Leaf disease identification is one of the most promising applications of convolutional neural networks(CNNs).This method represents a significant step towards revolutionizing agriculture by enabling the quick and accu...Leaf disease identification is one of the most promising applications of convolutional neural networks(CNNs).This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health.In this study,a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves.The researchers utilized a dataset of 3422 images,divided into four classes:healthy,fig rust,fig mosaic,and anthracnose.These diseases can significantly reduce the yield and quality of fig tree fruit.The objective of this research is to develop a CNN that can identify and categorize diseases in fig tree leaves.The data for this study was collected from gardens in the Amandi and Mamash Khail Bannu districts of the Khyber Pakhtunkhwa region in Pakistan.To minimize the risk of overfitting and enhance the model’s performance,early stopping techniques and data augmentation were employed.As a result,the model achieved a training accuracy of 91.53%and a validation accuracy of 90.12%,which are considered respectable.This comprehensive model assists farmers in the early identification and categorization of fig tree leaf diseases.Our experts believe that CNNs could serve as valuable tools for accurate disease classification and detection in precision agriculture.We recommend further research to explore additional data sources and more advanced neural networks to improve the model’s accuracy and applicability.Future research will focus on expanding the dataset by including new diseases and testing the model in real-world scenarios to enhance sustainable farming practices.展开更多
Accurate detection of fashion design attributes is essential for trend analyses and recommendation systems.Among these attributes,the neckline style plays a key role in shaping garment aesthetics.However,the presence ...Accurate detection of fashion design attributes is essential for trend analyses and recommendation systems.Among these attributes,the neckline style plays a key role in shaping garment aesthetics.However,the presence of complex backgrounds and varied body postures in real-world fashion images presents challenges for reliable neckline detection.To address this problem,this research builds a comprehensive fashion neckline database from online shop images and proposes an efficient fashion neckline detection model based on the YOLOv8 architecture(FN-YOLO).First,the proposed model incorporates a BiFormer attention mechanism into the backbone,enhancing its feature extraction capability.Second,a lightweight multi-level asymmetry detector head(LADH)is designed to replace the original head,effectively reducing the computational complexity and accelerating the detection speed.Last,the original loss function is replaced with Wise-IoU,which improves the localization accuracy of the detection box.The experimental results demonstrate that FN-YOLO achieves a mean average precision(mAP)of 81.7%,showing an absolute improvement of 3.9%over the original YOLOv8 model,and a detection speed of 215.6 frame/s,confirming its suitability for real-time applications in fashion neckline detection.展开更多
Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is availabl...Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is available to everyone,to look into howdeep learningmodels could be used to find powdery mildew,leaf rust,and yellow rust,which are three common fungal diseases in Punjab,India.We changed a few hyperparameters to test TensorFlowbased models,such as SSD and Faster R-CNN with ResNet50,ResNet101,and ResNet152 as backbones.Faster R-CNN with ResNet50 achieved amean average precision(mAP)of 0.68 among these models.We then used the PyTorch-based YOLOv8 model,which significantly outperformed the previous methods with an impressive mAP of 0.99.YOLOv8 proved to be a beneficial approach for the early-stage diagnosis of fungal diseases,especially when it comes to precisely identifying diseased areas and various object sizes in images.Problems,such as class imbalance and possible model overfitting,persisted despite these developments.The results show that YOLOv8 is a good automated disease diagnosis tool that helps farmers quickly find and treat fungal infections using image-based systems.展开更多
Forest carbon sinks are crucial for mitigating urban climate change.Their effectiveness depends on the balance between gross carbon losses and gains.However,quantitative and continuous monitoring of forest change/dist...Forest carbon sinks are crucial for mitigating urban climate change.Their effectiveness depends on the balance between gross carbon losses and gains.However,quantitative and continuous monitoring of forest change/disturbance carbon fluxes is still insufficient.To address this gap,we integrated an improved spatial carbon bookkeeping(SBK)model with the continuous change detection and classification(CCDC)algorithm,long-term Landsat observations,and ground measurements to track carbon emissions,uptakes,and net changes from forest cover changes in the Yangtze River Delta(YRD)of China from 2000 to 2020.The SBK model was refined by incorporating heterogeneous carbon response functions.Our results reveal that carbon emissions(-3.88 Tg C·year^(-1))were four times greater than carbon uptakes(0.93 