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Objectives. This study aimed to develop an initial deep-learning (DL) model based on computerized tomography (CT) scans for diagnosing lumbar spinal stenosis. Summary of Background Data. Magnetic resonance imaging is commonly used for diagnosing lumbar spinal stenosis due to its high soft tissue resolution, but CT is more portable, cost-effective, and has wider regional coverage. Using DL models to improve the accuracy of CT diagnosis can effectively reduce missed diagnoses and misdiagnoses in clinical practice. Materials and Methods. Axial lumbar spine CT scans obtained between March 2022 and September 2023 were included. The data set was divided into a training set (62.3%), a validation set (22.9%), and a control set (14.8%). All data were labeled by two spine surgeons using the widely accepted grading system for lumbar spinal stenosis. The training and validation sets were used to annotate the regions of interest by the two spine surgeons. First, a region of interest detection model and a convolutional neural network classifier were trained using the training set. After training, the model was preliminarily evaluated using a validation set. Finally, the performance of the DL model was evaluated on the control set, and a comparison was made between the model and the classification performance of specialists with varying levels of experience. Results. The central stenosis grading accuracies of DL Model Version 1 and DL Model Version 2 were 88% and 83%, respectively. The lateral recess grading accuracies of DL Model Version 1 and DL Model Version 2 were 75% and 71%, respectively. Conclusions. Our preliminarily developed DL system for assessing the degree of lumbar spinal stenosis in CT, including the central canal and lateral recess, has shown similar accuracy to experienced specialist physicians. This holds great value for further development and clinical application. [ABSTRACT FROM AUTHOR]

• A novel optimization algorithm integrating random and grid search mechanisms as function calls. • A cost-effective low inference time deep learning model for detecting 2D ionospheric plasma structures from all-sky airglow images. • The issue of uncertainty quantification has been addressed using the approach of bootstrapping. The ionosphere, part of the Earth's atmosphere, where different plasma irregularities/structures are generated through various electrodynamical processes. Airglow imaging is one of the tools to observe various morphological features of these irregularities. We aim to help all-sky airglow imaging-based ionospheric research by proposing novel deep learning at the edge framework for the detection of different types of mid-latitude ionospheric plasma structures (medium scale traveling ionospheric disturbances with single and multiple bands, and plasma bubbles). The primary challenge was to find an optimal deep neural network by considering the trade-off between accuracy and inference time. In order to address this, a novel hyperparameter optimization technique has been used that integrates the approaches of random and grid search mechanisms as a compact function. The random function generates the subspace for hyperparameters to check the convergence of the model while the grid search creates possible combinations to tune these hyperparameters. Our novel optimization method for deep learning inference at the edge led us to a low-cost, high-accuracy convolutional neural network (CNN) model, which outperformed the complex state-of-the-art deep learning models such as Inception-v3, DenseNet169, VGG16, and VGG19. For airglow image-based plasma structure detection, the proposed model recorded an accuracy of 99.9% with an inference time of 5.8 s. This was an improvement of about 4% in accuracy while 85% reduction in inference time over the best-performing baseline. There was about 93% reduction in the number of model parameters with respect to the best-performing baseline. Uncertainty quantification has also been performed in the present work through bootstrapping to validate the robustness of the proposed model. The findings of our study show the promise of deep learning at the edge for all-sky airglow imaging systems by demonstrating an alternate low-cost, high-accuracy deep neural network. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind power is crucial for the efficient operation and risk management of wind farms. This paper introduces a deep learning model for wind power prediction that integrates an Attention mechanism with a convolutional neural network (CNN) and a gated recurrent unit (GRU) neural network. Addressing the randomness, intermittency, volatility and uncertainty of wind speed, we first apply swarm decomposition (SWD) to preprocess the original wind power data into subsequences. Subsequently, the CNN extracts spatial features, and the GRU identifies temporal correlations. The Attention mechanism enhances feature significance, further optimizing prediction accuracy. Complex error sequences generated by the CNN–GRU–Attention (CGA) model are corrected using the autoregressive integrated moving average (ARIMA). We evaluated the model’s performance using three wind power datasets against 16 other models, employing six evaluation indices (MSE, RMSE, MAPE, Theil’s U, TIC and SPL) and the Diebold–Mariano (DM) test and model confidence set (MCS) for model testing. Our results demonstrate the proposed model’s superior accuracy and efficiency in predicting wind power. [ABSTRACT FROM AUTHOR]

Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes calculating the effort, time, and resources required. Although many researchers have worked to estimate it, the prediction accuracy results are still inaccurate and unstable. Estimating it requires a lot of effort. Therefore, there is an urgent need for modern techniques that contribute to cost estimation. This paper seeks to present a model based on deep learning and machine learning techniques by combining convolutional neural networks (CNN) and the particle swarm algorithm (PSO) in the context of time series forecasting, which enables feature extraction and automatic tuning of hyperparameters, which reduces the manual effort of selecting parameters and contributes to fine-tuning. The use of PSO also enhances the robustness and generalization ability of the CNN model and its iterative nature allows for efficient discovery of hyperparameter similarity. The model was trained and tested on 13 different benchmark datasets and evaluated through six metrics: mean absolute error (MAE), mean square error (MSE), mean magnitude relative error (MMRE), root mean square error (RMSE), median magnitude relative error (MdMRE), and prediction accuracy (PRED). Comparative results reveal that the performance of the proposed model is better than other methods for all datasets and evaluation criteria. The results were very promising for predicting software cost estimation. [ABSTRACT FROM AUTHOR]

In this paper, a method based on transfer learning is proposed to recover the three-dimensional shape of cultural relics faces from a single old photo. It can simultaneously reconstruct the three-dimensional facial structure and align the texture of the cultural relics with fewer training samples. The UV position map is used to represent the three-dimensional shape in space and act as the output of the network. A convolutional neural network is used to reconstruct the UV position map from a single 2D image. In the training process, the human face data is used for pre-training, and then a small amount of artifact data is used for fine-tuning. A deep learning model with strong generalization ability is trained with fewer artifact data, and a three-dimensional model of the cultural relic face can be reconstructed from a single old photograph. The methods can train more complex deep networks without a large amount of cultural relic data, and no over-fitting phenomenon occurs, which effectively solves the problem of fewer cultural relic samples. The method is verified by restoring a Chinese Terracotta Warrior with facial cracks and a Buddha with a broken nose. Other applications can be used in the fields such as texture recovery, facial feature extraction, and three-dimensional model estimation of the damaged cultural relics or sculptures in the photos. [ABSTRACT FROM AUTHOR]

