Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,...Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.展开更多
The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera im...The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera imaging,single-phase FFA from scanning laser ophthalmoscopy(SLO),and three-phase FFA also from SLO.Although many deep learning models are available,a single model can only perform one or two of these prediction tasks.To accomplish three prediction tasks using a unified method,we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network.The three prediction tasks are processed as follows:data preparation,network training under FFA supervision,and FFA image prediction from fundus structure images on a test set.By comparing the FFA images predicted by our model,pix2pix,and CycleGAN,we demonstrate the remarkable progress achieved by our proposal.The high performance of our model is validated in terms of the peak signal-to-noise ratio,structural similarity index,and mean squared error.展开更多
AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hos...AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included,and 13470 infrared pupil images were collected for the study.All infrared images for pupil segmentation were labeled using the Labelme software.The computation of pupil diameter is divided into four steps:image pre-processing,pupil identification and localization,pupil segmentation,and diameter calculation.Two major models are used in the computation process:the modified YoloV3 and Deeplabv 3+models,which must be trained beforehand.RESULTS:The test dataset included 1348 infrared pupil images.On the test dataset,the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils.The DeeplabV3+model achieved a background intersection over union(IOU)of 99.23%,a pupil IOU of 93.81%,and a mean IOU of 96.52%.The pupil diameters in the test dataset ranged from 20 to 56 pixels,with a mean of 36.06±6.85 pixels.The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels,with a mean absolute error(MAE)of 1.06±0.96 pixels.CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm,proven to be highly accurate and reliable for clinical application.展开更多
The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on ...The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on wind turbine blades,a blade surface defect detection and quantification method based on an improved Deeplabv3+deep learning model is proposed.Firstly,an improved method for wind turbine blade surface defect detection,utilizing Mobilenetv2 as the backbone feature extraction network,is proposed based on an original Deeplabv3+deep learning model to address the issue of limited robustness.Secondly,through integrating the concept of pre-trained weights from transfer learning and implementing a freeze training strategy,significant improvements have been made to enhance both the training speed and model training accuracy of this deep learning model.Finally,based on segmented blade surface defect images,a method for quantifying blade defects is proposed.This method combines image stitching algorithms to achieve overall quantification and risk assessment of the entire blade.Test results show that the improved Deeplabv3+deep learning model reduces training time by approximately 43.03%compared to the original model,while achieving mAP and MIoU values of 96.87%and 96.93%,respectively.Moreover,it demonstrates robustness in detecting different surface defects on blades across different back-grounds.The application of a blade surface defect quantification method enables the precise quantification of dif-ferent defects and facilitates the assessment of risk levels associated with defect measurements across the entire blade.This method enables non-contact,long-distance,high-precision detection and quantification of surface defects on the blades,providing a reference for assessing surface defects on wind turbine blades.展开更多
BACKGROUND Bleeding is one of the major complications after endoscopic submucosal dissection(ESD)in early gastric cancer(EGC)patients.There are limited studies on estimating the bleeding risk after ESD using an artifi...BACKGROUND Bleeding is one of the major complications after endoscopic submucosal dissection(ESD)in early gastric cancer(EGC)patients.There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system.AIM To derivate and verify the performance of the deep learning model and the clinical model for predicting bleeding risk after ESD in EGC patients.METHODS Patients with EGC who underwent ESD between January 2010 and June 2020 at the Samsung Medical Center were enrolled,and post-ESD bleeding(PEB)was investigated retrospectively.We split the entire cohort into a development set(80%)and a validation set(20%).The deep learning and clinical model were built on the development set and tested in the validation set.The performance of the deep learning model and the clinical model were compared using the area under the curve and the stratification of bleeding risk after ESD.RESULTS A total of 5629 patients were included,and PEB occurred in 325 patients.The area under the curve for predicting PEB was 0.71(95%confidence interval:0.63-0.78)in the deep learning model and 0.70(95%confidence interval:0.62-0.77)in the clinical model,without significant difference(P=0.730).The patients expected to the low-(<5%),intermediate-(≥5%,<9%),and high-risk(≥9%)categories were observed with actual bleeding rate of 2.2%,3.9%,and 11.6%,respectively,in the deep learning model;4.0%,8.8%,and 18.2%,respectively,in the clinical model.CONCLUSION A deep learning model can predict and stratify the bleeding risk after ESD in patients with EGC.展开更多
Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to...Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.展开更多
