The autoencoder (AE) is an efficient feature extraction tool that learns latent representations from raw data by minimizing the reconstruction loss. Building upon the AE architecture, deep clustering models are designed to jointly optimize the deep neural network and perform unsupervised clustering. However, existing methods directly impose the clustering objective on the latent features produced by the AE network, thereby neglecting the potential conflict between data clustering and data representation. Specifically, data clustering aims to enhance data aggregation, whereas data representation focuses on ensuring that latent features faithfully reflect the manifold structure of the raw data. To address this issue, this article proposes an innovative expanded deep embedding clustering (E-DEC) model, in which the AE network is employed to seek better latent representations, and a novel residual expansion module (REM) is integrated to construct an expanded feature space that better serves clustering tasks. Furthermore, adversarial learning between the soft cluster assignments and a prior one-hot distribution is adopted in lieu of the conventional Kullback–Leibler (KL) divergence, so as to enhance the discrimination of different clusters and avoid the degeneracy problem. Finally, an entropy regularization technique is incorporated to adaptively refine the affinity graph throughout the clustering process, thereby reducing the sensitivity of clustering performance to the initial affinity graph. Extensive experiments on real-world benchmark datasets demonstrate the superiority of the proposed model over state-of-the-art deep clustering methods.

Deep multiview clustering provides an efficient way to analyze the data consisting of multiple modalities and features. Recently, the autoencoder (AE)-based deep multiview clustering algorithms have attracted intensive attention by virtue of their rewarding capabilities of extracting inherent features. Nevertheless, most existing methods are still confronted by several problems. First, the multiview data usually contains abundant cross-view information, thus parallel performing an individual AE for each view and directly combining the extracted latent together can hardly construct an informative view-consensus feature space for clustering. Second, the intrinsic local structures of multiview data are complicated, hence simply embedding a preset graph constraint into multiview clustering models cannot guarantee expected performance. Third, current methods commonly utilize the Kullback-Leibler (KL) divergence as clustering loss and accordingly may yield appalling clusters that lack discriminate characters. To solve these issues, in this article we propose two new AE-based deep multiview clustering algorithms named AE-based deep multiview clustering model incorporating graph embedding (AG-DMC) and deep discriminative multiview clustering algorithm with adaptive graph constraint (ADG-DMC). In AG-DMC, a novel cross-view representation learning model is established delicately by performing decoding processes based on the cascaded view-specific latent to learn sound view-consensus features for inspiring clustering results. In addition, an entropy-regularized adaptive graph constraint is imposed on the obtained soft assignments of data to precisely preserve potential local structures. Furthermore, in the improved model ADG-DMC, the adversarial learning mechanism is adopted as clustering loss to strengthen the discrimination of different clusters for better performance. In the comprehensive experiments carried out on eight real-world datasets, the proposed algorithms have achieved superior performance in the comparison with other advanced multiview clustering algorithms.

BACKGROUND: Protein-protein interactions (PPIs) are crucial in various biological functions and cellular processes. Thus, many computational approaches have been proposed to predict PPI sites. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in sequences. Many feature extraction methods rely on the sliding window technique, which simply merges all the features of residues into a vector. The importance of some key residues may be weakened in the feature vector, leading to poor performance. RESULTS: We propose a novel sequence-based method for PPI sites prediction. The new network model, PPINet, contains multiple feature processing paths. For a residue, the PPINet extracts the features of the targeted residue and its context separately. These two types of features are processed by two paths in the network and combined to form a protein representation, where the two types of features are of relatively equal importance. The model ensembling technique is applied to make use of more features. The base models are trained with different features and then ensembled via stacking. In addition, a data balancing strategy is presented, by which our model can get significant improvement on highly unbalanced data. CONCLUSION: The proposed method is evaluated on a fused dataset constructed from Dset186, Dset_72, and PDBset_164, as well as the public Dset_448 dataset. Compared with current state-of-the-art methods, the performance of our method is better than the others. In the most important metrics, such as AUPRC and recall, it surpasses the second-best programmer on the latter dataset by 6.9% and 4.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model, especially, the hybrid feature. We share our code for reproducibility and future research at https://github.com/CandiceCong/StackingPPINet .

Plant vacuoles are essential organelles in the growth and development of plants, and accurate identification of their proteins is crucial for understanding their biological properties. In this study, we developed a novel model called GraphIdn for the identification of plant vacuole proteins. The model uses SeqVec, a deep representation learning model, to initialize the amino acid sequence. We utilized the AlphaFold2 algorithm to obtain the structural information of corresponding plant vacuole proteins, and then fed the calculated contact maps into a graph convolutional neural network. GraphIdn achieved accuracy values of 88.51% and 89.93% in independent testing and fivefold cross-validation, respectively, outperforming previous state-of-the-art predictors. As far as we know, this is the first model to use predicted protein topology structure graphs to identify plant vacuole proteins. Furthermore, we assessed the effectiveness and generalization capability of our GraphIdn model by applying it to identify and locate peroxisomal proteins, which yielded promising outcomes. The source code and datasets can be accessed at https://github.com/SJNNNN/GraphIdn .

