Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Prediction of the age of each individual is possible using the changing pattern of DNA methylation with age.In this paper an age prediction approach to work out multivariate regression problems using DNA methylation d...Prediction of the age of each individual is possible using the changing pattern of DNA methylation with age.In this paper an age prediction approach to work out multivariate regression problems using DNA methylation data is developed.In this research study a convolutional neural network(CNN)-based model optimised by the genetic algorithm(GA)is addressed.This paper contributes to enhancing age prediction as a regression problem using a union of two CNNs and exchanging knowledge be-tween them.This specifically re-starts the training process from a possibly higher-quality point in different iterations and,consequently,causes potentially yeilds better results at each iteration.The method proposed,which is called cooperative deep neural network(Co-DeepNet),is tested on two types of age prediction problems.Sixteen datasets containing 1899 healthy blood samples and nine datasets containing 2395 diseased blood samples are employed to examine the method's efficiency.As a result,the mean absolute deviation(MAD)is 1.49 and 3.61 years for training and testing data,respectively,when the healthy data is tested.The diseased blood data show MAD results of 3.81 and 5.43 years for training and testing data,respectively.The results of the Co-DeepNet are compared with six other methods proposed in previous studies and a single CNN using four prediction accuracy measurements(R^(2),MAD,MSE and RMSE).The effectiveness of the Co-DeepNet and superiority of its results is proved through the statistical analysis.展开更多
Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attentio...Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated the model on publicly available datasets, including Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV), which contain critical care patient data, and the Beijing Multi-Site Air Quality dataset, which measures environmental air quality. The proposed DRes-CNN method achieved a root mean square error (RMSE) of 0.00006, highlighting its high accuracy and robustness. We also compared with Low Light-Convolutional Neural Network (LL-CNN) and U-Net methods, which had RMSE values of 0.00075 and 0.00073, respectively. This represented an improvement of approximately 92% over LL-CNN and 91% over U-Net. The results showed that this DRes-CNN-based imputation method outperforms current state-of-the-art models. These results established DRes-CNN as a reliable solution for addressing missing data.展开更多
Gastrointestinal(GI)diseases,including gastric and colorectal cancers,signi-ficantly impact global health,necessitating accurate and efficient diagnostic me-thods.Endoscopic examination is the primary diagnostic tool;...Gastrointestinal(GI)diseases,including gastric and colorectal cancers,signi-ficantly impact global health,necessitating accurate and efficient diagnostic me-thods.Endoscopic examination is the primary diagnostic tool;however,its accu-racy is limited by operator dependency and interobserver variability.Advance-ments in deep learning,particularly convolutional neural networks(CNNs),show great potential for enhancing GI disease detection and classification.This review explores the application of CNNs in endoscopic imaging,focusing on polyp and tumor detection,disease classification,endoscopic ultrasound,and capsule endo-scopy analysis.We discuss the performance of CNN models with traditional dia-gnostic methods,highlighting their advantages in accuracy and real-time decision support.Despite promising results,challenges remain,including data availability,model interpretability,and clinical integration.Future directions include impro-ving model generalization,enhancing explainability,and conducting large-scale clinical trials.With continued advancements,CNN-powered artificial intelligence systems could revolutionize GI endoscopy by enhancing early disease detection,reducing diagnostic errors,and improving patient outcomes.展开更多
Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented reality.The narrow beam usag...Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented reality.The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links.In this research work,the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated.The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output(MIMO)which is utilized in a hyperdense environment called heterogeneous networks(HetNet).The optimization problem which arises while maximizing the Mean Opinion Score(MOS)is analyzed along with the QoE(Quality of Experience)metric by considering the Base Station(BS)powers in addition to the needed Quality of Service(QoS).Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic nature.Hence a deep reinforcement learning framework is developed for tackling the same opti-mization problem.In this work,a Fuzzy-based Deep Convolutional Neural Net-work(FDCNN)is proposed in addition to a Deep Reinforcing Learning Framework(DRLF)for extracting the features of highly correlated data.The investigational results prove that the proposed method yields the highest satisfac-tion to the user by increasing the number of antennas in addition with the small-scale antennas at the base stations.The proposed work outperforms in terms of MOS with multiple antennas.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising t...Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising technologies today,plays a crucial role in the effective assessment of water body health,which is essential for water resource management.This study models using both the original dataset and a dataset augmented with Generative Adversarial Networks(GAN).It integrates optimization algorithms(OA)with Convolutional Neural Networks(CNN)to propose a comprehensive water quality model evaluation method aiming at identifying the optimal models for different pollutants.Specifically,after preprocessing the spectral dataset,data augmentation was conducted to obtain two datasets.Then,six new models were developed on these datasets using particle swarm optimization(PSO),genetic algorithm(GA),and simulated annealing(SA)combined with CNN to simulate and forecast the concentrations of three water pollutants:Chemical Oxygen Demand(COD),Total Nitrogen(TN),and Total Phosphorus(TP).Finally,seven model evaluation methods,including uncertainty analysis,were used to evaluate the constructed models and select the optimal models for the three pollutants.The evaluation results indicate that the GPSCNN model performed best in predicting COD and TP concentrations,while the GGACNN model excelled in TN concentration prediction.Compared to existing technologies,the proposed models and evaluation methods provide a more comprehensive and rapid approach to water body prediction and assessment,offering new insights and methods for water pollution prevention and control.展开更多
The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the ...The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of sophistication.To resolve this problem,efficient and flexible malware detection tools are needed.This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations.Moreover,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:a.Trojan,b.Adware,c.Ransomware,d.Spyware,e.Worm.These network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained model.In addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional techniques.The success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.展开更多
During its growth stage,the plant is exposed to various diseases.Detection and early detection of crop diseases is amajor challenge in the horticulture industry.Crop infections can harmtotal crop yield and reduce farm...During its growth stage,the plant is exposed to various diseases.Detection and early detection of crop diseases is amajor challenge in the horticulture industry.Crop infections can harmtotal crop yield and reduce farmers’income if not identified early.Today’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant leaves.This is an excellent use case for Community Assessment and Treatment Services(CATS)due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of pathologists.An alternative to conventional Machine Learning(ML)methods,which require manual identification of parameters for exact results,is to develop a prototype that can be classified without pre-processing.To automatically diagnose tomato leaf disease,this research proposes a hybrid model using the Convolutional Auto-Encoders(CAE)network and the CNN-based deep learning architecture of DenseNet.To date,none of the modern systems described in this paper have a combined model based on DenseNet,CAE,and ConvolutionalNeuralNetwork(CNN)todiagnose the ailments of tomato leaves automatically.Themodelswere trained on a dataset obtained from the Plant Village repository.The dataset consisted of 9920 tomato leaves,and the model-tomodel accuracy ratio was 98.35%.Unlike other approaches discussed in this paper,this hybrid strategy requires fewer training components.Therefore,the training time to classify plant diseases with the trained algorithm,as well as the training time to automatically detect the ailments of tomato leaves,is significantly reduced.展开更多
With the increasing importance of supply chain transparency,blockchain-based data has emerged as a valuable and verifiable source for analyzing procurement transaction risks.This study extends the mathematical model a...With the increasing importance of supply chain transparency,blockchain-based data has emerged as a valuable and verifiable source for analyzing procurement transaction risks.This study extends the mathematical model and proof of‘the Overall Performance Characteristics of the Supply Chain’to encompass multiple variables within blockchain data.Utilizing graph theory,the model is further developed into a single-layer neural network,which serves as the foundation for constructing two multi-layer deep learning neural network models,Feedforward Neural Network(abbreviated as FNN)and Deep Clustering Network(abbreviated as DCN).Furthermore,this study retrieves corporate data from the Chunghwa Yellow Pages online resource and Taiwan Economic Journal database(abbreviated as TEJ).These data are then virtualized using‘the Metaverse Algorithm’,and the selected virtualized blockchain variables are utilized to train a neural network model for classification.The results demonstrate that a single-layer neural network model,leveraging blockchain data and employing the Proof of Relation algorithm(abbreviated as PoR)as the activation function,effectively identifies anomalous enterprises,which constitute 7.2%of the total sample,aligning with expectations.In contrast,the multi-layer neural network models,DCN and FNN,classify an excessively large proportion of enterprises as anomalous(ranging from one-fourth to one-third),which deviates from expectations.This indicates that deep learning may still be inadequate in effectively capturing or identifying malicious corporate behaviors associated with distortions in procurement transaction data.In other words,procurement transaction blockchain data possesses intrinsic value that cannot be replaced by artificial intelligence(abbreviated as AI).展开更多