Every day,websites and personal archives create more and more photos.The size of these archives is immeasurable.The comfort of use of these huge digital image gatherings donates to their admiration.However,not all of ...Every day,websites and personal archives create more and more photos.The size of these archives is immeasurable.The comfort of use of these huge digital image gatherings donates to their admiration.However,not all of these folders deliver relevant indexing information.From the outcomes,it is dif-ficult to discover data that the user can be absorbed in.Therefore,in order to determine the significance of the data,it is important to identify the contents in an informative manner.Image annotation can be one of the greatest problematic domains in multimedia research and computer vision.Hence,in this paper,Adap-tive Convolutional Deep Learning Model(ACDLM)is developed for automatic image annotation.Initially,the databases are collected from the open-source system which consists of some labelled images(for training phase)and some unlabeled images{Corel 5 K,MSRC v2}.After that,the images are sent to the pre-processing step such as colour space quantization and texture color class map.The pre-processed images are sent to the segmentation approach for efficient labelling technique using J-image segmentation(JSEG).Thefinal step is an auto-matic annotation using ACDLM which is a combination of Convolutional Neural Network(CNN)and Honey Badger Algorithm(HBA).Based on the proposed classifier,the unlabeled images are labelled.The proposed methodology is imple-mented in MATLAB and performance is evaluated by performance metrics such as accuracy,precision,recall and F1_Measure.With the assistance of the pro-posed methodology,the unlabeled images are labelled.展开更多
This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional te...This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional techniques.The work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells,namely,eosinophils,neutrophils,monocytes,and lymphocytes,known for their relationship with human body damage,inflammatory regions,and organ illnesses,in particular,and with the health of the immune system and other hazards,such as cardiovascular disease or infections,more in general.The results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%,97%,and 89%,respectively,with regard to the training,verification,and testing of the corresponding datasets.展开更多
Seasonal location and intensity changes in the western Pacific subtropical high(WPSH)are important factors dominating the synoptic weather and the distribution and magnitude of precipitation in the rain belt over East...Seasonal location and intensity changes in the western Pacific subtropical high(WPSH)are important factors dominating the synoptic weather and the distribution and magnitude of precipitation in the rain belt over East Asia.Therefore,this article delves into the forecast of the western Pacific subtropical high index during typhoon activity by adopting a hybrid deep learning model.Firstly,the predictors,which are the inputs of the model,are analysed based on three characteristics:the first is the statistical discipline of the WPSH index anomalies corresponding to the three types of typhoon paths;the second is the correspondence of distributions between sea surface temperature,850 hPa zonal wind(u),meridional wind(v),and 500 hPa potential height field;and the third is the numerical sensitivity experiment,which reflects the evident impact of variations in the physical field around the typhoon to the WPSH index.Secondly,the model is repeatedly trained through the backward propagation algorithm to predict the WPSH index using 2011–2018 atmospheric variables as the input of the training set.The model predicts the WPSH index after 6 h,24 h,48 h,and 72 h.The validation set using independent data in 2019 is utilized to illustrate the performance.Finally,the model is improved by changing the CNN2D module to the DeCNN module to enhance its ability to predict images.Taking the 2019 typhoon“Lekima”as an example,it shows the promising performance of this model to predict the 500 hPa potential height field.展开更多
Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored images.Thus,there were lots of eff...Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored images.Thus,there were lots of efforts trying to automate the classification operation and retrieve similar images accurately.To reach this goal,we developed a VGG19 deep convolutional neural network to extract the visual features from the images automatically.Then,the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural network.The Siamese model built and trained at first from scratch but,it didn’t generated high evaluation metrices.Thus,we re-built it from VGG19 pre-trained deep learning model to generate higher evaluation metrices.Afterward,three different distance metrics combined with the Sigmoid activation function are experimented looking for the most accurate method formeasuring the similarities among the retrieved images.Reaching that the highest evaluation parameters generated using the Cosine distance metric.Moreover,the Graphics Processing Unit(GPU)utilized to run the code instead of running it on the Central Processing Unit(CPU).This step optimized the execution further since it expedited both the training and the retrieval time efficiently.After extensive experimentation,we reached satisfactory solution recording 0.98 and 0.99 F-score for the classification and for the retrieval,respectively.展开更多
The prediction of the Tropical Cyclone(TC)intensity helps the government to take proper precautions and disseminate appropriate warnings to civilians.Intensity prediction for TC is a very challenging task due to its d...The prediction of the Tropical Cyclone(TC)intensity helps the government to take proper precautions and disseminate appropriate warnings to civilians.Intensity prediction for TC is a very challenging task due to its dynamically changing internal and external impact factors.We proposed a system to predict TC intensity using CNN-based ensemble deep-learning models that are trained by both satellite images and numerical data of the TC.This paper presents a thorough examination of several deep-learning models such as CNN,Recurrent Neural Networks(RNN)and transfer learning models(AlexNet and VGG)to determine their effectiveness in forecasting TC intensity.Our focus is on four widely recognized models:AlexNet,VGG16,RNN and,a customized CNN-based ensemble model all of which were trained exclusively on image data,as well as an ensemble model that utilized both image and numerical datasets for training.Our analysis evaluates the performance of each model in terms of the loss incurred.The results provide a comparative assessment of the deep learning models selected and offer insights into their respective prediction loss in the form of Mean Square Error(MSE)as 194 in 100 epochs and execution time 1229 s to forecasting TC intensity.We also emphasize the potential benefits of incorporating both image and numerical data into an ensemble model,which can lead to improved prediction accuracy.This research provides valuable knowledge to the field of meteorology and disaster management,paving the way for more resilient and precise TC intensity forecasting models.展开更多
