Search

The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence (AI) to preserve and promote cultural heritage has experienced significant growth in recent years. Among the various areas of cultural heritage, numismatics have emerged as a particularly promising field where we can develop AI solutions. Numismatics refers to the study of coins, tokens, paper money, and medals, which play a critical role in understanding human history and culture. However, there are still limited resources available to help researchers and collectors in the identification of coins. This is due to the vast number of coins in circulation, which presents a significant challenge in developing smart tools for classification tasks. This paper aims to provide a contribution to this setting. In particular, we start by creating a new dataset called EURO-Coin, which consists of images showing the side of coins with reliefs and is designed to facilitate the training and testing of AI models for euro coin classification. Then, we propose two approaches that leverage Convolutional Neural Networks and self-attention layers to classify the country and value of the coins. In our experiments, we obtain an accuracy of 86.9% for country classification and an accuracy of 96.4% for value classification. Finally, we conduct an ablation study to evaluate the impact of the preprocessing activities and attention layers in our approaches. [ABSTRACT FROM AUTHOR]

Facility management maintains building service quality through cycles of condition assessments and rehabilitation. Building components, however, differ in their nature, service lives, deterioration patterns, and textual/visual inspection data. This complicates the condition assessment process and subsequent rehabilitation decisions. This paper proposes a smart condition assessment framework that uses different artificial intelligence (AI) techniques that suit the condition data analysis of different building components. The framework has been applied to a dataset of over 2000 maintenance requests for roof and heating, ventilation, and air conditioning (HVAC) systems across a 600-villa portfolio. To address their varying needs, convolutional neural networks were used on images of roof defects, while enhanced data mining was used on textual data of HVAC systems. Accordingly, work packages of deteriorated components were identified, and a 60-day schedule was developed to repair 203 HVAC units. This research shows how AI can assist facility management with respect to condition assessment, rehabilitation planning, and resource allocation. [ABSTRACT FROM AUTHOR]

We present in this paper the state of the art and an analysis of recent research work and achievements performed in the domain of AI-based and vision-based systems for helping blind and visually impaired people (BVIP). We start by highlighting the recent and tremendous importance that AI has acquired following the use of convolutional neural networks (CNN) and their ability to solve image classification tasks efficiently. After that, we also note that VIP have high expectations about AI-based systems as a possible way to ease the perception of their environment and to improve their everyday life. Then, we set the scope of our survey: we concentrate our investigations on the use of CNN or related methods in a vision-based system for helping BVIP. We analyze the existing surveys, and we study the current work (a selection of 30 case studies) using several dimensions such as acquired data, learned models, and human–computer interfaces. We compare the different approaches, and conclude by analyzing future trends in this domain. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

This paper provides an overview of the applications of Convolutional Neural Networks (CNN) in the railway maintenance industry. Our research covers specifically the subdomain of railway track maintenance. In this study, we have analyzed the state-of-the-art of CNNs applied to railway track maintenance by conducting an extensive literature review, summarizing different tasks and problems related to the topic and presenting solutions based on CNNs with a special emphasis on the data used to create these models. The results of our research show different applications of CNNs within the scope, including the detection of defects in the surface of railway rails and railway track components, such as fasteners, joints, sleepers, switches and crossings, as well as the recognition of track components, and the continuous monitoring of railway tracks. The architecture of CNNs is fitting to learning spatial hierarchies of features directly from the input data, making them of great use for Computer Vision and other applications related to the topic at hand. The implementation of IoT devices and smart sensors aid the collection of real-time data which can be used to feed powerful CNN models to recognize patterns and identify complex events related to the maintenance of railway tracks. This and more insights are discussed in detail within the contents of this paper. [ABSTRACT FROM AUTHOR]

To protect our planet, the material recycling of domestic waste is necessary. Since the COVID-19 pandemic began, the volume of domestic waste has surged overwhelmingly, and many countries suffered from poor waste management. Increased demand for food delivery and online shopping led to a huge surge in plastic and paper waste which came from natural resources. To reduce the consumption of resources and protect the environment from pollution, such as that from landfills, waste should be recycled. One of precious recyclable materials from household waste is liquid cartons that are made of high-quality paper. To promote sustainable recycling, this paper proposes a vision-based inspection module based on convolutional neural networks via transfer learning (CNN-TL) for collecting liquid packaging cartons in the reverse vending machine (RVM). The RVM is an unmanned automatic waste collector, and thus it needs the intelligence to inspect whether a deposited item is acceptable or not. The whole processing algorithm for collecting cartons, including the inspection step, is presented. When the waste is inserted into the RVM by a user after scanning the barcode on the waste, it is relocated to the inspection module, and the item is weighed. To develop the inspector, an experimental set-up with a video camera was built for image data generation and preparation. Using the image data, the inspection agent was trained. To make a good selection for the model, 17 pretrained CNN models were evaluated, and DenseNet121 was selected. To access the performance of the cameras, four different types were also evaluated. With the same CNN model, this paper found the effect of the number of training epochs being set to 10, 100, and 500. In the results, the most accurate agent was the 500-epoch model, as expected. By using the RVM process logic with this model, the results showed that the accuracy of detection was over 99% (overall probability from three inspections), and the time to inspect one item was less than 2 s. In conclusion, the proposed model was verified for whether it would be applicable to the RVM, as it could distinguish liquid cartons from other types of paper waste. [ABSTRACT FROM AUTHOR]

This document discusses the advancements in deep learning technology and image sensing. It highlights the role of deep learning in improving image recognition data and sensor performance in various applications, such as autonomous driving and smartphone camera functionalities. The document presents eleven papers published in a special issue on deep learning technology and image sensing, covering topics like medical imaging, image enhancement, object detection, and innovation in image sensing technologies. The papers demonstrate innovative approaches and methodologies in diverse domains, including liver segmentation, brain tumor classification, optic cup and disc edge detection, image-to-image translation in astronomy, super-resolution imaging, and object detection systems. These studies contribute to the interdisciplinary field of deep learning-based imaging and sensing and offer valuable insights for various fields, including healthcare, astronomy, and robotics. [Extracted from the article]