Multi-modal medical image fusion (MMIF) is crucial for disease diagnosis and treatment because the images reconstructed from signals collected by different sensors can provide complementary information. In recent years, deep learning (DL) based methods have been widely used in MMIF. However, these methods often adopt a serial fusion strategy without feature decomposition, causing error accumulation and confusion of characteristics across different scales. To address these issues, we have proposed the Coupled Image Reconstruction and Fusion (CIRF) strategy. Our method parallels the image fusion and reconstruction branches which are linked by a common encoder. Firstly, CIRF uses the lightweight encoder to extract base and detail features, respectively, through the Vision Transformer (ViT) and the Convolutional Neural Network (CNN) branches, where the two branches interact to supplement information. Then, two types of features are fused separately via different blocks and finally decoded into fusion results. In the loss function, both the supervised loss from the reconstruction branch and the unsupervised loss from the fusion branch are included. As a whole, CIRF increases its expressivity by adding multi-task learning and feature decomposition. Additionally, we have also explored the impact of image masking on the network's feature extraction ability and validated the generalization capability of the model. Through experiments on three datasets, it has been demonstrated both subjectively and objectively, that the images fused by CIRF exhibit appropriate brightness and smooth edge transition with more competitive evaluation metrics than those fused by several other traditional and DL-based methods. [ABSTRACT FROM AUTHOR]

Research on transformers in remote sensing (RS), which started to increase after 2021, is facing the problem of a relative lack of review. To understand the trends of transformers in RS, we undertook a quantitative analysis of the major research on transformers over the past two years by dividing the application of transformers into eight domains: land use/land cover (LULC) classification, segmentation, fusion, change detection, object detection, object recognition, registration, and others. Quantitative results show that transformers achieve a higher accuracy in LULC classification and fusion, with more stable performance in segmentation and object detection. Combining the analysis results on LULC classification and segmentation, we have found that transformers need more parameters than convolutional neural networks (CNNs). Additionally, further research is also needed regarding inference speed to improve transformers' performance. It was determined that the most common application scenes for transformers in our database are urban, farmland, and water bodies. We also found that transformers are employed in the natural sciences such as agriculture and environmental protection rather than the humanities or economics. Finally, this work summarizes the analysis results of transformers in remote sensing obtained during the research process and provides a perspective on future directions of development. [ABSTRACT FROM AUTHOR]

The aim of this article is to introduce a novel approach to identifying flow regimes and void fractions in microchannel flow boiling, which is based on binary image segmentation using digital image processing and deep learning. The proposed image processing pipeline uses adaptive thresholding, blurring, gamma correction, contour detection, and histogram comparison to separate vapor from liquid areas, while the deep learning method uses a customized version of a convolutional neural network (CNN) called U-net to extract meaningful features from video frames. Both approaches enabled the automatic detection of flow boiling conditions, such as bubbly, slug, and annular flow, as well as automatic void fraction calculation. Especially CNN demonstrated its ability to deliver fast and dependable results, presenting an appealing substitute to manual feature extraction. The U-net-based CNN was able to segment flow boiling images with a Dice score of 99.1% and classify the above flow regimes with an overall classification accuracy of 91%. In addition, the neural network was able to predict resistance sensor readings from image data and assign them to a flow state with a mean squared error (MSE) < 10−6. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is an infectious disease caused by Mycobacterium. It primarily impacts the lungs but can also endanger other organs, such as the renal system, spine, and brain. When an infected individual sneezes, coughs, or speaks, the virus can spread through the air, which contributes to its high contagiousness. The goal is to enhance detection recognition with an X-ray image dataset. This paper proposed a novel approach, named the Tuberculosis Segmentation-Guided Diagnosis Model (TSSG-CNN) for Detecting Tuberculosis, using a combined semantic segmentation and adaptive convolutional neural network (CNN) architecture. The proposed approach is distinguished from most of the previously proposed approaches in that it uses the combination of a deep learning segmentation model with a follow-up classification model based on CNN layers to segment chest X-ray images more precisely as well as to improve the diagnosis of TB. It contrasts with other approaches like ILCM, which is optimized for sequential learning, and explainable AI approaches, which focus on explanations. Moreover, our model is beneficial for the simplified procedure of feature optimization from the perspectives of approach using the Mayfly Algorithm (MA). Other models, including simple CNN, Batch Normalized CNN (BN-CNN), and Dense CNN (DCNN), are also evaluated on this dataset to evaluate the effectiveness of the proposed approach. The performance of the TSSG-CNN model outperformed all the models with an impressive accuracy of 98.75% and an F1 score of 98.70%. The evaluation findings demonstrate how well the deep learning segmentation model works and the potential for further research. The results suggest that this is the most accurate strategy and highlight the potential of the TSSG-CNN Model as a useful technique for precise and early diagnosis of TB. [ABSTRACT FROM AUTHOR]

This study introduces a deep-learning-based automatic sleep scoring system to detect sleep apnea using a single-lead electrocardiography (ECG) signal, focusing on accurately estimating the apnea–hypopnea index (AHI). Unlike other research, this work emphasizes AHI estimation, crucial for the diagnosis and severity evaluation of sleep apnea. The suggested model, trained on 1465 ECG recordings, combines the deep-shallow fusion network for sleep apnea detection network (DSF-SANet) and gated recurrent units (GRUs) to analyze ECG signals at 1-min intervals, capturing sleep-related respiratory disturbances. Achieving a 0.87 correlation coefficient with actual AHI values, an accuracy of 0.82, an F1 score of 0.71, and an area under the receiver operating characteristic curve of 0.88 for per-segment classification, our model was effective in identifying sleep-breathing events and estimating the AHI, offering a promising tool for medical professionals. [ABSTRACT FROM AUTHOR]

Background: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios. Method: To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection. Results: The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy. Conclusion: The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings. [ABSTRACT FROM AUTHOR]

The evolution of image manipulation techniques has presented a paradoxical scenario in contemporary visual culture. This phenomenon operates as a double-edged sword, offering both creative liberation and ethical dilemmas. Consequently, there is a need to develop automated mechanisms capable of discerning such forged data. The proposed methodology leverages transfer learning, utilising pre-trained deep learning models as a foundation and fine-tuning them specifically for the task of copy-move forgery detection. This approach uses the knowledge learned from large datasets, enhancing the network's ability to discern subtle patterns indicative of copy-move manipulations in images. Further, this research introduces a custom-designed CNN architecture tailored to the intricacies of copy-move forgery, optimising feature extraction and classification. Experimental evaluations conducted on diverse datasets, namely MICC-F220, MICC-F600, MICC-F2000, and CoMoFoD demonstrate the effectiveness of the proposed method with a True Positive Rate (TPR) of 100%. [ABSTRACT FROM AUTHOR]

To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000–1400 nm and 1500–1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy. [ABSTRACT FROM AUTHOR]

With the massive amount of data available on the Internet, many users face problems when they need access to the information and goods they need. Adapting information to user needs has become a complex and time-consuming process. Recommender systems are powerful tools for guiding users in electronic settings to information, services, and goods of interest. With the ability to identify users and predict their preferences, recommender systems can extract information likely to be of interest to users from vast amounts of data and save time and energy by providing them with relevant recommendations. Therefore, in this paper, a framework for a recommender system is proposed, which consists of four main phases. In the first phase, the preprocessing operation is performed. The purpose of this phase is to create an ontology model for products based on the convolutional neural network method. Data collection operations are carried out in the second phase. The purpose of this phase is to receive and store user information based on behavior and different criteria. The third phase is related to creating an ontology. The purpose of this phase is to create an ontology model for user behavior using OWL libraries. Finally, in the last phase, finding similarities and presented a proposal. The purpose of this phase is the necessary calculations of the proposed framework in the similarity finding section using ontology models. Simulation results show that the proposed framework measures MAE (more than 10 and 15%), RMSE (more than 12 and 16%), precision (less than 10 and 13%), recall (more than 25 and 28%), F1-score (higher than 25 and 30%) and Score of User (higher than 15 and 17%) outperform two related approaches, namely CF and CF+ Ontology, respectively. [ABSTRACT FROM AUTHOR]