Tg C·year^(-1))from forest cover changes in the YRD during 2000-2020,despite a net forest cover gain of 10.95×10^(4) ha.These findings indicate that the carbon effect per hectare of forest cover loss is approximately 4.5 times that of forest cover gain.The asymmetric carbon effect suggests that forest cover change may act as a carbon source even with net-zero or net-positive forest cover change.Furthermore,carbon uptakes from forest gains in the YRD during 2000-2020 could only offset 0.28% of energy-related carbon emissions from 2000 to 2019.Urban and agricultural expansions accounted for 37% and 10% of carbon emissions,respectively,while the Grain for Green Project contributed to 45% of carbon uptakes.Our findings underscore the necessity of understanding the asymmetric carbon effects of forest cover loss and gain to accurately assess the capacity of forest carbon sinks.展开更多
The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger;the real threat lies in the obfuscation of these cyberattacks,as they constantly change their behavior,making...The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger;the real threat lies in the obfuscation of these cyberattacks,as they constantly change their behavior,making detection more difficult.Numerous researchers and developers have devoted considerable attention to this topic;however,the research field has not yet been fully saturated with high-quality studies that address these problems.For this reason,this paper presents a novel multi-objective Markov-enhanced adaptive whale optimization(MOMEAWO)cybersecurity model to improve the classification of binary and multi-class malware threats through the proposed MOMEAWO approach.The proposed MOMEAWO cybersecurity model aims to provide an innovative solution for analyzing,detecting,and classifying the behavior of obfuscated malware within their respective families.The proposed model includes three classification types:Binary classification and multi-class classification(e.g.,four families and 16 malware families).To evaluate the performance of this model,we used a recently published dataset called the Canadian Institute for Cybersecurity Malware Memory Analysis(CIC-MalMem-2022)that contains balanced data.The results show near-perfect accuracy in binary classification and high accuracy in multi-class classification compared with related work using the same dataset.展开更多
This paper proposes a tree kernel method of semantic relation detection and classification （RDC） between named entities. It resolves two critical problems in previous tree kernel methods of RDC. First, a new tree ke...This paper proposes a tree kernel method of semantic relation detection and classification （RDC） between named entities. It resolves two critical problems in previous tree kernel methods of RDC. First, a new tree kernel is presented to better capture the inherent structural information in a parse tree by enabling the standard convolution tree kernel with context-sensitiveness and approximate matching of sub-trees. Second, an enriched parse tree structure is proposed to well derive necessary structural information, e.g., proper latent annotations, from a parse tree. Evaluation on the ACE RDC corpora shows that both the new tree kernel and the enriched parse tree structure contribute significantly to RDC and our tree kernel method much outperforms the state-of-the-art ones.展开更多
We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network.Compared with the existing techniques,the proposed approach considers ex...We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network.Compared with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction ability.On this basis,a framework for transient fault detection and classification is created.Graph structure is generated to provide topology information to the task.Our approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results rapidly.Furthermore,the proposed approach is tested in various situations and its generalization ability is verified by experimental results.The results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.展开更多
While the internet has a lot of positive impact on society,there are negative components.Accessible to everyone through online platforms,pornography is,inducing psychological and health related issues among people of ...While the internet has a lot of positive impact on society,there are negative components.Accessible to everyone through online platforms,pornography is,inducing psychological and health related issues among people of all ages.While a difficult task,detecting pornography can be the important step in determining the porn and adult content in a video.In this paper,an architecture is proposed which yielded high scores for both training and testing.This dataset was produced from 190 videos,yielding more than 19 h of videos.The main sources for the content were from YouTube,movies,torrent,and websites that hosts both pornographic and non-pornographic contents.The videos were from different ethnicities and skin color which ensures the models can detect any kind of video.A VGG16,Inception V3 and Resnet 50 models were initially trained to detect these pornographic images but failed to achieve a high testing accuracy with accuracies of 0.49,0.49 and 0.78 respectively.Finally,utilizing transfer learning,a convolutional neural network was designed and yielded an accuracy of 0.98.展开更多