Both the ABO and Rhesus (Rh) blood groups play crucial roles in blood transfusion medicine. Herein, we report a simple and low-cost paper-based analytical device (PAD) for phenotyping red blood cell (RBC) antigens. Using this Rh typing format, 5 Rh antigens on RBCs can be simultaneously detected and macroscopically visualized within 12 min. The proposed Rh phenotyping relies on the presence or absence of hemagglutination in the sample zones after immobilizing the antibodies targeting each Rh antigen. The PAD was optimized in terms of filter paper type, antibodies, and distance of the visualization zone. In this study, the optimal conditions were Whatman filter paper Grade 4; anti-D, –C, -E, -c, and -e antibodies; RBC suspension of 30%; and a visualization zone of 1 cm above the sample zone. The accuracy of simultaneously phenotyping the five Rh RBC antigens in the blood samples (n = 4692) was 99.19%, comparable with the accuracy of the gold-standard tube method used by blood bank laboratories in several regions of Thailand. Furthermore, decision making based on this method can be assisted by deep learning. After implementing a two-stage objective detection algorithm (YOLO v4-tiny) and classification model (DenseNet-201), the ambiguous images (n = 48) were interpreted with 100% accuracy. The PAD integrated with customized-region convolutional neural networks can reduce the interpretation discrepancies in RBC antigen phenotyping in any laboratory. [Display omitted] • Five Rh antigens typing PADs combined with deep learning algorithm for an assisted human-decision. • Rh antigens typing PADs were tested with real blood samples and compared with the standard tube method. • Dataset of photographs following Rh PADs testing were trained and evaluated by the in-house deep learning platform CiRA CORE. • Misinterpretation caused by weak or ambiguous reactions was solved by the CiRA CORE platform. [ABSTRACT FROM AUTHOR]

Long Short-Term Memory (LSTM)-Based Dog Activity Detection Using Accelerometer and Gyroscope. The third paper, authored by Jeong et al. [[14]], introduced a fall detection system that combines a simple threshold method (STM) and LSTM, creating the STM-LSTM-based fall detection system. With rapid advancements in artificial intelligence (AI), the Internet of Things (IOTs), and communication networks, the future of information and communication engineering (ICE) promises remarkable opportunities and challenges. 10.3390/app12199801 18 Zhang M., Wang C., Zhu W., Shen Y. An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning. [Extracted from the article]

Applied computing and artificial intelligence methods have been attracting growing interest in recent years due to their effectiveness in solving technical problems. Hao et al. [[14]] present an unsupervised fault diagnosis methodology to leverage the generated MPCs of different working conditions to diagnose the actual unlabeled MPCs. The paper by Ainapure et al. [[17]] proposes a new cross-domain fault diagnosis method with enhanced robustness. The paper by Saeed et al. [[1]] proposes an approach to building an AutoML data-dependent CNN model (DeepPCANet) customized for DR screening automatically. [Extracted from the article]

In October 2021, the Secure Knowledge Management (SKM) Conference convened many of scholars and practitioners to discuss and share ideas about the use and development of AI for SKM and cybersecurity. Over the past half-decade, numerous federal funding agencies such as the National Science Foundation (NSF), National Academies of Science (NAS), and National Security and Technology Council (NSTC) have noted the significant role that Artificial Intelligence (AI)-enabled analytics techniques such as deep learning, machine learning, network science, generative models, reinforcement learning, text analytics, and other techniques could play for Secure Knowledge Management (SKM), and more broadly, cybersecurity. Papers in each theme had a component of AI that was based in one or more of the major themes of extant AI-enabled cybersecurity and SKM research summarized in the previous section. [Extracted from the article]

In recent years, the issue of preserving the privacy of parties involved in blockchain transactions has garnered significant attention. To ensure privacy protection for both sides of the transaction, many researchers are using ring signature technology instead of the original signature technology. However, in practice, identifying the signer of anillegal blockchain transactiononce ithas beenplacedon the chainnecessitates a signature technique that offers conditional anonymity. Some illegals can conduct illegal transactions and evade the lawusing ring signatures,which offer perfect anonymity. This paper firstly constructs a conditionally anonymous linkable ring signature using the Diffie-Hellman key exchange protocol and the Elliptic Curve Discrete Logarithm, which offers a non-interactive process for finding the signer of a ring signature in a specific case. Secondly, this paper's proposed scheme is proven correct and secure under Elliptic Curve Discrete Logarithm Assumptions. Lastly, compared to previous constructions, the scheme presented in this paper provides a non-interactive, efficient, and secure confirmation process. In addition, this paper presents the implementation of the proposed scheme on a personal computer, where the confirmation process takes only 2, 16, and 24ms for ring sizes of 4, 24 and 48, respectively, and the confirmation process can be combined with a smart contract on the blockchain with a tested millisecond level of running efficiency. In conclusion, the proposed scheme offers a solution to the challenge of identifying the signer of an illegal blockchain transaction, making it an essential contribution to the field. [ABSTRACT FROM AUTHOR]

Using the improved Johnson et al.'s style migration network as a starting point, this paper proposes a new loss function based on the position information Gram matrix. The new method adds the chunked Gram matrix with position information, and simultaneously, the structural similarity between the style map and the resultant image is added to the style training. The style position information is given to the resultant image, and finally, the resolution of the resultant image is improved with the SRGAN. The new model can effectively migrate the texture structure as well as the color space of the style image, while the data of the content image are kept intact. The simulation results reveal that the image processing results of the new model improve those of the classical Johnson et al.'s method, Google Brain team method, and CCPL method, and the SSIM values of the resulting map and style image are all greater than 0.3. As a comparison, the SSIM values of Johnson et al., Google Brain team, and CCPL are 0.14, 0.11, and 0.12, respectively, which is an obvious improvement. Moreover, with deeper training, the new method can improve the similarity of certain resulting images and style images to more than 0.37256. In addition, training other arbitrary content images on the basis of the trained model can quickly yield satisfactory results. [ABSTRACT FROM AUTHOR]

