Journalistic transparency rises as a key issue against the lack of credibility to which journalists are exposed, as well as the media manipulators and fake news providers. With the use of Natural Language Processing (NLP) and Machine Learning (ML), it is possible to automate the extraction of information from newspaper articles to know what the sources of information are to verify their veracity. Along

Multi-point shortest path planning problem is a typical problems in discrete optimization. The bat algorithm is a nature-inspired metaheuristic optimization algorithm that is used in a wide range of fields. However, there is one problem with the BA, which is easy to premature. To solve multi-point shortest path planning problem, an improved discrete bat algorithm (IDBA) is proposed in this paper. In

Recently, phishing emerges as one of the biggest threats to human’s daily networking environments. Phishing attackers disguise illegal URLs as normal ones to steal user’s private information with the social engineering techniques, such as emails and SMS, which calls for an effective method of preventing phishing attacks to relieve the loss by them. Neural networks can be used to detect and prevent

Recombination is a powerful way of generating new solutions in Evolutionary Algorithms. There are many ways to implement recombination. Traditional recombination operators do not use information about parents, evolutionary process, or models for variable interaction in order to find better ways to recombine solutions. Some modern recombination operators use information about parents and models for

Multi-hole parts made of difficult-to-cut materials like discs, blisks and casings are common and require high surface quality in aero engines. In workshops, a large number of holes in these parts are drilled successively in one process to ensure their positional accuracy. Due to the fast time-varying drill wear, the surface roughness of the holes is unstable and difficult to be satisfied. To this

The move towards sustainable development and energy efficient solutions in the built environment has led to develop innovative solutions, particularly in the area of indoor environmental comfort in buildings. Phase change materials (PCMs) are a potential approach to target building energy use reduction. PCMs are smart materials that have been commercialized and used in buildings for effective regulation

Forecasting multivariate time series is an important problem considered in many real-world scenarios. To deal with that problem, several forecasting models have already been proposed, where Fuzzy Cognitive Maps (FCMs) are proved to be a suitable alternative. The key limitation of the existing FCM-based forecasting models is the lack of time-efficient learning algorithms. In this paper, we plug that

The Longest Common Subsequence (LCS) problem aims at finding a longest string that is a subsequence of each string from a given set of input strings. This problem has applications, in particular, in the context of bioinformatics, where strings represent DNA or protein sequences. Existing approaches include numerous heuristics, but only a few exact approaches, limited to rather small problem instances

Wafer fabrication is an energy-consuming process. Achieving energy efficiency is, therefore, crucial for wafer fabrication factories (wafer fabs). However, limited studies have focused on resolving product quality problems for improving energy efficiency. This study proposed a novel fuzzy nonlinear programming (FNLP) approach for minimizing energy wastage caused by product quality problems. In the

This paper proposes a novel population-based metaheuristic optimization algorithm, called Adolescent Identity Search Algorithm (AISA), which is inspired by the process of identity development/search of adolescents. AISA simulates the identity formation behavior of adolescents in the peer group. This behavior is modeled mathematically to solve optimization problems. The proposed algorithm is evaluated

Two methods are developed for a two-dimensional cutting problem with irregular shaped items. The concepts of inner-fit raster and no-fit raster are used to search for a feasible positioning of items on a rectangular container. The first method is a Biased Random Key Genetic Algorithm, which is a population method, while the other is a Variable Neighborhood Search, which is a single trajectory method

The quantification of the uncertainty in crude oil price is of significance to improve the related financial decision-making. However, studies in this field have remained limited because the nonlinearity inherent in the crude oil price makes it challenging to model its uncertainty. In this paper, a novel learning system of ensemble probabilistic prediction combining five popular machine learning methods

Change detection (CD) is a process of identifying dissimilarities from two or more co-registered multitemporal images. In this paper, we have introduced a α-cut induced Fuzzy layer to the Deep Neural Network (αFDNN). Deep neural networks for change detection normally rely on the pre-classified labels of the clustering. But the pre-classified labels are more coarse and ambiguous, which is not able to

Well placement optimization is a critical part of the oil field development planning which aims to find the optimal locations of wells to maximize a traditional objective function (TOF), e.g., cumulative oil production (COP) or the net present value (NPV). However, the optimization process can be quite time-consuming since it requires iterative evaluations of the objective function and each evaluation

