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Optimal Placement and Sizing of Distributed Generations for Power Losses Minimization Using PSO-Based Deep Learning Techniques 认领 引用 被引量：2
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作者 Bello-Pierre Ngoussandou Nicodem Nisso +1 位作者 Dieudonné Kaoga Kidmo   Kitmo 《Smart Grid and Renewable Energy》 2023年第9期169-181,共13页
The integration of distributed generations (DGs) into distribution systems (DSs) is increasingly becoming a solution for compensating for isolated local energy systems (ILESs). Additionally, distributed generations ar... The integration of distributed generations (DGs) into distribution systems (DSs) is increasingly becoming a solution for compensating for isolated local energy systems (ILESs). Additionally, distributed generations are used for self-consumption with excess energy injected into centralized grids (CGs). However, the improper sizing of renewable energy systems (RESs) exposes the entire system to power losses. This work presents an optimization of a system consisting of distributed generations. Firstly, PSO algorithms evaluate the size of the entire system on the IEEE bus 14 test standard. Secondly, the size of the system is allocated using improved Particles Swarm Optimization (IPSO). The convergence speed of the objective function enables a conjecture to be made about the robustness of the proposed system. The power and voltage profile on the IEEE 14-bus standard displays a decrease in power losses and an appropriate response to energy demands (EDs), validating the proposed method. 展开更多
关键词 Distributed Generations Deep Learning Techniques Improved Particle Swarm Optimization Power Losses Power Losses Minimization Optimal Placement
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Research on Image Recognition Using Deep Learning Techniques 认领 引用
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作者 Shuntao Tang Wei Chen 《控制工程期刊（中英文版）》 2024年第1期1-5,共5页
This study delves into the applications,challenges,and future directions of deep learning techniques in the field of image recognition.Deep learning,particularly Convolutional Neural Networks(CNNs),Recurrent Neural Ne... This study delves into the applications,challenges,and future directions of deep learning techniques in the field of image recognition.Deep learning,particularly Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),and Generative Adversarial Networks(GANs),has become key to enhancing the precision and efficiency of image recognition.These models are capable of processing complex visual data,facilitating efficient feature extraction and image classification.However,acquiring and annotating high-quality,diverse datasets,addressing imbalances in datasets,and model training and optimization remain significant challenges in this domain.The paper proposes strategies for improving data augmentation,optimizing model architectures,and employing automated model optimization tools to address these challenges,while also emphasizing the importance of considering ethical issues in technological advancements.As technology continues to evolve,the application of deep learning in image recognition will further demonstrate its potent capability to solve complex problems,driving society towards more inclusive and diverse development. 展开更多
关键词 Deep Learning Techniques Image Recognition Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks
Diagnosis of different types of single suture craniosynostosis in infants with deep learning techniques 认领 引用
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作者 Amir Hossein Zinati Maliheh Sabeti +1 位作者 Hossein Kamyab Ehsan Moradi 《Medicine in Novel Technology and Devices》 2025年第3期96-105,共10页
Craniosynostosis(CsO)is characterized by premature fusion of skull sutures in infants.This early closure of one or more main sutures can lead to various skull and facial deformities and may cause developmental delay i... Craniosynostosis(CsO)is characterized by premature fusion of skull sutures in infants.This early closure of one or more main sutures can lead to various skull and facial deformities and may cause developmental delay in children.Early diagnosis,crucial for effective treatment,traditionally relies on physical examination and 3D cranial imaging,which are often inaccurate or with the risk of X-ray exposure.This study presents a fully-automated deep learning-based method for diagnosing common types of single suture CsO using routine digi-tal photographs of infants'heads.We employed a two-stage approach involving head segmentation and CsO type classification.First,mask region-based convolutional neural network(Mask R-CNN)was used for accurate head segmentation,achieving an average precision of 97.60%and an average recall of 96.20%.The segmented images were then classified into different CsO types using a modified VGG11 neural network.The classifier attained a training accuracy of 99.74%and a test accuracy of 94.44%,with high sensitivity and specificity for uni-coronal,metopic,and sagittal types.Our method illustrates high reliability and accuracy,offering non-invasive,accessible and accurate diagnostic instrument for early detection and patient screening. 展开更多