This paper [[7]] addresses the challenges Intrusion Detection Systems (IDS) face in the Internet of Things (IoT) context due to IoT data's high dimensionality and diversity. The Internet of Things (IoT) has become an increasingly popular technology in recent years, enabling interconnectivity and communication between devices and systems. As a result, there is a growing need for advanced data analysis techniques and artificial intelligence (AI) methods to process, analyze, and extract valuable insights from IoT data. The proposed technique for tracking a moving object of interest in a noisy video using a modified simplest color balance algorithm and a binarization algorithm proved to be effective in building training data for machine learning. [Extracted from the article]

Objectives: Early gastric cancer (EGC) is defined as aggressive gastric cancer involving the gastric mucosa and submucosa. Early detection and treatment of gastric cancer are beneficial to patients. In recent years, many studies have focused on endoscopic diagnosis and therapy for EGC. Exploring new methods to analyze data to enhance knowledge is a worthwhile endeavor, especially when numerous studies exist. This study aims to investigate research trends in endoscopy for EGC over the past 20 years using bibliometric analysis. Methods: Original articles and reviews examining the use of endoscopy for EGC published from 2000 to 2022 were retrieved from the Web of Science Core Collection, and bibliometric data were extracted. Microsoft Office Excel 2016 was used to show the annual number of published papers for the top 10 countries and specific topics. VOSviewer software was used to generate network maps of the cooccurrences of keywords, authors, and topics to perform visualization network analysis. Results: In total, 1,009 published papers met the inclusion criteria. Japan was the most productive country and had the highest number of publications (452, 44.8%), followed by South Korea (183, 18.1%), and China (150, 14.9%). The National Cancer Center of Japan was the institution with the highest number of publications (48, 4.8%). Ono was the most active author and had the highest number of cited publications. Through the network maps, exploring endoscopic diagnosis and therapy were major topics. Artificial intelligence (AI), convolutional neural networks (CNNs), and deep learning are hotspots in endoscopic diagnosis. Helicobacter pylori eradication, second-look endoscopy, and follow-up management were examined. Conclusions: This bibliometric analysis investigated research trends regarding the use of endoscopy for treating EGC over the past 20 years. AI and deep learning, second-look endoscopy, and management are hotspots in endoscopic diagnosis and endoscopic therapy in the future. [ABSTRACT FROM AUTHOR]

The model integrates artificial intelligence (AI) and big data analytics, utilizing IoMT devices for data acquisition and Hadoop ecosystem for managing big data. The field of medical diagnosis is currently undergoing a remarkable transformation with the emergence of artificial intelligence (AI) techniques, particularly deep learning and big data analytics. By harnessing the power of deep learning and big data analytics, AI-based e-diagnosis has the potential to revolutionize healthcare delivery. [Extracted from the article]

This paper is centred around the topic of Image Processing in Bharatnatyam, specifically based on the identification and classification of eye movements in classical Indian dance forms. These eight eye movements, also known as dhristi bedas, are extremely significant in the portrayal of the dance form and convey distinct emotions when acted out by the dancer. It would be very beneficial for Bharatnatyam students and avid classical dance enthusiasts to be able to easily identify the eye movements performed by a dancer so they can understand the story being conveyed. Hence, this paper aims to develop an algorithmic system using artificial intelligence and deep learning to process a dance video as input data and display the category of eye movements performed by the dancer in the video. The framework of the system is as follows: A convolutional neural network is developed with multiple layers and the frames from the video data undergo various stages in our methodology such as pre-processing, feature extraction and mapping the recognized patterns using the softmax activation function to each eye movement. The implementation of our proposed algorithm is done using Tensorflow and Keras. The proposed algorithm will allow the audience and students to comprehend the messages of the story through the dancer's eyes. [ABSTRACT FROM AUTHOR]

This systematic review aims to assess the efficacy of Artificial Intelligence (AI) applications in orthopedic surgery, with a focus on diagnostic accuracy and outcome prediction. In this review, we expose the findings of a systematic literature review awning the papers published from 2016 to October 2023 where authors worked on the application of an AI techniques and methods to an orthopedic purpose or problem. After application of inclusion and exclusion criteria on the extracted papers from PubMed and Google Scholar databases, 75 studies were included in this review. We examined, screened, and analyzed their content according to PRISMA guidelines. We also extracted data about the study design, the datasets included in the experiment, the reported performance measures and the results obtained. In this report, we will share the results of our survey by outlining the key machine and Deep Learning (DL) techniques, such as Convolutional Neural Network (CNN), Autoencoders and Generative Adversarial Network, that were mentioned, the various application domains in orthopedics, the type of source data and its modality, as well as the overall quality of their predictive capabilities. We aim to describe the content of the articles in detail and provide insights into the most notable trends and patterns observed in the survey data. [ABSTRACT FROM AUTHOR]

