Quantitative Evaluation Metrics Research Articles

Daily Runoff Prediction Based on FA-LSTM Model

Accurate and reliable short-term runoff prediction plays a pivotal role in water resource management, agriculture, and flood control, enabling decision-makers to implement timely and effective measures to enhance water use efficiency and minimize losses. To further enhance the accuracy of runoff prediction, this study proposes a FA-LSTM model that integrates the Firefly algorithm (FA) with the long short-term memory neural network (LSTM). The research focuses on historical daily runoff data from the Dahuangjiangkou and Wuzhou Hydrology Stations in the Xijiang River Basin. The FA-LSTM model is compared with RNN, LSTM, GRU, SVM, and RF models. The FA-LSTM model was used to carry out the generalization experiment in Qianjiang, Wuxuan, and Guigang hydrology stations. Additionally, the study analyzes the performance of the FA-LSTM model across different forecasting horizons (1–5 days). Four quantitative evaluation metrics—mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Kling–Gupta efficiency coefficient (KGE)—are utilized in the evaluation process. The results indicate that: (1) Compared to RNN, LSTM, GRU, SVM, and RF models, the FA-LSTM model exhibits the best prediction performance, with daily runoff prediction determination coefficients (R2) reaching as high as 0.966 and 0.971 at the Dahuangjiangkou and Wuzhou Stations, respectively, and the KGE is as high as 0.965 and 0.960, respectively. (2) FA-LSTM model was used to conduct generalization tests at Qianjiang, Wuxuan and Guigang hydrology stations, and its R2 and KGE are 0.96 or above, indicating that the model has good adaptability in different hydrology stations and strong robustness. (3) As the prediction period extends, the R2 and KGE of the FA-LSTM model show a decreasing trend, but the whole model still showed feasible forecasting ability. The FA-LSTM model introduced in this study presents an effective new approach for daily runoff prediction.

Single Image Super-Resolution via Wide-Activation Feature Distillation Network.

Feature extraction plays a pivotal role in the context of single image super-resolution. Nonetheless, relying on a single feature extraction method often undermines the full potential of feature representation, hampering the model's overall performance. To tackle this issue, this study introduces the wide-activation feature distillation network (WFDN), which realizes single image super-resolution through dual-path learning. Initially, a dual-path parallel network structure is employed, utilizing a residual network as the backbone and incorporating global residual connections to enhance feature exploitation and expedite network convergence. Subsequently, a feature distillation block is adopted, characterized by fast training speed and a low parameter count. Simultaneously, a wide-activation mechanism is integrated to further enhance the representational capacity of high-frequency features. Lastly, a gated fusion mechanism is introduced to weight the fusion of feature information extracted from the dual branches. This mechanism enhances reconstruction performance while mitigating information redundancy. Extensive experiments demonstrate that the proposed algorithm achieves stable and superior results compared to the state-of-the-art methods, as evidenced by quantitative evaluation metrics tests conducted on four benchmark datasets. Furthermore, our WFDN excels in reconstructing images with richer detailed textures, more realistic lines, and clearer structures, affirming its exceptional superiority and robustness.

Advanced image stitching method and evaluation for underwater structure inspection utilizing planar array cameras

The inspection of underwater structural health is crucial for comprehensive bridge health assessments. In underwater structure imaging, traditional methods include single-camera and binocular camera inspection. However, due to water turbidity and long working distances with a small field-of-view, obtaining clear and high-quality detection images with these methods takes much work. To address this problem, this paper presents a method for planar array image stitching based on Harris corner point extraction, utilizing the advantages of planar array cameras characterized by short working distances and wide field-of-view. The core contribution of this paper is the introduction of an innovative image sequence stitching algorithm utilizing Harris corner point extraction and the combination of the first proposed planar array cameras with the image sequence stitching algorithm, which solves the problem of long distance and small field-of-view during the underwater inspection. The image stitching method involves calibrating camera parameters with a checkerboard and stitching underwater images from planar array cameras to reveal underwater structural features. Furthermore, five quantitative evaluation metrics and the method for calculating the field-of-view loss rate are presented to evaluate and analyze the stitched images. A series of experiments were performed on concrete surfaces, aquatic and underwater, with a total field-of-view of the underwater image after stitching of 358.86 mm × 319.24 mm at a working distance of 160 mm. Five evaluation methods were used to quantitatively evaluate the quality of the stitched images and calculate the field-of-view loss rate of the images. The results indicate that the proposed method improves the ability to inspect underwater. The stitched images achieve notable metrics: an entropy of approximately 6.7, an average gradient of about 1.7, a spatial frequency of around 3.5, an edge strength of about 17, mutual information of approximately 1.2, and a field-of-view loss rate of <0.1, facilitating more effective underwater structure inspection.

