Convolutional Neural Network Training Research Articles

Exploring 2D X-ray diffraction phase fraction analysis with convolutional neural networks: Insights from kinematic-diffraction simulations

AbstractDeep-learning models are effective for analyzing the complex information in 2D X-ray diffraction (XRD) patterns. Accurately collecting parameters of the material sample is crucial during model training, significantly impacting model performance. In this study, we employ a kinematic-diffraction simulator to generate simulated 2D XRD patterns for Ti–6Al–4V alloy, allowing precise control of sample parameters. These simulated patterns are used to train convolutional neural networks, predicting $$\upbeta$$ β -phase volume fractions. The training data set consists exclusively of 2D XRD patterns with pure $$\upalpha$$ α - or pure $$\upbeta$$ β -phase, while the testing set incorporates patterns with intermediate phase volume fraction. In particular, we investigate how the architectures of the model influence prediction reliability and computational performance. Experimental results reveal that, with appropriate training, the convolutional neural network accurately detects intermediate phase volume fractions even trained with only pure-phase patterns, achieving a mean square error accuracy of $$9.4 \times 10^{-4}$$ 9.4 × 10 - 4 . Graphical abstract

Application of Convolutional Networks for Localization and Prediction of Scalar Parameters of Fractured Geological Inclusion

Seismic inversion is an important part of the modern geological exploration process. Novel applications of deep learning are capable of handling heterogeneous media, but require too much data for training. In this paper, we focus on the prediction of fracture inclusion location and its parameters in rock media and approach the problem in the multi-task manner. For this, several multi-task convolutional neural network (CNN) architectures are proposed and compared. The direct seismic problem is considered in the heterogeneous fractured geological model based on the well-known Marmousi2 model in a two-dimensional case. The model of the deformable solid medium containing slip planes with nonlinear slip conditions on them and explicit–implicit numerical method is applied to obtain the synthetic seismic dataset for CNN training and validation.

Student Progression and Dropout Rates Using Convolutional Neural Network: A Case Study of the Arab Open University

Pre-trained convolutional neural network (CNN) structures are considered as one of the emerging education management tools that can help improve the quality of education by allowing decision makers to manipulate important indicators. These indicators, which are categorized as student and institution specific factors, may influence student progress, retention or dropout rates. In this paper, we develop a deep learning model of predicting students’ satisfactions and their expected outcomes and associated early failures. The model can also predict dropout rates and identify the main baseline risk factors that influence such rates. The academic data of 12,000 students enrolled from 2018 in the Arab Open University student information system are used as CNNs training dataset to ensure that all institution levels are represented. Then, the trained network provides a probabilistic model that indicates, for each student, the probability of dropout. Based on the prediction model, the study presents an early warning system framework to generate alerts and recommendations to allow early and effective institutional intervention. Experiments are achieved by using the proposed dataset and the performance of our approach is considerably better compared to the competitive models in terms of training/validation accuracy and mean square errors.

Quality Evaluation of Road Surface Markings with Uncertainty Aware Regression and Progressive Pretraining

Maintaining high-quality road markings is essential for both safety and traffic flow. However, there has been limited research on automating the process of evaluating the quality of these markings and identifying degraded ones that need to be fixed. Our paper introduces a new approach that uses uncertainty aware (UA) regression to evaluate the quality of road surface markings. The approach is based on deep learning models and a unique training method called “progressive pretraining (PPT).” We used a dataset of RGB images which we converted to binary masks. These masks were then augmented and used to train convolutional neural networks models with a PPT strategy. The results showed that both the hybrid and UA models managed to outperform the baseline model in some metrics such as mean average error which was at 24.38% and accuracy with 81.27%. Additionally, each model showed unique strengths across various performance metrics, highlighting the efficacy of integrating uncertainty and progressive learning in quality assessment tasks. This study presents a solid proof of concept for the application of UA methods in quality evaluation tasks in general, and surface marking quality evaluation in particular.

The Impact of Drop Test Conditions on Brain Strain Location and Severity: A Novel Approach Using a Deep Learning Model.