The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stag...The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stage.Effectively identifying and predicting the smelt-ing stage poses a significant challenge within industrial production.Traditional image-based methodologies,which rely on a single static flame image as input,demonstrate low recognition accuracy and inadequately extract the dynamic changes in smelting stage.To address this issue,the present study introduces an innovative recognition model that preprocesses flame video sequences from the furnace mouth and then employs a convolutional recurrent neural network(CRNN)to extract spatiotemporal features and derive recognition outputs.Ad-ditionally,we adopt feature layer visualization techniques to verify the model’s effectiveness and further enhance model performance by integrating the Bayesian optimization algorithm.The results indicate that the ResNet18 with convolutional block attention module(CBAM)in the convolutional layer demonstrates superior image feature extraction capabilities,achieving an accuracy of 90.70%and an area under the curve of 98.05%.The constructed Bayesian optimization-CRNN(BO-CRNN)model exhibits a significant improvement in comprehensive performance,with an accuracy of 97.01%and an area under the curve of 99.85%.Furthermore,statistics on the model’s average recognition time,computational complexity,and parameter quantity(Average recognition time:5.49 ms,floating-point opera-tions per second:18260.21 M(1 M=1×10^(6)),parameters:11.58 M)demonstrate superior performance.Through extensive repeated ex-periments on real-world datasets,the proposed CRNN model is capable of rapidly and accurately identifying smelting stages,offering a novel approach for converter smelting endpoint control.展开更多
BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algor...BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algorithm similar to deep learning,has demonstrated its capability to recognise specific features that can detect pathological lesions.AIM To assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODS The databases PubMed,EMBASE,and the Web of Science and research books were systematically searched using related keywords.Studies analysing pathological anatomy,cellular,and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer,differentiating cancer from other lesions,or staging the lesion.The data were extracted as per a predefined extraction.The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed.The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTS A total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified.The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions(n=6),HCC from cirrhosis or development of new tumours(n=3),and HCC nuclei grading or segmentation(n=2).The CNNs showed satisfactory levels of accuracy.The studies aimed at detecting lesions(n=4),classification(n=5),and segmentation(n=2).Several methods were used to assess the accuracy of CNN models used.CONCLUSION The role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies.While a few limitations have been identified in these studies,overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liver cancers images.展开更多
Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the bes...Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.展开更多
The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-hei...The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.展开更多
Liver tumors segmentation from computed tomography (CT) images is an essential task for diagnosis and treatments of liver cancer. However, it is difficult owing to the variability of appearances, fuzzy boundaries, het...Liver tumors segmentation from computed tomography (CT) images is an essential task for diagnosis and treatments of liver cancer. However, it is difficult owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions. In this paper, an automatic method based on convolutional neural networks (CNNs) is presented to segment lesions from CT images. The CNNs is one of deep learning models with some convolutional filters which can learn hierarchical features from data. We compared the CNNs model to popular machine learning algorithms: AdaBoost, Random Forests (RF), and support vector machine (SVM). These classifiers were trained by handcrafted features containing mean, variance, and contextual features. Experimental evaluation was performed on 30 portal phase enhanced CT images using leave-one-out cross validation. The average Dice Similarity Coefficient (DSC), precision, and recall achieved of 80.06% ± 1.63%, 82.67% ± 1.43%, and 84.34% ± 1.61%, respectively. The results show that the CNNs method has better performance than other methods and is promising in liver tumor segmentation.展开更多
Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit(APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards ...Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit(APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So,actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network(HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term Memory(CNN-Bi LSTM) neural network is proposed for active hazard identification of APU in civil aircraft. In order to identify the risks caused by different types of failures, the proposed HDNN simultaneously integrates three CNN-Bi LSTM basic models with different time window sizes in parallel by using a fully connected neural network. The CNN-Bi LSTM basic model can automatically extract features representing the system state from the input data and learn the time information of irregular trends in the time series data. Nine benchmark models are compared with the proposed HDNN. The comparison results show that the proposed HDNN has the highest identification accuracy. The HDNN has the most stable identification performance for data with imbalanced samples.展开更多