Detection and distinguishing between different species of bacteria using experimental microbiology is an expensive, time-consuming, and risky process. Automatic computer-based methods for accurate detection and classification of bacteria species significantly reduce the cost, time, and avoiding scientists the risk of infection. This paper presents a novel computer-based approach for highly accurate

The genetic algorithm (GA), one of the best-known metaheuristic algorithms, has been extensively utilized in various fields of management science, operational research, and industrial engineering. The efficiency of GAs in solving large-scale optimization problems would be enhanced if the iterative processes required by the genetic operators can be implemented in a parallel and distributed computing

Accurate solar irradiance prediction plays an important role in renewable energy systems. Based on time series analysis, a serially connected multi-reservoir echo state network (MR-ESN) is developed to predict solar irradiance. MR-ESN is a fast yet efficient approach, which makes use of the high efficiency of ESN and the advantages of deep learning. MR-ESN consists of multiple reservoirs in series

To address the challenge of maintaining high robustness of target tracking in a 3D dynamic high-altitude scenario, this paper presents a method to formulate continuous strategic maneuvers for unmanned combat air vehicles (UCAVs) based on deep deterministic policy gradient (DDPG). DDPG is an efficient reinforcement learning approach that helps UCAV perform a variety of navigation tasks in real-time

The revolutionary development and implementation of smart technologies have triggered the manufacturers’ servitization trend towards smart product service system (PSS). Accurate selection of smart product service (SPS) module is critical to successful planning and development of smart PSS concept. This study constructs a list of criteria for SPS module selection from the perspectives of service implementation

Coupling relations analysis of monitoring variables in complex electromechanical system is a powerful and useful means for abnormal detection, false monitoring information identification and fault diagnosis. However, due to the characteristics of multivariable, nonlinear and non-stationarity in the actual production process, it is difficult to achieve multivariable coupling modeling of complex electromechanical

A machine learning approach based on hybridization of genetic programming and neural networks is used to derive mathematical models for music genre classification. We design three multi-label classifiers with different trade-offs between complexity and accuracy, which are able to identify the degree of belonging of music content to ten different music genres. Our approach is innovative as it entirely

The capital goods industry produces physical assets used for current and future production. Capital goods are highly customised. Production scheduling aims to synchronise material supply, component manufacturing, sub-assembly and final assembly processes to minimise the total costs of earliness and tardiness, whilst satisfying finite capacity, machining and assembly precedence constraints. This paper

Satisfaction with life scale is a comprehensive cognitive judgement of individual’s own life and becomes the dominant measure of life satisfaction. However, the individual may hesitate to assess his/her life satisfaction with a single value due to multiple criteria. Consequently, we propose the hesitant fuzzy satisfaction with life scale (HFSWLS) with the same form as hesitant fuzzy element (HFE),

Rheumatoid Arthritis (RA) is a type of dreadful autoimmune disease that affects the entire human body, especially joints. Early diagnosis of RA is a challenging task for General Physicians, since the actual triggering mechanism is unpredictable. The capability of C4.5 was explored using the hybridization of Grey Wolf Optimization (GWO) - Particle Swarm Optimization (PSO) to develop an effective RA

This paper proposes a novel non-dominated sorting genetic algorithm-based method for scheduling simultaneously different types of vehicles with different capacities in distribution centers working as cross dock. In this paper, a three-objective model is proposed, which minimizes operational time, lateness and earliness of delivering products. In many of real life cases, packs of products with different

With the limitations of the network resources and battery energy of wireless sensors, the competition of resources in the process of communication will increase the network energy consumption and reduce the Quality of Service (QoS), resulting in that the application of Multi-Radio Multi-Channel (MRMC) Wireless Sensor Networks (WSNs) face many challenges. In this paper, we concentrate on the resource

The research work carried out here deals with the application of a Hybrid Micro-Grid System (HMGS), which includes solar/wind/battery storage/diesel generator applied to three different parts of India. For a better analysis of all the three cases, an efficient and recent metaheuristic optimization method named hybrid multi-objective moth flame optimization (HMOMFO) technique has been used in MATLAB

Accurate segmentation is a necessary step in the clinical management of brain tumors. However, the task remains challenging due to not only large variations in the sizes and shapes of brain tumors but also wide variations among individuals. In this paper, we develop a novel fully convolutional neural network with a feature reuse module and feature conformity module (F2 FCN) to alleviate the above challenges