In contact sports such as rugby, players are at risk of sustaining traumatic brain injuries (TBI) due to high-intensity head impacts that generate high linear and rotational accelerations of the head. Previous studies have established a clear link between high-intensity head impacts and brain strains that result in concussions. This study presents a novel approach to investigating the effect of a range of laboratory controlled drop test parameters on regional peak and mean maximum principal strain (MPS) predictions within the brain using a trained convolutional neural network (CNN). The CNN is publicly available at https://github.com/Jilab-biomechanics/CNN-brain-strains . The results of this study corroborate previous findings that impacts to the side of the head result in significantly higher regional MPS than forehead impacts. Forehead impacts tend to result in the lowest region-averaged MPS values for impacts where the surface angle was at 0° and 45°, while side impacts tend to result in higher regional peak and mean MPS. The absence of a neck in drop tests resulted in lower regional peak and mean MPS values. The results indicated that the relationship between drop test parameters and resulting regional peak and mean MPS predictions is complex. The study's findings offer valuable insights into how deep learning models can be used to provide more detailed insights into how drop test conditions impact regional MPS. The novel approach used in this paper to predict brain strains can be applied in the development of better methods to reduce the brain strain resulting from head accelerations such as protective sports headgear.

Web-based diagnostic platform for microorganism-induced deterioration on paper-based cultural relics with iterative training from human feedback

PurposePaper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms’ nutrient source. Mold not only can damage papers’ structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper.MethodsMolds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model.Results & ConclusionIn the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation.

MA-XRF datasets analysis based on convolutional neural network: A case study on religious panel paintings

Macroscopic X-ray fluorescence (MA-XRF) datasets are analyzed using Artificial Neural Networks. Specifically, Convolutional Neural Networks (CNNs) are trained by coupling the spectra acquired during the MA-XRF scan of two religious panel paintings (“icons”) with the associated Ground-Truth counts per characteristic transition line, as they are extracted by X-ray fluorescence fundamental parameters analysis. In total, twenty thousand XRF spectra were used for the CNN training. The trained neural networks were applied to analyze millions of MA-XRF spectra acquired during the scan of religious painting panels by computing the counts per pixel of X-ray characteristic transition lines and creating the elemental transition maps. Comparison of the CNN extracted results to the Ground-Truth (GT) shows remarkable agreement. The successful MA-XRF datasets analysis applying the CNN method paves an analytical path to the direction of the auto-identification of spectral lines, offering the means for the non-experienced XRF analyst to provide a state-of-the-art analysis and supporting the experienced user not to overlook hardly resolved transition lines.

Machine-Learning driven STM images prediction of doped/defective graphene: Towards optimized tools for 2D nanomaterials characterization

Scanning tunneling microscopy (STM) is a key characterization technique that allows for visualization of specific features at nanostructures, given its ability to reveal changes in local charge densities in doping or defective sites. Besides the experimental acquisition of STM data from real samples, there is the theoretical route, which allows for STM images simulation from ab-initio calculations on defined nanostructures. This work presents an alternative route for theoretical STM image prediction by means of Machine Learning (ML) methods, based on the training of deep convolutional neural networks using simulated STM data. The ML-based STM predictions for defective, B- and N-doped graphene quantum dots are compared to ab-initio STM simulations, in terms of accuracy of structural rearrangements, charge density localizations, and computational resources requirements. The results demonstrate that STM images predicted by ML-methods are accurate at doping sites, whereas they show good similarity to simulations of large structural vacancies. This work represents a novel alternative for the use of ML methods in image-based characterization routines of doped/defective graphene, and potentially, for other 2-dimensional nanomaterials.

Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images

High-throughput materials research is strongly required to accelerate the development of safe and high energy-density lithium-ion battery (LIB) applicable to electric vehicle and energy storage system. The artificial intelligence, including machine learning with neural networks such as Boltzmann neural networks and convolutional neural networks (CNN), is a powerful tool to explore next-generation electrode materials and functional additives. In this paper, we develop a prediction model that classifies the major composition (e.g., 333, 523, 622, and 811) and different states (e.g., pristine, pre-cycled, and 100 times cycled) of various Li(Ni, Co, Mn)O2 (NCM) cathodes via CNN trained on scanning electron microscopy (SEM) images. Based on those results, our trained CNN model shows a high accuracy of 99.6% where the number of test set is 3840. In addition, the model can be applied to the case of untrained SEM data of NCM cathodes with functional electrolyte additives.