Acute flaccid paralysis(AFP)case surveillance is pivotal for the early detection of potential poliovirus,particularly in endemic countries such as Ethiopia.The community-based surveillance system implemented in Ethiop...Acute flaccid paralysis(AFP)case surveillance is pivotal for the early detection of potential poliovirus,particularly in endemic countries such as Ethiopia.The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance.However,challenges like delayed detection and disorganized communication persist.This work proposes a simple deep learning model for AFP surveillance,leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones.The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset.The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch,achieving superior accuracy,F1-score,precision,recall,and area under the receiver operating characteristic curve(AUC).It emerged as the optimal model,demonstrating the highest average AUC of 0.870±0.01.Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches(P<0.001).By bridging community reporting with health system response,this study offers a scalable solution for enhancing AFP surveillance in low-resource settings.The study is limited in terms of the quality of image data collected,necessitating future work on improving data quality.The establishment of a dedicated platform that facilitates data storage,analysis,and future learning can strengthen data quality.Nonetheless,this work represents a significant step toward leveraging artificial intelligence for community-based AFP surveillance from images,with substantial implications for addressing global health challenges and disease eradication strategies.展开更多
It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using...It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.展开更多
Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficie...Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.展开更多
Automatic detection of student engagement levels from videos,which is a spatio-temporal classification problem is crucial for enhancing the quality of online education.This paper addresses this challenge by proposing ...Automatic detection of student engagement levels from videos,which is a spatio-temporal classification problem is crucial for enhancing the quality of online education.This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos.The evaluation of these models utilizes the DAiSEE dataset,a public repository capturing student affective states in e-learning scenarios.The initial model integrates EfficientNetV2-L with Gated Recurrent Unit(GRU)and attains an accuracy of 61.45%.Subsequently,the second model combines EfficientNetV2-L with bidirectional GRU(Bi-GRU),yielding an accuracy of 61.56%.The third and fourth models leverage a fusion of EfficientNetV2-L with Long Short-Term Memory(LSTM)and bidirectional LSTM(Bi-LSTM),achieving accuracies of 62.11%and 61.67%,respectively.Our findings demonstrate the viability of these models in effectively discerning student engagement levels,with the EfficientNetV2-L+LSTM model emerging as the most proficient,reaching an accuracy of 62.11%.This study underscores the potential of hybrid spatio-temporal networks in automating the detection of student engagement,thereby contributing to advancements in online education quality.展开更多
Alzheimer's disease(AD)is gradually increasing in prevalence and the complexity of its pathogenesis has led to a lengthy process of developing therapeutic drugs with limited success.Faced with this challenge,we pr...Alzheimer's disease(AD)is gradually increasing in prevalence and the complexity of its pathogenesis has led to a lengthy process of developing therapeutic drugs with limited success.Faced with this challenge,we proposed using a state-of-the-art drug screening algorithm to identify potential therapeutic compounds for AD from traditional Chinese medicine formulas with strong empirical support.We developed four deep neural network(DNN)models for AD drugs screening at the disease and target levels.The AD model was trained with compounds labeled for AD activity to predict active compounds at the disease level,while the acetylcholinesterase(AChE),monoamine oxidase-A(MAO-A),and 5-hydroxytryptamine 6(5-HT6)models were trained for specific AD targets.All four models performed excellently and were used to identify potential AD agents in the Kaixinsan(KXS)formula.High-scoring compounds underwent experimental validation at the enzyme,cellular,and animal levels.Compounds like 2,4-di-tert-butylphenol and elemicin showed significant binding and inhibitory effects on AChE and MAO-A.Additionally,13 compounds,includingα-asarone,penetrated the blood-brain barrier(BBB),indicating potential brain target binding,and eight compounds enhanced microglialβ-amyloid phagocytosis,aiding in clearing AD pathological substances.Our results demonstrate the effectiveness of deep learning models in developing AD therapies and provide a strong platform for AD drug discovery.展开更多
The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evalu...The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.展开更多
