The linguistic Pythagorean fuzzy sets (LPFSs), which employ linguistic terms to express membership and non-membership degrees, can effectively deal with decision makers’ complicated evaluation values in the process of multiple attribute group decision-making (MAGDM). To improve the ability of LPFSs in depicting fuzzy information, this paper generalized LPFSs to cubic LPFSs (CLPFSs) and studied CLPFSs-based

Type-2 fuzzy sets (T2FSs) can deal with higher modeling and uncertainties which exist in the real-world application, specifically in the control systems. Particularly the climate changes are always uncertain and thus, the type-2 fuzzy controller is an effective system to handle those situations. Polyhouse is a methodology used to cultivate the plants. It breaks the seasonal hurdle of the formulation

Emotion recognition system from speech signal is a widely researched topic in the design of the Human–Computer Interface (HCI) models, since it provides insights into the mental states of human beings. Often, it is required to identify the emotional condition of the humans as cognitive feedback in the HCI. In this paper, an attempt to recognize seven emotional states from speech signals, known as sad

A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and more difficult to be detected

News is a medium that notifies people about the events that had happened worldwide. The menace of fake news on online platforms is on the rise which may lead to unwanted events. The majority of fake news is spread through social media platforms, since these platforms have a great reach. To identify the credibility of the news, various spam detection methods are generally used. In this work, a new stance

This paper considers how an online food delivery platform can improve last-mile delivery services’ performance using multi-source data. The delivery time is one critical but uncertain factor for online platforms that also regarded as the main challenges in order assignment and routing service. To tackle this challenge, we propose a data-driven optimization approach that combines machine learning techniques

Different from the usual surveillance task in which the goal is to achieve complete coverage of the specified area, the cooperative path planning problem of drones for persistent surveillance task is studied in this paper considering multiple constraints of the covered area. The goal is to maximize the combinational coverage area of drones while giving preference to the area that hasn’t been visited

Whether the design of product innovation can highly match the customer demand is the key to extend the product life cycle, and it is also the basis for enterprises to carry out continuous production and operation. The first step of product innovation is to identify the relationship between customer demands and design factors of product innovation. This paper focuses on the problems of market control

Research on the time series classification is gaining an increased attention in the machine learning and data mining areas due to the existence of the time series data almost everywhere, especially in our daily work and life. Recent studies have shown that the convolutional neural networks (CNN) can extract good features from the images and texts, but it often encounters the problem of low accuracy

This paper has represented a soft-set in the type-2 environment by its simplest form as an augmentation to soft-set theories. Furthermore, we have applied the type-2 fuzzy soft set(T2FSS) by using our most straightforward representation to find the solution of a decision-making-problem (DMP) based-on T2FSS as well as weighted type-2 fuzzy soft set (WT2FSS). We have proposed two definitions, namely

Failure mode and effect analysis (FMEA) is a risk analysis tool widely used in the manufacturing industry. However, traditional FMEA has limitations such as the inability to deal with uncertain failure data including subjective evaluations of experts, the absence of weight values of risk parameters, and not considering the conditionality between failure events. In this paper, we propose a holistic

In recent years, numerous researchers examined and analyzed several different types of uncertainty in shortest path (SP) problems. However, those SP problems in which the costs of arcs are expressed in terms of mixed interval-valued fuzzy numbers are less addressed. Here, for solving such uncertain SP problems, first a new procedure is extended to approximate the summation of mixed interval-valued

Multiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion

Recently, several socio-/bio-inspired algorithms have been proposed for solving a variety of problems. Generally, they perform well when applied for solving unconstrained problems; however, their performance degenerates when applied for solving constrained problems. Several types of penalty function approaches have been proposed so far for handling linear and non-linear constraints. Even though the

Human endeavours span a wide spectrum of activities which includes solving fascinating problems in the realms of engineering, arts, sciences, medical sciences, social sciences, economics and environment. To solve these problems, classical mathematics methods are insufficient. The real-world problems involve many uncertainties making them difficult to solve by classical means. The researchers world

