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Smart machine tools must be able to recognise the materials they interact with, in order to independently decide on what actions to be taken whenever needed. The purpose of this research is to offer a generic method to automate material identification and classification task utilising image processing and machine learning techniques so as to improve the cognitive capabilities of machine tools. A dataset of four material surfaces; Copper, Aluminium, Stainless Steel, and Bronze is generated and the RGB data are extracted. These colour channels are utilised as input features to train the machine learning algorithms. Convolutional Neural Network (CNN) and other classification methods like Support Vector Machine (SVM), Decision Trees, and k-Nearest Neighbour are also used to classify material images based on the generated dataset. A unique generalised technique, based on CNN, has been proposed to accurately recognise and categorise round surfaced materials during machining. The accuracy achieved by the classifier has reached 100% during training and testing. Test images are used to confirm the proposed methodology's capability to distinguish between materials based on various illumination environments and camera positions. [ABSTRACT FROM AUTHOR]

With the advances in smart sensing and data mining technologies of Industry 4.0, condition monitoring of key equipment in manufacturing has brought transformations in production and maintenance management. However, in practical applications, noise from both the working environment and the sensing devices is inevitable, which causes the low performance of data-driven fault diagnosis. To address this challenge, the paper develops a robust two-stage joint denoising method by integrating ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA), with fuzzy entropy discriminant as a threshold. The developed method can filter noisy components from decomposed modal components and reconstruct a new signal with denoised independent components. Moreover, an improved convolutional neural network (CNN) model based on the VGG structure has been constructed as a classifier to achieve end-to-end fault diagnosis. The experimental results demonstrate the high accuracy and superior anti-interference capability of the proposed method for rolling bearing fault diagnosis under various noise levels. Compared with state-of-the-art denoising methods and fault diagnosis methods, the proposed method achieves higher accuracy and robustness under variable noise interference. The proposed method can be applied to broader fault diagnosis tasks of production equipment in complex practical environments. [ABSTRACT FROM AUTHOR]

ObjectiveFast and accurate deformable image registration (DIR) that include DIR uncertainty estimation are essential for safe and reliable clinical deployment. While recent deep learning models have shown promise in predicting DIR with its uncertainty, challenges persist in proper uncertainty evaluation and hyperparameter optimization for these methods. This work aims to develop and evaluate a model that can perform fast DIR and predict its uncertainty in seconds.ApproachIn this study, we introduce a novel probabilistic multi-resolution image registration model utilizing convolutional neural networks (CNNs) to estimate a multivariate normal distributed dense displacement field (DDF) in a multimodal image registration problem. To assess the quality of the DDF distribution predicted by the model, we propose a new metric based on the Kullback-Leibler divergence. The performance of our approach was evaluated against three other DIR algorithms (VoxelMorph, Monte Carlo Drop-Out, and Monte Carlo B-splines) capable of predicting uncertainty. The evaluation of the models included not only the quality of the deformation but also the reliability of the estimated uncertainty. Our application investigated registration of a treatment planning computed tomography (CT) to follow-up cone beam CT for daily adaptive radiotherapy.Main resultsThe hyperparameter tuning of the models showed that there is a trade-off between the reliability of the estimated uncertainty and the accuracy of the deformation. In the optimal trade-off our model excelled in contour propagation and uncertainty estimation (p < 0.01) compared to existing uncertainty estimation models. We obtained an average dice similarity coefficient of 0.89 and a KL-divergence of 0.15.SignificanceBy addressing challenges in DIR uncertainty estimation and evaluation, our work showed that both the DIR and its uncertainty can be reliably predicted paving the way for safe deployment in clinical environment..

Due to the harsh working environments of certain bridges, the bridge structural health monitoring systems (SHMs) are prone to error warning because of anomaly data. Therefore, it is of great significance to accurately classify and locate the anomaly data for effectively addressing these issues. This paper proposes a method for classifying and locating anomaly data utilizing one-dimensional monitoring data based on convolutional neural networks. Compared to previous research reliant on visual features, the proposed method has lower computational costs. By incorporating the anomaly data localization network, manual localization operations for data restoration are replaced. The analysis in this paper is based on monitoring data from a large-span cable-stayed bridge, along with artificially generated anomaly data. The two neural network frameworks proposed in this paper are trained and validated, showcasing precise classification and localization of anomaly data. Furthermore, the paper discusses the impact of common errors in labeling data categories and locating training samples in practical operations. The results demonstrate that even in the presence of noticeable yet non-extreme labeling errors in the training set, the proposed method still achieves accurate classification and localization, highlighting its robustness. [ABSTRACT FROM AUTHOR]

As the globe struggles to recover from COVID-19, the monkeypox virus has emerged as a new global pandemic threat. Monkeypox cases are still being reported daily from different nations despite the virus not being as harmful or contagious as COVID-19. As a result, the possibility of another worldwide pandemic occurring directly due to a lack of adequate preventative measures will not come as a complete shock to everyone. Diagnosing Monkeypox in its early stages may be challenging because it resembles chickenpox and measles. When confirmatory Polymerase Chain Reaction assays are not readily available, monitoring suspected cases and swiftly detecting them may be possible with computer-assisted detection of monkeypox lesions. Recent research has shown that deep learning models have significant promise for image-based diagnostics, including cancer diagnosis, identifying tumor cells, and detecting COVID-19 patients. To address these challenges, we built a deep learning model based on transfer learning that can assist medical professionals and other individuals in determining whether they are suffering from Monkeypox. The InceptionV3 model utilized in this study was trained with the publicly accessible Monkeypox dataset. During the studies, the model attained an accuracy of 98%. [ABSTRACT FROM AUTHOR]

Considering the noise impact in the bearing operating environment and the time-consuming and non-universal design of traditional diagnostic algorithms, this paper proposes a new model for rolling bearing fault diagnosis, which uses convolutional pooling group (CPG) to extract features from data, At the same time, expanding the dual convolutional kernel to obtain a larger receptive field obtained the WCPGCNN (A CPG Convolutional Neural Network with Wide Convolutional Kernel as the First Lay) model based on the CPG network architecture. Firstly, the fault features of the input signal are automatically extracted through four convolutional pooling groups; Next, fault features are further extracted using the fully connected layer, and finally input into the Softmax layer for fault identification. By utilizing algorithms such as Adam, dropout, and batch normalization, the model performs well in terms of accuracy, noise resistance, and timeliness, while also possessing good cross dataset high generalization ability. This article uses the rolling bearing fault standard data from Case Western Reserve University (CWRU) and the American Society for Mechanical Fault Prevention Technology (MFPT), and verifies through multiple controlled experiments that the model established in this article has high accuracy and good generalization characteristics. [ABSTRACT FROM AUTHOR]

In the fast modernised world, usage of vehicles increased day by day, leads to vehicle traffic and congestions. Traditional way of monitoring traffic is less efficient and requires man power. Besides, safety of traffic controllers is the major concern in the manual monitoring. For that reason, effective prediction of vehicular traffic and monitoring the level of congestion is significant in VANET (Vehicular Adhoc Network) for mitigating the delays and danger of accidents. In order to make clear prediction of vehicles and the collision free path in the network, numerous existing algorithms have been provided for prediction and classification. However, the conventional techniques have faced complications in satisfying the accuracy while making predictions of path images. The accuracy in prediction along with the faster computational time have been addressed as a drawback which hinders in making appropriate travel path decisions. In order to overcome such challenges, proposed system employed Yolo v5 algorithm, GGA (Greedy based Genetic Algorithm) and Deep CNN (Convolutional Neural Network) with LSTM (Long Short Term Memory) for traffic monitoring and congestion control in VANET. The Yolo v5 algorithm is utilised for the vehicle prediction mechanism, for the capability of detection with high speed and accuracy. GGA is utilised for the feature selection for the capability of handling numerous features in data and to increase the computational speed. The Deep CNN with LSTM is utilised for Classification for the capability of handling larger datasets and to enhance the accuracy. Though, DCNN is the effective algorithm for classification, it has few limitations like slow processing time. To resolve this, DCNN is utilised with LSTM for enhancing the speed in the projected system. Vehicle detection dataset is used in the proposed system. Further, the experimental evaluations and comparative analysis exhibits the efficiency of the system in terms of accuracy, precision, recall and F1 score when compared to several existing algorithms. The proposed model holds the potential in efficient vehicular congestion management with expected accuracy and it is intended to contribute in to the path identification for vehicles in VANET applications. [ABSTRACT FROM AUTHOR]

