In the era of Internet of things (IoT), network Connection of an enormous number of agriculture machines and service centers is an expectation. However, it will be with a generation of massive volume of data, thus overwhelming the network traffic and storage system especially when manufacturers give maintenance service typically by various data analytic applications on the cloud. The situation is more

Using the concept of swap operation and swap sequence on the sequence of paths of a Traveling Salesman Problem(TSP) Artificial Bee Colony (ABC) algorithm is modified to solve multi-objective TSP. The fitness of a solution is determined using a rule following the dominance property of a multi-objective optimization problem. This fitness is used for the selection process of the onlooker bee phase of

In the past few months, several works were published in regards to the dynamics and early detection of COVID-19 via mathematical modeling and Artificial intelligence (AI). The aim of this work is to provide the research community with comprehensive overview of the methods used in these studies as well as a compendium of available open source datasets in regards to COVID-19. In all, 61 journal articles

Symbolic regression is a process to find a mathematical expression that represents the relationship between a set of explanatory variables and a measured variable. It has become a best-known problem for GP (genetic programming), as GP can use the tree representation to represent solutions as expression trees. Since the success of memetic algorithms (MAs (Memetic algorithms (MAs) can be regarded as

Network embedding is an effective method aiming to learn the low-dimensional vector representation of nodes in networks, which has been widely used in various network analytic tasks such as node classification, node clustering, and link prediction. The objective of network embedding is to capture the structural information and inherent characteristics of the network as much as possible in the low-dimensional

This article offers a new physical-based meta-heuristic optimization algorithm, which is named Transient Search Optimization (TSO) algorithm. This algorithm is inspired by the transient behavior of switched electrical circuits that include storage elements such as inductance and capacitance. The exploration and exploitation of the TSO algorithm are verified by using twenty-three benchmark, where its

Knowledge tracing (KT) is essential for adaptive learning to obtain learners’ current states of knowledge for the purpose of providing adaptive service. Generally, the knowledge construction procedure is constantly evolving because students dynamically learn and forget over time. Unfortunately, to the best of our knowledge most existing approaches consider only a fragment of the information that relates

This paper investigates the problem of the stock closing price forecasting for the stock market. Based on existing two-stage fusion models in the literature, two new prediction models based on clustering have been proposed, where k-means clustering method is adopted to cluster several common technical indicators. In addition, ensemble learning has also been applied to improve the prediction accuracy

Traditional computational techniques are recently being improved with the use of prior biological knowledge from open-access repositories in the area of gene expression data analysis. In this work, we propose the use of prior knowledge as heuristic in an inference method of gene-gene associations from gene expression profiles. In this paper, we use Gene Ontology, which is an open-access ontology where

Deceptive reviews on Web are a common phenomenon and how to detect them has a very important impact on products, services, and even business policies. In order to filter out deceptive reviews more accurately, a new model called Sentence Joint Topic Sentiment Model (SJTSM) is presented in this paper, which incorporates the sentence structure of reviews and the sentiment label information of words based

Multi-sensor data fusion (MSDF) problems have attracted widespread attention recently. However, it is still an open issue about how to make the fusion process effectively even if the collected data conflict due to several unpredictable reasons. Moreover, most existing approaches mainly concentrated on the distinction of evidence sources, which cannot well consider the feature of individual belief degree

Since recommended treatment for Non-small cell lung cancer (NSCLC) after surgery is chemotherapy, the prediction of effectiveness or futileness of adjuvant chemotherapy (ACT) in early stage is important for future decision. Classification of NSCLC in gene expression data is performed to predict effectiveness or futileness of ACT. Selection of genes highly correlated with the class attribute, affects

Set based image classification technology has been developed successfully in recent decades. Previous approaches dispose set based image classification by employing all the gallery sets to learn metrics or construct the model using a typical number of parameters. However, they are based on the assumption that the global structure is consistent with the local structure, which is rigid in real applications

In the anti-jamming field of radio communication, the problem of online and multiclass jamming recognition is fundamental to implement reasonable anti-jamming measures. The incremental small sphere and large margin (IncSSLM) is proposed, this model can learn the compact boundary for own communication signals and known jamming, which relieves the open-set problem of radio data. Meanwhile it can also

Automatic image annotation is a crucial area in computer vision, which plays a significant role in image retrieval, image description, and so on. Along with the internet technique developing, there are numerous images posted on the web, resulting in the fact that it is a challenge to annotate images only by humans. Hence, many computer vision researchers are interested in automatic image annotation

