In this paper, the problem of exponential dissipation stability of T-S fuzzy system with state quantization is studied by using non-fragile sampled-data control. First, A Lyapunov-Krasovskii function containing all sampled-data and quantization information is constructed. Second, the better results can be obtained by using the integral inequality and the Newton Leibniz formula. In addition, a dissipative

Arguably, the main purpose of cluster analysis is to develop algorithms to reveal natural groupings (clusterings) over a set of data points based on their similarity. On the other hand, the focus of cooperative game theory (CGT) is to study the formation of groups (coalitions) of decision makers (players) and ways to split the resulting income among them. Due to the conceptual similitude between these

Periodic frequent patterns are sets of events or items that periodically appear in a sequence of events or transactions. Many algorithms have been designed to identify periodic frequent patterns in data. However, most assume that the periodic behavior of a pattern does not change much over time. To address this limitation, this paper proposes to discover a novel type of periodic patterns in a sequence

Since the recent challenge that humanity is facing against COVID-19, several initiatives have been put forward with the goal of creating measures to help control the spread of the pandemic. In this paper we present a series of experiments using supervised learning models in order to perform an accurate classification on datasets consisting of medical images from COVID-19 patients and medical images

During the last 15 to 20 years, evolving (neuro-)fuzzy systems (E(N)FS) have enjoyed more and more attraction in the context of data stream mining and modeling processes. This is because they can be updated on the fly in a single-pass sample-wise manner and are able to perform autonomous changes of the models on structural level in order to react onto process drifts. A wide variety of evolving (neuro-)fuzzy

Machine learning techniques have received much attention in many areas for regression and classification tasks. In this paper, two new support vector regression (SVR) models, namely, least-square SVR and ε-SVR, are developed under the Bayesian inference framework with a square loss function and a ε-insensitive squared one respectively. In this framework, a Gaussian process prior is assigned to the

The single-pair all-shortest-path problem is to find all possible shortest paths, given a single source-destination pair in a graph. Due to the lack of efficient algorithms for single-pair all-shortest-path problem, many applications used diverse types of modifications to the existing shortest-path algorithms such as Dijkstra’s algorithm. Such approaches can facilitate the analysis of medium-sized

In this paper, we define a new fuzzy squareness measure to quantify how much a given fuzzy shape matches a fuzzy square. The new fuzzy shape-based measure is naturally defined and theoretically well-founded, resulted in that its behavior can be understood and predicted in advance. It runs through the interval (0,1] and takes the maximum value equals 1 if and only if the shape measured is a fuzzy square

Uncertainty pertaining to multiple parameters is a critical issue in designing complex systems. Whether or not to acquire more information to reduce uncertainty, and how to acquire information are the meta-level decisions to be made. Key challenges in making such decisions are that there are multiple information sources to choose from, and the cost of information as well as its effects on the overall

Automatic programming is a branch of artificial intelligence that presents each solution as a mathematical formula based on heuristic mechanisms. In this study, artificial bee colony expression programming (ABCEP) is presented, which is combined simultaneously with expression programming. By using expression sharing to generate new solutions, the proposed method can minimize certain deficiencies of

The interest for patient trajectory prediction, a sort of computer-aided medicine, has steadily increased with the pace of artificial intelligence innovation. Notwithstanding, the design of effective systems able to predict clinical outcomes based on the history of a patient is far from trivial. Works so far are based on neural architectures with low performance, especially when using low-cardinality

Partial Multi-Label learning (PML) aims to learn from training data where each example is associated with a set of candidate labels, among which only a subset of them is correct. The major challenge of PML lies in that the training procedure is prone to be misled by the label noise. To address this problem, nearly all existing PML methods focus on solely label disambiguation, i.e., dislodging the noisy

Least squares regression (LSR) has attracted widespread attention in the fields of statistics, machine learning, and pattern recognition. However, it utilizes strict zero-one regression targets, which leads to inferior performance on classification tasks. Furthermore, LSR ignores the local manifold structures of data and lacks robustness. To address these issues, this paper proposes a general regression

In the past decade, deep learning models have shown to be promising tools for time series forecasting. However, owing to significant differences in the volatility characteristics among different types of time series data, it is difficult for an individual deep learning model to maintain robust forecasting performance. In this study, a novel ensemble deep learning model is proposed to achieve accurate

Review-based recommender systems represent users and items with reviews associated with them. As such, the recommender systems are highly dependent on the number of reviews, which is usually few in number. Thus, they produce inaccurate recommendations to users who rarely wrote reviews. An approach to generate better recommendations for the cold-start users is to augment the scarce reviews using other