Remote sensing image registration method based on synchronous atmospheric correction

Image registration is a crucial preprocessing step in remote sensing applications, integrating information from multiple images to achieve synergistic advantages. Nevertheless, aerosols characterized by spatiotemporal heterogeneity can result in the blurring of remote-sensing images, thereby compromising the accuracy of image registration. This paper begins by analyzing the basic principles of atmospheric correction and image registration. The variations in atmospheric radiative contribution caused by aerosol changes in real-world scenarios were simulated, along with an examination of the relationship between atmospheric effects and the quantity of image features. Subsequently, addressing the challenge posed by insufficient synchronicity in aerosol parameters and the influence of atmospheric effects on remote sensing image registration, we propose a registration method based on synchronous atmospheric correction. This approach utilizes the Airborne Synchronous Monitoring Atmospheric Corrector (ASMAC) to obtain aerosol optical depth and column water vapor images for synchronous atmospheric correction of remote sensing images, along with the assessment of the registration transformation matrix. Finally, airborne experiments involving ASMAC and high-resolution cameras are conducted to validate the proposed method's improvement in remote sensing image registration accuracy. Experimental results demonstrate the effectiveness of the proposed method, showcasing an increase in the number of features and improvements in quantitative evaluation metrics. Specifically, the normalized correlation coefficient improved by up to 2.408%, while the normalized mutual information increased by a maximum of 1.395%, a maximum feature count and successfully matched features improvement of 21.1% and 38.5%

Enhancing Clinical Documentation with Synthetic Data: Leveraging Generative Models for Improved Accuracy

Accurate and comprehensive clinical documentation is crucial for delivering high-quality healthcare, facilitating effective communication among providers, and ensuring compliance with regulatory requirements. However, manual transcription and data entry processes can be time-consuming, error-prone, and susceptible to inconsistencies, leading to incomplete or inaccurate medical records. This paper proposes a novel approach to augment clinical documentation by leveraging synthetic data generation techniques to generate realistic and diverse clinical transcripts. We present a methodology that combines state-of- the-art generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), with real-world clinical transcript and other forms of clinical data to generate synthetic transcripts. These synthetic transcripts can then be used to supplement existing documentation workflows, providing additional training data for natural language processing models and enabling more accurate and efficient transcription processes. Through extensive experiments on a large dataset of anonymized clinical transcripts, we demonstrate the effectiveness of our approach in generating high- quality synthetic transcripts that closely resemble real- world data. Quantitative evaluation metrics, including perplexity scores and BLEU scores, as well as qualitative assessments by domain experts, validate the fidelity and utility of the generated synthetic transcripts. Our findings highlight synthetic data generation's potential to address clinical documentation challenges, improving patient care, reducing administrative burdens, and enhancing healthcare system efficiency.

Analysis and Comparison of Artifact Removal Techniques for Epilepsy EEG Signal

Objective: Accurate epilepsy diagnosis demands precise EEG analysis, hindered by non-neuronal artifacts. This study evaluates artifact removal methods, specifically Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD), aiming to enhance signal quality. We introduce a hybrid approach, combining ICA and EMD. Methods: ICA and EMD are applied to preprocess epilepsy EEG recordings. Quantitative evaluation metrics, including Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Standard Deviation (SD), are calculated and compared for both methods. Findings: ICA outperforms EMD, showing higher SNR and PSNR, notably in BONN and CHB-MIT datasets. ICA achieves significant reductions in MSE, RMSE, and SD. The hybrid approach surpasses existing methods, supported by quantitative data. Novelty: Rigorous application of ICA and EMD to diverse datasets quantitatively establishes ICA's superiority. The hybrid approach, backed by quantitative evidence, proves effective beyond epilepsy EEG. Conclusion: This abstract provides clear, quantitative support for ICA's superiority and the hybrid approach's efficacy, offering valuable insights into artifact removal in EEG analysis. Keywords: Epilepsy, Artifact removal, EEG, ICA, DWT, EMD, Performance metrics

RMSRGAN: A Real Multispectral Imagery Super-Resolution Reconstruction for Enhancing Ginkgo Biloba Yield Prediction

