Wound classification is a critical task in healthcare,requiring accurate and efficient diagnostic tools to support clinicians.In this paper,we investigated the effectiveness of the YOLO11n model in classifying differe...Wound classification is a critical task in healthcare,requiring accurate and efficient diagnostic tools to support clinicians.In this paper,we investigated the effectiveness of the YOLO11n model in classifying different types of wound images.This study presents the training and evaluation of a lightweight YOLO11n model for automated wound classification using the AZH dataset,which includes six wound classes:Background(BG),Normal Skin(N),Diabetic(D),Pressure(P),Surgical(S),and Venous(V).The model’s architecture,optimized through experiments with varying batch sizes and epochs,ensures efficient deployment in resource-constrained environments.The model’s architecture is discussed in detail.The visual representation of different blocks of the model is also presented.The visual results of training and validation are shown.Our experiments emphasize the model’s ability to classify wounds with high precision and recall,leveraging its lightweight architecture for efficient computation.The findings demonstrate that fine-tuning hyperparameters has a significant impact on the model’s detection performance,making it suitable for real-world medical applications.This research contributes to advancing automated wound classification through deep learning,while addressing challenges such as dataset imbalance and classification intricacies.We conducted a comprehensive evaluation of YOLO11n for wound classification across multiple configurations,including 6,5,4,and 3-way classification,using the AZH dataset.YOLO11n acquires the highest F1 score and mean Average Precision of 0.836 and 0.893 for classifying wounds into six classes,respectively.It outperforms the existing methods in classifying wounds using the AZH dataset.Moreover,Gradient-weighted Class Activation Mapping(Grad-CAM)is applied to the YOLO11n model to visualize class-relevant regions in wound images.展开更多
Software Defined Networking(SDN)being an emerging network control model is widely recognized as a control and management platform.This model provides efficient techniques to control and manage the enterprise network.A...Software Defined Networking(SDN)being an emerging network control model is widely recognized as a control and management platform.This model provides efficient techniques to control and manage the enterprise network.Another emerging paradigm is edge computing in which data processing is performed at the edges of the network instead of a central controller.This data processing at the edge nodes reduces the latency and bandwidth requirements.In SDN,the controller is a single point of failure.Several security issues related to the traditional network can be solved by using SDN central management and control.Address Spoofing and Network Intrusion are the most common attacks.These attacks severely degrade performance and security.We propose an edge computing-based mechanism that automatically detects and mitigates those attacks.In this mechanism,an edge system gets the network topology from the controller and the Address Resolution Protocol(ARP)traffic is directed to it for further analysis.As such,the controller is saved from unnecessary processing related to addressing translation.We propose a graph computation based method to identify the location of an attacker or intruder by implementing a graph difference method.By using the correct location information,the exact attacker or intruder is blocked,while the legitimate users get access to the network resources.The proposed mechanism is evaluated in a Mininet simulator and a POX controller.The results show that it improves system performance in terms of attack mitigation time,attack detection time,and bandwidth requirements.展开更多
Intracavity absorption spectroscopy is a strikingly sensitive technique that has been integrated with a two-wavelength setup to develop a sensor for human breath.Various factors are considered in such a scenario,out o...Intracavity absorption spectroscopy is a strikingly sensitive technique that has been integrated with a two-wavelength setup to develop a sensor for human breath.Various factors are considered in such a scenario,out of which Relative Intensity Noise(RIN)has been exploited as an important parameter to characterize and calibrate the said setup.During the performance of an electrical based assessment arrangement which has been developed in the laboratory as an alternative to the expensive Agilent setup,the optical amplifier plays a pivotal role in its development and operation,along with other components and their significance.Therefore,the investigation and technical analysis of the amplifier in the system has been explored in detail.The algorithm developed for the automatic measurements of the system has been effectively deployed in terms of the laser’s performance.With this in perspective,a frequency dependent calibration has been pursued in depth with this scheme which enhances the sensor’s efficiency in terms of its sensitivity.In this way,our investigation helps us in a better understanding and implementation perspective of the proposed system,as the outcomes of our analysis adds to the precision and accuracy of the entire system.展开更多
基金funded by the Deanship of Graduate Studies and Scientific Research,Jazan University,Saudi Arabia,through project number:(RG24-S0150).