Today＇s industry requires more reliable information on the current status of their hard assets; prognosis for continued usability of systems and better predictability of equipment life cycle maintenance. Therefore, a...Today＇s industry requires more reliable information on the current status of their hard assets; prognosis for continued usability of systems and better predictability of equipment life cycle maintenance. Therefore, an innovative technique for early detection of potential failure and condition monitoring is urgently required by many engineers. This document describes a novel approach to improve industrial equipment safety, reliability and life cycle management. A new field portable instrument called the ＂IMS （indicator of mechanical stresses）＂ utilizes magneto-anisotropic （＂cross＂） transducers to measure anisotropy of magnetic properties in ferromagnetic material. Mechanical stresses including residual stresses in Ferro-magnetic parts, are ＂not visible＂ to most traditional NDT （non-destructive testing） methods; for example, radiography and ultrasonic inspection. Stress build-up can be the first indicator that something is faulty with a structure. This can be the result of a manufacturing defect; or as assets age and fatigue, stress loads can become unevenly distributed throughout the metal. We outline the evaluation of IMS as a fast screening tool to provide structural condition or deterioration feedback in novel applications for pipelines, petrochemical refinery, cranes, and municipal infrastructure.展开更多
This study examined wetland trends in the St.Lawrence Seaway(~500,000 km^(2))in Canada over the past four decades.To this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were proce...This study examined wetland trends in the St.Lawrence Seaway(~500,000 km^(2))in Canada over the past four decades.To this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were processed.Reference samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged samples.These spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to 2021.Subsequently,a change analysis was conducted to calculate the loss and gain of different wetland types.Overall,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,respectively.It was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 years.Overall,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an increase.Finally,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),respectively.This study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.展开更多
Intelligent detection and classification of kitchen waste can promote ecological sustainability by replacing inefficient manual processes.However,the presence of non-degradable waste mixed in kitchen waste often follo...Intelligent detection and classification of kitchen waste can promote ecological sustainability by replacing inefficient manual processes.However,the presence of non-degradable waste mixed in kitchen waste often follows a long-tailed distribution,making it challenging to train convolutional neural network-based object detectors,which results in the unsatisfactory detection of tailclass waste.To address this challenge,we propose a class-instance balanced detector(CIB-Det) for intelligent detection and classification of kitchen waste.CIB-Det implements two strategies for the loss function:the class-balanced strategy(CBS)and the instance-balanced strategy(IBS).The CBS focuses more on tail classes,and the IBS concentrates on hard-to-classify instances adaptively during training.Consequently,CIB-Det comprehensively and adaptively addresses the long-tailed issue.Our experiments on a real dataset of kitchen waste images support the effectiveness of CIB-Det for kitchen waste detection.展开更多
Purpose-Taking into consideration the current human need for agricultural produce such as rice that requires water for growth,the optimal consumption of this valuable liquid is important.Unfortunately,the traditional ...Purpose-Taking into consideration the current human need for agricultural produce such as rice that requires water for growth,the optimal consumption of this valuable liquid is important.Unfortunately,the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption.Therefore,designing and implementing a mechanized irrigation system is of the highest importance.This system includes hardware equipment such as liquid altimeter sensors,valves and pumps which have a failure phenomenon as an integral part,causing faults in the system.Naturally,these faults occur at probable time intervals,and the probability function with exponential distribution is used to simulate this interval.Thus,before the implementation of such high-cost systems,its evaluation is essential during the design phase.Design/methodology/approach-The proposed approach included two main steps:offline and online.The offline phase included the simulation of the studied system(i.e.the irrigation system of paddy fields)and the acquisition of a data set for training machine learning algorithms such as decision trees to detect,locate(classification)and evaluate faults.In the online phase,C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.Findings-The proposed approach is a comprehensive online component-oriented method,which is a combination of supervisedmachine learning methods to investigate system faults.Each