Real-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be

This study has been inspired by golf ball movements during the game to improve particle swarm optimization. Because, all movements from the first to the last move of the golf ball are the moves made by the player to win the game. Winning this game is also a result of successful implementation of the desired moves. Therefore, the movements of the golf ball are also an optimization, and this has a meaning

Numerous research papers and several engineering applications have proved that the fuzzy set theory is an intelligent effective tool to represent complex uncertain information. In fuzzy multi-criteria decision-making (fuzzy MCDM) methods, intelligent information system and fuzzy control-theoretic models, complex qualitative information are extracted from expert’s knowledge as linguistic variables and

In this paper, a new variant of the electric vehicle (EV) routing problem, which considers heterogeneous EVs, partial recharge, and vehicle recycling, is investigated based on logistic companies' practical operation. A mixed integer linear programming (MILP) model is proposed to formulate the problem. For small-scale scenarios, commercial solver, e.g., CPLEX, is leveraged. For large-scale instances

With the rapid development of e-economy, ordering via online food delivery platforms has become prevalent in recent years. Nevertheless, the platforms are facing lots of challenges such as time-limitation and uncertainty. This paper addresses a complex stochastic online route-planning problem (SORPP) which is mathematically formulated as a two-stage stochastic programming model. To meet the immediacy

Internet of things (IoT) is made up of many devices like sensors, tags, actuators, mobile devices, and many more. These devices interact with each other without human interaction. Radio-frequency identification (RFID) devices are used to track people, assets, objects, etc. Along with the small memory capacity and low-power battery issues, these devices suffer from various security-related issues. These

The distribution of relief materials is an important part of post-disaster emergency rescue. To meet the needs of the relief materials in the affected areas after a sudden disaster and ensure its smooth progress, an optimized dispatch model for multiple periods and multiple modes of transportation supported by the Internet of Things is established according to the characteristics of relief materials

Non-negative matrix factorization and its extensions were applied to various areas (i.e., dimensionality reduction, clustering, etc.). When the original data are corrupted by outliers and noise, most of non-negative matrix factorization methods cannot achieve robust factorization and learn a subspace with binary codes. This paper puts forward a robust semi-supervised non-negative matrix factorization

A dynamic programming (DP) approach with adaptive state generation and conflicts resolution is developed to address the timetable-rescheduling problem (TRP) at relatively lower computation costs. A multi-stage decision-making model is first developed to represent the timetable-rescheduling procedure in high-speed railways. Then, an adaptive state generation method by reordering the trains at each station

An efficient classification method to categorize histopathological images is a challenging research problem. In this paper, an improved bag-of-features approach is presented as an efficient image classification method. In bag-of-features, a large number of keypoints are extracted from histopathological images that increases the computational cost of the codebook construction step. Therefore, to select

Surrogate-assisted meta-heuristic algorithms have shown good performance to solve the computationally expensive problems within a limited computational resource. Compared to the method that only one surrogate model is utilized, the surrogate ensembles have shown more efficiency to get a good optimal solution. In this paper, we propose a bi-stage surrogate-assisted hybrid algorithm to solve the expensive

This paper investigates the problem of finite-time stability (FTS) for a class of delayed genetic regulatory networks with reaction-diffusion terms. In order to fully utilize the system information, a linear parameterization method is proposed. Firstly, by applying the Lagrange’s mean-value theorem, the linear parameterization method is applied to transform the nonlinear system into a linear one with

The collaborative filtering method is widely used in the traditional recommendation system. The collaborative filtering method based on matrix factorization treats the user’s preference for the item as a linear combination of the user and the item latent vectors, and cannot learn a deeper feature representation. In addition, the cold start and data sparsity remain major problems for collaborative filtering

Chaotic phenomena are observed in several practical and scientific fields; however, the chaos is harmful to systems as they can lead them to be unstable. Consequently, the purpose of this study is to analyze the bifurcation of permanent magnet direct current (PMDC) motor and develop a controller that can suppress chaotic behavior resulted from parameter variation such as the loading effect. The nonlinear

Since 2011, when the concepts of Industry 4.0 were first announced, this industrial revolution has grown and expanded from some theoretical concepts to real-world applications. Its practicalities can be found in many fields and affect nearly all of us in so many ways. While we are adapting to new changes, adjustments are starting to reveal on national and international levels. It is becoming clear

