Aimed at finding a scientific and effective method to diagnose faults in rotating machinery, the algorithm based on Supervision Kernel Entropy Component Analysis with Whale Optimization Algorithm (WOSKECA) for feature selection has been proposed in this paper. Firstly, for ensure sufficient information gathering from the rotating machinery, multi-features parameters of the time domain, frequency domain

Image steganography is a technique of hiding secret data into a cover image and so as to prevent the intruders from accessing the secret data. The efficiency of image steganography techniques are usually evaluated based on perceptual transparency, payload capacity, security, temper resistance and computational costs. Though there has been significant improvement in related research over the decades

In this paper, a heuristic algorithm fusing with niche identification (NIT) and Artificial Bee Colony (ABC) technique is developed to solve multimodal optimization problems, and is then applied for structural damage detection. In order to improve the detection accuracy of the proposed algorithm, the Depth First Search (DFS) is adopted, and a new particle update scheme is proposed to maintain the diversity

In practice, as a well-known statistical method, the logistic regression model is used to evaluate the credit-worthiness of borrowers due to its simplicity and transparency in predictions. However, in literature, sophisticated machine learning models can be found that can replace the logistic regression model. Despite the advances and applications of machine learning models in credit scoring, there

Breast cancer is the most common cancer type attacking women worldwide. Also, breast cancer has been phenotypically classified into five subtypes. Each subtype group has unique characteristics that demonstrate the heterogeneity present within the breast cancer tumour. In 2012, the American Association for Cancer Research provided a population based molecular integrative clusters for the METABRIC (Molecular

In this paper, the Ridge, LASSO and Elastic Net regression methods are adapted for the task of selecting feature. In order to enhance the feature selection performance via these methods, a Hesitant Fuzzy Correlation Matrix (HFCM) is added to the objective functions of these models for addressing the minimum redundancy of features. To this end, the fuzzy C-means clustering is utilized, and the obtained

Shortwave radio broadcast resource allocation (SRBRA) is an NP-hard combinatorial optimization problem with practical significance in many countries. The aim of SRBRA is to allocate radio programs to transmission devices so as to broadcast all radio programs felicitously with a maximized objective of total qualified monitoring sites. To solve such an issue presented by the State Administration of Press

In control of switched systems with undetectable switching signals, robustness and precision are on two different sides of a spectrum. In one hand, robustness to significant modeling uncertainties arising from undetectability of switching modes can be attained by designing the control scheme for worst-case switching configurations. On the other hand, such a control scheme would potentially be overly

The evolvable hardware literature reports several methods for the evolution of digital circuits. However, there is a large variability in the set of problems and the appropriate metrics used for the evaluation of the results. In this paper, we propose a set of representative problems to comparatively evaluate metaheuristics when designing Combinational Logic Circuits (CLCs). We also define a set of

Credit risk evaluation model is generally regarded as a valid method for business risk management. Although the most of literatures about credit risk evaluation always use class-balanced data as sample sets, the study on class-imbalanced datasets is more suitable for actual situation. This paper proposes a new ensemble model to evaluate class-imbalanced credit risk, which integrates multiple sampling

Over the past years, the surge in the necessity for early detection and diagnosis of breast cancer has resulted in many innovative research directions. According to the World Health Organization, an early and accurate detection of breast cancer successfully leads to a correct decision towards its treatment. Development of computer-aided diagnosis (CAD) system is considered to be a major stead in current

The design of an efficient computer-aided diagnosis (CADx) system for psoriasis severity assessment demands both accurate segmentation and classification of psoriasis lesions. Recently, few studies have been conducted to design automatic CADx systems for psoriasis severity assessment using traditional machine learning approaches. However, these approaches are highly featured dependent and require extensive

Supporting the ultra-reliable and low-latency communications (URLLCs) has become one of the major goals for future wireless networks. In this paper, we present an analytical reliability model for user equipment (UE) connecting multiple base stations (BSs) with multi-stream carrier aggregation (MSCA) technology. We first derive a closed-form expression for reliability characterization using signal-