of thesemethods is considered a component determined by the dimensions and complexity of the case study(to discover,classify and evaluate fault tolerance).These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods.As a result,depending on the conditions under study,the most efficient method is selected in the components.Before the system implementation phase,its reliability is checked by evaluating the predicted faults(in the system design phase).Therefore,this approach avoids the construction of a high-risk system.Compared to existing methods,the proposed approach is more comprehensive and has greater flexibility.Research limitations/implications-By expanding the dimensions of the problem,the model verification space grows exponentially using automata.Originality/value-Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection,classification and fault-tolerance evaluation,this paper proposes a comprehensive processoriented approach that investigates all three aspects of fault analysis concurrently.展开更多
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Leaf disease identification is one of the most promising applications of convolutional neural networks(CNNs).This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health.In this study,a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves.The researchers utilized a dataset of 3422 images,divided into four classes:healthy,fig rust,fig mosaic,and anthracnose.These diseases can significantly reduce the yield and quality of fig tree fruit.The objective of this research is to develop a CNN that can identify and categorize diseases in fig tree leaves.The data for this study was collected from gardens in the Amandi and Mamash Khail Bannu districts of the Khyber Pakhtunkhwa region in Pakistan.To minimize the risk of overfitting and enhance the model’s performance,early stopping techniques and data augmentation were employed.As a result,the model achieved a training accuracy of 91.53%and a validation accuracy of 90.12%,which are considered respectable.This comprehensive model assists farmers in the early identification and categorization of fig tree leaf diseases.Our experts believe that CNNs could serve as valuable tools for accurate disease classification and detection in precision agriculture.We recommend further research to explore additional data sources and more advanced neural networks to improve the model’s accuracy and applicability.Future research will focus on expanding the dataset by including new diseases and testing the model in real-world scenarios to enhance sustainable farming practices.
基金Fundamental Research Funds for the Central Universities,China(Nos.2232020G-08 and 2232020E-03)Shanghai University Knowledge Service Platform,China(No.13S107024)。
文摘Accurate detection of fashion design attributes is essential for trend analyses and recommendation systems.Among these attributes,the neckline style plays a key role in shaping garment aesthetics.However,the presence of complex backgrounds and varied body postures in real-world fashion images presents challenges for reliable neckline detection.To address this problem,this research builds a comprehensive fashion neckline database from online shop images and proposes an efficient fashion neckline detection model based on the YOLOv8 architecture(FN-YOLO).First,the proposed model incorporates a BiFormer attention mechanism into the backbone,enhancing its feature extraction capability.Second,a lightweight multi-level asymmetry detector head(LADH)is designed to replace the original head,effectively reducing the computational complexity and accelerating the detection speed.Last,the original loss function is replaced with Wise-IoU,which improves the localization accuracy of the detection box.The experimental results demonstrate that FN-YOLO achieves a mean average precision(mAP)of 81.7%,showing an absolute improvement of 3.9%over the original YOLOv8 model,and a detection speed of 215.6 frame/s,confirming its suitability for real-time applications in fashion neckline detection.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R432),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is available to everyone,to look into howdeep learningmodels could be used to find powdery mildew,leaf rust,and yellow rust,which are three common fungal diseases in Punjab,India.We changed a few hyperparameters to test TensorFlowbased models,such as SSD and Faster R-CNN with ResNet50,ResNet101,and ResNet152 as backbones.Faster R-CNN with ResNet50 achieved amean average precision(mAP)of 0.68 among these models.We then used the PyTorch-based YOLOv8 model,which significantly outperformed the previous methods with an impressive mAP of 0.99.YOLOv8 proved to be a beneficial approach for the early-stage diagnosis of fungal diseases,especially when it comes to precisely identifying diseased areas and various object sizes in images.Problems,such as class imbalance and possible model overfitting,persisted despite these developments.The results show that YOLOv8 is a good automated disease diagnosis tool that helps farmers quickly find and treat fungal infections using image-based systems.
基金supported by the Natural Science Foundation of Zhejiang Province(No.ZCLQN25C0301)the National Key Research and Development Program of China(No.2016YFC0502700)the General Program of Education Department of Zhejiang(No.23056209-F).
