Although metaheuristics are used for solving complex and real-world problems, they do not provide an exact solution, but only an approximate solution in feasible time. However, for very large instances, a metaheuristic may take a considerable amount of time. Therefore, we utilize a highly parallel metaheuristic to run on a modern massively parallel graphics processing unit (GPU) to further reduce the

The need to forecast COVID-19 related variables continues to be pressing as the epidemic unfolds. Different efforts have been made, with compartmental models in epidemiology and statistical models such as AutoRegressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS) or computing intelligence models. These efforts have proved useful in some instances by allowing decision makers to distinguish

To solve global optimization problems, this paper proposed a novel improved version of sine cosine algorithm—the dimension by dimension dynamic sine cosine algorithm (DDSCA). In the update equation of sine cosine algorithm (SCA), the dimension by dimension strategy evaluates the solutions in each dimension, and the greedy strategy is used to form new solutions after combined them with other dimensions

This paper proposes a heuristic method (Bayesian cosine maximization method (BCCM)) to rank the alternatives in the Analytic Hierarchy Process (AHP) synthesis, based on the multiplicative AHP model, which focuses on the revision of the pair-wise comparison matrices (PCMs) and derivation of the priority vectors from the PCMs in whole hierarchy, considering both the consistency of the PCMs and total

As an unsupervised approach of machine learning, clustering is an important method to understand and learn structural information from data. However, current adaptive clustering approach based on multi-objective genetic algorithm have two apparent limitations. The first is that prior knowledge, i.e., sample information is needed to get the correct cluster number. The second is that no effective method

In this work, a surrogate-assisted teaching-learning-based optimization algorithm is presented for high-dimensional and computationally expensive black-box optimization problems. In the presented method, a two-phase searching framework is proposed to realize the exploitation of surrogates and the metaheuristic exploration. Specifically, radial basis functions are used to build the dynamically updated

Resource-constrained project scheduling problem (RCPSP) has been one of the most important topics in project scheduling in recent decades. RCPSP, due to the strategic importance of the projects and internal and external pressures for timely completion, is a very challenging task. When executing a project, controlling and monitoring it also becomes vital. This paper aims to present a new soft computing

Multivariate time series data come as a collection of time series describing different aspects of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a challenging problem yet with numerous applications in science and engineering because anomaly scores come from the simultaneous consideration of the temporal and variable relationships. In this paper, we propose a clustering-based

It is technically difficult to accurately establish a dynamic adaptive model of an aluminium electrolysis manufacturing system (AEMS) because the system has many complex characteristics, such as multiple parameters, dynamic time variance, and a non-Gaussian distribution of process data. Inspired by the overall superiority of particle filtering theory in dealing with non-linear non-Gaussian problems

In this paper the problem of multi-synchronization with time-varying switching modes is introduced and a novel adaptive control method is designed. Unlike to the most studies, in addition to the unknown dynamics, the values of fractional-orders and control input nonlinearities are also considered to be unknown. A new fractional-order dynamic model on basis of non-singleton interval type-3 fuzzy logic

Different from single-label learning, multi-label learning has rich semantic information. Label embedding obtains the inherent intelligence of the label space by projecting the label space into a latent one. However, the label space is inevitably incomplete. The missing label data will lead to the label embedding model capturing incomplete inherent information. Therefore, label data recovery becomes

Learning human daily behavior is important for enabling robots to perform tasks and assist people. However, most prior work either requires specific sensors for capturing data or heavily relies on prior knowledge of human motion, which can be difficult to obtain. To alleviate the above problems, we propose a novel pipeline for robots to learn human behavior based on coarse-to-fine video summarization

Concerning the needs of multi-service and network performance prediction in the Internet of Things (IoT), we propose an asymmetric two-priority polling control system model, and use the neural network algorithm to predict and analyze its performance. Firstly, the mathematical model of the system in the continuous time state is established by using the embedded Markov chain theory and the probability

Based on the idea of fractional calculus, reverse accumulated generating operators of real number’s order are proposed in this paper. We claim the set of these operators forms a 1-dimensional additive real Lie group, which is isomorphic to R. As a direct consequence, the weakening buffer operators of positive real number’s order are constructed. Compared with traditional weakening buffer operators