Skin cancer is a highly frequent kind of cancer.Early identification of a phenomenon significantly improves outcomes and mitigates the risk of fatalities.Melanoma,basal,and squamous cell carcinomas are well-recognized...Skin cancer is a highly frequent kind of cancer.Early identification of a phenomenon significantly improves outcomes and mitigates the risk of fatalities.Melanoma,basal,and squamous cell carcinomas are well-recognized cutaneous malignancies.Malignant We can differentiate Melanoma from non-pigmented carcinomas like basal and squamous cell carcinoma.The research on developing automated skin cancer detection systems has primarily focused on pigmented malignant type melanoma.The limited availability of datasets with a wide range of lesion categories has hindered in-depth exploration of non-pigmented malignant skin lesions.The present study investigates the feasibility of automated methods for detecting pigmented skin lesions with potential malignancy.To diagnose skin lesions,medical professionals employ a two-step approach.Before detecting malignant types with other deep learning(DL)models,a preliminary step involves using a DL model to identify the skin lesions as either pigmented or non-pigmented.The performance assessments accurately assessed four distinct DL models:Long short-term memory(LSTM),Visual Geometry Group(VGG19),Residual Blocks(ResNet50),and AlexNet.The LSTM model exhibited higher classification accuracy compared to the other models used.The accuracy of LSTM for pigmented and non-pigmented,pigmented tumours and benign classes,and melanomas and pigmented nevus classes was 0.9491,0.9531,and 0.949,respectively.Automated computerized skin cancer detection promises to enhance diagnostic efficiency and precision significantly.展开更多
Accurate Global Horizontal Irradiance(GHI)forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouri...Accurate Global Horizontal Irradiance(GHI)forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources.Particularly considering the implications of the aggressive GHG emission targets,accurate GHI forecasting has become vital for developing,designing,and operational managing solar energy systems.This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA(Autoregressive Integrated Moving Average),Elaman NN(Elman Neural Network),RBFN(Radial Basis Function Neural Network),SVM(Support Vector Machine),LSTM(Long Short-Term Memory),Persistent,BPN(Back Propagation Neural Network),MLP(Multilayer Perceptron Neural Network),RF(Random Forest),and XGBoost(eXtreme Gradient Boosting)for assessing multi-seasonal forecasting of GHI.Used the India region data to evaluate the models’performance and forecasting ability.Research using forecasting models for seasonal Global Horizontal Irradiance(GHI)forecasting in winter,spring,summer,monsoon,and autumn.Substantiated performance effectiveness through evaluation metrics,such as Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),and R-squared(R^(2)),coded using Python programming.The performance experimentation analysis inferred that the most accurate forecasts in all the seasons compared to the other forecasting models the Random Forest and eXtreme Gradient Boosting,are the superior and competing models that yield Winter season-based forecasting XGBoost is the best forecasting model with MAE:1.6325,RMSE:4.8338,and R^(2):0.9998.Spring season-based forecasting XGBoost is the best forecasting model with MAE:2.599599,RMSE:5.58539,and R^(2):0.999784.Summer season-based forecasting RF is the best forecasting model with MAE:1.03843,RMSE:2.116325,and R^(2):0.999967.Monsoon season-based forecasting RF is the best forecasting model with MAE:0.892385,RMSE:2.417587,and R^(2):0.999942.Autumn season-based forecasting RF is the best forecasting model with MAE:0.810462,RMSE:1.928215,and R^(2):0.999958.Based on seasonal variations and computing constraints,the findings enable energy system operators to make helpful recommendations for choosing the most effective forecasting models.展开更多
Deep learning(DL),derived from the domain of Artificial Neural Networks(ANN),forms one of the most essential components of modern deep learning algorithms.DL segmentation models rely on layer-by-layer convolution-base...Deep learning(DL),derived from the domain of Artificial Neural Networks(ANN),forms one of the most essential components of modern deep learning algorithms.DL segmentation models rely on layer-by-layer convolution-based feature representation,guided by forward and backward propagation.Acritical aspect of this process is the selection of an appropriate activation function(AF)to ensure robustmodel learning.However,existing activation functions often fail to effectively address the vanishing gradient problem or are complicated by the need for manual parameter tuning.Most current research on activation function design focuses on classification tasks using natural image datasets such asMNIST,CIFAR-10,and CIFAR-100.To address this gap,this study proposesMed-ReLU,a novel activation function specifically designed for medical image segmentation.Med-ReLU prevents deep learning models fromsuffering dead neurons or vanishing gradient issues.It is a hybrid activation function that combines the properties of ReLU and Softsign.For positive inputs,Med-ReLU adopts the linear behavior of ReLU to avoid vanishing gradients,while for negative inputs,it exhibits the Softsign’s polynomial convergence,ensuring robust training and avoiding inactive neurons across the training set.The training performance and segmentation accuracy ofMed-ReLU have been thoroughly evaluated,demonstrating stable learning behavior and resistance to overfitting.It consistently outperforms state-of-the-art activation functions inmedical image segmentation tasks.Designed as a parameter-free function,Med-ReLU is simple to implement in complex deep learning architectures,and its effectiveness spans various neural network models and anomaly detection scenarios.展开更多
基金supported by the Project of Stable Support for Youth Team in Basic Research Field,CAS(grant No.YSBR-018)the National Natural Science Foundation of China(grant Nos.42188101,42130204)+4 种基金the B-type Strategic Priority Program of CAS(grant no.XDB41000000)the National Natural Science Foundation of China(NSFC)Distinguished Overseas Young Talents Program,Innovation Program for Quantum Science and Technology(2021ZD0300301)the Open Research Project of Large Research Infrastructures of CAS-“Study on the interaction between low/mid-latitude atmosphere and ionosphere based on the Chinese Meridian Project”.The project was supported also by the National Key Laboratory of Deep Space Exploration(Grant No.NKLDSE2023A002)the Open Fund of Anhui Provincial Key Laboratory of Intelligent Underground Detection(Grant No.APKLIUD23KF01)the China National Space Administration(CNSA)pre-research Project on Civil Aerospace Technologies No.D010305,D010301.