This article defines a new methodology for pre-recognition of events with object motion analysis in a video without any prior knowledge. This unsupervised application is named as ’conceptualization’. This conceptualization technique is also tested with real-time video data in an internet of things (IoT) architecture. The merits of rough sets in the framework of granular computing are explored to execute

This paper mainly addresses the statistical classification robust to small training data size. We develop a label constrained convolutional factor analysis (LCCFA) model, which unifies the factor analysis (FA), convolution operation and supervised learning. In the LCCFA model, each dictionary atom is used as a small-sized convolution kernel with the goal of learning the observations’ basic structures

Smoothness regularization derives the optimal regression function by minimizing the squared loss combined with a smoothness regularizer that restricts the variation of the function within a neighboring region. Thus, the regression function can effectively accommodate intrinsic data structures, and prediction performance can be improved when the label information is insufficient. In this study, we propose

Compressing real-world graphs has many benefits such as improving or enabling the visualization in small memory devices, graph query processing, community search, and mining algorithms. This work proposes a novel compact representation for real sparse and clustered undirected graphs. The approach lists all the maximal cliques by using a fast algorithm and defines a clique graph based on its maximal

A tag-aware recommender system (TRS) presents the challenge of tag sparsity in a user profile. Previous work focuses on expanding similar tags and does not link the tags with corresponding resources, therefore leading to a static user profile in the recommendation. In this article, we have proposed a new social tag expansion model (STEM) to generate a dynamic user profile to improve the recommendation

The way in which people learn and institutions teach is changing due to the ever-increasing impact of technology. People access the Internet anywhere, anytime and request online training. This has brought about the creation of numerous online learning platforms which offer comprehensive and effective educational solutions which are 100% online. These platforms benefit from intelligent tutoring systems

Benefit-to-cost ratio (BCR) measuring is a useful approach for selecting portfolios of projects when they are evaluated applying multiple and conflicting criteria. A multiattribute value function can be used to measure the benefit of each project, which allows one to evaluate the BCRs, rank projects, and select them according to the available budget. However, here, there is a significant difficulty

Existing winner determination models tend to overlook the sustainable attributes of third party logistics (3PL) providers. This paper investigates a novel green winner determination problem that has several features, including (i) the sustainable attributes with conflicting and interactive properties of potential 3PLs, and (ii) the loss-averse behavior with an internal reference point of a fourth party

The subject of this study is three fundamental procedures of contemporary data analysis: outlier detection, clustering and classification. These issues are considered in a conditional approach – the introduction of specific (e.g., current) values to the model allows, in practice, a significantly precise description of the reality under research. The same methodology has been used for all three of the

Multimodal optimization aims to find and maintain as many global and local optima of a function as possible. Niching techniques based on multi-populations and clustering proved to be efficient for tackling multimodal optimization problems. The main focus of this work is to enhance the diversity of the population and improve the global search ability to locate more optima. A Double-Layer-Clustering

Human motion prediction from its historical poses is an essential task in computer vision; it is successfully applied for human-machine interaction and intelligent driving. Recently, significant progress has been made with variants of RNNs or LSTMs. Despite alleviating the vanishing gradient problem, the chain RNN often leads to deformities and convergence to the mean pose because of its low ability

With the advent of fast data streams, real-time machine learning has become a challenging task, demanding many processing resources. In addition, they can be affected by the concept drift effect, by which learning methods have to detect changes in the data distribution and adapt to these evolving conditions. Several emerging paradigms such as the so-called Smart Dust, Utility Fog, or Swarm Robotics

Accurately forecasting user’s purchase intention over time is a huge challenge for personalized recommend systems, of which a critical problem is how to model the changes of user preference and temporal correlation of items. In this paper, aiming at addressing this question, we first introduce attention flow network to model users’ purchase records by leveraging attention flow that describes the changing

Sparse representation has been in a widespread use in hyperspectral image (HSI) classification task. The samples to be classified can be linearly represented with a few samples from the same class. However, when samples from different classes are highly correlated with each other, it makes the classification task challenging. To solve this problem, we take the Euclidean distance information between

Due to the increasing importance of in the decision-making and a broad range of functions in our digitalized society, the trusty of data is becoming increasingly critical. For example, in a traditional trading process, there usually exists a trusted middle-person to guide the transaction and arbitrate any arising dispute. However, having such a trusted middle person can be labor-intensive, increases

