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Similar Journals
 Soft ComputingJournal Prestige (SJR): 0.593 Citation Impact (citeScore): 2Number of Followers: 7      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1433-7479 - ISSN (Online) 1433-7479 Published by Springer-Verlag  [2469 journals]
• Maritime anomaly detection based on a support vector machine

Abstract: Abstract This paper designs a maritime anomaly detection algorithm based on a support vector machine (SVM) that considers the spatiotemporal and motion features of trajectories. Since trajectories are two-dimensional, it is difficult to present their motion features. To accurately describe trajectory features, a novel trajectory feature extraction method based on statistical theory is proposed in this paper. This method maps trajectories onto a high-dimensional space, which can account for both the spatiotemporal features and motion features of the trajectories. With the proposed feature extraction method, the density-based spatial clustering of applications with noise algorithm is employed to recognize vessel traffic patterns by simultaneously considering the spatiotemporal and motion features. Then, an improved SVM is designed by employing a weighted hybrid kernel function and differential operator to detect anomalous behaviours from recognized vessel traffic patterns that include the spatiotemporal and motion characteristics. Compared with standard SVM, it can adaptively determine the optimal kernel function according to sample set. Finally, a numerical example based on automatic identification system data from the waters off Chengshan Jiao is fulfilled to verify the proposed algorithm effectiveness and accuracy.
PubDate: 2022-08-07

• Improving waiting time and energy consumption performance of a
bi-objective genetic algorithm embedded in an elevator group control
system through passenger flow estimation

Abstract: Abstract Passenger waiting time is a significant issue related to the quality of service of a multiple lift system; however, energy consumption reduction is also an important concern in the lift industry. In this paper, we evaluate different versions of a genetic algorithm (GA) published previously by the authors with several relevant adjustments for the lift dispatching problem to minimize passenger waiting time and/or energy consumption. To the raw GA with adjustments (that works under the assumption one call-one passenger), we incorporated several elements: a passenger-counting module using origin–destination matrices, and the activation of certain policies (zoning and/or parking) under different detected traffic profiles (up-peak, interfloor or down-peak profiles). Besides, we added a proportional integral controller (PI) to assign different weights to passenger waiting time and energy consumption to evaluate the performance of our GA. Different versions of this GA, minimizing passenger waiting time and/or energy consumption, were compared among them and to a conventional control algorithm using three different types of simulated profiles: a mixed one, three well-known full day office profiles and three different step profiles. The results showed that the bi-objective GA version with the estimation of the number of passengers behind a call, i.e. the passenger forecasting, together with the parking policy for up-peak or down-peak conditions significantly improved performance of passenger waiting time, and in some cases in energy consumption as well. The addition of the PI controller to the GA proved to be especially useful when the system was under a high intensity traffic demand. The advantages of all these elements to forecast the passenger flow and detect the traffic profile to help the controller show unquestionable benefits to minimize passenger waiting time and energy consumption.
PubDate: 2022-08-07

• Dombi operations for linguistic T-spherical fuzzy number: an approach for
selection of the best variety of maize

Abstract: Abstract The operations proposed by Dombi based on t-norn (TN) and t-corom (TCN) are generally known as Dombi operations, which offer versatility in the working behavior of parameters. Over the last decade, group decision-making has been a very active research field. Especially, the development of new operational rules, aggregation operators, and multi-attribute group decision-making techniques based on these rules and operators have recently piqued the interest of scientists. Acknowledging the importance of t-spherical fuzzy sets and linguistic variable in this paper, firstly, we define the notion of linguistic T-spherical fuzzy set (Lt-SFS) where membership degree, abstinence degree and non-membership degree are presented in the form of linguistic variables. Dombi operations, score function, accuracy function for linguistic T-spherical fuzzy numbers (Lt-SFNs) are defined and some prominent properties of Dombi operations are then investigated. Furthermore, two aggregations operators based on Dombi operations namely, linguistic t-spherical fuzzy Dombi weighted averaging operator and linguistic t-spherical fuzzy Dombi weighted geometric operator are also developed. At that point, these Dombi operators are used to establish an extension of the technique for order of preference by similarity to ideal solution (TOPSIS) method, and a multi-attribute group decision-making approach is proposed to solve decision-making problems which is the key innovation of this paper. Finally, we apply the proposed technique for the selection of the best variety of maize and a comparison analysis is provided to demonstrate its applicability and feasibility.
PubDate: 2022-08-07

