A novel method named probabilistic multimodal optimization (PMO) algorithm is proposed in this paper to competently optimize noisy objective function. In the PMO algorithm, we propose two new strategies to make the algorithm have the capability of probability prediction and multiple extreme points optimization. The first strategy is concerned with the partition strategy of search space based on Buffon

This paper proposes a hybrid soft computing approach that integrates the Dempster–Shafer (D–S) evidence theory and cluster analysis for probabilistic risk analysis in complex projects under uncertainty. The fusion model tends to solve multi-criteria decision-making problems with a focus on the information content reflected from evidence. Risk factors are quantified into a continuous numeric scale for

Graph coloring is one of the most studied NP-hard problems with a wide range of applications. In this work, the first solution-driven multilevel algorithm for this computationally challenging problem is investigated. Following the general idea of multilevel optimization, the proposed algorithm combines an original solution-driven coarsening procedure with an uncoarsening procedure as well as an effective

Taking actions to prevent the occurrence of failure in advance, rather than improving reliability through post-mortem testing is crucial for improvement of products’ quality and efficiency. As a typical prevention reliability analysis method, failure mode and effects analysis (FMEA) has an innate advantage in conducting this improvement. However, traditional FMEA also contains some deficiencies in

The novel coronavirus termed as covid-19 has taken the world by its crutches affecting innumerable lives with devastating impact on the global economy and public health. One of the major ways to control the spread of this disease is identification in the initial stage, so that isolation and treatment could be initiated. Due to the lack of automated auxiliary diagnostic medical tools, availability of

The current universally challenging SARS-COV-2 pandemic has transcended all the social, logical, economic, and mortal boundaries regarding global operations. Although myriad global societies tried to address this issue, most of the employed efforts seem superficial and failed to deal with the problem, especially in the healthcare sector. On the other hand, the Internet of Things (IoT) has enabled healthcare

Just-in-time production in large enterprises along with the factory’s limited space highlights the need for scheduling tools that consider blocking conditions. This study contributes to the scheduling literature by developing an effective metaheuristic to address the Blocking Flowshop Scheduling Problems with Sequence-Dependent Setup-Times (BFSP with SDSTs). Including a new constructive heuristic and

Crowdsourced indoor localization methods have grasped much attention in recent years as a method of reducing the cost of constructing the fingerprint database. In a crowdsourcing environment, however, the localization system is vulnerable to malicious attacks, which possibly lead to serious localization errors. In this paper, we conclude the potential attacks during fingerprint database updates and

Biclustering is an unsupervised classification technique that plays an increasingly important role in the study of modern biology. This data mining technique has provided answers to several challenges raised by the analysis of biological data and more particularly the analysis of gene expression data. It aims to cluster simultaneously genes and conditions. These unsupervised techniques are based essentially

The comprehensive intention of this paper is to evaluate the optimal welding parameters for joining two dissimilar materials by friction stir welding (FSW) process which is termed as a green manufacturing technology in order to generate quality joints. Conventionally, the optimization of process parameters experimentally is carried out by a time-consuming trial and error technique. Also, the effect

Accurately predicting the remaining useful life of lithium-ion batteries is critical to battery health management systems. Aiming at the problems of low long-term prediction accuracy, unstable model output, and difficult key parameter selection, this paper proposes a self-adaptive differential evolution optimized monotonic echo state network prediction method. First, we analyze the life decay characteristics

This paper develops a hybrid approach combining the neural network and the nonlinear filtering to model and predict terrain profiles for both air and ground vehicles. To simplify the neural network structures and reduce the number of synaptic weights and biases, the multiplicative neuron model (MNM) is utilized to describe the relationship between the unknown elevation ahead and the last few height

In this investigation, nature-inspired heuristic strategy exploiting moth flame optimization (MFO) algorithm combined with active-set algorithm (ASA), interior point algorithm (IPA) and sequential quadratic programming (SQP) are presented to take care of the enhancement issues of economic load dispatch (ELD) problem involving valve point loading effect (VPLE) and stochastic wind (SW). The strength

Multi-label rank support vector machine (RankSVM) is an effective technique to deal with multi-label classification problems, which has been widely used in various fields. However, it is sensitive to noise points and can not delete redundant features for high dimensional problems. Therefore, to address the above two limitations, a sparse elastic net multi-label RankSVM with pinball loss (pin-ENR) is