Objectives: Transcranial sonography has been used as a valid neuroimaging tool to diagnose Parkinson's disease (PD). This study aimed to develop a modified transcranial sonography (TCS) technique based on a deep convolutional neural network (DCNN) model to predict Parkinson's disease. Methods: This retrospective diagnostic study was conducted using 1529 transcranial sonography images collected from 854 patients with PD and 775 normal controls admitted to the Second Affiliated Hospital of Soochow University (Suzhou, Jiangsu, China) between September 2019 and May 2022. The data set was divided into training cohorts (570 PD patients and 541 normal controls), and the validation set (184 PD patients and 234 normal controls). Using these datasets, we developed four different DCNN models (ResNet18, ResNet50, ResNet152, and DenseNet121). We then assessed their diagnostic performance, including the area under the receiver operating characteristic (AUROC) curve, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and F1 score and compared with traditional diagnostic criteria. Results: Among the 1529 TCS images, 570 PD patients and 541 normal controls from 4 of 6 sonographers of the TCS team were selected as the training cohort, and 184 PD patients and 234 normal controls from the other 2 sonographers were chosen as the validation cohort. There were no sex and age differences between PD patients and normal control subjects in the training and validation cohorts (P values > 0.05). All DCNN models achieved good performance in distinguishing PD patients from normal control subjects on the validation datasets, with diagnostic AUROCs and accuracy of 0.949 (95% CI 0.925, 0.965) and 86.60 for the RestNet18 model, 0.949 (95% CI 0.929, 0.971) and 87.56 for ResNet50, 0.945 (95% CI 0.931, 0.969) and 88.04 for ResNet152, 0.953 (95% CI 0.935, 0.971) and 87.80 for DenseNet121, respectively. On the other hand, the diagnostic accuracy of the traditional diagnostic method was 82.30. The accuracy of all DCNN models was higher than that of traditional diagnostic method. Moreover, the 5k-fold cross-validation results in train datasets showed that these DCNN models are robust. Conclusion: The developed transcranial sonography-based DCNN models performed better than traditional diagnostic criteria, thus improving the sonographer's accuracy in diagnosing PD. [ABSTRACT FROM AUTHOR]

Matching optical and synthetic aperture radar (SAR) images is often challenged by intricate geometric distortion and nonlinear radiation differences, leading to insufficient and unevenly distributed corresponding points. To tackle this issue, we propose a lightweight deep convolutional network with inverted residuals for optical and SAR image matching. Initially, a fully convolutional neural network (FCNN) is designed to extract high-level and semantic features, robustly capturing universal characteristics between optical and SAR images, adept at handling geometric distortion and nonlinear radiation changes. Notably, we integrate a lightweight architecture with inverted residuals into FCNN to adeptly extract local and global contextual information, facilitating feature reuse and minimizing the loss of crucial features. Additionally, a vector-refined module is deployed to refine dense features, filtering out redundant information. Subsequently, a coarse-to-fine strategy is employed to further eliminate gross errors or incorrect matches. Finally, we evaluate the performance of the proposed network in optical and SAR image matching against manually-designed methods and state-of-the-art deep learning techniques. Experimental results demonstrate that our network significantly surpasses existing methods in terms of the number of correct matches and matching accuracy. Specifically, our proposed network achieves at least a 2.8 times increase in correct matches and an 18% improvement in matching accuracy compared to existing methods. [ABSTRACT FROM AUTHOR]

Decomposing an image into meaningful components is an effective way for image processing. Making full use of cartoon features and texture features with deep neural networks may further achieve the stable noise cancellation performance for low-dose computed tomography (LDCT) denoising. However, the performance of conventional deep neural networks is limited by their black-box nature. To overcome this drawback, we proposed an algorithm unrolling approach for LDCT denoising, namely, the cartoon-texture features guided network (CTFG-Net). The CTFG-Net integrates a cartoon sub-network, a texture sub-network, and a fusion sub-network into an end-to-end convolutional neural network model. A cartoon decomposition algorithm and a texture decomposition algorithm were unrolled to the cartoon sub-network and the texture sub-network, respectively. These two sub-networks were employed to decompose a LDCT image into its cartoon components and texture components. During the process of image decomposition, these two sub-networks exploited cartoon features and texture features to greatly suppress noise and remove artifacts in the corresponding components. At last, the final denoised results were yielded through a data fusion of the denoised cartoon components and the denoised texture components. The proposed model was tested with three datasets, Mayo dataset, Piglet dataset, and AbdomenCT-1K dataset. Qualitative results show that the CTFG-Net achieved better denoising effects for LDCT images than the competing algorithms. Compared to the competing algorithms, the proposed CTFG-Net also improved the peak signal-to-noise ratio, structural similarity index measure, and visual information fidelity by (0.3994 dB and 0.7692 dB), (0.0115 and 0.0056), and (0.0751 and 0.0433) with Mayo dataset and Piglet dataset. [ABSTRACT FROM AUTHOR]

Evaluation of left ventricular (LV) function in critical care patients is useful for guidance of therapy and early detection of LV dysfunction, but the tools currently available are too time-consuming. To resolve this issue, we previously proposed a method for the continuous and automatic quantification of global LV function in critical care patients based on the detection and tracking of anatomical landmarks on transesophageal heart ultrasound. In the present study, our aim was to improve the performance of mitral annulus detection in transesophageal echocardiography (TEE). We investigated several state-of-the-art networks for both the detection and tracking of the mitral annulus in TEE. We integrated the networks into a pipeline for automatic assessment of LV function through estimation of the mitral annular plane systolic excursion (MAPSE), called autoMAPSE. TEE recordings from a total of 245 patients were collected from St. Olav's University Hospital and used to train and test the respective networks. We evaluated the agreement between autoMAPSE estimates and manual references annotated by expert echocardiographers in 30 Echolab patients and 50 critical care patients. Furthermore, we proposed a prototype of autoMAPSE for clinical integration and tested it in critical care patients in the intensive care unit. Compared with manual references, we achieved a mean difference of 0.8 (95% limits of agreement: –2.9 to 4.7) mm in Echolab patients, with a feasibility of 85.7%. In critical care patients, we reached a mean difference of 0.6 (95% limits of agreement: –2.3 to 3.5) mm and a feasibility of 88.1%. The clinical prototype of autoMAPSE achieved real-time performance. Automatic quantification of LV function had high feasibility in clinical settings. The agreement with manual references was comparable to inter-observer variability of clinical experts. [ABSTRACT FROM AUTHOR]

