Trained Convolutional Neural Network Model Research Articles

Distance and stellar parameter estimations of solar-like stars from the LAMOST spectroscopic survey

Context. The Gaia mission has opened up a new era for the precise astrometry of stars, thus revolutionizing our understanding of the Milky Way. However, beyond a few kiloparseconds from the Sun, parallax measurements become less reliable, and even within 2 kpc, there still exist stars with large uncertainties. Aims. Our aim was to determine the distance and stellar parameters of 521 424 solar-like stars from LAMOST DR9; these stars lacked precise distance measurements (uncertainties higher than 20% or even without any distance estimations) when checked with Gaia. Methods. We proposed a convolutional neural network (CNN) model to predict the absolute magnitudes, colors, and stellar parameters (Teff, log ɡ, and [Fe/H]) directly from low-resolution spectra. For spectra with signal-to-noise ratios at ɡ band (S/Ng) greater than 10, the model achieves a precision of 85 K for Teff, 0.07 dex for log ɡ, 0.06 dex for [Fe/H], 0.25 mag for MG, and 0.03 mag for (BP – RP)0. The estimated distances have a median fractional error of 4% with a standard deviation of 8%. Results. We applied the trained CNN model to 521 424 solar-like stars to derive the distance and stellar parameters. Compared with other distance estimation studies and spectroscopic surveys, the results show good consistency. Additionally, we investigated the metallicity gradients of the Milky Way from a subsample, and find a radial gradient ranging from −0.05 < Δ[Fe/H]/ΔR < 0.0 dex kpc−1 and a vertical gradient ranging from −0.26 < Δ[Fe/H]/ΔZ < −0.07 dex kpc−1. Conclusions. We conclude that our method is effective in estimating distances and stellar parameters for solar-like stars with limited astrometric data. Our measurements are reliable for Galactic structure studies and hopefully will be useful for exoplanet researches.

Identifikasi Pembuluh Darah Jari Tangan, Menggunakan Bahasa Pemrograman Python

Biometric systems automatically identify people based on their behavioral characteristics. Finger veins are more secure as biometric data than other biometric data. Finger vein patterns are difficult to forge because they are below the surface of the skin. To make the vascular pattern visible on the skin, a special method is needed to identify it. This research aims to develop a finger vein recognition system using the Convolutional Neural Network (CNN) method implemented in Python, with the aim of replacing the fingerprint-based employee attendance system in hospitals. This method offers higher security and resistance to forgery, given that finger veins are difficult to access from outside the body. This research utilizes the blood vessel dataset from Kaggle and goes through three main stages: preprocessing, training, and testing. The trained CNN model showed high performance with 99.99% accuracy in recognizing patterns in the finger vein images. The results of this study confirm that the developed system can provide accurate

Exploring the interplay between colorectal cancer subtypes genomic variants and cellular morphology: A deep-learning approach.

Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CRC subtype identification using H&E stained histopathological images, the correlation between CRC subtype genomic variants and their corresponding cellular morphology expressed by their imaging phenotypes is yet to be fully explored. The goal of this study was to determine such correlations by incorporating genomic variants in CNN models for CRC subtype classification from H&E images. We utilized the publicly available TCGA-CRC-DX dataset, which comprises whole slide images from 360 CRC-diagnosed patients (260 for training and 100 for testing). This dataset also provides information on CRC subtype classifications and genomic variations. We trained CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology patterns. We assessed the interplay between CRC subtypes' genomic variations and cellular morphology patterns by evaluating the CRC subtype classification accuracy of the different models in a stratified 5-fold cross-validation experimental setup using the area under the ROC curve (AUROC) and average precision (AP) as the performance metrics. The CNN models that account for potential correlation between genomic variations within CRC subtypes and their cellular morphology pattern achieved superior accuracy compared to the baseline CNN classification model that does not account for genomic variations when using either single-nucleotide-polymorphism (SNP) molecular features (AUROC: 0.824±0.02 vs. 0.761±0.04, p<0.05, AP: 0.652±0.06 vs. 0.58±0.08) or CpG-Island methylation phenotype (CIMP) molecular features (AUROC: 0.834±0.01 vs. 0.787±0.03, p<0.05, AP: 0.687±0.02 vs. 0.64±0.05). Combining the CNN models account for variations in CIMP and SNP further improved classification accuracy (AUROC: 0.847±0.01 vs. 0.787±0.03, p = 0.01, AP: 0.68±0.02 vs. 0.64±0.05). The improved accuracy of CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology as expressed by H&E imaging phenotypes may elucidate the biological cues impacting cancer histopathological imaging phenotypes. Moreover, considering CRC subtypes genomic variations has the potential to improve the accuracy of deep-learning models in discerning cancer subtype from histopathological imaging data.