Abstract Obtaining accurate segmentation of central serous chorioretinopathy in spectral‐domain optical coherence tomography (SD‐OCT) is critical for the determination of the disease severity. Although existing methods achieve considerable segmentation results, they heavily depend on large‐scale data with high‐quality annotations. Also, the lesions bear a large shape variation across different patients, which are often difficult to encode. To address the above problems, we propose a fine‐to‐coarse‐to‐fine weakly supervised framework. Specifically, global alternate max‐avg pooling (GTP) network can be employed to locate the lesion regions accurately by using only image‐level annotations. A network module based on the GTP network and a semantic transfer module are proposed to iteratively guide the network to continuously discover and expand the target lesion regions. Then, we employ 3D grey distribution histogram to generate pseudo‐volumetric labels. Finally, a novel 3D level set loss function is proposed to perform coarse‐to‐fine volumetric segmentation. Experiments on a challenging dataset demonstrate that the performance of our proposed method is closer to those of models trained with pixel‐level supervision.

Membrane protein amphiphilic helices play an important role in many biological processes. Based on the graph convolution network and the horizontal visibility graph the prediction method of membrane protein amphiphilic helix structure is proposed in this paper. The new dataset of amphiphilic helix is constructed. In this paper, we propose the novel feature extraction method, which characterize the amphiphilicity of membrane protein. We also extract three commonly used protein features together with the new features as protein node features. The neighbor information and long-distance dependence information of proteins are further extracted by sliding window and bidirectional long-short term memory network respectively. From the perspective of horizontal visibility algorithm, we transform protein sequences into complex networks to obtain the graph features of proteins. Then, graph convolutional network model is employed to predict the amphiphilic helix structure of membrane protein. A rigorous ten-fold cross-validation shows that the proposed method outperforms other AH prediction methods on the newly constructed dataset.

Distributed clustering based on the Gaussian mixture model (GMM) has exhibited excellent clustering capabilities in peer-to-peer (P2P) networks. However, more iterative numbers and communication overhead are required to achieve the consensus in existing distributed GMM clustering algorithms. In addition, the truth that it cannot find a closed form for the update of parameters in GMM causes the imprecise clustering accuracy. To solve these issues, by utilizing the transfer learning technique, a general transfer distributed GMM clustering framework is exploited to promote the clustering performance and accelerate the clustering convergence. In this work, each node is treated as both the source domain and the target domain, and these nodes can learn from each other to complete the clustering task in distributed P2P networks. Based on this framework, the transfer distributed expectation-maximization algorithm with the fixed learning rate is first presented for data clustering. Then, an improved version is designed to obtain the stable clustering accuracy, in which an adaptive transfer learning strategy is adopted to adjust the learning rate automatically instead of a fixed value. To demonstrate the extensibility of the proposed framework, a representative GMM clustering method, the entropy-type classification maximum-likelihood algorithm, is further extended to the transfer distributed counterpart. Experimental results verify the effectiveness of the presented algorithms in contrast with the existing GMM clustering approaches.

Collaborative multiview clustering methods can efficiently realize the view fusion by exploring complementary and consistent information among multiple views. However, these studies ignore all the differences between multiple views in fusion. In fact, in the multiview clustering, the data are diverse from view to view. The larger the difference between any two views is, the more the fusion of these views is required. Moreover, a global tradeoff parameter is generally adopted to restrain the penalty related to the disagreement of all views, which is often defined empirically. Inspired by the idea of transfer learning, a series of novel collaborative multiview clustering algorithms are proposed to tackle these challenges. In the most basic one, each view performs clustering independently and learns from others to improve its own clustering performance, in which a global learning factor is defined to control the interaction between multiple views. The fuzzy memberships are regarded as the important knowledge to provide guidance between views, and the consensus constraint is defined to ensure the consistent partitions of all views. In addition, the local adaptive learning factors between any two views instead of a global fixed one are adopted in an improved version to emphasize the difference between views, and the adjustment strategy for the learning factor is further designed to guarantee the stability of multiview clustering without the influence of initial values. Finally, to identify the significance of different views to the clustering, the extended versions are excavated with the assignment of view weights and the maximum entropy regularization technique is employed to optimize the weights. Experiments on various real-world multiview datasets verify the superiority of the presented approaches.

Kernel clustering has the ability to get the inherent nonlinear structure of the data. But the high computational complexity and the unknown representation of the kernel space make it unavailable for the data clustering in distributed peer-to-peer (P2P) networks. To solve this issue, we propose a new series of random feature-based collaborative kernel clustering algorithms in this article. In the most basic algorithm, each node in a distributed P2P network first maps its data into a low-dimensional random feature space with the approximation of the given kernel by using the random Fourier feature mapping method. Then, each node independently searches the clusters with its local data and the collaborative knowledge from its neighbor nodes, and the distributed clustering is performed among all network nodes until reaching the global consensus result, i.e., all nodes have the same cluster centers. In addition, an improved version is designed with assignment of feature weights, which is optimized by the maximum-entropy technique to extract important features for the cluster identification. What’s more, to relief the impact of different kernel functions and related parameters on clustering results, the combination of multiple kernels rather than a single kernel is adopted for the low-dimensional approximation, and the optimized weights are assigned to provide the guidance on the choice of the kernels and their parameters and discover significant features at the same time. Experiments on synthetic and real-world datasets show that the proposed methods achieve similar and even better results than the traditional kernel clustering methods on various performance metrics, including the average classification rate, the average normalized mutual information, and the average adjusted rand index. More importantly, the low-dimensional random features approximated to kernels and the distributed clustering mechanism adopted in these methods bring the greatly lower temporal complexity.