Multispectral remote sensing data with abundant spectral information can be used to compute vegetation indices to improve the accuracy of Ginkgo biloba yield prediction. The limited spatial resolution of multispectral cameras restricts the detail capture over wide farmland, but super-resolution (SR) reconstruction methods can enhance image quality. However, most existing SR models have been trained on images processed from downsampled high-resolution (HR) images, making them less effective in reconstructing real low-resolution (LR) images. This study proposes a GAN-based super-resolution reconstruction method (RMSRGAN) for multispectral remote sensing images of Ginkgo biloba trees in real scenes. A U-Net-based network is employed instead of the traditional discriminator. Convolutional block attention modules (CBAMs) are incorporated into the Residual-in-Residual Dense Blocks (RRDBs) of the generator and the U-Net of the discriminator to preserve image details and texture features. An unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to capture field multispectral remote sensing images of Ginkgo biloba trees at different spatial resolutions. Four matching HR and LR datasets were created from these images to train RMSRGAN. The proposed model outperforms the traditional models by achieving superior results in both quantitative evaluation metrics (peak signal-to-noise ratio (PSNR) is 32.490, 31.085, 27.084, 26.819, and structural similarity index (SSIM) is 0.894, 0.881, 0.832, 0.818, respectively) and qualitative evaluation visualization. Furthermore, the efficiency of our proposed method was tested by generating individual vegetation indices (VIs) from images taken before and after reconstruction to predict the yield of Ginkgo biloba. The results show that the SR images exhibit better R2 and RMSE values than LR images. These findings show that RMSRGAN can improve the spatial resolution of real multispectral images, increasing the accuracy of Ginkgo biloba yield prediction and providing more effective and accurate data support for crop management.

Time-gated imaging through dense fog via physics-driven Swin transformer

Imaging through the fog is valuable for many areas, such as autonomous driving and cosmic exploration. However, due to the influence of strong backscattering and diffuse reflection generated by the dense fog on the temporal-spatial correlations of photons returning from the target object, the reconstruction quality of most existing methods is significantly reduced under dense fog conditions. In this study, we describe the optical scatter imaging process and propose a physics-driven Swin Transformer method utilizing Time-of-Flight (ToF) and Deep Learning principles to mitigate scattering effects and reconstruct targets in conditions of heterogeneous dense fog. The results suggest that, despite the exponential decrease in the number of ballistic photons as the optical thickness of fog increases, the Physics-Driven Swin Transformer method demonstrates satisfactory performance in imaging targets obscured by dense fog. Importantly, this article highlights that even in dense fog imaging experiments with optical thickness reaching up to 3.0, which exceeds previous studies, commonly utilized quantitative evaluation metrics like PSNR and SSIM indicate that our method is cutting-edge in imaging through dense fog.

Fracture propagation morphology and parameter optimization design of pre-CO2 hybrid fracturing in shale oil reservoirs, Ordos Basin

Shale reservoirs typically exhibit the characteristics of large burial depth, low permeability, and well-developed bedding. Despite the implementation of water-based fracturing techniques with high displacement, attaining the optimal reservoir stimulation effect remains a formidable challenge. Consequently, in recent years, a novel method of hybrid fracturing method using pre-CO2 + sand-carrying slick water has been proposed. Within this framework, the meticulous design of fracturing operation parameters stands out as a pivotal factor influencing the efficacy of reservoir stimulation. This paper presents a coupled 3-D hydraulic fracture propagation model, investigating the influence of injection displacement, injection volume, and three-stage pre-injection fluid volume ratio on the fracture propagation. Two quantitative evaluation metrics, namely fracture surface area S and fracture transverse plane fractal dimension D, are precisely defined. Additionally, real-time analysis of bottom-hole pressure is conducted to further comprehend the dynamics of the process. Numerical investigations have revealed that both pre-CO2 injection displacement and volume exert a substantial influence on fracture half-length and complexity. Furthermore, the proportion of hybrid fracturing fluid demonstrates a notable impact on fracture height and complexity. The design of injection parameters does not exhibit a significant effect on the formation breakdown pressure. This study quantitatively evaluates fracture propagation under different working conditions from the perspective of fracture morphology, and serve as practical guidance for optimizing the process parameters design for hybrid fracturing in shale oil reservoirs, Ordos Basin.