文摘Forest carbon sinks are crucial for mitigating urban climate change.Their effectiveness depends on the balance between gross carbon losses and gains.However,quantitative and continuous monitoring of forest change/disturbance carbon fluxes is still insufficient.To address this gap,we integrated an improved spatial carbon bookkeeping(SBK)model with the continuous change detection and classification(CCDC)algorithm,long-term Landsat observations,and ground measurements to track carbon emissions,uptakes,and net changes from forest cover changes in the Yangtze River Delta(YRD)of China from 2000 to 2020.The SBK model was refined by incorporating heterogeneous carbon response functions.Our results reveal that carbon emissions(-3.88 Tg C·year^(-1))were four times greater than carbon uptakes(0.93 Tg C·year^(-1))from forest cover changes in the YRD during 2000-2020,despite a net forest cover gain of 10.95×10^(4) ha.These findings indicate that the carbon effect per hectare of forest cover loss is approximately 4.5 times that of forest cover gain.The asymmetric carbon effect suggests that forest cover change may act as a carbon source even with net-zero or net-positive forest cover change.Furthermore,carbon uptakes from forest gains in the YRD during 2000-2020 could only offset 0.28% of energy-related carbon emissions from 2000 to 2019.Urban and agricultural expansions accounted for 37% and 10% of carbon emissions,respectively,while the Grain for Green Project contributed to 45% of carbon uptakes.Our findings underscore the necessity of understanding the asymmetric carbon effects of forest cover loss and gain to accurately assess the capacity of forest carbon sinks.
文摘The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger;the real threat lies in the obfuscation of these cyberattacks,as they constantly change their behavior,making detection more difficult.Numerous researchers and developers have devoted considerable attention to this topic;however,the research field has not yet been fully saturated with high-quality studies that address these problems.For this reason,this paper presents a novel multi-objective Markov-enhanced adaptive whale optimization(MOMEAWO)cybersecurity model to improve the classification of binary and multi-class malware threats through the proposed MOMEAWO approach.The proposed MOMEAWO cybersecurity model aims to provide an innovative solution for analyzing,detecting,and classifying the behavior of obfuscated malware within their respective families.The proposed model includes three classification types:Binary classification and multi-class classification(e.g.,four families and 16 malware families).To evaluate the performance of this model,we used a recently published dataset called the Canadian Institute for Cybersecurity Malware Memory Analysis(CIC-MalMem-2022)that contains balanced data.The results show near-perfect accuracy in binary classification and high accuracy in multi-class classification compared with related work using the same dataset.
基金Supported by the National Natural Science Foundation of China under Grant Nos.60873150,60970056 and 90920004
文摘This paper proposes a tree kernel method of semantic relation detection and classification （RDC） between named entities. It resolves two critical problems in previous tree kernel methods of RDC. First, a new tree kernel is presented to better capture the inherent structural information in a parse tree by enabling the standard convolution tree kernel with context-sensitiveness and approximate matching of sub-trees. Second, an enriched parse tree structure is proposed to well derive necessary structural information, e.g., proper latent annotations, from a parse tree. Evaluation on the ACE RDC corpora shows that both the new tree kernel and the enriched parse tree structure contribute significantly to RDC and our tree kernel method much outperforms the state-of-the-art ones.
基金This work was supported by the National Key Research and Development Program of China under Grant 2018YFF0214704.
文摘We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network.Compared with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction ability.On this basis,a framework for transient fault detection and classification is created.Graph structure is generated to provide topology information to the task.Our approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results rapidly.Furthermore,the proposed approach is tested in various situations and its generalization ability is verified by experimental results.The results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.
文摘While the internet has a lot of positive impact on society,there are negative components.Accessible to everyone through online platforms,pornography is,inducing psychological and health related issues among people of all ages.While a difficult task,detecting pornography can be the important step in determining the porn and adult content in a video.In this paper,an architecture is proposed which yielded high scores for both training and testing.This dataset was produced from 190 videos,yielding more than 19 h of videos.The main sources for the content were from YouTube,movies,torrent,and websites that hosts both pornographic and non-pornographic contents.The videos were from different ethnicities and skin color which ensures the models can detect any kind of video.A VGG16,Inception V3 and Resnet 50 models were initially trained to detect these pornographic images but failed to achieve a high testing accuracy with accuracies of 0.49,0.49 and 0.78 respectively.Finally,utilizing transfer learning,a convolutional neural network was designed and yielded an accuracy of 0.98.