Structured pruning has received ever-increasing attention as a method for compressing convolutional neural networks. However, most existing methods directly prune the network structure according to the statistical information of the parameters. Besides, these methods differentiate the pruning rates only in each pruning stage or even use the same pruning rate across all layers, rather than using learnable parameters. In this article, we propose a network redundancy elimination approach guided by the pruned model. Our proposed method can easily tackle multiple architectures and is scalable to the deeper neural networks because of the use of joint optimization during the pruning procedure. More specifically, we first construct a sparse self-representation for the filters or neurons of the well-trained model, which is useful for analyzing the relationship among filters. Then, we employ particle swarm optimization to learn pruning rates in a layerwise manner according to the performance of the pruned model, which can determine optimal pruning rates with the best performance of the pruned model. Under this criterion, the proposed pruning approach can remove more parameters without undermining the performance of the model. Experimental results demonstrate the effectiveness of our proposed method on different datasets and different architectures. For example, it can reduce 58.1% FLOPs for ResNet50 on ImageNet with only a 1.6% top-five error increase and 44.1% FLOPs for FCN_ResNet50 on COCO2017 with a 3% error increase, outperforming most state-of-the-art methods.

The integration of reinforcement learning with fuzzy logic can be effective in compensating the external disturbance and complex dynamic while designing the control strategy for vehicle suspension. The main contribution of this paper is that a learning-based adaptive vibration control strategy is proposed for semi-active suspension system, which combines the fuzzy logic with the reward function of reinforcement learning to improve the robustness and feasibility of the vibration control strategy. What’s more, an improved proximal policy optimization algorithm combined with fuzzy logic is proposed for realizing the trial-and-error reinforcement learning. Specially, the reward function with fuzzy logic is formulated to meet the requirements of suspension performance under different road conditions, in which the fuzzy logic is designed to fuzzily the process the collected road information, real-time update the weight matrix coefficients, and adjust the optimization objectives adaptively. Finally, numerical simulation results are given to prove the effectiveness of the proposed vibration control strategy.

The reinforcement learning (RL) with deep neural network, as a data-driven approach, is promising for adaptive traffic signal control (ATSC) in traffic scenarios. The majority of the existing studies focus on designing efficient agents and policy optimization for ATSC, but neglect to observe more detailed states of the environment. In this paper, an adaptive traffic signal control strategy, named as A2C RTQL, is proposed for scheduling the traffic signal in an intersection, by combining the real-time lane-based queue lengths with deep RL agent. First, the Lighthill-Whitham-Richards (LWR) shockwave theory is employed for obtaining the real-time queue lengths in each lane. After that, by defining the obtained queue lengths as the inputs, A2C RTQL strategy is designed for traffic signal control based on the advanced actor-critic (A2C) agent, where the lanes are divided into multiple parallel environments based on the phases of traffic signal. Simulation results demonstrate the optimality and efficiency of the proposed strategy compared with other methods in SUMO under simulated peak-hour traffic dynamics.

The traditional collaborative fuzzy clustering can effectively perform data clustering in distributed peer-to-peer networks, which is an impossible task to complete for the centralized clustering methods due to privacy and security requirements or network transmission technology constraints. But it will increase the number of clustering iterations and lead to lower efficiency of the clustering. Moreover, the collaborative mechanism hidden in the iterative process of clustering cannot be well revealed and explained. In this article, a novel series of transfer collaborative fuzzy clustering algorithms are proposed to solve these issues. In the first basic algorithm, the transfer learning among neighbor nodes vividly expresses the collaborative mechanism and enhances the information collaboration to accelerate the convergence of fuzzy clustering. Meanwhile, neighbor nodes can learn the knowledge from each other to further promote their respective clustering performance. Then, an improved version, with the learning-rate-adjustable strategy instead of fixed values, is designed to highlight the different influence between neighbor nodes, and the appropriate learning rates between neighbor nodes are achieved to ensure the stable clustering accuracy. Finally, two extended versions with the attribute-weight-entropy regularization technique are presented for the clustering of high dimensional sparse data and the extraction of important subspace features. Experiments show the efficiency of the proposed algorithms compared with the related prototype-based clustering methods.

