查看更多>>摘要：In recent years, many efficient evolutionary multitasking (EMT) algorithms have been proposed to solve multi-objective multi-task optimization problems. However, EMT algorithms often face negative transfer problems. In this paper, a novel multi-objective evolution strategy, called CT-EMT-MOES, is proposed based on a cultural transmission theory for solving multi-objective multitask optimization problems. First, two evolutionary operators inspired by cultural transmission theory are proposed. The elite-guided variation strategy can transfer the information from the current Pareto front to all individuals and guide the population to quickly converge. The horizontal cultural transmission strategy can efficiently transfer information from the source task to the target task. Second, to solve the negative transfer problem, an adaptive information transfer strategy is proposed to adaptively adjust the probability of an information transfer. Third, the proposed algorithm can gain a Pareto front with good convergence and diversity by utilizing a smaller population size and fewer computing resources. As a result, the proposed algorithm can effectively utilize the implicit similarity and complementarity between simultaneous optimized tasks to improve the overall convergence efficiency and reduce a negative transfer. Finally, comprehensive experimental results show that the proposed algorithm can achieve a better performance compared with previous state-of-the-art multi-objective EMT algorithms.
查看更多>>摘要：The Takagi-Sugeno-Kang fuzzy system has wide applications across different areas, e.g., regression, classification and decision making, attributed to its high precision and interpretability. However, the existing Takagi-Sugeno-Kang fuzzy system is not an ideal solution to some special scenarios, particularly for those that are constrained monotonically. To this end, a monotonic relation-constrained Takagi-Sugeno-Kang fuzzy system classifier is proposed in this paper. The proposed method introduces a monotonic relation between the inputs and the outputs, where the objective function is expressed in a monotonically constrained form and a strategy for generating monotonicity constraint pairs is developed. Furthermore, to address the convexity loss caused by the increasing monotonicity constraints, the proposed method introduces the Tikhonov regularization strategy to ensure the uniqueness and boundedness of the solution. The results from extensive experiments show that the proposed method exhibits better classification performance than the original Takagi-Sugeno-Kang fuzzy system and state-of-the-art monotonic classification methods in handling monotonic datasets.
查看更多>>摘要：The interval type-2 Takagi-Sugeno (T-S) fuzzy dynamic output feedback and H-infinity stability analysis is studied for a class of networked control systems with multiple time-varying additive uncertainties, time-varying signal communication delay and external disturbance. Firstly, the interval type-2T-S fuzzy is employed to denote the system plant. Secondly, the multiple time-varying additive uncertainties are introduced in the controller design and the state variables depending on additive uncertainties. Thirdly, the delay-dependent Lyapunov-Krasovskii functional with double integral terms is designed to derive the less conservative stability conditions in terms of linear matrix inequalities (LMIs). The characteristic of the state variables are reflected effectively by employing the controller with multiple time-varying additive uncertainties. The closed-loop system is asymptotically stable with prescribed H-infinity performance index γ by employing the matrices transformation information. The less conservative stability conditions are derived and extended into the networked control system without additive uncertainties. Finally, simulations are presented to show the effectiveness of the proposed methods.
查看更多>>摘要：RNA-binding proteins (RBPs) play a significant part in several biological processes in the living cell, such as gene regulation and mRNA localization. The research indicates that the mutation of RBPs will lead to some serious diseases. Several deep learning methods, especially the model based on convolutional neural network (CNN), have been used to predict the binding sites. However, these methods only use single-scale filters to extract a fixed length of motifs features, which restricts the performance of prediction. For the sequence data, different sizes of filters may learn different biological information of the RNA sequence. Therefore, a deep multi-scale attention network (DeepMSA) based on convolutional neural network is proposed to predict the sequence-binding preferences of RBPs. DeepMSA extracts features by multi-scale CNNs and integrates these features with an attention model to predict the RBPs and binding motifs. Experiments demonstrate DeepMSA outperforms several state-of-the-art methods on the invivo and invitro datasets. The results indicate that attention can make the model learn the consistent pattern of candidate motifs, which can provide some important guiding advice for RBP motifs.
查看更多>>摘要：This paper considers switching stochastic delay neural networks (SSDNNs) with all unstable subsystems. By using discretized Lyapunov-Krasovskii functions (DLKFs) combined with the dwell time method, exponential stability of SSDNNs with all unstable subsystems are analyzed, and several novel stability criteria in mean square are obtained. Comparing with the existing works, our results focus on all unstable subsystems rather than other combinations such as all stable or partially stable subsystems, which is of more research significance. Finally, the correctness of the conclusion is checked by the feasible solutions of two numerical examples.