文摘Wound classification is a critical task in healthcare,requiring accurate and efficient diagnostic tools to support clinicians.In this paper,we investigated the effectiveness of the YOLO11n model in classifying different types of wound images.This study presents the training and evaluation of a lightweight YOLO11n model for automated wound classification using the AZH dataset,which includes six wound classes:Background(BG),Normal Skin(N),Diabetic(D),Pressure(P),Surgical(S),and Venous(V).The model’s architecture,optimized through experiments with varying batch sizes and epochs,ensures efficient deployment in resource-constrained environments.The model’s architecture is discussed in detail.The visual representation of different blocks of the model is also presented.The visual results of training and validation are shown.Our experiments emphasize the model’s ability to classify wounds with high precision and recall,leveraging its lightweight architecture for efficient computation.The findings demonstrate that fine-tuning hyperparameters has a significant impact on the model’s detection performance,making it suitable for real-world medical applications.This research contributes to advancing automated wound classification through deep learning,while addressing challenges such as dataset imbalance and classification intricacies.We conducted a comprehensive evaluation of YOLO11n for wound classification across multiple configurations,including 6,5,4,and 3-way classification,using the AZH dataset.YOLO11n acquires the highest F1 score and mean Average Precision of 0.836 and 0.893 for classifying wounds into six classes,respectively.It outperforms the existing methods in classifying wounds using the AZH dataset.Moreover,Gradient-weighted Class Activation Mapping(Grad-CAM)is applied to the YOLO11n model to visualize class-relevant regions in wound images.
文摘Software Defined Networking(SDN)being an emerging network control model is widely recognized as a control and management platform.This model provides efficient techniques to control and manage the enterprise network.Another emerging paradigm is edge computing in which data processing is performed at the edges of the network instead of a central controller.This data processing at the edge nodes reduces the latency and bandwidth requirements.In SDN,the controller is a single point of failure.Several security issues related to the traditional network can be solved by using SDN central management and control.Address Spoofing and Network Intrusion are the most common attacks.These attacks severely degrade performance and security.We propose an edge computing-based mechanism that automatically detects and mitigates those attacks.In this mechanism,an edge system gets the network topology from the controller and the Address Resolution Protocol(ARP)traffic is directed to it for further analysis.As such,the controller is saved from unnecessary processing related to addressing translation.We propose a graph computation based method to identify the location of an attacker or intruder by implementing a graph difference method.By using the correct location information,the exact attacker or intruder is blocked,while the legitimate users get access to the network resources.The proposed mechanism is evaluated in a Mininet simulator and a POX controller.The results show that it improves system performance in terms of attack mitigation time,attack detection time,and bandwidth requirements.
基金This work was supported in part by the German Academic Exchange Service(Deutsche Akademische Austausch Dienst(DAAD)),and in part by the University of Kassel.
文摘Intracavity absorption spectroscopy is a strikingly sensitive technique that has been integrated with a two-wavelength setup to develop a sensor for human breath.Various factors are considered in such a scenario,out of which Relative Intensity Noise(RIN)has been exploited as an important parameter to characterize and calibrate the said setup.During the performance of an electrical based assessment arrangement which has been developed in the laboratory as an alternative to the expensive Agilent setup,the optical amplifier plays a pivotal role in its development and operation,along with other components and their significance.Therefore,the investigation and technical analysis of the amplifier in the system has been explored in detail.The algorithm developed for the automatic measurements of the system has been effectively deployed in terms of the laser’s performance.With this in perspective,a frequency dependent calibration has been pursued in depth with this scheme which enhances the sensor’s efficiency in terms of its sensitivity.In this way,our investigation helps us in a better understanding and implementation perspective of the proposed system,as the outcomes of our analysis adds to the precision and accuracy of the entire system.
