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A probabilistic multimodal optimization algorithm based on Buffon principle and Nyquist sampling theorem for noisy environment Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-06 Xia Wang; Yaomin Wang; Xinling Shi; Lian Gao; Peng Li
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
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Cluster-based information fusion for probabilistic risk analysis in complex projects under uncertainty Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-18 Limao Zhang; Ying Wang; Xianguo Wu
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
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A solution-driven multilevel approach for graph coloring Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-10 Wen Sun; Jin-Kao Hao; Yuhao Zang; Xiangjing Lai
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
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Interval-valued q-rung orthopair fuzzy FMEA application to improve risk evaluation process of tool changing manipulator Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-18 Chuanxi Jin; Yan Ran; Genbao Zhang
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
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A generative adversarial neural network model for industrial boiler data repair Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-24 Xiaobin Hu; Guoqiang Li; Peifeng Niu; Jianmei Wang; Linlin Zha
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Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-24 Sameena Pathan; P.C. Siddalingaswamy; Tanweer Ali
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
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Utilizing IoT to design a relief supply chain network for the SARS-COV-2 pandemic Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-24 Ali Zahedi; Amirhossein Salehi-Amiri; Neale R. Smith; Mostafa Hajiaghaei-Keshteli
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
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Minimizing Total Completion Time in blocking flowshops with sequence-dependent setup times Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-26 Chen-Yang Cheng; Pourya Pourhejazy; Kuo-Ching Ying; Shi-Yao Huang
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
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BERT-ADLOC: A secure crowdsourced indoor localization system based on BLE fingerprints Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-26 Xu Sun; Haojun Ai; Jingjie Tao; Tan Hu; Yusong Cheng
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
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Homotopy-based hyper-heuristic searching approach for reciprocal feedback inversion of groundwater contamination source and aquifer parameters Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-18 Zeyu Hou; Wangmei Lao; Yu Wang; Wenxi Lu
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Evolutionary Local Search Algorithm for the biclustering of gene expression data based on biological knowledge Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-19 Ons Maâtouk; Wassim Ayadi; Hend Bouziri; Béatrice Duval
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
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An intelligent multi-objective framework for optimizing friction-stir welding process parameters Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-18 Tanmoy Medhi; Syed Abou Iltaf Hussain; Barnik Saha Roy; Subhash Chandra Saha
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
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An RUL prediction approach for lithium-ion battery based on SADE-MESN Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-18 Yufan Ji; Zewang Chen; Yong Shen; Ke Yang; Youren Wang; Jiang Cui
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
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Data-driven time series prediction based on multiplicative neuron model artificial neuron network Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-10 Wenping Pan; Limao Zhang; Chunlin Shen
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
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Design of moth flame optimization heuristics for integrated power plant system containing stochastic wind Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-18 Babar Sattar Khan; Muhammad Asif Zahoor Raja; Affaq Qamar; Naveed Ishtiaq Chaudhary
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
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Sparse elastic net multi-label rank support vector machine with pinball loss and its applications Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-24 Hongmei Wang; Yitian Xu
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
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Meta deep learning based rotating machinery health prognostics toward few-shot prognostics Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-24 Peng Ding; Minping Jia; Xiaoli Zhao
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
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Social learning discrete Particle Swarm Optimization based two-stage X-routing for IC design under Intelligent Edge Computing architecture Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-24 Genggeng Liu; Xiaohua Chen; Ruping Zhou; Saijuan Xu; Yeh-Cheng Chen; Guolong Chen
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Evolving simple and accurate symbolic regression models via asynchronous parallel computing Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-24 Aliyu Sani Sambo; R. Muhammad Atif Azad; Yevgeniya Kovalchuk; Vivek Padmanaabhan Indramohan; Hanifa Shah
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
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Balancing accuracy and diversity in ensemble learning using a two-phase artificial bee colony approach Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-24 Yeou-Ren Shiue; Gui-Rong You; Chao-Ton Su; Hua Chen
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