Plant diseases cause significant agricultural losses, demanding accurate detection methods. Traditional approaches relying on expert knowledge may be biased, but advancements in computing, particularly deep learning, offer non-experts effective tools. This study focuses on fine-tuning cutting-edge pre-trained CNN and vision transformer models to classify grape leaves and diagnose grape leaf diseases through digital images. Our research examined a PlantVillage dataset, which comprises 4062 leaf images distributed across four categories. Additionally, we utilized the Grapevine dataset, consisting of 500 leaf images. This dataset is organized into five distinct groups, with each group containing 100 images corresponding to one of the five grape types. The PlantVillage dataset focuses on four classes related to grape diseases, namely Black Rot, Leaf Blight, Healthy, and Esca leaves. On the other hand, the Grapevine dataset includes five classes for leaf recognition, specifically Ak, Alaidris, Buzgulu, Dimnit, and Nazli. In experiments with 14 CNN and 17 vision transformer models, deep learning demonstrated high accuracy in distinguishing grape diseases and recognizing leaves. Notably, four models achieved 100% accuracy on PlantVillage and Grapevine datasets, with Swinv2-Base standing out. This approach holds promise for enhancing crop productivity through early disease detection and providing insights into grape variety characterization in agriculture. [ABSTRACT FROM AUTHOR]

In the bridge structure, the main bearing element is the spans, which are directly subject to random and continuous loads, environmental influences, ageing, etc. These factors are easy to lead to material deterioration. Therefore, this study proposes a novel approach to investigate the mechanical properties of the material. The span of the bridge is the selected structure for investigation. This study focuses on the general evaluation of mechanical factors consistent with reality, so the following main factors are to be carried out. First, the viscoelastic model of material will be applied to set up and solve the governing differential equation of the beams with material characteristics involving the elastic modulus (E) and the viscous coefficient (C). The viscoelastic model is different from the traditional elastic model due to its non-linearity, reflecting the actual state of the structure. Second, the random vibration signal-based Loss Factor function (LF) calculation using the Power Spectral Density (PSD) to detect changes in structures. LF is a feature representing changes in material properties, including elasticity and viscosity, and is suitable for many types of bridge structures. Also, the paper uses Cubic Interpolation (CI) to generate a surface representing the LF distribution. This interpolation results in surfaces with respect to the LF values distributed by frequencies and spectral amplitudes. Finally, the LF distribution-based material investigation using Convolutional Neural Network (CNN) is proposed with high performance and accuracy. This study applies the proposed method to several bridges in Ho Chi Minh City, Vietnam. It demonstrates that LF is highly suitable for bridge health monitoring. [ABSTRACT FROM AUTHOR]

The formation dip angle is an important characteristic parameter that reflects underground structures, and it is widely used in seismic exploration and geological interpretation. However, the routine dip angle calculation method suffers from the problems of artifact interference and insufficient accuracy, which restricts the development of fine exploration technology. More accurately obtaining the formation dip angle has become a general concern. To solve these problems, this paper proposes a method for calculating the formation dip angle using deep learning. The dip angle calculation is considered a regression problem. By establishing a database of synthetic seismic data and dip data tags, data-driven fitting of nonlinear functional relationships between the seismic data and dip angles are achieved, and intelligent dip calculations are realized. The method is verified using the synthetic sedimentary model and actual data and is compared with the mainstream dip angle calculation method in the industry. The results show that this method more realistically reflects the undulating characteristics of a structure with both high accuracy and anti-interference ability. [ABSTRACT FROM AUTHOR]

In the current era, remote sensing is a powerful platform for detecting and predicting landslides. Moreover, the advancement in computing technologies has proven significant in artificial intelligence (AI) research. Researchers have made significant attempts in the existing literature by introducing landslide detection procedures from remote sensing images (RSIs) through deep learning (DpLr) algorithms. This research work aims to survey those methods. Our database consists of 204 published research articles. In addition, 50% (approximately) of the papers are directly related to landslide information extraction from satellite and unmanned aerial vehicles (UAV) images exploiting DpLr models. The suggested methods have been categorized into seven parts based on the applied model. Further, the evaluation methods have been discussed. The quantitative results are based on the following parameters: (1) contributing nations, (2) key study locations, (3) data set distribution, and (4) model utilization. Lastly, challenges in the studies of DpLr algorithms and the opportunities in landslide detection problems are discussed to motivate future research. [ABSTRACT FROM AUTHOR]

Background: Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, due to the variations in their modes of action, identification of effective AEDs often relies on ad hoc trials, which is particularly challenging for pediatric patients. Thus, there is significant value in computational methods capable of assisting in the selection of AEDs, aiming to minimize unnecessary medication and improve treatment efficacy. Results: In this study, we collected 7,507 medical records from 1,000 pediatric epilepsy patients and developed a computational clinical decision-supporting system for AED selection. This system leverages three multi-channel convolutional neural network (CNN) models tailored to three specific AEDs (vigabatrin, prednisolone, and clobazam). Each CNN model predicts whether a respective AED is effective on a given patient or not. The CNN models showed AUROCs of 0.90, 0.80, and 0.92 in 10-fold cross-validation, respectively. Evaluation on a hold-out test dataset further revealed positive predictive values (PPVs) of 0.92, 0.97, and 0.91 for the three respective CNN models, representing that suggested AEDs by our models would be effective in controlling epilepsy with a high accuracy and thereby reducing unnecessary medications for pediatric patients. Conclusion: Our CNN models in the system demonstrated high PPVs for the three AEDs, which signifies the potential of our approach to support the clinical decision-making by assisting doctors in recommending effective AEDs within the three AEDs for patients based on their medical history. This would result in a reduction in the number of unnecessary ad hoc attempts to find an effective AED for pediatric epilepsy patients. [ABSTRACT FROM AUTHOR]

Intracranial hemorrhage (ICH) is a common and critical disease in clinical, with rapid progression, high disability, and mortality rates. Existing segmentation methods, such as U-Net and TransUNet, perform poorly for the problems in ICH segmentation such as small bleeding lesions, partial volume effects, and edge tissue adhesion with edema in ICH data. To solve these issues more efficiently, a deformable mixed-attention model based on deep supervision for ICH lesion segmentation (DFMA-ICH) is proposed in this study. DFMA-ICH consists of the short-term dense concatenate network (STDC) as the backbone with a mixed-attention method, an attention refining residual module (ARRM), and a mixed feature fusion module (MFFM). The mixed-attention method includes multi-scale spatial attention (MSP) and channel attention mechanism (SE) to extract rich lesion information. The double-pooling attention module (DPA) in ARRM is designed to correct features. In MFFM, different attention modules are constructed to reasonably combine low- and high-level features, and deformable convolution (DConv) is applied for boundary optimization. DFMA-ICH is trained by the deep supervision method to equalize the corresponding outputs at different stages. Overall, DFMA-ICH outperforms other advanced models on both spontaneous and traumatic ICH datasets by transfer learning with the Dice of 86.03, 80.98%, and HD of 12.35, 47.28 mm, respectively. Moreover, DFMA-ICH incurs the lowest time-space cost and exhibits the fastest inference speed. The study confirms that the proposed DFMA-ICH can provide an accurate and efficient method for ICH segmentation. [ABSTRACT FROM AUTHOR]

