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This document is a summary of a special issue of the journal Quality & Reliability Engineering International. The issue showcases original research presented at the 2023 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering. The research papers cover a diverse range of topics related to reliability and safety in modern engineering systems. The papers address areas such as reliability modeling and analysis, reliability-based design and optimization, failure/safety analysis and prevention, and maintainability. The authors express their appreciation to the journal and the reviewers for their support and hope that the research presented will be valuable to researchers, engineers, scientists, and students in the field. [Extracted from the article]

The paper classification method aims to correctly divide the paper data according to the similarity of its content. However, how to accurately classify according to the content expressed in the paper has always been a problem that various classification algorithms need to face. At present, there is a kind of paper classification method based on deep learning and implemented by the encoder‐decoder structure. This method inputs the words from a large number of papers into encoder, after calculating by NN (neural network) algorithm, the similarity degree of different papers is compared to achieve the purpose of classification. However, this type of method only considers the similarity between words, a NN algorithm can only calculate a large number of word information once, and it cannot find the regularity of classification through word information. But it has a difference with the similarity of the content. This paper starts from the perspective of considering the content, its label information is extracted, and the input vector of encoder‐decoder structure is formed with labels and words. This improves the original paper classification method based on encoder‐decoder structure. Firstly, the label information is based on the content, which can reflect the content of the paper. Secondly, the classification method which combines label information and word information can reflect the content of the paper comprehensively. Thirdly, the label information is independent of word information and NN algorithm is used separately to make this part of the content more consistent in the encoder‐decoder structure. Finally, the label information and the word information are combined, respectively, with the output values obtained by different NN algorithms to realize the classification of the content. This paper proves the effectiveness of the proposed method by evaluating the paper data in web of science and obtaining relevant experimental results. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to evaluate the significance of background noise in machine learning models assessing the GRBAS scale for voice disorders., Methods: A dataset of 1406 voice samples was collected from retrospective data, and a 5-layer 1D convolutional neural network (CNN) model was constructed using TensorFlow. The dataset was divided into training, validation, and test data. Gaussian noise was added to test samples at various intensities to assess the model's noise resilience. The model's performance was evaluated using accuracy, F1 score, and quadratic weighted Cohen's kappa score., Results: The model's performance on the GRBAS scale generally declined with increasing noise intensities. For the G scale, accuracy dropped from 70.9% (original) to 8.5% (at the highest noise), F1 score from 69.2% to 1.3%, and Cohen's kappa from 0.679 to 0.0. Similar declines were observed for the remaining RBAS components., Conclusion: The model's performance was affected by background noise, with substantial decreases in evaluation metrics as noise levels intensified. Future research should explore noise-tolerant techniques, such as data augmentation, to improve the model's noise resilience in real-world settings., Level of Evidence: This study evaluates a machine learning model using a single dataset without comparative controls. Given its non-comparative design and specific focus, it aligns with Level 4 evidence (Case-series) under the 2011 OCEBM guidelines Laryngoscope, 134:3537-3541, 2024., (© 2024 The Authors. The Laryngoscope published by Wiley Periodicals LLC on behalf of The American Laryngological, Rhinological and Otological Society, Inc.)

Since deep low‐frequency tremors are considered to be associated with large earthquakes that occur adjacently on the same subducting plate interface, it is important to investigate tremors that occurred before the establishment of modern seismograph networks such as the High Sensitivity Seismograph Network (Hi‐net). We propose a deep‐learning solution to detect evidence of tremors in scanned images of paper seismogram records from over 50 years ago. In this study, we fine‐tuned a convolutional neural network (CNN) based on the Residual Network, which was pre‐trained using images of synthetic waveforms from our previous study, using a data set comprised of images generated from real seismic data recorded digitally by Hi‐net to facilitate a supervised analysis. The fine‐tuned CNN was able to predict the presence or absence of tremors in the Hi‐net images with an accuracy of 98.64%. Gradient‐weighted Class Activation Mapping heatmaps created to visualize model predictions indicated that the CNN's ability to detect tremors is not degraded by the presence of teleseisms. Once validated using the Hi‐net images, the CNN was applied to paper seismograms recorded from 1966 to 1977 at the Kumano observatory in southwest Japan, operated by Earthquake Research Institute, The University of Tokyo. The CNN showed potential for detecting tremors in scanned images of paper seismogram records from the past, facilitating downstream tasks such as the creation of new tremor catalogs. However, further training using an augmented data set to control for variables such as inconsistent plotting pen thickness is required to develop a universally applicable model. Plain Language Summary: In 2002, deep low‐frequency tremors, which periodically occur with much smaller amplitudes and longer durations than common earthquakes, were discovered owing to the establishment of dense seismic arrays in Japan. These tremors are considered to be associated with megathrust earthquakes because they occur in deeper regions of plate boundaries compared with common earthquakes. Investigating tremors that occurred before the establishment of dense seismic arrays is important since megathrust earthquakes recur on intervals of 100–200 years, and digital records from modern seismic arrays cover only a fraction of that time. In this study, we developed a convolutional neural network based on the Residual Network (ResNet) that extracts tremors from images of waveforms recorded more than 50 years ago, when seismometers drew waveforms directly on drum‐rolled paper using a pen. The proposed ResNet, which was trained on seismogram images generated from synthetic waveforms and real data recorded digitally by modern seismic arrays, was applied to scanned images of paper seismograms recorded from 1966 to 1977 at an observatory in southwest Japan. A list of tremors was successfully obtained; however, further training using data that accounts for variables such as inconsistent plotting pen thickness is required to develop a universally applicable model. Key Points: Convolutional neural network model for detection of deep low‐frequency tremors from seismogram images is proposedThe model trained with seismogram images converted from real seismic data successfully detects tremorsThe detection performances of the trained model for the paper records at the Kumano observatory in southwest Japan are discussed [ABSTRACT FROM AUTHOR]