Robust acoustic directional sensing enabled by synergy between resonator-based sensor and deep learning

We demonstrate enhanced acoustic sensing arising from the synergy between resonator-based acoustic sensor and deep learning. We numerically verify that both vibration amplitude and phase are enhanced and preserved at and off the resonance in our compact acoustic sensor housing three cavities. In addition, we experimentally measure the response of our sensor to single-frequency and siren signals, based on which we train convolutional neural networks (CNNs). We observe that the CNN trained by using both amplitude and phase features achieve the best accuracy on predicting the incident direction of both types of signals. This is even though the signals are broadband and affected by noise thought to be difficult for resonators. We attribute the improvement to a complementary effect between the two features enabled by the combination of resonant effect and deep learning. This observation is further supported by comparing to the CNNs trained by the features extracted from signals measured on reference sensor without resonators, whose performances fall far behind. Our results suggest the advantage of this synergetic approach to enhance the sensing performance of compact acoustic sensors on both narrow- and broad-band signals, which paves the way for the development of advanced sensing technology that has potential applications in autonomous driving systems to detect emergency vehicles.

Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1

Abstract. Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. By transporting significant amounts of latent energy towards the poles, they are crucial to maintaining the climate system's energy balance. Since there is no first-principle definition of an AR grounded in geophysical fluid mechanics, AR identification is currently performed by a multitude of expert-defined, threshold-based algorithms. The variety of AR detection algorithms has introduced uncertainty into the study of ARs, and the thresholds of the algorithms may not generalize to new climate datasets and resolutions. We train convolutional neural networks (CNNs) to detect ARs while representing this uncertainty; we name these models ARCNNs. To detect ARs without requiring new labeled data and labor-intensive AR detection campaigns, we present a semi-supervised learning framework based on image style transfer. This framework generalizes ARCNNs across climate datasets and input fields. Using idealized and realistic numerical models, together with observations, we assess the performance of the ARCNNs. We test the ARCNNs in an idealized simulation of a shallow-water fluid in which nearly all the tracer transport can be attributed to AR-like filamentary structures. In reanalysis and a high-resolution climate model, we use ARCNNs to calculate the contribution of ARs to meridional latent heat transport, and we demonstrate that this quantity varies considerably due to AR detection uncertainty.

Machine learning for non-experts: A more accessible and simpler approach to automatic benthic habitat classification

Automating identification of benthic habitats from imagery, with Machine Learning (ML), is necessary to contribute efficiently and effectively to marine spatial planning. A promising method is to adapt pre-trained general convolutional neural networks (CNNs) to a new classification task (transfer learning). However, this is often inaccessible to a non-specialist, requiring large investments in computational resources and time (for user comprehension and model training). In this paper, we demonstrate a simpler transfer learning framework for classifying broad deep-sea benthic habitats. Specifically, we take an ‘off-the-shelf’ CNN (VGG16) and use it to extract features (pixel patterns) from benthic images (without further training). The default outputs of VGG16 are then fed in to a Support Vector Machine (SVM), a classical and simpler method than deep networks. For comparison, we also train the remaining classification layers of VGG16 using stochastic gradient descent. The discriminative power of these approaches is demonstrated on three benthic datasets (574–8353 images) from Norwegian waters; each using a unique imaging platform. Benthic habitats are broadly classified as Soft Substrate (sands, muds), Hard Substrate (gravels, cobbles and boulders) and Reef (Desmophyllum pertusum). We found that the relatively simplicity of the SVM classifier did not compromise performance. Results were competitive with the CNN classifier and consistently high, with test accuracy ranging from 0.87 to 0.95 (average = 0.9 (±0.04)) across datasets, somewhat increasing with dataset size. Impressively, these results were achieved 2.4–5× faster than CNN training and had significantly less dependency on high-specification hardware. Our suggested approach maximises conceptual and practical simplicity, representing a realistic baseline for novice users when approaching benthic habitat classification. This method has wide potential. It allows automated image grouping to aid annotation or further model selection, as well as screening of old-datasets. It is especially suited to offshore scenarios as it can provide quick, albeit crude, insights into habitat presence, allowing adaptation of sampling protocols in near real-time.

Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models

The impacts of climate change on hydrology underscore the urgency of understanding watershed hydrological patterns for sustainable water resource management. The conventional physics-based fully distributed hydrological models are limited due to computational demands, particularly in the case of large-scale watersheds. Deep learning (DL) offers a promising solution for handling large datasets and extracting intricate data relationships. Here, we propose a DL modeling framework, incorporating convolutional neural networks (CNNs) to efficiently replicate physics-based model outputs at high spatial resolution. The goal was to estimate groundwater head and surface water depth in the Sabgyo Stream Watershed, South Korea. The model datasets consisted of input variables, including elevation, land cover, soil type, evapotranspiration, rainfall, and initial hydrological conditions. The initial conditions and target data were obtained from the fully distributed hydrological model HydroGeoSphere (HGS), whereas the other inputs were actual measurements in the field. By optimizing the training sample size, input design, CNN structure, and hyperparameters, we found that CNNs with residual architectures (ResNets) yielded superior performance. The optimal DL model reduces computation time by 45 times compared to the HGS model for monthly hydrological estimations over five years (RMSE 2.35 and 0.29 m for groundwater and surface water, respectively). In addition, our DL framework explored the predictive capabilities of hydrological responses to future climate scenarios. Although the proposed model is cost-effective for hydrological simulations, further enhancements are needed to improve the accuracy of long-term predictions. Ultimately, the proposed DL framework has the potential to facilitate decision-making, particularly in large-scale and complex watersheds.