Nowadays,the amount of wed data is increasing at a rapid speed,which presents a serious challenge to the web monitoring.Text sentiment analysis,an important research topic in the area of natural language processing,is...Nowadays,the amount of wed data is increasing at a rapid speed,which presents a serious challenge to the web monitoring.Text sentiment analysis,an important research topic in the area of natural language processing,is a crucial task in the web monitoring area.The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data.Deep learning is a hot research topic of the artificial intelligence in the recent years.By now,several research groups have studied the sentiment analysis of English texts using deep learning methods.In contrary,relatively few works have so far considered the Chinese text sentiment analysis toward this direction.In this paper,a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network(CNN)in deep learning in order to improve the analysis accuracy.The feature values of the CNN after the training process are nonuniformly distributed.In order to overcome this problem,a method for normalizing the feature values is proposed.Moreover,the dimensions of the text features are optimized through simulations.Finally,a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances.Experiment results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods,e.g.,the support vector machine method.展开更多
Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly acc...Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed,equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties,performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance.Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.展开更多
Compressive strength of concrete is a significant factor to assess building structure health and safety.Therefore,various methods have been developed to evaluate the compressive strength of concrete structures.However...Compressive strength of concrete is a significant factor to assess building structure health and safety.Therefore,various methods have been developed to evaluate the compressive strength of concrete structures.However,previous methods have several challenges in costly,time-consuming,and unsafety.To address these drawbacks,this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network(DCNN).The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy.The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.The experimental results indicated a root mean square error(RMSE)value of 3.56(MPa),demonstrating a strong feasibility that the proposed model can be utilized to predict the concrete strength with digital images of their surfaces and advantages to overcome the previous limitations.This experiment contributed to provide the basis that could be extended to future research with image analysis technique and artificial neural network in the diagnosis of concrete building structures.展开更多
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金supported by the Universiti Kebangsaan Malaysia(DIP-2016-024).
文摘Prediction of the age of each individual is possible using the changing pattern of DNA methylation with age.In this paper an age prediction approach to work out multivariate regression problems using DNA methylation data is developed.In this research study a convolutional neural network(CNN)-based model optimised by the genetic algorithm(GA)is addressed.This paper contributes to enhancing age prediction as a regression problem using a union of two CNNs and exchanging knowledge be-tween them.This specifically re-starts the training process from a possibly higher-quality point in different iterations and,consequently,causes potentially yeilds better results at each iteration.The method proposed,which is called cooperative deep neural network(Co-DeepNet),is tested on two types of age prediction problems.Sixteen datasets containing 1899 healthy blood samples and nine datasets containing 2395 diseased blood samples are employed to examine the method's efficiency.As a result,the mean absolute deviation(MAD)is 1.49 and 3.61 years for training and testing data,respectively,when the healthy data is tested.The diseased blood data show MAD results of 3.81 and 5.43 years for training and testing data,respectively.The results of the Co-DeepNet are compared with six other methods proposed in previous studies and a single CNN using four prediction accuracy measurements(R^(2),MAD,MSE and RMSE).The effectiveness of the Co-DeepNet and superiority of its results is proved through the statistical analysis.
基金supported by the Intelligent System Research Group(ISysRG)supported by Universitas Sriwijaya funded by the Competitive Research 2024.
文摘Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated the model on publicly available datasets, including Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV), which contain critical care patient data, and the Beijing Multi-Site Air Quality dataset, which measures environmental air quality. The proposed DRes-CNN method achieved a root mean square error (RMSE) of 0.00006, highlighting its high accuracy and robustness. We also compared with Low Light-Convolutional Neural Network (LL-CNN) and U-Net methods, which had RMSE values of 0.00075 and 0.00073, respectively. This represented an improvement of approximately 92% over LL-CNN and 91% over U-Net. The results showed that this DRes-CNN-based imputation method outperforms current state-of-the-art models. These results established DRes-CNN as a reliable solution for addressing missing data.
基金Supported by Open Funds for Shaanxi Provincial Key Laboratory of Infection and Immune Diseases,No.2023-KFMS-1.