Researchers usually face difficulties in finding scientific papers relevant to their research interests due to increasing growth. Recommender systems emerge as a leading solution to filter valuable items intelligently. Recently, deep learning algorithms, such as convolutional neural network, improved traditional recommendation technologies, for example, the graph‐based or content‐based methods. However, existing graph‐based methods ignore high‐order association between users and items on graphs, and content‐based methods ignore global features of texts for explicit user preferences. Therefore, this paper proposes a Content‐based and knowledge Graph‐based Paper Recommendation method (CGPRec), which uses a two‐layer self‐attention block to obtain global features of texts for more complete explicit user preferences, and proposes an improved graph convolutional network for modeling high‐order associations on the knowledge graph to mine implicit user preferences. And the knowledge graph in this paper is constructed with concept nodes, user nodes, paper nodes, and other meta‐data nodes. Experimental results on a public dataset, CiteULike‐a, and a real application log dataset, AHData, show that our model outperforms compared with baseline methods. [ABSTRACT FROM AUTHOR]

Background: Precapillary pulmonary hypertension (PH) is characterized by a sustained increase in right ventricular (RV) afterload, impairing systolic function. Two-dimensional (2D) echocardiography is the most performed cardiac imaging tool to assess RV systolic function; however, an accurate evaluation requires expertise. We aimed to develop a fully automated deep learning (DL)-based tool to estimate the RV ejection fraction (RVEF) from 2D echocardiographic videos of apical four-chamber views in patients with precapillary PH., Methods: We identified 85 patients with suspected precapillary PH who underwent cardiac magnetic resonance imaging (MRI) and echocardiography. The data was divided into training (80%) and testing (20%) datasets, and a regression model was constructed using 3D-ResNet50. Accuracy was assessed using five-fold cross validation., Results: The DL model predicted the cardiac MRI-derived RVEF with a mean absolute error of 7.67%. The DL model identified severe RV systolic dysfunction (defined as cardiac MRI-derived RVEF < 37%) with an area under the curve (AUC) of .84, which was comparable to the AUC of RV fractional area change (FAC) and tricuspid annular plane systolic excursion (TAPSE) measured by experienced sonographers (.87 and .72, respectively). To detect mild RV systolic dysfunction (defined as RVEF ≤ 45%), the AUC from the DL-predicted RVEF also demonstrated a high discriminatory power of .87, comparable to that of FAC (.90), and significantly higher than that of TAPSE (.67)., Conclusion: The fully automated DL-based tool using 2D echocardiography could accurately estimate RVEF and exhibited a diagnostic performance for RV systolic dysfunction comparable to that of human readers., (© 2024 Wiley Periodicals LLC.)

Beamforming technique can effectively improve the spectrum utilization in the multi‐antenna systems, while the dirty‐paper coding (DPC) technique can reduce the inter‐user interference. In this letter, it is aimed to maximize the weighted sum‐rate under the total power constraint in the multiple‐input‐single‐output (MISO) system with the DPC technique. However, the existing methods of beamforming optimization mainly rely on customized iterative algorithms, which have high computational complexity. To address this issue, the beamforming neural network (BFNNet) is devised by utilizing the deep learning technique and the uplink‐downlink duality and exploring the optimal solution structure, which includes the deep neural network module and the signal processing module. Simulation results show that the BFNNet can achieve near‐optimal solutions and significantly reduce computational complexity. [ABSTRACT FROM AUTHOR]

Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [ABSTRACT FROM AUTHOR]

In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber‐security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Samples were analysed for plasma endogenous adrenocorticotrophic hormone (eACTH), serum cortisol, serum thyroid hormone and plasma histamine. I This study by i [1] I in the United States examined the incidence of abnormal behaviours and their association with concentrations of ovarian hormones associated with a granulosa cell tumour (GCT) i . ELECTROHYDRAULIC SHOCK WAVE THERAPY I In this study i [2] I in the United States examined the effect of the number of electrohydraulic shock wave therapy (ESWT) treatments in the management of superficial digital flexor tendinitis (SDFT) and proximal suspensory desmitis (PSD) injuries and compared short- and long-term outcomes i . [Extracted from the article]