This paper proposes a similarity-based Particle Filter (PF) method for Remaining Useful Life (RUL) prediction with improved performance. In the proposed methodology, Maximum Mean Discrepancy (MMD) and Kernel Two Sample Test are firstly adopted to query similar Run-To-Failure (R2F) profiles from historical data library. The states and parameters of degradation are initialized based on the similar R2F

In this paper, an innovative hybrid multi-variable generator’s actual-output-power predicting model is proposed based on ant colony optimization algorithm and extreme learning machine network, and a data-driven performance evaluation model is presented based on the two indices, K-means clustering algorithm and Markov chain for the performance evaluation of the wind turbines. Ant colony optimization

Forecasting models have been widely used in wind-speed time series forecasting that are often nonlinear, irregular, and non-stationary. Current forecasting models based on artificial neural network can adapt to various wind-speed time series. However, they cannot simultaneously and effectively forecast the entire wind-speed time series of a wind farm. In this paper, a novel combined forecasting system

Implementation of distributed energy resources (DERs) has led to a decrement in the cost of supplying demand in distribution networks. Integration of DERs in the forms of micro-grids (MGs) is a solution to enhance the operation of these resources in the low voltage networks. To meet the demand by MG operator, both technical and economic characteristics as well as the prices offered by retailers are

Reviewers play a significant role in academic peer review activities, including conference paper assignment and funding selection, because their evaluation of proposals impacts the final decision. Several studies have proposed reviewer selection strategies or reviewer evaluation methods for solving the problem of selecting appropriate reviewers. Identifying reviewers who are familiar with the proposals

Scheduling under nonrenewable resources is one of the challenging issues in project scheduling problems. There are many cases where the projects are subject to some nonrenewable resources. In most of the literature, nonrenewable resources are assumed to be available in full amount at the beginning of the project. However, in practice, it is prevalent that these resources are procured along the project

Software reliability is an important attribute of software quality. To achieve higher reliability, software development must include a testing phase in which faults can be detected and corrected. The software reliability growth model (SRGM) has evolved from modelling merely the fault detection process (FDP) into incorporating the fault correction process (FCP) as well. However, restricted by mathematical

The problem of optimally designing an interplanetary trajectory for a space mission is considered in this paper. To tackle the extreme non-linearity of the search space, a case learning-based differential evolution algorithm, named CLDE, is proposed. It stores successful control parameters (scaling factor and crossover possibility) and retrieve the available reference information according to a geographic

Vehicle routing problem (VRP) is about finding optimal routes for a fixed fleet of vehicles in order that they can meet the demands for a set of given customers by traveling through those paths. This problem and its numerous expansions are one of the most important and most applicable transportation and logistics problems. In this study, the green vehicle routing and scheduling problem with heterogeneous

Compliant mechanisms are promising candidates in precision engineering, soft robotics, space, and bioengineering due to their advantages of free friction, free lubricant, no backlash, monolithic structure, and minimal assembly. However, designing and analyzing of compliant mechanisms are facing the high complexity due to a coupling of kinematic and mechanical behavior in comparison to rigid-body mechanisms

In this paper, we propose a novel asymmetric ϵ-insensitive pinball loss function for quantile estimation. There exists some pinball loss functions which attempt to incorporate the ϵ-insensitive zone approach in it but, they fail to extend the ϵ-insensitive approach for quantile estimation in true sense. The proposed asymmetric ϵ-insensitive pinball loss function can make an asymmetric ϵ- insensitive

In this paper, a binary integer programming model of an unbiased capability-based distributed layout (UBCB-DL) problem without demand and process flow information is first developed. Then, it is extended to a mixed-integer program for a biased capability-based distributed layout (BCB-DL) problem where the demand information and processing requirements of several parts are taken into account. Since

Rough set (RS) is a mathematical framework used to deal with incomplete and uncertain information. It has been widely used in decision analysis, data mining, artificial intelligence, economic management and many other fields. Up to now, there have been tens of thousands of research papers on this topic, and the area has made a rapid growth. In light of these factors, a comprehensive and systematic