文摘Gastrointestinal(GI)diseases,including gastric and colorectal cancers,signi-ficantly impact global health,necessitating accurate and efficient diagnostic me-thods.Endoscopic examination is the primary diagnostic tool;however,its accu-racy is limited by operator dependency and interobserver variability.Advance-ments in deep learning,particularly convolutional neural networks(CNNs),show great potential for enhancing GI disease detection and classification.This review explores the application of CNNs in endoscopic imaging,focusing on polyp and tumor detection,disease classification,endoscopic ultrasound,and capsule endo-scopy analysis.We discuss the performance of CNN models with traditional dia-gnostic methods,highlighting their advantages in accuracy and real-time decision support.Despite promising results,challenges remain,including data availability,model interpretability,and clinical integration.Future directions include impro-ving model generalization,enhancing explainability,and conducting large-scale clinical trials.With continued advancements,CNN-powered artificial intelligence systems could revolutionize GI endoscopy by enhancing early disease detection,reducing diagnostic errors,and improving patient outcomes.
文摘Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented reality.The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links.In this research work,the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated.The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output(MIMO)which is utilized in a hyperdense environment called heterogeneous networks(HetNet).The optimization problem which arises while maximizing the Mean Opinion Score(MOS)is analyzed along with the QoE(Quality of Experience)metric by considering the Base Station(BS)powers in addition to the needed Quality of Service(QoS).Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic nature.Hence a deep reinforcement learning framework is developed for tackling the same opti-mization problem.In this work,a Fuzzy-based Deep Convolutional Neural Net-work(FDCNN)is proposed in addition to a Deep Reinforcing Learning Framework(DRLF)for extracting the features of highly correlated data.The investigational results prove that the proposed method yields the highest satisfac-tion to the user by increasing the number of antennas in addition with the small-scale antennas at the base stations.The proposed work outperforms in terms of MOS with multiple antennas.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
基金Supported by Natural Science Basic Research Plan in Shaanxi Province of China(Program No.2022JM-396)the Strategic Priority Research Program of the Chinese Academy of Sciences,Grant No.XDA23040101+4 种基金Shaanxi Province Key Research and Development Projects(Program No.2023-YBSF-437)Xi'an Shiyou University Graduate Student Innovation Fund Program(Program No.YCX2412041)State Key Laboratory of Air Traffic Management System and Technology(SKLATM202001)Tianjin Education Commission Research Program Project(2020KJ028)Fundamental Research Funds for the Central Universities(3122019132)。
文摘Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising technologies today,plays a crucial role in the effective assessment of water body health,which is essential for water resource management.This study models using both the original dataset and a dataset augmented with Generative Adversarial Networks(GAN).It integrates optimization algorithms(OA)with Convolutional Neural Networks(CNN)to propose a comprehensive water quality model evaluation method aiming at identifying the optimal models for different pollutants.Specifically,after preprocessing the spectral dataset,data augmentation was conducted to obtain two datasets.Then,six new models were developed on these datasets using particle swarm optimization(PSO),genetic algorithm(GA),and simulated annealing(SA)combined with CNN to simulate and forecast the concentrations of three water pollutants:Chemical Oxygen Demand(COD),Total Nitrogen(TN),and Total Phosphorus(TP).Finally,seven model evaluation methods,including uncertainty analysis,were used to evaluate the constructed models and select the optimal models for the three pollutants.The evaluation results indicate that the GPSCNN model performed best in predicting COD and TP concentrations,while the GGACNN model excelled in TN concentration prediction.Compared to existing technologies,the proposed models and evaluation methods provide a more comprehensive and rapid approach to water body prediction and assessment,offering new insights and methods for water pollution prevention and control.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Funding Program,Grant No.(FRP-1443-15).
文摘The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of sophistication.To resolve this problem,efficient and flexible malware detection tools are needed.This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations.Moreover,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:a.Trojan,b.Adware,c.Ransomware,d.Spyware,e.Worm.These network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained model.In addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional techniques.The success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.
基金funded by UKRI EPSRC Grant EP/W020408/1 Project SPRITE+2:The Security,Privacy,Identity,and Trust Engagement Network plus(phase 2)for this studyfunded by PhD project RS718 on Explainable AI through the UKRI EPSRC Grant-funded Doctoral Training Centre at Swansea University.