文摘Today＇s industry requires more reliable information on the current status of their hard assets; prognosis for continued usability of systems and better predictability of equipment life cycle maintenance. Therefore, an innovative technique for early detection of potential failure and condition monitoring is urgently required by many engineers. This document describes a novel approach to improve industrial equipment safety, reliability and life cycle management. A new field portable instrument called the ＂IMS （indicator of mechanical stresses）＂ utilizes magneto-anisotropic （＂cross＂） transducers to measure anisotropy of magnetic properties in ferromagnetic material. Mechanical stresses including residual stresses in Ferro-magnetic parts, are ＂not visible＂ to most traditional NDT （non-destructive testing） methods; for example, radiography and ultrasonic inspection. Stress build-up can be the first indicator that something is faulty with a structure. This can be the result of a manufacturing defect; or as assets age and fatigue, stress loads can become unevenly distributed throughout the metal. We outline the evaluation of IMS as a fast screening tool to provide structural condition or deterioration feedback in novel applications for pipelines, petrochemical refinery, cranes, and municipal infrastructure.
文摘This study examined wetland trends in the St.Lawrence Seaway(~500,000 km^(2))in Canada over the past four decades.To this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were processed.Reference samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged samples.These spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to 2021.Subsequently,a change analysis was conducted to calculate the loss and gain of different wetland types.Overall,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,respectively.It was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 years.Overall,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an increase.Finally,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),respectively.This study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.
基金supported by the National Key Research and Development Program of China (Grant No. 2021YFC1910402)。
文摘Intelligent detection and classification of kitchen waste can promote ecological sustainability by replacing inefficient manual processes.However,the presence of non-degradable waste mixed in kitchen waste often follows a long-tailed distribution,making it challenging to train convolutional neural network-based object detectors,which results in the unsatisfactory detection of tailclass waste.To address this challenge,we propose a class-instance balanced detector(CIB-Det) for intelligent detection and classification of kitchen waste.CIB-Det implements two strategies for the loss function:the class-balanced strategy(CBS)and the instance-balanced strategy(IBS).The CBS focuses more on tail classes,and the IBS concentrates on hard-to-classify instances adaptively during training.Consequently,CIB-Det comprehensively and adaptively addresses the long-tailed issue.Our experiments on a real dataset of kitchen waste images support the effectiveness of CIB-Det for kitchen waste detection.
文摘Purpose-Taking into consideration the current human need for agricultural produce such as rice that requires water for growth,the optimal consumption of this valuable liquid is important.Unfortunately,the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption.Therefore,designing and implementing a mechanized irrigation system is of the highest importance.This system includes hardware equipment such as liquid altimeter sensors,valves and pumps which have a failure phenomenon as an integral part,causing faults in the system.Naturally,these faults occur at probable time intervals,and the probability function with exponential distribution is used to simulate this interval.Thus,before the implementation of such high-cost systems,its evaluation is essential during the design phase.Design/methodology/approach-The proposed approach included two main steps:offline and online.The offline phase included the simulation of the studied system(i.e.the irrigation system of paddy fields)and the acquisition of a data set for training machine learning algorithms such as decision trees to detect,locate(classification)and evaluate faults.In the online phase,C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.Findings-The proposed approach is a comprehensive online component-oriented method,which is a combination of supervisedmachine learning methods to investigate system faults.Each of thesemethods is considered a component determined by the dimensions and complexity of the case study(to discover,classify and evaluate fault tolerance).These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods.As a result,depending on the conditions under study,the most efficient method is selected in the components.Before the system implementation phase,its reliability is checked by evaluating the predicted faults(in the system design phase).Therefore,this approach avoids the construction of a high-risk system.Compared to existing methods,the proposed approach is more comprehensive and has greater flexibility.Research limitations/implications-By expanding the dimensions of the problem,the model verification space grows exponentially using automata.Originality/value-Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection,classification and fault-tolerance evaluation,this paper proposes a comprehensive processoriented approach that investigates all three aspects of fault analysis concurrently.
