Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified ne...Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.展开更多
The use of the internet is increasing all over the world on a daily basis in the last two decades.The increase in the internet causes many sexual crimes,such as sexual misuse,domestic violence,and child pornography.Va...The use of the internet is increasing all over the world on a daily basis in the last two decades.The increase in the internet causes many sexual crimes,such as sexual misuse,domestic violence,and child pornography.Various research has been done for pornographic image detection and classification.Most of the used models used machine learning techniques and deep learning models which show less accuracy,while the deep learning model ware used for classification and detection performed better as compared to machine learning.Therefore,this research evaluates the performance analysis of intelligent neural-based deep learning models which are based on Convolution neural network(CNN),Visual geometry group(VGG-16),VGG-14,and Residual Network(ResNet-50)with the expanded dataset,trained using transfer learning approaches applied in the fully connected layer for datasets to classify rank(Pornographic vs.Nonpornographic)classification in images.The simulation result shows that VGG-16 performed better than the used model in this study without augmented data.The VGG-16 model with augmented data reached a training and validation accuracy of 0.97,0.94 with a loss of 0.070,0.16.The precision,recall,and f-measure values for explicit and non-explicit images are(0.94,0.94,0.94)and(0.94,0.94,0.94).Similarly,The VGG-14 model with augmented data reached a training and validation accuracy of 0.98,0.96 with a loss of 0.059,0.11.The f-measure,recall,and precision values for explicit and non-explicit images are(0.98,0.98,0.98)and(0.98,0.98,0.98).The CNN model with augmented data reached a training and validation accuracy of 0.776&0.78 with losses of 0.48&0.46.The f-measure,recall,and precision values for explicit and non-explicit images are(0.80,0.80,0.80)and(0.78,0.79,0.78).The ResNet-50 model with expanded data reached with training accuracy of 0.89 with a loss of 0.389 and 0.86 of validation accuracy and a loss of 0.47.The f-measure,recall,and precision values for explicit and non-explicit images are(0.86,0.97,0.91)and(0.86,0.93,0.89).Where else without augmented data the VGG-16 model reached a training and validation accuracy of 0.997,0.986 with a loss of 0.008,0.056.The f-measure,recall,and precision values for explicit and non-explicit images are(0.94,0.99,0.97)and(0.99,0.93,0.96)which outperforms the used models with the augmented dataset in this study.展开更多
Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate,ECG,nasal/oral airflow,temperature,light sensor,and fall detection sensor.Different researchers have done diff...Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate,ECG,nasal/oral airflow,temperature,light sensor,and fall detection sensor.Different researchers have done different work in the field of health monitoring with sensor networks.Different researchers used built-in apps,such as some used a small number of parameters,while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate,and outdated tools for study development.While no efficient,cheap,and updated work is proposed in the field of sensor-based health monitoring systems.Therefore,this study developed an android-based mobile system that can remotely monitor electrocardiograms(ECGs),pulse oximetry,heart rate,and body temperature.The microcontroller’s Wi-Fi device is used to manage wireless data transport.The findings of the patient are saved on the Firebase server for further usage in the mobile app.The performance of the proposed device is tested on ten numbers of different patients age-wise in terms of beats per minute(BPM),ECG,Temperature,and SpO2.This system uses temperature,pulse,ECG,blood pressure,and eye blink sensors.This device makes the usage of a tiny pulse sensor that has been designed to provide an accurate and optimal readout of the pulse rate and a temperature sensor is also included.With the help of an MCU,our system measures the pulse rate in beats per minute(bpm),blood oxygen level temperature measurements,and ECG readings and communicates this information to the Firebase server.To check the performance of the proposed system first,the BPM parameter was checked on the cardiac monitor.Then,the proposed model is tested on different patients age-wise.The simulation result shows that the BPM reading is not much different than the BPM of the cardiac monitor.According to the simulation findings,the proposed model achieved the best performance as compared to commercially available devices.展开更多
With the rising demand for data access,network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for a...With the rising demand for data access,network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access.To increase efficacy of Software Defined Network(SDN)and Network Function Virtualization(NFV)framework,we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency,reduce network performance,and increase maintenance cost.The existing frameworks lack in security,and computer systems face few abnormalities,which prompts the need for different recognition and mitigation methods to keep the system in the operational state proactively.The fundamental concept behind SDN-NFV is the encroachment from specific resource execution to the programming-based structure.This research is around the combination of SDN and NFV for rational decision making to control and monitor traffic in the virtualized environment.The combination is often seen as an extra burden