关键词 Craniosynostosis Deep learning techniques Patient screening
Advanced Techniques for Dynamic Malware Detection and Classification in Digital Security Using Deep Learning 认领 引用
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作者 Taher Alzahrani 《Computers, Materials & Continua》 SCIE EI 2025年第6期4575-4606,共32页
The rapid evolution of malware presents a critical cybersecurity challenge,rendering traditional signature-based detection methods ineffective against novel variants.This growing threat affects individuals,organizatio... The rapid evolution of malware presents a critical cybersecurity challenge,rendering traditional signature-based detection methods ineffective against novel variants.This growing threat affects individuals,organizations,and governments,highlighting the urgent need for robust malware detection mechanisms.Conventional machine learning-based approaches rely on static and dynamicmalware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures.Although machine learning algorithms(MLAs)offer promising detection capabilities,their reliance on extensive feature engineering limits real-time applicability.Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,affecting deployment efficiency.This research evaluates classical MLAs and deep learningmodels to enhance malware detection performance across diverse datasets.The proposed approach integrates a novel text and imagebased detection framework,employing an optimized Support Vector Machine(SVM)for textual data analysis and EfficientNet-B0 for image-based malware classification.Experimental analysis,conducted across multiple train-test splits over varying timescales,demonstrates 99.97%accuracy on textual datasets using SVM and 96.7%accuracy on image-based datasets with EfficientNet-B0,significantly improving zero-day malware detection.Furthermore,a comparative analysis with existing competitive techniques,such as Random Forest,XGBoost,and CNN-based(Convolutional Neural Network)classifiers,highlights the superior performance of the proposed model in terms of accuracy,efficiency,and robustness. 展开更多
关键词 Machine learning EffiicientNet B0 malimg dataset XceptionNet malware detection deep learning techniques support vector machines(SVM)
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Retraction: A Lightweight Multimodal Deep Fusion Network for Face Antis Poofing with Cross-Axial Attention and Deep Reinforcement Learning Technique 认领 引用
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作者 无 《Computers, Materials & Continua》 SCIE EI 2026年第7期1981-1981,共1页
The published article titled“A Lightweight Multimodal Deep Fusion Network for Face Antis Poofing with Cross-Axial Attention and Deep Reinforcement Learning Technique”has been retracted from Computers,Materials&C... The published article titled“A Lightweight Multimodal Deep Fusion Network for Face Antis Poofing with Cross-Axial Attention and Deep Reinforcement Learning Technique”has been retracted from Computers,Materials&Continua,Vol.85,No.3,2025,pp.5671-5702. 展开更多
关键词 face antis poofing deep reinforcement learning technique deep reinforcement learning face antispoofing lightweight multimodal deep fusion network cross axial attention
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A critical evaluation of deep-learning based phylogenetic inference programs using simulated datasets 认领 引用
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作者 Yixiao Zhu Yonglin Li +2 位作者 Chuhao Li Xing-Xing Shen Xiaofan Zhou 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2025年第5期714-717,共4页
Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus o... Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus often suffer from model misspecification or inadequacy.The on-rising deep learning(DL)techniques offer a powerful alternative.Deep learning employs multi-layered artificial neural networks to progressively transform input data into more abstract and complex representations.DL methods can autonomously uncover meaningful patterns from data,thereby bypassing potential biases introduced by predefined features(Franklin,2005;Murphy,2012).Recent efforts have aimed to apply deep neural networks(DNNs)to phylogenetics,with a growing number of applications in tree reconstruction(Suvorov et al.,2020;Zou et al.,2020;Nesterenko et al.,2022;Smith and Hahn,2023;Wang et al.,2023),substitution model selection(Abadi et al.,2020;Burgstaller-Muehlbacher et al.,2023),and diversification rate inference(Voznica et al.,2022;Lajaaiti et al.,2023;Lambert et al.,2023).In phylogenetic tree reconstruction,PhyDL(Zou et al.,2020)and Tree_learning(Suvorov et al.,2020)are two notable DNN-based programs designed to infer unrooted quartet trees directly from alignments of four amino acid(AA)and DNA sequences,respectively. 展开更多
