With the exponential growth in Android apps, Android based devices are becoming victims of target attackers in the “silent battle” of cybernetics. To protect Android based devices from malware has become more complex and crucial for academicians and researchers. The main vulnerability lies in the underlying permission model of Android apps. Android apps demand permission or permission sets at the time

Recently, multilayer extreme learning machine (ELM) algorithms have been extensively studied for hierarchical abstract representation learning in the ELM community. In this paper, we investigate the specific combination of \(L_{21}\)-norm based loss function and regularization to improve the robustness and the sparsity of multilayer ELM. As we all known, the mean square error (MSE) cost function (or

As a generalization algorithm of spectral clustering, p-spectral clustering has gradually attracted extensive attention of researchers. Gaussian kernel function is generally used in traditional p-spectral clustering to construct the similarity matrix of data. However, the Gaussian kernel function based on Euclidean distance is not effective when the data-set is complex with multiple density peaks or

Automatic text classification using machine learning is significantly affected by the text representation model. The structural information in text is necessary for natural language understanding, which is usually ignored in vector-based representations. In this paper, we present a graph kernel-based text classification framework which utilises the structural information in text effectively through

Spectral clustering (SC) transforms the dataset into a graph structure, and then finds the optimal subgraph by the way of graph-partition to complete the clustering. However, SC algorithm constructs the similarity matrix and feature decomposition for overall datasets, which needs high consumption. Secondly, k-means is taken at the clustering stage and it selects the initial cluster centers randomly

In real world, multi-attribute group decision making (MAGDM) is a complicated cognitive process that involves expression, fusion and analysis of multi-source uncertain information. Among diverse soft computing tools for addressing MAGDM, the ones from granular computing (GrC) frameworks perform excellently via efficient strategies for multi-source uncertain information. However, they usually lack convincing

Ensemble reinforcement learning, which combines the decisions of a set of base agents, is proposed to enhance the decision making process and speed up training time. Many studies indicate that an ensemble model may achieve better results than a single agent because of the complement of base agents, in which the error of an agent may be corrected by others. However, the fusion method is a fundamental

This paper proposed automated systems for analyzing elemental processes and for evaluating work skills. The systems use labeled latent Dirichlet allocation (L-LDA) to classify worker motions obtained from sensors into four elemental processes. L-LDA automatically learns characteristic motions, so there is no need to define and identify motion features. The proposed system predicts elemental processes

Roughness recognition is an important function in the nervous system that facilitates our interactions with the environment. Previous studies have focused on the neuro-cognitive aspects and frequency-based changes in response to the roughness stimuli. In this study, we investigate the effect of different roughness levels on the nonlinear characteristics of EEG signals. Nine healthy subjects participated

In real-world applications, data can be represented using different units/scales. For example, weight in kilograms or pounds and fuel-efficiency in km/l or l/100 km. One unit can be a linear or non-linear scaling of another. The variation in metrics due to the non-linear scaling makes Anomaly Detection (AD) challenging. Most existing AD algorithms rely on distance- or density-based functions, which

Feature extraction is an essential component in many classification tasks. Popular feature extraction approaches especially deep learning-based methods, need large training samples to achieve satisfactory performance. Although dictionary learning-based methods are successfully used for feature extraction on both small and large datasets, however, when dealing with high-dimensional datasets, a large

With the rapid development of internet economy, personal recommender system plays an increasingly important role in e-commerce. In order to improve the quality of recommendation, a variety of scholars and engineers devoted themselves in developing the recommendation algorithms. Traditional collaborative filtering algorithms are only dependent on rating information or attribute information. Most of

Multigranulation rough set model (MGRS) uses multiple equivalence relations on the universe to calculate the approximations, which can solve problem in mutigranulation spaces. In practical applications, information systems often dynamically update due to the variation of objects, attributes or attribute values. Incremental approach is an effective method to calculate approximations for dynamically

The development of mobile Internet and sharing economy brings the prosperity of spatial crowdsourcing (SC). Pricing is a crucial step for SC platforms to solve the Profit-driven Task Assignment (PTA) problem to maximize their total profit. However, dynamic pricing is still large unexplored in PTA. In addition, existing works seek solutions without considering the uncertainty of workers’ acceptance

In this paper we propose a knowledge discovery and visualisation method for unearthing emotional states from physiological data typically available from wearable devices. In addition we investigate the viability of using a limited set of wearable sensors to extract decision tree rules which are representative of physiological changes taking place during emotional changes. Our method utilised a fusion