文摘Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.
基金supported in part by the Gusu Innovation and Entrepreneurship Leading Talents in Suzhou City,grant numbers ZXL2021425 and ZXL2022476Doctor of Innovation and Entrepreneurship Program in Jiangsu Province,grant number JSSCBS20211440+6 种基金Jiangsu Province Key R&D Program,grant number BE2019682Natural Science Foundation of Jiangsu Province,grant number BK20200214National Key R&D Program of China,grant number 2017YFB0403701National Natural Science Foundation of China,grant numbers 61605210,61675226,and 62075235Youth Innovation Promotion Association of Chinese Academy of Sciences,grant number 2019320Frontier Science Research Project of the Chinese Academy of Sciences,grant number QYZDB-SSW-JSC03Strategic Priority Research Program of the Chinese Academy of Sciences,grant number XDB02060000.
文摘The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera imaging,single-phase FFA from scanning laser ophthalmoscopy(SLO),and three-phase FFA also from SLO.Although many deep learning models are available,a single model can only perform one or two of these prediction tasks.To accomplish three prediction tasks using a unified method,we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network.The three prediction tasks are processed as follows:data preparation,network training under FFA supervision,and FFA image prediction from fundus structure images on a test set.By comparing the FFA images predicted by our model,pix2pix,and CycleGAN,we demonstrate the remarkable progress achieved by our proposal.The high performance of our model is validated in terms of the peak signal-to-noise ratio,structural similarity index,and mean squared error.
文摘AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included,and 13470 infrared pupil images were collected for the study.All infrared images for pupil segmentation were labeled using the Labelme software.The computation of pupil diameter is divided into four steps:image pre-processing,pupil identification and localization,pupil segmentation,and diameter calculation.Two major models are used in the computation process:the modified YoloV3 and Deeplabv 3+models,which must be trained beforehand.RESULTS:The test dataset included 1348 infrared pupil images.On the test dataset,the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils.The DeeplabV3+model achieved a background intersection over union(IOU)of 99.23%,a pupil IOU of 93.81%,and a mean IOU of 96.52%.The pupil diameters in the test dataset ranged from 20 to 56 pixels,with a mean of 36.06±6.85 pixels.The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels,with a mean absolute error(MAE)of 1.06±0.96 pixels.CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm,proven to be highly accurate and reliable for clinical application.
基金supported by the National Science Foundation of China(Grant Nos.52068049 and 51908266)the Science Fund for Distinguished Young Scholars of Gansu Province(No.21JR7RA267)Hongliu Outstanding Young Talents Program of Lanzhou University of Technology.
文摘The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on wind turbine blades,a blade surface defect detection and quantification method based on an improved Deeplabv3+deep learning model is proposed.Firstly,an improved method for wind turbine blade surface defect detection,utilizing Mobilenetv2 as the backbone feature extraction network,is proposed based on an original Deeplabv3+deep learning model to address the issue of limited robustness.Secondly,through integrating the concept of pre-trained weights from transfer learning and implementing a freeze training strategy,significant improvements have been made to enhance both the training speed and model training accuracy of this deep learning model.Finally,based on segmented blade surface defect images,a method for quantifying blade defects is proposed.This method combines image stitching algorithms to achieve overall quantification and risk assessment of the entire blade.Test results show that the improved Deeplabv3+deep learning model reduces training time by approximately 43.03%compared to the original model,while achieving mAP and MIoU values of 96.87%and 96.93%,respectively.Moreover,it demonstrates robustness in detecting different surface defects on blades across different back-grounds.The application of a blade surface defect quantification method enables the precise quantification of dif-ferent defects and facilitates the assessment of risk levels associated with defect measurements across the entire blade.This method enables non-contact,long-distance,high-precision detection and quantification of surface defects on the blades,providing a reference for assessing surface defects on wind turbine blades.
文摘BACKGROUND Bleeding is one of the major complications after endoscopic submucosal dissection(ESD)in early gastric cancer(EGC)patients.There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system.AIM To derivate and verify the performance of the deep learning model and the clinical model for predicting bleeding risk after ESD in EGC patients.METHODS Patients with EGC who underwent ESD between January 2010 and June 2020 at the Samsung Medical Center were enrolled,and post-ESD bleeding(PEB)was investigated retrospectively.We split the entire cohort into a development set(80%)and a validation set(20%).The deep learning and clinical model were built on the development set and tested in the validation set.The performance of the deep learning model and the clinical model were compared using the area under the curve and the stratification of bleeding risk after ESD.RESULTS A total of 5629 patients were included,and PEB occurred in 325 patients.The area under the curve for predicting PEB was 0.71(95%confidence interval:0.63-0.78)in the deep learning model and 0.70(95%confidence interval:0.62-0.77)in the clinical model,without significant difference(P=0.730).The patients expected to the low-(<5%),intermediate-(≥5%,<9%),and high-risk(≥9%)categories were observed with actual bleeding rate of 2.2%,3.9%,and 11.6%,respectively,in the deep learning model;4.0%,8.8%,and 18.2%,respectively,in the clinical model.CONCLUSION A deep learning model can predict and stratify the bleeding risk after ESD in patients with EGC.