关键词 phylogenetic inference explicit models sequence evolution deep learning deep learning dl techniques molecular sequences simulated datasets phylogenetic methods such evolutionary biologymany
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An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique 认领 引用 被引量：3
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作者 Sumaia Mohamed Elhassan Saad Mohamed Darwish Saleh Mesbah Elkaffas 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期835-867,共33页
Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of suc... Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance. 展开更多
关键词 Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8
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Research on deep learning decoding method for polar codes in ACO-OFDM spatial optical communication system 认领 引用
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作者 LIU Kangrui LI Ming +2 位作者 CHEN Sizhe QU Jiashun ZHOU Ming’ou 《Optoelectronics Letters》 EI 2025年第7期427-433,共7页
Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbule... Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 102compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder. 展开更多
关键词 frequency conduction polar codes deep learning signal demodulation deep learning technique decoding ACO OFDM polarization code decoding
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Deep-learning-based in-field citrus fruit detection and tracking 认领 引用 被引量：13
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作者 Wenli Zhang Jiaqi Wang +6 位作者 Yuxin Liu Kaizhen Chen Huibin Li Yulin Duan Wenbin Wu Yun Shi Wei Guo 《Horticulture Research》 SCIE CSCD 2022年第1期1708-1722,共15页
Fruit yield estimation is crucial for establishing fruit harvest and marketing strategies.Recently,computer vision and deep learning techniques have been used to estimate citrus fruit yield and have exhibited notable ... Fruit yield estimation is crucial for establishing fruit harvest and marketing strategies.Recently,computer vision and deep learning techniques have been used to estimate citrus fruit yield and have exhibited notable fruit detection ability.However,computer-visionbased citrus fruit counting has two key limitations:inconsistent fruit detection accuracy and double-counting of the same fruit.Using oranges as the experimental material,this paper proposes a deep-learning-based orange counting algorithm using video sequences to help overcome these problems.The algorithm consists of two sub-algorithms,OrangeYolo for fruit detection and OrangeSort for fruit tracking.The OrangeYolo backbone network is partially based on the YOLOv3 algorithm,which has been improved upon to detect small objects(fruits)at multiple scales.The network structure was adjusted to detect small-scale targets while enabling multiscale target detection.A channel attention and spatial attention multiscale fusion module was introduced to fuse the semantic features of the deep network with the shallow textural detail features.OrangeYolo can achieve mean Average Precision(mAP)values of 0.957 in the citrus dataset,higher than the 0.905,0.911,and 0.917 achieved with the YOLOv3,YOLOv4,and YOLOv5 algorithms.OrangeSort was designed to alleviate the double-counting problem associated with occluded fruits.A specific tracking region counting strategy and tracking algorithm based on motion displacement estimation were established.Six video sequences taken from two fields containing 22 trees were used as the validation dataset.The proposed method showed better performance(Mean Absolute Error(MAE)=0.081,Standard Deviation(SD)=0.08)than video-based manual counting and produced more accurate results than the existing standards Sort and DeepSort(MAE=0.45 and 1.212;SD=0.4741 and 1.3975). 展开更多
关键词 deep learning citrus fruit detection establishing fruit harvest marketing strategiesrecentlycomputer vision deep learning techniques fruit yield estimation orange counting estimate citrus fruit yield fruit detection
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Deep learning in tropical leaf disease detection:advantages and applications 认领 引用 被引量：1
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作者 Zhiye Yao Mengxing Huang 《Tropical Plants》 2024年第1期257-267,共11页