Many day-to-day operation decisions in a smart city need short term load forecasting (STLF) of its customers. STLF is a challenging task because the forecasting accuracy is affected by external factors whose relationships are usually complex and nonlinear. In this paper, a novel hybrid forecasting algorithm is proposed. The proposed hybrid forecasting method is based on locally weighted support vector

The position information of aspect is essential and useful for aspect-based sentiment analysis, while how to model the position of the aspect effectively during aspect-based sentiment analysis has not been well studied. Inspired by the intuition that the position prediction can help boost the performance of aspect-based sentiment analysis, we propose a D eep M ulti-T ask L earning (DMTL) model, which

Knowledge base completion has been an active research topic for knowledge graph. However, existing methods are of low learning and generalization abilities, and neglect the rich internal logic between entities and relationships. To solve the above problems, this paper proposes the modeling of complex internal logic for knowledge base completion. This method first integrates the semantic information

Whale optimization algorithm (WOA) is a recently proposed meta-heuristic algorithm which imitates the hunting behavior of humpback whales. Due to its characteristic advantages, it has found its place in the mature population-based methods in many scientific and engineering fields. Because WOA was proposed for continuous optimization, it cannot be directly used to solve discrete optimization problems

Due to the limitation of assessors’ knowledge and the uncertainty of risks, risk assessment data reasonably are given in the form of linguistic terms, safety risk assessment in petrochemical industry is often a multi-criterion and multi-expert information fusion based on linguistic terms(MCMEIF-LT) problem. A novel model dealing with the MCMEIF-LT problem is presented in this paper. Firstly, the individual

Multi-objective reinforcement learning (MORL) algorithms aim to approximate the Pareto frontier uniformly in multi-objective decision making problems. In the scenario of deep reinforcement learning (RL), gradient-based methods are often adopted to learn deep policies/value functions due to the fast convergence speed, while pure gradient-based methods can not guarantee a uniformly approximated Pareto

Object tracking is a significant problem of computer vision due to challenging environmental variations. Single cue appearance model is not sufficient to handle the variations. To this end, we propose a multi-cue tracking framework in which complementary cues namely, LBP and HOG were exploited to develop a robust appearance model. The proposed feature fusion captures the high-level relationship between

In today’s interconnected society, cyberattacks have become more frequent and sophisticated, and existing intrusion detection systems may not be adequate in the complex cyberthreat landscape. For instance, existing intrusion detection systems may have overfitting, low classification accuracy, and high false positive rate (FPR) when faced with significantly large volume and variety of network data.

Hyperspectral (HS) images captured from Earth by satellite and aircraft have become increasingly important in several environmental and ecological contexts (e.g. agriculture and urban areas). In the present study we propose an iterative learning methodology for the change detection of HS scenes taken at different times in the same areas. It cascades clustering and classification through iterative learning

Brain storm optimization is a swarm intelligence algorithm inspired by the brainstorming process in human beings. Many researchers have paid much more attention to it, and many attempts have been made to improve it’s performance. The search ability of brain storm optimization is maintained by the creating process of ideas, but it still suffers from sticking into stagnation during exploitation phase

According to the problem that the multi-scale vehicle objects in traffic surveillance video are difficult to detect and the overlapping objects are prone to missed detection, an improved vehicle object detection method based on YOLOv3 was proposed. In order to extract feature more efficiently, we first use the inverted residuals technique to improve the convolutional layer of YOLOv3. To solve the multi-scale

In this paper, a novel zig-zag scan-based feedback convolution algorithm for image encryption against differential attacks is proposed. The two measures Number of Pixel Change Rate (NPCR) and Unified Average Changed Intensity (UACI) are commonly utilized for analyzing the differential attacks. From the study of the existing papers, even though high Number of Pixel Change Rate and Unified Average Changed

Online learning represents an important family of efficient and scalable algorithms for large-scale classification problems. Many of them are linear with fast computational speed, but when faced with complex classification, they more likely have low accuracies. In order to improve accuracies, kernel trick is applied, however, it often brings high computational cost. In fact, discriminative information

The recent advances in deep convolutional neural networks (DCNNs) have convincingly demonstrated high-capability reconstruction for single image super-resolution (SR). However, it is a big challenge for most DCNNs-based SR models when the scaling factor increases. In this paper, we propose a novel Progressive Residual Network (PRNet) to integrate hierarchical and scale features for single image SR

Rain streaks usually give rise to visual degradation and cause many computer vision algorithms to fail. So it is necessary to develop an effective deraining algorithm as preprocess of high-level vision tasks. In this paper, we propose a novel deep learning based deraining method. Specifically, the multi-scale kernels and feature maps are both important for single image deraining. However, the previous