The use of computer aided diagnosis (CAD) systems, which are computer based tools for the automatic analysis of medical images such as mammogram and prostate MRI, can assist in the early detection and diagnosis of developing cancer. In the process of CAD for mammogram, the task of image processing (IP) plays a fundamental role in providing promising diagnostic results, by exploiting high-quality features

The use of dual probabilistic linguistic term sets (DPLTSs) to represent the use’s preferences in decision making can reflect the decision maker’s cognitive certainty and uncertainty. Additionally, the appearance of incomplete preferences is a recurring phenomenon that must be taken into account if you want to make a successful decision. This paper presents a new multi-criteria decision model based

Solving the real-world optimization problems often needs a large number of expensive function evaluations (FEs) by using evolutionary algorithms (EAs). To alleviate this difficulty, surrogate-assisted EAs (SAEAs) have attracted more and more attention from academia and industry. However, the existing SAEAs need a large amount of sample points to construct surrogate model within the expected times of

Multi-focus image fusion is an important approach to obtain the composite image with all objects in focus, and it can be treated as an image segmentation problem, which is solved by convolutional neural networks (CNN). For CNN-based multi-focus image fusion methods, public training dataset does not exist, and the network model determines the recognition accuracy of the focused and defocused pixels

The accurate estimation of missing data plays a vital role in ensuring a high level of data quality. The missing values should be imputed before performing data mining, machine learning, and other data processing tasks. Ten correlation-based imputation methods are proposed in this paper. All of these methods try to maximize the correlation between a missing feature and other features. The maximization

In this work, we introduce, for the first time, the design of a compact neuromorphic architecture to efficiently support a filtered-x error-coded affine projection-like (FXECAP-L) algorithm that is based on affine projection (AP) algorithms for active noise cancellation (ANC) in an acoustic duct. To date, few practical ANC implementations have used AP algorithms because of their high computational

In this paper a new robust fractional-order predictive controller is presented and employed to regulate the glucose level in type-1 diabetes. The dynamics of the system is fully unknown an it is online estimated by a fractional-order model using interval Type-2 (T2) fuzzy logic system. The proposed control system is composed of two main controllers which are the predictive General T2 Fuzzy Logic Controller

To predict the effluent ammonia nitrogen (NH4-N) of wastewater treatment process (WWTP), the soft computing methods are widely used, in which the mean square error (MSE) is usually adopted as the performance criterion. However, the MSE based methods cannot fully utilize the statistic information of data and are vulnerable to the nonzero-mean noise. To address these issues, the modeling-error probability

A new algorithm for data prediction based on the Laplacian Eigenmap (LE) is presented. We construct the Long Short-Term Memory model with the application of the LE in artificial neural networks. The new Long Short-Term Memory model based on Laplacian Eigenmap (LE-LSTM) reserves the characteristics of original data using the eigenvectors derived from the Laplacian matrix of the data matrix. LE-LSTM

This work proposes the implementation of a recently invented meta-heuristic optimization solver namely, an adaptive multiple teams perturbation-guiding Jaya (AMTPG-Jaya) technique to tackle with diverse single goal optimum power flow (OPF) forms. The AMTPG-Jaya solver employs numerous populations named as teams to investigate the search domain. Each team is guided by a number of movement equations

One-class Classification (OCC) is an important field of machine learning which aims at predicting a single class on the basis of its lonely representatives and potentially some additional counter-examples. OCC is thus opposed to traditional classification problems involving two or more classes, and addresses the issue of class unbalance. There is a wide range of one-class models which give satisfaction

Evolvable hardware (EH) architectures are capable of changing their configuration and behavior dynamically based on inputs from the environment. In this paper, we investigate the feasibility of using EH to prevent Hardware Trojan Horses (HTHs) from being inserted, activated or propagated in a digital electronic chip. HTHs are malicious hardware components that intend to leak secret information or cause

Although the structural regularized support vector machine (SRSVM) can enhance the generalization capability of the standard support vector machine (SVM), its current version is used only for binary classification. To make SRSVM adapt to the K-class classification, the most direct approach is combining it with partitioning strategies, which may however lead to the following shortcomings: (1) Extracting

Design concept evaluation in the early phase of product design plays a crucial role in new product development as it considerably determines the direction of subsequent design activities. However, it is a process involving uncertainty and subjectivity. The evaluation information mainly relies on expert’s subjective judgment, which is imprecise and uncertain. How to effectively and objectively evaluate