In recent years, action recognition has become a popular and challenging task in computer vision. Nowadays, two-stream networks with appearance stream and motion stream can make judgment jointly and get excellent action classification results. But many of these networks fused the features or scores simply, and the characteristics in different streams were not utilized effectively. Meanwhile, the spatial

Machine learning algorithms play a critical role in electroencephalograpy (EEG)-based brain-computer interface (BCI) systems. However, collecting labeled samples for classifier training and calibration is still difficult and time-consuming, especially for patients. As a promising alternative way to address the problem, semi-supervised learning has attracted much attention by exploiting both labeled

In the era of big data, the research on clustering technologies is a popular topic because they can discover the structure of complex data sets with minimal prior knowledge. Among the existing soft clustering technologies, as an extension of fuzzy c-means (FCM) algorithm, the intuitionistic FCM (IFCM) algorithm has been widely used due to its superiority in reducing the effects of outliers/noise and

Speech quality evaluation (SQE) under complex noisy environment is important for audio processing systems and quality of service. Recently, the non-intrusive SQE is getting more and more attentive due to its efficient and ease of use. However, non-intrusive SQEs are expected to be underperformed the intrusive ones since it has no prior knowledge of the clean speech. In this paper, a novel quasi-clean

Large-scale multi-robot task allocation (MRTA) problem is an important part of intelligent logistics scheduling. And the load capacity of robot and picking station are important factors affecting the MRTA problem. In this paper, the MRTA problem is built as a many-objective optimization model with four objectives, which takes the load capacity of single robot, single picking station, all robots and

The stock investment selection could be regarded as a classical multiple attribute group decision making (MAGDM) issue. The intuitionistic fuzzy sets (IFSs) can fully describes the uncertain information for stock investment selection. Furthermore, the classical TODIM method based on the cumulative prospect theory (CPT-TODIM) is built, which is a selectable method in reflecting the DMs’ psychological

The distance measure plays an important role in the hesitant fuzzy theory. Experts always focus on the attributes aggregation of hesitant fuzzy information, ignoring the preference relationships between alternatives. Therefore, it is necessary to consider the competition effect between alternatives and develop a more suitable distance measure. Considering the background information of the connections

As a state-of-the-art multi-class supervised novelty detection method, supervised novelty detection-support vector machine (SND-SVM) is extended from one-class support vector machine (OC-SVM). It still requires to slove a more time-consuming quadratic programming (QP) whose scale is the number of training samples multiplied by the number of normal classes. In order to speed up SND-SVM learning, we

Dimensionality reduction (DR) has been widely used to deal with high-dimensional data, and plays an important role in alleviating the so-called “curse of dimensionality”. In this paper, we propose a novel unsupervised DR method with applications to face recognition, i.e., Nonnegative Representation based Discriminant Projection (NRDP). Different with other locality or globality preserving DR methods

One of the applications of center-based clustering algorithms such as K-means is partitioning data points into K clusters. In some examples, the feature space relates to the underlying problem we are trying to solve, and sometimes we can obtain a suitable feature space. Nevertheless, while K-means is one of the most efficient offline clustering algorithms, it is not equipped to estimate the number

Clustering is a fundamental approach to discover the valuable information in data mining and machine learning. Density peaks clustering is a typical density based clustering and has received increasing attention in recent years. However DPC and most of its improvements still suffer from some drawbacks. For example, it is difficult to find peaks in the sparse cluster regions; assignment for the remaining

Among machine learning paradigms, unsupervised transductive transfer learning is useful when no labeled data from the target domain are available at training time, but there is accessible unlabeled target data during training phase instead. The current paper proposes a novel unsupervised transductive transfer learning method to find the specific and shared features across the source and the target

With the introduction of the interval-valued intuitionistic fuzzy sets, the interval-valued intuitionistic fuzzy numbers are used instead of precise numbers to provide fuzzy characterization of feature attribute values and misclassification loss function values, which is more in line with the realistic fuzzy decision-making environment. Also, the constructive covering algorithm is introduced into the

This research mainly focuses on presenting an innovative study of a multi-stage multi-objective fixed-charge solid transportation problem (MMFSTP) with a green supply chain network system under an intuitionistic fuzzy environment. The most controversial issue in recent years is that greenhouse gas emissions such as carbon dioxide, methane, etc. induce air pollution and global warming, thus motivating

In this paper, a new adaptive super-twisting sliding mode control (STSMC) scheme based on a double loop fuzzy neural network (DLFNN) is proposed to solve the problem of the external disturbances and approximate the unknown model for a micro gyroscopes. The STSMC algorithm can effectively suppress chattering since it can hide the high-frequency switching part in the high-order derivative of the sliding