Breaking the limitations with sparse inputs by variational frameworks (BLIss) in terahertz super-resolution 3D reconstruction

Data acquisition, image processing, and image quality are the long-lasting issues for terahertz (THz) 3D reconstructed imaging. Existing methods are primarily designed for 2D scenarios, given the challenges associated with obtaining super-resolution (SR) data and the absence of an efficient SR 3D reconstruction framework in conventional computed tomography (CT). Here, we demonstrate BLIss, a new approach for THz SR 3D reconstruction with sparse 2D data input. BLIss seamlessly integrates conventional CT techniques and variational framework with the core of the adapted Euler-Elastica-based model. The quantitative 3D image evaluation metrics, including the standard deviation of Gaussian, mean curvatures, and the multi-scale structural similarity index measure (MS-SSIM), validate the superior smoothness and fidelity achieved with our variational framework approach compared with conventional THz CT modal. Beyond its contributions to advancing THz SR 3D reconstruction, BLIss demonstrates potential applicability in other imaging modalities, such as X-ray and MRI. This suggests extensive impacts on the broader field of imaging applications.

Spatiotemporal prediction and characterization of microstructure evolution during in-situ heat treatment using a deep learning algorithm

The austenite grain growth plays a significant role in improving the structures and properties of carbon steels. Modeling, characterizing, and predicting the temporal evolution of microstructures is crucial for understanding the relationship of processing-structure-property. Therefore, in this study, we designed a customized in-situ heating device that could be installed in the scanning electron microscope chamber to observe the temporal evolution process of the microstructures of 45# steel. We collected the in-situ experimental images during heating for downstream deep learning-based grain boundary extraction, spatiotemporal sequence characterization, and prediction. The proposed pipeline was fully verified in three experimental datasets under two distinct heating schemes in terms of (1) short-term and long-term prediction, (2) feeding different lengths of input sequence, and (3) transferring to other datasets. The proposed pipeline presented a high performance in quantitative evaluation metrics and qualitative comparisons between the predicted results and the ground truths in all three verification scenarios. These results are inspiring for researchers as they can preview the evolutional state in advance, thereby saving money, labor, and time with the assistance of deep learning-based predictive models. We believe that this pipeline opens opportunities for modeling, characterizing, and predicting evolutional state, which will aid in the analysis of processing-structure-property relationship, tailoring heat treatment, and improving material properties.

Learning Subject-Aware Cropping by Outpainting Professional Photos

How to frame (or crop) a photo often depends on the image subject and its context; e.g., a human portrait. Recent works have defined the subject-aware image cropping task as a nuanced and practical version of image cropping. We propose a weakly-supervised approach (GenCrop) to learn what makes a high-quality, subject-aware crop from professional stock images. Unlike supervised prior work, GenCrop requires no new manual annotations beyond the existing stock image collection. The key challenge in learning from this data, however, is that the images are already cropped and we do not know what regions were removed. Our insight is to combine a library of stock images with a modern, pre-trained text-to-image diffusion model. The stock image collection provides diversity, and its images serve as pseudo-labels for a good crop. The text-image diffusion model is used to out-paint (i.e., outward inpainting) realistic uncropped images. Using this procedure, we are able to automatically generate a large dataset of cropped-uncropped training pairs to train a cropping model. Despite being weakly-supervised, GenCrop is competitive with state-of-the-art supervised methods and significantly better than comparable weakly-supervised baselines on quantitative and qualitative evaluation metrics.

Sustainable Development Strategies in Power Systems: Day-Ahead Stochastic Scheduling with Multi-Sources and Customer Directrix Load Demand Response

Increasing the installed capacity of renewable energy sources (RESs) in the power system is significant for advancing sustainable development. As the proportion of RESs rapidly increases in power systems, the inherent stochasticity and variability of renewable energies significantly reduce the regulatory capacity of generation resources. To compensate for the lack of power system flexibility, it is necessary to coordinate the participation of load-side resources in demand response (DR). Therefore, this paper proposes a solution to the diminished flexibility of power systems. It introduces a day-ahead stochastic scheduling model for an integrated thermal-hydro-wind-solar system. This model relies on customer directrix load (CDL) to efficiently absorb RES output. CDL represents an ideal load profile shape. Firstly, the stochastic scenario sets of RES output were modeled using Monte Carlo simulations, and the complementary characteristics between wind and solar output are considered using Copula theory. Then, CDL is introduced into day-ahead scheduling model, which considers relevant demand-side responsive load constraints. Secondly, customer-side DR effectiveness model is proposed to obtain the shaping load profile after DR, based on quantitative customer response effectiveness evaluation metrics. Lastly, system-side stochastic scheduling model of high-proportion RES power system is proposed based on the shaping load profile. Case studies were conducted on a modified IEEE-6 bus system. These studies show that the model effectively addresses the uncertainty of RES. It improves the power system’s regulation capability. Additionally, it promotes the absorption of RES.