in terms of resources usage in a heterogeneous network environment,but as well as it provides the solution for critical problems specially regarding massive network traffic issues.The attacks have been expanding step by step;therefore,it is hard to recognize and protect by conventional methods.To overcome these issues,there must be an autonomous system to recognize and characterize the network traffic’s abnormal conduct if there is any.Only four types of assaults,including HTTP Flood,UDP Flood,Smurf Flood,and SiDDoS Flood,are considered in the identified dataset,to optimize the stability of the SDN-NFVenvironment and security management,through several machine learning based characterization techniques like Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Logistic Regression(LR)and Isolation Forest(IF).Python is used for simulation purposes,including several valuable utilities like the mine package,the open-source Python ML libraries Scikit-learn,NumPy,SciPy,Matplotlib.Few Flood assaults and Structured Query Language(SQL)injections anomalies are validated and effectively-identified through the anticipated procedure.The classification results are promising and show that overall accuracy lies between 87%to 95%for SVM,LR,KNN,and IF classifiers in the scrutiny of traffic,whether the network traffic is normal or anomalous in the SDN-NFV environment.展开更多
Security is a significant issue for everyone due to new and creative ways to commit cybercrime.The Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect ...Security is a significant issue for everyone due to new and creative ways to commit cybercrime.The Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect lives.Operators monitor CCTV;however,it is difficult for a single person to monitor the actions of multiple people at one time.Consequently,there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study,we have designed a new Intelligent Ammunition Detection and Classification(IADC)system using Convolutional Neural Network(CNN).The proposed system is designed to identify persons carrying weapons and ammunition using CCTV cameras.When weapons are identified,the cameras sound an alarm.In the proposed IADC system,CNN was used to detect firearms and ammunition.The CNN model which is a Deep Learning technique consists of neural networks,most commonly applied to analyzing visual imagery has gained popularity for unstructured(images,videos)data classification.Additionally,this system generates an early warning through detection of ammunition before conditions become critical.Hence the faster and earlier the prediction,the lower the response time,loses and potential victims.The proposed IADC system provides better results than earlier published models like VGGNet,OverFeat-1,OverFeat-2,and OverFeat-3.展开更多
Clinical image processing plays a signicant role in healthcare systems and is currently a widely used methodology.In carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at a...Clinical image processing plays a signicant role in healthcare systems and is currently a widely used methodology.In carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at an early stage.Ductal carcinoma in situ(DCIS)and lobular carcinoma in situ(LCIS)are common types of malignancies that affect both women and men.The number of cases of DCIS and LCIS has increased every year since 2002,while it still takes a considerable amount of time to recommend a controlling technique.Image processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing operations.In this paper,we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our results.In this proposed study,mammograms are primarily used to diagnose,more precisely,the breast’s tumor component.The detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram equalization.The resulting images’tumor portions are then isolated by a segmentation process,such as threshold detection.Furthermore,morphological operations,such as erosion and dilation,are applied to the images,then a gray-level co-occurrence matrix texture features,Harlick texture features,and shape features are extracted from the regions of interest.For classication purposes,a support vector machine(SVM)classier is used to categorize normal and abnormal patterns.Finally,the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images,and the exact categorization of prior patterns is gained through the SVM.Early detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these diseases.Substantial results are obtained through cubic support vector machine(CSVM),respectively,showing 98.95%and 98.01%accuracies for normal and abnormal mammograms.Through ANFIS,promising results of mean square error(MSE)0.01866,0.18397,and 0.19640 for DCIS and LCIS differentiation during the training,testing,and checking phases.展开更多
Plant diseases are a major impendence to food security,and due to a lack of key infrastructure in many regions of the world,quick identification is still challenging.Harvest losses owing to illnesses are a severe prob...Plant diseases are a major impendence to food security,and due to a lack of key infrastructure in many regions of the world,quick identification is still challenging.Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities,motivating our mission.Because of the large range of diseases,identifying and classifying diseases with human eyes is not only time-consuming and labor intensive,but also prone to being mistaken with a high error rate.Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and diagnosis.The proposed work describes a deep learning approach for detection plant disease.Therefore,we proposed a deep learning model strategy for detecting plant disease and classification of plant leaf diseases.In our research,we focused on detecting plant diseases in five crops divided into 25 different types of classes(wheat,cotton,grape,corn,and cucumbers).In this task,we used a public image database of healthy and diseased plant leaves acquired under realistic conditions.For our work,a deep convolutional neural model AlexNet and Particle Swarm optimization was trained for this task we found that the metrics(accuracy,specificity,Sensitivity,precision,and Fscore)of the tested deep learning networks achieves an accuracy of 98.83%,specificity of 98.56%,Sensitivity of 98.78%,precision of 98.67%,and F-score of 98.47%,demonstrating the feasibility of this approach.展开更多