Female mortality is frequently caused by ovarian cancer (OC). Because of its late detection, ovarian cancer seems to have a low survival rate, and new techniques are required for its early identification. One of the more prevalent gynecologic cancers is ovarian cancer. The various diagnoses of ovarian cancer depend on the efficient classification of the various forms. Patients with ovarian tumours require accurate diagnosis. When compared to a deep convolutional neural network, previous neural networks are an outmoded technology that offers fewer characteristics, which demonstrates that deep convolutional layers supply essential and healthy features. To get over these limitations, ovarian tumours are identified using the krill herd optimization-based convolutional neural network (KHO-CNN) mechanism, a novel optimized deep neural network approach. The system analyses datasets related to ultrasound-detected ovarian cancer. The obtained real-world ultrasound images of ovarian cancer also contain additional noise, which is removed using a Wavelet Transform. An enhanced KHO model has been used in the segmentation process. Features were extracted by use of a local binary pattern. Ovarian tumours are classified as benign, malignant, or normal by the KHO-CNN. To identify ovarian cancers using deep learning techniques that utilize optimised convolutional neural networks, this model was developed and utilised with a set. [ABSTRACT FROM AUTHOR]

Breast cancer poses a threat to women's health and contributes to an increase in mortality rates. Mammography has proven to be an effective tool for the early detection of breast cancer. However, it faces many challenges in early breast cancer detection due to poor image quality, traditional segmentation, and feature extraction. Therefore, this work addresses these issues and proposes an attention-based backpropagation convolutional neural network (ABB-CNN) to detect breast cancer from mammogram images more accurately. The proposed work includes image enhancement, reinforcement learning-based semantic segmentation (RLSS), and multiview feature extraction and classification. The image enhancement is performed by removing noise and artefacts through a hybrid filter (HF), image scaling through a pixel-based bilinear interpolation (PBI), and contrast enhancement through an election-based optimization (EO) algorithm. In addition, the RLSS introduces intelligent segmentation by utilizing a deep Q network (DQN) to segment the region of interest (ROI) strategically. Moreover, the proposed ABB-CNN facilitates multiview feature extraction from the segmented region to classify the mammograms into normal, malignant, and benign classes. The proposed framework is evaluated on the collected and the digital database for screening mammography (DDSM) datasets. The proposed framework provides better outcomes in terms of accuracy, sensitivity, specificity, precision, f-measure, false-negative rate (FNR) and area under the curve (AUC). This work achieved (99.20%, 99.35%), (99.56%, 99.66%), (98.96%, 98.99%), (99.05%, 99.12%), (0.44%, 0.34%), (99.31%, 99.39%) and (99.27%, 99.32%) of accuracy, sensitivity, specificity, precision, FNR, f-measure and AUC on (collected, DDSM datasets), respectively. This research addresses the prevalent challenges in breast cancer identification and offers a robust and highly accurate solution by integrating advanced deep-learning techniques. The evaluated results reveal the proposed framework's potential in early breast cancer detection. [ABSTRACT FROM AUTHOR]

3D neuromorphic hardware system is first demonstrated in neuromorphic application as on‐chip level by integrating array devices with CMOS circuits after wafer bonding (WB) and interconnection process. The memory window of synaptic device is degraded after WB and 3 Dimesional (3D) integration due to process defects and thermal stress. To address this degradation, Ag diffusion in materials of Ta2O5 and HfO2 is studied in a volatile memristor, furthermore, the interconnection and gate metal Ru are investigated to reduce defective traps of gate interface in non‐volatile memory devices. As a result, a memory window is improved over 106 in both types of devices. Improved and 3D integrated 12 × 14 array devices are identified in the synaptic characteristics according to the change of the synaptic weight from the interconnected Test Element Group (TEG) of the Complementary Metal Oxide Semiconductor (CMOS) circuits. The trained array devices present recognizable image of letters, achieving an accuracy rate of 92% when utilizing a convolutional neural network, comparing the normalized accuracy of 93% achieved by an ideal synapse device. This study proposes to modulate the memory windows up to 106 in an integrated hardware‐based neural system, considering the possibility of device degradation in both volatile and non‐volatile memory devices demonstrated by the hardware neural system. [ABSTRACT FROM AUTHOR]

A key study area throughout the medical sector for image processing and analysis is medical image segmentation. The diagnosis and treatment strategies of doctors may have a solid foundation owing to accurate and effective medical image segmentation. Conventional approaches in this field rely on manual feature extraction, which makes segmentation complex, costs doctors’ time and energy, and involves a subjective evaluation that is readily susceptible to diagnostic errors. Researchers have applied convolutional neural network-based deep learning techniques to the segmentation of medical images as a result of their impressive advancements and successes in the field of computer vision. The research described here uses the U-Net network’s outstanding feature learning capabilities and end-to-end processing mode for lung CT image segmentation via fully convolutional network (FCN) research. However, focusing on valuable, crucial information aspects in the U-Net network is challenging. This study employed multilevel attention mechanisms on the basis of U-Net networks to enhance the model’s accuracy in lung CT image segmentation. These mechanisms were inspired by attention mechanisms. By improving the segmentation accuracy and optimizing the segmentation effect, the new model embeds a self-attention module in front of each upsampling layer in the U-Net model. This module provides more detailed information by stitching the self-attention module of the original image and then suppresses irrelevant and redundant information by using the effect of feature extraction of the upsampling layer. Several additional comparative experiments were conducted on the 2019nCoVR dataset. The outcomes demonstrate the efficacy of the optimized model described in this paper and its application results in improved segmentation effects in lung CT images. Additionally, the new model has distinct advantages over existing approaches that are typical of medical image segmentation, which represent its higher level of lung CT image segmentation. [ABSTRACT FROM AUTHOR]

Resistance spot welding (RSW) is considered a preferred technique for joining metal parts in various industries, mainly for its efficiency and cost-effectiveness. The mechanical properties of spot welds are pivotal in ensuring structural integrity and overall assembly performance. In this work, the quality attributes of resistance spot welding, such as both nugget and corona bond sizes, are assessed by analyzing the thermal behavior of the joint using a physical information neural network (PINN). Starting from the thermal signal phase gradient and amplitude gradient maps, a convolutional neural network (CNN) estimates the size of nuggets and corona bonds. The CNN architecture is based on the Inception V3 architecture, a state-of-the-art neural network that excels in image recognition tasks. This study suggests adopting a new methodology for automatic RSW quality control based on thermal signal analysis. [ABSTRACT FROM AUTHOR]

