Integrating Artificial Bee Colony Algorithm and BP Neural Network for Software Aging Prediction in IoT Environment

Jing, Liu; Meng, Lingze

doi:10.1109/access.2019.2903081

Cited by 30 publications

(10 citation statements)

References 25 publications

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“…Another issue in IoT environments is occurring faults in the result of software aging. Liu and Meng [45] proposed a method to predict software aging. The method works based on a neural network with Back Propagation (BP) error.…”

Section: Related Workmentioning

confidence: 99%

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

et al. 2020

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By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT applications. Most of the current research studies evaluated the fault prediction methods using simulation environments. However, formal verification of the correctness of a fault prediction method has not been reported yet. This paper presents a behavioral modeling and formal verification of a hybrid machine learning-based fault prediction model with Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) algorithms. In particular, the PSO is used for feature selection. Then, the fault prediction is considered as a behavior to be verified formally. The fault prediction behavior is divided into two types of behaviors: dimension reduction behavior and prediction behavior. For each of the behaviors, one formal model is designed. The behavioral models designed are mapped into the Labeled Transition System (LTS). The Process Analysis Toolkit (PAT) model checker is employed to evaluate the behavioral models. The accuracy of the fault prediction method is done by some existing specifications such as deadlock-free and reachability properties in terms of linear temporal logic formulas. Also, the verification of the fault prediction behaviors is used to detect the defect metrics of information-centric IoT applications. Experimental results showed that our proposed verification method has minimum verification time and memory usage for evaluating critical specification rules than other research studies. INDEX TERMS Internet of Things applications, fault prediction, formal verification, process analysis toolkit, multi-layer perceptron, particle swarm optimization.

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Section: Related Workmentioning

confidence: 99%

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

et al. 2020

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“…They showed that this method increased the prediction precision and recall. In order to forecast performance anomaly in IoT, Liu and Meng et al [24 ] utilised BP neural network union artificial bee colony method to fit Google's data and then judged whether software system was in ageing state. However, this method only considered the non‐linear characteristics of data and ignored the influence of network structure for software ageing prediction.…”

Section: Related Workmentioning

confidence: 99%

Software ageing prediction using neural network with ridge

Yan

2020

IET softw.

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Since software systems become more complex than before, software ageing problems have a big impact on the performance of running software systems. To find software ageing in advance, some prediction methods were used to forecast those parameters which can indicate software ageing occurrences. Since the unsuitable parameters can reduce the prediction ability of an algorithm, in this study, multilayer perceptron (MLP) with ridge is proposed to improve the prediction accuracy of MLP and apply in software ageing problems. The proposed approach is a three‐step method. First, a pre‐processing process needs to be done by using outlier recognition, dispose, and normalisation. Second, MLP with ridge is proposed and used to optimise network structure. Third, a glowworm swarm optimisation method is utilised to automatically find optimal values of model parameters. In the experimental section, the results indicate that the proposed algorithm owns higher forecast accuracy than other state‐of‐the‐art methods on two levels.

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“…A great progress in solving complex numerical optimization problems has achieved in [19,20]. With the continuous improvement and optimization, ABC has been applied in more fields, such as workshop scheduling [21][22][23][24][25] software aging prediction [26], machine learning [27], multi-objective optimization [28], dynamic optimization [29,30] and so on.…”

Section: Introductionmentioning

confidence: 99%

K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony

2021

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K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colony algorithm (CAABC) clustering algorithm is presented to optimally partition objects into K clusters in this study. This algorithm adopts the max–min distance product method for initialization. In addition, a new fitness function is adapted to the KMC algorithm. This paper also reports that the iteration abides by the adaptive search strategy, and Fuch chaotic disturbance is added to avoid converging on local optimum. The step length decreases linearly during the iteration. In order to overcome the shortcomings of the classic ABC algorithm, the simulated annealing criterion is introduced to the CAABC. Finally, the confluent algorithm is compared with other stochastic heuristic algorithms on the 20 standard test functions and 11 datasets. The results demonstrate that improvements in CAABA-K-means have an advantage on speed and accuracy of convergence over some conventional algorithms for solving clustering problems.

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Integrating Artificial Bee Colony Algorithm and BP Neural Network for Software Aging Prediction in IoT Environment

Cited by 30 publications

References 25 publications

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

Software ageing prediction using neural network with ridge

K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony

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