Cushing's disease (CD) is a rare disease that occurs in 1.2–1.4 persons per million population per year. Recurrence prediction after transsphenoidal surgery (TSS) is important for determining individual treatment and follow-up strategies. Between 2000 and 2017, 354 CD patients with initial postoperative remission and long-term follow-up data were enrolled from Peking union medical college hospital

This study introduces the evolutionary multi-objective version of seagull optimization algorithm (SOA), entitled Evolutionary Multi-objective Seagull Optimization Algorithm (EMoSOA). In this algorithm, a dynamic archive concept, grid mechanism, leader selection, and genetic operators are employed with the capability to cache the solutions from the non-dominated Pareto. The roulette-wheel method is

Recently, deep neural networks have demonstrated their efficiency in image classification tasks, which are commonly achieved by an extended depth and width of network architecture. However, poor convergence, over-fitting and gradient disappearance might be generated with such comprehensive architectures. Therefore, DenseNet is developed to address these problems. Although DenseNet adopts bottleneck

With the increasing complexity of scientific research and the expanding scale of projects, scientific research cooperation is an important trend in large-scale research. The analysis of co-authorship networks is a big data problem due to the expanding scale of the literature. Without sufficient data mining, research cooperation will be limited to a similar group, namely, a “small group”, in the co-author

Review rating prediction is commonly approached from the perspective of either Collaborative Filtering (CF) or Sentiment Classification (SC). CF-based approach usually resorts to matrix factorization based on user–item interaction, and does not fully utilize the valuable review text features. In contrast, SC-based approach is focused on mining review content, but can just incorporate some user- and

In the field of machine learning, offline training and online training occupy the same important position because they coexist in many real applications. The extreme learning machine (ELM) has the characteristics of fast learning speed and high accuracy for offline training, and online sequential ELM (OS-ELM) is a variant of ELM that supports online training. With the explosive growth of data volume

Fault detection and isolation (FDI) is considered as one of the most critical problems in biological processes. Therefore, in this paper, we consider a new FDI framework that aims to improve the monitoring of biological processes. To do that, a machine learning-based statistical hypothesis approach, which can identify the model, detect and isolate the faults, will be developed. In the developed approach

Clinical quantitative information contains crucial measurable expressions of patients’ diseases and treatment conditions, which are commonly exist in free-text electronic medical records. Although the clinical quantitative information is of considerable significance in assisting the analysis of health care, few researches have yet focused on the topic and it remains an ongoing challenge. Focusing on

5G cellular networks are expected to be the key infrastructure to deliver the emerging services. These services bring new requirements and challenges that obstruct the desired goal of forthcoming networks. Mobile operators are rethinking their network design to provide more flexible, dynamic, cost-effective and intelligent solutions. This paper starts with describing the background of the 5G wireless

The rapid development and application of AI in intelligent transportation systems has widely impacted daily life. The application of an intelligent visual aid for traffic sign information recognition can provide assistance and even control vehicles to ensure safe driving. The field of autonomous driving is booming, and great progress has been made. Many traffic sign recognition algorithms based on

Time series data is ubiquitous in financial, biomedical, and other areas. Anomaly detection in time series has been widely researched in these areas. However, most existing algorithms suffer from “curse of dimension” and may lose some information in the process of feature extraction. In this paper, we propose two new data structures named interval table (ITable) and extend interval table (EITable)

There exist two formulations of the theory of rough sets, consisting of the conceptual formulations and the computational formulations. Class-specific and classification-based attribute reducts are two crucial notions in three-way probabilistic rough set models. In terms of conceptual formulations, the two types of attribute reducts can be defined by considering probabilistic positive or negative region

The particle swarm optimization (PSO) algorithm is a popular evolutionary computation approach that has received an ever-increasing interest in the past decade owing to its wide application potential. Despite the many variants of the PSO algorithm with improved search ability by means of both the convergence rate and the population diversity, the local optima problem remains a major obstacle that hinders

Since p-spectral clustering has good performance in many practical problems, it has attracted great attention. The Cheeger cut criterion is used in p-spectral clustering to do graph partition. However, due to the improper affinity measure and outliers, the original p-spectral clustering algorithm is not effective in dealing with manifold data. To solve this problem, we propose a manifold p-spectral

The medical literature is the most important way to demonstrate academic achievements and academic exchanges. Massive medical literature has become a huge treasure trove of knowledge. It is necessary to automatically extract implicit medical knowledge from the medical literature. Medical relation extraction aims to automatically extract medical relations from the medical text for various medical researches