Data-driven health prognostic is attracting more and more attention to machinery prognostic and health management. It enables machinery to realize predictive maintenance and rarely depends on prior knowledge of degradation mechanisms. However, cross-domain health prognostic may lack enough measured data as supports, and this bottleneck is particularly prominent in high-end manufacturing. As such, this

In machine learning, reducing the complexity of a model can help to improve its computational efficiency and avoid overfitting. In genetic programming (GP), the model complexity reduction is often achieved by reducing the size of evolved expressions. However, previous studies have demonstrated that the expression size reduction does not necessarily prevent model overfitting. Therefore, this paper uses

In ensemble learning, it is necessary to build a balancing mechanism to balance the accuracy of individual learners with the diversity between individual learners to achieve excellent ensemble learning performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this

A reliable prediction of CO2 emissions from marine fleets plays an important role in the low carbon development of shipping industry. However, CO2 emissions from marine fleets have its own inherent trends and the influencing factors might have interaction effects, and these problems make it difficult to build an accurate prediction model. To this end, a novel multivariable trend interaction grey model

With increasing demand for using robotic manipulators in industrial applications, controllers specific for performing repeatable tasks are required. These controllers must also be robust to model uncertainties. To address this research issue, a repetitive learning control method fused with adaptive fuzzy logic techniques is designed. Specifically, modeling uncertainties are first modeled with a fuzzy

In real-world machine learning applications, unlabeled training data are readily available, but labeled data are expensive and hard to obtain. Therefore, semi-supervised learning algorithms have gathered much attention. Previous studies in this area mainly focused on a semi-supervised classification problem, whereas semi-supervised regression has received less attention. In this paper, we proposed

To efficiently increase the conversion of solar energy into electricity, it is vitally important to find the appropriate equivalent circuit parameters to execute the modeling, evaluation, and maximum power point tracking on photovoltaic (PV) systems in high quality and efficiency. In this study, an enhanced JAYA (EJAYA) algorithm is proposed for accurately and efficiently estimating the PV system parameters

This paper describes a class of novel initializations in Deterministic Particle Swarm Optimization (DPSO) for approximately solving costly unconstrained global optimization problems. The initializations are based on choosing specific dense initial positions and velocities for particles. These choices tend to induce in some sense orthogonality of particles’ trajectories, in the early iterations, in

In the complex environment of telemedicine, Electroencephalogram (EEG) signals are easily overwhelmed by noise, which affects the intelligent diagnosis of diseases. Since the time-frequency domain characteristics of some noise in EEG signals are complex the distribution is unknown, and the spectrum of some noise overlaps with the original EEG signal spectrum, it is difficult to filter those noise by

Traditional machine learning methods may not yield satisfactory generalization capability when samples in different classes are imbalanced. These methods tend to sacrifice the accuracy of the minority class to improve the overall accuracy without regarding the fact that misclassifications of minority samples usually costs more in many real world applications. Therefore, we propose a neural network

Assessing and ranking private health insurance companies provides insurance agencies, insurance customers, and authorities with a reliable instrument for the insurance decision-making process. Moreover, because the world’s insurance sector suffers from a gap of evaluation of private health insurance companies during the COVID-19 outbreak, the need for a reliable, useful, and comprehensive decision

In this paper, we present a clustering model for energy optimization based on the nature-inspired behaviour of animals. This clustering model finds the optimal distance to send data packets from one location to another, either long or short distances, so as to maintain the lifetime of the sensor network. The challenge with sensor networks is how to balance the energy load, which can be achieved by

Network representation learning endeavors to learn low-dimensional dense representations for nodes in a network. With the rapid development of online social platforms, the analysis of social networks has become increasingly significant. Although network representation learning can facilitate the social network analysis, most existing algorithms merely exploit the explicit structure among nodes to obtain

COVID-19 is a global pandemic that aroused the interest of scientists to prevent it and design a drug for it. Nowadays, presenting intelligent biological data analysis tools at a low cost is important to analyze the biological structure of COVID-19. The global alignment algorithm is one of the important bioinformatics tools that measure the most accurate similarity between a pair of biological sequences