This article presents two new hybrid swarm-human based meta-heuristic optimization algorithms benefitting from the synergy of whale optimization algorithm (WOA) and social group optimization (SGO) known as Hybrid Social Whale Optimization Algorithm (HS-WOA and HS-WOA+). HS-WOA and HS-WOA+ are hybridized combining the exploratory capabilities of WOA and convergence capabilities of SGO with a perfect

Drilling is an important means of obtaining resources. Since the complicated bottom hole conditions and coupling relationship among subsystems, it is difficult to accurately predict the drilling states and simultaneously improve drilling efficiency and safety. A novel modeling and coordinated optimization method has been developed to solve that. In the modeling part, based on the analysis of the correlation

Coronavirus disease 2019 (COVID-2019), which emerged in Wuhan, China in 2019 and has spread rapidly all over the world since the beginning of 2020, has infected millions of people and caused many deaths. For this pandemic, which is still in effect, mobilization has started all over the world, and various restrictions and precautions have been taken to prevent the spread of this disease. In addition

In recent years, many scholars have paid more attention to the service-oriented computing for Internet of Things (IoT). With the increasing smart devices are deployed in IoT, it has become a challenging problem that how to quickly combine the services provided by IoT components into a comprehensive composite service to balance energy and quality of service (QoS). In this article, a fast energy-centered

Nowadays, the most important aspects of wireless sensor networks (WSNs) are to make optimal use of and direct the limited energy of sensor nodes towards the desired application and prolong the network lifetime for that application. Although a few studies exist that have addressed these special goals, they have been mostly focused on the process of selecting cluster heads (CHs) and forwarders. No studies

Wind speed forecasting with artificial neural networks (ANNs) plays important role in safely utilizing and integrating the wind power. With the rapid updated wind speed data, however, the only way to guarantee forecasting accuracy for these ANN models is re-training from scratch with an updated training dataset. Obviously, it is an inefficient work due to the resumption of constructing the new training

In the intelligent traffic transportation, the security and stability are vital for the sustainable transportation and efficient logistics. The fault diagnosis on the catenary system is crucial for the railway transportation. For purpose of improving the detection capability for the faulted current-carrying ring and maintaining the efficiency of the railway system, a fault diagnosis method for the

DNA-binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research. Considering the high-priced

In this paper, we present an automatic coloring model for ethnic costume sketches based on a generative adversarial networks. The proposed model is composed of a 6-layer U-net structure generator and a discriminator with 5-layer convolutional neural network. Then the loss function and the weights of true reliability value in the discriminator are optimized. And, finally the constructed sketch database

Surrogate modeling is becoming a popular tool to approximate computationally-expensive simulations for complex engineering problems. In practice, there are still difficulties in surrogate modeling as follows: (1) efficient learning for functional relationship of simulation models and (2) diagnostics for the surrogate model. In order to address these difficulties simultaneously, this paper proposes

Divide-and-conquer-based (DC-based) evolutionary algorithms (EAs) have achieved notable success in dealing with large-scale optimization problems (LSOPs). However, the appealing performance of this type of algorithms generally requires a high-precision decomposition of the optimization problem, which is still a challenging task for existing decomposition methods. This study attempts to address the

The classification of biometric data is always a difficult task because these data are often unstable and depends on behavioural condition of a person. Personal keystroke activity is difficult to imitate and can be used for identity authentication. In this article, we suggest that the analysis of keystroke dynamics can dramatically increase the level of security of a computer system without disturbing

With the increasing popularity and acceptance of cloud computing, it is being applied in services like executing large-scale applications, where cloud environment is selected by the scientific associations to easily execute the computation intensive workflows. However, cloud computing can have higher failure rates due to the larger number of servers and components filled with the intensive workloads

Most existing machine learning techniques require complete data. However, incomplete patterns are common in many real-world scenarios due to the missing values (MVs). Various Missing value imputation (MVI) methods have been proposed to recover the MVs. Each of them has its own advantages in some scenarios. However, on the one hand, few of them consider taking advantages of different MVI methods; on