A reliable adaptive hybrid classifier (hAHC), which combines a posture-based adaptive signal segmentation algorithm with a multi-layer perceptron (MLP) classifier, together with a plurality voting approach, was proposed and evaluated in this study. The hAHC model was evaluated using a real-time posture recognition framework that sought to identify five behaviours (sitting, walking, standing, running

Many real-world datasets nowadays are of regression type, while only a few dimensionality reduction methods have been developed for regression problems. On the other hand, most existing regression methods are based on the computation of the covariance matrix, rendering them inefficient in the reduction process. Therefore, a BMA-based multi-objective feature selection method, GBMA, is introduced by

Optimal scale combination selection plays a critical role for knowledge discovery in multi-scale decision tables (MDTs) and has attracted considerable attention. However, searching for all optimal scale combinations from the scale collection may result in a combinatorial explosion, and the existing approaches are time-consuming. The main goal of this study is to improve the efficiency of searching

Learning low-dimensional vector representations of words from a large corpus is one of the basic tasks in natural language processing (NLP). The existing universal word embedding model learns word vectors mainly through grammar and semantic information from the context, while ignoring the sentiment information contained in the words. Some approaches, although they model sentiment information in the

Many real-world data have multiple class labels known as multi-label data, where the labels are correlated with each other, and as such, they are not independent. Since these data are usually high-dimensional, and the current multi-label feature selection methods have not been precise enough, then a new feature selection method is necessary. In this paper, for the first time, we have modeled the problem

The problem of frequent itemset mining is one of the more important problems in data mining which has been extensively employed across a wide range of other relevant tasks such as market basket analysis in marketing, or text analysis in text mining applications. The majority of the deterministic frequent itemset mining algorithms which have been proposed in recent years use some sort or another of

Outlier ensembles that use more base detectors recently become an attractive approach to solving problems of single detectors. However, existing outlier ensembles often assume that base detectors make independent errors, which is difficult to satisfy in practical applications. To this end, this paper proposes a dynamic outlier ensemble to loose this error independence assumption. In our method, it

Disaster has great destructiveness and a wide influence on urban and rural construction, and more than one place is affected by disaster. Thus, it needs to take multiple disaster points into account. In view of different rescue missions, both medical rescue teams and search rescue teams also need to be dispatched. That is the multi-sided matching among medical rescue teams, search rescue teams and

The product knapsack problem (PKP) is a new variation of the knapsack problem which arises in social choice computation. Although some deterministic algorithms have been reported to handle small-scale problems, the solution to the middle and large-scale problems is still lack of progress. For efficiently solving this problem, a new ideal of solving PKP by evolutionary algorithms is proposed in the

Side-information based exponential discriminant analysis (SIEDA) is more efficient than side-information based linear discriminant analysis (SILDA) in computing the discriminant vectors because it maximizes the Fisher criterion function. In this paper, we develop a novel criterion, named side-information based weighted exponential discriminant analysis (SIWEDA), that is based on the classical SIEDA

In this paper, our focus is on using a rule-based approach to develop agents with artificial general intelligence. In rule-based systems, developing effective rules is a huge challenge, and coding rules for agents requires a large amount of manual work. Association rules mining (ARM) can be used for discovering specific rules from data sets and determining relationships between data sets. In this paper

Task-space control needs the inverse kinematics solution or Jacobian matrix for the transformation from task space to joint space. However, they are not always available for redundant robots because there are more joint degrees-of-freedom than Cartesian degrees-of-freedom. Intelligent learning methods, such as neural networks (NN) and reinforcement learning (RL) can learn the inverse kinematics solution

Optimization is used in different fields of engineering to solve complex problems. In image processing, multilevel thresholding requires to find the optimal configuration of thresholds to obtain accurate segmented images. In this case, the use of two-dimensional histograms is helpful because they permit us to combine information from the image preserving different features. This paper introduces a

The rapid development in 5G cellular and IoT technologies is expected to be deployed widespread in the next few years. At the same time, crime rates are also increasing to a greater extent while the investigation officers are held responsible to deal with a broad range of cyber and internet issues in investigations. Therefore, advanced IT technologies and IoT devices can be deployed to ease the investigation

Image has become a main medium of Internet information dissemination, makes it easy for an Internet visitor to get pornographic images with just few clicks on websites. It is necessary to build pornographic image recognition systems since uncontrolled spreading of adult content could be harm to the adolescents. Previous solutions for pornographic image recognition are usually based on hand-crafted