With the massive success of deep networks,there have been signi-cant efforts to analyze cancer diseases,especially skin cancer.For this purpose,this work investigates the capability of deep networks in diagnosing a va...With the massive success of deep networks,there have been signi-cant efforts to analyze cancer diseases,especially skin cancer.For this purpose,this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images.This paper aims to develop and ne-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images.Fine-tuning is a powerful method to obtain enhanced classication results by the customized pre-trained network.Regularization,batch normalization,and hyperparameter optimization are performed for ne-tuning the proposed deep network.The proposed ne-tuned ResNet50 model successfully classied 7-respective classes of dermoscopic lesions using the publicly available HAM10000 dataset.The developed deep model was compared against two powerful models,i.e.,InceptionV3 and VGG16,using the Dice similarity coefcient(DSC)and the area under the curve(AUC).The evaluation results show that the proposed model achieved higher results than some recent and robust models.展开更多
Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious individuals.The accuracy of HAR systems is completely reliant on the extraction of promi...Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious individuals.The accuracy of HAR systems is completely reliant on the extraction of prominent features.Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities,thereby reducing recognition performance.In this paper,we propose a robust feature extraction method for HAR systems based on template matching.Essentially,in this method,we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette.In this regard,the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the frame.This process is replicated for the whole frame,and the pixel is directed to the optimum match.The best count is estimated to be the pixel where the silhouette(provided via the template)presented inside the frame.In this way,the feature vector is generated.After feature vector generation,the hiddenMarkovmodel(HMM)has been utilized to label the incoming activity.We utilized different publicly available standard datasets for experiments.The proposed method achieved the best accuracy against existing state-of-the-art systems.展开更多
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant Number(DSR2022-RG-0102).
文摘Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.
基金funded by the Deanship of Scientific Research at Jouf University under Gran Number DSR–2022–RG–0101.
文摘The use of the internet is increasing all over the world on a daily basis in the last two decades.The increase in the internet causes many sexual crimes,such as sexual misuse,domestic violence,and child pornography.Various research has been done for pornographic image detection and classification.Most of the used models used machine learning techniques and deep learning models which show less accuracy,while the deep learning model ware used for classification and detection performed better as compared to machine learning.Therefore,this research evaluates the performance analysis of intelligent neural-based deep learning models which are based on Convolution neural network(CNN),Visual geometry group(VGG-16),VGG-14,and Residual Network(ResNet-50)with the expanded dataset,trained using transfer learning approaches applied in the fully connected layer for datasets to classify rank(Pornographic vs.Nonpornographic)classification in images.The simulation result shows that VGG-16 performed better than the used model in this study without augmented data.The VGG-16 model with augmented data reached a training and validation accuracy of 0.97,0.94 with a loss of 0.070,0.16.The precision,recall,and f-measure values for explicit and non-explicit images are(0.94,0.94,0.94)and(0.94,0.94,0.94).Similarly,The VGG-14 model with augmented data reached a training and validation accuracy of 0.98,0.96 with a loss of 0.059,0.11.The f-measure,recall,and precision values for explicit and non-explicit images are(0.98,0.98,0.98)and(0.98,0.98,0.98).The CNN model with augmented data reached a training and validation accuracy of 0.776&0.78 with losses of 0.48&0.46.The f-measure,recall,and precision values for explicit and non-explicit images are(0.80,0.80,0.80)and(0.78,0.79,0.78).The ResNet-50 model with expanded data reached with training accuracy of 0.89 with a loss of 0.389 and 0.86 of validation accuracy and a loss of 0.47.The f-measure,recall,and precision values for explicit and non-explicit images are(0.86,0.97,0.91)and(0.86,0.93,0.89).Where else without augmented data the VGG-16 model reached a training and validation accuracy of 0.997,0.986 with a loss of 0.008,0.056.The f-measure,recall,and precision values for explicit and non-explicit images are(0.94,0.99,0.97)and(0.99,0.93,0.96)which outperforms the used models with the augmented dataset in this study.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number 223202.