文摘During its growth stage,the plant is exposed to various diseases.Detection and early detection of crop diseases is amajor challenge in the horticulture industry.Crop infections can harmtotal crop yield and reduce farmers’income if not identified early.Today’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant leaves.This is an excellent use case for Community Assessment and Treatment Services(CATS)due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of pathologists.An alternative to conventional Machine Learning(ML)methods,which require manual identification of parameters for exact results,is to develop a prototype that can be classified without pre-processing.To automatically diagnose tomato leaf disease,this research proposes a hybrid model using the Convolutional Auto-Encoders(CAE)network and the CNN-based deep learning architecture of DenseNet.To date,none of the modern systems described in this paper have a combined model based on DenseNet,CAE,and ConvolutionalNeuralNetwork(CNN)todiagnose the ailments of tomato leaves automatically.Themodelswere trained on a dataset obtained from the Plant Village repository.The dataset consisted of 9920 tomato leaves,and the model-tomodel accuracy ratio was 98.35%.Unlike other approaches discussed in this paper,this hybrid strategy requires fewer training components.Therefore,the training time to classify plant diseases with the trained algorithm,as well as the training time to automatically detect the ailments of tomato leaves,is significantly reduced.
文摘With the increasing importance of supply chain transparency,blockchain-based data has emerged as a valuable and verifiable source for analyzing procurement transaction risks.This study extends the mathematical model and proof of‘the Overall Performance Characteristics of the Supply Chain’to encompass multiple variables within blockchain data.Utilizing graph theory,the model is further developed into a single-layer neural network,which serves as the foundation for constructing two multi-layer deep learning neural network models,Feedforward Neural Network(abbreviated as FNN)and Deep Clustering Network(abbreviated as DCN).Furthermore,this study retrieves corporate data from the Chunghwa Yellow Pages online resource and Taiwan Economic Journal database(abbreviated as TEJ).These data are then virtualized using‘the Metaverse Algorithm’,and the selected virtualized blockchain variables are utilized to train a neural network model for classification.The results demonstrate that a single-layer neural network model,leveraging blockchain data and employing the Proof of Relation algorithm(abbreviated as PoR)as the activation function,effectively identifies anomalous enterprises,which constitute 7.2%of the total sample,aligning with expectations.In contrast,the multi-layer neural network models,DCN and FNN,classify an excessively large proportion of enterprises as anomalous(ranging from one-fourth to one-third),which deviates from expectations.This indicates that deep learning may still be inadequate in effectively capturing or identifying malicious corporate behaviors associated with distortions in procurement transaction data.In other words,procurement transaction blockchain data possesses intrinsic value that cannot be replaced by artificial intelligence(abbreviated as AI).
基金financially supported by the National Natural Science Foundation of China(No.52374320).
文摘The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stage.Effectively identifying and predicting the smelt-ing stage poses a significant challenge within industrial production.Traditional image-based methodologies,which rely on a single static flame image as input,demonstrate low recognition accuracy and inadequately extract the dynamic changes in smelting stage.To address this issue,the present study introduces an innovative recognition model that preprocesses flame video sequences from the furnace mouth and then employs a convolutional recurrent neural network(CRNN)to extract spatiotemporal features and derive recognition outputs.Ad-ditionally,we adopt feature layer visualization techniques to verify the model’s effectiveness and further enhance model performance by integrating the Bayesian optimization algorithm.The results indicate that the ResNet18 with convolutional block attention module(CBAM)in the convolutional layer demonstrates superior image feature extraction capabilities,achieving an accuracy of 90.70%and an area under the curve of 98.05%.The constructed Bayesian optimization-CRNN(BO-CRNN)model exhibits a significant improvement in comprehensive performance,with an accuracy of 97.01%and an area under the curve of 99.85%.Furthermore,statistics on the model’s average recognition time,computational complexity,and parameter quantity(Average recognition time:5.49 ms,floating-point opera-tions per second:18260.21 M(1 M=1×10^(6)),parameters:11.58 M)demonstrate superior performance.Through extensive repeated ex-periments on real-world datasets,the proposed CRNN model is capable of rapidly and accurately identifying smelting stages,offering a novel approach for converter smelting endpoint control.