• On level spaces of fuzzy bitopological spaces

Abstract: Abstract In this article, we introduce the notion of $$(\alpha -\beta )$$ -level spaces by considering the concept of fuzzy bitopological space (shortly, fbts). We also define the fuzzy bitopological $$(\alpha -\beta )$$ -Hausdorff space and, with the help of $$(\alpha -\beta )^{*}$$ -disjoint sets, the idea of fuzzy bitopological $$(\alpha -\beta )^{*}$$ -Hausdorff space is introduced as well as investigate related results. Two new notions of shading families are presented and called $$(\alpha -\beta )$$ -shading and $$(\alpha -\beta )^{*}$$ -shading. Further, we define the notions of fuzzy bitopological $$(\alpha -\beta )$$ -compact space involving $$(\alpha -\beta )$$ -shading, fuzzy bitopological locally $$(\alpha -\beta )$$ -compact and fuzzy bitopological $$(\alpha -\beta )$$ -connected spaces and obtain several interesting results. Moreover, we give some illustrative examples to indicate the validity of aforesaid notions.
PubDate: 2022-08-05

• Back-propagation extreme learning machine

Abstract: Abstract Incremental Extreme Learning Machine (I-ELM) is a typical constructive feed-forward neural network with random hidden nodes, which can automatically determine the appropriate number of hidden nodes. However I-ELM and its variants suffer from a notorious problem, that is, the input parameters of these algorithms are randomly assigned and kept fixed throughout the training process, which results in a very unstable performance of the model. To solve this problem, we propose a novel Back-Propagation ELM (BP-ELM) in this study, which can dynamically assign the most appropriate input parameters according to the current residual error of the model during the increasing process of the hidden nodes. In this way, BP-ELM can greatly improve the quality of newly added nodes and then accelerate the convergence rate and improve the model performance. Moreover, under the same error level, the network structure of the model obtained by BP-ELM is more compact than that of the I-ELM. We also prove the universal approximation ability of BP-ELM in this study. Experimental results on three benchmark regression problems and a real-life traffic flow prediction problem empirically show that BP-ELM has better stability and generalization ability than other I-ELM-based algorithms.
PubDate: 2022-08-04

• A derived least square extreme learning machine

Abstract: Abstract Extreme learning machine (ELM) is a single hidden layer feedforward neural network and is proved to be a good machine learning tool. However, the singularity of the ELM activation function results in the poor generalization ability of the systems. This study proposes a least squares ELM with derivative characteristics (DLSELM). The activation function of the network consists of the original and derivative functions due to the introduction of derivative characteristics in the network. All weights and biases of the network are determined by a twice least squares method. Derivative characteristics increase the diversity of activation functions in the network. The regression accuracy of the network and the generalization ability of the system were greatly improved due to the weighs and biases of the DLSELM calculated by twice least methods. DLSELM is applied to different datasets for verifying their performance. Moreover, DLSELM possesses the best regression accuracy, stability, and generalization performance compared with the other networks.
PubDate: 2022-08-04

• Modeling the leader–follower supply chain network under uncertainty and
solving by the HGALO algorithm