Instance selection aims to search for the best patterns in the training set and main instance selection methods include condensation methods, edition methods and hybrid methods. Hybrid methods combine advantages of both edition methods and condensation methods. Nevertheless, most of existing hybrid approaches heavily rely on parameters and are relatively time-consuming, resulting in the performance

In this paper, a novel self-organizing fuzzy neural network with an adaptive learning algorithm (SOFNN-ALA) for nonlinear system modeling and identification in industrial processes is proposed. To efficiently enhance the generalization capability, the proposed SOFNN-ALA is designed by using both structure identification and parameter estimation simultaneously in the learning process. In the structure

Selfish herd optimizer is a new and effective continuous optimization algorithm, but the algorithm cannot solve the specific discrete problem. Therefore, we design a discrete selfish herd optimizer (DSHO) specifically for solving graph coloring problems. At present, many existing methods cannot effectively solve graph coloring problems. To deal with this difficult problem, in DSHO, we propose an effective

Extreme learning machine (ELM) and its variants have been widely used in the field of object recognition and other complex classification tasks. Traditional deep learning architectures like Convolutional Neural Network (CNN) are capable of extracting high-level features, which are the key for the models to make right decisions. However, traditional deep architectures are confronted with solving a tough

In this paper an action recognition method that can adaptively handle the problems of variations in camera viewpoint is introduced. Our contribution is three-fold. First, a space-sampling algorithm based on affine transform in multiple scales is proposed to yield a series of different viewpoints from a single one. A histogram of dense optical flow is then extracted over each fixed-size patch for a

This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantum-inspired evolutionary models to automatically configure the ensemble members and combine their output. The quantum-inspired evolutionary models identify the most appropriate

Mining uncertain frequent patterns (UFPs) from uncertain databases was recently introduced, and there are various approaches to solve this problem in the last decade. However, systems are often faced with the problem of too many UFPs being discovered by the traditional approaches to this issue, and thus will spend a lot of time and resources to rank and find the most promising patterns. Therefore,

Abductive inference derives explanations for encountered anomalies and thus embodies a natural approach for diagnostic reasoning. Yet its computational complexity, which is inherent to the expressiveness of the underlying theory, remains a disadvantage. Even when restricting the representation to Horn formulae the problem is NP-complete. Hence, finding procedures that can efficiently solve abductive

User-based collaborative filtering is widely used in recommendation systems, which normally comprises three steps: (1) finding the nearest conceptual neighbors, (2) aggregating the neighbors’ ratings to predict the ratings of unrated items, and (3) generating recommendations based on the prediction. Existing algorithms mainly focus on steps 1 and 3 but neglect subtle treatments of aggregating neighbors’

Short text stream clustering has become an important problem for mining textual data in diverse social media platforms (e.g., Twitter). However, most of the existing clustering methods (e.g., LDA and PLSA) are developed based on the assumption of a static corpus of long texts, while little attention has been given to short text streams. Different from the long texts, the clustering of short texts is

This paper is designed based on the mathematical models for bi-level programming in Stackelberg game under type-2 fuzzy environment. The parameters of the objective functions on both levels are considered as type-2 fuzzy numbers in the first case whereas the parameters of the objective functions and the constraints are chosen as type-2 fuzzy numbers in the second case. Critical value based reduction

Differential evolution (DE) is an efficient population-based search algorithm with good robustness, but it faces challenges in dealing with Large-Scale Global Optimization (LSGO). In this paper, we proposed an improved multi-population differential evolution with best-random mutation strategy (called mDE-brM). The population is divided into three sub-populations based on the fitness values, each sub-population

With the explosion of unlabelled and high-dimensional data, unsupervised feature selection has become an critical and challenging problem in machine learning. Recently, data representation based model has been successfully deployed for unsupervised feature selection, which defines feature importance as the capability to represent original data via a reconstruction function. However, most existing algorithms

Inthis article, wefocus on the classification problem to semi-supervised learning in non-stationary environment. Semi-supervised learning is a learning task from both labeled and unlabeled data points. There are several approaches to semi-supervised learning in stationary environment which are not applicable directly for data streams. We propose a novel semi-supervised learning algorithm, named STDS

In video-on-demand (VoD) services, large volumes of digital data are kept at hubs which are spatially distributed over large geographic areas and users are connected to these hubs based on their demands. In this article, we consider a large database of video files, that are pre-partitioned to multiple segments based on the demand patterns of users. These segments are restricted to be located only in