We study the state estimation for cyber-physical systems (CPSs) whose communication channels are subject to mixed denial-of-service (DoS) and false data injection (FDI) attacks. Cyber-attacks compromise the security and privacy of sensor and communication information. Unlike systems subject to only one single type of attacks (either DoS or FDI attacks), systems under mixed attacks will make the implementation

In recent years, with the improvement in the accuracy of remote sensing image classification and target recognition, the feature level fusion technology of remote sensing images has attracted much attention and become a research hotspot. However, this kind of fusion technology is not as mature as pixel-level fusion technology, and there are still many problems to be solved. This paper proposes a multi-spectral

Unmanned aerial vehicles (UAVs) face a lot of security challenges due to the openness of their wireless communication. In this paper, we investigate how to improve the security of wireless communication when the UAV is utilized as a wireless relay. We consider a new scenario where the dual UAVs are employed cooperatively to transmit confidential information and an eavesdropper whose actual position

Personalised 3D face creation has always been a hot topic in the computer vision community. Many methods have been proposed including the statistic model, the non-rigid registration and high-end depth acquisition equipment. However, in practical applications, those existing methods still have their own limitations. For example, the performance of the statistic model-based methods highly depends on

Click-through rate prediction is an important method for online advertising and marketing evaluations. However, for environmental reasons, there is a scarcity and imbalance in the advertising data available. We found that a feature transfer can be applied in a transfer learning method to obtain potential connections from less relevant advertisement data. Considering the complexity and diverse features

With the increasing popularity of Internet of Energy (IoE), more and more physical devices connected to IoE depend on the information system for energy control and coordination. Under this condition, how to achieve information flow control in IoE becomes a great challenge. Access control encryption (ACE) is a promising technology to address the problem. However, existing ACE requires a centralized

In recent years, XACML (eXtensible Access Control Markup Language) has been widely used in the development of various applications, especially Web services. The evaluation time of a PDP (Policy Decision Point) grows significantly when the PDP loads a large-scale policy set coded in XACML. In order to improve the PDP evaluation performance, we propose an optimized policy evaluation engine, namely XDLEngine

The concept of fuzzy random variable combines two sources of uncertainty: randomness and fuzziness, whereas the model of ordered fuzzy numbers provides a representation of inaccurate quantitative data, and is an alternative to the standard fuzzy numbers model proposed by Zadeh. This paper develops the model of ordered fuzzy numbers by defining the concept of fuzzy random variables for these numbers

Acute hypotensive episode (AHE) is a common serious postoperative complication in ICU, which may raise multiple system failure (especially of cardiac and respiratory kinds), and even cause death. Timely and effective clinical intervention is obviously vital to the saving of patients. AHE detection involves physiological time-series monitoring, processing and prediction technologies, which can offer

Dynamic Multi-objective Optimization Problem (DMOP) is emerging in recent years as a major real-world optimization problem receiving considerable attention. Tracking the movement of Pareto front efficiently and effectively over time has been a central issue in solving DMOPs. In this paper, a reinforcement learning-based dynamic multi-objective evolutionary algorithm, called RL-DMOEA, which seamlessly

The paper introduces a novel many-objective evolutionary method, with a diversity-based selection operator and aims to fill the “gaps” in the Pareto Front approximation and to increase its spread. It shows, that guiding the evolution process towards the least explored parts of a space increases overall diversity, but can also lead to increased convergence. A set of experiments is carried out on a many–objective

The increasing presence of renewable energy plants has created new challenges such as grid integration, load balancing and energy trading, making it fundamental to provide effective prediction models. Recent approaches in the literature have shown that exploiting spatio-temporal autocorrelation in data coming from multiple plants can lead to better predictions. Although tensor models and techniques

Deep learning models are being widely used in almost every field of computing and information processing. The advantages offered by these models are unparalleled, however, similar to any other computing discipline, they are also vulnerable to security threats. A compromised deep neural network can significantly impact its robustness and accuracy. In this work, we present a novel targeted attack method

Convolutional neural network (CNN)-based encoder-decoder models have profoundly inspired recent works in the field of salient object detection (SOD). With the rapid development of encoder-decoder models with respect to most pixel-level dense prediction tasks, an empirical study still does not exist that evaluates performance by applying a large body of encoder-decoder models on SOD tasks. In this paper

Online social networks provide convenience for users to propagate ideas, products, opinions, and many other items that compete with different items for influence spread. How to accurately model the spread of competitive influence is still a challenging problem. Almost all reported methods ignore the effect of trust relationships in the spread of competitive influence. Maximizing competitive influence