查看更多>>摘要：Multi-modal brain imaging data reflect brain structural and functional information from different aspects, which have been widely used in brain disease diagnosis, including epilepsy and Alzheimer's disease. In practice, it is difficult to obtain all the modalities of each subject due to high cost or equipment limitation. Therefore, it is highly essential to fuse incomplete multi-modality data to improve the diagnostic accuracy. The traditional methods need to perform data cleansing and discard incomplete subjects from the data, which leads to inefficient training and poor robustness. For addressing this problem, this paper proposes an incomplete multi-modality data fusion method based on low-rank representation for the diagnosis of epilepsy and its subtypes. Specifically, we designed an objective function that simultaneously learns the low-rank representation of the complete modality part, and recovers the incomplete modality by the correlation between different modalities. The proposed model can be optimized by using alternating direction method of multipliers. Extensive evaluation of the proposed method on epilepsy classification task with incomplete DTI and fMRI data showed that our method can achieve promising classification results in identifying epilepsy and its subtypes.
查看更多>>摘要：Quality function deployment (QFD) is a team-based product development technique for determining critical engineering characteristics based on customer requirements to realize higher customer satisfaction. Given its simplicity and visibility, the QFD has been broadly utilized in a variety of industries for product or service planning. However, traditional QFD method assumes that the behavior of domain experts is completely rational, and is only suitable for product development problems with a small number of experts. In this paper, we aim to develop a new large group QFD approach based on an extended TODIM (Portuguese acronym for interactive multi-criteria decision making) method under interval type-2 fuzzy context. The interval type-2 fuzzy sets are utilized to describe the imprecise and uncertain correlation assessment information between customer requirements and engineering characteristics. The normal TODIM is modified and used to derive the priority of engineering characteristics considering the psychological factors of experts. Also, an entropy weighting method was adopted to derive the weights of customer requirements objectively. Finally, we provide an electric vehicle product development case and perform a comparison analysis with previous methods to reveal the effectiveness and practicability of the proposed large group QFD approach.
查看更多>>摘要：In the Internet age, analyzing the behavior of online users can help webstore owners understand customers’ interests. Insights from such analysis can be used to improve both user experience and website design. A prominent task for online behavior analysis is clickstream mining, which consists of identifying customer browsing patterns that reveal how users interact with websites. Recently, this task was extended to consider weights to find more impactful patterns. However, most algorithms for mining weighted clickstream patterns are serial algorithms, which are sequentially executed from the start to the end on one running thread. In real life, data is often very large, and serial algorithms can have long runtimes as they do not fully take advantage of the parallelism capabilities of modern multi-core CPUs. To address this limitation, this paper presents two parallel algorithms named DPCompact-SPADE (Depth load balancing Parallel Compact-SPADE) and APCompact-SPADE (Adaptive Parallel Compact-SPADE) for weighted clickstream pattern mining. Experiments on various datasets show that the proposed parallel algorithm is efficient, and outperforms state-of-the-art serial algorithms in terms of runtime, memory consumption, and scalability.
查看更多>>摘要：The increased amount of high-dimensional imbalanced data in online social networks challenges existing feature selection methods. Although feature selection methods such as principal component analysis (PCA) are effective for solving high-dimensional imbalanced data problems, they can be computationally expensive. Hence, an effortless approach for identifying meaningful features that are indicative of anomalous behaviour between humans and malicious bots is presented herein. The most recent Twitter dataset that encompasses the behaviour of various types of malicious bots (including fake followers, retweet spam, fake advertisements, and traditional spambots) is used to understand the behavioural traits of such bots. The approach is based on Benford's law for predicting the frequency distribution of significant leading digits. This study demonstrates that features closely obey Benford's law on a human dataset, whereas the same features violate Benford's law on a malicious bot dataset. Finally, it is demonstrated that the features identified by Benford's law are consistent with those identified via PCA and the ensemble random forest method on the same datasets. This study contributes to the intelligent detection of malicious bots such that their malicious activities, such as the dissemination of spam, can be minimised.
查看更多>>摘要：Elastic net support vector machine (ENSVM) is an effective and popular classification technique. It has been widely used in many practical applications. However, solving large-scale problems still remains challenging. Inspired by its sparsity, a safe double screening rule (DSR) is proposed for accelerating ENSVM. Its main idea is to reduce the scale of the model by discarding the inactive features and samples simultaneously. In this way, the computational speed can be accelerated. Another superiority of DSR is safety, i.e., the discarded features and samples must be inactive. Its key strategy is to estimate the region containing optimal solution based on the feasible solution and duality gap. Thus, the DSR can be embedded into the process of solving the model until the algorithm converges. In addition, the safe keeping rule is constructed to identify the active features and samples. So, the DSR only needs to work on the remaining set after safe keeping. In this way, the screening process of DSR can be accelerated. Moreover, the Stochastic Dual Coordinate Ascent (SDCA) method is employed as an efficient solver. Numerical experiments on an artificial dataset and eighteen benchmark datasets demonstrate the feasibility and validity of our proposed method.
NSTL
Elsevier