White Blood Cell(WBC)cancer or leukemia is one of the serious cancers that threaten the existence of human beings.In spite of its prevalence and serious consequences,it is mostly diagnosed through manual practices.The...White Blood Cell(WBC)cancer or leukemia is one of the serious cancers that threaten the existence of human beings.In spite of its prevalence and serious consequences,it is mostly diagnosed through manual practices.The risks of inappropriate,sub-standard and wrong or biased diagnosis are high in manual methods.So,there is a need exists for automatic diagnosis and classification method that can replace the manual process.Leukemia is mainly classified into acute and chronic types.The current research work proposed a computer-based application to classify the disease.In the feature extraction stage,we use excellent physical properties to improve the diagnostic system’s accuracy,based on Enhanced Color Co-Occurrence Matrix.The study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network(EVNN)classification.The proposed method achieved optimum accuracy in detection and classification of leukemia from WBC images.Thus,the study results establish the superiority of the proposed method in automated diagnosis of leukemia.The values achieved by the proposed method in terms of sensitivity,specificity,accuracy,and error rate were 97.8%,89.9%,76.6%,and 2.2%,respectively.Furthermore,the system could predict the disease in prior through images,and the probabilities of disease detection are also highly optimistic.展开更多
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
The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe.Radiologists use X-Rays or Computed Tomography(CT)images to confirm the presence of COVID-19.So,...The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe.Radiologists use X-Rays or Computed Tomography(CT)images to confirm the presence of COVID-19.So,image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times.The current research work introduces Multi-objective Black Widow Optimization(MBWO)-based Convolutional Neural Network i.e.,MBWOCNN technique for diagnosis and classification of COVID-19.MBWOCNN model involves four steps such as preprocessing,feature extraction,parameter tuning,and classification.In the beginning,the input images undergo preprocessing followed by CNN-based feature extraction.Then,Multi-objective Black Widow Optimization(MBWO)technique is applied to fine tune the hyperparameters of CNN.Finally,Extreme Learning Machine with autoencoder(ELM-AE)is applied as a classifier to confirm the presence of COVID-19 and classify the disease under different class labels.The proposed MBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing techniques.The experimental results ensured the superior results of the ELM-AE model by attaining maximum classification performance with the accuracy of 96.43%.The effectiveness of the technique is proved through promising results and the model can be applied in diagnosis and classification of COVID-19.展开更多
Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December 2019.It was tagged as a pandemic soon by the WHO being a serious public medical conditionworldwide.In spite of the fact that the virus can ...Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December 2019.It was tagged as a pandemic soon by the WHO being a serious public medical conditionworldwide.In spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)images.In this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features.Impressive features like Speeded-Up Robust Features(SURF),Features from Accelerated Segment Test(FAST)and Scale-Invariant Feature Transform(SIFT)are used in the test images to detect the presence of virus.The optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning rate.This task is accomplished by exceptional mating conduct of Black Widow spiders.In this strategy,cannibalism is incorporated.During this phase,fitness outcomes are rejected and are not satisfied by the proposed model.The results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing spaces.VGG16 model identifies the imagewhich has a place with which it is dependent on the distinctions in images.The impact of the distinctions on labels during training stage is studied and predicted for test images.The proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen,Spec,Accuracy and F1 score were promising.展开更多
文摘White Blood Cell(WBC)cancer or leukemia is one of the serious cancers that threaten the existence of human beings.In spite of its prevalence and serious consequences,it is mostly diagnosed through manual practices.The risks of inappropriate,sub-standard and wrong or biased diagnosis are high in manual methods.So,there is a need exists for automatic diagnosis and classification method that can replace the manual process.Leukemia is mainly classified into acute and chronic types.The current research work proposed a computer-based application to classify the disease.In the feature extraction stage,we use excellent physical properties to improve the diagnostic system’s accuracy,based on Enhanced Color Co-Occurrence Matrix.The study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network(EVNN)classification.The proposed method achieved optimum accuracy in detection and classification of leukemia from WBC images.Thus,the study results establish the superiority of the proposed method in automated diagnosis of leukemia.The values achieved by the proposed method in terms of sensitivity,specificity,accuracy,and error rate were 97.8%,89.9%,76.6%,and 2.2%,respectively.Furthermore,the system could predict the disease in prior through images,and the probabilities of disease detection are also highly optimistic.
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.
文摘The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe.Radiologists use X-Rays or Computed Tomography(CT)images to confirm the presence of COVID-19.So,image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times.The current research work introduces Multi-objective Black Widow Optimization(MBWO)-based Convolutional Neural Network i.e.,MBWOCNN technique for diagnosis and classification of COVID-19.MBWOCNN model involves four steps such as preprocessing,feature extraction,parameter tuning,and classification.In the beginning,the input images undergo preprocessing followed by CNN-based feature extraction.Then,Multi-objective Black Widow Optimization(MBWO)technique is applied to fine tune the hyperparameters of CNN.Finally,Extreme Learning Machine with autoencoder(ELM-AE)is applied as a classifier to confirm the presence of COVID-19 and classify the disease under different class labels.The proposed MBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing techniques.The experimental results ensured the superior results of the ELM-AE model by attaining maximum classification performance with the accuracy of 96.43%.The effectiveness of the technique is proved through promising results and the model can be applied in diagnosis and classification of COVID-19.
基金The authors are grateful to Taif University Researchers Supporting Project Number(TURSP-2020/215)Taif University,Taif,Saudi Arabia.This work is also supported by the Faculty of Computer Science and Information Technology,University of Malaya,under Postgraduate Research Grant(PG035-2016A).
文摘Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December 2019.It was tagged as a pandemic soon by the WHO being a serious public medical conditionworldwide.In spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)images.In this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features.Impressive features like Speeded-Up Robust Features(SURF),Features from Accelerated Segment Test(FAST)and Scale-Invariant Feature Transform(SIFT)are used in the test images to detect the presence of virus.The optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning rate.This task is accomplished by exceptional mating conduct of Black Widow spiders.In this strategy,cannibalism is incorporated.During this phase,fitness outcomes are rejected and are not satisfied by the proposed model.The results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing spaces.VGG16 model identifies the imagewhich has a place with which it is dependent on the distinctions in images.The impact of the distinctions on labels during training stage is studied and predicted for test images.The proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen,Spec,Accuracy and F1 score were promising.