Deep learning approaches to recover the plasma current density profile from the safety factor based on Grad–Shafranov solutions across multiple tokamaks

Many magnetohydrodynamic stability analyses require generation of a set of equilibria with a fixed safety factor q-profile while varying other plasma parameters. A neural network (NN)-based approach is investigated that facilitates such a process. Both multilayer perceptron (MLP)-based NN and convolutional neural network (CNN) models are trained to map the q-profile to the plasma current density J-profile, and vice versa, while satisfying the Grad–Shafranov radial force balance constraint. When the initial target models are trained, using a database of semi-analytically constructed numerical equilibria, an initial CNN with one convolutional layer is found to perform better than an initial MLP model. In particular, a trained initial CNN model can also predict the q- or J-profile for experimental tokamak equilibria. The performance of both initial target models is further improved by fine-tuning the training database, i.e. by adding realistic experimental equilibria with Gaussian noise. The fine-tuned target models, referred to as fine-tuned MLP and fine-tuned CNN, well reproduce the target q- or J-profile across multiple tokamak devices. As an important application, these NN-based equilibrium profile convertors can be utilized to provide a good initial guess for iterative equilibrium solvers, where the desired input quantity is the safety factor instead of the plasma current density.

Development of a Method for Estimating the Angle of Lumbar Spine X-ray Images Using Deep Learning with Pseudo X-ray Images Generated from Computed Tomography

Background and Objectives: In lumbar spine radiography, the oblique view is frequently utilized to assess the presence of spondylolysis and the morphology of facet joints. It is crucial to instantly determine whether the oblique angle is appropriate for the evaluation and the necessity of retakes after imaging. This study investigates the feasibility of using a convolutional neural network (CNN) to estimate the angle of lumbar oblique images. Since there are no existing lumbar oblique images with known angles, we aimed to generate synthetic lumbar X-ray images at arbitrary angles from computed tomography (CT) images and to estimate the angles of these images using a trained CNN. Methods: Synthetic lumbar spine X-ray images were created from CT images of 174 individuals by rotating the lumbar spine from 0° to 60° in 5° increments. A line connecting the center of the spinal canal and the spinous process was used as the baseline to define the shooting angle of the synthetic X-ray images based on how much they were tilted from the baseline. These images were divided into five subsets and trained using ResNet50, a CNN for image classification, implementing 5-fold cross-validation. The models were trained for angle estimation regression and image classification into 13 classes at 5° increments from 0° to 60°. For model evaluation, mean squared error (MSE), root mean squared error (RMSE), and the correlation coefficient (r) were calculated for regression analysis, and the area under the curve (AUC) was calculated for classification. Results: In the regression analysis for angles from 0° to 60°, the MSE was 14.833 degree2, the RMSE was 3.820 degrees, and r was 0.981. The average AUC for the 13-class classification was 0.953. Conclusion: The CNN developed in this study was able to estimate the angle of an lumbar oblique image with high accuracy, suggesting its usefulness.

Detection of Skin Disease by using Image Processing

There are many types and forms of skin diseases and have many causes, including internal ones related to hormones and body glands such as acne, or external related to air pollution or sensitivity to sunlight such as rashes. The skin disease may be contagious, such as scabies and lice, or it may not be contagious, such as drug allergy and rosacea, or it may be chronic, such as psoriasis and atopic eczema, or it may be rare, such as sweet syndrome and ofuji disease. The outlook of society, in general, reduces the importance of skin disease when it appears and it is preferable not to visit the doctor. Skin diseases are a major and worrying problem in societies due to their physical and psychological effects on patients. Detecting skin diseases at an early stage has an important role in treatment. The process of diagnosing and treating skin injury is related to the skill and experience of the specialist doctor. skin disease prediction by using a neural network based on image analysis. Diagnosis and prediction of skin disease take a very long process because it requires a patient’s history, physical examination, and proper laboratory diagnostic tests. st of such diagnosis is still limited and very expensive. So, image processing techniques help to build automated screening system for dermatology at an initial stage. The extraction of features plays a key role in helping to classify skin disease. proach works on the inputs of a color image. Then resize the of the image to extract features using pre trained convolutional neural network. After that classified feature using Multiclass SVM. Finally, the results are shown to the user, including the type.