Integrating Lean Manufacturing tools with artificial intelligence (AI) is emerging as a revolutionary approach to optimize production processes, reduce waste, and enhance efficiency. Traditional Lean practices focus on waste reduction and process improvement, often relying on human expertise for problem identification and resolution. AI algorithms, on the other hand, excel in pattern recognition, data analysis, and decision-making. Lean tools and AI can offer more precise, data-driven solutions for common manufacturing challenges when integrated. AI algorithms can automate and refine Lean techniques like value stream mapping, Kanban, and 5S by providing real-time, actionable insights drawn from big data. This fusion of Lean and AI aids in predictive maintenance, quality control, and optimization, enhancing the efficiency and responsiveness of the manufacturing process. Moreover, AI's capability for machine learning allows the system to adapt and improve autonomously over time, further aligning with Lean's continuous improvement ethos. Seven case studies were conducted to show how this alignment might aid Lean Manufacturing. However, successful implementation necessitates overcoming data quality and algorithmic bias challenges. Despite these hurdles, integrating Lean tools and AI can redefine best practices in manufacturing, setting new standards for operational excellence. [ABSTRACT FROM AUTHOR]

Introduction: The cardiothoracic ratio (CTR) based on postero-anterior chest X-rays (P-A CXR) images is one of the most commonly used cardiac measurement methods and an indicator for initially evaluating cardiac diseases. However, the hearts are not readily observable on P-A CXR images compared to the lung fields. Therefore, radiologists often manually determine the CTR's right and left heart border points of the adjacent left and right lung fields to the heart based on P-A CXR images. Meanwhile, manual CTR measurement based on the P-A CXR image requires experienced radiologists and is timeconsuming and laborious. Methods: Based on the above, this article proposes a novel, fully automatic CTR calculation method based on lung fields abstracted from the P-A CXR images using convolutional neural networks (CNNs), overcoming the limitations to heart segmentation and avoiding errors in heart segmentation. First, the lung field mask images are abstracted from the P-A CXR images based on the pre-trained CNNs. Second, a novel localization method of the heart's right and left border points is proposed based on the two-dimensional projection morphology of the lung field mask images using graphics. Results: The results show that the mean distance errors at the x-axis direction of the CTR's four key points in the test sets T1 (21 × 512 × 512 static P-A CXR images) and T2 (13 × 512 × 512 dynamic P-A CXR images) based on various pre-trained CNNs are 4.1161 and 3.2116 pixels, respectively. In addition, the mean CTR errors on the test sets T1 and T2 based on four proposed models are 0.0208 and 0.0180, respectively. Discussion: Our proposed model achieves the equivalent performance of CTR calculation as the previous CardioNet model, overcomes heart segmentation, and takes less time. Therefore, our proposed method is practical and feasible and may become an effective tool for initially evaluating cardiac diseases. [ABSTRACT FROM AUTHOR]

Epileptic seizure (ES) detection is an active research area, that aims at patient-specific ES detection with high accuracy from electroencephalogram (EEG) signals. The early detection of seizure is crucial for timely medical intervention and prevention of further injuries of the patients. This work proposes a robust deep learning framework called HyEpiSeiD that extracts self-trained features from the pre-processed EEG signals using a hybrid combination of convolutional neural network followed by two gated recurrent unit layers and performs prediction based on those extracted features. The proposed HyEpiSeiD framework is evaluated on two public datasets, the UCI Epilepsy and Mendeley datasets. The proposed HyEpiSeiD model achieved 99.01% and 97.50% classification accuracy, respectively, outperforming most of the state-of-the-art methods in epilepsy detection domain. [ABSTRACT FROM AUTHOR]

Introduction: Developing effective breast cancer survival prediction models is critical to breast cancer prognosis. With the widespread use of next-generation sequencing technologies, numerous studies have focused on survival prediction. However, previous methods predominantly relied on single-omics data, and survival prediction using multi-omics data remains a significant challenge. Methods: In this study, considering the similarity of patients and the relevance of multi-omics data, we propose a novel multi-omics stacked fusion network (MSFN) based on a stacking strategy to predict the survival of breast cancer patients. MSFN first constructs a patient similarity network (PSN) and employs a residual graph neural network (ResGCN) to obtain correlative prognostic information from PSN. Simultaneously, it employs convolutional neural networks (CNNs) to obtain specificity prognostic information from multiomics data. Finally, MSFN stacks the prognostic information from these networks and feeds into AdaboostRF for survival prediction. Results: Experiments results demonstrated that our method outperformed several state-of-the-art methods, and biologically validated by Kaplan-Meier and t-SNE. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disease that is well-known for causing continuous loss of memory, cognition, and other higher brain functions. AD is not a single disease, but rather a group of related diseases with similar characteristics. The use of deep neural network-based pattern classification techniques, such as convolutional neural networks, is effective in classifying patients into different sub-types of AD and in distinguishing the different stages of severity of the disease. in the medical field, early detection of its start can be quite beneficial. This article focuses on the early detection of various stages of cognitive aging and AD using neuroimaging and transfer learning (TL). Images of imagery via resonance magnetic (IRM) obtained from a Kaggle database called Alzheimer's Dataset (4 class of Images) with several classes of non-dementia (NONDEM), very mild dementia(VERDEM), mild dementia(MILDEM), moderate dementia(MODDEM) are classified using a transfer learning approach. In this work, we compare the classification performance of six pre-trained networks, which are VGG-19, VGG-16, ResNet-50, InceptionV3, Xception, and DenseNet169. They were enthralled and tested using 6400 images from the Kaggle data pool. The confusion matrix and its parameters are used to assess the classification performance of these six networks. VGG-19, VGG-16, Inception-V3, Xception, ResNet-50, and DenseNet169 all have 92.86%, 92.83%, 91.04%, 90.57%, 85.99%, and 88.64% overall precision in MA detection, respectively. [ABSTRACT FROM AUTHOR]

This study investigated the usefulness of deep learning-based automatic detection of temporomandibular joint (TMJ) effusion using magnetic resonance imaging (MRI) in patients with temporomandibular disorder and whether the diagnostic accuracy of the model improved when patients' clinical information was provided in addition to MRI images. The sagittal MR images of 2948 TMJs were collected from 1017 women and 457 men (mean age 37.19 ± 18.64 years). The TMJ effusion diagnostic performances of three convolutional neural networks (scratch, fine-tuning, and freeze schemes) were compared with those of human experts based on areas under the curve (AUCs) and diagnosis accuracies. The fine-tuning model with proton density (PD) images showed acceptable prediction performance (AUC = 0.7895), and the from-scratch (0.6193) and freeze (0.6149) models showed lower performances (p < 0.05). The fine-tuning model had excellent specificity compared to the human experts (87.25% vs. 58.17%). However, the human experts were superior in sensitivity (80.00% vs. 57.43%) (all p < 0.001). In gradient-weighted class activation mapping (Grad-CAM) visualizations, the fine-tuning scheme focused more on effusion than on other structures of the TMJ, and the sparsity was higher than that of the from-scratch scheme (82.40% vs. 49.83%, p < 0.05). The Grad-CAM visualizations agreed with the model learned through important features in the TMJ area, particularly around the articular disc. Two fine-tuning models on PD and T2-weighted images showed that the diagnostic performance did not improve compared with using PD alone (p < 0.05). Diverse AUCs were observed across each group when the patients were divided according to age (0.7083–0.8375) and sex (male:0.7576, female:0.7083). The prediction accuracy of the ensemble model was higher than that of the human experts when all the data were used (74.21% vs. 67.71%, p < 0.05). A deep neural network (DNN) was developed to process multimodal data, including MRI and patient clinical data. Analysis of four age groups with the DNN model showed that the 41–60 age group had the best performance (AUC = 0.8258). The fine-tuning model and DNN were optimal for judging TMJ effusion and may be used to prevent true negative cases and aid in human diagnostic performance. Assistive automated diagnostic methods have the potential to increase clinicians' diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Lung cancer patients’ chances of survival can increase with early detection. For the diagnosis of lung cancer, precise lung nodule identification in computed tomography (CT) images is crucial. The variety of the lung nodules and the complexity of the surroundings, however, have made robust nodule identification a challenging problem. To address this challenge, the Extensible Hunt Optimization-based deep Convolutional Neural Network classifier (EHO-deep CNN), is developed in this research for detecting the lung nodules present in the CT images. The proposed model in which the preprocessing is performed through the median filter to eliminate the artifacts in the image. Specifically, the images are segmented by adopting the centralized superpixels segmentation-based iterative clustering (CSSBIC) technique that enhances the accuracy of segmentation. The proposed approach relies on the GLCM and the VGG16-based feature extraction to explicate the discrimination of the lung nodule features. In addition, the retrieved features are combined and fed forwarded to the Deep CNN classifier that is optimally tuned via the EHO algorithm. The EHO algorithm is developed by fusing the fitness function of Border collie dogs with the hunting characteristics of Dingo to enhance convergence rate and performance. The proposed EHO-deep CNN is reported in terms of accuracy, sensitivity, and specificity values as 98.13%, 98.98%, and 97.28% when measuring TP 80 for the LIDC-IDRI dataset and 96.09%, 96.70%, and 95.48% for measuring k-fold 10. Further, implementing the suggested model obtains the values of 95.34%, 92.88%, and 97.80% for the LUNA 16 dataset when measuring TP 80 and 98.21%, 98.06%, 98.35% when measuring k-fold 10, which outperforms other existing techniques in effectiveness. [ABSTRACT FROM AUTHOR]