Blasting operations typically have several negative impacts upon human beings and constructions in adjacent region. Among all, air-overpressure (AOp) has been persistently attractive to practitioners and researchers. To control the AOp-induced damage, its strength should be predicted before conducting a blasting operation. This paper analyzes the AOp consequences through the use of the Fuzzy Delphi

Traditional job scheduling is an independent optimization phase without considering material flow among machines. However, in actual production, the machine layout will affect material flow time, and therefore affecting job completion time. In this paper, a novel multi-task-oriented production layout problem is proposed, with the goal to optimize material flow by adjusting machine placement. To solve

The hesitant fuzzy linguistic term set, which is characterized by a set of possible linguistic terms, becomes a useful tool to describe the complex cognitions of experts. In many cases, it is much more flexible for experts to provide evaluation information via pairwise comparisons rather than give their evaluation information on options directly. In this regard, the hesitant fuzzy linguistic preference

COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly between people and approaches millions of people worldwide in few months. It is very much essential to quickly and accurately identify the infected

Deep learning approaches have shown superior advantages than shallow techniques in the field of electrical load forecasting; however, their applications in existing studies encounter thorny issues despite their excellent forecasting performance: heavy computing costs due to complicated network structure and restricted to the deterministic point forecasting. This paper aims to solve above two problems

In this manuscript, a hybrid control method is implemented to model and design bidirectional DC-DC converters for the energy management of interconnected renewable energy sources (RES). The proposed hybrid control method is the joint execution of Hybrid Crow Search Algorithm (HCSA) and Radial Basis Function Neural Network (RBFNN). The modeling and design of the Bidirectional DC-DC converter modelling

High-dimensional data analysis has attracted increasingly attention in machine learning or data mining tasks. Due to the existence of irrelevant and redundant features, classification accuracy is often degraded seriously. Feature selection (FS), which aims to improve the predictive accuracy by selecting a subset of features, plays a very important role. In this paper, we proposed an improved discretization-based

Diagnosing abnormal events in nuclear power plants (NPPs) is a challenging issue given the hundreds of possible abnormal events that can occur and the thousands of plant parameters that require monitoring. This study proposes a convolutional neural network model for abnormality diagnosis in an NPP. The distinct feature of the proposed approach is the use of two-channel two-dimensional images to deal

The technical indicators are highly uncertain therefore possess greater influence on the stock market prediction. Among different techniques developed for effective prediction of the financial market the AI techniques show better prediction efficiency. In this paper, a hybrid model combined with autoencoder (AE) and kernel extreme learning machine (KELM) is proposed for further improvement in the quality

Affective state recognition of an individual is based on the emotional cues, such as the activation level. Body expression is a modal able to convey emotions and can be used for autism diagnosis through the presence of stereotyped behaviors (SBs). These behaviors are atypical and repetitive movements of the body, which can be related to a low mental health condition. The development of systems able

The neural network (NN)-based direct uncertainty quantification (UQ) methods have achieved the state of the art performance since the first inauguration, known as the lower–upper-bound estimation (LUBE) method. However, currently-available cost functions for uncertainty guided NN training are not always converging, and all converged NNs do not generate optimized prediction intervals (PIs). In recent

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building

When contagious diseases hit a city, such as MERS, SARS, and COVID-19, the problem arises as how to assign the limited supermarket resources to urban residential communities for government measures. In this study, in order to solve the assignment problem from supermarket resources to urban residential communities under the situation of the epidemic control, the discrete multi-objective particle swarm

The COVID-19 pandemic has brought lots of losses to the global economy. Within the context of COVID-19 outbreak, many emergency decision-making problems with uncertain information arose and a number of individuals were involved to solve such complicated problems. For instance, the selection of the first entry point to China is important for oversea flights during the epidemic outbreak given that reducing

To solve constrained portfolio selection model effectively, an improved quantum-behaved particle swarm optimization algorithm(LQPSO) is presented. Firstly, considering its practicality in real dealing process, a class of fuzzy portfolio models with transaction costs and background risk is established. Then in the design of improved algorithm, Lévy flight strategy and contraction-expansion coefficient

In the recent years, the fast development of e-commerce technologies made it possible for people to select the most desirable items in terms of suggested price, quality and quantity among various services, facilities, shops and stores from all around the world. However, it also made it easier for fraudsters to abuse this huge opportunity. As credit card has become the most popular mode of payment,

