To ensure the safe operation of any software controlled critical systems,quality factors like reliability and safety are given utmost importance.In this paper,we have chosen to analyze the impact of logic error that i...To ensure the safe operation of any software controlled critical systems,quality factors like reliability and safety are given utmost importance.In this paper,we have chosen to analyze the impact of logic error that is one of the contributors to the above factors.In view of this,we propose a novel framework based on a data driven approach known as software failure estimation with logic error(SFELE).Here,the probabilistic nature of software error is explored by observing the operation of a safety critical system by injecting logic fault.The occurrence of error,its propagations and transformations are analyzed from its inception to end of its execution cycle through the hidden Markov model(HMM)technique.We found that the proposed framework SFELE supports in labeling and quantifying the behavioral properties of selected errors in a safety critical system while traversing across its system components in addition to reliability estimation of the system.Our attempt at the design level can help the design engineers to improve their system quality in a costeffective manner.展开更多
With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT dev...With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly preferable.At the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic processes.With this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT environment.The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT environment.The proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct clusters.Besides,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter tuning.The design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the work.For show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.展开更多
文摘To ensure the safe operation of any software controlled critical systems,quality factors like reliability and safety are given utmost importance.In this paper,we have chosen to analyze the impact of logic error that is one of the contributors to the above factors.In view of this,we propose a novel framework based on a data driven approach known as software failure estimation with logic error(SFELE).Here,the probabilistic nature of software error is explored by observing the operation of a safety critical system by injecting logic fault.The occurrence of error,its propagations and transformations are analyzed from its inception to end of its execution cycle through the hidden Markov model(HMM)technique.We found that the proposed framework SFELE supports in labeling and quantifying the behavioral properties of selected errors in a safety critical system while traversing across its system components in addition to reliability estimation of the system.Our attempt at the design level can help the design engineers to improve their system quality in a costeffective manner.
文摘With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly preferable.At the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic processes.With this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT environment.The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT environment.The proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct clusters.Besides,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter tuning.The design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the work.For show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.