This paper aims to present a novel multiple attribute group decision-making process under the intuitionistic multiplicative preference set environment. In it, Saaty’s 1/9-9 scale is used to express the imprecise information which is asymmetrical distribution about 1. To achieve it, the present work is divided into three folds. First, a concept of connection number-based intuitionistic multiplicative

Consumer demand and retailing models nowadays are being upgraded more frequently. More and more retailers are switching to the Omni-channel retailing model. Choosing a reasonable location for a front distribution center (FDC) helps control an enterprise's cost and improves its service level. This is especially true in the existence of fierce competition. In this paper, two important and contradictory

In this article, some properties of neutrosophic derivative and neutrosophic numbers have been presented. This properties have been used to develop the neutrosophic differential calculus. By considering different types of first- and second-order derivatives, different kind of systems of derivatives have been developed. This is the first time where a second-order neutrosophic boundary-value problem

Over the past 2 decades, several multi-objective optimizers (MOOs) have been proposed to address the different aspects of multi-objective optimization problems (MOPs). Unfortunately, it has been observed that many of MOOs experiences performance degradation when applied over MOPs having a large number of decision variables and objective functions. Specially, the performance of MOOs rapidly decreases

During past few decades, fuzzy decision is an important attention in the areas of science, engineering, economic system, business, etc. To solve day-to-day problem, researchers use fuzzy data in transportation problem for presenting the uncontrollable factors; and most of multi-objective transportation problems are solved using goal programming. However, when the problem contains interval-valued data

Selective maintenance problem plays an essential role in reliability optimization decision-making problems. Systems are a configuration of several components, and there are situations the system needs small intervals or break for maintenance actions, during the intervals expert carried out the maintenance actions to replace or repair the deteriorated components of the systems. Because of the uncertainty

Compared to the intuitionistic fuzzy sets, the Pythagorean fuzzy sets (PFSs) can provide the decision makers with more freedom to express their evaluation information. There exist some research results on the correlation coefficient between PFSs, but sometimes they fail to deal with the problems of disease diagnosis and cluster analysis. To tackle the drawbacks of the existing correlation coefficients

Supply and distribution management of blood products is a challenging task due to their short lifespan. The problem is even more sophisticated considering uncertain demand for these products. This paper addresses integrated inventory-routing of blood in a supply chain network consisting of a single supplier and a group of blood centers. Transshipment among blood centers is allowed to decrease the cost

Cartesian Genetic Programming (CGP) is a variant of Genetic Programming (GP) with the individuals represented by a two-dimensional acyclic directed graph, which can flexibly encode many computing structures. In general, CGP only uses a point mutation operator and the genotype of an individual is of fixed size, which may lead to the lack of population diversity and then cause the premature convergence

Receiving an accurate emotional response from robots has been a challenging task for researchers for the past few years. With the advancements in technology, robots like service robots interact with users of different cultural and lingual backgrounds. The traditional approach towards speech emotion recognition cannot be utilized to enable the robot and give an efficient and emotional response. The

Multi-agent systems are widely studied due to its ability of solving complex tasks in many fields, especially in deep reinforcement learning. Recently, distributed optimization problem over multi-agent systems has drawn much attention because of its extensive applications. This paper presents a projection-based continuous-time algorithm for solving convex distributed optimization problem with equality

As a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete

To deploy deep neural networks to edge devices with limited computation and storage costs, model compression is necessary for the application of deep learning. Pruning, as a traditional way of model compression, seeks to reduce the parameters of model weights. However, when a deep neural network is pruned, the accuracy of the network will significantly decrease. The traditional way to decrease the

Wireless sensor network (WSN) is used to sense the environment, collect the data, and further transmit it to the base station (BS) for analysis. A synchronized tree-based approach is an efficient approach to aggregate data from various sensor nodes in a WSN environment. However, achieving energy efficiency in such a tree formation is challenging. In this research work, an algorithm named fuzzy attribute-based