Restoration of UAV-Based Backlit Images for Geological Mapping of a High-Steep Slope.

Unmanned aerial vehicle (UAV)-based geological mapping is significant for understanding the geological structure in the high-steep slopes, but the images obtained in these areas are inevitably influenced by the backlit effect because of the undulating terrain and the viewpoint change of the camera mounted on the UAV. To handle this concern, a novel backlit image restoration method is proposed that takes the real-world application into account and addresses the color distortion issue existing in backlit images captured in high-steep slope scenes. Specifically, there are two main steps in the proposed method, which consist of the backlit removal and the color and detail enhancement. The backlit removal first eliminates the backlit effect using the Retinex strategy, and then the color and detail enhancement step improves the image color and sharpness. The author designs extensive comparison experiments from multiple angles and applies the proposed method to different engineering applications. The experimental results show that the proposed method has potential compared to other main-stream methods both in qualitative visual effects and universal quantitative evaluation metrics. The backlit images processed by the proposed method are significantly improved by the process of feature key point matching, which is very conducive to the fine construction of 3D geological models of the high-steep slope.

Deep learning framework for three-dimensional surface reconstruction of object of interest in photoacoustic tomography.

Photoacoustic tomography (PAT) is a non-ionizing hybrid imaging technology of clinical importance that combines the high contrast of optical imaging with the high penetration of ultrasonic imaging. Two-dimensional (2D) tomographic images can only provide the cross-sectional structure of the imaging target rather than its overall spatial morphology. This work proposes a deep learning framework for reconstructing three-dimensional (3D) surface of an object of interest from a series of 2D images. It achieves end-to-end mapping from a series of 2D images to a 3D image, visually displaying the overall morphology of the object. The framework consists of four modules: segmentation module, point cloud generation module, point cloud completion module, and mesh conversion module, which respectively implement the tasks of segmenting a region of interest, generating a sparse point cloud, completing sparse point cloud and reconstructing 3D surface. The network model is trained on simulation data sets and verified on simulation, phantom, and in vivo data sets. The results showed superior 3D reconstruction performance both visually and on the basis of quantitative evaluation metrics compared to the state-of-the-art non-learning and learning approaches. This method potentially enables high-precision 3D surface reconstruction from the tomographic images output by the preclinical PAT system without changing the imaging system. It provides a general deep learning scheme for 3D reconstruction from tomographic scanning data.

Double Augmentation: A Modal Transforming Method for Ship Detection in Remote Sensing Imagery

Ship detection in remote sensing images plays an important role in maritime surveillance. Recently, convolution neural network (CNN)-based methods have achieved state-of-the-art performance in ship detection. Even so, there are still two problems that remain in remote sensing. One is that the different modal images observed by multiple satellite sensors and the existing dataset cannot satisfy network-training requirements. The other is the false alarms in detection, as the ship target is usually faint in real view remote sensing images and many false-alarm targets can be detected in ocean backgrounds. To solve these issues, we propose a double augmentation framework for ship detection in cross-modal remote sensing imagery. Our method can be divided into two main steps: the front augmentation in the training process and the back augmentation verification in the detection process; the front augmentation uses a modal recognition network to reduce the modal difference in training and in using the detection network. The back augmentation verification uses batch augmentation and results clustering to reduce the rate of false-alarm detections and improve detection accuracy. Real-satellite-sensing experiments have been conducted to demonstrate the effectiveness of our method, which shows promising performance in quantitative evaluation metrics.

PIDNET: Polar Transformation Based Implicit Disentanglement Network for Truncation Artifacts.

The interior problem, a persistent ill-posed challenge in CT imaging, gives rise to truncation artifacts capable of distorting CT values, thereby significantly impacting clinical diagnoses. Traditional methods have long struggled to effectively solve this issue until the advent of supervised models built on deep neural networks. However, supervised models are constrained by the need for paired data, limiting their practical application. Therefore, we propose a simple and efficient unsupervised method based on the Cycle-GAN framework. Introducing an implicit disentanglement strategy, we aim to separate truncation artifacts from content information. The separated artifact features serve as complementary constraints and the source of generating simulated paired data to enhance the training of the sub-network dedicated to removing truncation artifacts. Additionally, we incorporate polar transformation and an innovative constraint tailored specifically for truncation artifact features, further contributing to the effectiveness of our approach. Experiments conducted on multiple datasets demonstrate that our unsupervised network outperforms the traditional Cycle-GAN model significantly. When compared to state-of-the-art supervised models trained on paired datasets, our model achieves comparable visual results and closely aligns with quantitative evaluation metrics.