Deep learning-aided active subspace exploration of free-stream effects for fan-shaped film cooling

Film cooling plays a critical role in protecting engine components from high temperatures that can influence safety and performance of gas turbines. However, the process is fraught with uncertainties due to complex inflow conditions and geometrical configurations. These uncertainties significantly impact cooling effectiveness, underscoring the importance of identifying the dominant factors in a quantified manner. Traditional methods, such as the Monte Carlo approach, encounter the “curse of dimensionality,” making them computationally intensive as the number of variable increases. This study tackles these challenges by employing a deep learning strategy with a convolutional neural network (CNN) model to predict film cooling effectiveness efficiently, reducing computational loads compared to traditional computational fluid dynamics (CFD) simulations. It enables the CNN model to act as a surrogate for the CFD simulation and provides gradient information for subsequent analyses. Additionally, this study employs active subspace (AS) analysis for dimensionality reduction, identifying dominant parameters based on the trained CNN model. This approach not only enhances the speed of simulations but also provides an effective way to analyze dominant parameters and carry out optimizations. Results demonstrate a strong correlation between the CNN predictions and detailed CFD simulations, with all the mean absolute errors below 0.08, validating the model's efficacy in capturing complex cooling dynamics. The dominant analysis based on the AS method gives the blowing ratio as the most important factors, and the subsequent parameter dimension reduction and suggested optimization region are also explored by applying the low-dimensional active variable. The tested optimal factors yield higher spatially averaged cooling effectiveness above 0.35 and show reasonable pattern changes compared to the maximum and minimum results in the sample.

Deep learning with uncertainty estimation for automatic tumor segmentation in PET/CT of head and neck cancers: impact of model complexity, image processing and augmentation

Objective. Target volumes for radiotherapy are usually contoured manually, which can be time-consuming and prone to inter- and intra-observer variability. Automatic contouring by convolutional neural networks (CNN) can be fast and consistent but may produce unrealistic contours or miss relevant structures. We evaluate approaches for increasing the quality and assessing the uncertainty of CNN-generated contours of head and neck cancers with PET/CT as input. Approach. Two patient cohorts with head and neck squamous cell carcinoma and baseline 18F-fluorodeoxyglucose positron emission tomography and computed tomography images (FDG-PET/CT) were collected retrospectively from two centers. The union of manual contours of the gross primary tumor and involved nodes was used to train CNN models for generating automatic contours. The impact of image preprocessing, image augmentation, transfer learning and CNN complexity, architecture, and dimension (2D or 3D) on model performance and generalizability across centers was evaluated. A Monte Carlo dropout technique was used to quantify and visualize the uncertainty of the automatic contours. Main results. CNN models provided contours with good overlap with the manually contoured ground truth (median Dice Similarity Coefficient: 0.75–0.77), consistent with reported inter-observer variations and previous auto-contouring studies. Image augmentation and model dimension, rather than model complexity, architecture, or advanced image preprocessing, had the largest impact on model performance and cross-center generalizability. Transfer learning on a limited number of patients from a separate center increased model generalizability without decreasing model performance on the original training cohort. High model uncertainty was associated with false positive and false negative voxels as well as low Dice coefficients. Significance. High quality automatic contours can be obtained using deep learning architectures that are not overly complex. Uncertainty estimation of the predicted contours shows potential for highlighting regions of the contour requiring manual revision or flagging segmentations requiring manual inspection and intervention.

CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data

This study presents a convolutional neural network (CNN) deep learning approach for identifying damage in submerged structure-foundation systems using vibration data. Firstly, foundation damage in a lab-scale caisson-foundation system is simulated to measure time-history responses. Singular value decomposition (SVD) responses are derived from the time-history responses. Secondly, the 1-D CNN deep learning model is trained using both the time-history responses and SVD responses. Finally, the trained CNN models are implemented to evaluate the foundation damage under conditions of noise contamination and partially untrained data. The experimental results demonstrate the effectiveness of CNN models for damage identification and highlight the comparative strengths of time-history and SVD data. The CNN model trained using SVD data outperforms the other model when under noise contamination conditions, while the CNN model trained using time-history data maintains better accuracy in partially untrained data conditions. Integrating both types of data enhances the accuracy of damage classification.

Integrated deep learning model for automatic detection and classification of stenosis in coronary angiography

Coronary artery disease poses a significant threat to human health. In clinical settings, coronary angiography remains the gold standard for diagnosing coronary heart disease. A crucial aspect of this diagnosis involves detecting arterial narrowings. Categorizing these narrowings can provide insight into whether patients should receive vascular revascularization treatment. The majority of current deep learning methods for analyzing coronary angiography are mostly confined to the theoretical research domain, with limited studies offering direct practical support to clinical practitioners. This paper proposes an integrated deep-learning model for the localization and classification of narrowings in coronary angiography images. The experimentation employed 1606 coronary angiography images obtained from 132 patients, resulting in an accuracy of 88.9 %, a recall rate of 85.4 %, an F1 score of 0.871, and a MAP value of 0.875 for vascular stenosis detection. Furthermore, we developed the "Hemadostenosis" web platform (http://bioinfor.imu.edu.cn/hemadostenosis) using Django, a highly mature HTTP framework. Users are able to submit coronary angiography image data for assessment via a visual interface. Subsequently, the system sends the images to a trained convolutional neural network model to localize and categorize the narrowings. Finally, the visualized outcomes are displayed to users and are downloadable. Our proposed approach pioneers the recognition and categorization of arterial narrowings in vascular angiography, offering practical support to clinical practitioners in their learning and diagnostic processes.

Translating Test Responses to Images for Test-termination Prediction via Multiple Machine Learning Strategies

Failure diagnosis is a software-based, data-driven procedure. Collecting an excessive amount of fail data not only increases the overall test cost but can also potentially reduce diagnostic resolution. Thus, test-termination prediction is proposed to dynamically determine the appropriate failing test pattern to terminate testing, producing an amount of test data that is sufficient for an accurate diagnosis analysis. In this work, we describe a set of novel methods utilizing advanced machine learning techniques for efficient test-termination prediction. To implement this approach, we first generate images representing failing test responses from failure-log files. These images are then used to train a multi-layer convolutional neural network (CNN) incorporating a residual block. The trained CNN model leverages the images and known diagnostic results to determine the optimal test-termination strategy within the testing process, ensuring efficient and high-quality diagnosis. In addition to the integration of test response-to-image translation, our approach harnesses two cutting-edge learning strategies to enhance fail data and boost performance in subsequent tasks. The first strategy is transfer learning, which utilizes sample-label information from one circuit to guide the decision of whether to continue or stop testing for another circuit lacking labels. The second strategy involves the use of a generative deep model to generate fail data in the form of synthetic images. This technique increases the modeling effectiveness by expanding the volume of training samples. Experimental results conducted on actual failing chips and standard benchmarks validate that our proposed method surpasses existing approaches. Our method creates opportunities to harness the power of recent advances in machine learning for improving test and diagnosis efficiency.

Hand Gesture Recognition Using Deep Learning

Hand gesture recognition (HGR) has gained significant attention due to its potential for various applications. This paper explores the use of deep learning, specifically Convolutional Neural Networks (CNNs), for HGR using the TensorFlow library. We investigate existing research on CNN-based HGR, focusing on image classification tasks. We then provide a brief overview of CNNs and their suitability for image recognition. Subsequently, we describe the typical workflow of a deep learning-based HGR system, including data preprocessing, hand detection, feature extraction with CNNs, and classification. We highlight the advantages of using TensorFlow to build and train CNN models for HGR. Finally, we conclude by summarizing the key findings from related work and mentioning the specific dataset and number of gestures classified in our research. This work contributes to the growing body of research on CNN-based HGR using TensorFlow and emphasizes its potential for developing accurate and efficient HGR systems.