PubDate: 2022-08-03

• Approximation operators via TD-matroids on two sets

Abstract: Abstract Rough set theory is an extension of set theory with two additional unary set-theoretic operators known as approximation in order to extract information and knowledge. It needs the basic, or say definable, knowledge to approximate the undefinable knowledge in a knowledge space using the pair of approximation operators. Many existed approximation operators are expressed with unary form. How to mine the knowledge which is asked by binary form with rough set has received less research attention, though there are strong needs to reveal the answer for this challenging problem. There exist many information with matroid constraints since matroid provides a platform for combinatorial algorithms especially greedy algorithm. Hence, it is necessary to consider a matroidal structure on two sets no matter the two sets are the same or not. In this paper, we investigate the construction of approximation operators expressed by binary form with matroid theory, and the constructions of matroidal structure aided by a pair of approximation operators expressed by binary form. First, we provide a kind of matroidal structure—TD-matroid defined on two sets as a generalization of Whitney classical matroid. Second, we introduce this new matroidal construction to rough set and construct a pair of approximation operators expressed with binary form. Third, using the existed pair of approximation operators expressed with binary form, we build up two concrete TD-matroids. Fourth, for TD-matroid and the approximation operators expressed by binary form on two sets, we seek out their properties with aspect of posets, respectively. Through the paper, we use some biological examples to explain and test the correct of obtained results. In summary, this paper provides a new approach to research rough set theory and matroid theory on two sets, and to study on their applications each other.
PubDate: 2022-08-03

• Cold start aware hybrid recommender system approach for E-commerce users

Abstract: Abstract The recommendation system (RS) suffers badly from the cold start problem (CSP) that occurs due to the lack of sufficient information about the new customers, purchase history, and browsing data. Moreover, data sparsity problems also arise when the interaction is made among a limited number of items. These issues not only pose a negative impact on the recommendation but also significantly condense the diversity of choices available on the particular platform. To tackle these issues, a novel methodological approach called sparsity and cold start aware hybrid recommended system (SCSHRS) has been designed to suppress data sparsity and CSP in RS. The performance of the proposed SCSHRS method is tested on MovieLens-20 M, Last.FM and Book-Crossing data sets and compared with the prevailing techniques. Based on the evaluation reports with the standards, the proposed SCSHRS system gives Mean Absolute Percentage Error of 40%, and, precision (0.16), recall (0.08), F-measure (0.1), and Normalized Discounted Cumulative Gain of 0.65. This study completely describes the SCSHRS mechanism and its comparison with other pre-proposed historic and traditional processes based on collaborative filtering.
PubDate: 2022-08-03

• Sustainable configuration paths of marine eco-efficiency: based on
fuzzy-set qualitative comparative analysis of 11 coastal areas in China

Abstract: Abstract Studying the sustainable configuration path of marine ecological efficiency (MEE) is significant for China's efforts to build a marine power and achieve high-quality development of the marine economy. The existing studies have deficiencies in explaining the complex interaction among multiple land-based influence factors on MEE. This study puts forward an integrated analysis framework to understand the differences in MEE among different coastal areas based on the Technology-Organization-Environment (TOE) framework and combined with the characteristics of government behavior under the background of the Chinese system. Taking 11 coastal areas in China as cases, the fuzzy-set qualitative comparative analysis (fsQCA) method is used for configuration analysis, and the findings are as follows: (1) Conditions based on technology, organization, and environment cannot, by themselves, explain why MEE is high or low. This means that a single condition can't explain much about MEE. (2) There are three configuration paths for high MEE: a path of development based on innovation, a path of ecological industry based on unique geographical location, and a path of development based on political pressure. Among them, the path of development based on innovation plays a more important role in improving MEE and has more value in making effective policy. (3) There are two ways to have a low MEE: one with a lot of innovation and one with a lot of environmental regulation and political pressure on the technology. These two paths imply that the lack of green technology capability is an important conditional variable for low MEE. The findings of this study will help people understand MEE's long-term development path in a more rational way and give them useful practical advice.
PubDate: 2022-08-02

• Correction to: The control mode study of PPP project financing management
information system

PubDate: 2022-08-01

• Special issue on advances in pattern recognition and computer vision,
applications and systems

PubDate: 2022-08-01

• A communication security anti-interference decision model using deep
learning in intelligent industrial IoT environment

