The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthca...The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.展开更多
In modern scenarios,Industry 4.0 entails invention with various advanced technology,and blockchain is one among them.Blockchains are incorporated to enhance privacy,data transparency aswell as security for both large ...In modern scenarios,Industry 4.0 entails invention with various advanced technology,and blockchain is one among them.Blockchains are incorporated to enhance privacy,data transparency aswell as security for both large and small scale enterprises.Industry 4.0 is considered as a new synthesis fabrication technique that permits the manufacturers to attain their target effectively.However,because numerous devices and machines are involved,data security and privacy are always concerns.To achieve intelligence in Industry 4.0,blockchain technologies can overcome potential cybersecurity constraints.Nowadays,the blockchain and internet of things(IoT)are gaining more attention because of their favorable outcome in several applications.Though they generate massive data that need to be effectively optimized and in this research work,deep learning-based techniques are employed for this.This paper proposes a novel mutated leader sine cosine algorithm-based deep convolutional neural network(MLSC-DCNN)in order to attain a secure and optimized IoT blockchain for Industry 4.0.Here,an MLSC is hybridized using a mutated leader and sine cosine algorithm to enhance the weight function and minimize the loss factor of DCNN.Finally,the experimentation is carried out for various simulation measures.The comparative analysis is made for Best Tip Selection Method(BTSM),Smart Block-Software Defined Networking(SDN),and the proposed approach.The evaluation results show that the proposed approach attains better performances than BTSM and SDN.展开更多
基金funded by King Saud University through Researchers Supporting Program Number (RSP2024R499).
文摘The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.
基金The authors extend their appreciation to King Saud University for funding this work through Researchers Supporting Project Number(RSP2022R499)King Saud University,Riyadh,Saudi Arabia.
文摘In modern scenarios,Industry 4.0 entails invention with various advanced technology,and blockchain is one among them.Blockchains are incorporated to enhance privacy,data transparency aswell as security for both large and small scale enterprises.Industry 4.0 is considered as a new synthesis fabrication technique that permits the manufacturers to attain their target effectively.However,because numerous devices and machines are involved,data security and privacy are always concerns.To achieve intelligence in Industry 4.0,blockchain technologies can overcome potential cybersecurity constraints.Nowadays,the blockchain and internet of things(IoT)are gaining more attention because of their favorable outcome in several applications.Though they generate massive data that need to be effectively optimized and in this research work,deep learning-based techniques are employed for this.This paper proposes a novel mutated leader sine cosine algorithm-based deep convolutional neural network(MLSC-DCNN)in order to attain a secure and optimized IoT blockchain for Industry 4.0.Here,an MLSC is hybridized using a mutated leader and sine cosine algorithm to enhance the weight function and minimize the loss factor of DCNN.Finally,the experimentation is carried out for various simulation measures.The comparative analysis is made for Best Tip Selection Method(BTSM),Smart Block-Software Defined Networking(SDN),and the proposed approach.The evaluation results show that the proposed approach attains better performances than BTSM and SDN.