Measurement of Centroid Distance in Determining Stunting Clusters

This study aims to develop a vegetable freshness classification model using Convolutional Neural Networks (CNN). The methodology used includes collecting vegetable image datasets, image preprocessing, training CNN models, and evaluating classification accuracy. The dataset used consists of various types of vegetables with varying levels of freshness. The results of the study show that the developed CNN model is able to classify vegetable freshness with high accuracy, so it can assist in the automation process of determining vegetable freshness in the food industry. Model evaluation is carried out using accuracy metrics and mean squared error (MSE).

Deep learning-based prediction of velocity and temperature distributions in metal foam with hierarchical pore structure

Constrained by the substantial computational time required for numerical simulation, a deep learning technique is applied to investigate fluid flow and heat transfer processes in metal foam with a hierarchical pore structure. This work adopted a 3D convolutional neural network (CNN) combining U-Net architecture to predict velocity and temperature distributions, alongside corresponding permeability and overall heat transfer coefficient. This approach demonstrates excellent capability in intricate image segmentation. The training sets were acquired by lattice Boltzmann method (LBM) simulations. The CNN model, trained on a substantial amount of data, demonstrates remarkable precision, exhibiting mean relative errors of 0.57% for permeability prediction and 2.27% for overall heat transfer coefficient prediction. Moreover, in CNN prediction, a broader range of structure parameters and boundary conditions beyond those in the training set was used to evaluate the practicability of the trained CNN model. In contrast to numerical simulation, the CNN model economizes approximately 95.41% and 99.57% of computational time for velocity and temperature distribution prediction, respectively, providing a novel approach for exploring transport processes in metal foam with hierarchical pore structure.

Categorizing Dermatological Malignancies Via Computational Methods

In this study, a machine learning model is developed to classify different types of cancer using convolutional neural networks (CNNs) for image processing. The core objective is to achieve a performance level comparable to that of dermatologists. The model is trained on a substantial dataset of medical images, enabling it to learn and recognize various characteristics indicative of different cancer types. By leveraging the power of CNNs, the model can process these images effectively, identifying subtle patterns and features that are often challenging to detect with the naked eye. The training process involves feeding the CNN with labelled images, enabling it to differentiate between benign and malignant cases with high accuracy. Through rigorous testing, the model demonstrates competence on par with experienced dermatologists, both in terms of sensitivity and specificity. This equivalence in performance is particularly significant as it underscores the model's potential to aid in clinical settings, providing reliable second opinions and enhancing diagnostic workflows. A user interface is also developed to allow input images to be analysed by the trained CNN model. This interface not only displays the model’s predictions but also provides essential metrics such as confidence scores and probability distributions. These metrics offer valuable insights into the model's decision-making process, aiding clinicians in understanding and trusting the AI's assessments. Overall, the findings suggest that convolutional neural networks hold substantial promise for improving cancer diagnosis. The model's high performance in classification tasks demonstrates its viability as a tool for supporting dermatologists in clinical practice. By reducing diagnostic errors and accelerating the identification process, this technology has the potential to significantly impact patient outcomes and advance the field of medical imaging and diagnostics. Keywords: Convolutional Neural Networks (CNNs); Cancer Classification; Medical Image Processing; Dermatology AI; Diagnostic Accuracy

Unveiling Anomalies in Surveillance Videos

Abstract: The implementation of Convolutional Neural Network (CNN) algorithm for the detection of anomalies like fire and potholes in images or videos. Leveraging deep learning technique, the model aims to accurately identify fire, potholes, etc. within various contexts. The CNN architecture is trained on a dataset comprising diverse images to enhance its ability to generalize. This proposed project not only addresses safety concerns by promoting helmet usage and proper maintenance of roads but also contributes to the broader field of computer vision and object detection. This implementation involves training the CNN on annotated datasets, fine-tuning the model for optimal performance, and integrating it into a practical application for efficient anomaly identification. With a focus on real-time detection, the system utilizes image processing techniques and a trained CNN model to analyze visual data from cameras or video feeds. The system's versatility allows integration into surveillance systems, industrial sites, or any environment where fire detection is crucial for safety. By automating the detection process, the project contributes to minimizing human error and ensuring consistent monitoring. The proposed solution holds the potential to significantly impact safety protocols, particularly in industries where protective headgear is paramount. Potholes pose a significant threat to both drivers and pedestrians, leading to accidents and infrastructure damage. The proposed solution leverages CNN's ability to effectively process visual data, making it well-suited for image recognition tasks. Our approach involves training the CNN on a diverse dataset of road images to enable it to accurately identify potholes. The model will learn distinctive features and patterns associated with potholes, allowing for robust detection under various environmental conditions. Real-time implementation on embedded systems or cameras along roadways will enable instantaneous identification and alerting.