In this paper, a higher-order terminal sliding mode control is proposed for fault accommodation of a class of lipschitz second-order nonlinear systems. This approach is designed based on a combining between a novel third-order fast terminal sliding mode surface (TOFTSMS), which is designed to preserve the merits of the PID sliding surface and the fast terminal sliding mode (FTSM) surface, and a continuous

The proof development process in interactive theorem provers (ITPs) requires the users to manually search for proofs by interacting with proof assistants. The activity of finding the correct proofs can become quite cumbersome and time consuming for users. To make the proof searching process easier in proof assistants, we provide an evolutionary/heuristic-based framework. The basic idea for the framework

The rapid growth of the COVID-19 pandemic in the world and the importance of controlling it in all regions have made managing this crisis a great challenge for all countries. In addition to imposing various monetary costs on countries, this pandemic has left many serious damages and casualties. Proper control of this crisis will provide better medical services. Controlling travel and tourists in this

Background: The ongoing fight with Novel Corona Virus, getting quick treatment, and rapid diagnosis reports have become an act of high priority. With millions getting infected daily and a fatality rate of 2%, we made it our motive to contribute a little to solve this real-world problem by accomplishing a significant and substantial method for diagnosing COVID-19 patients. Aim: The Exponential growth

In this paper, we propose a novel multiple attribute group decision making (MAGDM) method based on the correlation coefficient and hesitancy degrees under interval-valued intuitionistic fuzzy environment. Firstly, the conception of individual hesitancy degree and group hesitancy degree are defined to ensure the effective communication of information among group members and overcome the restrictions

Public–private partnership (PPP) is increasingly encouraged to deliver public services in developing countries. Many studies have been conducted to identify factors that affect PPP contract failure. Although a country’s PPP experience is of great importance in controlling the contract failure rate, most of the current studies are based on a qualitative perspective. This research develops a data-driven

As a new computing technology proposed with the development of 5G, IoT technologies and increasing requirement of mobile applications and services, edge computing enables mobile application developers and content providers to serve context-aware mobile services (e.g., mobile app recommendation). Mobile app recommendation is known as an effective solution to overcome the information overload in mobile

Recognizing human activities has become a trend in smart surveillance that contains several challenges, such as performing effective analyses of huge video data streams, while maintaining low computational complexity, and performing this task in real-time. Current activity recognition techniques are using convolutional neural network (CNN) models with computationally complex classifiers, creating hurdles

Segmentation of 3D micro-Computed Tomographic (μCT) images of rock samples is essential for further Digital Rock Physics (DRP) analysis, however, conventional methods such as thresholding and watershed segmentation are susceptible to user-bias. Deep Convolutional Neural Networks (CNNs) have produced accurate pixelwise semantic (multi-category) segmentation results with natural images and μCT rock images

The growing popularity of social networks and their easy acceptance of new users have the unintended consequence of fostering an environment where anonymous users can act in malicious ways. Although these platforms have many incentives to prevent such occurrences, they have not been able to cope with the sheer volume of information that must be processed. Moreover, the tendency of attackers to rapidly

This paper presents a minimalistic reasoning algorithm to solve imprecise instance retrieval in fuzzy ontologies with application to querying Building Information Models (BIMs)—a knowledge representation formalism used in the construction industry. Our proposal is based on a novel lossless reduction of fuzzy to crisp reasoning tasks, which can be processed by any Description Logics reasoner. We implemented

Missing Values Management is one of the challenges faced by Data Analysts. Therefore, the creation of effective data models will be the right decision for missing data imputation. However, learning, training, and Data Analysis must be implemented through machine learning algorithms. Missing Data is a problem with no feedback or variables. This problem (missing data) can result in serious Data Analysis

The analytic hierarchy process (AHP) is widely used as a multi-criteria decision-making method in practical applications. Several researchers have expanded the AHP method to D numbers AHP (D-AHP) to apply AHP to an uncertain decision-making environment. D numbers is an extension of the Dempster–Shafer (D–S) theory, which overcomes the shortcomings of the D–S theory and can effectively express uncertain

