Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and thei...Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and their implementation elevating the environment.Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe.Therefore,the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent.Therefore,this paper focuses on the design of automated forest fire detection using a fusion-based deep learning(AFFD-FDL)model for environmental monitoring.The AFFDFDL technique involves the design of an entropy-based fusion model for feature extraction.The combination of the handcrafted features using histogram of gradients(HOG)with deep features using SqueezeNet and Inception v3 models.Besides,an optimal extreme learning machine(ELM)based classifier is used to identify the existence of fire or not.In order to properly tune the parameters of the ELM model,the oppositional glowworm swarm optimization(OGSO)algorithm is employed and thereby improves the forest fire detection performance.A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several aspects.The experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques.展开更多
Social media is a platform in which user can create,share and exchange the knowledge/information.Social media marketing is to identify the different consumer’s demands and engages them to create marketing resources.T...Social media is a platform in which user can create,share and exchange the knowledge/information.Social media marketing is to identify the different consumer’s demands and engages them to create marketing resources.The popular social media platforms are Microsoft,Snapchat,Amazon,Flipkart,Google,eBay,Instagram,Facebook,Pin interest,and Twitter.The main aim of social media marketing deals with various business partners and build good relationship with millions of customers by satisfying their needs.Disruptive technology is replacing old approaches in the social media marketing to new technology-based marketing.However,this disruptive technology creates some issues like fake news,insecure,inconsistency,inaccuracy and so on.These issues contribute economic instability in the society,diminishing the level of trustworthy.To overcome these issues,this paper we present blockchain as disruptive technology for social media marketing.Blockchain plays a vital role on social media marketing by providing secure to the company page in the website.The properties of disruptive potential of blockchain on social media marketing is transparency,security,reliability and immutability.This paper presents a new framework for disruptive technology in blockchain social media marketing using fusion of CryptoNight mining algorithm with YAC consensus algorithm[BCDSMM-CNYAC].This mining algorithm provides high CPU efficiency,high dimensionality of secure and detecting falsifying data attack in the social media marketing.For the data analysis we proposed ANOVA analysis method regarding to the factors of age,time,frequency visiting times of social media platform.For reliability analysis of data Cronbach’s alpha tests are implemented.展开更多
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.1/172/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R191)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.This study is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444).
文摘Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and their implementation elevating the environment.Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe.Therefore,the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent.Therefore,this paper focuses on the design of automated forest fire detection using a fusion-based deep learning(AFFD-FDL)model for environmental monitoring.The AFFDFDL technique involves the design of an entropy-based fusion model for feature extraction.The combination of the handcrafted features using histogram of gradients(HOG)with deep features using SqueezeNet and Inception v3 models.Besides,an optimal extreme learning machine(ELM)based classifier is used to identify the existence of fire or not.In order to properly tune the parameters of the ELM model,the oppositional glowworm swarm optimization(OGSO)algorithm is employed and thereby improves the forest fire detection performance.A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several aspects.The experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques.
文摘Social media is a platform in which user can create,share and exchange the knowledge/information.Social media marketing is to identify the different consumer’s demands and engages them to create marketing resources.The popular social media platforms are Microsoft,Snapchat,Amazon,Flipkart,Google,eBay,Instagram,Facebook,Pin interest,and Twitter.The main aim of social media marketing deals with various business partners and build good relationship with millions of customers by satisfying their needs.Disruptive technology is replacing old approaches in the social media marketing to new technology-based marketing.However,this disruptive technology creates some issues like fake news,insecure,inconsistency,inaccuracy and so on.These issues contribute economic instability in the society,diminishing the level of trustworthy.To overcome these issues,this paper we present blockchain as disruptive technology for social media marketing.Blockchain plays a vital role on social media marketing by providing secure to the company page in the website.The properties of disruptive potential of blockchain on social media marketing is transparency,security,reliability and immutability.This paper presents a new framework for disruptive technology in blockchain social media marketing using fusion of CryptoNight mining algorithm with YAC consensus algorithm[BCDSMM-CNYAC].This mining algorithm provides high CPU efficiency,high dimensionality of secure and detecting falsifying data attack in the social media marketing.For the data analysis we proposed ANOVA analysis method regarding to the factors of age,time,frequency visiting times of social media platform.For reliability analysis of data Cronbach’s alpha tests are implemented.