With the rapid advancement of digital technology, three-dimensional designs of Grid Information Models (GIMs) are increasingly applied in the power industry. Addressing the challenges of extracting key parameters during the GIM's process for power grid equipment, this paper explores an innovative approach that integrates artificial intelligence with image recognition technologies into power design engineering. The traditional methods of "template matching, feature extraction and classification, and symbol recognition" have enabled the automated processing of electrical grid equipment engineering drawings, allowing for the extraction of key information related to grid equipment. However, these methods still rely on manually designed and selected feature regions, which limits their potential for achieving full automation. This study introduces an optimized algorithm that combines enhanced Convolutional Neural Networks (CNNs) with Depth-First Search (DFS) strategies, and is specifically designed for the automated extraction of crucial GIM parameters from power grid equipment. Implemented on the design schematics of power engineering projects, this algorithm utilizes an improved CNN to precisely identify component symbols on schematics, and continues to extract text data associated with these symbols. Utilizing a scene text detector, the text data are matched with corresponding component symbols. Finally, the DFS strategy is applied to identify connections between these component symbols in the diagram, thus facilitating the automatic extraction of key GIM parameters. Experimental results demonstrate that this optimized algorithm can accurately identify basic GIM parameters, providing technical support for the automated extraction of parameters using the GIM. This study's recognition accuracy is 91.31%, while a traditional CNN achieves 71.23% and a Faster R-CNN achieves 89.59%. Compared to existing research, the main innovation of this paper lies in the application of the combined enhanced CNN and DFS strategies for the extraction of GIM parameters in the power industry. This method not only improves the accuracy of parameter extraction but also significantly enhances processing speed, enabling the rapid and effective identification and extraction of critical information in complex power design environments. Moreover, the automated process reduces manual intervention, offering a novel solution in the field of power design. These features make this research broadly applicable and of significant practical value in the construction and maintenance of smart grids. [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]

Facial emotion detection systems have witnessed significant advancements, particularly with the utilization of convolutional neural networks (CNNs). This paper provides a thorough survey of such systems, beginning with an introduction to artificial intelligence and the evolutionary trajectory of neural networks, including artificial neural networks (ANNs), recurrent neural networks (RNNs), and CNNs. The paper elaborates on CNNs' architecture and functionality, elucidating key components such as convolutional layers, pooling layers, and fully connected layers, while also spotlighting prominent CNN architectures like AlexNet and ResNet. It delineates the broad scope and diverse applications of facial emotion detection systems across various domains, including marketing research, crowd testing, AI robots, banking, and entertainment. In the literature review section, recent research papers on CNN models for facial expression recognition are synthesized, highlighting variances in datasets, methodologies, and accuracy levels. The paper concludes that CNNs represent the current pinnacle of facial emotion classification techniques, surpassing previous methodologies such as eigenfaces. It underscores the efficacy of deep CNN architectures trained on extensive facial image datasets in proficiently identifying emotions from facial expressions. Moreover, the paper emphasizes the necessity for ongoing endeavors to enhance accuracy, particularly concerning complex emotions like disgust. In essence, CNNs exhibit substantial promise for the development of real-world facial emotion detection systems, heralding a new era of sophisticated emotion recognition technology. [ABSTRACT FROM AUTHOR]

Deep learning algorithms have simplified the process of creating indistinguishable synthetic videos, or deep fakes, because of the unparalleled increase in processing power. It is concerning because these face-swapped manipulations are often used in a variety of contexts, such as blackmail and political manipulation. This paper presents a revolutionary deep learning-based approach to accurately discriminating between real and Artificial Intelligence (AI)-generated false films. Using a ResNext Convolutional Neural Network (CNN) for frame-level feature extraction, this method makes use of an automated mechanism intended to identify replacement and re-enactment deep fakes. A Recurrent Neural Network (RNN) equipped with Long Short-Term Memory (LSTM) training is utilized to classify videos and distinguish between real and modified ones. The system demonstrates the effectiveness of a straightforward and reliable methodology, in addition to utilizing complex neural network topologies. Through testing, this paper showcases how well the system can accurately identify videos playing a crucial role in ongoing initiatives to combat the increasing dangers posed by the proliferation of deep fake content in society. [ABSTRACT FROM AUTHOR]

This paper is on the autonomous detection of humans in off-limits mountains. In off-limits mountains, a human rarely exists, thus human detection is an extremely rare event. Due to the advances in artificial intelligence, object detection–classification algorithms based on a Convolution Neural Network (CNN) can be used for this application. However, considering off-limits mountains, there should be no person in general. Thus, it is not desirable to run object detection–classification algorithms continuously, since they are computationally heavy. This paper addresses a time-efficient human detector system, based on both motion detection and object classification. The proposed scheme is to run a motion detection algorithm from time to time. In the camera image, we define a feasible human space where a human can appear. Once motion is detected inside the feasible human space, one enables the object classification, only inside the bounding box where motion is detected. Since motion detection inside the feasible human space runs much faster than an object detection–classification method, the proposed approach is suitable for real-time human detection with low computational loads. As far as we know, no paper in the literature used the feasible human space, as in our paper. The outperformance of our human detector system is verified by comparing it with other state-of-the-art object detection–classification algorithms (HOG detector, YOLOv7 and YOLOv7-tiny) under experiments. This paper demonstrates that the accuracy of the proposed human detector system is comparable to other state-of-the-art algorithms, while outperforming in computational speed. Our experiments show that in environments with no humans, the proposed human detector runs 62 times faster than YOLOv7 method, while showing comparable accuracy. [ABSTRACT FROM AUTHOR]

The past few decades have witnessed remarkable progress in the application of artificial intelligence (AI) and machine learning (ML) in medicine, notably in medical imaging. The application of ML to dental and oral imaging has also been developed, powered by the availability of clinical dental images. The present work aims to investigate recent progress concerning the application of ML in the diagnosis of oral diseases using oral X-ray imaging, namely the quality and outcome of such methods. The specific research question was developed using the PICOT methodology. The review was conducted in the Web of Science, Science Direct, and IEEE Xplore databases, for articles reporting the use of ML and AI for diagnostic purposes in X-ray-based oral imaging. Imaging types included panoramic, periapical, bitewing X-ray images, and oral cone beam computed tomography (CBCT). The search was limited to papers published in the English language from 2018 to 2022. The initial search included 104 papers that were assessed for eligibility. Of these, 22 were included for a final appraisal. The full text of the articles was carefully analyzed and the relevant data such as the clinical application, the ML models, the metrics used to assess their performance, and the characteristics of the datasets, were registered for further analysis. The paper discusses the opportunities, challenges, and limitations found. [ABSTRACT FROM AUTHOR]