Detection Of Deep Fakes Using CNN

The rise of technology has emphasized the importance of identifying altered images and videos to preserve trust and authenticity in digital content. Convolutional Neural Networks (CNNs) have emerged as tools, for tasks like detecting fakes. This study introduces a method for identifying fakes using CNNs. Our strategy involves training a CNN model on a dataset containing both synthetic visuals. The network is structured to learn features that differentiate between manipulated content. We use a mix of layers pooling layers and connected layers to extract and process features from the input data. To strengthen the reliability of our model we employ techniques like data augmentation and transfer learning. Data augmentation includes applying transformations such as rotation, scaling and cropping to the training data to enhance its variety. Transfer learning allows us to utilize trained CNN models and adjust them for deep fake detection purposes. We assess the effectiveness of our approach on datasets. Compare it with existing techniques. Our experiments show that our CNN based method achieves accuracy in spotting fake content in different scenarios. Additionally we examine how well the model withstands attacks and variations, in input data quality.

Multi-task learning with self-learning weight for image denoising

BackgroundImage denoising technology removes noise from the corrupted image by utilizing different features between image and noise. Convolutional neural network (CNN)-based algorithms have been the concern of the recent progress on diverse image restoration problems and become an efficient solution in image denoising.ObjectiveAlthough a quite number of existing CNN-based image denoising methods perform well on the simplified additive white Gaussian noise (AWGN) model, their performance often degrades severely on the real-world noisy images which are corrupted by more complicated noise.MethodsIn this paper, we utilized the multi-task learning (MTL) framework to integrate multiple loss functions for collaborative training of CNN. This approach aims to improve the denoising performance of CNNs on real-world images with non-Gaussian noise. Simultaneously, to automatically optimize the weights of individual sub-tasks within the MTL framework, we incorporated a self-learning weight layer into the CNN.ResultsExtensive experiments demonstrate that our approach effectively enhances the denoising performance of CNN-based image denoising algorithms on real-world images. It reduces excessive image smoothing, improves quantitative metrics, and enhances visual quality in the restored images.ConclusionOur method shows the effectiveness of the improved performance of denoising CNNS for real-world image denoising processing.

Feature space separation by conformity loss driven training of CNN

Convolutional neural networks (CNNs) have enabled tremendous achievements in image classification, as the model can automatically extract image features and assign a proper classification. Nevertheless, the classification is lacking robustness to — for humans’ invisible perturbations on the input. To improve the robustness of the CNN model, it is necessary to understand the decision-making procedure of CNN models. By inspecting the learned feature space, we found that the classification regions are not always clearly separated by the CNN model. The overlap of classification regions increases the possibility to less perturbation induced input changes on classification results. Therefore, the clear separation of feature spaces of the CNN model should support decision robustness. In this paper, we propose to use a novel loss function called “conformity loss” to strengthen disjoint feature spaces during learning at different layers of the CNN, in order to improve the intra-class compactness and inter-class differences in trained representations. The same function was used as an evaluation metric to measure the feature space separation during the testing process. In conclusion, the conformity loss driven trained model has shown better feature space separation at comparable output performance.

WiKA: A Vision Based Sign Language Recognition from Extracted Hand Joint Features using DeepLabCut

The use of hand gesture recognition for sign language translation to address the communication gap between the hearing majority and the deaf population has had significant breakthroughs over the years. While the contact-based approach uses wearable devices, a vision-based solution is preferred owing to the convenience it offers and since it obviates the need for complicated gears. This study presents the development of WiKA, an open-source software designed to track the joints of the hands and interpret them into their corresponding sign language counterparts. DeepLabCut, a markerless pose estimation software, was employed to develop the Hand-Joint Tracking Model through the training of a sequential Convolutional Neural Network, utilizing extracted Hand-Joint features to predict the sign language alphabets (A-Z) and numbers (1-9) based on the positioning of the joints. The developed Hand-Joint tracking model exhibited a 4.92% training error and a 5.74% test error with a p-cut-off of 60%. On the other hand, the developed sign language recognition achieved a 96.44% prediction accuracy with only 0.0356% misclass. This model can be further integrated into mobile phones for seamless conversations between the signing and non-signing populations.