Highlights A novel image‐driven deep learning approach embedded with model‐data‐knowledge information can directly predict the full field stress distribution and mechanical properties of composites solely based on initial scanning geometry images. The fiber spatial distribution and morphological features are identified by Scanning Electron Microscopy (SEM) initially. An effective random fiber generation algorithm is further utilized to generate images database comprising equivalent geometric models of various microstructures. Subsequently, the geometry model images database is analyzed by fast Fourier transform (FFT) to produce the database including the elastic modulus parameters and full field stress distributions of composites with distinct microstructures. Finally, an innovative image‐learning comprehensive paradigm uniting convolutional neural networks and convolutional autoencoders is elaborated systematically to learn the inherent laws of geometry images and field images. The results show that the proposed method have capacity to effectively and accurately predict the elastic modulus and full field stress distributions combined with error analysis even for stochastic boundary composites. The proposed methodology is a symbiosis of cutting‐edge image learning and non‐destructive testing techniques for composites, which can directly use the local non‐destructive or scanning images of composite structural components to quickly obtain field information and further predict damage evolution online. Proposing a novel deep learning approach combined with FFT directly to predict stress distribution of stochastic boundary composites solely based on micro‐images. Adopting equivalent geometric models dispersed by higher pixels mesh density considering extreme thin interface. Incorporate adaptive algorithms for streamlined training optimization. An innovative integration of deep learning and non‐destructive testing technology for the stress distribution and properties prediction online. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion In radiotherapy, the delineation of the gross tumor volume (GTV) in brain metastases using computed tomography (CT) simulation localization is very important. However, despite the criticality of this process, a pronounced gap exists in the availability of tools tailored for the automatic segmentation of the GTV based on CT simulation localization images.This study aims to fill this gap by devising an effective tool specifically for the automatic segmentation of the GTV using CT simulation localization images.A dual‐network generative adversarial network (GAN) architecture was developed, wherein the generator focused on refining CT images for more precise delineation, and the discriminator differentiated between real and augmented images. This architecture was coupled with the Mask R‐CNN model to achieve meticulous GTV segmentation. An end‐to‐end training process facilitated the integration between the GAN and Mask R‐CNN functionalities. Furthermore, a conditional random field (CRF) was incorporated to refine the initial masks generated by the Mask R‐CNN model to ensure optimal segmentation accuracy. The performance was assessed using key metrics, namely, the Dice coefficient (DSC), intersection over union (IoU), accuracy, specificity, and sensitivity.The GAN+Mask R‐CNN+CRF integration method in this study performs well in GTV segmentation. In particular, the model has an overall average DSC of 0.819 ± 0.102 and an IoU of 0.712 ± 0.111 in the internal validation. The overall average DSC in the external validation data is 0.726 ± 0.128 and the IoU is 0.640 ± 0.136. It demonstrates favorable generalization ability.The integration of the GAN, Mask R‐CNN, and CRF optimization provides a pioneering tool for the sophisticated segmentation of the GTV in brain metastases using CT simulation localization images. The method proposed in this study can provide a robust automatic segmentation approach for brain metastases in the absence of MRI. [ABSTRACT FROM AUTHOR]

Background: X-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice (Oryza sativa) root segments in CT volumes. Results: VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data. Conclusions: A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating. [ABSTRACT FROM AUTHOR]

Timely and effective fault diagnosis is crucial to guarantee the reliability and durability of proton exchange membrane fuel cell (PEMFC). Segmented cell technology has been proven to be a powerful tool for exploring the internal characteristics of fuel cells, yet it is seldom applied for fault diagnosis. This paper proposes a data-driven fault diagnosis method that combines segmented cell and deep learning technology for PEMFC water management fault diagnosis. The dynamic characteristics of the current distribution of the fuel cell under different levels of faults are explored with segmented cell technology. A dual-input convolutional neural network is applied to combine current distribution data and sensor data for a more comprehensive system status for fault diagnosis. The results show that the model can efficiently identify flooding and drying faults with more than 98.5% accuracy, faster and more accurately than other algorithms. This work highlights the significance of segmented cell technology for the comprehension of water management inside PEMFC and provides a new concept for PEMFC fault diagnosis. [Display omitted] • The segmented cell technology is applied to investigate the current distribution during flooding and drying process. • A dual-input CNN is adopted to establish a fault diagnosis model for PEMFC water management. • The proposed method exhibits excellent diagnostic performance, with an accuracy rate of over 98.5%. [ABSTRACT FROM AUTHOR]

Hydraulic infrastructures are susceptible to deformation over time, necessitating reliable monitoring and prediction methods. In this study, we address this challenge by proposing a novel approach based on the combination of Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) methods for Global Navigation Satellite Systems (GNSS) deformation monitoring and prediction modeling. The VMD method is utilized to decompose the complex deformation signals into intrinsic mode functions, which are then fed into a CNN method for feature extraction. The extracted features are input into an LSTM method to capture temporal dependencies and make predictions. The experimental results demonstrate that the proposed VMD-CNN-LSTM method exhibits an improvement by about 75%. This research contributes to the advancement of deformation monitoring technologies in water conservancy engineering, offering a promising solution for proactive maintenance and risk mitigation strategies. [ABSTRACT FROM AUTHOR]

On-shelf availability (OSA) refers to the number of products available in saleable condition to a customer at the place he expects and at the time he wants to buy the product. OSA of the products in retail stores is important to enhance the customer shopping experience and profitability. The products on the retail store shelves are arranged strategically. This arrangement may be changed during shopping, because it is observed that customers may not move the products in the same or at the right place when they pick them up from the shelf. In such real-time scenarios, it would be appropriate to perform shelf image analysis to detect and identify these misplaced objects. This will help the shopkeeper to maintain a large-scale store effortlessly, otherwise it will require massive human effort and labour along with time. Many computer vision-based technology solutions have been developed by researchers to solve this problem. This article discusses a convolutional neural network (CNN)-based method for classifying shelf imagery into the correct semantic classes (that is whether it has misplaced products or not). The proposed architecture was evaluated with a modified COIL-100 dataset. The results of the proposed CNN model were compared with the variant of the proposed model in addition to lightweight pre-trained deep learning models viz; mobilenetV2, densenet121, efficientnetb0, efficientnetb3, and NASNetMobile using transfer learning (TL) concept. According to this study, it has been found that the TL-based MobilenetV2 model is lightweight and achieves better classification performance with 91.28% accuracy. Furthermore, a CNN-based model with 11 user-defined layers provides an accuracy of 90.36%. This shows the efficacy of the proposed models for change detection. [ABSTRACT FROM AUTHOR]

The large number of near-Earth asteroids (NEAs) has greatly impacted human space activities and Earth security. However, detecting NEAs in astronomical images with complex, varying backgrounds is still extremely challenging. In this paper, we propose a deep segmentation assisted asteroid tracking algorithm, termed DSAT, to construct a possible pipeline for faint NEA tracking in astronomical images. First, the single-frame object detection problem is converted to a segmentation problem, enabling robust extraction of faint potential moving objects. Then, a multiframe motion prior-based moving object tracking algorithm is proposed to find real NEAs. We further propose a distance tolerance criterion to help DSAT achieve effective tracking in practical situations when detection has partially failed. Finally, the pipeline is tested with both simulated and real astronomical images at different SNRs and in crowded fields. The results showed that our pipeline has the potential to detect and track faint NEAs in complex backgrounds. Our code is publicly available at https://github.com/zhenhongdu/DeepSegAsteroidTracker. [ABSTRACT FROM AUTHOR]