The increasing prevalence of malware targeting android mobile devices has raised significant concerns regarding user privacy and security. In response, effective methods for malware classification and detection are crucial to protect users from malicious applications. This paper presents an approach that leverages deep learning techniques and explainable artificial intelligence (XAI) for android mobile malware classification and detection. Convolutional neural networks (CNNs) are deep learning model that has shown impressive performance in several application areas, including image and text classification. In the context of android mobile malware, CNNs have shown promising results in capturing intricate patterns and features inherent in malware samples. By training these models on large datasets of benign and malicious applications, accurate classification can be achieved. To enhance transparency and interpretability, XAI techniques are integrated into the classification process. These techniques provide insights into the decision-making process of the deep learning models, enabling the identification of critical features and characteristics that contribute to the classification results. This research, by combining deep learning and XAI methods, presents a fresh strategy for identifying and categorizing Android malware. This research paper will focus on a fascinating CNN-based malware categorization technique. [ABSTRACT FROM AUTHOR]

This paper addresses issues concerning biometric authentication based on handwritten signatures. Our research aimed to check whether a handwritten signature acquired with a mobile device can effectively verify a user's identity. We present a novel online signature verification method using coordinates of points and pressure values at each point collected with a mobile device. Convolutional neural networks are used for signature verification. In this paper, three neural network models are investigated, i.e., two self-made light SigNet and SigNetExt models and the VGG-16 model commonly used in image processing. The convolutional neural networks aim to determine whether the acquired signature sample matches the class declared by the signer. Thus, the scenario of closed set verification is performed. The effectiveness of our method was tested on signatures acquired with mobile phones. We used the subset of the multimodal database, MobiBits, that was captured using a custom-made application and consists of samples acquired from 53 people of diverse ages. The experimental results on accurate data demonstrate that developed architectures of deep neural networks can be successfully used for online handwritten signature verification. We achieved an equal error rate (EER) of 0.63% for random forgeries and 6.66% for skilled forgeries. [ABSTRACT FROM AUTHOR]

Human error estimating food intake is a major source of bias in nutrition research. Artificial intelligence (AI) methods may reduce bias, but the overall accuracy of AI estimates is unknown. This study was a systematic review of peer-reviewed journal articles comparing fully automated AI-based (e.g. deep learning) methods of dietary assessment from digital images to human assessors and ground truth (e.g. doubly labelled water). Literature was searched through May 2023 in four electronic databases plus reference mining. Eligible articles reported AI estimated volume, energy, or nutrients. Independent investigators screened articles and extracted data. Potential sources of bias were documented in absence of an applicable risk of bias assessment tool. Database and hand searches identified 14,059 unique publications; fifty-two papers (studies) published from 2010 to 2023 were retained. For food detection and classification, 79% of papers used a convolutional neural network. Common ground truth sources were calculation using nutrient tables (51%) and weighed food (27%). Included papers varied widely in food image databases and results reported, so meta-analytic synthesis could not be conducted. Relative errors were extracted or calculated from 69% of papers. Average overall relative errors (AI vs. ground truth) ranged from 0.10% to 38.3% for calories and 0.09% to 33% for volume, suggesting similar performance. Ranges of relative error were lower when images had single/simple foods. Relative errors for volume and calorie estimations suggest that AI methods align with – and have the potential to exceed – accuracy of human estimations. However, variability in food image databases and results reported prevented meta-analytic synthesis. The field can advance by testing AI architectures on a limited number of large-scale food image and nutrition databases that the field determines to be adequate for training and testing and by reporting accuracy of at least absolute and relative error for volume or calorie estimations. These results suggest that AI methods are in line with – and have the potential to exceed – accuracy of human estimations of nutrient content based on digital food images. Variability in food image databases used and results reported prevented meta-analytic synthesis. The field can advance by testing AI architectures on a limited number of large-scale food image and nutrition databases that the field determines to be accurate and by reporting accuracy of at least absolute and relative error for volume or calorie estimations. Overall, the tools currently available need more development before deployment as stand-alone dietary assessment methods in nutrition research or clinical practice. [ABSTRACT FROM AUTHOR]

Artificial intelligence is changing and influencing our world. As one of the main algorithms in the field of artificial intelligence, convolutional neural networks (CNNs) have developed rapidly in recent years. Especially after the emergence of NASNet, CNNs have gradually pushed the idea of AutoML to the public's attention, and large numbers of new structures designed by automatic searches are appearing. These networks are usually based on reinforcement learning and evolutionary learning algorithms. However, sometimes, the blocks of these networks are complex, and there is no small model for simpler tasks. Therefore, this paper proposes POSS-CNN aiming at target recognition and detection, which employs a multi-branch CNN structure with PSNC and a method of automatic parallel selection for super parameters based on a multi-branch CNN structure. Moreover, POSS-CNN can be broken up. By choosing a single branch or the combination of two branches as the "benchmark", as well as the overall POSS-CNN, we can achieve seven models with different precision and operations. The test accuracy of POSS-CNN for a recognition task tested on a CIFAR10 dataset can reach 86.4%, which is equivalent to AlexNet and VggNet, but the operation and parameters of the whole model in this paper are 45.9% and 45.8% of AlexNet, and 29.5% and 29.4% of VggNet. The mAP of POSS-CNN for a detection task tested on the LSVH dataset is 45.8, inferior to the 62.3 of YOLOv3. However, compared with YOLOv3, the operation and parameters of the model in this paper are reduced by 57.4% and 15.6%, respectively. After being accelerated by WRA, POSS-CNN for a detection task tested on an LSVH dataset can achieve 27 fps, and the energy efficiency is 0.42 J/f, which is 5 times and 96.6 times better than GPU 2080Ti in performance and energy efficiency, respectively. [ABSTRACT FROM AUTHOR]