This study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in TOF-MRAs, (2) improve the approach by testing various fully automatic pre-processing pipelines, and (3) rigorously validate the model's transferability on independent, external test-datasets. A convolutional neural network was trained on 235 TOF-MRAs acquired on local scanners from a single vendor to segment intracranial aneurysms. Different pre-processing pipelines including bias field correction, resampling, cropping and intensity-normalization were compared regarding their effect on model performance. The models were tested on independent, external same-vendor and other-vendor test-datasets, each comprised of 70 TOF-MRAs, including patients with and without aneurysms. The best-performing model achieved excellent results on the external same-vendor test-dataset, surpassing the results of the previous publication with an improved sensitivity (0.97 vs. ~ 0.86), a higher Dice score coefficient (DSC, 0.60 ± 0.25 vs. 0.53 ± 0.31), and an improved false-positive rate (0.87 ± 1.35 vs. ~ 2.7 FPs/case). The model further showed excellent performance in the external other-vendor test-datasets (DSC 0.65 ± 0.26; sensitivity 0.92, 0.96 ± 2.38 FPs/case). Specificity was 0.38 and 0.53, respectively. Raising the voxel-size from 0.5 × 0.5×0.5 mm to 1 × 1×1 mm reduced the false-positive rate seven-fold. This study successfully replicated core principles of a previous approach for detecting and segmenting cerebral aneurysms in TOF-MRAs with a robust, fully automatable pre-processing pipeline. The model demonstrated robust transferability on two independent external datasets using TOF-MRAs from the same scanner vendor as the training dataset and from other vendors. These findings are very encouraging regarding the clinical application of such an approach. [ABSTRACT FROM AUTHOR]

Smart agriculture has shifted the paradigm by integrating advanced technologies, particularly weed management. This paper introduces an innovative approach to weed control by applying a Wavelet-based Convolution Neural Network (WCNN). In the era of precision agriculture, our study explores the integration of WCNN into real-world scenarios, emphasizing its adaptability to diverse environmental conditions. Utilizing the spatial-frequency analysis features of wavelets and convolutional neural networks, the WCNN model is the most effective at finding weeds, classifying them, and managing them specifically in agricultural fields in real-time. This research contributes to the scientific discourse on smart agriculture and addresses the challenges of invasive weeds, presenting a sustainable solution for optimizing resource utilization. Our investigation includes a detailed exploration of WCNN’s adaptive learning mechanisms and dynamic adjustment to changing agricultural landscapes. The model seamlessly integrates with existing smart farming infrastructure, showcasing a substantial reduction in manual intervention and a simultaneous increase in agricultural productivity. We incorporate fog computing and resource optimization into our framework, enhancing the efficiency of onboard data processing. To evaluate the real-world efficacy of WCNN, we conducted comprehensive experiments in texture classification and image labelling using two distinct datasets: the plant seedling and soybean weed datasets. Results demonstrate the superior performance of WCNN, achieving higher accuracy in training and test scenarios with significantly fewer parameters than traditional CNNs. For the soybean weed dataset, WCNN achieved remarkable accuracy in the training (0.9970) and testing (0.9987) phases, with correspondingly low losses of 0.0109 and 0.0048. The WCNN model demonstrated high accuracy during training (0.9739) and testing (0.9902), with minimal losses of 0.0898 and 0.0239 in the plant seedling dataset. [ABSTRACT FROM AUTHOR]

The accurate health status evaluation of lithium-ion batteries is crucial for preemptive identification of potential battery failures and averting hazardous incidents, given its essential role in indicating the extent of battery degradation. The challenge in determining the State of Health (SOH) arises from the absence of a precise and standardized definition, as well as the difficulty in measuring essential input variables. Therefore, this paper utilizes current and voltage data during the charge and discharge process as direct inputs for SOH estimation and proposes a deep learning-based lithium-ion battery SOH estimation approach. Specifically, it leverages Bayesian optimized Convolutional Neural Network (CNN) within a data-driven framework. Experimental results demonstrate that the proposed deep learning method achieves a Mean Absolute Error (MAE) of 1% and a Maximum Error (MAX) below 4% in estimation accuracy, highlighting its enhanced precision and robustness. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusions This study aimed to explore the value of ultrasound (US) images in chronic kidney disease (CKD) screening by constructing a CKD screening model based on grey‐scale US images.According to the CKD diagnostic criteria, 1049 patients from Tongde Hospital of Zhejiang Province were retrospectively enrolled in the study. A total of 4365 renal US images were collected from these patients. Convolutional neural networks were used for feature extractions and a screening model was constructed by fusing ResNet34 and texture features to identify CKD and its stage. A comparative analysis was performed to compare the diagnosis results of the model with physicians.When diagnosing CKD or non‐CKD, the receiver operating characteristic curve (AUC) of our model was 0.918 and that of the senior physician group was 0.869 (p < .05). For the diagnosis of CKD stage, the AUC of our model for CKD G1–G3 was 0.781, 0.880, and 0.905, respectively, while the AUC of the senior physician group for CKD G1–G3 was 0.506, 0.586, and 0.796, respectively; all differences were statistically significant (p < .05). The diagnostic efficiency of our model for CKD G4 and G5 reached the level of the senior physicians group. Specifically, the AUC of our model for CKD G4‐G5 was 0.867 and 0.931, respectively, while the AUC of the senior physician group for CKD G4‐G5 was 0.838 and 0.963, respectively (all p > .05).Our deep learning radiomics model is more effective than senior physicians in the diagnosis of early CKD. [ABSTRACT FROM AUTHOR]