Background and Aims: While biopsy is the gold standard for liver fibrosis staging, it poses significant risks. Noninvasive assessment of liver fibrosis is a growing field. Recently, deep learning (DL) technology has revolutionized medical image analysis. This technology has the potential to enhance noninvasive fibrosis assessment. We systematically examined the application of DL in noninvasive liver fibrosis imaging., Methods: Embase, MEDLINE, Web of Science, and IEEE Xplore databases were used to identify studies that reported on the accuracy of DL for classification of liver fibrosis on noninvasive imaging. The search keywords were "liver or hepatic," "fibrosis or cirrhosis," and "neural or DL networks." Risk of bias and applicability were evaluated using the QUADAS-2 tool., Results: Sixteen studies were retrieved. Imaging modalities included ultrasound (n = 10), computed tomography (n = 3), and magnetic resonance imaging (n = 3). The studies analyzed a total of 40 405 radiological images from 15 853 patients. All but two of the studies were retrospective. In most studies the "ground truth" reference was the METAVIR score for pathological staging (n = 9.56%). The majority of the studies reported an accuracy >85% when compared to histopathology. Fourteen studies (87.5%) had a high risk of bias and concerns regarding applicability., Conclusions: Deep learning has the potential to play an emerging role in liver fibrosis classification. Yet, it is still limited by a relatively small number of retrospective studies. Clinicians should facilitate the use of this technology by sharing databases and standardized reports. This may optimize the noninvasive evaluation of liver fibrosis on a large scale., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objectives: Convolutional neural network (CNN) is a state-of-art deep learning (DL) method with superior performance in image classification. Here, a CNN-based workflow is proposed to discriminate hominid teeth. Our hope is that this method could help confirm otherwise questionable records of Homo from Pleistocene deposits where there is a standing risk of mis-attributing molars of Pongo to Homo., Methods and Materials: A two-step workflow was designed. The first step is converting the enamel-dentine junction (EDJ) into EDJ card, that is, a two-dimensional image conversion of the three-dimensional EDJ surface. In this step, researchers must carefully orient the teeth according to the cervical plane. The second step is training the CNN learner with labeled EDJ cards. A sample consisting of 53 fossil Pongo and 53 Homo (modern human and Neanderthal) was adopted to generate EDJ cards, which were then separated into training set (n = 84) and validation set (n = 22). To assess the feasibility of this workflow, a Pongo-Homo classifier was trained from the aforementioned EDJ card set, and then the classifier was used to predict the taxonomic affinities of six samples (test set) from von Koenigswald's Chinese Apothecary collection., Results: Results show that EDJ cards in validation set are classified accurately by the CNN learner. More importantly, taxonomic predictions for six specimens in test set match well with the diagnosis results deduced from multiple lines of evidence, implying the great potential of CNN method., Discussion: This workflow paves a way for future studies using CNN to address taxonomic complexity (e.g., distinguishing Pongo and Homo teeth from the Pleistocene of Asia). Further improvements include visual interpretation and extending the applicability to moderately worn teeth., (© 2021 Wiley Periodicals LLC.)

Computer vision system (CVSs) are effective tools that enable large-scale phenotyping with a low-cost and non-invasive method, which avoids animal stress. Economically important traits, such as rib and loin yield, are difficult to measure; therefore, the use of CVS is crucial to accurately predict several measures to allow their inclusion in breeding goals by indirect predictors. Therefore, this study aimed (1) to validate CVS by a deep learning approach and to automatically predict morphometric measurements in tambaqui and (2) to estimate genetic parameters for growth traits and body yield. Data from 365 individuals belonging to 11 full-sib families were evaluated. Seven growth traits were measured. After biometrics, each fish was processed in the following body regions: head, rib, loin, R + L (rib + loin). For deep learning image segmentation, we adopted a method based on the instance segmentation of the Mask R-CNN (Region-based Convolutional Neural Networks) model. Pearson's correlation values between measurements predicted manually and automatically by the CVS were high and positive. Regarding the classification performance, visible differences were detected in only about 3% of the images. Heritability estimates for growth and body yield traits ranged from low to high. The genetic correlations between the percentage of body parts and morphometric characteristics were favorable and highly correlated, except for percentage head, whose correlations were unfavorable. In conclusion, the CVS validated in this image dataset proved to be resilient and can be used for large-scale phenotyping in tambaqui. The weight of the rib and loin are traits under moderate genetic control and should respond to selection. In addition, standard length and pelvis length can be used as an efficient and indirect selection criterion for body yield in this tambaqui population., (© 2023 Stichting International Foundation for Animal Genetics.)

Skin cancer is among the 10 most common cancers. Recent research revealed the superiority of artificial intelligence (AI) over dermatologists to diagnose skin cancer from predesignated and cropped images. However, there remain several uncertainties for AI in diagnosing skin cancers, including lack of testing for consistency, lack of pathological proof or ambiguous comparisons. Hence, to develop a reliable, feasible and user-friendly platform to facilitate the automatic diagnostic algorithm is important. The aim of this study was to build a light-weight skin cancer classification model based on deep learning methods for aiding first-line medical care. The developed model can be deployed on cloud platforms as well as mobile devices for remote diagnostic applications. We reviewed the medical records and clinical images of patients who received a histological diagnosis of basal cell carcinoma, squamous cell carcinoma, melanoma, seborrheic keratosis and melanocytic nevus in 2006-2017 in the Department of Dermatology in Kaohsiung Chang Gung Memorial Hospital (KCGMH). We used the deep learning models to identify skin cancers and benign skin tumors in the manner of binary classification and multi-class classification in the KCGMH and HAM10000 datasets to construct a skin cancer classification model. The accuracy reached 89.5% for the binary classifications (benign vs malignant) in the KCGMH dataset; the accuracy was 85.8% in the HAM10000 dataset in seven-class classification and 72.1% in the KCGMH dataset in five-class classification. Our results demonstrate that our skin cancer classification model based on deep learning methods is a highly promising aid for the clinical diagnosis and early identification of skin cancers and benign tumors., (© 2020 Japanese Dermatological Association.)