The dominant intrusion detection models in internet of things industrial internet of things cybersecurity use network-based datasets. The Modbus protocol is one of the most often targeted protocols and cyberattacks against IoT/IIoT devices have grown to be a major threat in recent years. Due to the intricacy of the protocol and the quick evolution of cyber threats, detecting these attacks using conventional techniques might be difficult. This paper proposes an architecture that consistently outperforms the state-of-the-art methods of performing intrusion Detection that includes binary classification of whether an intrusion occurred or not and multi-class classification that classifies the different types of attacks using an embedding layer in a neural network to model the register values. The best accuracy results were obtained with a convolutional neural network, with an accuracy of 98.91% in the Modbus Binary dataset, a fully connected neural network with an accuracy of 98.06% in the multi-class classification of the Modbus dataset, and long short-term memory neural networks with an accuracy of 99.97%, 99.7%, and 80.20% in Binary, multi-class, and multi-class sub-categories, respectively which conclude that the proposed architecture performs consistently better than the control NN. Three NN are designed with and without the proposed architecture. All experiments performed in this paper conclude that the proposed architecture performs consistently better than the control NN. This paper shows that a NN with an embedding function can effectively be used to model whether an attack occurred on a device and the class of attack that occurred. This network can be utilized in the future to lessen DoS attacks and other types of network attacks. The network will be able to protect itself against a lot of damage if attacks can be predicted either before they occur or at the same moment they are launched. [ABSTRACT FROM AUTHOR]

The integration of an oral cancer early detection and staging system through the implementation of a mobile application is the focus of this research paper. The mobile app comprises three main components: 1) detecting oral cancer using lips and tongue images, 2) oral cancer detection using CT scan images, 3) oral cancer detection employing histopathological images and assessing the severity of the patient's cancer using medical data. Convolutional Neural Networks (CNNs) were utilized to train models for the first two parts, while logistic regression was employed to determine the severity of patients' conditions. This paper presents a comprehensive study on these integrated approaches with promising results in advancing early detection and accurate staging methods for oral cancer patients. [ABSTRACT FROM AUTHOR]

A full connected world is expected to be introduced in the sixth generation mobile network (6G). As a typical fully connected scenario, the internet of vehicle (IoV) enables intelligent vehicle operations via artificial intelligence (AI) and edge computing technologies. Thus, integrating intelligence into edge computing is, no doubt, a promising development trend. In the future of vehicular networks, a massive variety of services need powerful computing resources and higher quality of service (QoS). Existing computing resources are insufficient to match those increasing requirements. Most works on this problem focused on finding the power-delay's trade-off, ignoring QoS and stable load balance. In this study, we found that the computing power and redundancy of vehicles' in IoV is increasing. So, those redundant computing resources are possible to be used to solve the shortage of computing resource. CNN is a typical AI technique. This technology is very suitable for solving the problems in this article. So, we adopted CNN technique of AI to design and algorithm of convolutional long short-term memory (CN_LSTM) based traffic prediction (ACLBTP). ACLBTP was designed to gain the predicted number of vehicles belonging to the edge node. Secondly, according to the problem of insufficient computing resources on remote servers, we found that a large amount of redundant computing resources exist in edge nodes. So, we used edge computing technique to solve the problem of insufficient computing resources on remote servers. ASOBCL was designed to distribute computing tasks to edge nodes. Meanwhile, an intelligent service offloading framework was provided in this article. Based on the framework, an algorithm of improved gradient descent (AIGD) was created to accelerate the speed of iteration. So, the ACLBTP's convergence of convolutional neural network (CNN) based on AIGD was able to be accelerated too. In ASOBCL, a sorting technique was adopted to speed up the offloading work. Simulation results demonstrated the fact that the prediction strategy designed in this paper had high accuracy. The low offloading time and maintaining stable load balance were gained via running ASOBCL. Low offloading time means short response time. Additionally, the QoS was guaranteed. So, these strategies designed in this paper were effective and valuable. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease. [ABSTRACT FROM AUTHOR]

Currently, digitalization and digital transformation are actively expanding into various areas of human activity, and researchers are identifying urgent problems and offering new solutions regarding digital environments in industry [[1]], economics [[3]], medicine [[5]], ecology [[7]], education [[9]], etc. Conclusions The purpose of this Special Issue was to attract high-quality new papers in the field of applied and computational mathematics for digital environments, offering original solutions to various problems that are relevant and in demand in various fields of human activity. 10.3390/math10224297 21 Demidova L.A. A Novel Approach to Decision-Making on Diagnosing Oncological Diseases Using Machine Learning Classifiers Based on Datasets Combining Known and/or New Generated Features of a Different Nature. I hope that these selected research papers are recognized as important and meaningful by the international scientific community and can form the basis for further research in the field of applied and computational mathematics for digital environments, solving complex problems in various disciplines and application areas. [Extracted from the article]