Deep learning-based detection of lumbar spinal canal stenosis using convolutional neural networks

BACKGROUND CONTEXTLumbar spinal canal stenosis (LSCS) is the most common spinal degenerative disorder in elderly people and usually first seen by primary care physicians or orthopedic surgeons who are not spine surgery specialists. Magnetic resonance imaging (MRI) is useful in the diagnosis of LSCS, but the equipment is often not available or difficult to read. LSCS patients with progressive neurologic deficits have difficulty with recovery if surgical treatment is delayed. So, early diagnosis and determination of appropriate surgical indications are crucial in the treatment of LSCS. Convolutional neural networks (CNNs), a type of deep learning, offers significant advantages for image recognition and classification, and work well with radiographs, which can be easily taken at any facility. PURPOSEOur purpose was to develop an algorithm to diagnose the presence or absence of LSCS requiring surgery from plain radiographs using CNNs. STUDY DESIGNRetrospective analysis of consecutive, nonrandomized series of patients at a single institution. PATIENT SAMPLEData of 150 patients who underwent surgery for LSCS, including degenerative spondylolisthesis, at a single institution from January 2022 to August 2022, were collected. Additionally, 25 patients who underwent surgery at 2 other hospitals were included for extra external validation. OUTCOME MEASURESIn annotation 1, the area under the curve (AUC) computed from the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated. In annotation 2, correlation coefficients were used. METHODSFour intervertebral levels from L1/2 to L4/5 were extracted as region of interest from lateral plain lumbar spine radiographs totaling 600 images were obtained. Based on the date of surgery, 500 images derived from the first 125 cases were used for internal validation, and 100 images from the subsequent 25 cases used for external validation. Additionally, 100 images from other hospitals were used for extra external validation. In annotation 1, binary classification of operative and nonoperative levels was used, and in annotation 2, the spinal canal area measured on axial MRI was labeled as the output layer. For internal validation, the 500 images were divided into each 5 dataset on per-patient basis and 5-fold cross-validation was performed. Five trained models were registered in the external validation prediction performance. Grad-CAM was used to visualize area with the high features extracted by CNNs. RESULTSIn internal validation, the AUC and accuracy for annotation 1 ranged between 0.85–0.89 and 79–83%, respectively, and the correlation coefficients for annotation 2 ranged between 0.53 and 0.64 (all p<.01). In external validation, the AUC and accuracy for annotation 1 were 0.90 and 82%, respectively, and the correlation coefficient for annotation 2 was 0.69, using 5 trained CNN models. In the extra external validation, the AUC and accuracy for annotation 1 were 0.89 and 84%, respectively, and the correlation coefficient for annotation 2 was 0.56. Grad-CAM showed high feature density in the intervertebral joints and posterior intervertebral discs. CONCLUSIONSThis technology automatically detects LSCS from plain lumbar spine radiographs, making it possible for medical facilities without MRI or nonspecialists to diagnose LSCS, suggesting the possibility of eliminating delays in the diagnosis and treatment of LSCS that require early treatment.

A Predictive Model for Intraoperative Cerebrospinal Fluid Leak During Endonasal Pituitary Adenoma Resection Using a Convolutional Neural Network