This paper proposes two efficient approximation methods to solve high-dimensional fully nonlinear partial differential equations (NPDEs) and second-order backward stochastic differential equations (2BSDEs), where such high-dimensional fully NPDEs are extremely difficult to solve because the computational cost of standard approximation methods grows exponentially with the number of dimensions. Therefore, we consider the following methods to overcome this difficulty. For the merged fully NPDEs and 2BSDEs system, combined with the time forward discretization and ReLU function, we use multiscale deep learning fusion and convolutional neural network (CNN) techniques to obtain two numerical approximation schemes, respectively. Finally, three practical high-dimensional test problems involving Allen-Cahn, Black-Scholes-Barenblatt, and Hamilton-Jacobi-Bellman equations are given so that the first proposed method exhibits higher efficiency and accuracy than the existing method, while the second proposed method can extend the dimensionality of the completely NPDEs-2BSDEs system over 400 dimensions, from which the numerical results highlight the effectiveness of proposed methods. [ABSTRACT FROM AUTHOR]

BACKGROUND: Coronary heart disease (CHD) is one of the deadliest diseases and a risk prediction model for cardiovascular conditions is needed. Due to the huge number of features that lead to heart problems, it is often difficult for an expert to evaluate these huge features into account. So, there is a need of appropriate feature selection for the given CHD dataset. For early CHD detection, deep learning modes (DL) show promising results in the existing studies. OBJECTIVE: This study aimed to develop a deep convolution neural network (CNN) model for classification with a selected number of efficient features using the LASSO (least absolute shrinkage and selection operator) technique. Also, aims to compare the model with similar studies and analyze the performance of the proposed model using accuracy measures. METHODS: The CHD dataset of NHANES (National Health and Nutritional Examination Survey) was examined with 49 features using LASSO technique. This research work is an attempt to apply an improved CNN model for the classification of the CHD dataset with huge features CNN model with feature extractor consists of a fully connected layer with two convolution 1D layers, and classifier part consists of two fully connected layers with SoftMax function was trained on this dataset. Metrics like accuracy recall, specificity, and ROC were used for the evaluation of the proposed model. RESULTS: The feature selection was performed by applying the LASSO model. The proposed CNN model achieved 99.36% accuracy, while previous studies model achieved over 80 to 92% accuracy. CONCLUSION: The application of the proposed CNN with the LASSO model for the classification of CHD can speed up the diagnosis of CHD and appears to be effective in predicting cardiovascular disease based on risk features. [ABSTRACT FROM AUTHOR]

BACKGROUND: Lung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, it is difficult to diagnose in the early stages. OBJECTIVE: Image processing techniques developed using machine learning methods have played a crucial role in the development of decision support systems. This study aimed to classify benign and malignant lung lesions with a deep learning approach and convolutional neural networks (CNNs). METHODS: The image dataset includes 4459 Computed tomography (CT) scans (benign, 2242; malignant, 2217). The research type was retrospective; the case-control analysis. A method based on GoogLeNet architecture, which is one of the deep learning approaches, was used to make maximum inference on images and minimize manual control. RESULTS: The dataset used to develop the CNNs model is included in the training (3567) and testing (892) datasets. The model's highest accuracy rate in the training phase was estimated as 0.98. According to accuracy, sensitivity, specificity, positive predictive value, and negative predictive values of testing data, the highest classification performance ratio was positive predictive value with 0.984. CONCLUSION: The deep learning methods are beneficial in the diagnosis and classification of lung cancer through computed tomography images. [ABSTRACT FROM AUTHOR]

In this work, we used the convolutional neural network (CNN) method to invert sea surface height (SSH) from the Global Navigation Satellite System (GNSS) delayed Doppler map (DDM) data during 2009–2017 and compared the CNN inversion data with those obtained from traditional simple random forest (RF) method. SSH observations from the OSTM/Jason-2 satellite were used to judge the merits of the two methods. The results show that both methods yield good SSH inversion results, but when the training set is 9000, the root mean square errors of the SSH inversion results based on the CNN and the RF method are 16.78 and 15.96 respectively; as the training set increases above 9000, the accuracy of the CNN method is significantly better than that of the RF method. This suggests that SSH inversion based on the CNN method will become more advantageous as more data become available. [ABSTRACT FROM AUTHOR]

During the manufacturing process, hot-rolled steel strip surface defects occur frequently. These defects cause economic losses and risks in the use of these products. Therefore, it is crucial to develop automatic inspection systems to identify these defects. In the last few years, computer vision has emerged as an effective tool to identify these defects. Deep learning-based computer vision techniques, especially Convolutional Neural Networks (CNNs), achieved state-of-the-art results for most computer vision tasks, including image classification. These results are obtained using a large amount of data. However, collecting such large datasets in the manufacturing field remains a challenging task. To overcome such a problem, a transfer learning-based framework with multiple CNN variants is proposed in this study. Therefore, different state-of-the-art and widely used pre-trained CNN architectures, including VGG-16, VGG-19, ResNet50, ResNet50V2, InceptionV3, InceptionResNet-V2, MobileNet-V1, MobileNet-V2, MobileNet-V3 Small, and NASNetMobile, combined with transfer learning were investigated to evaluate their performances in classifying hot-rolled steel strips surface defects. The experimental results showed that MobileNet-V2-based and InceptionResNet-V2-based methods achieve better performance than all other models in terms of accuracy, loss, training and inference times, and model size in the case of NEU-CLS dataset. Similarly, MobileNet-V1 and MobileNet-V2 provide the best performances on X-SDD dataset among the adopted models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Assessing the strength of complex networks is crucial for evaluating their functionality and monitoring system security. Two primary perspectives for measuring network robustness are connectivity and controllability, which quantify how well a network maintains connectivity and controllability after experiencing complete attacks. However, conventional robustness calculation methods that use simulation attacks can be impractical for large-scale networks. Recent research has explored the use of deep learning methods for network robustness prediction, including PCR (short for predictor of controllability robustness), which uses a VGG (short for visual geometry group)-based convolutional neural network (CNN) and a linear filter. However, PCR has limitations, including inaccurate edge importance differentiation and fluctuating output curves. This paper presents ATTRP, an attention-based robustness predictor, which addresses these limitations. ATTRP uses a novel convolutional neural network with channel and spatial attention mechanisms to improve prediction accuracy and a robustness filter based on Savitzky–Golay smoothing to rectify the robustness curve. Experimental studies demonstrate that ATTRP outperforms other predictors, with much lower errors, while its computational speed is far superior to that of traditional attacking simulation. Overall, our research provides a more effective and efficient means of predicting network robustness, with potential applications in monitoring system security. [ABSTRACT FROM AUTHOR]