Currently, due to the overflow of textual information on the Internet, automatic text summarization methods are becoming increasingly important in many fields of knowledge. Extractive multi-document text summarization approaches are intended to automatically generate summaries from a document collection, covering the main content and avoiding redundant information. These approaches can be addressed

Email spam is a serious problem that annoys recipients and wastes their time. Machine- learning methods have been prevalent in spam detection systems owing to their efficiency in classifying mail as solicited or unsolicited. However, existing spam detection techniques usually suffer from low detection rates and cannot efficiently handle high-dimensional data. Therefore, we propose a novel spam detection

Absenteeism is a complex phenomenon characterized by the physical absence of the individual, usually at his workplace. Such absences generally lead to innumerable personal, social, and economic losses, particularly in public security institutions, where incidence is higher than the one verified in other occupational categories. Identifying preponderant absenteeism factors and allowing preventive actions

Complex and flexible access mode, and frequent data interaction bring about large security risks to data transmission for active distribution networks. How to ensure data security is critical to the safe and stable operation of active distribution networks. Traditional methods, like access control, data encryption, and text filtering based on intelligent algorithms, are difficult to ensure the security

Several applications of supervised learning involve the prediction of multiple continuous target variables from a dataset. When the target variables exhibit statistical dependencies among them, a multi-target regression (MTR) modelling permits to improve the predictive performance in comparison to induce a separate model for each target. Apart from describing the dependencies among the targets, the

The accuracy of cooperative localization for multiple autonomous underwater vehicles (AUVs) equipped with low precise proprioceptive localization sensors can be improved by using relative location information between individuals and Bayesian filtering. However, when the relative location measurement errors are high, its accuracy will be reduced. Two measurement for rough estimation algorithms under

Since greening scheduling is arousing increasing attention from many manufacturing enterprises, this paper focuses on a flexible job shop greening scheduling problem with crane transportation (FJSGSP-CT). Distinguished from the traditional scheduling model which merely concentrates on machining processes, FJSGSP-CT takes the comprehensive effect of machining and crane transportation processes into

In this paper, we focus on generalized fuzzy complex numbers and provide several examples of membership functions and graphical representations of these numbers. Then, a specific type of fuzzy complex linear systems, called the rectangular fuzzy complex linear system, is considered and its algebraic and general solutions are defined. Finally an approach based on restricting the general solution is

Distinguishing contact lenses from real iris is an attention-grabbing task within the field of iris recognition (IR). The textured lens may be used to forge the IR system by exploiting its external texture. Besides, the soft lens may also result an upsurge in the false rejection rate as it reduces the clearness of the iris texture. Therefore, it is foremost to identify contact lens in human eyes before

Originating from the belief-rule-based (BRB) system, the extended belief rule-based (EBRB) system combined the advantages of the rule-based method and those of data-driven methods. By transforming the data set into extended belief rules and using evidential reasoning (ER), the EBRB system has expanded the application of BRB systems and demonstrated their capability in addressing classification problems

In modern manufacturing industry, dynamic scheduling methods are urgently needed with the sharp increase of uncertainty and complexity in production process. To this end, this paper addresses the dynamic flexible job shop scheduling problem (DFJSP) under new job insertions aiming at minimizing the total tardiness. Without lose of generality, the DFJSP can be modeled as a Markov decision process (MDP)

Information is one of the most important assets of an organization. In recent years, the volume of data stored in organizations, varying user requirements, time constraints, and query management complexities have grown exponentially. Due to these problems, the performance modeling of queries in data warehouses (DWs) has assumed a key role in organizations. DWs make relevant information available to

Analyzing explicit and clear sentiment is challenging owing to the growing use of emblematic and multilingual language constructs. This research proposes sarcasm detection using deep learning in code-switch tweets, specifically the mash-up of English with Indian native language, Hindi. The proposed model is a hybrid of bidirectional long short-term memory with a softmax attention layer and convolution