BACKGROUND: Nerve discharge is the carrier of information transmission, which can reveal the basic rules of various nerve activities. Recognition of the nerve discharge rhythm is the key to correctly understand the dynamic behavior of the nervous system. The previous methods for the nerve discharge recognition almost depended on the traditional statistical features, and the nonlinear dynamical features of the discharge activity. The artificial extraction and the empirical judgment of the features were required for the recognition. Thus, these methods suffered from subjective factors and were not conducive to the identification of a large number of discharge rhythms. RESULTS: The ability of automatic feature extraction along with the development of the neural network has been greatly improved. In this paper, an effective discharge rhythm classification model based on sparse auto-encoder was proposed. The sparse auto-encoder was used to construct the feature learning network. The simulated discharge data from the Chay model and its variants were taken as the input of the network, and the fused features, including the network learning features, covariance and approximate entropy of nerve discharge, were classified by Softmax. The results showed that the accuracy of the classification on the testing data was 87.5%, which could provide more accurate classification results. Compared with other methods for the identification of nerve discharge types, this method could extract the characteristics of nerve discharge rhythm automatically without artificial design, and show a higher accuracy. CONCLUSIONS: The sparse auto-encoder, even neural network has not been used to classify the basic nerve discharge from neither biological experiment data nor model simulation data. The automatic classification method of nerve discharge rhythm based on the sparse auto-encoder in this paper reduced the subjectivity and misjudgment of the artificial feature extraction, saved the time for the comparison with the traditional method, and improved the intelligence of the classification of discharge types. It could further help us to recognize and identify the nerve discharge activities in a new way.

BACKGROUND: The growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics. To establish the interactions between genes or proteins and understand the intrinsic mechanisms of biological systems have become an urgent need and study hotspot. RESULTS: In order to forecast gene expression data and identify more accurate gene regulatory network, complex-valued version of ordinary differential equation (CVODE) is proposed in this paper. In order to optimize CVODE model, a complex-valued hybrid evolutionary method based on Grammar-guided genetic programming and complex-valued firefly algorithm is presented. CONCLUSIONS: When tested on three real gene expression datasets from E. coli and Human Cell, the experiment results suggest that CVODE model could improve 20-50% prediction accuracy of gene expression data, which could also infer more true-positive regulatory relationships and less false-positive regulations than ordinary differential equation.

BACKGROUND: Correctly classifying the subtypes of cancer is of great significance for the in-depth study of cancer pathogenesis and the realization of personalized treatment for cancer patients. In recent years, classification of cancer subtypes using deep neural networks and gene expression data has gradually become a research hotspot. However, most classifiers may face overfitting and low classification accuracy when dealing with small sample size and high-dimensional biology data. RESULTS: In this paper, a laminar augmented cascading flexible neural forest (LACFNForest) model was proposed to complete the classification of cancer subtypes. This model is a cascading flexible neural forest using deep flexible neural forest (DFNForest) as the base classifier. A hierarchical broadening ensemble method was proposed, which ensures the robustness of classification results and avoids the waste of model structure and function as much as possible. We also introduced an output judgment mechanism to each layer of the forest to reduce the computational complexity of the model. The deep neural forest was extended to the densely connected deep neural forest to improve the prediction results. The experiments on RNA-seq gene expression data showed that LACFNForest has better performance in the classification of cancer subtypes compared to the conventional methods. CONCLUSION: The LACFNForest model effectively improves the accuracy of cancer subtype classification with good robustness. It provides a new approach for the ensemble learning of classifiers in terms of structural design.

In this article, the fault-tolerant control (FTC) problem of vehicle active suspension is concerned in the discrete-time domain, in which the road disturbances and faults in actuator and measurement are considered. The main contribution consists of proposing an active physically realizable fault-tolerant controller based on a reduced-order observer, which makes up an optimal vibration control component and an event-triggered FTC component. More specifically, by discussing a discrete vehicle active suspension subject to road disturbances generated from the output of a designed exosystem, the optimal vibration control component is derived from maximum principle to offset the inevitable vibrations. Meanwhile, based on the real-time system output of vehicle suspension rather than residual error, a reduced-order observer is proposed to cover the physically unrealizable problem for the designed optimal vibration control component. After that, an event-triggered FTC component and an event-triggered restructured system output are designed to compensate the faults in actuator and measurement, respectively. Finally, extensive experiments are conduced to the control performance of vehicle active suspension under the proposed controller, and confirm its effectiveness and superiority over other control schemes.

Combining the computational intelligence with dynamic responses of vehicle suspension for estimating the road profiles provides effective tool for designing various control strategies. In this paper, a FNT-based road profile classification method is proposed based on the dynamic responses of a quarter semi-active suspension under PID controller and road disturbances generated from power spectral density under the ISO 8608 standard. More specially, a data preprocessing method is designed to reduce the impact of vehicle velocity on dynamic response and determine the appropriate size of the spatial domain for data collection. After that, FNT is employed as the basic model to screen these extracted features for road profile classification with low computational consumption of road evaluation. From the numerical simulation results, the classification accuracy is 98.41% under the proposed road profile classification with six input variables.