In order to carry out machine fault diagnosis earlier and more accurately such as the automatic detection for the ship’s level scale, and the existing literatures didn’t discuss these until now. However, for solving the problem, this paper presents a semigroup of fuzzy automata and its properties, constructs a fuzzy inference system on the semigroup of fuzzy automata, and discusses its application

Among the existing clustering algorithms, the k-Means algorithm is one of the most commonly used clustering methods. As an extension of the k-Means algorithm, the k-Modes algorithm has been widely applied to categorical data clustering by replacing means with modes. However, there are more mixed-type data containing categorical, ordinal and numerical attributes. Mixed-type data clustering problem has

Abstract The alignment and fusion of knowledge graphs have been at entity alignment and fusion which match and align knowledge graphs (KGs) by measuring similarity of the entities in KGs. Nevertheless, false fusion of completely different KGs can be easily caused if only considering entity similarity but ignoring entity relationship similarity. This paper focuses on entity relationship similarity model

Abstract Multimodal Biometric Systems are extensively employed over unimodal counterparts for user authentication in the digital world. However, the application of multimodal systems to security-critical applications is limited mainly due to non-adaptiveness of these systems to the dynamic environment and inability to distinguish between spoofing attack and the noisy input image. In order to address

Abstract Feature representation is generally applied to reducing the dimensions of high-dimensional data to accelerate the process of data handling and enhance the performance of pattern recognition. However, the dimensionality of data nowadays appears to be a rapidly increasing trend. Existing unsupervised feature representation methods are susceptible to the rapidly increasing dimensionality of data

Abstract Existing smoky vehicle detection methods are vulnerable to false alarms because of the continuous interferences from common passed vehicles and the complex characteristics of smoke. This paper presents a video smoky vehicle detection method based on dynamic features. Three groups of features, including Multi-Sequence Integral Projection (MS-IP), Center-Symmetric Local Binary Patterns on Three

Abstract Distracted driver action is the main cause of road traffic crashes, which threatens the security of human life and public property. Based on the observation that cues (like the hand holding the cigarette) reveal what the driver is doing, a driver action recognition model is proposed, which is called deformable and dilated Faster R-CNN (DD-RCNN). Our approach utilizes the detection of motion-specific

Abstract Semantic segmentation can be considered as a per-pixel localization and classification problem, which gives a meaningful label to each pixel in an input image. Deep convolutional neural networks have made extremely successful in semantic segmentation in recent years. However, some challenges still exist. The first challenge task is that most current networks are complex and it is hard to deploy

Abstract Noise leads to the ambiguity in regions of interest detection by corrupting the pixel information and is a vital problem in image processing domain. A novel hybrid technique based on fuzzy filtering and fuzzy thresholding is proposed here to extract the object regions accurately in presence of Gaussian noises. The proposed method is automated, does not need any parameter tuning as well does

Abstract The Sine Cosine Algorithm (SCA) is a recently developed efficient metaheuristic algorithm to find the solution of global optimization problems. However, in some circumstances, this algorithm suffers the problem of low exploitation, skipping of true solutions and insufficient balance between exploration and exploitation. Therefore, the present paper aims to alleviate these issues from SCA by

Abstract In the current paper, a novel approach is proposed for bi-clustering of gene expression data using the fusion of differential evolution framework and self-organizing map (SOM), named as BiClustSMEA. Variable number of gene and condition cluster centers are encoded in different solutions of the population to determine the number of bi-clusters from a dataset in an automated way. The concept

Abstract High quality crowd density maps preserve a large amount of spatial information of crowd distribution, which provides significant priori information for the field of crowd behavior analysis and anomaly detection. Recent work on crowd density estimation pays more attention to the accuracy of crowd counting, ignoring the quality of crowd density map estimation. Hence, in this paper, we propose

Abstract Intuitionistic Fuzzy Sets is one of the most influential extension and development of Zadeh’s fuzzy set theory. It has strong performance in dealing with uncertain information, while taking into account information on membership degree, non-membership degree and hesitation degree. In this paper, a new loose Pareto dominant relationship named intuitionistic fuzzy dominance is adopted to research

Abstract The aim of the subspace clustering is to segment the high-dimensional data into the corresponding subspace. The structured sparse subspace clustering and the block diagonal representation clustering are quite advanced spectral-type subspace clustering algorithms when handling to the linear subspaces. In this paper, the respective advantages of these two algorithms are fully exploited, and

Abstract Supplier selection can be regarded as a classic multiple criteria decision making (MCDM) problem. To a great extent, experts’ evaluations play a decisive role in the decision-making process. There will inevitable exist a variety of indefinite factors, which result from imprecision, uncertainty, and fuzziness due to the subjective judgment of human beings. As an effective tool to express uncertain