Automated Skin Cancer Diagnosis and Localization Using Deep Reinforcement Learning

Skin cancer is a significant and life-threatening medical condition caused by abnormal cell growth in the skin. Early detection and accurate diagnosis are critical for effective treatment and improved patient outcomes. However, manual diagnosis of skin cancer can be time-consuming and prone to inter-observer variability, necessitating the development of automated and objective diagnostic approaches. This research purports to diagnose and localize skin cancer automatically utilizing a novel Deep Reinforcement Learning technique. The proposed approach leverages a modified version of the Asynchronous Advantage Actor Critic (A3C) algorithm, comprising multiple independent agents represented as Convolutional Neural Networks (CNNs). These agents interact with the environment (skin images) and perform segmentation actions based on policies that maximize expected rewards, promoting accurate segmentation and penalizing false negatives as well as false positives. The research employs three diverse skin cancer datasets, namely PH2, ISIC 2018, and ISIC 2017 in order to assess the proposed approach’s efficiency. Quantitative evaluation metrics, including Precision, Specificity, Accuracy, Jaccard Index (IoU), Sensitivity, Dice coefficient, and F1-score are used to assess segmentation accuracy. The proposed method is compared with advanced deep-learning algorithms commonly used for skin cancer segmentation. The results demonstrate the superiority of the proposed approach in accurately localizing cancerous regions under various challenging conditions, such as hair presence, image blurriness, and the presence of other objects or obstacles. The proposed approach outperforms existing methods in terms of segmentation accuracy (98.8%) in diverse skin image sources.

Research and comparison of pavement performance prediction based on neural networks and fusion transformer architecture

<abstract> <p>The decision-making process for pavement maintenance from a scientific perspective is based on accurate predictions of pavement performance. To improve the rationality of pavement performance indicators, comprehensive consideration of various influencing factors is necessary. To this end, four typical pavement performance indicators (i.e., Rutting Depth, International Roughness Index, Longitudinal Cracking, and Alligator Cracking) were predicted using the Long Term Pavement Performance (LTPP) database. Two types of data, i.e., local input variables and global input variables, were selected, and S-ANN and L-ANN models were constructed using a fully connected neural network. A comparative analysis of the predictive outcomes reveals the superior optimization of the L-ANN model. Subsequently, by incorporating structures such as self-attention mechanism, a novel predictive approach based on the Transformer architecture was proposed. The objective is to devise a more accurate predictive methodology for pavement performance indices, with the goal of guiding pavement maintenance and management efforts. Experimental results indicate that, through comparative analysis of three quantitative evaluation metrics (root mean square error, mean absolute error, coefficient of determination), along with visual scatter plots, the predictive model employing the fused Transformer architecture demonstrates higher robustness and accuracy within the domain of pavement performance prediction when compared to the L-ANN model. This outcome substantiates the efficacy and superiority of the model in terms of predictive performance, establishing it as a reliable tool for accurately reflecting the evolution of asphalt pavement performance. Furthermore, it furnishes a theoretical reference for determining optimal preventive maintenance timing for pavements.</p> </abstract>

TL-P3GAN: An Efficient Temporal Learning-based Generative Adversarial Network for Precise P300 Signal Generation for P300 Spellers

Problem of data imbalance among target and non-target classes is inherent in the oddball paradigm-based P300 speller. This class imbalance is a critical issue and requires advanced rigorous learning to deal with. Conventionally, data level-like sampling approaches and algorithmic level-like ensemble approaches had been attempted in past research for data augmentation. However, information loss, overfitting and subject variability were their major pitfalls. Alternatively, generative adversarial network (GAN) based data augmentation managed to alleviate information loss but exhibits problems of overfitting and subject variability due to lack of temporal learning. To compensate for those problems, authors have proposed novel temporal learning-based GAN (TL-P3GAN) to generate precise P300 signals and augment the minority class i.e., P300. The TL-P3GAN comprises novel contribution of multiscale morphological learning in both generator and discriminator. Moreover, the multiscale hybrid model in generator learns multi-resolution morphological information and considers sample-wise latency information from original P300. The effectiveness of TL-P3GAN was confirmed by qualitative and quantitative evaluation metrics with two standard datasets. Further, the work is extended to analyze the effect of generated P300 signals on P300 classification and found significant performance improvement of 8-16% with both datasets in comparison with existing conventional and GAN-based data augmentation approaches.