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Forecasting CO2 emissions from Chinese marine fleets using multivariable trend interaction grey model Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-24 Yun Cao; Kedong Yin; Xuemei Li; Chenchen Zhai
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
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Adaptive fuzzy logic with self-tuned membership functions based repetitive learning control of robotic manipulators Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-16 B. Melih Yilmaz; Enver Tatlicioglu; Aydogan Savran; Musa Alci
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
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Semi-supervised regression using diffusion on graphs Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-18 Mohan Timilsina; Alejandro Figueroa; Mathieu d’Aquin; Haixuan Yang
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
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Opposition-based JAYA with population reduction for parameter estimation of photovoltaic solar cells and modules Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-20 Xi Yang; Wenyin Gong
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
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Dense conjugate initialization for deterministic PSO in applications: ORTHOinit+ Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-10 Cecilia Leotardi; Andrea Serani; Matteo Diez; Emilio F. Campana; Giovanni Fasano; Riccardo Gusso
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
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EEG denoising through a wide and deep echo state network optimized by UPSO algorithm Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-23 Weitong Sun; Yuping Su; Xia Wu; Xiaojun Wu; Yumei Zhang
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
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Maximizing minority accuracy for imbalanced pattern classification problems using cost-sensitive Localized Generalization Error Model Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-16 Wing W.Y. Ng; Zhengxi Liu; Jianjun Zhang; Witold Pedrycz
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
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MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-18 Fatih Ecer; Dragan Pamucar
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
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A real-time hostile activities analyses and detection system Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-15 Sajjad Dadkhah; Farzaneh Shoeleh; Mohammad Mehdi Yadollahi; Xichen Zhang; Ali A. Ghorbani
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Clustering algorithm based on nature-inspired approach for energy optimization in heterogeneous wireless sensor network Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-13 Israel Edem Agbehadji; Richard C. Millham; Abdultaofeek Abayomi; Jason J. Jung; Simon James Fong; Samuel Ofori Frimpong
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
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Influence-aware graph neural networks Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-16 Bin Yu; Yu Zhang; Yu Xie; Chen Zhang; Ke Pan
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
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Expeditious COVID-19 similarity measure tool based on consolidated SCA algorithm with mutation and opposition operators Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-20 Mohamed Issa
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
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Higher-order terminal sliding mode controller for fault accommodation of Lipschitz second-order nonlinear systems using fuzzy neural network Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-20 Mien Van
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
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An evolutionary/heuristic-based proof searching framework for interactive theorem prover Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-20 M. Saqib Nawaz; M. Zohaib Nawaz; Osman Hasan; Philippe Fournier-Viger; Meng Sun
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
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Supply chain design to tackle coronavirus pandemic crisis by tourism management Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-20 Faezeh Motevalli-Taher; Mohammad Mahdi Paydar
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
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CoVNet-19: A Deep Learning model for the detection and analysis of COVID-19 patients Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-15 Priyansh Kedia; Anjum; Rahul Katarya
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
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A multi-attribute group decision making method considering both the correlation coefficient and hesitancy degrees under interval-valued intuitionistic fuzzy environment Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-19 You Peng; Liu Xiaohe; Sun Jianbo
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
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An outlier detection algorithm for categorical matrix-object data Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-15 Fuyuan Cao; Xiaolin Wu; Liqin Yu; Jiye Liang
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Data-driven quantification of public–private partnership experience levels under uncertainty with Bayesian hierarchical model Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-11 Yongqi Wang; Zengqi Xiao; Robert L.K. Tiong; Limao Zhang
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
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Mobile app recommendation via heterogeneous graph neural network in edge computing Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-10 Tingting Liang; Xuan Sheng; Li Zhou; Youhuizi Li; Honghao Gao; Yuyu Yin; Liang Chen
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