This paper delves into the realm of artificial intelligence,where an array of deep learning techniques has proven effective in automating crop leaf disease identification and classification.The current paper shows mat... This paper delves into the realm of artificial intelligence,where an array of deep learning techniques has proven effective in automating crop leaf disease identification and classification.The current paper shows mature detection methodologies for apple,tomato,rice,mango,coconut,and durian leaf diseases with examples while demonstrating research on leaf disease detection in tropical plants.Through this exploration,valuable insights into the benefits and applications of detection techniques based on deep learning methods are provided for leaf disease detection.Highlighting the advantages of deep learning methods are provided for automated feature extraction and disease detection,the paper describes the salient features and challenges of the application of leaf disease detection in the tropics.In this paper,an introductory overview of a leaf disease detection model is offered and delve into the factors influencing detection accuracy and speed while proposing ways to mitigate the inherent trade-offs between these indicators.Furthermore,the challenges,such as multi-scale detection and leaf overlapping,that may occur in plants in the tropics,have been examined,enriching our understanding of deep learning-driven leaf disease detection in tropical agriculture. 展开更多
关键词 deep learning tropical leaf disease detection detection techniques deep lear leaf disease detection artificial intelligencewhere deep learning techniques automating crop leaf disease identification classificationthe
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Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19 认领 引用 被引量：2
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作者 Sally M.Elghamrawy Aboul Ella Hassnien Vaclav Snasel 《Computers, Materials & Continua》 SCIE EI 2021年第5期2353-2371,共19页
Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients.This study a... Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients.This study aimed to develop a COVID-19 diagnosis and prediction(AIMDP)model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography(CT)scans.The proposed system uses convolutional neural networks(CNNs)as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment.We employed the whale optimization algorithm(WOA)to select the most relevant patient signs.A set of experiments validated AIMDP performance.It demonstrated the superiority of AIMDP in terms of the area under the curve-receiver operating characteristic(AUC-ROC)curve,positive predictive value(PPV),negative predictive rate(NPR)and negative predictive value(NPV).AIMDP was applied to a dataset of hundreds of real data and CT images,and it was found to achieve 96%AUC for diagnosing COVID-19 and 98%for overall accuracy.The results showed the promising performance of AIMDP for diagnosing COVID-19 when compared to other recent diagnosing and predicting models. 展开更多
关键词 Convolutional neural networks coronavirus disease 2019(COVID-19) CT chest scan imaging deep learning technique feature selection whale optimization algorithm
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Solar cycle prediction using a long short-term memory deep learning model 认领 引用 被引量：4
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作者 Qi-Jie Wang Jia-Chen Li Liang-Qi Guo 《Research in Astronomy and Astrophysics》 SCIE CAS CSCD 2021年第1期119-126,共8页
In this paper,we propose a long short-term memory(LSTM)deep learning model to deal with the smoothed monthly sunspot number(SSN),aiming to address the problem whereby the prediction results of the existing sunspot pre... In this paper,we propose a long short-term memory(LSTM)deep learning model to deal with the smoothed monthly sunspot number(SSN),aiming to address the problem whereby the prediction results of the existing sunspot prediction methods are not uniform and have large deviations.Our method optimizes the number of hidden nodes and batch sizes of the LSTM network structures to 19 and 20,respectively.The best length of time series and the value of the timesteps were then determined for the network training,and one-step and multi-step predictions for Cycle 22 to Cycle 24 were made using the well-established network.The results showed that the maximum root-mean-square error(RMSE)of the one-step prediction model was6.12 and the minimum was only 2.45.The maximum amplitude prediction error of the multi-step prediction was 17.2%and the minimum was only 3.0%.Finally,the next solar cycles(Cycle 25)peak amplitude was predicted to occur around 2023,with a peak value of about 114.3.The accuracy of this prediction method is better than that of the other commonly used methods,and the method has high applicability. 展开更多
关键词 Sun:solar activity Sun:sunspot number techniques:deep learning techniques:long short-term memory
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A Critical Analysis of Machine Learning and Deep Learning Methods for Cervical Cancer Screening 认领 引用