For image segmentation, fuzzy clustering methods with single spatial information cannot ensure robustness to the image corrupted by different noises. In this paper, to figure out this problem, we propose a multiple spatial information weighted fuzzy clustering method, in which the original pixel intensity and its two spatial information, the mean and median of neighbors within a local window, are combined with different weights to obtain precise segmentation results of noise images. And the entropy-regularized method is employed to optimize the weight of each term to handle the images with different noise. What's more, the kernelization of the proposed method is presented to relief the impact of outliers. It is worth noting that our methods can be further extended by combining with other spatial information. Experiments on synthetic images and natural images show the superiority and efficiency of the proposed methods.

BACKGROUND: Apoptosis, also called programmed cell death, refers to the spontaneous and orderly death of cells controlled by genes in order to maintain a stable internal environment. Identifying the subcellular location of apoptosis proteins is very helpful in understanding the mechanism of apoptosis and designing drugs. Therefore, the subcellular localization of apoptosis proteins has attracted increased attention in computational biology. Effective feature extraction methods play a critical role in predicting the subcellular location of proteins. RESULTS: In this paper, we proposed two novel feature extraction methods based on evolutionary information. One of the features obtained the evolutionary information via the transition matrix of the consensus sequence (CTM). And the other utilized the evolutionary information from PSSM based on absolute entropy correlation analysis (AECA-PSSM). After fusing the two kinds of features, linear discriminant analysis (LDA) was used to reduce the dimension of the proposed features. Finally, the support vector machine (SVM) was adopted to predict the protein subcellular locations. The proposed CTM-AECA-PSSM-LDA subcellular location prediction method was evaluated using the CL317 dataset and ZW225 dataset. By jackknife test, the overall accuracy was 99.7% (CL317) and 95.6% (ZW225) respectively. CONCLUSIONS: The experimental results show that the proposed method which is hopefully to be a complementary tool for the existing methods of subcellular localization, can effectively extract more abundant features of protein sequence and is feasible in predicting the subcellular location of apoptosis proteins.

Quantitative assessment of retinal layer thickness in spectral domain-optical coherence tomography (SD-OCT) images is vital for clinicians to determine the degree of ophthalmic lesions. However, due to the complex retinal tissues, high-level speckle noises and low intensity constraint, how to accurately recognize the retinal layer structure still remains a challenge. To overcome this problem, this paper proposes an adaptive-guided-coupling-probability level set method for retinal layer segmentation in SD-OCT images. Specifically, based on Bayes's theorem, each voxel probability representation is composed of two probability terms in our method. The first term is constructed as neighborhood Gaussian fitting distribution to characterize intensity information for each intra-retinal layer. The second one is boundary probability map generated by combining anatomical priors and adaptive thickness information to ensure surfaces evolve within a proper range. Then, the voxel probability representation is introduced into the proposed segmentation framework based on coupling probability level set to detect layer boundaries. A total of 1792 retinal B-scan images from 4 SD-OCT cubes in healthy eyes, 5 cubes in abnormal eyes with central serous chorioretinaopathy and 5 SD-OCT cubes in abnormal eyes with age-related macular disease are used to evaluate the proposed method. The experiment demonstrates that the segmentation results obtained by the proposed method have a good consistency with ground truth, and the proposed method outperforms six methods in the layer segmentation of uneven retinal SD-OCT images.

The centralized longitudinal control problem for Cooperative Adaptive Cruise Control (CACC) systems is discussed in this paper, in which the imperfect wireless communication surroundings and actuator dynamics are taken into consideration. From the large-scale system standpoint, the centralized longitudinal control problem for platoon vehicles equipped with CACC functionality is formulated as minimizing a quadratic performance index under the constrains of a large-scale discrete-time system with actuator delay and system output delay, in which the leader vehicle is set as the control center. After that, benefiting from a designed delay-free transformed vector, a delay-free two-point-boundary-value problem is derived from the equivalent reconstruction forms for original system delay model and performance index. Thus the centralized longitudinal controller is obtained by solving a Riccati equation. Finally, simulation results demonstrate that the ego vehicle can reasonable response the accelerating or decelerating behaviors of the preceding vehicles under the proposed controller, thereby the desired CACC control performance is satisfied, and the wireless communication delay and actuator delay are compensated effectively.

BACKGROUND: Cancer subtype classification attains the great importance for accurate diagnosis and personalized treatment of cancer. Latest developments in high-throughput sequencing technologies have rapidly produced multi-omics data of the same cancer sample. Many computational methods have been proposed to classify cancer subtypes, however most of them generate the model by only employing gene expression data. It has been shown that integration of multi-omics data contributes to cancer subtype classification. RESULTS: A new hierarchical integration deep flexible neural forest framework is proposed to integrate multi-omics data for cancer subtype classification named as HI-DFNForest. Stacked autoencoder (SAE) is used to learn high-level representations in each omics data, then the complex representations are learned by integrating all learned representations into a layer of autoencoder. Final learned data representations (from the stacked autoencoder) are used to classify patients into different cancer subtypes using deep flexible neural forest (DFNForest) model.Cancer subtype classification is verified on BRCA, GBM and OV data sets from TCGA by integrating gene expression, miRNA expression and DNA methylation data. These results demonstrated that integrating multiple omics data improves the accuracy of cancer subtype classification than only using gene expression data and the proposed framework has achieved better performance compared with other conventional methods. CONCLUSION: The new hierarchical integration deep flexible neural forest framework(HI-DFNForest) is an effective method to integrate multi-omics data to classify cancer subtypes.