Abstract: Abstract To traditional anti-jamming decision algorithm that cannot meet the security needs of smart city development, this paper proposes a communication security anti-interference decision algorithm using deep learning in an intelligent industrial IoT environment. Firstly, an interactive system model of cognitive users and disruptors with intelligent perception function is constructed. Besides, the interference intensity and channel gain are comprehensively analyzed to design the optimization goal to maximize network capacity. Then, by modeling the interaction between cognitive environment and decision engine as the interaction between environment and agent in deep reinforcement learning, the Q-learning algorithm integrating reinforcement learning is used to explore the maximum action reward feedback to cognitive decision engine, so as to intelligently obtain the effective interference parameters of communication state. Finally, the proposed algorithm is experimentally demonstrated based on MATLAB simulation platform. The results show that when the number of links is 300, the network capacity of proposed algorithm is about 960 $$\text{bit} \cdot \text{s}^{ - 1} \cdot \text{Hz}^{ - 1}$$ , and the cumulative average reward value reaches 0.59, which is better than the comparison algorithm, and realizes high reliable autonomous decision-making.
PubDate: 2022-08-01

• Classification of Gurumukhi month’s name images using various
convolutional neural network optimizers

Abstract: Abstract The Gurumukhi script has a complex structure for which text recognition based on an analytical approach can misinterpret the script. For error-free results in text recognition, the author has proposed a holistic approach based on classification of Gurumukhi month’s name images. For this, a new convolutional neural model has been developed for automatic feature extraction from Gurumukhi text images. The proposed convolutional neural network is designed with five convolutional, three polling layers, one flatten layer and one dense layer. To validate the results of the proposed model, the dataset was self-created from 500 distinct writers. The performance of the model has been analyzed with 100 epochs, 40 batch sizes and different optimizers. The various optimizers that have been used for this experimentation are SGD, Adagrad, Adadelta, RMSprop, Adam, and Nadam. The experimental results show that the proposed CNN model performed best with Adam optimizer in terms of accuracy, computational time, F1 score, precision and recall.
PubDate: 2022-08-01

• An evolutionary trajectory planning algorithm for multi-UAV-assisted MEC
system

Abstract: Abstract This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where multiple UAVs are used to serve mobile users. We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises four phases. In the first phase, a variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, a differential evolution clustering algorithm is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system.
PubDate: 2022-08-01

• Deep learning-based election results prediction using Twitter activity

Abstract: Abstract Nowadays, political parties have widely adopted social media for their party promotions and election campaigns. During the election, Twitter and other social media platforms are used for political coverage to promote the party and its candidates. This research discusses and estimates the stability of many volumetric social media approaches to forecast election results from social media activities. Numerous machine learning approaches are applied to opinions shared on social media for predicting election results. This paper presents a machine learning model based on sentiment analysis to predict Pakistan's general election results. In a general election, voters vote for their favorite party or candidate based on their personal interests. Social media has been extensively used for the campaign in Pakistan general election 2018. Using a machine learning technique, we provide a five-step process to analyze the overall election results, whether fair or unfair. The work is concluded with detailed experimental results along with discussion on the outcomes of sentiment analysis for real-world forecasting and approval of general elections in Pakistan.
PubDate: 2022-08-01

• An effective nonlocal means image denoising framework based on
non-subsampled shearlet transform

Abstract: Abstract Image denoising is a fundamental task in computer vision and image processing system with an aim of estimating the original image by eliminating the noise and artefact from the noise-corrupted version of the image. In this study, a nonlocal means (NLM) algorithm with NSST (non-subsampled shearlet transform) has been designed to surface a computationally simple image denoising algorithm. Initially, NSST is employed to decompose source image into coarser and finer layers. The number of decomposition levels of NSST is set to two, resulting in set of low-frequency coefficients (coarser layer) and four sets high-frequency coefficients (finer layers). The two number of levels of decomposition are used in order to preserve memory, reduce processing time, and mitigate the influence of noise and misregistration errors. The finer layers are then processed using NLM algorithm, while the coarser layer is left as it is. The NL-Means algorithm reduces noise in finer layers while maintaining the sharpness of strong edges, such as the image silhouette. When compared to noisy images, this filter preserves textured regions, resulting in retaining more information. To obtain a final denoised image, inverse NSST is performed to the coarser layer and the NL-means filtered finer layers. The robustness of our method has been tested on the different multisensor and medical image dataset with diverse levels of noise. In the context of both subjective assessment and objective measurement, our method outperforms numerous other existing denoising algorithms notably in terms of retaining fine image structures. It is also clearly exhibited that the proposed method is computationally more effective as compared to other prevailing algorithms.
PubDate: 2022-08-01