基金Supported by the College of Medicine Research Centre,Deanship of Scientific Research,King Saud University,Riyadh,Saudi Arabia
文摘BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algorithm similar to deep learning,has demonstrated its capability to recognise specific features that can detect pathological lesions.AIM To assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODS The databases PubMed,EMBASE,and the Web of Science and research books were systematically searched using related keywords.Studies analysing pathological anatomy,cellular,and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer,differentiating cancer from other lesions,or staging the lesion.The data were extracted as per a predefined extraction.The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed.The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTS A total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified.The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions(n=6),HCC from cirrhosis or development of new tumours(n=3),and HCC nuclei grading or segmentation(n=2).The CNNs showed satisfactory levels of accuracy.The studies aimed at detecting lesions(n=4),classification(n=5),and segmentation(n=2).Several methods were used to assess the accuracy of CNN models used.CONCLUSION The role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies.While a few limitations have been identified in these studies,overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liver cancers images.
基金supported by the National Natural Science Foundation of China(41601369)the Young Talents Program of Institute of Crop Sciences,Chinese Academy of Agricultural Sciences(S2019YC04)
文摘Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.
基金supported by the Fundamental Research Funds for the Central Universities of China(Grant No.2013SCU11006)the Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying,Mapping and Geoinformation of China(Grant NO.DM2014SC02)the Key Laboratory of Geospecial Information Technology,Ministry of Land and Resources of China(Grant NO.KLGSIT201504)
文摘The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.
文摘Liver tumors segmentation from computed tomography (CT) images is an essential task for diagnosis and treatments of liver cancer. However, it is difficult owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions. In this paper, an automatic method based on convolutional neural networks (CNNs) is presented to segment lesions from CT images. The CNNs is one of deep learning models with some convolutional filters which can learn hierarchical features from data. We compared the CNNs model to popular machine learning algorithms: AdaBoost, Random Forests (RF), and support vector machine (SVM). These classifiers were trained by handcrafted features containing mean, variance, and contextual features. Experimental evaluation was performed on 30 portal phase enhanced CT images using leave-one-out cross validation. The average Dice Similarity Coefficient (DSC), precision, and recall achieved of 80.06% ± 1.63%, 82.67% ± 1.43%, and 84.34% ± 1.61%, respectively. The results show that the CNNs method has better performance than other methods and is promising in liver tumor segmentation.
基金co-supported by National Natural Science Foundation of China(No.U1933202)Natural Science Foundation of Civil Aviation University of China(No.U1733201)+1 种基金China Scholarship Council(CSC)(No.201906830043)Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Nos.KYCX18_0310 and KYCX18_0265)。
文摘Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit(APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So,actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network(HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term Memory(CNN-Bi LSTM) neural network is proposed for active hazard identification of APU in civil aircraft. In order to identify the risks caused by different types of failures, the proposed HDNN simultaneously integrates three CNN-Bi LSTM basic models with different time window sizes in parallel by using a fully connected neural network. The CNN-Bi LSTM basic model can automatically extract features representing the system state from the input data and learn the time information of irregular trends in the time series data. Nine benchmark models are compared with the proposed HDNN. The comparison results show that the proposed HDNN has the highest identification accuracy. The HDNN has the most stable identification performance for data with imbalanced samples.
文摘Nowadays,the amount of wed data is increasing at a rapid speed,which presents a serious challenge to the web monitoring.Text sentiment analysis,an important research topic in the area of natural language processing,is a crucial task in the web monitoring area.The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data.Deep learning is a hot research topic of the artificial intelligence in the recent years.By now,several research groups have studied the sentiment analysis of English texts using deep learning methods.In contrary,relatively few works have so far considered the Chinese text sentiment analysis toward this direction.In this paper,a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network(CNN)in deep learning in order to improve the analysis accuracy.The feature values of the CNN after the training process are nonuniformly distributed.In order to overcome this problem,a method for normalizing the feature values is proposed.Moreover,the dimensions of the text features are optimized through simulations.Finally,a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances.Experiment results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods,e.g.,the support vector machine method.
文摘Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed,equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties,performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance.Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2018R1A2B6007333).
文摘Compressive strength of concrete is a significant factor to assess building structure health and safety.Therefore,various methods have been developed to evaluate the compressive strength of concrete structures.However,previous methods have several challenges in costly,time-consuming,and unsafety.To address these drawbacks,this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network(DCNN).The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy.The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.The experimental results indicated a root mean square error(RMSE)value of 3.56(MPa),demonstrating a strong feasibility that the proposed model can be utilized to predict the concrete strength with digital images of their surfaces and advantages to overcome the previous limitations.This experiment contributed to provide the basis that could be extended to future research with image analysis technique and artificial neural network in the diagnosis of concrete building structures.
