In recent years,cervical cancer is one of the most common diseases which occur in any woman regardless of any age.This is the deadliest disease since there were no symptoms shown till it is diagnosed to be the last st...In recent years,cervical cancer is one of the most common diseases which occur in any woman regardless of any age.This is the deadliest disease since there were no symptoms shown till it is diagnosed to be the last stage.For women at a certain age,it is better to have a proper screening for cervical can-cer.In most underdeveloped nations,it is very difficult to have frequent scanning for cervical cancer.Data Mining and machine learning methodologies help widely infinding the important causes for cervical cancer.The proposed work describes a multi-class classification approach is implemented for the dataset using Support Vector Machine(SVM)and the perception learning method.It is known that most classification algorithms are designed for solving binary classification problems.From a heuristic approach,the problem is addressed as a multiclass classification problem.A Gradient Boosting Machine(GBM)is also used in implementation in order to increase the classifier accuracy.The proposed model is evaluated in terms of accuracy,sensitivity and found that this model works well in identifying the risk factors of cervical cancer.展开更多
The major environmental hazard in this pandemic is the unhygienic dis-posal of medical waste.Medical wastage is not properly managed it will become a hazard to the environment and humans.Managing medical wastage is a ...The major environmental hazard in this pandemic is the unhygienic dis-posal of medical waste.Medical wastage is not properly managed it will become a hazard to the environment and humans.Managing medical wastage is a major issue in the city,municipalities in the aspects of the environment,and logistics.An efficient supply chain with edge computing technology is used in managing medical waste.The supply chain operations include processing of waste collec-tion,transportation,and disposal of waste.Many research works have been applied to improve the management of wastage.The main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security,trustworthiness,and transparency.To over-come these issues,in this paper we implement an efficient Naive Bayes classifier algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm(NBQ-BBOA).This proposed work is used to track,detect,and manage medical waste.To minimize the transferring cost of medical wastage from various nodes,the Q-Learning algorithm is used.The accuracy obtained for the Naïve Bayes algorithm is 88%,the Q-Learning algo-rithm is 82%and NBQ-BBOA is 98%.The error rate of Root Mean Square Error(RMSE)and Mean Error(MAE)for the proposed work NBQ-BBOA are 0.012 and 0.045.展开更多
Data management becomes essential component of patient healthcare.Internet of Medical Things(IoMT)performs a wireless communication between E-medical applications and human being.Instead of consulting a doctor in the ...Data management becomes essential component of patient healthcare.Internet of Medical Things(IoMT)performs a wireless communication between E-medical applications and human being.Instead of consulting a doctor in the hospital,patients get health related information remotely from the physician.The main issues in the E-Medical application are lack of safety,security and priv-acy preservation of patient’s health care data.To overcome these issues,this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage.Patients health data is collected from physician,smart devices etc.The main goal is to store this highly valuable health related data in a secure,safety,easy access and less cost-effective manner.In this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work(PBFT-PoW).The implementation is done using cloud technology setup with smart contracts(PBFT-PoW).The accuracy rate of PBFT is 90.15%,for PoW is 92.75%and our proposed work PBFT-PoW is 99.88%.展开更多
文摘In recent years,cervical cancer is one of the most common diseases which occur in any woman regardless of any age.This is the deadliest disease since there were no symptoms shown till it is diagnosed to be the last stage.For women at a certain age,it is better to have a proper screening for cervical can-cer.In most underdeveloped nations,it is very difficult to have frequent scanning for cervical cancer.Data Mining and machine learning methodologies help widely infinding the important causes for cervical cancer.The proposed work describes a multi-class classification approach is implemented for the dataset using Support Vector Machine(SVM)and the perception learning method.It is known that most classification algorithms are designed for solving binary classification problems.From a heuristic approach,the problem is addressed as a multiclass classification problem.A Gradient Boosting Machine(GBM)is also used in implementation in order to increase the classifier accuracy.The proposed model is evaluated in terms of accuracy,sensitivity and found that this model works well in identifying the risk factors of cervical cancer.
文摘The major environmental hazard in this pandemic is the unhygienic dis-posal of medical waste.Medical wastage is not properly managed it will become a hazard to the environment and humans.Managing medical wastage is a major issue in the city,municipalities in the aspects of the environment,and logistics.An efficient supply chain with edge computing technology is used in managing medical waste.The supply chain operations include processing of waste collec-tion,transportation,and disposal of waste.Many research works have been applied to improve the management of wastage.The main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security,trustworthiness,and transparency.To over-come these issues,in this paper we implement an efficient Naive Bayes classifier algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm(NBQ-BBOA).This proposed work is used to track,detect,and manage medical waste.To minimize the transferring cost of medical wastage from various nodes,the Q-Learning algorithm is used.The accuracy obtained for the Naïve Bayes algorithm is 88%,the Q-Learning algo-rithm is 82%and NBQ-BBOA is 98%.The error rate of Root Mean Square Error(RMSE)and Mean Error(MAE)for the proposed work NBQ-BBOA are 0.012 and 0.045.
基金Research Supporting Project number(RSP-2021/323)King Saud University,Riyadh,Saudi Arabia.
文摘Data management becomes essential component of patient healthcare.Internet of Medical Things(IoMT)performs a wireless communication between E-medical applications and human being.Instead of consulting a doctor in the hospital,patients get health related information remotely from the physician.The main issues in the E-Medical application are lack of safety,security and priv-acy preservation of patient’s health care data.To overcome these issues,this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage.Patients health data is collected from physician,smart devices etc.The main goal is to store this highly valuable health related data in a secure,safety,easy access and less cost-effective manner.In this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work(PBFT-PoW).The implementation is done using cloud technology setup with smart contracts(PBFT-PoW).The accuracy rate of PBFT is 90.15%,for PoW is 92.75%and our proposed work PBFT-PoW is 99.88%.