Cerebrospinal fluid (CSF) leak during endoscopic endonasal transsphenoidal surgery can lead to postoperative complications. The clinical and anatomic risk factors of intraoperative CSF leak are not well defined. We applied a two-dimensional (2D) convolutional neural network (CNN) machine learning model to identify risk factors from preoperative magnetic resonance imaging. All adults who underwent endoscopic endonasal transsphenoidal surgery at our institution from January 2007 to March 2023 who had accessible preoperative stereotactic magnetic resonance imaging were included. A retrospective classic statistical analysis was performed to identify demographic, clinical, and anatomic risk factors of intraoperative CSF leak. Stereotactic T2-weighted brain magnetic resonance imaging scans were used to train and test a 2D CNN model. Of 220 included patients, 81 (36.8%) experienced intraoperative CSF leak. Among all preoperative variables, visual disturbance was the only statistically significant identified risk factor (P= 0.008). The trained 2D CNN model predicted CSF leak with 92% accuracy and area under receiver operating characteristic curve of 0.90 (sensitivity of 86% and specificity of 93%). Class activation mapping of this model revealed that anatomic regions of CSF flow were most important in predicting CSF leak. Further review of the class activation mapping gradients revealed regions of the diaphragma sellae, clinoid processes, temporal horns, and optic nerves to have anatomic correlation to intraoperative CSF leak risk. Additionally, visual disturbances from anatomic compression of the optic chiasm were the only identified clinical risk factor. Our 2D CNN model can help a treating team to better anticipate and prepare for intraoperative CSF leak.

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.

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.

Pre- and post-fire forest canopy height mapping in Southeast Australia through the integration of multi-temporal GEDI data, satellite images, and Convolution Neural Network

ABSTRACT This study leveraged Convolutional Neural Network (CNN) models to estimate canopy height in Southeast Australian forests before and after the 2019–2020 bushfire event, using inputs from Sentinel-1, Sentinel-2, spectral indices, and terrain features and GEDI canopy height (rh99) as target variables. Our research consisted of three primary objectives: (1) an evaluation of GEDI rh99 canopy height in relation to an nDSM derived from airborne LiDAR, (2) the development of two CNN models for pre- and post-fire scenarios, and (3) utilizing the trained CNN models to generate pre- and post-fire canopy height maps and canopy height change maps across the continuous landscape. GEDI rh99 accuracy analysis revealed R 2 = 0.85, RMSE = 7.25 m and nRMSE = 0.23 for pre-fire GEDI and R 2 = 0.78, RMSE = 10.1 m and nRMSE = 0.3 for post-fire GEDI. The pre-fire CNN model achieved R 2 = 0.75, RMSE = 7.29 m and nRMSE = 0.22, and post-fire CNN model achieved R 2 = 0.62, RMSE = 11.67 m, and nRMSE = 0.34 when validated against nDSM. Bias analysis was conducted, revealing that GEDI rh99 underestimated nDSM across all canopy height ranges, and slope has no significant impact on GEDI accuracy. Notably, post-fire GEDI rh99 exhibited significant underestimation in high to extreme fire severity. The CNN models’ prediction displayed overestimation in short forests < 20 m and increasing underestimation in taller forests > 20 m and underestimation in high and extreme fire severity. Subsequent investigation indicated that canopy layer thinning in high fire severity conditions resulted in weak GEDI waveform signals and consequent bias. Additionally, the GEDI rh99 bias was propagated to the CNN models, together with insensitivity of spectral band values to canopy height in dense and burnt forests potentially contributed to models’ bias. Finally, we demonstrated CNN models’ ability to monitor forest recovery by generating canopy height maps spanning East Gippsland from 2019 to 2023.

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.

Real Time Emotion Based Music Player

Abstract: In this paper, we present the development and implementation of an Emotion-based Music Player System utilizing facial emotion data, Convolutional Neural Networks (CNN), Flask, OpenCV for face detection, and Spotify API for music playlist integration. The system aims to provide users with personalized music recommendations based on their current emotional state, detected through real-time analysis of facial expressions. The proposed system consists of several key components, including data collection, preprocessing, CNN algorithm implementation for emotion classification, integration with Flask for web application development, and real-time emotion detection using OpenCV. The trained CNN model is capable of accurately classifying emotions such as anger, disgust, fear, happiness, neutrality, sadness, and surprise. Results from the implementation demonstrate the system's effectiveness in accurately detecting emotions from facial expressions and providing corresponding music recommendations. The CNN model achieved a final accuracy of 62.44% after 10 epochs of training. Realtime emotion detection using OpenCV successfully identifies facial expressions, allowing for dynamic adjustments to the music playlist. Overall, the Emotion-based Music Player System presents a novel approach to enhancing user experience by leveraging facial emotion data and advanced machine learning techniques to deliver personalized music recommendations tailored to individual emotional states.