Objectives: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. Method: A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. Results: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data‐driven phenotypes, as well as by predicting transition to the disorder in high‐risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non‐stationary distribution of the data, and lack of appropriate funding. Conclusion: Machine learning‐based studies, including atheoretical data‐driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse‐relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings. [ABSTRACT FROM AUTHOR]

Objectives/hypothesis: To develop a deep-learning-based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngoscopy findings., Study Design: Retrospective study., Methods: A total of 24,667 laryngoscopy images (normal, vocal nodule, polyps, leukoplakia and malignancy) were collected to develop and test a convolutional neural network (CNN)-based classifier. A comparison between the proposed CNN-based classifier and the clinical visual assessments (CVAs) by 12 otolaryngologists was conducted., Results: In the independent testing dataset, an overall accuracy of 96.24% was achieved; for leukoplakia, benign, malignancy, normal, and vocal nodule, the sensitivity and specificity were 92.8% vs. 98.9%, 97% vs. 99.7%, 89% vs. 99.3%, 99.0% vs. 99.4%, and 97.2% vs. 99.1%, respectively. Furthermore, when compared with CVAs on the randomly selected test dataset, the CNN-based classifier outperformed physicians for most laryngeal conditions, with striking improvements in the ability to distinguish nodules (98% vs. 45%, P < .001), polyps (91% vs. 86%, P < .001), leukoplakia (91% vs. 65%, P < .001), and malignancy (90% vs. 54%, P < .001)., Conclusions: The CNN-based classifier can provide a valuable reference for the diagnosis of laryngeal neoplasms during laryngoscopy, especially for distinguishing benign, precancerous, and cancer lesions., Level of Evidence: NA Laryngoscope, 130:E686-E693, 2020., (© 2020 The American Laryngological, Rhinological and Otological Society, Inc.)

This document is a guest editorial from the journal IET Computer Vision, discussing the topic of advanced image restoration and enhancement. The editorial highlights the challenges faced in this field, such as the complexity of degradation models for real-world low-quality images and the difficulty of acquiring paired data. It also introduces a special issue of the journal that includes five accepted papers, which focus on video reconstruction and image super-resolution. The editorial concludes by providing brief summaries of each accepted paper. The guest editors of the special issue are also mentioned, along with their research interests and affiliations. [Extracted from the article]

Display front‐of‐screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process. However, the severe imbalanced data, especially the limited number of defective samples, has been a long‐standing problem that hinders the successful application of deep learning algorithms. Synthetic defect data generation can help address this issue. This paper reviews the state‐of‐the‐art synthetic data generation methods and the evaluation metrics that can potentially be applied to display FOS quality inspection tasks. [ABSTRACT FROM AUTHOR]

High dynamic range (HDR) techniques have received significant attention in generating realistic, high‐quality images and videos and improving visual quality in new display systems. We have witnessed remarkable advances in HDR reconstruction using deep learning technologies in recent years. This review examines recent developments in HDR reconstruction using a deep learning approach, which takes a single low dynamic range (LDR) image as an input and aims to restore an HDR image featuring higher color gamut and a higher detail retention than the LDR image. We aim to provide a comprehensive survey in this field. Since there are numerous HDR algorithms, it is necessary to evaluate and organize theirperformance, therefore, we evaluate them using two objective evaluation metrics. [ABSTRACT FROM AUTHOR]

Considering the problem of missing fields in the criminal case system, this article proposes a deep learning algorithm to extract the features of the case description and fill in the missing value. Due to Chinese expressions and characteristics of criminal cases, we make both character vectors and word vectors to present text embedding. Character vectors are from bert model. Word vector is trained by long short‐term memory model with attention. The experiment uses 13,890 data totally. This work is an extension of our short conference proceeding paper. The results show that the combination of characters and words can effectively improve the accuracy of the conference paper by 9%. This is the first time to cascade the character and word dimensions on the criminal case information preprocess and it can provide higher quality data especially for the crime data mining. [ABSTRACT FROM AUTHOR]

Image quality acts as a major factor in determining its performance in a vision inspection task. The moire pattern caused by frequency aliasing severely degrades the visual quality in display devices, where such high‐quality images are required. To remove these quality undermining patterns, the images are acquired by intentional defocusing. Then, to restore the details lost during the image acquisition, image deblurring is used. The existing deblurring methods fail to output satisfactory results for low contrast Mura images. To solve this problem, we present a novel approach using a generalized Gaussian kernel for real‐world vision inspection tasks. We evaluated the performance and experimented under different settings to validate the robustness of the proposed method. The performance for the proposed method has improved in no‐reference image quality assessment metrics. [ABSTRACT FROM AUTHOR]

The accuracy of short‐term photovoltaic (PV) power prediction is crucial for maintaining power system stability and grid scheduling. Here, a short‐term PV power prediction framework is proposed considering combined weather similarity day screening, signal decomposition noise reduction and hybrid deep learning to realize PV power prediction. First, a combined meteorological similar day screening model is constructed to screen out historical days similar to the day, which reduces the number of training sets; Second, Synchrosqueezing Wavelet Transform is utilized to eliminate data noise. Third, a Convolution Neural Network‐Gate Recurrent Unit (CNN‐GRU) network is constructed to extract periodic and nonlinear features in the PV power generation data series and to capture the relationship features between PV power generation and meteorological factors to improve the prediction accuracy. Fourth, the Sparrow Search Algorithm is introduced to perform hyper‐parameter optimization of the CNN‐GRU network to accelerate the model convergence and improve the model training efficiency. Finally, this paper conducts simulation experiments and the experimental results show that the prediction method proposed in this paper can effectively improve the prediction accuracy of short‐term PV power compared to the baseline model, and the method proposed in this paper is superior to other conventional methods. [ABSTRACT FROM AUTHOR]

In this paper, simulation‐based artificial intelligence is applied to discover new materials, and to aid in backplane and module design in order to realize high performance organic light emitting diode displays. Additionally, image quality restoration based on deep learning is introduced for a new display form factor in which the camera is positioned beneath the display panel. [ABSTRACT FROM AUTHOR]