Document classification is one of the predominant tasks in Natural Language Processing. However, some document classification tasks do not have ground truth while other similar datasets may have ground truth. Transfer learning can utilize similar datasets with ground truth to train effective classifiers on the dataset without ground truth. This paper introduces a transductive transfer learning method

Metaheuristic algorithms are important methods to solve optimization problems and maintaining a balance between the global exploration and local exploitation is crucial to the performance of such algorithms. We propose a self-adaptive multi strategy cuckoo search algorithm (MSACS) based on the cuckoo search algorithm (CS). First, five different search strategies were proposed to calculate the use probability

For solving the knapsack problem with a single continuous variable (KPC), a binary team game algorithm (TGA) with one-way mutation strategy is proposed. Firstly, without changing the evolution mode of TGA, three basic operations are reconstructed based on modulo 2 operation. Then, a binary TGA (BTGA) suitable for solving binary optimization problem is proposed. In order to use BTGA to solve KPC problem

Bow-Tie is an efficient and emerging safety management tool to identify the root causes of an accident. Conventional quantification approaches for bow-tie suffer from a lack of information, insufficient data, and uncertainty. Therefore, experts’ views and knowledge about the occurrence probability of the basic events and the success probability of safety measures can be utilized to deal with the lack

This paper revisits interval-valued fuzzy regression and proposes a new unified framework to address interval-valued type-1 and type-2 fuzzy regression models. The paper focuses on two main objectives. First, some philosophical and methodological reflections about interval-valued type-1 fuzzy regression (IV-T1FR) and interval-valued type-2 fuzzy regression (IV-T2FR) are discussed and analyzed. These

Emotion recognition has become one of the most active research areas in pattern recognition due to the emergence of human–machine interaction systems. Describing facial expression is a very challenging problem since it relies on the quality of the face representation. A multitude of features have been proposed in the literature to describe facial expression. None of these features is universal for

This work aims at intensifying text-independent speaker identification performance in real application situations such as noisy and emotional talking conditions. This is achieved by incorporating two different modules: a Computational Auditory Scene Analysis (CASA) based pre-processing module for noise reduction and “cascaded Gaussian Mixture Model – Convolutional Neural Network (GMM-CNN) classifier

Most countries are reopening or considering lifting the stringent prevention policies such as lockdowns, consequently, daily coronavirus disease (COVID-19) cases (confirmed, recovered and deaths) are increasing significantly. As of July 25th, there are 16.5 million global cumulative confirmed cases, 9.4 million cumulative recovered cases and 0.65 million deaths. There is a tremendous necessity of supervising

Continuum manipulators are a new generation of robotic systems that possess infinite number of degrees of freedom associated with inherent compliance, unlike traditional robotic manipulators which consist of a finite number of rigid links. Because of this characteristic, controlling continuum manipulators is more complicated and difficult based on only traditional control theory. Soft computing techniques

Multi-objectivization is a term used to describe strategies developed for optimizing single-objective problems by multi-objective algorithms. This paper focuses on multi-objectivizing the sum-of-the-parts combinatorial optimization problems, which include the traveling salesman problem, the unconstrained binary quadratic programming and other well-known combinatorial optimization problem. For a sum-of-the-parts

Unlike other linguistic modellings, probabilistic linguistic terms can clearly describe the importance of different linguistic terms. With respect to group decision-making (GDM) problems, it is convenient for experts to express their evaluation opinions with probabilistic linguistic preference relations (PLPRs), which can transform experts’ quantitative descriptions into qualitative probabilistic linguistic

This paper studies the integration of the vehicle routing problem with cross-docking (VRPCD). The aim is to find a set of routes to deliver products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic based on column

A hydraulic jump is an abrupt transition between subcritical and supercritical flows which is associated with energy dissipation, air entrainment, spray, splashing, and surface waves. Both physical and numerical modeling were largely applied to study hydrodynamics, turbulence and air-entrainment in the hydraulic jump, while the literature about the application of classifier models is quite limited

Caching and offloading in Mobile Edge Computing (MEC) are hot topics recently. Existing caching strategies at the edge ignore the programming ability of edge network and design strategies independently thus network resource is under utilization and the quality of experience (QOE) for end users is far from satisfactory. In this paper, we design intelligently joint caching and offloading strategies under