Applying risk assessment approaches to improve quality in enterprises is of great importance especially for sectors that are labor-intensive and thus frequently encountered failures. One of the methods frequently used to take precautions against failures caused by high variability in this type of sector is failure mode and effects analysis (FMEA). In this study, a hybrid FMEA approach is proposed so

Topological index is a numerical value associated with a chemical constitution for correlation of chemical structure with various physical properties, chemical reactivity or biological activity. In this work, some new indices based on neighborhood degree sum of nodes are proposed. To make the computation of the novel indices convenient, an algorithm is designed. Quantitative structure property relationship

Real-time water quality monitoring is a complex system as it involves many quality parameters to be monitored, the nature of these parameters, and non-linear interdependence between themselves. Intelligent algorithms crucial in building intelligent systems are good candidates for building a reliable and convenient monitoring system. To analyze water quality, we need to understand, model, and monitor

Brain stroke is an emergency medical condition which occurs mainly due to insufficient blood flow to the brain. It results in permanent cellular-level damage. There are two main types of brain stroke, ischemic and hemorrhagic. Ischemic brain stroke is caused by a lack of blood flow, and the haemorrhagic form is due to internal bleeding. The affected part of brain will not function properly after this

If the sample or population has vague, inaccurate, unidentified, deficient, indecisive, or fuzzy data, then the available sampling plans could not be suitable to use for decision-making. In this article, an improved group-sampling plan based on time truncated life tests for Weibull distribution under neutrosophic statistics (NS) has been developed. We developed improved single and double group-sampling

The mains signal is a complex fusion of various electrical equipment load signals in a building. In the non-intrusive load monitoring recognition, our main aim is to be able to extract as much load features as possible from the complex aggregate mains signal in a simpler way through a computer vision-based approach as opposed to the powers series signal approach. Power series methods, which are one

This paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process.

Genetic Programming (GP) is a popular and powerful evolutionary optimization algorithm that has a wide range of applications such as symbolic regression, classification and program synthesis. However, existing GPs often ignore the intrinsic structure of the ground truth equation of the symbolic regression problem. To improve the search efficacy of GP on symbolic regression problems by fully exploiting

Surrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this

Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern. Meanwhile, neural architecture search has become very popular in deep learning for automatically tuning the architecture and hyperparameters of deep neural networks. While both federated learning and neural architecture

Fuzzy set and neutrosophic set are two efficient tools to handle the uncertainties and vagueness of any real-world problems. Neutrosophic set is more capable than fuzzy set to deal the uncertainties of a real-life problem. This research paper introduces some new concept of single-valued neutrosophic graph (SVNG). We have also presented some different operations on SVNG such as rejection, symmetric

At present, the entire globe gets engaged in importing and exporting the products for promoting their business in which supply chain management is playing a vital role. The main aspect of any effective supply chain management is the transportation of cargoes. To avoid the damages of cargoes during transportation and for minimizing the cost, the returnable containers are used. The present research deals

In this paper, a fuzzy fractional two-stage transshipment problem where all the parameters are represented by fuzzy numbers is studied. The problem uses the ratio of costs divided by benefits as the objective function. A solution method which employs the extension principle is used to find the fuzzy objective value of the problem. For this purpose, the fuzzy fractional two-stage transshipment problem

Sustainable Computing has advanced the technological evolution of the Internet and information-based communication technology. It is nowadays emerging in the form of the Cloud of Medical Things (CoMT) to develop smart healthcare systems. The academic community has lately made great strides for the development of security for the CoMT based application systems, such as e-healthcare systems, industrial

In this paper, public services are analyzed for implementations of Industry 4.0 tools to satisfy citizen expectations. To be able to prioritize public services for digitalization, fuzzy Z-AHP and fuzzy Z-WASPAS are used in the analysis. The decision criteria are determined as reduced cost, fast response, ease of accessibility, reduced service times, increase in the available information and increased

In any multi-script environment, handwritten script classification is an unavoidable pre-requisite before the document images are fed to their respective Optical Character Recognition (OCR) engines. Over the years, this complex pattern classification problem has been solved by researchers proposing various feature vectors mostly having large dimensions, thereby increasing the computation complexity