基金Funding is provided by Taif University Researchers Supporting Project Number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.
文摘Every day,websites and personal archives create more and more photos.The size of these archives is immeasurable.The comfort of use of these huge digital image gatherings donates to their admiration.However,not all of these folders deliver relevant indexing information.From the outcomes,it is dif-ficult to discover data that the user can be absorbed in.Therefore,in order to determine the significance of the data,it is important to identify the contents in an informative manner.Image annotation can be one of the greatest problematic domains in multimedia research and computer vision.Hence,in this paper,Adap-tive Convolutional Deep Learning Model(ACDLM)is developed for automatic image annotation.Initially,the databases are collected from the open-source system which consists of some labelled images(for training phase)and some unlabeled images{Corel 5 K,MSRC v2}.After that,the images are sent to the pre-processing step such as colour space quantization and texture color class map.The pre-processed images are sent to the segmentation approach for efficient labelling technique using J-image segmentation(JSEG).Thefinal step is an auto-matic annotation using ACDLM which is a combination of Convolutional Neural Network(CNN)and Honey Badger Algorithm(HBA).Based on the proposed classifier,the unlabeled images are labelled.The proposed methodology is imple-mented in MATLAB and performance is evaluated by performance metrics such as accuracy,precision,recall and F1_Measure.With the assistance of the pro-posed methodology,the unlabeled images are labelled.
基金supported by National Natural Science Foundation of China(NSFC)(Nos.61806087,61902158).
文摘This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional techniques.The work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells,namely,eosinophils,neutrophils,monocytes,and lymphocytes,known for their relationship with human body damage,inflammatory regions,and organ illnesses,in particular,and with the health of the immune system and other hazards,such as cardiovascular disease or infections,more in general.The results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%,97%,and 89%,respectively,with regard to the training,verification,and testing of the corresponding datasets.
文摘Seasonal location and intensity changes in the western Pacific subtropical high(WPSH)are important factors dominating the synoptic weather and the distribution and magnitude of precipitation in the rain belt over East Asia.Therefore,this article delves into the forecast of the western Pacific subtropical high index during typhoon activity by adopting a hybrid deep learning model.Firstly,the predictors,which are the inputs of the model,are analysed based on three characteristics:the first is the statistical discipline of the WPSH index anomalies corresponding to the three types of typhoon paths;the second is the correspondence of distributions between sea surface temperature,850 hPa zonal wind(u),meridional wind(v),and 500 hPa potential height field;and the third is the numerical sensitivity experiment,which reflects the evident impact of variations in the physical field around the typhoon to the WPSH index.Secondly,the model is repeatedly trained through the backward propagation algorithm to predict the WPSH index using 2011–2018 atmospheric variables as the input of the training set.The model predicts the WPSH index after 6 h,24 h,48 h,and 72 h.The validation set using independent data in 2019 is utilized to illustrate the performance.Finally,the model is improved by changing the CNN2D module to the DeCNN module to enhance its ability to predict images.Taking the 2019 typhoon“Lekima”as an example,it shows the promising performance of this model to predict the 500 hPa potential height field.
基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4400271DSR01).
文摘Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored images.Thus,there were lots of efforts trying to automate the classification operation and retrieve similar images accurately.To reach this goal,we developed a VGG19 deep convolutional neural network to extract the visual features from the images automatically.Then,the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural network.The Siamese model built and trained at first from scratch but,it didn’t generated high evaluation metrices.Thus,we re-built it from VGG19 pre-trained deep learning model to generate higher evaluation metrices.Afterward,three different distance metrics combined with the Sigmoid activation function are experimented looking for the most accurate method formeasuring the similarities among the retrieved images.Reaching that the highest evaluation parameters generated using the Cosine distance metric.Moreover,the Graphics Processing Unit(GPU)utilized to run the code instead of running it on the Central Processing Unit(CPU).This step optimized the execution further since it expedited both the training and the retrieval time efficiently.After extensive experimentation,we reached satisfactory solution recording 0.98 and 0.99 F-score for the classification and for the retrieval,respectively.
