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
Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest can...Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal world.The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease.Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time.From the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)technique.Existence of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant cells.Number of image processing techniques are available for processing the images to identify the affected area.This study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI image.Preprocessing involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization technique.The traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting mechanism.The results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its standard.The experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind.展开更多
Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so...Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so on.SA has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source tools.The current research work designs a novel Modified Red Deer Algorithm(MRDA)Extreme Learning Machine Sparse Autoencoder(ELMSAE)model for SA and classification.The proposed MRDA-ELMSAE technique initially performs pre-processing to transform the data into a compatible format.Moreover,TF-IDF vec-torizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments.Furthermore,optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of MRDA-ELMSAE technique against other recent techniques.展开更多
Energy management and packet delivery rate are the important factors in ad hoc networks.It is the major network where nodes share the information without administration.Due to the mobility of nodes,maximum energy is s...Energy management and packet delivery rate are the important factors in ad hoc networks.It is the major network where nodes share the information without administration.Due to the mobility of nodes,maximum energy is spent on transmission of packets.Mostly energy is wasted on packet dropping and false route discovery.In this research work,Fuzzy Based Reliable Load Balanced Routing Approach(RLRA)is proposed to provide high energy efficiency and more network lifetime using optimal multicast route discovery mechanism.It contains three phases.In first phase,optimal multicast route discovery is initiated to resolve the link failures.In second phase,the link quality is estimated and set to threshold value to meet the requirements of high energy efficiency.In third phase,energy model is shown to obtain total energy of network after transmission of packets.A multicast routing is established Based on path reliability and fault tolerant calculation is done and integrated with multicast routing.The routes can withstand the malicious issues.Fuzzy decision model is integrated with propose protocol to decide the performance of network lifetime.The network simulation tool is used for evaluating the RLRA with existing schemes and performance of RLRA is good compared to others.展开更多
Sensing the content of macronutrients in the agricultural soil is an essential task in precision agriculture.It helps the farmers in the optimal use of fertilizers.It reduces the cost of food production and also the n...Sensing the content of macronutrients in the agricultural soil is an essential task in precision agriculture.It helps the farmers in the optimal use of fertilizers.It reduces the cost of food production and also the negative environmentalimpacts on atmosphere and water bodies due to indiscriminate dosageof fertilizers.The traditional chemical-based laboratory soil analysis methodsdo not serve the purpose as they are hardly suitable for site specific soil management.Moreover,the spectral range used in the chemical-based laboratory soil analysismay be of 350-2500 nm,which leads to redundancy and confusion.Developing sensors based on the discovery of spectral wavebands that respondto soil macronutrient concentrations,on the other hand,is an innovative and successfultechnology since the results are dependable and timely.The goal of thisarticle is to use a supervised neuro-fuzzy based dimensionality reduction approachin the sensor development process to determine sensitive wavebands of soilmacronutrients.Accordingly,the spectral signatures of the soil are collected inan outdoor environment and mapped with its macronutrient concentrations.In thisspectral analysis,the spectral reflectance of 424 wavelengths has been obtainedand these wavelengths are evaluated through combined and individual modesas well.Appropriate wavelengths are selected in each case by minimizing the fuzzy reflectance assessment index.The effectiveness of these selected wavelengthsin each mode is validated by modeling the relation between the reduced reflectancespace and each macronutrient concentration using Partial Least Squares Multi Variable Regression(PLS-MVR)method.Set of optimal wavebands areidentified and the results are compared with the existing systems.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.
文摘Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal world.The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease.Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time.From the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)technique.Existence of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant cells.Number of image processing techniques are available for processing the images to identify the affected area.This study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI image.Preprocessing involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization technique.The traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting mechanism.The results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its standard.The experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind.
基金We acknowledge Taif University for Supporting this study through Taif University Researchers Supporting Project number(TURSP-2020/173)Taif University,Taif,Saudi Arabia.
文摘Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so on.SA has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source tools.The current research work designs a novel Modified Red Deer Algorithm(MRDA)Extreme Learning Machine Sparse Autoencoder(ELMSAE)model for SA and classification.The proposed MRDA-ELMSAE technique initially performs pre-processing to transform the data into a compatible format.Moreover,TF-IDF vec-torizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments.Furthermore,optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of MRDA-ELMSAE technique against other recent techniques.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University,Kingdom of Saudi Arabia for funding this workthrough General Research Project Under the grant number(RGP.1/262/42).
文摘Energy management and packet delivery rate are the important factors in ad hoc networks.It is the major network where nodes share the information without administration.Due to the mobility of nodes,maximum energy is spent on transmission of packets.Mostly energy is wasted on packet dropping and false route discovery.In this research work,Fuzzy Based Reliable Load Balanced Routing Approach(RLRA)is proposed to provide high energy efficiency and more network lifetime using optimal multicast route discovery mechanism.It contains three phases.In first phase,optimal multicast route discovery is initiated to resolve the link failures.In second phase,the link quality is estimated and set to threshold value to meet the requirements of high energy efficiency.In third phase,energy model is shown to obtain total energy of network after transmission of packets.A multicast routing is established Based on path reliability and fault tolerant calculation is done and integrated with multicast routing.The routes can withstand the malicious issues.Fuzzy decision model is integrated with propose protocol to decide the performance of network lifetime.The network simulation tool is used for evaluating the RLRA with existing schemes and performance of RLRA is good compared to others.
文摘Sensing the content of macronutrients in the agricultural soil is an essential task in precision agriculture.It helps the farmers in the optimal use of fertilizers.It reduces the cost of food production and also the negative environmentalimpacts on atmosphere and water bodies due to indiscriminate dosageof fertilizers.The traditional chemical-based laboratory soil analysis methodsdo not serve the purpose as they are hardly suitable for site specific soil management.Moreover,the spectral range used in the chemical-based laboratory soil analysismay be of 350-2500 nm,which leads to redundancy and confusion.Developing sensors based on the discovery of spectral wavebands that respondto soil macronutrient concentrations,on the other hand,is an innovative and successfultechnology since the results are dependable and timely.The goal of thisarticle is to use a supervised neuro-fuzzy based dimensionality reduction approachin the sensor development process to determine sensitive wavebands of soilmacronutrients.Accordingly,the spectral signatures of the soil are collected inan outdoor environment and mapped with its macronutrient concentrations.In thisspectral analysis,the spectral reflectance of 424 wavelengths has been obtainedand these wavelengths are evaluated through combined and individual modesas well.Appropriate wavelengths are selected in each case by minimizing the fuzzy reflectance assessment index.The effectiveness of these selected wavelengthsin each mode is validated by modeling the relation between the reduced reflectancespace and each macronutrient concentration using Partial Least Squares Multi Variable Regression(PLS-MVR)method.Set of optimal wavebands areidentified and the results are compared with the existing systems.