Considering that the non-linear path of monthly time-series for the iron ore imported to China is under reciprocal influences of multiple factors, the process of data generation is not easily represented in a time-series model. Based on the decomposition–integration method, superiorities of empirical mode decomposition (EMD), non-linear autoregressive neural network (NARNN), and autoregressive integrated

In recent years, tourism demand forecasting has attracted more interests not only in tourism area but in data science field. In this study, we follow the previous relevant data science literatures and propose a new neural network enhanced hidden Markovian structural time series model (NehM-STSM). This model takes a multiplicative error structure of a trend and a seasonal element. The trend is modelled

The fuzzy local information C-means clustering algorithm (FLICM) is an important robust fuzzy clustering segmentation method, which has attracted considerable attention over the years. However, it lacks certain robustness to high noise or severe outliers. To improve the accuracy and robustness of the FLICM algorithm for images corrupted by high noise, a novel fuzzy local information c-means clustering

Sustainable supplier selection is known as a crucial objective in supply chains due to its impact on profitability, adorability, flexibility, and agility of the system. This study proposes a new multi-stage hierarchical fuzzy index-based approach with which decision-makers are empowered to select the most sustainable supplier based on sustainability triple bottom line criteria. Besides, a new fuzzy

The concept of modeling the behavior of industrial processes is of great importance as it describes the possible states of equipment used in large industries, which once damaged, it usually costs both in time and money. In this paper, we propose a data-driven methodology for depicting three distinct states of a chemical reactor, (1) normal, (2) warning, (3) alert, by using machine learning techniques

We propose a novel convex loss function termed as ‘ϵ-penalty loss function’, to be used in Support Vector Regression (SVR) model. The proposed ϵ-penalty loss function is shown to be optimal for a more general noise distribution. The popular ϵ-insensitive loss function and the Laplace loss function are particular cases of the proposed loss function. Making the use of the proposed loss function, we have

We developed a contour detection based image processing algorithm based on Mamdani (Type-2) fuzzy rules for detection of blood vessels in retinal fundus images. The method uses the green channel data from eye fundus images as input, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, and median filter for background exclusion. The Mamdani (Type-2) fuzzy rules applied

Experts typically have unequal authority levels in collaborative forecasting tasks. Most current fuzzy collaborative forecasting methods address this problem by applying a (fuzzy) weighted average to aggregate experts’ fuzzy forecasts. However, the aggregation result may be unreasonable, hence fuzzy weighted intersection operators have been proposed for fuzzy collaborative forecasting. This paper proposes

Feature selection for a given model can be transformed into an optimization task. The essential idea behind it is to find the most suitable subset of features according to some criterion. Nature-inspired optimization can mitigate this problem by producing compelling yet straightforward solutions when dealing with complicated fitness functions. Additionally, new mathematical representations, such as

This paper proposes a memetic algorithm (MA) with novel semi-constructive crossover and mutation operators (MASC) to minimize makespan in permutation flowshop scheduling problem (PFSP). MASC combines the strengths of genetic algorithm (GA), simulated annealing (SA), and Nawaz–Enscore–Ham (NEH) algorithm. The aim is to enhance GA in identifying promising areas in the search space, whose local optima

In the multi-sensor target identification problem involving multiple frames, it is important to fuse the potential information characterizing inherent relations among frames with uncertain decision inputs for enhancing the decision-making process. However, due to the influence of environments or other interference factors, the priori knowledge that accurately represents these relations is usually hard

Mining machinery failure is almost an everyday occurrence. Usually the failures bare certain consequences, which require additional financial costs to repair and restore the system to its operational state. The consequences are viewed through negative and damaging effects a failure has on the machine, health and safety of the employees, work environment, and on the environment. The removal of the consequences

This paper proposes a new extended zero-order Autonomous Learning Multiple-Model (ALMMo-0*) neuro-fuzzy approach in order to classify different heart disorders through sounds. ALMMo-0* is build upon the recently introduced ALMMo-0. In this paper ALMMo-0 is extended by adding a pre-processing structure which improves the performance of the proposed method. ALMMo-0* has as a learning engine composed

Due to the combined navigation system consisting of both Inertial Navigation System (INS) and Global Positioning System (GPS) in a complementary mode which assure a reliable, accurate, and continuous navigation system, we use a GPS/INS navigation system in our research. Because of the conditions of navigation system such as low-cost MEMS-based inertial sensors with considerable uncertainty in INS sensors