Accurate classification on protein structural is playing an important role in Bioinformatics. An increase in evidence demonstrates that a variety of classification methods have been employed in such a field. In this research, the features of amino acids composition, secondary structure's feature, and correlation coefficient of amino acid dimers and amino acid triplets have been used. Flexible neutral tree (FNT), a particular tree structure neutral network, has been employed as the classification model in the protein structures' classification framework. Considering different feature groups owing diverse roles in the model, impact factors of different groups have been put forward in this research. In order to evaluate different impact factors, Impact Factors Scaling (IFS) algorithm, which aim at reducing redundant information of the selected features in some degree, have been put forward. To examine the performance of such framework, the 640, 1189, and ASTRAL datasets are employed as the low-homology protein structure benchmark datasets. Experimental results demonstrate that the performance of the proposed method is better than the other methods in the low-homology protein tertiary structures.

This paper presents a nearest neighbor partitioning method designed to improve the performance of a neural-network classifier. For neural-network classifiers, usually the number, positions, and labels of centroids are fixed in partition space before training. However, that approach limits the search for potential neural networks during optimization; the quality of a neural network classifier is based on how clear the decision boundaries are between classes. Although attempts have been made to generate floating centroids automatically, these methods still tend to generate sphere-like partitions and cannot produce flexible decision boundaries. We propose the use of nearest neighbor classification in conjunction with a neural-network classifier. Instead of being bound by sphere-like boundaries (such as the case with centroid-based methods), the flexibility of nearest neighbors increases the chance of finding potential neural networks that have arbitrarily shaped boundaries in partition space. Experimental results demonstrate that the proposed method exhibits superior performance on accuracy and average f-measure.

This paper considers the sliding mode control (SMC) for a class of state delayed systems subject to persistent disturbances. First, a disturbance compensator is proposed to eliminate the influence from persistent disturbances, and the stability of control system is discussed. Then, the control problem is transformed into sliding mode control problem for state delayed system without expression of disturbances. The reduced-order sliding mode surface function is proposed based on the Lyapunov-Functional and the designed switching function. Furthermore, sliding mode control law is obtained. Finally, the simulation results demonstrate that the proposed control law can guarantee the stability of state delayed systems.

Protein sub-cellular localization prediction has attracted much attention in recent years because of its importance for protein function studying and targeted drug discovery, and that makes it to be an important research field in bioinformatics. Traditional experimental methods which ascertain the protein sub-cellular locations are costly and time consuming. In the last two decades, machine learning methods got increasing development, and a large number of machine learning based protein sub-cellular location predictors have been developed. However, most of such predictors can only predict proteins in only one subcellular location. With the development of biology techniques, more and more proteins which have two or even more sub-cellular locations have been found. It is much more significant to study such proteins because they have extremely useful implication for both basic biology and bioinformatics research. In order to improve the accuracy of prediction, much more feature information which can represent the protein sequence should be extracted. In this paper, several feature extraction methods were fused together to extract the feature information, then the multi-label k nearest neighbors (ML-KNN) algorithm was used to predict protein sub-cellular locations. The best overall accuracies we got for dataset s1 in constructing Gpos-mploc is 66.7304 and 59.9206 percent for dataset s2 in constructing Virus-mPLoc.

The study of epilepsy detection has great clinical significance. The focus of this study is feature extraction method, which has significant impacts on the performance of epilepsy detection. Recently, the statistic properties of complex network show ability to describe the dynamics of nonlinear time series. In this paper, a feature extraction method of epileptic EEG, based on statistical properties of weighted complex network, is proposed. The weighted network of epileptic EEG is first constructed and the vertex strength distribution of the converted network is studied. Then the weighted mean value of the vertex strength distribution is defined and extracted as the classification feature. Experimental results indicate that the extracted feature can clearly reflect the difference between ictal EEGs and interictal EEGs and the single feature classification based on extracted feature gets higher classification accuracy up to 95.50%.

This paper presents a longitudinal tracking control law for Cooperative Adaptive Cruise Control (CACC) systems in a platoon that can comprehensively enable tracking capability of various spacing policies, designed expected velocity, and designed expected acceleration. Taking into account heterogeneous traffic, i.e., a platoon of vehicles with possibly different characteristics, the longitudinal control problem is formulated as an output tracking control problem with a quadratic function so that the contradictions among the different tracking requirements are realized, which include inter-vehicle spacing, velocity and acceleration. Then, the decentralized longitudinal tracking control law is proposed by using a limited communication structure and maximum principle (in this case, a wireless communication link with the nearest preceding vehicle and designed platoon leader only), in which the feedback items are composed of the states of host vehicles, and additional information of the nearest preceding vehicle and designed platoon leader are used as feed forward items. In addition, the concepts of "expected velocity" and "expected acceleration" are introduced to design the desired velocity and acceleration, realize additional objectives, and improve the predictive abilities. Numerous simulation results show that the proposed tracking controller provides a reliable tool for a systematic and efficient design of a platoon controller within CACC systems.