Pulmonary fat embolism (PFE) as a cause of death often occurs in trauma cases such as fractures and soft tissue contusions. Traditional PFE diagnosis relies on subjective methods and special stains like oil red O. This study utilizes computational pathology, combining digital pathology and deep learning algorithms, to precisely quantify fat emboli in whole slide images using conventional hematoxylin-eosin (H&E) staining. The results demonstrate deep learning's ability to identify fat droplet morphology in lung microvessels, achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.98. The AI-quantified fat globules generally matched the Falzi scoring system with oil red O staining. The relative quantity of fat emboli against lung area was calculated by the algorithm, determining a diagnostic threshold of 8.275% for fatal PFE. A diagnostic strategy based on this threshold achieved a high AUC of 0.984, similar to manual identification with special stains but surpassing H&E staining. This demonstrates computational pathology's potential as an affordable, rapid, and precise method for fatal PFE diagnosis in forensic practice. [ABSTRACT FROM AUTHOR]

The proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML) models for mapping plant species in natural environments. However, a critical gap exists in the literature regarding the use of deep learning (DL) techniques that integrate MS data and vegetation indices (VIs) with different feature extraction techniques to map invasive species in complex natural environments. This research addresses this gap by focusing on mapping the distribution of the Broad-leaved pepper (BLP) along the coastal strip in the Sunshine Coast region of Southern Queensland in Australia. The methodology employs a dual approach, utilising classical ML models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) in conjunction with the U-Net DL model. This comparative analysis allows for an in-depth evaluation of the performance and effectiveness of both classical ML and advanced DL techniques in mapping the distribution of BLP along the coastal strip. Results indicate that the DL U-Net model outperforms classical ML models, achieving a precision of 83%, recall of 81%, and F1–score of 82% for BLP classification during training and validation. The DL U-Net model attains a precision of 86%, recall of 76%, and F1–score of 81% for BLP classification, along with an Intersection over Union (IoU) of 68% on the separate test dataset not used for training. These findings contribute valuable insights to environmental conservation efforts, emphasising the significance of integrating MS data with DL techniques for the accurate mapping of invasive plant species. [ABSTRACT FROM AUTHOR]

Low-dose computed tomography (LDCT) technology has attracted more and more attention in the field of medical imaging because of the reduction of radiation damage to the human body. However, the large amount of quantum noise contained in LDCT images can affect physicians' judgment. To solve the problem of large amounts of quantum noise and artifacts in LDCT images, a convolutional neural network denoising model based on cartoon texture decomposition of images (CATCNN) is developed in this study based on deep learning. The model first uses a U-Net-based image decomposition sub-network to decompose LDCT images into cartoon images and texture images. The texture images are then denoised using a texture denoising sub-network based on edge protection and Efficient Channel Attention, finally, cartoon images are summed with the denoised texture images to obtain images with improved quality. Our experimental results demonstrate that the proposed model outperforms existing technologies, achieving a peak signal-to-noise ratio value of 33.4666 dB and a structural similarity value of 0.9193. The visual and quantitative evaluation results suggest that the CATCNN model effectively improves the quality of LDCT images. [ABSTRACT FROM AUTHOR]

The precise identification of drug–protein inter action (DPI) can significantly speed up the drug discovery process. Bioassay methods are time-consuming and expensive to screen for each pair of drug proteins. Machine-learning-based methods cannot accurately predict a large number of DPIs. Compared with traditional computing methods, deep learning methods need less domain knowledge and have strong data learning ability. In this study, we construct a DPI prediction model based on dual channel neural networks with an efficient path attention mechanism, called DCA-DPI. The drug molecular graph and protein sequence are used as the data input of the model, and the residual graph neural network and the residual convolution network are used to learn the feature representation of the drug and protein, respectively, to obtain the feature vector of the drug and the hidden vector of protein. To get a more accurate protein feature vector, the weighted sum of the hidden vector of protein is applied using the neural attention mechanism. In the end, drug and protein vectors are concatenated and input into the full connection layer for classification. In order to evaluate the performance of DCA-DPI, three widely used public data, Human, C.elegans and DUD-E, are used in the experiment. The evaluation metrics values in the experiment are superior to other relevant methods. Experiments show that our model is efficient for DPI prediction. [ABSTRACT FROM AUTHOR]

Development of a reliable and intelligent traffic monitoring system is highly desired to improve the transportation safety and establish future transportation plans due to the fast growth of vehicle population. Vehicle classification is one of the most critical subsystems where existing ones suffer from privacy concerns, requirements of complicated systems, and high maintenance cost. This paper reports a novel vehicle classification method by utilizing a triboelectric sensor to accurately identify vehicles. Novelty of this method originates from using triboelectric sensor and machine learning method with important advantages over current alternatives by providing an easy installation, simple operation, noninvasive measurement, cost-effective manufacturing, and highly accurate classification. To make a classification, vehicle toys' signals were acquired from triboelectric sensor and then applied to a deep learning algorithm. The 1932 sensor output data were grouped into a set of seven vehicle toys with different wheelbases, and number of tires passing on are used to train and optimize 1D-CNN model. The utilized 1D-CNN model achieved accuracy, f1-score, precision, and recall as 96.38%, 0.9638, 0.9658, and 0.9637, respectively. [ABSTRACT FROM AUTHOR]

The progress of incorporating deep learning in the field of medical image interpretation has been greatly hindered due to the tremendous cost and time associated with generating ground truth for supervised machine learning, alongside concerns about the inconsistent quality of images acquired. Active learning offers a potential solution to these problems of expanding dataset ground truth by algorithmically choosing the most informative samples for ground truth labeling. Still, this effort incurs the costs of human labeling, which needs minimization. Furthermore, automatic labeling approaches employing active learning often exhibit overfitting tendencies while selecting samples closely aligned with the training set distribution and excluding out-of-distribution samples, which could potentially improve the model's effectiveness. We propose that the majority of out-of-distribution instances can be attributed to inconsistent cross images. Since the FDA approved the first whole-slide image system for medical diagnosis in 2017, whole-slide images have provided enriched critical information to advance the field of automated histopathology. Here, we exemplify the benefits of a novel deep learning strategy that utilizes high-resolution whole-slide microscopic images. We quantitatively assess and visually highlight the inconsistencies within the whole-slide image dataset employed in this study. Accordingly, we introduce a deep learning-based preprocessing algorithm designed to normalize unknown samples to the training set distribution, effectively mitigating the overfitting issue. Consequently, our approach significantly increases the amount of automatic region-of-interest ground truth labeling on high-resolution whole-slide images using active deep learning. We accept 92% of the automatic labels generated for our unlabeled data cohort, expanding the labeled dataset by 845%. Additionally, we demonstrate expert time savings of 96% relative to manual expert ground-truth labeling. [ABSTRACT FROM AUTHOR]

