As a commonly used technique in data preprocessing, feature selection selects a subset of informative attributes or variables to build models describing data. By removing redundant and irrelevant or noise features, feature selection can improve the predictive accuracy and the comprehensibility of the predictors or classifiers. Many feature selection algorithms with different selection criteria has been introduced by researchers. However, it is discovered that no single criterion is best for all applications. In this paper, we propose a framework based on a genetic algorithm (GA) for feature subset selection that combines various existing feature selection methods. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for a particular inductive learning algorithm of interest to build the classifier. We conducted experiments using three data sets and three existing feature selection methods. The experimental results demonstrate that our approach is a robust and effective approach to find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm.

A new neural network prediction model is proposed for predicting ship motion attitude with high accuracy. This prediction model is based on an adaptive dynamic particle swarm optimization algorithm (ADPSO) and bidirectional long short-term memory (BiLSTM) neural network, which is to optimize the hyperparameters of BiLSTM neural network by the proposed ADPSO algorithm. The ADPSO algorithm introduces dynamic search space strategy into the classical particle swarm optimization algorithm and adjusts the learning factor adaptively to balance the global and local search ability, so as to improve the optimization performance and improve its optimization effect in BiLSTM parameter optimization process. The results show that the model can obtain higher prediction accuracy and faster convergence speed, and has better prediction performance in the prediction of ship motion attitude. INDEX TERMS Ship motion attitude, BiLSTM neural network, ADPSO algorithm, prediction accuracy.

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Abstract-Hybrid systems model checking is a great success in guaranteeing the safety of computerized control cyber-physical systems (CPS). However, when applying hybrid systems model checking to Medical Device Plug-and-Play (MDPnP) CPS, we encounter two challenges due to the complexity of human body: i) there are no good offline differential equation based models for many human body parameters; ii) the complexity of human body can result in many variables, complicating the system model. In an attempt to address the challenges, we propose to alter the traditional approach of offline hybrid systems model checking of time-unbounded (i.e., infinite-horizon, a.k.a., long-run) future behavior to online hybrid systems model checking of time-bounded (i.e., finite-horizon, a.k.a., short-run) future behavior. According to this proposal, online model checking runs as a real-time task to prevent faults. To meet the real-time requirements, certain design patterns must be followed, which brings up the co-design issue. We propose two sets of system co-design patterns for hard real-time and soft real-time respectively. To evaluate our proposals, a case study on laser tracheotomy MDPnP is carried out. The study shows the necessity of online model checking. Furthermore, test results based on real-world human subject trace show the feasibility and effectiveness of our proposed co-design.

Abstract. Due to the huge number of genes and comparatively small number of samples from microarray gene expression data, accurate classification of diseases becomes challenging. Feature selection techniques can improve the classification accuracy by removing irrelevant and redundant genes. However, the performance of different feature selection algorithms based on different theoretic arguments varies even when they are applied to the same data set. In this paper, we propose a hybrid approach to combine useful outcomes from different feature selection methods through a genetic algorithm. The experimental results demonstrate that our approach can achieve better classification accuracy with a smaller gene subset than each individual feature selection algorithm does.