文摘Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate,ECG,nasal/oral airflow,temperature,light sensor,and fall detection sensor.Different researchers have done different work in the field of health monitoring with sensor networks.Different researchers used built-in apps,such as some used a small number of parameters,while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate,and outdated tools for study development.While no efficient,cheap,and updated work is proposed in the field of sensor-based health monitoring systems.Therefore,this study developed an android-based mobile system that can remotely monitor electrocardiograms(ECGs),pulse oximetry,heart rate,and body temperature.The microcontroller’s Wi-Fi device is used to manage wireless data transport.The findings of the patient are saved on the Firebase server for further usage in the mobile app.The performance of the proposed device is tested on ten numbers of different patients age-wise in terms of beats per minute(BPM),ECG,Temperature,and SpO2.This system uses temperature,pulse,ECG,blood pressure,and eye blink sensors.This device makes the usage of a tiny pulse sensor that has been designed to provide an accurate and optimal readout of the pulse rate and a temperature sensor is also included.With the help of an MCU,our system measures the pulse rate in beats per minute(bpm),blood oxygen level temperature measurements,and ECG readings and communicates this information to the Firebase server.To check the performance of the proposed system first,the BPM parameter was checked on the cardiac monitor.Then,the proposed model is tested on different patients age-wise.The simulation result shows that the BPM reading is not much different than the BPM of the cardiac monitor.According to the simulation findings,the proposed model achieved the best performance as compared to commercially available devices.
文摘With the rising demand for data access,network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access.To increase efficacy of Software Defined Network(SDN)and Network Function Virtualization(NFV)framework,we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency,reduce network performance,and increase maintenance cost.The existing frameworks lack in security,and computer systems face few abnormalities,which prompts the need for different recognition and mitigation methods to keep the system in the operational state proactively.The fundamental concept behind SDN-NFV is the encroachment from specific resource execution to the programming-based structure.This research is around the combination of SDN and NFV for rational decision making to control and monitor traffic in the virtualized environment.The combination is often seen as an extra burden in terms of resources usage in a heterogeneous network environment,but as well as it provides the solution for critical problems specially regarding massive network traffic issues.The attacks have been expanding step by step;therefore,it is hard to recognize and protect by conventional methods.To overcome these issues,there must be an autonomous system to recognize and characterize the network traffic’s abnormal conduct if there is any.Only four types of assaults,including HTTP Flood,UDP Flood,Smurf Flood,and SiDDoS Flood,are considered in the identified dataset,to optimize the stability of the SDN-NFVenvironment and security management,through several machine learning based characterization techniques like Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Logistic Regression(LR)and Isolation Forest(IF).Python is used for simulation purposes,including several valuable utilities like the mine package,the open-source Python ML libraries Scikit-learn,NumPy,SciPy,Matplotlib.Few Flood assaults and Structured Query Language(SQL)injections anomalies are validated and effectively-identified through the anticipated procedure.The classification results are promising and show that overall accuracy lies between 87%to 95%for SVM,LR,KNN,and IF classifiers in the scrutiny of traffic,whether the network traffic is normal or anomalous in the SDN-NFV environment.
文摘Security is a significant issue for everyone due to new and creative ways to commit cybercrime.The Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect lives.Operators monitor CCTV;however,it is difficult for a single person to monitor the actions of multiple people at one time.Consequently,there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study,we have designed a new Intelligent Ammunition Detection and Classification(IADC)system using Convolutional Neural Network(CNN).The proposed system is designed to identify persons carrying weapons and ammunition using CCTV cameras.When weapons are identified,the cameras sound an alarm.In the proposed IADC system,CNN was used to detect firearms and ammunition.The CNN model which is a Deep Learning technique consists of neural networks,most commonly applied to analyzing visual imagery has gained popularity for unstructured(images,videos)data classification.Additionally,this system generates an early warning through detection of ammunition before conditions become critical.Hence the faster and earlier the prediction,the lower the response time,loses and potential victims.The proposed IADC system provides better results than earlier published models like VGGNet,OverFeat-1,OverFeat-2,and OverFeat-3.