Due to the increasing awareness of environmental and social issues, sustainable supplier selection (SSS) becomes an important problem. In order to scientifically evaluation the SSS, the aim of this paper is to develop a novel SSS method considering the robustness and relationship among experts. Firstly, a novel concept of intuitionistic linguistic rough numbers (ILRNs) is proposed to accurately express

Video streaming, whether on demand or live, has become one of the most popular internet applications. However, financial investments required for it is a severe problem since it needs more real time storage, higher data transfer and a significant amount of computation than other kinds of multimedia data. To tackle this problem, cloud computing, offering services without investing in hardware or software

Existing optimal control systems for vehicles that consider the effect of road slopes use a cost function with fixed weights related to speed deviation, regardless of driving states on slopes. As a result, gravitational potential energy is not efficiently exploited and braking at down-slopes (which wastes energy) becomes unavoidable. Thus, there is still significant scope to improve fuel saving behavior

The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports

In this study, a novel metaheuristic algorithm, namely, carnivorous plant algorithm (CPA), inspired by how the carnivorous plants adapting to survive in the harsh environment, was proposed. The CPA was first evaluated on thirty well-known benchmark functions with different characteristics and seven CEC 2017 test functions. Its convergence characteristic and computational time were analysed and compared

In general fuzzy system, the nature of a fuzzy number is usually viewed as specific and deterministic but, in fuzzy reasoning the membership function is developed in a randomised sense. Here, we have studied the concept of fuzzy approximate reasoning over the modelling of a cost minimization classical economic order quantity (EOQ) inventory management problem. We have developed the model where all

Accurate diagnosis and segmentation of skin lesion is critical for early detection and diagnosis of skin cancer. Recent multi-task learning methods require expensive annotations for skin lesion analysis while single-task driven models cannot fully utilize the potential knowledge. The aim of this study is to utilize the neglected knowledge by a flexible architecture in dermoscopy skin lesion classification

The demand for natural gas is expected to continuously increase due to its significant role in the transition towards a low-carbon energy structure. Based on the nonhomogeneous grey model, a new method for estimating natural gas production and consumption is developed, namely, the conformable fractional nonhomogeneous grey Bernoulli model (denoted as CFNHGBM(1,1,k) for short). In the new method, the

Elderly care is a significant livelihood project in the increasingly serious aging society. Nowadays, the wider application of the Internet of Things (IoT) technology on assistant means has making an importance contribution to elderly care in institutions and at home. On the broader data foundation collected by IoT devices, Human Activity Recognition (HAR) with its high demand in various elderly care

Resource assignment is one of the emerging research area in the cloud scenario. Cloud computing provides a shared pool of resources in a distributed environment. It supports the features of utility-based computing. Efficient task provisioning on virtual machines is the major concern in an extensible cloud computing environment. The task provisioning minimizes the performance metrics total completion

With the continuous advancement of intelligent manufacturing and industry 4.0, production scheduling has become a significant problem that most enterprises must deal with. Thereinto, (hybrid) multi-objective flexible job-shop scheduling problem, widely existing in the real-life manufacturing systems, is one of the NP-hard problems in various scheduling problems. Consequently, in this paper, an improved

The intelligent decision making of multi-energy management in a microgrid is a non-trivial task due to the intermittent and stochastic nature of highly penetrated renewable energy sources and demand. To address such a challenge, the energy management system often adopts the prediction based day-ahead energy scheduling and real-time energy dispatch to optimally coordinate the operation of dispatchable

Recent years, cross-domain fault diagnosis of rotating machinery has been a hot topic, and various kinds of methods taking advantage of transfer learning are proposed correspondingly. Despite their success, they mainly focus on marginal distribution alignments, which ignore weighing between marginal and conditional distributions in network training. However, this kind of weighting can boost diagnosis

Face image retrieval underpins numerous applications in many computer vision domains, however facial appearance variations including age, gender and race make this task challenging. Prior art methods rely on geometric properties and relationship between local features. However, their performance is still short of what is needed, mainly because (1) they ignore the demographic information, and (2) lack