文摘The prediction of the Tropical Cyclone(TC)intensity helps the government to take proper precautions and disseminate appropriate warnings to civilians.Intensity prediction for TC is a very challenging task due to its dynamically changing internal and external impact factors.We proposed a system to predict TC intensity using CNN-based ensemble deep-learning models that are trained by both satellite images and numerical data of the TC.This paper presents a thorough examination of several deep-learning models such as CNN,Recurrent Neural Networks(RNN)and transfer learning models(AlexNet and VGG)to determine their effectiveness in forecasting TC intensity.Our focus is on four widely recognized models:AlexNet,VGG16,RNN and,a customized CNN-based ensemble model all of which were trained exclusively on image data,as well as an ensemble model that utilized both image and numerical datasets for training.Our analysis evaluates the performance of each model in terms of the loss incurred.The results provide a comparative assessment of the deep learning models selected and offer insights into their respective prediction loss in the form of Mean Square Error(MSE)as 194 in 100 epochs and execution time 1229 s to forecasting TC intensity.We also emphasize the potential benefits of incorporating both image and numerical data into an ensemble model,which can lead to improved prediction accuracy.This research provides valuable knowledge to the field of meteorology and disaster management,paving the way for more resilient and precise TC intensity forecasting models.
基金supported by a fund from the International Development Research Centre(IDRC)(Grant No.109981e001)funded by Canada’s International Development Research Centre(IDRC)(Grant No.109981-001)+3 种基金support from IDRC and UK's Foreign,Commonwealth and Development Office(FCDO)(Grant No.110554-001)support from NSERC Discovery Grant(Grant No.RGPIN-2022-04559)NSERC Discovery Launch Supplement(Grant No:DGECR-2022-00454)New Frontier in Research Fund-Exploratory(Grant No.NFRFE-2021-00879).
文摘Acute flaccid paralysis(AFP)case surveillance is pivotal for the early detection of potential poliovirus,particularly in endemic countries such as Ethiopia.The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance.However,challenges like delayed detection and disorganized communication persist.This work proposes a simple deep learning model for AFP surveillance,leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones.The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset.The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch,achieving superior accuracy,F1-score,precision,recall,and area under the receiver operating characteristic curve(AUC).It emerged as the optimal model,demonstrating the highest average AUC of 0.870±0.01.Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches(P<0.001).By bridging community reporting with health system response,this study offers a scalable solution for enhancing AFP surveillance in low-resource settings.The study is limited in terms of the quality of image data collected,necessitating future work on improving data quality.The establishment of a dedicated platform that facilitates data storage,analysis,and future learning can strengthen data quality.Nonetheless,this work represents a significant step toward leveraging artificial intelligence for community-based AFP surveillance from images,with substantial implications for addressing global health challenges and disease eradication strategies.
基金supported by the National Natural Science Foundation of China(Grant Nos.42375062 and 42275158)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)the Natural Science Foundation of Gansu Province(Grant No.22JR5RF1080)。
文摘It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.
文摘Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.
文摘Automatic detection of student engagement levels from videos,which is a spatio-temporal classification problem is crucial for enhancing the quality of online education.This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos.The evaluation of these models utilizes the DAiSEE dataset,a public repository capturing student affective states in e-learning scenarios.The initial model integrates EfficientNetV2-L with Gated Recurrent Unit(GRU)and attains an accuracy of 61.45%.Subsequently,the second model combines EfficientNetV2-L with bidirectional GRU(Bi-GRU),yielding an accuracy of 61.56%.The third and fourth models leverage a fusion of EfficientNetV2-L with Long Short-Term Memory(LSTM)and bidirectional LSTM(Bi-LSTM),achieving accuracies of 62.11%and 61.67%,respectively.Our findings demonstrate the viability of these models in effectively discerning student engagement levels,with the EfficientNetV2-L+LSTM model emerging as the most proficient,reaching an accuracy of 62.11%.This study underscores the potential of hybrid spatio-temporal networks in automating the detection of student engagement,thereby contributing to advancements in online education quality.
基金This work was supported by the Science and Technology Project of Haihe Laboratory of Modern Chinese Medicine,China(Grant No.:22HHZYSS00003)the Science and Technology Program of Tianjin,China(Grant No.:22ZYJDSS00100)The authors would like to thank the support from Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine,China(Grant No:ZYYCXTD-D-202002).
文摘Alzheimer's disease(AD)is gradually increasing in prevalence and the complexity of its pathogenesis has led to a lengthy process of developing therapeutic drugs with limited success.Faced with this challenge,we proposed using a state-of-the-art drug screening algorithm to identify potential therapeutic compounds for AD from traditional Chinese medicine formulas with strong empirical support.We developed four deep neural network(DNN)models for AD drugs screening at the disease and target levels.The AD model was trained with compounds labeled for AD activity to predict active compounds at the disease level,while the acetylcholinesterase(AChE),monoamine oxidase-A(MAO-A),and 5-hydroxytryptamine 6(5-HT6)models were trained for specific AD targets.All four models performed excellently and were used to identify potential AD agents in the Kaixinsan(KXS)formula.High-scoring compounds underwent experimental validation at the enzyme,cellular,and animal levels.Compounds like 2,4-di-tert-butylphenol and elemicin showed significant binding and inhibitory effects on AChE and MAO-A.Additionally,13 compounds,includingα-asarone,penetrated the blood-brain barrier(BBB),indicating potential brain target binding,and eight compounds enhanced microglialβ-amyloid phagocytosis,aiding in clearing AD pathological substances.Our results demonstrate the effectiveness of deep learning models in developing AD therapies and provide a strong platform for AD drug discovery.