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作者 Muhtasim   Mahmudur Rahman +3 位作者 Jakir Khan Abu Sale Mohammad Mostafizur Rahman Redoanul Haque Md. Sumon Ali 《Journal of Computer and Communications》 2023年第12期64-85,共22页
Cervical cancer is a serious public health issue worldwide, and early identification is crucial for better patient outcomes. Recent study has investigated how ML and DL approaches may be used to increase the accuracy ... Cervical cancer is a serious public health issue worldwide, and early identification is crucial for better patient outcomes. Recent study has investigated how ML and DL approaches may be used to increase the accuracy of vagina tests. In this piece, we conducted a thorough review of 50 research studies that applied these techniques. Our investigation compared the outcomes to well-known screening techniques and concentrated on the datasets used and performance measurements reported. According to the research, convolutional neural networks and other deep learning approaches have potential for lowering false positives and boosting screening precision. Although several research used small sample sizes or constrained datasets, this raises questions about how applicable the findings are. This paper discusses the advantages and disadvantages of the articles that were chosen, as well as prospective topics for future research, to further the application of ml and dl in cervical cancer screening. The development of cervical cancer screening technologies that are more precise, accessible, and can lead to better public health outcomes is significantly affected by these findings. 展开更多
关键词 Cervical Cancer Neoplasms Screening Machine Learning Techniques Deep Learning Techniques
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Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection 认领 引用 被引量：2
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作者 T.Thanya S.Wilfred Franklin 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期97-112,共16页
At an early point,the diagnosis of pancreatic cancer is mediocre,since the radiologist is skill deficient.Serious threats have been posed due to the above reasons,hence became mandatory for the need of skilled technici... At an early point,the diagnosis of pancreatic cancer is mediocre,since the radiologist is skill deficient.Serious threats have been posed due to the above reasons,hence became mandatory for the need of skilled technicians.However,it also became a time-consuming process.Hence the need for automated diagnosis became mandatory.In order to identify the tumor accurately,this research pro-poses a novel Convolution Neural Network(CNN)based superior image classi-fication technique.The proposed deep learning classification strategy has a precision of 97.7%,allowing for more effective usage of the automatically exe-cuted feature extraction technique to diagnose cancer cells.Comparative analysis with CNN-Grey Wolf Optimization(GWO)is carried based on varied testing and training outcomes.The suggested study is carried out at a rate of 90%–10%,80%–20%,and 70%–30%,indicating the robustness of the proposed research work.Outcomes show that the suggested method is effective.GWO-CNN is reli-able and accurate relative to other detection methods available in the literatures. 展开更多
关键词 Convolution neural network deep learning technique feature extraction grey wolf optimizer
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Intelligent recognition of weak discontinuities on outcrops of hard rock masses 认领 引用
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作者 Wen Zhang Guanglu Xu +4 位作者 Tengyue Li Danyang Wu Huiyu Zhou Long Chen Xiaoxue Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第5期3742-3759,共18页
Rock mass discontinuities arise from tectonic movements and other geological processes,reflecting the evolution of the Earth's crust.These discontinuities significantly influence the physical properties,deformatio... Rock mass discontinuities arise from tectonic movements and other geological processes,reflecting the evolution of the Earth's crust.These discontinuities significantly influence the physical properties,deformation characteristics,and energy release mechanisms of the crust.Therefore,recognizing discontinuities is crucial for understanding the evolution of geological structures,analyzing the physical and mechanical properties of geological bodies,and investigating geological hazards.Traditionally,discontinuity recognition has relied on manual interpretation or automated algorithms based on pixel brightness.However,these methods often struggle to strike a balance between efficiency and robustness.To overcome these limitations,we leveraged deep learning techniques that integrate the strengths of both approaches,enabling the recognition of automated discontinuity with expert-level accuracy.To accomplish this objective,we