Insufficient and imbalance data samples often prevent the development of accurate deep learning models for manufacturing defect detection. By applying data augmentation methods ‐ including VAE latent space oversampling and random data generation, and GAN multi‐modal complementary data generation, we overcome the dataset limitations and achieve Pass/No‐Pass accuracies of over 90%. [ABSTRACT FROM AUTHOR]

Background: Oral cancer is a deadly disease among the most common malignant tumors worldwide, and it has become an increasingly important public health problem in developing and low-to-middle income countries. This study aims to use the convolutional neural network (CNN) deep learning algorithms to develop an automated classification and detection model for oral cancer screening., Methods: The study included 700 clinical oral photographs, collected retrospectively from the oral and maxillofacial center, which were divided into 350 images of oral squamous cell carcinoma and 350 images of normal oral mucosa. The classification and detection models were created by using DenseNet121 and faster R-CNN, respectively. Four hundred and ninety images were randomly selected as training data. In addition, 70 and 140 images were assigned as validating and testing data, respectively., Results: The classification accuracy of DenseNet121 model achieved a precision of 99%, a recall of 100%, an F1 score of 99%, a sensitivity of 98.75%, a specificity of 100%, and an area under the receiver operating characteristic curve of 99%. The detection accuracy of a faster R-CNN model achieved a precision of 76.67%, a recall of 82.14%, an F1 score of 79.31%, and an area under the precision-recall curve of 0.79., Conclusion: The DenseNet121 and faster R-CNN algorithm were proved to offer the acceptable potential for classification and detection of cancerous lesions in oral photographic images., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Drug-induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration of the inner ear. Herein, we focused on the in silico modeling of drug-induced ototoxicity of chemicals. We collected 1,102 ototoxic medications and 1,705 non-ototoxic drugs. Based on the data set, a series of computational models were developed with different traditional machine learning and deep learning algorithms implemented on an online chemical database and modeling environment. Six ML models performed best on 5-fold cross-validation and test set. A consensus model was developed with the best individual models. These models were further validated with an external validation. The consensus model showed best predictive ability, with high accuracy of 0.95 on test set and 0.90 on validation set. The consensus model and the data sets used for model development are available at https://ochem.eu/model/46566321. Besides, 16 structural alerts responsible for drug-induced ototoxicity were identified. We hope the results could provide meaningful knowledge and useful tools for ototoxicity evaluation in drug discovery and environmental risk assessment., (© 2021 John Wiley & Sons A/S.)

Currently, deep learning methods for low‐light image enhancement tasks mainly focus on the illumination of images, while neglecting the problems of image noise and feature loss. To address this issue, this paper proposes a novel low‐light image enhancement network called DAF‐Retinex, based on the Retinex‐Net. To address the issue of image noise, different from traditional image denoise methods, this paper utilizes a fully convolutional neural network to denoise the reflection component, additionally, a denoising loss function is introduced to suppress noise. For preserving image details and extracting features, this paper creatively introduces self‐calibrated convolutions into low‐light image enhancement tasks, furthermore, a feature augmented attention block consisting of feature‐guided attention (FGA) is designed for feature learning to effectively enhance image illumination and extract image detail features. Experimental results demonstrate that the proposed algorithm in this paper effectively removes image noise and extracts detailed features, resulting in visually improved outcomes. On public datasets, the average improvement in objective evaluation metrics of image quality such as PSNR, SSIM, and NIQE are 1.13%, 4.12%, and 1.28%, respectively. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special issue on computational methods and artificial intelligence applications in low-carbon energy systems. The editorial highlights the urgent need for advanced computing and artificial intelligence in the clean energy transition to improve system reliability, economics, and sustainability. The special issue includes 19 original research articles covering topics such as energy forecasting, situational awareness, multi-energy system dispatch, and power system operation. The articles present state-of-the-art methods and techniques in these areas, including wind power forecasting, demand-side flexibility, fault diagnosis of photovoltaic strings, and energy management strategies. The authors express their gratitude to the participating authors and anonymous reviewers for their contributions to the special section. [Extracted from the article]

This document is a guest editorial for a special issue of the CAAI Transactions on Intelligence Technology journal. The special issue focuses on advances in representation learning for computer vision. The editorial highlights the success of deep learning methods in deriving powerful representations from visual data, but also acknowledges the challenges of conducting representation learning with deep models, especially with large and noisy datasets. The document provides summaries of several research papers included in the special issue, covering topics such as cancellable biometrics, medical image analysis, watermarking for medical images, facial pattern description, multi-biometric strategies, semantic segmentation, image enhancement, image classification, and hyperspectral image super-resolution. The authors express their hope that these papers will enhance readers' understanding of current trends and guide future research in the field. The document also includes brief biographies of the authors. [Extracted from the article]

The freshness of aquatic products is directly related to the safety and health of the people. Traditional methods of detecting the freshness of Conger myriaster rely on manual operations, which are labor‐intensive, inefficient, and highly subjective. This paper combines computer vision and the DWG‐YOLOv8 network model to establish an intelligent classification method for C. myriaster freshness. Through image augmentation, 484 C. myriaster samples were expanded to 2904 samples. The YOLOv8n model was improved by simplifying the network backbone, introducing Ghost convolution and the new DW‐GhostConv, thereby reducing the number of parameters and computational load. Test results show that the recognition accuracy of the DWG‐YOLOv8 model reached 98.958%, outperforming models such as ResNet18, Mobilenetv3 small, and Swin transformer v2 tiny. The model's parameter count is 16.609 K, the inference time is 57.80 ms, and the model size is only 102 KB. The research provides a reliable method for online intelligent and nondestructive detection of C. myriaster freshness. [ABSTRACT FROM AUTHOR]