• A note on “Dealer using a new trapezoidal cubic hesitant fuzzy TOPSIS
method and application to group decision-making program”

Abstract: Abstract Amin et al. (Soft Comput 23:5353–5366, 2019), firstly, proposed a trapezoidal cubic hesitant fuzzy weighted geometric (TrCHFWG) aggregation operator for aggregating trapezoidal cubic hesitant fuzzy sets (TrCHFSs). Then, using the proposed aggregation operator, Amin et al. proposed TOPSIS (Technique for order preference by similarity to ideal solution) method for solving multi-attribute group decision making problems under TrCHFS environment. In this note, it is pointed out that Amin et al.’s aggregation operator is not valid as the monotonicity property is not satisfied for Amin et al.’s TrCHFWG aggregation operator. Hence, it is inappropriate to use Amin et al.’s TOPSIS method. Also, it is pointed out that to resolve the inappropriateness of Amin et al.’s TrCHFWG aggregation operator as well as Amin et al.’s TOPSIS method is an open challenging research problem.
PubDate: 2022-08-01

• An effective structure of multi-modal deep convolutional neural network

Abstract: Abstract Deep learning models have been extensively used in pattern recognition and image processing. The conventional deep learning methods highly focused on the learning feature for a unique data type. In our work, a wavelet-based multi-modal deep convolutional neural network with an Adaptive group teaching optimization algorithm is proposed for learning hierarchical features from big data. Initially, the dataset is provided as the input to the network model for learning the optimal features. The wavelet with Haar transform has been implemented within the network to reduce the features by eliminating the redundant data. This step reduces the overall architectural complexities involved. The backpropagation algorithm with the stochastic gradient descent algorithm within the network model layers is utilized to enhance the training step. The adaptive group teaching optimization algorithm is utilized in the proposed network model to update the learned weight values by minimizing the loss function. Thus, the complexity of the architecture and the learning time can be decreased, which will lead to greater accuracy. Two datasets, such as Canadian Institute for advanced research-10 and self-taught learning-10, are used for evaluating the performances of wavelet-based multi-modal deep convolutional neural networks with an Adaptive group teaching optimization algorithm. The simulation results showed the overall performance of the proposed method in terms of accuracy, recall, and precision based on the two datasets is better than the existing methods. The existing methods include convolutional neural network, convolutional autoencoder neural network, and multichannel convolutional neural network.
PubDate: 2022-08-01

• Inclusion degree-based multigranulation rough fuzzy set over heterogeneous
preference information and application to multiple attribute group
decision making

Abstract: Abstract The knowledge background and preference of decision-makers and interaction among criteria play an important role in actual multiple attribute group decision-making(MAGDM) problem. Consequently, the attribute set and evaluation information chosen by decision-makers are different according to their preference. In this situation, this paper proposes a multigranulation rough fuzzy set model under heterogeneous preference information. We firstly present heterogeneous preference information system and the definition is given. What’s more, the weight information can be depicted at different levels from the perspective of granular computing. Considering the decision-makers or experts have different weights for they chosen the set of criterion, we recalculate the weight of attribute by using Choquet integral and the generalized Shapley index. Then we construct the arbitrary binary relation classes based on the inclusion measurement of the heterogeneous preference information system between any alternatives. We then give the lower and upper approximations of any fuzzy decision-making object over the heterogeneous preference information system. At the same time, several interesting properties for the defined model are given and optimistic and pessimistic multigranulation rough fuzzy set models are deduced, respectively. Moreover, the interrelationship among the established multigranulation rough fuzzy set over the heterogeneous preference information system as well as the existing multigranulation rough set models are discussed in detail. After that, we present a new approach to multiple attribute group decision making problem by using the multigranulation rough fuzzy set method with the heterogeneous preference information. The basic principle and the methodology as well as the algorithm of the decision making given in this paper are given. Finally, the optimal renewable energy technologies alternative determination problem is used as a case study to illustrate the application.
PubDate: 2022-08-01

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