文摘Clinical image processing plays a signicant role in healthcare systems and is currently a widely used methodology.In carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at an early stage.Ductal carcinoma in situ(DCIS)and lobular carcinoma in situ(LCIS)are common types of malignancies that affect both women and men.The number of cases of DCIS and LCIS has increased every year since 2002,while it still takes a considerable amount of time to recommend a controlling technique.Image processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing operations.In this paper,we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our results.In this proposed study,mammograms are primarily used to diagnose,more precisely,the breast’s tumor component.The detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram equalization.The resulting images’tumor portions are then isolated by a segmentation process,such as threshold detection.Furthermore,morphological operations,such as erosion and dilation,are applied to the images,then a gray-level co-occurrence matrix texture features,Harlick texture features,and shape features are extracted from the regions of interest.For classication purposes,a support vector machine(SVM)classier is used to categorize normal and abnormal patterns.Finally,the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images,and the exact categorization of prior patterns is gained through the SVM.Early detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these diseases.Substantial results are obtained through cubic support vector machine(CSVM),respectively,showing 98.95%and 98.01%accuracies for normal and abnormal mammograms.Through ANFIS,promising results of mean square error(MSE)0.01866,0.18397,and 0.19640 for DCIS and LCIS differentiation during the training,testing,and checking phases.
文摘Plant diseases are a major impendence to food security,and due to a lack of key infrastructure in many regions of the world,quick identification is still challenging.Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities,motivating our mission.Because of the large range of diseases,identifying and classifying diseases with human eyes is not only time-consuming and labor intensive,but also prone to being mistaken with a high error rate.Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and diagnosis.The proposed work describes a deep learning approach for detection plant disease.Therefore,we proposed a deep learning model strategy for detecting plant disease and classification of plant leaf diseases.In our research,we focused on detecting plant diseases in five crops divided into 25 different types of classes(wheat,cotton,grape,corn,and cucumbers).In this task,we used a public image database of healthy and diseased plant leaves acquired under realistic conditions.For our work,a deep convolutional neural model AlexNet and Particle Swarm optimization was trained for this task we found that the metrics(accuracy,specificity,Sensitivity,precision,and Fscore)of the tested deep learning networks achieves an accuracy of 98.83%,specificity of 98.56%,Sensitivity of 98.78%,precision of 98.67%,and F-score of 98.47%,demonstrating the feasibility of this approach.
文摘With the massive success of deep networks,there have been signi-cant efforts to analyze cancer diseases,especially skin cancer.For this purpose,this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images.This paper aims to develop and ne-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images.Fine-tuning is a powerful method to obtain enhanced classication results by the customized pre-trained network.Regularization,batch normalization,and hyperparameter optimization are performed for ne-tuning the proposed deep network.The proposed ne-tuned ResNet50 model successfully classied 7-respective classes of dermoscopic lesions using the publicly available HAM10000 dataset.The developed deep model was compared against two powerful models,i.e.,InceptionV3 and VGG16,using the Dice similarity coefcient(DSC)and the area under the curve(AUC).The evaluation results show that the proposed model achieved higher results than some recent and robust models.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this work through the Project Number“375213500”.
文摘Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious individuals.The accuracy of HAR systems is completely reliant on the extraction of prominent features.Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities,thereby reducing recognition performance.In this paper,we propose a robust feature extraction method for HAR systems based on template matching.Essentially,in this method,we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette.In this regard,the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the frame.This process is replicated for the whole frame,and the pixel is directed to the optimum match.The best count is estimated to be the pixel where the silhouette(provided via the template)presented inside the frame.In this way,the feature vector is generated.After feature vector generation,the hiddenMarkovmodel(HMM)has been utilized to label the incoming activity.We utilized different publicly available standard datasets for experiments.The proposed method achieved the best accuracy against existing state-of-the-art systems.