The hydration of Portland cement paste has an important impact on the formation of microstructure and development of strength. Manual derivation of cement hydration kinetic equation is very difficult because of the extreme complexity in Portland cement hydration. It can be reversely extracted automatically from the observed time series using evolutionary computation method. However, the physical meaning of coefficients of the extracted kinetic equation can not be understood easily, which limits the scope of application of kinetic equation in predicting hydration reaction. In this paper, in order to predict the reaction process of Portland cement, an evolutionary approach to predict the development of cement hydration using extreme early-age data and differential evolution algorithm is proposed. The experimental results indicate that the proposed method is very suitable for the forecasting of the development of degree of hydration for Portland cement.

Most previous methods of inferring the S-system models have a significant limitation. That is, the structure of S-system models is fixed, and the only goal is to optimize its parameters and coefficients. Because the number of S-system parameters is proportional to the square of the number of variable, a large number of S-system parameters need to be simultaneously estimated, when the number of variable is very large. This limit may lead to the overwhelming computational complexities. To overcome this limitation we propose the restricted additive tree model for inferring the S-system models. In this approach, the evolution algorithm based on tree-structure and the particle swarm optimization (PSO) are employed to evolve the structure and the parameters of the S-system models, respectively. And the partitioning strategy is used to reduce the search space. We make three experiments and Simulation results using both synthetic data and real microarray measurements show that the structures and parameters of the S-system models can be identified correctly, which demonstrate the effectiveness of the proposed methods. The experiment results show that the structures and parameters of the S-system models can be identified correctly. And compared with other methods, the spent time is sharply reduced.

Traditional traffic classification techniques like port-based and payload-based techniques are becoming ineffective owning to more and more Internet applications using dynamic port number and encryption techniques. Therefore, in the past few years, many researches have addressed machine learning-based techniques. Most researches of machine learning-based traffic identification use traffic samples collected on key nodes of networks for their learning. These samples do not have accurate application information i. e. the ground truth which is crucial for machine learning algorithms. In this paper, we first designed a distributed host based traffic collecting platform (DHTCP) to gather traffic samples with accurate application information on user hosts. Then we built a data set using DHTCP, and applied Flexible Neural Trees (FNT) - a special kind of artificial neural network which has been successfully applied in many areas, for traffic identification. Web and P2P traffics were studied in our work. Although the proposed technique is at an early stage of development, experimental results show that it is a promising solution of Internet traffic identification.

Risk assessment is often done by human experts, because there is no exact and mathematical solution to the problem. Usually the human reasoning and perception process cannot be expressed precisely. This paper propose a light weight risk assessment system based on an Hierarchical Takagi-Sugeno model designed using evolutionary algorithms. Performance comparison is done with neuro-fuzzy and genetic programming methods. Empirical results indicate that the techniques are robust and suitable for developing light weight risk assessment models, which could be integrated with intrusion detection and prevention systems.

Extracting motifs in the sea of DNA sequences is an intricate task but have great significance. We propose an alternative solution integrating bacterial foraging optimization (BFO) algorithm and Tabu search (TS) algorithm namely TS-BFO. We modify the original BFO via established a self-control multi-length chemotactic step mechanism, and introduce Rao metric. The experiments on real data set extracted from TRANSFAC and SCPD database have predicted meaningful motif which demonstrated that TS BFO is a promising approach for motif discovery.

This paper introduces a novel numerical stochastic optimization algorithm called Region Reproduction Algorithm (RRA) to solve global numerical optimization problems. The algorithm firstly generates some regions in space which the individual in the population exists. Then we evaluate the regions according to the fitness of the individuals in them. The number of offspring in the region is reproduced by the fitness in the regions. With the algorithm goes on, there would be more offspring in the superior regions than the poorer regions. Because the algorithm is more concerned in the superior regions, it has more probability to find the optimal solution than traditional algorithms. Experiments show that the algorithm is more effective and stable in terms of the solution quality and standard deviation compared with other existing methods, such as GA, PSO, Canonical PSO and EO.

This paper presents a new face recognition approach by using correlation analysis and ensemble classifiers based on Support Vector Machine (SVM). In this approach, image pre-processing techniques such as histogram equalization, edge detection and geometrical transformation are first used in order to improve the quality of the face images. We further employ correlation analysis method to extract features. At last, ensemble classifiers based on SVM are selected to construct the classification committee using Binary Particle Swarm Optimization (BPSO). Comparisons with other popular classification methods show that our scheme is very promising in face recognition.

Protein structure classification is an important issue in understanding the associations between sequence and structure as well as possible functional and evolutionary relationships. Recently structural genomes initiatives and other high-throughput experiments have populated the biological databases at a rapid pace. In this paper, three types of classifiers, k nearest neighbors, class center and nearest neighbor and probabilistic neural networks and their homogenous ensemble for multiclass protein fold recognition problem are evaluated firstly, and then a heterogenous ensemble Voting System is designed for the same problem. The different features and/or their combinations extracted from the protein fold dataset are used in these classification models. The heterogenous classification results are then put into a voting system to get the final result. The experimental results show that the proposed method can improve prediction accuracy by 4%–10% on a benchmark dataset containing 27 SCOP folds.