In the paper 'Data-driven optimal scheduling for underground space based integrated hydrogen energy system', Li et al. present a deep deterministic policy gradient (DDPG)-based optimal scheduling method for underground space-based integrated hydrogen energy system. In the paper 'Low-carbon operation of a multi-energy system with hydrogen-based vehicle applications', Mei et al. report the optimal scheduling of a real multi-energy system with hydrogen-based vehicle applications. In the paper 'Low-carbon coordinated scheduling of integrated electricity-gas distribution system with hybrid AC/DC network', Qing et al. proposes a low-carbon coordinated operation method considering hybrid AC/DC distribution network, carbon capture and carbon storage. Human society is facing a dilemma between "less carbon" and "more energy". [Extracted from the article]

Bibliometric analysis was performed to study the development of publications related to Industry 4.0 and its key technologies in Vietnam. Comparisons with data from other ASEAN countries, and with global data have been done to identify distinctive characteristics of Industry 4.0 literature from Vietnam. The collection of 1,470 retrieved papers was analysed to answer seven research questions. Our results highlighted some valuable insights of Industry 4.0 literature in Vietnam. The number of papers in Industry 4.0 in Vietnam increased rapidly in recent years, mostly focused on Computer Science, Engineering, and Mathematics. Iran, China, and South Korea were the most productive partner countries with Vietnam in Industry 4.0. Machine learning, artificial intelligence, big data, deep learning, Internet of things, neural networks, and data mining were among the most popular research themes in Industry 4.0 in Vietnam. Vietnam ranked third among 10 Southeast Asian countries, based on the number of published papers in Industry 4.0, but the gap with the two top countries was large. Compared to the global data, the annual growth rate of Industry 4.0 papers in Vietnam, and other Southeast Asian countries was lower. Findings from this work can be helpful for other scholars in establishing potential future research lines related to Industry 4.0 in Vietnam. [ABSTRACT FROM AUTHOR]

License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a hot topic in computer vision and various deep learning networks are designed to achieve higher accuracy on that by fully exploring the capability of neural networks. This paper aims to address the issue and proposes the substructures with novelty for popular networks. Meanwhile, we present a cross‐channel structure, which simultaneously reduces parameter while the kernel size becomes larger. After that, to overcome the weakness of insufficient dataset which refers to satellite image data, we propose a feature attention mechanism with generative adversarial network to enhance the images' features. We show the recognition result on the satellite image dataset with a large picture. This paper evaluates substructures on the PASCAL VOC2012 dataset and improves the mIOU from 74.68% to 88.15%. [ABSTRACT FROM AUTHOR]

Recent geometric approaches to visual‐inertial odometry have shown impressive accuracy with real‐time performance in autonomous tracking applications in several fields including virtual and augmented reality (VR & AR) as well as robotics. But these methods are still not robust to challenging conditions due to their dependence on hand‐engineered features, heuristics, sensor calibration and manual synchronization (when using visual and inertial sensors). In this paper, we review the recent advances in deep learning based approaches to odometry and identify some future research directions. [ABSTRACT FROM AUTHOR]

Sound signals play a crucial role in immersive perceptual experiences, such as virtual and augmented reality applications and hearing assistant devices. Traditional approaches enhance speech by estimating the background noise or the speech based on its statistics or a parametric model. However, the performance of such an approach has plateaued due to mismatches between its assumptions and actual background noise and speech. Recently, deep learning (DL) has been applied to solve such a challenging problem by taking advantage of its ability to learn a nonlinear mapping and to recognize a pattern without making explicit assumptions about the background noise or speech. In this paper, we will provide a systematic review of single‐microphone DL‐based speech enhancement approaches. Through an analysis of their advantages and disadvantages, we will provide some insight into future research directions for speech enhancement for hearing devices. [ABSTRACT FROM AUTHOR]

Recently, deep learning (DL) based dynamic security assessment (DSA) methods have been very successful. However, although a DSA model can be trained well for a specific topology, it often does not perform well for other topologies. Since the topology in real‐world power systems is frequently changing, the performance reduction of DL‐based DSA methods is very serious, which is a challenging and urgent problem. This paper proposes a novel DSA method based on a convolutional neural network (CNN) to solve this problem. In the proposed method, a strong yet simple domain adaptation approach named adaptive batch normalization (AdaBN) is used, which significantly enhances the extensibility and generalizability of the DSA model when the topology changes and eliminates the need to train a large number of models. This approach achieves a deep adaptation effect by modulating the statistics from the source domain to the target domain in all batch normalization layers across the model. Unlike other domain adaptation methods, this method is parameter‐free, requires no additional components, and has advanced performance despite its simplicity. In addition, this paper introduces TGAN‐based data augmentation to deal with the difficulty of costly data collection and labelling. This data augmentation makes the proposed model applicable to small databases. The test results of the proposed method on IEEE 39‐bus and IEEE 118‐bus systems show that this method can evaluate system dynamic security during topology changes and in the face of data noise with high accuracy. [ABSTRACT FROM AUTHOR]