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected were schools, colleges, and universities. The education system of entire nations had shifted to online education during this time. Many shortcomings of Learning Management Systems (LMSs) were detected to support education in an online mode that spawned the research in Artificial Intelligence (AI) based tools that are being developed by the research community to improve the effectiveness of LMSs. This paper presents a detailed survey of the different enhancements to LMSs, which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience. The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources, including AI-based chatbots. Session layer enhancements are also required, such as AI-based online proctoring and user authentication using Biometrics. These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms. It also needs the support of the Networking layer for IP-based geolocation features, the Virtual Private Network (VPN) feature, and the support of Software-Defined Networks (SDN) for optimum Quality of Service (QoS). Finally, in addition to these, non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is rapidly advancing, aiming to mimic human cognitive abilities, and is addressing complex medical challenges in the field of biological science. Over the past decade, AI has experienced exponential growth and proven its effectiveness in processing massive datasets and optimizing decision-making. The main content of this review paper emphasizes the active utilization of AI in the field of stem cells. Stem cell therapies use diverse stem cells for drug development, disease modeling, and medical treatment research. However, cultivating and differentiating stem cells, along with demonstrating cell efficacy, require significant time and labor. In this review paper, convolutional neural networks (CNNs) are widely used to overcome these limitations by analyzing stem cell images, predicting cell types and differentiation efficiency, and enhancing therapeutic outcomes. In the biomedical sciences field, AI algorithms are used to automatically screen large compound databases, identify potential molecular structures and characteristics, and evaluate the efficacy and safety of candidate drugs for specific diseases. Also, AI aids in predicting disease occurrence by analyzing patients' genetic data, medical images, and physiological signals, facilitating early diagnosis. The stem cell field also actively utilizes AI. Artificial intelligence has the potential to make significant advances in disease risk prediction, diagnosis, prognosis, and treatment and to reshape the future of healthcare. This review summarizes the applications and advancements of AI technology in fields such as drug development, regenerative medicine, and stem cell research. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to improve the manufacturability of additive manufacturing (AM) for topology-optimized (TO) structures. Enhancement of manufacturability focuses on modifying geometric constraints and classifying the building orientation (BO) of AM parts to reduce stresses and support structures (SSs). To this end, artificial intelligence (AI) networks are being developed to automate design for additive manufacturing (DfAM). Design/methodology/approach: This study considers three geometric constraints for their correction by convolutional autoencoders (CAEs) and transfer learning (TL). Furthermore, BOs of AM parts are classified using generative adversarial (GAN) and classification networks to reduce the SS. To verify the results, finite element analysis (FEA) is performed to compare the stresses of modified components with the original ones. Moreover, one sample is produced by the laser-based powder bed fusion (LB-PBF) in the BO predicted by the AI to observe its SSs. Findings: CAE and TL resulted in promoting the manufacturability of TO components. FEA demonstrated that enhancing manufacturability leads to a 50% reduction in stresses. Additionally, training GAN and pre-training the ResNet-18 resulted in 80%, 95% and 96% accuracy for training, validation and testing. The production of a sample with LB-PBF demonstrated that the predicted BO by ResNet-18 does not require SSs. Originality/value: This paper provides an automatic platform for DfAM of TO parts. Consequently, complex TO parts can be designed most feasibly and manufactured by AM technologies with minimal material usage, residual stresses and distortions. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence, hand gesture detection has gradually become a research hotspot in human–computer interaction. However, the traditional hand gesture detection has low robustness and detection accuracy, and brings the problem of privacy protection. Therefore, this paper proposes a Faster Region-based Convolutional Neural Network (F-RCNN) hand gesture detection method based on Frequency Modulated Continuous Wave (FMCW) radar with 3-Dimensions deep convolutional Generative Adversarial Networks (3-DCGAN). In particular, the range Doppler and angle of the hand gestures are calculated by FMCW radar. Then, the semantic label maps of the Range-Time-Map, Doppler-Time-Map and Angle-Time-Map images are respectively sent to the 3-DCGAN to expand the datasets. After that, the original and the 3-DCGAN generated images are sent to F-RCNN for jointly training. The classifier function is designed, and a learning ranking based assessment called Rank based Quality Score (RQS) is applied to improve the detection performance. The experimental results show that the mean average precision reaches to 80.8%. Moreover, the RQS for hand gesture detection is as high as 96.3%, which is increased by 5.8% compared to the traditional F-RCNN. [ABSTRACT FROM AUTHOR]

Deep learning, a subset of machine learning, stands at the forefront of artificial intelligence, striving to bridge the gap to its ultimate goal. This paper employs summary and induction methodologies to research into the area of deep learning. It begins by surveying the global development and current landscape of deep learning. Next, it elucidates the structural principles, characteristics, and key models, including stacked auto encoders, deep belief networks, deep Boltzmann machines, and convolutional neural networks. Furthermore, it examines the latest advancements and applications of deep learning across diverse domains such as speech processing, computer vision, natural language processing, and medical diagnostics. Finally, the paper outlines the challenges and future research directions within the realm of deep learning. [ABSTRACT FROM AUTHOR]

This document provides a summary of a special issue on the application of artificial intelligence (AI) in hydraulic engineering. The papers in this issue cover a range of topics including safety monitoring, optimization algorithms, nondestructive sensors, numerical simulations, and experiments. The research presented in these papers is significant for analyzing and managing the safety of major structures in reservoir dams. The authors hope that these papers will be of interest to researchers, designers, and practitioners in the field and will inspire further research. [Extracted from the article]

Convolutional neural networks (CNNs) have been widely used in satellite remote sensing. However, satellites in orbit with limited resources and power consumption cannot meet the storage and computing power requirements of current million‐scale artificial intelligence models. This paper proposes a new generation of high flexibility and intelligent CNNs hardware accelerator for satellite remote sensing in order to make its computing carrier more lightweight and efficient. A data quantization scheme for INT16 or INT8 is designed based on the idea of dynamic fixed point numbers and is applied to different scenarios. The operation mode of the systolic array is divided into channel blocks, and the calculation method is optimized to increase the utilization of on‐chip computing resources and enhance the calculation efficiency. An RTL‐level CNNs field programable gate arrays accelerator with microinstruction sequence scheduling data flow is then designed. The hardware framework is built upon the Xilinx VC709. The results show that, under INT16 or INT8 precision, the system achieves remarkable throughput in most convolutional layers of the network, with an average performance of 153.14 giga operations per second (GOPS) or 301.52 GOPS, which is close to the system's peak performance, taking full advantage of the platform's parallel computing capabilities. [ABSTRACT FROM AUTHOR]

The rising interest in Artificial Intelligence and the increasing time invested in functional verification processes are driving the demand for AI solutions in this field. Functional verification is the process of verifying that the Register Transfer Layer (RTL) implementation behaves according to the specifications provided. This is performed using a hardware verification language (HVL) such as SystemVerilog combined with the Universal Verification Methodology (UVM). Reading, identifying the key elements from multiple documentations, creating the verification plan, building the verification environment, implementing the tests defined, and achieving 100% coverage are usually the steps performed in order to complete the verification process. The verification process is considered finalized when functional coverage is at 100%. There are multiple ideas on how the process can be aided by AI, such as underlining the essential information from documentation, which would help in understanding faster how the Register Transfer Layer implementation works, thus vastly reducing time. In this paper, to greatly reduce the time spent on functional verification, two Convolutional Neural Network (CNN) architectures are implemented to properly classify the information across different documents; both approaches have significant and promising results. The database used for this classification task was created by the researchers using different documentations available. [ABSTRACT FROM AUTHOR]