Semantic segmentation is a challenging task as each pixel should be labeled accurately in the image. To improve the performance of semantic segmentation, some Fully Convolutional Network (FCN) based semantic segmentation methods adopt a spatial pyramid pooling structure to enrich contextual information. Others employ an encoder–decoder architecture to recover object details gradually. In this paper

Machine tool selection has been an important issue in the manufacturing industry because improper machine tool selection can have a negative effect on productivity, accuracy, flexibility, and the responsive manufacturing capabilities of a company. The current multi-criteria decision making (MCDM) approach of machine tool selection mostly focuses on the subjective perspective. However, as the objective

The membrane fouling is an important factor of restricting wide application of MBR (Membrane Bio-Reactor), which causes the fall of membrane flux and reduces the membrane cleaning period. So the Bandelet neural network is proposed through combining Bandelet transform and neural network, which predicts membrane flux and its recovery rate for making proper membrane cleaning decision. Firstly, the main

Stock investment is characterized by high risk and massive profit, so it is necessary to propose a scientific and accurate stock assessment and selection method for avoiding investment risks and obtaining high returns. Stock investment evaluation and selection can be regarded as a three-way decision (3WD) problem. Decision-theoretic rough sets (DTRSs) are an excellent tool to cope with 3WDs under risks

Many studies have been carried out on stock price trend prediction, but most of them focused on the public market data and did not utilize the trading behaviors owing to the unavailability of real transaction records data. In fact, trading behaviors can better reflect the market movements, and the fusion of trading information and market information can further improve the prediction accuracy. In this

Numerous algorithms have been introduced in solving multi-/many-objective optimization problems. However, some rigorous elite definitions may result in the side-effect that new solutions are hard to be admitted by the elite archive, which will restrain the searching ability. In this paper, a universal strengthened searching (SS) module is proposed as an accessorial searching procedure to improve the

Image segmentation is an active research topic in image processing. The Fuzzy C-means (FCM) clustering analysis has been widely used in image segmentation. As there is a large amount of delicate tissues such as blood vessels and nerves in medical images, noise generated during imaging process can easily affect successful segmentation of these tissues. The traditional FCM algorithm is not ideal for

To date, deep learning techniques, especially the combination of convolutional neural networks and recurrent neural networks with the attention mechanism, have been the state-of-the-art solutions for processing relation extraction and classification tasks. However, the neural network model constructed by this method cannot make full use of the labeled entities and their positional information in the

This paper presents a novel Soft Computing Approach called “Neuro-Fuzzy Analytical Network Process (NFANP)” for the group decision-making problems based on the conventional Analytic Network Process (ANP) method. The proposed approach deals with the interval values of judgements in a fuzzy environment using mobile, not fixed, trapezoidal and triangular membership functions, as well as the interval numerical

Optimization is more and more significant due to its application in the real engineering problems. The recently proposed imperialist competitive algorithm (ICA) is a successful method in mono-objective optimization. Nevertheless, ICA cannot handle simultaneously the conflicting objectives in multi-objective design problem. In addition, the ICA has the drawback of trapping in local optimum solutions

The popularity of Wi-Fi networks coupled with the intrinsic vulnerability of wireless interfaces has promoted the investigation and proposal of traffic analysis and anomaly detection algorithms targeted to that application. We propose a scalable and modular algorithm architecture to set up a lightweight classifier, able to detect malicious frames with high reliability, allowing a simple implementation

This article deals with product Family: a group of products with similar modules produced based on assembling to order (ATO) approach to cover diverse customer needs. The demand level and the customer requirements of these products are dynamically changing, which necessitate a novel model for co-reconfiguration of the product family (PF) and the supply chain (SC). Therefore, this article aims to apply

This work proposes a system based on machine learning aimed at creating an investment strategy capable of trading on the cryptocurrency exchange markets. Additionally, with the goal of generating investments with higher returns and lower risk, rather than investing on predictions based on time sampled financial series, a novel method for resampling financial series was developed and employed in this

While traditional Pareto-based evolutionary multi-objective optimization (EMO) algorithms have shown an excellent balance between convergence and diversity on a wide range of practical problems with two or three objectives in real applications, the decision maker (DM) is interested in a unique set of solutions rather than the whole population on Pareto optimal front (POF). In addition, Pareto-based