Dental age estimation, a cornerstone in forensic age assessment, has been extensively tried and tested, yet manual methods are impeded by tedium and interobserver variability. Automated approaches using deep transfer learning encounter challenges like data scarcity, suboptimal training, and fine-tuning complexities, necessitating robust training methods. This study explores the impact of convolutional neural network hyperparameters, model complexity, training batch size, and sample quantity on age estimation. EfficientNet-B4, DenseNet-201, and MobileNet V3 models underwent cross-validation on a dataset of 3896 orthopantomograms (OPGs) with batch sizes escalating from 10 to 160 in a doubling progression, as well as random subsets of this training dataset. Results demonstrate the EfficientNet-B4 model, trained on the complete dataset with a batch size of 160, as the top performer with a mean absolute error of 0.562 years on the test set, notably surpassing the MAE of 1.01 at a batch size of 10. Increasing batch size consistently improved performance for EfficientNet-B4 and DenseNet-201, whereas MobileNet V3 performance peaked at batch size 40. Similar trends emerged in training with reduced sample sizes, though they were outperformed by the complete models. This underscores the critical role of hyperparameter optimization in adopting deep learning for age estimation from complete OPGs. The findings not only highlight the nuanced interplay of hyperparameters and performance but also underscore the potential for accurate age estimation models through optimization. This study contributes to advancing the application of deep learning in forensic age estimation, emphasizing the significance of tailored training methodologies for optimal outcomes. [ABSTRACT FROM AUTHOR]

Bone tumors are one of the most common diseases in bone and soft tissue tumors, and accurate classification is crucial for developing effective treatment strategies. However, traditional pathological morphology diagnosis is subjective and uncertain, requiring highly specialized knowledge and experience. Therefore, how to efficiently and accurately classify bone tumor types based on pathological images is an urgent problem to be solved. In this study, a lightweight convolutional neural network (CNN) model called Efficient and Enhance Network (EENet) was proposed for the automatic recognition of bone tumor pathological images. The model introduced efficient channel attention (ECA) blocks to improve the efficiency of feature extraction and reduce computational complexity. A comprehensive dataset of bone pathological images, including five types of bone lesion tissues and normal bone tissue, was compiled for the research. Due to the low incidence of bone tumors and the difficulty in obtaining a large amount of data, the transfer learning method was chosen to overcome the problem of limited data volume. The experimental results from the fivefold cross‐validation demonstrate that the proposed model achieves 99.06% accuracy, 99.08% precision, 99.19% recall, 99.81% specificity, and 99.03% F1‐score in the bone tumor classification task, highlighting its potential as a clinical diagnostic tool. [ABSTRACT FROM AUTHOR]

In recent years, although significant progress has been made in infrared and visible image fusion, existing methods typically assume that the source images have been rigorously registered or aligned prior to image fusion. However, the difference in modalities of infrared and visible images poses a great challenge to achieve strict alignment automatically, affecting the quality of the subsequent fusion procedure. To address this problem, this paper proposes a deep learning framework for misaligned infrared and visible image fusion, aiming to free the fusion algorithm from strict registration. Technically, we design a convolutional neural network (CNN)-Transformer Hierarchical Interactive Embedding (CTHIE) module, which can combine the respective advantages of CNN and Transformer, to extract features from the source images. In addition, by characterizing the correlation between the features extracted from misaligned source images, a Dynamic Re-aggregation Feature Representation (DRFR) module is devised to align the features with a self-attention-based feature re-aggregation scheme. Finally, to effectively utilize the features at different levels of the network, a Fully Perceptual Forward Fusion (FPFF) module via interactive transmission of multi-modal features is introduced for feature fusion to reconstruct the fused image. Experimental results on both synthetic and real-world data demonstrate the effectiveness of the proposed method, verifying the feasibility of directly fusing infrared and visible images without strict registration. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) has emerged as the most favorable source for recognizing brain disorders like epileptic seizure (ES) using deep learning (DL) methods. This study investigated the well-performed EEG-based ES detection method by decomposing EEG signals. Specifically, empirical mode decomposition (EMD) decomposes EEG signals into six intrinsic mode functions (IMFs). Three distinct features, namely, fluctuation index, variance, and ellipse area of the second order difference plot (SODP), were extracted from each of the IMFs. The feature values from all EEG channels were arranged in two composite feature forms: a 1D (i.e., unidimensional) form and a 2D image-like form. For ES recognition, the convolutional neural network (CNN), the most prominent DL model for 2D input, was considered for the 2D feature form, and a 1D version of CNN was employed for the 1D feature form. The experiment was conducted on a benchmark CHB-MIT dataset as well as a dataset prepared from the EEG signals of ES patients from Prince Hospital Khulna (PHK), Bangladesh. The 2D feature-based CNN model outperformed the other 1D feature-based models, showing an accuracy of 99.78% for CHB-MIT and 95.26% for PHK. Furthermore, the cross-dataset evaluations also showed favorable outcomes. Therefore, the proposed method with 2D composite feature form can be a promising ES detection method. [ABSTRACT FROM AUTHOR]

Background: Glaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), convolutional neural networks (CNNs), and image processing for accurate glaucoma detection using medical imaging data, surpassing prior research efforts. Method: Developing a hybrid glaucoma detection framework using CNNs (ResNet50, VGG-16) and Random Forest. Models analyze pre-processed retinal images independently, and post-processing rules combine predictions for an overall glaucoma impact assessment. Result: The hybrid framework achieves a significant 95.41% accuracy, with precision and recall at 99.37% and 88.37%, respectively. The F1 score, balancing precision and recall, reaches a commendable 93.52%. These results highlight the robustness and effectiveness of the hybrid framework in accurate glaucoma diagnosis. Conclusion: In summary, our research presents an innovative hybrid framework combining CNNs and traditional ML models for glaucoma detection. Using ResNet50, VGG-16, and Random Forest in an ensemble approach yields remarkable accuracy, precision, recall, and F1 score. These results showcase the methodology's potential to enhance glaucoma diagnosis, emphasizing its promising role in early detection and preventing irreversible vision loss. The integration of ML and DNNs in medical imaging analysis suggests a valuable path for future advancements in ophthalmic healthcare. [ABSTRACT FROM AUTHOR]

Background: Most of the important biological mechanisms and functions of transmembrane proteins (TMPs) are realized through their interactions with non-transmembrane proteins(nonTMPs). The interactions between TMPs and nonTMPs in cells play vital roles in intracellular signaling, energy metabolism, investigating membrane-crossing mechanisms, correlations between disease and drugs. Results: Despite the importance of TMP-nonTMP interactions, the study of them remains in the wet experimental stage, lacking specific and comprehensive studies in the field of bioinformatics. To fill this gap, we performed a comprehensive statistical analysis of known TMP-nonTMP interactions and constructed a deep learning-based predictor to identify potential interactions. The statistical analysis describes known TMP-nonTMP interactions from various perspectives, such as distributions of species and protein families, enrichment of GO and KEGG pathways, as well as hub proteins and subnetwork modules in the PPI network. The predictor implemented by an end-to-end deep learning model can identify potential interactions from protein primary sequence information. The experimental results over the independent validation demonstrated considerable prediction performance with an MCC of 0.541. Conclusions: To our knowledge, we were the first to focus on TMP-nonTMP interactions. We comprehensively analyzed them using bioinformatics methods and predicted them via deep learning-based solely on their sequence. This research completes a key link in the protein network, benefits the understanding of protein functions, and helps in pathogenesis studies of diseases and associated drug development. [ABSTRACT FROM AUTHOR]