Crowdsourcing services provide a fast, efficient, and cost-effective approach to obtaining labeled data, particularly for human-like tasks. In a crowdsourcing scenario, after ground truth inference methods have been employed to obtain integrated instance labels, label noise remains present in the integrated labels. Label noise handling techniques can then be implemented to mitigate the effects of this

Geological hazards caused by rock failure severely threaten the safety of underground projects, and thus microseismic monitoring systems have been deployed to monitor the rock mass stability. However, due to implicit subseries patterns and sparse distinguishing features, automatic discrimination of the microseismic waveforms of rock fracturing remains a great challenge. Deep neural networks offer powerful

Nowadays, efficient and effective stream processing has become increasingly important in many real-world applications such as sensor data monitoring, network intrusion detection, IP network traffic analysis, and so on. In practice, stream data often encounter the problem of having some data attributes missing, due to reasons such as packet losses, network congestion/failure, and so on. In such a scenario

Recently, it has been demonstrated that deep neural networks can significantly improve the performance of single image super-resolution (SISR). Numerous studies have concentrated on raising the quantitative quality of super-resolved (SR) images. However, these methods that target PSNR maximization usually produce blurred images at large upscaling factor. The introduction of generative adversarial networks

The k-medoids (k-median) problem is one of the best known unsupervised clustering problems. Due to its complexity, finding high-quality solutions for huge-scale datasets remains extremely challenging. The application of many approaches finding optimal or quality solutions is limited to only small and medium-size instances. On the other hand, many parallel, distributed algorithms that can handle huge-scale

Change-point problems which originated from the field of quality control have become an important area of research. Although existing methods have been successful in detecting change-points, most of them require the underlying data to follow a specific distribution. Heuristically speaking, those methods only perform well when the data set is hypothesised to follow a normal distribution. In this paper

This paper tackles the observer-based finite-time H∞ control problem for networked nonlinear semi-Markovian jump systems (SMJSs) with actuator fault, parameter uncertainties and partly unknown transition rates via an improved dynamic event-triggered scheme (DETS). In comparison with traditional dynamic event-triggered scheme, the observer-based improved dynamic event-triggered scheme which is not restricted

APT attacks have posed serious threats to the security of cyberspace nowadays which are usually tailored for specific targets. Identification and understanding of APT attacks remains a key issue for society. Attackers often utilize malware as the weapons to launch cyber-attacks. For this reason, detecting APT malware and gaining an insight of its malicious behaviors can strengthen the power to understand

In many-objective optimization problems (MaOPs), the decomposition-based algorithms are widely used since they have promising performances in maintaining the diversity of solutions. However, few studies have been reported on how to utilize relationships between subproblems to promote global convergence. To fill this gap, we develop an automatic estimation mechanism based on the modified Ant Colony

Embedding diversity into knowledge discovery is important: the patterns mined will be more novel, more meaningful, and broader. Surprisingly, in the classic problem of influence maximization in social networks, relatively little study has been devoted to diversity and its integration into the objective function of an influence maximization method. In this work, we propose the integration of a categorical-based

The theory of three-way decision plays an important role in decision making and knowledge reasoning. However, little attention has been paid to the problem of learning from partially labeled data with three-way decision. In this paper, we propose a three-way co-decision model for partially labeled data. More specifically, the problem of attribute reduction for partially labeled data is first investigated

Under the framework of cyber-physical systems (CPSs), this paper investigates the secure synchronization control problem of complex networks against asynchronous attacks. Different from the previous works that considered either connection attack on network connections or denial-of-service (DoS) attack in control (controller-to-actuator) channels, the asynchronous case of both types of attacks in complex

We investigate two aspects of network architecture design for dynamic scene deblurring: (1) Learning blur characteristics and their location in dynamic scenes, which corresponds to learning what and where to attend in the channel and spatial axes, respectively. In this regard, we design an attention-adaptive module (AAM), the innovation of which is that it adaptively determines the arrangement of channel

Nearest neighbor search using random projection trees has recently been shown to achieve superior performance, in terms of better accuracy while retrieving less number of data points, compared to locality sensitive hashing based methods. However, to achieve acceptable nearest neighbor search accuracy for large scale applications, where number of data points and/or number of features can be very large

Uni-nullnorms generalize both uninorms and nullnorms. In this paper, we investigate the migrative property for uni-nullnorms. We give a common characterization for the α-migrative uni-nullnorms over a fixed uni-nullnorm, which generalizes and unifies characterizations for migrative uninorms and migrative nullnorms to some extent.