Trash or garbage is one of the most dangerous health and environmental problems that affect pollution. Pollution affects nature, human life, and wildlife. In this paper, we propose modern solutions for cleaning the environment of trash pollution by enforcing strict action against people who dump trash inappropriately on streets, outside the home, and in unnecessary places. Artificial Intelligence (AI), especially Deep Learning (DL), has been used to automate and solve issues in the world. We availed this as an excellent opportunity to develop a system that identifies trash using a deep convolutional neural network (CNN). This paper proposes a real-time garbage identification system based on a deep CNN architecture with eight distinct classes for the training dataset. After identifying the garbage, the CCTV camera captures a video of the individual placing the trash in the incorrect location and sends an alert notice to the relevant authority. [ABSTRACT FROM AUTHOR]

Sign language, a visual-gestural language used by the deaf and hard-of-hearing community, plays a crucial role in facilitating communication and promoting inclusivity. Sign language recognition (SLR), the process of automatically recognizing and interpreting sign language gestures, has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world. The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR. This paper presents a comprehensive and up-to-date analysis of the advancements, challenges, and opportunities in deep learning-based sign language recognition, focusing on the past five years of research. We explore various aspects of SLR, including sign data acquisition technologies, sign language datasets, evaluation methods, and different types of neural networks. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have shown promising results in fingerspelling and isolated sign recognition. However, the continuous nature of sign language poses challenges, leading to the exploration of advanced neural network models such as the Transformer model for continuous sign language recognition (CSLR). Despite significant advancements, several challenges remain in the field of SLR. These challenges include expanding sign language datasets, achieving user independence in recognition systems, exploring different input modalities, effectively fusing features, modeling co-articulation, and improving semantic and syntactic understanding. Additionally, developing lightweight network architectures for mobile applications is crucial for practical implementation. By addressing these challenges, we can further advance the field of deep learning for sign language recognition and improve communication for the hearing-impaired community. [ABSTRACT FROM AUTHOR]

This paper presents two experiments in which, using artificial intelligence (specifically Deep Learning with convolutional neural networks), we were able to predict Alzheimer's disease based on MRI images. In order to have better results and to minimize the computational effort in the laboratory, two pre-trained AI models were used, models trained previously on more than a million images from the ImageNet database (which provide tens of millions of clean, labelled and sorted images). The top-layers of the models were trained, for our specific task of Alzheimer’s prediction, with 500 public MRI images from Kaggle, an online community of data scientists and machine learning engineers and a subsidiary of Google. In this paper we describe the code used in the laboratory for the specific task. [ABSTRACT FROM AUTHOR]

Deep learning, a crucial technique for achieving artificial intelligence (AI), has been successfully applied in many fields. The gradual application of the latest architectures of deep learning in the field of time series forecasting (TSF), such as Transformers, has shown excellent performance and results compared to traditional statistical methods. These applications are widely present in academia and in our daily lives, covering many areas including forecasting electricity consumption in power systems, meteorological rainfall, traffic flow, quantitative trading, risk control in finance, sales operations and price predictions for commercial companies, and pandemic prediction in the medical field. Deep learning-based TSF tasks stand out as one of the most valuable AI scenarios for research, playing an important role in explaining complex real-world phenomena. However, deep learning models still face challenges: they need to deal with the challenge of large-scale data in the information age, achieve longer forecasting ranges, reduce excessively high computational complexity, etc. Therefore, novel methods and more effective solutions are essential. In this paper, we review the latest developments in deep learning for TSF. We begin by introducing the recent development trends in the field of TSF and then propose a new taxonomy from the perspective of deep neural network models, comprehensively covering articles published over the past five years. We also organize commonly used experimental evaluation metrics and datasets. Finally, we point out current issues with the existing solutions and suggest promising future directions in the field of deep learning combined with TSF. This paper is the most comprehensive review related to TSF in recent years and will provide a detailed index for researchers in this field and those who are just starting out. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence, computer vision techniques have been successfully applied to concrete defect diagnosis in bridge structural health monitoring. To enhance the accuracy of identifying the location and type of concrete defects (cracks, exposed bars, spalling, efflorescence and voids), this paper proposes improvements to the existing Mask Region Convolution Neural Network (Mask R-CNN). The improvements are as follows: (i) The residual network (ResNet101), the backbone network of Mask R-CNN which has too many convolution layers, is replaced by the lightweight network MobileNetV2. This can solve the problem that the large number of parameters leads to a slow training speed of the model, and improve the ability to extract features of smaller targets. (ii) Embedding attention mechanism modules in Feature Pyramid Networks (FPNs) to better extract the target features. (iii) A path aggregation network (PANet) is added to solve the problem that the model Mask R-CNN lacks the ability to extract shallow layer feature information. To validate the superiority of the proposed improved Mask R-CNN, the multi-class concrete defect image dataset was constructed, and using the K-means clustering algorithm to determine the aspect ratio of the most suitable prior bounding box for the dataset. Following, the identification results of improved Mask-RCNN, original Mask-RCNN and other mainstream deep learning networks on five types of concrete defects (cracks, exposed bars, spalling, efflorescence and voids) in the dataset were compared. Finally, the intelligent identification system for concrete defects has been established by innovatively combining images taken by unmanned aerial vehicles (UAVs) with our improved defect identification model. The reinforced concrete bridge defects images collected by UAVs were used as test set for testing. The result is the improved Mask R-CNN with superior accuracy, and the identification accuracy is higher than the original Mask-RCNN and other deep learning networks. The improved Mask-RCNN can identify the new untrained concrete defects images taken by UAVs, and the identification accuracy can meet the requirements of bridge structural health monitoring. [ABSTRACT FROM AUTHOR]