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Landslide disasters, due to their widespread distribution and clustered occurrences, pose a significant threat to human society. Rainfall is considered a primary triggering factor, and the frequent clustering of landslides underscores the importance of early warning systems for regional landslide disasters in preventing and mitigating rainfall-induced landslides. Research on early warning models is crucial for accurately predicting rainfall-induced landslides. However, traditional models face challenges such as the complexity of landslide causes, insufficient data, and limited analysis methods, resulting in low accuracy and inadequate precision. This study focuses on Fujian Province, China, proposing a four-step process for building a regional landslide early warning model based on machine learning. The process includes data integration and cleaning, sample set construction, model training and validation, and practical application. By integrating and cleaning the latest and most detailed data, a training sample set (15,589 samples) for the regional landslide disaster early warning model is established. Three machine learning algorithms--Random Forest, Multilayer Perceptron, and Convolutional Neural Network--are employed and compared, the evaluation results indicated that the RF-based warning model achieved an accuracy of 0.930-0.957 and an AUC value of 0.955. The CNNbased warning model demonstrated an accuracy of 0.945-0.948 with an AUC value of 0.940. The MLP-based warning model achieved an accuracy of 0.930-0.953 and an AUC value of 0.930. The results showed comparable accuracy metrics among the three models, with RF exhibiting a significant advantage in AUC values. Finally, the models are applied to the regional landslide disasters induced by heavy rainfall in Fujian Province on 5 August 2021. The results showed that in the binary classification warning strategy, the accuracy of the Random Forest and Convolutional Neural Network was 92.9%, while that of the Multilayer Perceptron was 85.8%, all performing well. In the multi-classification hierarchical warning strategy, the Random Forest excelled, while the performance of the Convolutional Neural Network and Multilayer Perceptron was relatively limited. The findings of this study Frontiers in Earth Science 01 frontiersin.org Liu et al. 10.3389/feart.2024.1419421 contribute to valuable attempts in landslide disaster warning model research, with anticipated further improvements through the gradual accumulation of samples and practical application verification. [ABSTRACT FROM AUTHOR]

Background and objective: Epilepsy, which is associated with neuronal damage and functional decline, typically presents patients with numerous challenges in their daily lives. An early diagnosis plays a crucial role in managing the condition and alleviating the patients' suffering. Electroencephalogram (EEG)- based approaches are commonly employed for diagnosing epilepsy due to their effectiveness and non-invasiveness. In this study, a classification method is proposed that use fast Fourier Transform (FFT) extraction in conjunction with convolutional neural networks (CNN) and long short-term memory (LSTM) models. Methods: Most methods use traditional frameworks to classify epilepsy, we propose a new approach to this problem by extracting features from the source data and then feeding them into a network for training and recognition. It preprocesses the source data into training and validation data and then uses CNN and LSTM to classify the style of the data. Results: Upon analyzing a public test dataset, the top-performing features in the fully CNN nested LSTM model for epilepsy classification are FFT features among three types of features. Notably, all conducted experiments yielded high accuracy rates, with values exceeding 96% for accuracy, 93% for sensitivity, and 96% for specificity. These results are further benchmarked against current methodologies, showcasing consistent and robust performance across all trials. Our approach consistently achieves an accuracy rate surpassing 97.00%, with values ranging from 97.95 to 99.83% in individual experiments. Particularly noteworthy is the superior accuracy of our method in the AB versus (vs.) CDE comparison, registering at 99.06%. Conclusion: Our method exhibits precise classification abilities distinguishing between epileptic and non-epileptic individuals, irrespective of whether the participant's eyes are closed or open. Furthermore, our technique shows remarkable performance in effectively categorizing epilepsy type, distinguishing between epileptic ictal and interictal states versus non-epileptic conditions. An inherent advantage of our automated classification approach is its capability to disregard EEG data acquired during states of eye closure or eye-opening. Such innovation holds promise for real-world applications, potentially aiding medical professionals in diagnosing epilepsy more efficiently. [ABSTRACT FROM AUTHOR]

The accurate identification of low-voltage distribution substation topology plays a crucial role in research applications such as line loss management, fault location, imbalance correction, and intelligent operation and maintenance of substations. In response to the challenges posed by the large scale of users and the complex connection relationships in low-voltage distribution substations, which complicate the identification of their topology, a method for identifying low-voltage distribution substation topology based on user profiling technology is proposed. This method is supported by big data technology and introduces the concept of user profiling into the research of substation topology identification. Firstly, by deeply studying the theoretical knowledge of the relationship between the supply of distribution transformers and the power consumption of substation users, as well as the voltage similarity, we establish the feature labels for electricity coefficient and voltage similarity. Then, we use the continuous relaxation method and branch-and-bound method to solve the electricity coefficient matrix; and use the Gaussian kernel function to solve the voltage similarity matrix. Finally, by constructing a comprehensive attribution matrix using the electricity coefficient matrix and the voltage similarity matrix, employing a convolutional neural network to cluster and solve the comprehensive attribution matrix, outputting user-area attribution information, forming user-area attribution profiles, and completing the identification of area attribution relationships in low-voltage distribution systems. Simulation results demonstrate that the proposed method not only effectively identifies the area information to which low-voltage users belong but also discerns the connection relationships between users and area transformers. [ABSTRACT FROM AUTHOR]

Head pose estimation methods can be generally classified into two categories: model-based and appearance-based methods. The model-based approach relies on facial landmarks for three-dimensional reconstruction, aiming to achieve high-precision results. However, this method is heavily dependent on the accuracy of these landmarks. The appearance-based approach utilizes images as input and employs feature extraction and calculations to generate outcomes. While the appearance-based method boasts greater robustness, its accuracy falls short of the former. In this paper, a new and effective hybrid method is proposed. This hybrid approach combines the strengths of both methods. Unlike the conventional model-based methods, the proposed method regards the facial landmarks in 2D images as a sequence of neural network inputs and then obtains the head pose estimation results for users by neural network regression. The proposed method solves the fuzzy rotation labeling problem by using a rotation matrix representation, introducing a 6D rotation matrix representation as an intermediate state of the rotation matrix to achieve effective direct regression. Introducing face processing enhances the robustness of the model in cross-dataset scenarios. The proposed method achieves remarkable results based on imprecise face recognition and a simplistic model. The proposed method can be divided into three parts. First, the proposed method applies face processing on the input image; second, the method detects facial landmarks; and third, it converts these facial landmarks into sequences and obtains the 6D rotation representation of the head pose by regression. Extensive experiments on the publicly available BIWI, PRIMA, and DrivFace datasets show that this method is functional and performs better than other state-of-the-art methods. Compared to other methods, this approach demonstrates an average performance improvement of at least 10% across the dataset. [ABSTRACT FROM AUTHOR]