基金supported by the National Natural Science Foundation of China(Grant Nos.42090054,41931295)the Natural Science Foundation of Hubei Province of China(2022CFA002)。
文摘The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.
文摘Skin cancer is a highly frequent kind of cancer.Early identification of a phenomenon significantly improves outcomes and mitigates the risk of fatalities.Melanoma,basal,and squamous cell carcinomas are well-recognized cutaneous malignancies.Malignant We can differentiate Melanoma from non-pigmented carcinomas like basal and squamous cell carcinoma.The research on developing automated skin cancer detection systems has primarily focused on pigmented malignant type melanoma.The limited availability of datasets with a wide range of lesion categories has hindered in-depth exploration of non-pigmented malignant skin lesions.The present study investigates the feasibility of automated methods for detecting pigmented skin lesions with potential malignancy.To diagnose skin lesions,medical professionals employ a two-step approach.Before detecting malignant types with other deep learning(DL)models,a preliminary step involves using a DL model to identify the skin lesions as either pigmented or non-pigmented.The performance assessments accurately assessed four distinct DL models:Long short-term memory(LSTM),Visual Geometry Group(VGG19),Residual Blocks(ResNet50),and AlexNet.The LSTM model exhibited higher classification accuracy compared to the other models used.The accuracy of LSTM for pigmented and non-pigmented,pigmented tumours and benign classes,and melanomas and pigmented nevus classes was 0.9491,0.9531,and 0.949,respectively.Automated computerized skin cancer detection promises to enhance diagnostic efficiency and precision significantly.
文摘Accurate Global Horizontal Irradiance(GHI)forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources.Particularly considering the implications of the aggressive GHG emission targets,accurate GHI forecasting has become vital for developing,designing,and operational managing solar energy systems.This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA(Autoregressive Integrated Moving Average),Elaman NN(Elman Neural Network),RBFN(Radial Basis Function Neural Network),SVM(Support Vector Machine),LSTM(Long Short-Term Memory),Persistent,BPN(Back Propagation Neural Network),MLP(Multilayer Perceptron Neural Network),RF(Random Forest),and XGBoost(eXtreme Gradient Boosting)for assessing multi-seasonal forecasting of GHI.Used the India region data to evaluate the models’performance and forecasting ability.Research using forecasting models for seasonal Global Horizontal Irradiance(GHI)forecasting in winter,spring,summer,monsoon,and autumn.Substantiated performance effectiveness through evaluation metrics,such as Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),and R-squared(R^(2)),coded using Python programming.The performance experimentation analysis inferred that the most accurate forecasts in all the seasons compared to the other forecasting models the Random Forest and eXtreme Gradient Boosting,are the superior and competing models that yield Winter season-based forecasting XGBoost is the best forecasting model with MAE:1.6325,RMSE:4.8338,and R^(2):0.9998.Spring season-based forecasting XGBoost is the best forecasting model with MAE:2.599599,RMSE:5.58539,and R^(2):0.999784.Summer season-based forecasting RF is the best forecasting model with MAE:1.03843,RMSE:2.116325,and R^(2):0.999967.Monsoon season-based forecasting RF is the best forecasting model with MAE:0.892385,RMSE:2.417587,and R^(2):0.999942.Autumn season-based forecasting RF is the best forecasting model with MAE:0.810462,RMSE:1.928215,and R^(2):0.999958.Based on seasonal variations and computing constraints,the findings enable energy system operators to make helpful recommendations for choosing the most effective forecasting models.
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Deep learning(DL),derived from the domain of Artificial Neural Networks(ANN),forms one of the most essential components of modern deep learning algorithms.DL segmentation models rely on layer-by-layer convolution-based feature representation,guided by forward and backward propagation.Acritical aspect of this process is the selection of an appropriate activation function(AF)to ensure robustmodel learning.However,existing activation functions often fail to effectively address the vanishing gradient problem or are complicated by the need for manual parameter tuning.Most current research on activation function design focuses on classification tasks using natural image datasets such asMNIST,CIFAR-10,and CIFAR-100.To address this gap,this study proposesMed-ReLU,a novel activation function specifically designed for medical image segmentation.Med-ReLU prevents deep learning models fromsuffering dead neurons or vanishing gradient issues.It is a hybrid activation function that combines the properties of ReLU and Softsign.For positive inputs,Med-ReLU adopts the linear behavior of ReLU to avoid vanishing gradients,while for negative inputs,it exhibits the Softsign’s polynomial convergence,ensuring robust training and avoiding inactive neurons across the training set.The training performance and segmentation accuracy ofMed-ReLU have been thoroughly evaluated,demonstrating stable learning behavior and resistance to overfitting.It consistently outperforms state-of-the-art activation functions inmedical image segmentation tasks.Designed as a parameter-free function,Med-ReLU is simple to implement in complex deep learning architectures,and its effectiveness spans various neural network models and anomaly detection scenarios.
