Mathematics

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28 pages, 8248 KiB

Open AccessArticle

Quantum Inspired Task Optimization for IoT Edge Fog Computing Environment

by Tariq Ahamed Ahanger, Fadl Dahan, Usman Tariq and Imdad Ullah

Mathematics 2023, 11(1), 156; https://doi.org/10.3390/math11010156 - 28 Dec 2022

Cited by 8 | Viewed by 2930

IoT-Edge-Fog Computing presents a trio-logical model for decentralized computing in a time-sensitive manner. However, to address the rising need for real-time information processing and decision modeling, task allocation among dispersed Edge Computing nodes has been a major challenge. State-of-the-art task allocation techniques such [...] Read more.

IoT-Edge-Fog Computing presents a trio-logical model for decentralized computing in a time-sensitive manner. However, to address the rising need for real-time information processing and decision modeling, task allocation among dispersed Edge Computing nodes has been a major challenge. State-of-the-art task allocation techniques such as Min–Max, Minimum Completion time, and Round Robin perform task allocation, butv several limitations persist including large energy consumption, delay, and error rate. Henceforth, the current work provides a Quantum Computing-inspired optimization technique for efficient task allocation in an Edge Computing environment for real-time IoT applications. Furthermore, the QC-Neural Network Model is employed for predicting optimal computing nodes for delivering real-time services. To acquire the performance enhancement, simulations were performed by employing 6, 10, 14, and 20 Edge nodes at different times to schedule more than 600 heterogeneous tasks. Empirical results show that an average improvement of 5.02% was registered for prediction efficiency. Similarly, the error reduction of 2.03% was acquired in comparison to state-of-the-art techniques. Full article

(This article belongs to the Special Issue Artificial Intelligence with Applications of Soft Computing)

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27 pages, 5708 KiB

Open AccessArticle

Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network

by Hazem Noori Abdulrazzak, Goh Chin Hock, Nurul Asyikin Mohamed Radzi, Nadia M. L. Tan and Chiew Foong Kwong

Mathematics 2022, 10(24), 4720; https://doi.org/10.3390/math10244720 - 12 Dec 2022

Cited by 9 | Viewed by 2249

Abstract

Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that [...] Read more.

Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. The problems with the K-Means algorithm concern the selection of a suitable number of clusters, the creation of a highly reliable cluster, and achieving high similarity within a cluster. To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. Firstly, RK-Means creates multi-groups of vehicles using a covering rough set based on effective parameters. Secondly, the K-value-calculating algorithm computes the optimal number of clusters. Finally, the classical K-Means algorithm is applied to create the vehicle clusters for each covering rough set group. The datasets used in this work were imported from Simulation of Urban Mobility (SUMO), representing two highway scenarios, high-density and low-density. Four evaluation indexes, namely, the root mean square error (RMSE), silhouette coefficient (SC), Davies–Bouldin (DB) index, and Dunn index (DI), were used directly to test and evaluate the results of the clustering. The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. The result of the compression showed that RK-Means had high cluster similarity, greater reliability, and error reductions of 32.5% and 24.2% compared with OK-Means and K-Means++, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence with Applications of Soft Computing)

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21 pages, 572 KiB

Open AccessArticle

Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems

by Ivona Brajević, Predrag S. Stanimirović, Shuai Li, Xinwei Cao, Ameer Tamoor Khan and Lev A. Kazakovtsev

Mathematics 2022, 10(23), 4555; https://doi.org/10.3390/math10234555 - 1 Dec 2022

Cited by 23 | Viewed by 2961

Abstract

Engineering design optimization problems are difficult to solve because the objective function is often complex, with a mix of continuous and discrete design variables and various design constraints. Our research presents a novel hybrid algorithm that integrates the benefits of the sine cosine [...] Read more.