developed and open-sourced the first large-scale deep learning database for rock mass discontinuities,featuring over 300,000 annotated discontinuities.The YOLOv8x-seg model was extensively trained on this database and evaluated across diverse and complex scenarios.The results demonstrated the model's capability to accurately recognize discontinuities even under challenging conditions.Furthermore,we expanded the test set to include rock masses from various global locations,as well as underground rock masses,soils,and artificial structures,where the model consistently achieved effective recognition.The model consistently delivered accurate results,highlighting its strong generalization capability.A comparative analysis revealed that its performance closely aligns with expert manual interpretations.Our open-source database enables researchers to train various deep learning models and achieve equally high-performance results. 展开更多
关键词 Rock mass discontinuity recognition Deep learning techniques Expert-level accuracy Deep learning database YOLOv8x-seg model
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Automated Concrete Bridge Damage Detection Using an Efficient Vision Transformer-Enhanced Anchor-Free YOLO 认领 引用
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作者 Xiaofei Yang Enrique del Rey Castillo +3 位作者 Yang Zou Liam Wotherspoon Jianxi Yang Hao Li 《Engineering》 SCIE EI CSCD 2025年第8期311-326,共16页
Deep learning techniques have recently been the most popular method for automatically detecting bridge damage captured by unmanned aerial vehicles(UAVs).However,their wider application to real-world scenarios is hinde... Deep learning techniques have recently been the most popular method for automatically detecting bridge damage captured by unmanned aerial vehicles(UAVs).However,their wider application to real-world scenarios is hindered by three challenges:①defect scale variance,motion blur,and strong illumination significantly affect the accuracy and reliability of damage detectors;②existing commonly used anchor-based damage detectors struggle to effectively generalize to harsh real-world scenarios;and③convolutional neural networks(CNNs)lack the capability to model long-range dependencies across the entire image.This paper presents an efficient Vision Transformer-enhanced anchor-free YOLO(you only look once)method to address these challenges.First,a concrete bridge damage dataset was established,augmented by motion blur and varying brightness.Four key enhancements were then applied to an anchor-based YOLO method:①Four detection heads were introduced to alleviate the multi-scale damage detection issue;②decoupled heads were employed to address the conflict between classification and bounding box regression tasks inherent in the original coupled head design;③an anchor-free mechanism was incorporated to reduce the computational complexity and improve generalization to real-world scenarios;and④a novel Vision Transformer block,C3MaxViT,was added to enable CNNs to model long-range dependencies.These enhancements were integrated into an advanced anchor-based YOLOv5l algorithm,and the proposed Vision Transformer-enhanced anchor-free YOLO method was then compared against cutting-edge damage detection methods.The experimental results demonstrated the effectiveness of the proposed method,with an increase of 8.1%in mean average precision at intersection over union threshold of 0.5(mAP50)and an improvement of 8.4%in mAP@[0.5:.05:.95]respectively.Furthermore,extensive ablation studies revealed that the four detection heads,decoupled head design,anchor-free mechanism,and C3MaxViT contributed improvements of 2.4%,1.2%,2.6%,and 1.9%in mAP50,respectively. 展开更多
关键词 Computer vision Deep learning techniques Vision Transformer Object detection Bridge visual inspection
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YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security 认领 引用 被引量：2
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作者 Fares Alharbi Reem Alshahrani +2 位作者 Mohammed Zakariah Amjad Aldweesh Abdulrahman Abdullah Alghamdi 《Computers, Materials & Continua》 SCIE EI 2023年第12期3697-3722,共26页
Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless... Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels,optical fiber,and blockchain technology.The Internet of Things(IoT)is a network of connected,interconnected gadgets.Privacy issues occasionally arise due to the amount of data generated.However,they have been primarily addressed by blockchain and smart contract technology.While there are still security issues with smart contracts,primarily due to the complexity of writing the code,there are still many challenges to consider when designing blockchain designs for the IoT environment.This study uses traditional blockchain technology with the“You Only Look Once”(YOLO)object detection method to accurately locate and identify license plates.While YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising,they have received limited research attention.Real-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network(RCNN)built with the tensor flow core open source libraries.This method works reasonably well for identifying any license plate.The Automatic License Plate Recognition(ALPR)approach delivered outstanding results in various datasets.First,with a recognition rate of 96.2%,our system(UFPR-ALPR)surpassed the previously used technology,consisting of 4500 frames and around 150 films.Second,a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR dataset.Third,the license plate’s characters were complicated for standard methods to identify because of the shifting lighting correctly.The proposed model,however,produced beneficial outcomes. 展开更多
关键词 Intelligent transportation system blockchain technology license plate recognition privacy YOLO deep learning technique ALPR
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Text-to-Sketch Synthesis via Adversarial Network 认领 引用
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作者 Jason Elroy Martis Sannidhan Manjaya Shetty +2 位作者 Manas Ranjan Pradhan Usha Desai Biswaranjan Acharya 《Computers, Materials & Continua》 SCIE EI 2023年第7期915-938,共24页
In the past,sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security purposes.However,relying on eyewitness observations can lead to d... In the past,sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security purposes.However,relying on eyewitness observations can lead to discrepancies in the depictions of the sketch,depending on the experience and skills of the sketch artist.With the emergence of modern technologies such as Generative Adversarial Networks(GANs),generating images using verbal and textual cues is now possible,resulting in more accurate sketch depictions.In this study,we propose an adversarial network that generates human facial sketches using such cues provided by an observer.Additionally,we have introduced an Inverse Gamma Correction Technique to improve the training and enhance the quality of the generated sketches.To evaluate the effectiveness of our proposed method,we conducted experiments and analyzed the results using the inception score and Frechet Inception Distance metrics.Our proposed method achieved an overall inception score of 1.438±0.049 and a Frechet Inception Distance of 65.29,outperforming other state-of-the-art techniques. 展开更多
关键词 Generative adversarial networks inverse gamma correction sketch attributes text-to-sketch synthesis deep learning techniques
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DSC based Dual-Resunet for radio frequency interference identification 认领 引用 被引量：1
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作者 Yan-Jun Zhang Yan-Zuo Li +1 位作者 Jun Cheng Yi-Hua Yan 《Research in Astronomy and Astrophysics》 SCIE CAS CSCD 2021年第12期315-325,共11页
Radio frequency interference(RFI)will pollute the weak astronomical signals received by radio telescopes,which in return will seriously affect the time-domain astronomical observation and research.In this paper,we use... Radio frequency interference(RFI)will pollute the weak astronomical signals received by radio telescopes,which in return will seriously affect the time-domain astronomical observation and research.In this paper,we use a deep learning method to identify RFI in frequency spectrum data,and propose a neural network based on Unet that combines the principles of depthwise separable convolution and residual,named DSC Based Dual-Resunet.Compared with the existing Unet network,DSC Based Dual-Resunet performs better in terms of accuracy,F1 score,and MIoU,and is also better in terms of computation cost where the model size and parameter amount are 12.5%of Unet and the amount of computation is 38%of Unet.The experimental results show that the proposed network is a high-performance and lightweight network,and it is hopeful to be applied to RFI identification of radio telescopes on a large scale. 展开更多
关键词 techniques:deep learning and image processing radio frequency interference telescopes Sun:radio radiation
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Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture 认领 引用
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作者 Meherab Mamun Ratul Kazi Ayesha Rahman +2 位作者 Javeria Fazal Naimur Rahman Abanto Riasat Khan 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期3641-3658,共18页
The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial ma... The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores. 展开更多
关键词 Artificial intelligence COVID-19 deep learning technique face mask detection social distance monitor you only look once
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