Leukemia is a type of blood cell cancer that is in the bone marrow's blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances fast and chronic growth gradually which are further classified into lymphocytic and myeloid leukemias. This work evaluates a unique deep convolutional neural network (CNN) classifier that improves identification precision by carefully examining concatenated peptide patterns. The study uses leukemia protein expression for experiments supporting two different techniques including independence and applied cross-validation. In addition to CNN, multilayer perceptron (MLP), gated recurrent unit (GRU), and recurrent neural network (RNN) are applied. The experimental results show that the CNN model surpasses competitors with its outstanding predictability in independent and cross-validation testing applied on different features extracted from protein expressions such as amino acid composition (AAC) with a group of AAC (GAAC), tripeptide composition (TPC) with a group of TPC (GTPC), and dipeptide composition (DPC) for calculating its accuracies with their receiver operating characteristic (ROC) curve. In independence testing, a feature expression of AAC and a group of GAAC are applied using MLP and CNN modules, and ROC curves are achieved with overall 100% accuracy for the detection of protein patterns. In cross-validation testing, a feature expression on a group of AAC and GAAC patterns achieved 98.33% accuracy which is the highest for the CNN module. Furthermore, ROC curves show a 0.965% extraordinary result for the GRU module. The findings show that the CNN model is excellent at figuring out leukemia illnesses from protein expressions with higher accuracy. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the performance and factors affecting the species classification of convolutional neural network (CNN) architecture using whole-part and earlywood-part cross-sectional datasets of six Korean Quercus species. The accuracy of species classification for each condition was analyzed using the datasets, data augmentation, and optimizers—stochastic gradient descent (SGD), adaptive moment estimation (Adam), and root mean square propagation (RMSProp)—based on a CNN architecture with three to four convolutional layers. The model trained with the augmented dataset yielded significantly superior results in terms of classification accuracy compared to the model trained with the non-augmented dataset. The augmented dataset was the only factor affecting classification accuracy in the final five epochs. In contrast, four factors in the entire epoch, such as the Adam and SGD optimizers and the earlywood-part and whole-part datasets, affected species classification. The arrangement of earlywood vessels, broad rays, and axial parenchyma was identified as a major influential factor in the CNN species classification using gradient-weighted class activation mapping (Grad-CAM) analysis. The augmented whole-part dataset with the Adam optimizer achieved the highest classification accuracy of 85.7% during the final five epochs of the test phase. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing applications have facilitated the distribution of heterogeneous, unstructured digitized data among users' social networks of varying opinions. Text processing at a large scale requires high-precision computational techniques, which increases the computational burden. The advent of big data analytics along with Natural Language Processing is a powerful factor in improving the efficiency of processing large-scale text data, using the kernel of the MapReduce big data analytics frameworks that allow parallelization of large computational operations. In this paper, we propose an intelligent sentiment prediction approach based on deep learning, batch, and streaming big data analytics. In fact, our main objective is to take advantages of the powerful tools provided by the distributed platforms, such as, Hadoop and Spark to preprocess streaming data. This involves various tasks such as cleaning the data, reducing its size, minimizing access time, and decreasing storage volume. This step prepares the big streaming data to be fully exploited by Deep Learning models. This work includes a research study on processing big data related to short volume scripts based on batch and streaming distributed frameworks as well as deep learning approaches in Natural Language Processing. We detail our idea for analyzing short texts to determine their semantic context and categorize them into pros and cons poles. There were different stages in building this model, the first involving data reduction and refinement using selected features and big data analysis tools. In the second stage, words are embedded by global vector to be computed in layers of convolutional and recurrent neural networks. The experimental study and the analysis of the results confirm the usefulness of our proposed model and its superiority over the main approaches studied in the literature. Our model achieved a performance of 96% in terms of accuracy. [ABSTRACT FROM AUTHOR]

Protein sequence classification needs to be performed quickly and accurately to progress bioinformatics advancements and the production of pharmaceutical products. Extensive comparisons between large databases of known proteins and unknown sequences are necessary in traditional protein classification methods, which can be time‐consuming. This labour‐intensive and slow manual matching and classification method depends on functional and biological commonalities. Protein classification is one of the many fields in which deep learning has recently revolutionized. The data on proteins are organized hierarchically and sequentially, and the most advanced algorithms, such as Deep Family‐based Method (DeepFam) and Protein Convolutional Neural Network (ProtCNN), have shown promising results in classifying proteins into relative groups. On the other hand, these methods frequently refuse to acknowledge this fact. We propose a novel hybrid model called ProteinCNN‐BLSTM to overcome these particular challenges. To produce more accurate protein sequence classification, it combines the techniques of amino acid embedding with bidirectional long short‐term memory (BLSTM) and convolutional neural networks (CNNs). The CNN component is the most effective at capturing local features, while the BLSTM component is the most capable of modeling long‐term dependencies across protein sequences. Through the process of amino acid embedding, sequences of proteins are transformed into numeric vectors, which significantly improves the precision of prediction and the representation of features. Using the standard protein samples PDB‐14189 and PDB‐2272, we analyzed the proposed ProteinCNN‐BLSTM model and the existing deep‐learning models. Compared to the existing models, such as CNN, LSTM, GCNs, CNN‐LSTM, RNNs, GCN‐RNN, DeepFam, and ProtCNN, the proposed model performed more accurately and better than the existing models. [ABSTRACT FROM AUTHOR]

Concrete cracking is one of the most significant damage types in reinforced concrete structures due to its potential to adversely affect durability, safety, and serviceability and even reduce the bearing capacity during operation. Thus, damage inspection of damage caused by concrete cracking is important for management, maintenance, and structural assessment for both damaged and undamaged existing buildings but with concrete cracking after a long time of use that needs reconstruction or renovation. This study provides an improved building damage inspection approach by applying Unmanned Aerial Vehicles (UAVs) and state‐of‐the‐art deep learning algorithms to detect concrete cracks on building surfaces. Two distinct architectures for Convolutional Neural Networks (CNNs), namely ResNet50 and YOLOv8 based on classification, and object detection approaches to create a total of 11 models are established, trained, and compared. The classification models attained accuracy levels exceeding 99%, whereas the object detection models achieved approximately 85%. All models effectively identified and accurately located concrete cracks on building surfaces. Besides, the CNN models' capacity to detect cracks is influenced by a variety of model hyperparameters, encompassing factors such as model architecture, the number of network layers, different training dataset sizes, and the quantity of trainable parameters necessary to learn the specific features of detection targets during the training process. The results of this study ultimately demonstrate that the proposed approach can yield accurate detection results and holds high potential for application in crack inspection to advance automatic damage inspection in building structures to a greater extent. In addition, it is important to note that a universal rule cannot be established rule as a larger and more complex model, or an increased number of trainable parameters, necessarily leads to improved detection performance. Models that are trained from scratch using local datasets might not necessarily result in enhanced performance in comparison to the improvements gained through fine‐tuning via transfer learning. Therefore, an appropriate training type, dataset size, task complexity, computational resources, and time demands to achieve a balance between accuracy and efficiency should be considered for specific application scenarios. [ABSTRACT FROM AUTHOR]

Background: Bladder prolapse is a common clinical disorder of pelvic floor dysfunction in women, and early diagnosis and treatment can help them recover. Pelvic magnetic resonance imaging (MRI) is one of the most important methods used by physicians to diagnose bladder prolapse; however, it is highly subjective and largely dependent on the clinical experience of physicians. The application of computer‐aided diagnostic techniques to achieve a graded diagnosis of bladder prolapse can help improve its accuracy and shorten the learning curve. Purpose: The purpose of this study is to combine convolutional neural network (CNN) and vision transformer (ViT) for grading bladder prolapse in place of traditional neural networks, and to incorporate attention mechanisms into mobile vision transformer (MobileViT) for assisting in the grading of bladder prolapse. Methods: This study focuses on the grading of bladder prolapse in pelvic organs using a combination of a CNN and a ViT. First, this study used MobileNetV2 to extract the local features of the images. Next, a ViT was used to extract the global features by modeling the non‐local dependencies at a distance. Finally, a channel attention module (i.e., squeeze‐and‐excitation network) was used to improve the feature extraction network and enhance its feature representation capability. The final grading of the degree of bladder prolapse was thus achieved. Results: Using pelvic MRI images provided by a Huzhou Maternal and Child Health Care Hospital, this study used the proposed method to grade patients with bladder prolapse. The accuracy, Kappa value, sensitivity, specificity, precision, and area under the curve of our method were 86.34%, 78.27%, 83.75%, 95.43%, 85.70%, and 95.05%, respectively. In comparison with other CNN models, the proposed method performed better. Conclusions: Thus, the model based on attention mechanisms exhibits better classification performance than existing methods for grading bladder prolapse in pelvic organs, and it can effectively assist physicians in achieving a more accurate bladder prolapse diagnosis. [ABSTRACT FROM AUTHOR]

