication receiver is proposed. Conventional orthogonal frequency-division multiplexing (OFDM) receivers perform channel estimation using linear interpolation. However, due to the significant delay spread in multipath UA channels, the frequency response often exhibits strong non-linearity between pilot subcarriers. Since the channel delay profile is generally unknown, this non-linearity cannot be modeled precisely. A neural network (NN)-based receiver effectively tackles this challenge by learning and compensating for the non-linearity through NN training. The performance of the DNN-based UA communication receiver was tested recently in river trials in Western Australia. The results obtained from the trials prove that the DNN-based receiver performs better than the conventional least-squares (LS) estimator-based receiver. This paper suggests that UA communication using DNN receivers holds great potential for revolutionizing underwater communication systems, enabling higher data rates, improved reliability, and enhanced adaptability to changing underwater conditions.

Clarifying the impact of engine operating parameters of heavy-duty diesel vehicles on NOx and CO2 emissions using multimodal fusion methods.

The issue of air pollution from transportation sources remains a major concern, particularly the emissions from heavy-duty diesel vehicles, which pose serious threats to ecosystems and human health. China VI emission standards mandate On-Board Diagnostics (OBD) systems in heavy-duty diesel vehicles for real-time data transmission, yet the current data quality, especially concerning crucial parameters like NOx output, remains inadequate for effective regulation. To address this, a novel approach integrating Multimodal Feature Fusion with Particle Swarm Optimization (OBD-PSOMFF) is proposed. This network employs Long Short-Term Memory (LSTM) networks to extract features from OBD indicators, capturing temporal dependencies. PSO optimizes feature weights, enhancing prediction accuracy. Testing on 23 heavy-duty vehicles demonstrates significant improvements in predicting NOx and CO2 mass emission rates, with mean squared errors reduced by 65.205 % and 70.936 % respectively compared to basic LSTM models. This innovative multimodal fusion method offers a robust framework for emission prediction, crucial for effective vehicle emission regulation and environmental preservation.

Design of modified long short-term memory-based zebra optimization algorithm for limiting the issue of SHEPWM in multi-level inverter.

This research work proposes unique MLSTM-ZOA to quickly measure SAs for an MLI. Within a certain MI range, the suggested method may calculate a greater count of SAs with various solutions. Here, the main objective lies in minimizing the THD with consideration of three parameters such as MI, number of pulses per quarter cycle, and duty cycle. Furthermore, by achieving a lower fitness value in less iteration, the suggested method has definitely outperformed other methods with respect to convergence behavior, according to the data. Lastly, analysis and performance associated with the experimental validation of SHE in multi-level inverter are also carried out. The proposed MLSTM-ZOA model in terms of THD is 56.25%, 81.58%, 53.33%, and 41.67% better than MPA, HHO, MGMPA, and SF-BOA respectively. Similarly, the proposed MLSTM-ZOA model with respect to HDP is 64.03%, 47.16%, 84.01%, and 27.62% advanced than MPA, HHO, MGMPA, and SF-BOA respectively.

Navigating the mind's eye: Understanding gaze shifts in visuospatial bootstrapping.

Visuospatial bootstrapping refers to the well-replicated phenomena in which serial recall in a purely verbal task is boosted by presenting digits within the familiar spatial layout of a typical telephone keypad. The visuospatial bootstrapping phenomena indicates that additional support comes from long-term knowledge of a fixed spatial pattern, and prior experimentation supports the idea that access to this benefit depends on the availability of the visuospatial motor system. We investigate this by tracking participants' eye movements during encoding and retention of verbal lists to learn whether gaze patterns support verbal memory differently when verbal information is presented in the familiar visual layout. Participants' gaze was recorded during attempts to recall lists of seven digits in three formats: centre of the screen, typical telephone keypad, or a spatially identical layout with randomised number placement. Performance was better with the typical than with the novel layout. Our data show that eye movements differ when encoding and retaining verbal information that has a familiar layout compared with the same verbal information presented in a novel layout, suggesting recruitment of different spatial rehearsal strategies. However, no clear link between gaze pattern and recall accuracy was observed, which suggests that gazes play a limited role in retention, at best.

Optimizing electric load forecasting with support vector regression/LSTM optimized by flexible Gorilla troops algorithm and neural networks a case study.

This research work focuses on addressing the challenges of electric load forecasting through the combination of Support Vector Regression and Long Short-Term Memory (SVR/LSTM) methodology. The model has been modified by a flexible version of the Gorilla Troops optimization algorithm. The objective of this study is to enhance the precision and effectiveness of load forecasting models by integrating the adaptive functionalities of the Gorilla Troops algorithm within the SVR/LSTM framework. To assess the efficacy of the proposed methodology, a comprehensive series of experiments and evaluations have been undertaken, utilizing authentic data obtained from 200 residential properties located in Texas, United States of America. The dataset comprises historical records of electricity consumption, meteorological data, and other pertinent variables that exert an impact on energy demand. The presence of this general dataset enhances the dependability and inclusiveness of the empirical findings. The proposed methodology was evaluated against various contemporary load forecasting techniques that are widely employed in the industry. The results of a comprehensive evaluation and performance analysis indicate that the modified SVR/LSTM model exhibits superior performance compared to the existing methods in terms of accuracy and robustness. The comparison results unequivocally demonstrate the superiority of the proposed method in accurately forecasting electric load demand.

Exploring the contribution of joint angles and sEMG signals on joint torque prediction accuracy using LSTM-based deep learning techniques.

Machine learning (ML) has been used to predict lower extremity joint torques from joint angles and surface electromyography (sEMG) signals. This study trained three bidirectional Long Short-Term Memory (LSTM) models, which utilize joint angle, sEMG, and combined modalities as inputs, using a publicly accessible dataset to estimate joint torques during normal walking and assessed the performance of models, that used specific inputs independently plus the accuracy of the joint-specific torque prediction. The performance of each model was evaluated using normalized root mean square error (nRMSE) and Pearson correlation coefficient (PCC). Each model's median scores for the PCC and nRMSE values were highly convergent and the bulk of the mean nRMSE values of all joints were less than 10%. The ankle joint torque was the most successfully predicted output, having a mean nRMSE of less than 9% for all models. The knee joint torque prediction has reached the highest accuracy with a mean nRMSE of 11% and the hip joint torque prediction of 10%. The PCC values of each model were significantly high and remarkably comparable for the ankle (∼ 0.98), knee (∼ 0.92), and hip (∼ 0.95) joints. The model obtained significantly close accuracy with single and combined input modalities, indicating that one of either input may be sufficient for predicting the torque of a particular joint, obviating the need for the other in certain contexts.