The high‐precision prediction of total ionospheric electron content (TEC) is of great significance for improving the accuracy of global navigation satellite systems. There are two problems with the current prediction of TEC: (a) The existing TEC prediction models mainly based on stacked structure, which has insufficient predictive ability when the network has fewer layers, and loss of fine‐grained features when there are more layers, resulting in a decrease in predictive performance; (b) The existing research on ionospheric TEC prediction mainly focuses on building deep learning prediction models, while there is little research on optimizing the hyper‐parameters of TEC prediction models. Optimization can help find a better quasi‐optimal hyperparameter combination and improve the performance of the model. This paper proposed an automatic deep learning framework for global TEC map prediction, named MAOOA‐Residual‐Attitude‐BiConvLSTM. This framework includes a TEC prediction model, Residual‐Attention‐BiConvLSTM, which can simultaneously consider both coarse‐grained and fine‐grained spatiotemporal features. It also includes an optimization algorithm, MAOOA, for optimizing the hyper‐parameters of the model. We conducted comparative experiments between our framework and C1PG, ConvLSTM, ConvGRU, and ED‐ConvLSTM during high solar activity years, low solar activity years, and a magnetic storm event. The results indicate that in all cases, the framework proposed in this paper outperforms the comparative models. Plain Language Summary: There are two main problems I n the existing TEC prediction field: (a) The prediction model mainly adopts a sequential stacking structure, losing fine‐grained spatiotemporal features, which leads to insufficient prediction ability of the model. (b) Current research mainly focuses on TEC prediction models, with little attention paid to hyper‐parameter optimization of prediction models. Model optimization can find hyperparameter combinations that make the model's performance close to the optimal solution. This paper proposed a TEC prediction and optimization framework, MAOOA‐Residual‐Attitude‐BiConvLSTM, in which Residual‐Attitude‐BiConvLSTM is used for TEC map prediction. By incorporating skip layer design, this model can simultaneously focus on fine‐grained and coarse‐grained features, resulting in higher prediction accuracy. MAOOA is used to find a better quasi‐optimal hyperparameters for Residual‐Attitude‐BiConvLSTM. This paper provides a new approach for building TEC prediction models. At the same time, this study also indicates that in TEC prediction, in addition to focusing on designing prediction models with higher accuracy, attention should also be paid to the optimization algorithms of the models. Key Points: Proposed multi‐strategy assisted Osprey optimization algorithmuses three new strategies to boost its optimization capabilityNew TEC model Residual‐Attention‐BiConvLSTM improves feature retention and predictionMAOOA‐Residual‐Attention‐BiConvLSTM, a novel TEC prediction framework, merges deep learning with hyper‐parameter optimization [ABSTRACT FROM AUTHOR]

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large‐scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, insights into the transformative potential of ML in shaping the future of power system asset management are provided. [ABSTRACT FROM AUTHOR]

Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images. [ABSTRACT FROM AUTHOR]

The proposed network provides a quantitative similarity score for any two given faces and has been applied to large-scale face datasets to identify similar face pairs. This special issue of IET Biometrics, "BIOSIG 2021 Special Issue on Efficient, Reliable, and Privacy-Friendly Biometrics", has as starting point the 2021 edition of the Biometric Special Interest Group (BIOSIG) conference. ACKNOWLEDGEMENTS The Guest Editorial Board would like to thank all of the authors for their contributions to this Special Issue: "BIOSIG 2021 Special Issue on Efficient, Reliable, and Privacy-Friendly Biometrics". [Extracted from the article]

We developed a neural network‐based method for evaluation of display luminance and color non‐uniformity (which we call Mura). We studied a correlation between our developed method and human visual evaluation because visual evaluation is the gold standard for Mura evaluation. We achieved Pearson correlation coefficient of 0.82. [ABSTRACT FROM AUTHOR]

An editorial is presented on the advancements in knowledge-based deep learning systems (KDLS) in biomedicine. Topics include the application of KDLS for evaluating functional connectivity and neurological disorders, the use of deep learning for brain tumor classification and Alzheimer's disease diagnosis, and novel methods for medical image encryption and enhancement.

Gamma spectrum denoising can reduce the adverse effects of statistical fluctuations of radioactivity, gamma ray scattering, and electronic noise on the measured gamma spectrum. Traditional denoising methods are intricate and require analytical expertise in gamma spectrum analysis. This paper proposes a segmentation‐enhanced Convolutional Neural Network‐Stacked Denoising Autoencoder (CNN‐SDAE) method based on convolutional feature extraction network and stacked denoising autoencoder to achieve gamma spectrum denoising, which adopts the idea of data segmentation to enhance the learning ability of the neural network. By dividing the complete gamma spectrum into multiple segments and then using the segmentation‐enhanced CNN‐SDAE method for denoising, the method can achieve adaptive denoising without manually setting the threshold. The experimental results show that our method can effectively achieve gamma spectrum denoising while retaining the characteristics of the gamma spectrum. Compared with traditional methods, the denoising speed and effectiveness have been significantly improved, and the proposed method demonstrates an approximately 1.72‐fold enhancement in smoothing performance than the empirical mode decomposition method. Furthermore, in terms of retaining gamma spectrum characteristics, it also achieves a performance improvement of approximately three orders of magnitude than the wavelet method. [ABSTRACT FROM AUTHOR]