Protein data contain discriminative patterns that can be used in many beneficial applications if they are defined correctly. Protein classification in terms of fold recognition plays an important role in computational protein analysis, since it can contribute to the determination of the function of a protein whose structure is unknown. In this paper, a probabilistic neural network ensemble (PNNE) model is proposed for multi-class protein folds recognition problem. For training and evaluating the proposed method we use two datasets containing 27 SCOP folds. Experimental results show that the proposed method can improve the prediction accuracy and outperform other related approaches.

The forecasting models for time series forecasting using computational intelligence such as artificial neural networks (ANNs) , genetic programming (GP) and gene expression programming (GEP), especially hybrid particle swarm optimization (PSO) algorithm and artificial neural networks (ANNs) have achieved favorable results. However, these studies, have assumed a static environment. This paper investigates the development of a new dynamic decision forecasting model. The input size of the ANNs will be dynamical changed in the process of evolution. Application results prove the higher precision and generalization capacity obtained by this new method than the static models.

The paper presented a new signal processing technique to accomplish blind source separation when given only a single-channel mixture signal. One signal source can be generated by a set of weighted linear superposition of the time domain sub-band functions with independent component characteristic. By combining the independent sub-band function components into the single-channel mixture signal, making the single-channel mixture signal is transformed into a multi-dimensional vector from one-dimensional. Thus ICA can be applied to separate the extended single-channel mixture signal. The simulation results demonstrated the effectiveness and adaptability of the proposed method. What is more, similitude phase graph is also proposed in this paper, which can show the performance of blind separation algorithm straightly.

An intrusion is defined as a violation of the security policy of the system, and, hence, intrusion detection mainly refers to the mechanisms that are developed to detect violations of system security policy. Current intrusion detection systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this study is to identify important input features in building an IDS that is computationally efficient and effective. This article proposes an IDS model based on a general and enhanced flexible neural tree (FNT). Based on the predefined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, overlayer connections, and different activation functions for the various nodes involved. The FNT structure is developed using an evolutionary algorithm, and the parameters are optimized by a particle swarm optimization algorithm. Empirical results indicate that the proposed method is efficient. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 337–352, 2007.

This paper presents an automatic way of evolving hierarchical Takagi-Sugeno fuzzy systems (TS-FS). The hierarchical structure is evolved using probabilistic incremental program evolution (PIPE) with specific instructions. The fine tuning of the if - then rule's parameters encoded in the structure is accomplished using evolutionary programming (EP). The proposed method interleaves both PIPE and EP optimizations. Starting with random structures and rules' parameters, it first tries to improve the hierarchical structure and then as soon as an improved structure is found, it further fine tunes the rules' parameters. It then goes back to improve the structure and the rules' parameters. This loop continues until a satisfactory solution (hierarchical TS-FS model) is found or a time limit is reached. The proposed hierarchical TS-FS is evaluated using some well known benchmark applications namely identification of nonlinear systems, prediction of the Mackey-Glass chaotic time-series and some classification problems. When compared to other neural networks and fuzzy systems, the developed hierarchical TS-FS exhibits competing results with high accuracy and smaller size of hierarchical architecture.

Technology trends in today's cooperative design environments are making it more and more important to monitor the network performance and ensure the network security. This paper describes the design and implementation of a distributed network traffic monitoring system based on embedded NetFlow hardware and software engines. The system architecture and design principles were introduced in the paper, some discussions were also presented about the NetFlow-based network monitoring technologies. The system had been successfully used to monitor highspeed campus networks at full wire speed without packet sampling in scenarios where commercial NetFlow collectors could not be used due to their limitations. Results show that this is an effective mechanism to identify, diagnose, and determine controls for network activities in CSCW environments and other network-based applications.

This paper introduces a flexible neural tree model. The model is computed as a flexible multi-layer feed-forward neural network. A hybrid learning/evolutionary approach to automatically optimize the neural tree model is also proposed. The approach includes a modified probabilistic incremental program evolution algorithm (MPIPE) to evolve and determine a optimal structure of the neural tree and a parameter learning algorithm to optimize the free parameters embedded in the neural tree. The performance and effectiveness of the proposed method are evaluated using function approximation, time series prediction and system identification problems and compared with the related methods.

An indispensable ability for intelligent control is to comprehend and learn about plants, disturbances, environment, and operating conditions. In this paper, a modified probabilistic incremental program evolution (MPIPE) algorithm and a random search algorithm are used as a promising tool for such purposes. In order to identify and evolve the structure and parameters of the additive models simultaneously, a hybrid method is proposed, in which the MPIPE is used for the identification of structure of the additive models, and the parameters used in additive models are optimized by a random search algorithm. Simulation results for the identification of linear/nonlinear systems show the feasibility and effectiveness of the proposed method.