Existing studies based on convolutional neural networks mainly focus on the accuracy of image reconstruction, ignoring problems such as excessive parameters, insufficient feature extraction, and resource waste. In response to the above, this paper proposed multi-frequency feature extraction network (MFEN), which designed a lightweight lattice information interaction structure and used channel segmentation with multi-mode convolution combination to reduce the number of parameters. By separating the low-frequency, mid-frequency, and high-frequency information of the image and extracting the feature heterogeneity, it improved the expressiveness and feature differentiation of the network, made the network pay more attention to the restoration of texture detail features, and reasonably allocated the computational resources. In addition, it integrated the local binary pattern (LBP) algorithm into the network to enhance texture sensitivity, which further improved the network s ability to extract details. It experimentally verifies that the proposed method balances complexity and performance well. In the 2X zooming experiments on the Set5 dataset, compared to the conventional image super-resolution algorithm (SRCNN) based on convolutional neural network and the newer algorithm (MADNet), the peak signal-to-noise ratio (PSNR) of the proposed method is improved by 1.31 dB and 0. 12 dB respectively, and the number of parameters is reduced by 55% compared to MADNet. [ABSTRACT FROM AUTHOR]

Mainstream knowledge graph-based recommendation model rarely consider the relationship between source nodes and target nodes when fusing high-order information, leading to the introduction of too much noise information and thus affecting recommendation performance in complex network scenarios. To address this problem, this paper proposed a knowledge graph recommendation model with integrated meta-graph neighborhoods, with the goal of reducing the impact of noise information by constructing and integrating meta-graph neighborhoods, thereby improving recommendation performance. Firstly, the model obtained the initial similar sequence of the source node based on meta-graph similarity. Then, the model enhanced the initial sequence using self-attention networks and linear networks, which resulted in a set of enhanced feature vectors that serve as the meta-graph neighborhoods of the node. Secondly, the model designed an attention mechanism based on the user s different preferences for each meta-graph to perform convolution and aggregation on the resulting meta-graph neighborhoods. Then, the model integrated the meta-graph neighborhoods into the source node to enhance the feature representation of the source node. Finally, the model used the inner product of the enhanced vector and the user vector as the probability of user interaction with the item, which was then utilized to complete the recommendation. Experimental results on the MovieLens-20M and Last-FM datasets show that the proposed model achieves an AUC of 97.3% and 94.3%, and F₁-score of 83.1% and 75. 6%, respectively. The recall@50 are 35.4% and 31.7%, respectively. These performance metrics outperform models such as NGCF, KGCN, LKGR, and other models. The results demonstrate that the knowledge graph recommendation model with integrated meta-graph neighborhoods is effective in improving recommendation performance. [ABSTRACT FROM AUTHOR]

The development of a bridge damage detection method relies on comprehensive dynamic responses pertaining to damage. The numerical model of a bridge can conveniently considers various damage scenarios and acquire pertinent data, while the entity of a bridge or its physical model proves challenging. Traditional methods for identifying bridge damage often struggle to effectively utilize data acquired from diverse domains, presenting a significant hurdle in addressing cross-domain issues. This study proposes a novel cross-domain damage identification method for suspension bridges using recurrence plots and convolutional neural networks. By employing parameter identification-based modal modification of numerical model, the gap between numerical model and physical models eliminated. Un-threshold multivariate recurrence plots are used for accurately characterizing dynamic responses and extracting deeper damage features. Due to the scarcity of experimental data, which limits the training of robust neural networks, a transfer learning tailored for convolutional neural networks is implemented. This strategy not only addresses the issue of small sample sizes but also significantly enhances the network's ability to identify structural damage across diverse bridge domains. The proposed damage identification method is validated using a combination of numerical simulations and physical experiments on a specific single-span suspension bridge. Results demonstrate that un-threshold multivariate recurrence plots reveal detailed internal structure and damage information. Furthermore, the utilization of improved convolutional neural networks effectively facilitates cross-domain structural damage identification, marking a significant advancement in the field of structural health monitoring. [ABSTRACT FROM AUTHOR]

In urban system management and public safety, accurate traffic flow forecasting is pivotal for real-world applications such as traffic control, resource-sharing scheduling platforms, and intelligent transportation systems. The challenge lies in effectively capturing complex spatio-temporal correlations. To address this, we introduce ST-DSTN, a novel spatial-temporal fusion attention-based deep neural network that significantly advances the current state-of-the-art. ST-DSTN utilizes three distinct temporal branches to capture temporal dependencies and a convolutional neural network to encapsulate spatial dependencies. Our innovative contribution includes a spatio-temporal fusion attention module, effectively modeling the dynamic spatio-temporal dependencies of traffic data, thus improving upon existing methods. Additionally, we propose a hierarchical fusion method that permeates the entire model, capturing complex correlations at various levels through multi-level fusion. Our comprehensive experiments on the BikeNYC dataset demonstrate ST-DSTN's superior performance, supported by numerical results that substantiate its improvement over current methods, surpassing the DeepSTN+ model by 4.31%. [ABSTRACT FROM AUTHOR]

Currently, the decision boundary of the multi-class anomaly detection algorithm based on deep learning does not sufficiently capture the positive class region, posing a risk of abnormal sample features falling into the domain of normal sample features and potentially leading to misleading outcomes in practical applications. In response to the above problems, this paper proposes a new method called multi-class hypersphere anomaly detection (MMHAD) based on the edge outlier exposure set and margin. The method aims to utilize convolutional neural networks for joint training of all normal object classes, identifying a shared set of outlier exposures, learning compact identification features, and setting appropriate edge parameters to guide the model in mapping outliers outside the hypersphere. This approach enables more comprehensive detection of various types of exceptions. The experiments demonstrate that the algorithm is superior to the most advanced baseline method, with an improvement of 26.0%, 8.2%, and 20.1% on CIFAR-10 and 14.8%, 12.0%, and 20.1% on FMNIST in the cases of (2/8), (5/5), and (9,1), respectively. Furthermore, we investigate the challenging (2/18) case on CIFAR-100, where our method achieves approximately 17.4% AUROC gain. Lastly, for a recycling waste dataset with the (4/1) case, our MMHAD yields a notable 22% enhancement in performance. Experimental results show the effectiveness of the proposed model in multi-classification anomaly detection. [ABSTRACT FROM AUTHOR]

Energy is a foundational element of the modern industrial economy. Prices of metals play a crucial role in energy sectors' revenue evaluations, making them the cornerstone of effective payment management employed by resource policymakers. Copper is one of the most important industrial metals, and plays a vital role in various aspects of today's economies. Copper is strongly associated with many industries, such as electrical wiring, construction, and equipment manufacturing; therefore, the price of copper has become a significant impact factor on the performance of related energy companies and economies. The accurate prediction of copper prices holds particular significance for market participants and policymakers. This study carried out research to address the gap in copper price forecasting using a one-dimensional convolutional neural network (1D-CNN). The proposed method was implemented and tested using extensive data spanning from November 1991 to May 2023. To assess the performance of the CNN model, standard evaluation metrics, such as the R-value, mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), were employed. For the prediction of global copper prices, the proposed artificial intelligence algorithm demonstrated high accuracy. Lastly, future global copper prices were predicted up to 2027 by the CNN and compared with forecasts published by the International Monetary Fund and the International Society of Automation. The results show the exceptional performance of the CNN, establishing it as a reliable tool for monitoring copper prices and predicting global copper price volatilities near reality, and as carrying significant implications for policymakers and governments in shaping energy policies and ensuring equitable implementation of energy strategies. [ABSTRACT FROM AUTHOR]

