Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,etc.Several issues that exist in the designing of WSN...Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,etc.Several issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so on.In spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of anchors.The efficiency of the WSN can be considerably influenced by the node localization accuracy.Therefore,this paper presents a modified search and rescue optimization based node localization technique(MSRONLT)forWSN.The major aim of theMSRO-NLT technique is to determine the positioning of the unknown nodes in theWSN.Since the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the technique.The application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO algorithm.In order to validate the effective node localization performance of the MSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.展开更多
Recent developments in digital cameras and electronic gadgets coupled with Machine Learning(ML)and Deep Learning(DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives...Recent developments in digital cameras and electronic gadgets coupled with Machine Learning(ML)and Deep Learning(DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models.In this background,the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection(ESFO-EALD)model.The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically.In this scenario,Median Filtering(MF)approach is utilized to boost the quality of apple plant leaf images.Moreover,SFO with Kapur’s entropy-based segmentation technique is also utilized for the identification of the affected plant region from test image.Furthermore,Adam optimizer with EfficientNet-based feature extraction and Spiking Neural Network(SNN)-based classification are employed to detect and classify the apple plant leaf images.A wide range of simulations was conducted to ensure the effective outcomes of ESFO-EALD technique on benchmark dataset.The results reported the supremacy of the proposed ESFO-EALD approach than the existing approaches.展开更多
文摘Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,etc.Several issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so on.In spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of anchors.The efficiency of the WSN can be considerably influenced by the node localization accuracy.Therefore,this paper presents a modified search and rescue optimization based node localization technique(MSRONLT)forWSN.The major aim of theMSRO-NLT technique is to determine the positioning of the unknown nodes in theWSN.Since the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the technique.The application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO algorithm.In order to validate the effective node localization performance of the MSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/209/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R191)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recent developments in digital cameras and electronic gadgets coupled with Machine Learning(ML)and Deep Learning(DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models.In this background,the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection(ESFO-EALD)model.The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically.In this scenario,Median Filtering(MF)approach is utilized to boost the quality of apple plant leaf images.Moreover,SFO with Kapur’s entropy-based segmentation technique is also utilized for the identification of the affected plant region from test image.Furthermore,Adam optimizer with EfficientNet-based feature extraction and Spiking Neural Network(SNN)-based classification are employed to detect and classify the apple plant leaf images.A wide range of simulations was conducted to ensure the effective outcomes of ESFO-EALD technique on benchmark dataset.The results reported the supremacy of the proposed ESFO-EALD approach than the existing approaches.