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Efficient activity recognition using lightweight CNN and DS-GRU network for surveillance applications Appl. Soft Comput. (IF 5.472) Pub Date : 2021-01-15 Amin Ullah; Khan Muhammad; Weiping Ding; Vasile Palade; Ijaz Ul Haq; Sung Wook Baik
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
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Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-16 Ying Da Wang; Mehdi Shabaninejad; Ryan T. Armstrong; Peyman Mostaghimi
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
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User trustworthiness in online social networks: A systematic review Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-06 Majed Alkhamees; Saleh Alsaleem; Muhammad Al-Qurishi; Majed Al-Rubaian; Amir Hussain
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
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Minimalistic fuzzy ontology reasoning: An application to Building Information Modeling Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-10 Ignacio Huitzil; Miguel Molina-Solana; Juan Gómez-Romero; Fernando Bobillo
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
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Modulo 9 model-based learning for missing data imputation Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-10 Alladoumbaye Ngueilbaye; Hongzhi Wang; Daouda Ahmat Mahamat; Sahalu B. Junaidu
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
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A weighting model based on best–worst method and its application for environmental performance evaluation Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-09 Peide Liu; Baoying Zhu; Peng Wang
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
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Transductive transfer learning based Genetic Programming for balanced and unbalanced document classification using different types of features Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-11 Wenlong Fu; Bing Xue; Xiaoying Gao; Mengjie Zhang
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
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Adaptive cuckoo algorithm with multiple search strategies Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-13 Shuzhi Gao; Yue Gao; Yimin Zhang; Tianchi Li
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
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Binary team game algorithm based on modulo operation for knapsack problem with a single continuous variable Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-13 Yichao He; Xiang Hao; Wenbin Li; Qinglei Zhai
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
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A comprehensive methodology for quantification of Bow-tie under type II fuzzy data Appl. Soft Comput. (IF 5.472) Pub Date : 2021-01-29 Souvik Das; Ashish Garg; J. Maiti; O.B. Krishna; Jitesh J. Thakkar; R.K. Gangwar
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
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Interval-valued fuzzy regression: Philosophical and methodological issues Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-05 Reda Boukezzoula; Didier Coquin
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
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A genetic programming-based feature selection and fusion for facial expression recognition Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-08 Haythem Ghazouani
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
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CASA-based speaker identification using cascaded GMM-CNN classifier in noisy and emotional talking conditions Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-08 Ali Bou Nassif; Ismail Shahin; Shibani Hamsa; Nawel Nemmour; Keikichi Hirose
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
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Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-08 K.E. ArunKumar; Dinesh V. Kalaga; Ch. Mohan Sai Kumar; Govinda Chilkoor; Masahiro Kawaji; Timothy M. Brenza
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
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Neural-dynamics-enabled Jacobian inversion for model-based kinematic control of multi-section continuum manipulators Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-10 Ning Tan; Mingwei Huang; Peng Yu; Tao Wang
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
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Multi-objectivization inspired metaheuristics for the sum-of-the-parts combinatorial optimization problems Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-12 Jialong Shi; Jianyong Sun; Qingfu Zhang
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
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Consistency and trust relationship-driven social network group decision-making method with probabilistic linguistic information Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-11 Feifei Jin; Meng Cao; Jinpei Liu; Luis Martínez; Huayou Chen
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
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A matheuristic algorithm for the vehicle routing problem with cross-docking Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-03 Aldy Gunawan; Audrey Tedja Widjaja; Pieter Vansteenwegen; Vincent F. Yu
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
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Estimation of air-flow parameters and turbulent intensity in hydraulic jump on rough bed using Bayesian model averaging Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-04 Narges Taravatrooy; Farhad Bahmanpouri; Mohammad Reza Nikoo; Carlo Gualtieri; Azizallah Izady
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
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Joint optimization of service chain caching and task offloading in mobile edge computing Appl. Soft Comput. (IF 5.472) Pub Date : 2021-02-04 Kai Peng; Jiangtian Nie; Neeraj Kumar; Chao Cai; Jiawen Kang; Zehui Xiong; Yang Zhang
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
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