There are various blocking and filtering algorithms for protection against harmful contents on the Internet. However, it is impossible to classify particularly the visual contents according to their genres and block them through traditional methods. In order to block the harmful visual contents, such as various advertisements and social media posts, we need to review and classify them as per their contents. Deep learning method is today’s most efficient method to review the visual contents. In this study, only the harmful images were blocked without completely blocking the entire website. Alcoholic drinks were selected as the harmful content data set. For this purpose, a training was provided with 4.6 million images by using CNN (Convolutional Neural Networks) and GoogLeNet architecture. At the end of this training, 97.6469% of accuracy was achieved. F1 score was calculated as 87.75526188% at the end of the test conducted with 154501 images. The images were determined through the network traffic via mitmproxy and classified as harmful or harmless thanks to the trained model, and the filtering process was successfully completed. [ABSTRACT FROM AUTHOR]

Segmenting the left ventricle from the transgastric short-axis views (TSVs) on transesophageal echocardiography (TEE) is the cornerstone for cardiovascular assessment during perioperative management. Even for seasoned professionals, the procedure remains time-consuming and experience-dependent. The current study aims to evaluate the feasibility of deep learning for automatic segmentation by assessing the validity of different U-Net algorithms. A large dataset containing 1388 TSV acquisitions was retrospectively collected from 451 patients (32% women, average age 53.42 years) who underwent perioperative TEE between July 2015 and October 2023. With image preprocessing and data augmentation, 3336 images were included in the training set, 138 images in the validation set, and 138 images in the test set. Four deep neural networks (U-Net, Attention U-Net, UNet++, and UNeXt) were employed for left ventricle segmentation and compared in terms of the Jaccard similarity coefficient (JSC) and Dice similarity coefficient (DSC) on the test set, as well as the number of network parameters, training time, and inference time. The Attention U-Net and U-Net++ models performed better in terms of JSC (the highest average JSC: 86.02%) and DSC (the highest average DSC: 92.00%), the UNeXt model had the smallest network parameters (1.47 million), and the U-Net model had the least training time (6428.65 s) and inference time for a single image (101.75 ms). The Attention U-Net model outperformed the other three models in challenging cases, including the impaired boundary of left ventricle and the artifact of the papillary muscle. This pioneering exploration demonstrated the feasibility of deep learning for the segmentation of the left ventricle from TSV on TEE, which will facilitate an accelerated and objective alternative of cardiovascular assessment for perioperative management. [ABSTRACT FROM AUTHOR]

(1) Background: Identifying acute aortic syndrome (AAS) and thoracic aortic aneurysm (TAA) in busy emergency departments (EDs) is crucial due to their life-threatening nature, necessitating timely and accurate diagnosis. (2) Methods: This retrospective case-control study was conducted in the ED of three hospitals. Adult patients visiting the ED between 1 January 2010 and 1 January 2020 with a chief complaint of chest or back pain were enrolled in the study. The collected chest radiography (CXRs) data were divided into training (80%) and testing (20%) datasets. The training dataset was trained by four different convolutional neural network (CNN) models. (3) Results: A total of 1625 patients were enrolled in this study. The InceptionV3 model achieved the highest F1 score of 0.76. (4) Conclusions: Analysis of CXRs using a CNN-based model provides a novel tool for clinicians to interpret ED patients with chest pain and suspected AAS and TAA. The integration of such imaging tools into ED could be considered in the future to enhance the diagnostic workflow for clinically fatal diseases. [ABSTRACT FROM AUTHOR]

Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model's capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) images play an indispensable role in many key fields such as environmental monitoring, precision agriculture, and urban resource management. Traditional deep convolutional neural networks have the problem of limited receptive fields. To address this problem, this paper introduces a hybrid network model that combines the advantages of CNN and Transformer, called MBT-UNet. First, a multi-branch encoder design based on the pyramid vision transformer (PVT) is proposed to effectively capture multi-scale feature information; second, an efficient feature fusion module (FFM) is proposed to optimize the collaboration and integration of features at different scales; finally, in the decoder stage, a multi-scale upsampling module (MSUM) is proposed to further refine the segmentation results and enhance segmentation accuracy. We conduct experiments on the ISPRS Vaihingen dataset, the Potsdam dataset, the LoveDA dataset, and the UAVid dataset. Experimental results show that MBT-UNet surpasses state-of-the-art algorithms in key performance indicators, confirming its superior performance in high-precision remote sensing image segmentation tasks. [ABSTRACT FROM AUTHOR]

Scene recognition has been the foundation of research in computer vision fields. Because scene images typically are composed of specific regions distributed in some layout, so modeling layouts of various scenes is a key clue for scene recognition. Existing methods usually require an additional stream to detect regions for subsequent modeling, which accumulate errors and may miss important information. Meanwhile, they use manual features to model relations between regions, which weakens the representation ability of layouts. In this paper, we propose a single-stream adaptive scene layout modeling approach based on a layout modeling module (LMM), which constructs layouts without additional detection streams and adaptively captures the relations to take advantage of graph attention network. LMM is directly concatenated to a convolutional neural network, where each pixel of the activation maps of the last convolutional layer is defined as a region that is the initial input node of the LMM. LMM first models the layout of each region, and then uses all regions with layout information to model the entire scene. Layout relations are encoded as edges, which are automatically analyzed according to region co-occurrence and relative position. Our work can be understood as optimizing features of the activation maps from a scene layout modeling perspective for scene recognition. Experimental results on MIT67, SUN397, and Places365 show that our single-stream model achieves competitive performance. [ABSTRACT FROM AUTHOR]

Low-light images are prevalent in intelligent monitoring and many other applications, with low brightness hindering further processing. Although low-light image enhancement can reduce the influence of such problems, current methods often involve a complex network structure or many iterations, which are not conducive to their efficiency. This paper proposes a Zero-Reference Camera Response Network using a camera response model to achieve efficient enhancement for arbitrary low-light images. A double-layer parameter-generating network with a streamlined structure is established to extract the exposure ratio K from the radiation map, which is obtained by inverting the input through a camera response function. Then, K is used as the parameter of a brightness transformation function for one transformation on the low-light image to realize enhancement. In addition, a contrast-preserving brightness loss and an edge-preserving smoothness loss are designed without the requirement for references from the dataset. Both can further retain some key information in the inputs to improve precision. The enhancement is simplified and can reach more than twice the speed of similar methods. Extensive experiments on several LLIE datasets and the DARK FACE face detection dataset fully demonstrate our method's advantages, both subjectively and objectively. [ABSTRACT FROM AUTHOR]

In this paper, artificial intelligence (AI) technology is applied to the electromagnetic imaging of anisotropic objects. Advances in magnetic anomaly sensing systems and electromagnetic imaging use electromagnetic principles to detect and characterize subsurface or hidden objects. We use measured multifrequency scattered fields to calculate the initial dielectric constant distribution of anisotropic objects through the backpropagation scheme (BPS). Later, the estimated multifrequency permittivity distribution is input to a convolutional neural network (CNN) for the adaptive moment estimation (ADAM) method to reconstruct a more accurate image. In the meantime, we also improve the definition of loss function in the CNN. Numerical results show that the improved loss function unifying the structural similarity index measure (SSIM) and root mean square error (RMSE) can effectively enhance image quality. In our simulation environment, noise interference is considered for both TE (transverse electric) and TM (transverse magnetic) waves to reconstruct anisotropic scatterers. Lastly, we conclude that multifrequency reconstructions are more stable and precise than single-frequency reconstructions. [ABSTRACT FROM AUTHOR]