Background: Unlike Papanicolaou tests, there are no commercially available computer-assisted automated screening systems for urine specimens. Despite The Paris System for Reporting Urinary Cytology, there still is poor interobserver agreement with urine cytology and many cases in which a definitive diagnosis cannot be made. In the current study, the authors have reported on the development of an image algorithm that applies computational methods to digitized liquid-based urine cytology slides., Methods: A total of 2405 archival ThinPrep glass slides, including voided and instrumented urine cytology cases, were digitized. A deep learning computational pipeline with multiple tiers of convolutional neural network models was developed for processing whole slide images (WSIs) and predicting diagnoses. The algorithm was validated using a separate test data set comprised of consecutive cases encountered in routine clinical practice., Results: There were 1.9 million urothelial cells analyzed. An average of 5400 urothelial cells were identified in each WSI. The algorithm achieved an area under the curve of 0.88 (95% CI, 0.83-0.93). Using the optimal operating point, the algorithm's sensitivity was 79.5% (95% CI, 64.7%-90.2%) and the specificity was 84.5% (95% CI, 81.6%-87.1%) for high-grade urothelial carcinoma., Conclusions: The authors successfully developed a computational algorithm capable of accurately analyzing WSIs of urine cytology cases. Compared with prior studies, this effort used a much larger data set, exploited whole slide-level and not just cell-level features, and used a cell gallery to display the algorithm's output for easy end-user review. This algorithm provides computer-assisted interpretation of urine cytology cases, akin to the machine learning technology currently used for automated Papanicolaou test screening., (© 2019 American Cancer Society.)

In this paper, the authors apply the feature pyramid network (FPN) to the single‐stage anchor‐free object detection algorithm CenterNet, and the effectiveness of the multi‐level feature fusion of FPN for the object detection algorithm is proved by experiments. However, multi‐level feature fusion leads to an increase in computational cost. In this regard, this paper proposes an object detection algorithm, called DDE‐Net, that does not use multi‐level feature fusion and only uses single‐level feature for optimization. The key component in it: the dense dilated encoder, which encourages dense information exchange of features between different spatial scales. This paper presents extensive experiments, and DDE‐Net shows strong performance compared to that of other popular models on the PASCAL VOC and on the COCO2017 dataset. On the COCO2017 dataset, the authors' DDE‐Net achieves comparable results with its feature pyramids counterpart RetinaNet, while applying the same backbone with smaller params and GFLOPs than RetinaNet. With an image size of 512 × 512, DDE‐Net achieves 37.3 AP running at 81 fps on 2080 Ti. [ABSTRACT FROM AUTHOR]

Offshore wind power is an important technology for low‐carbon power grids. To improve the accuracy, a short‐term offshore wind power prediction method based on significant weather process classification and multitask learning considering neighboring powers is presented in this paper. First, a novel weather process classification method, in which the samples are divided into pieces of waves based on extreme points and are quantified with labels of energy level and fluctuation level, is proposed to classify samples into multiple types of significant weather processes for independent modeling. Second, a multitask learning method, in which the power sequences in neighboring offshore wind farms are innovatively introduced as a new input feature, is proposed for modeling wind power prediction for each wind farm inside a neighboring region under each weather process class. Case studies are presented to verify the effectiveness and superiority of the proposed method. Based on this new method, the 4‐h ultra‐short‐term root mean squared error (RMSE), 24‐h day‐ahead RMSE, 4‐h ultra‐short‐term mean absolute error (MAE), and 24‐h day‐ahead MAE can be reduced by 1.45%, 2.1%, 1.15%, and 1.85%, respectively, compared with benchmark methods, which verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Background and Aims: Alzheimer's disease (AD) is a degenerative neurological condition that worsens over time and leads to deterioration in cognitive abilities, reduced memory, and, eventually, a decrease in overall functioning. Timely and correct identification of Alzheimer's is essential for effective treatment. The systematic study specifically examines the application of deep learning (DL) algorithms in identifying AD using three‐dimensional (3D) imaging methods. The main goal is to evaluate these methods' current state, efficiency, and potential enhancements, offering valuable insights into how DL could improve AD's rapid and precise diagnosis. Methods: We searched different online repositories, such as IEEE Xplore, Elsevier, MDPI, PubMed Central, Science Direct, ACM, Springer, and others, to thoroughly summarize current research on DL methods to diagnose AD by analyzing 3D imaging data published between 2020 and 2024. We use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) guidelines to ensure the organization and understandability of the information collection process. We thoroughly analyzed the literature to determine the primary techniques used in these investigations and their findings. Results and Conclusion: The ability of convolutional neural networks (CNNs) and their variations, including 3D CNNs and recurrent neural networks, to detect both temporal and spatial characteristics in volumetric data has led to their widespread use. Methods such as transfer learning, combining multimodal data, and using attention procedures have improved models' precision and reliability. We selected 87 articles for evaluation. Out of these, 31 papers included various concepts, explanations, and elucidations of models and theories, while the other 56 papers primarily concentrated on issues related to practical implementation. This article introduces popular imaging types, 3D imaging for Alzheimer's detection, discusses the benefits and restrictions of the DL‐based approach to AD assessment, and gives a view toward future developments resulting from critical evaluation. [ABSTRACT FROM AUTHOR]

Optic disc (OD) and optic cup (OC) segmentation is an important task in ophthalmic medicine and is crucial for aiding glaucoma screening. With the development of smart healthcare and the increase of large datasets, there is an increasing number of research efforts targeting OD and OC segmentation, making it particularly important to provide a systematic review of the latest advances in the field. This paper presents a systematic review of commonly used datasets, evaluation metrics, and related research results in the field of OD and OC segmentation. The advantages and disadvantages of segmentation techniques based on traditional and deep learning methods are comparatively analysed. In addition, this study emphasizes the importance of OD and OC segmentation efforts in smart healthcare. Despite the technological advances, the lack of generalization capability is still a major obstacle limiting its clinical application. To address this issue, this study explores unsupervised domain adaptation methods to enhance the generalization performance of segmentation techniques and provide new strategies for clinical diagnosis. Finally, this paper discusses the challenges and future research directions faced by OD and OC segmentation when applied in the medical field to help readers comprehensively grasp the research dynamics in this area. [ABSTRACT FROM AUTHOR]