Engineering design optimization problems are difficult to solve because the objective function is often complex, with a mix of continuous and discrete design variables and various design constraints. Our research presents a novel hybrid algorithm that integrates the benefits of the sine cosine algorithm (SCA) and artificial bee colony (ABC) to address engineering design optimization problems. The SCA is a recently developed metaheuristic algorithm with many advantages, such as good search ability and reasonable execution time, but it may suffer from premature convergence. The enhanced SCA search equation is proposed to avoid this drawback and reach a preferable balance between exploitation and exploration abilities. In the proposed hybrid method, named HSCA, the SCA with improved search strategy and the ABC algorithm with two distinct search equations are run alternately during working on the same population. The ABC with multiple search equations can provide proper diversity in the population so that both algorithms complement each other to create beneficial cooperation from their merger. Certain feasibility rules are incorporated in the HSCA to steer the search towards feasible areas of the search space. The HSCA is applied to fifteen demanding engineering design problems to investigate its performance. The presented experimental results indicate that the developed method performs better than the basic SCA and ABC. The HSCA accomplishes pretty competitive results compared to other recent state-of-the-art methods. Full article

(This article belongs to the Special Issue Artificial Intelligence with Applications of Soft Computing)

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28 pages, 5721 KiB

Open AccessArticle

LAE-GAN-Based Face Image Restoration for Low-Light Age Estimation

by Se Hyun Nam, Yu Hwan Kim, Jiho Choi, Seung Baek Hong, Muhammad Owais and Kang Ryoung Park

Mathematics 2021, 9(18), 2329; https://doi.org/10.3390/math9182329 - 19 Sep 2021

Cited by 2 | Viewed by 3134

Abstract

Age estimation is applicable in various fields, and among them, research on age estimation using human facial images, which are the easiest to acquire, is being actively conducted. Since the emergence of deep learning, studies on age estimation using various types of convolutional [...] Read more.

Age estimation is applicable in various fields, and among them, research on age estimation using human facial images, which are the easiest to acquire, is being actively conducted. Since the emergence of deep learning, studies on age estimation using various types of convolutional neural networks (CNN) have been conducted, and they have resulted in good performances, as clear images with high illumination were typically used in these studies. However, human facial images are typically captured in low-light environments. Age information can be lost in facial images captured in low-illumination environments, where noise and blur generated by the camera in the captured image reduce the age estimation performance. No study has yet been conducted on age estimation using facial images captured under low light. In order to overcome this problem, this study proposes a new generative adversarial network for low-light age estimation (LAE-GAN), which compensates for the brightness of human facial images captured in low-light environments, and a CNN-based age estimation method in which compensated images are input. When the experiment was conducted using the MORPH, AFAD, and FG-NET databases—which are open databases—the proposed method exhibited more accurate age estimation performance and brightness compensation in low-light images compared to state-of-the-art methods. Full article

(This article belongs to the Special Issue Artificial Intelligence with Applications of Soft Computing)

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45 pages, 8681 KiB

Open AccessArticle

Clustering of Latvian Pension Funds Using Convolutional Neural Network Extracted Features

by Vitalija Serapinaitė and Audrius Kabašinskas

Mathematics 2021, 9(17), 2086; https://doi.org/10.3390/math9172086 - 29 Aug 2021

Cited by 1 | Viewed by 2632

Abstract

Pension funds became a fundamental part of financial security in pensioners’ lives, guaranteeing stable income throughout the years and reducing the chance of living below the poverty level. However, participating in a pension accumulation scheme does not ensure financial safety at an older [...] Read more.

Pension funds became a fundamental part of financial security in pensioners’ lives, guaranteeing stable income throughout the years and reducing the chance of living below the poverty level. However, participating in a pension accumulation scheme does not ensure financial safety at an older age. Various pension funds exist that result in different investment outcomes ranging from high return rates to underperformance. This paper aims to demonstrate alternative clustering of Latvian second pillar pension funds, which may help system participants make long-range decisions. Due to the demonstrated ability to extract meaningful features from raw time-series data, the convolutional neural network was chosen as a pension fund feature extractor that was used prior to the clustering process. In this paper, pension fund cluster analysis was performed using trained (on daily stock prices) convolutional neural network feature extractors. The extractors were combined with different clustering algorithms. The feature extractors operate using the black-box principle, meaning the features they learned to recognize have low explainability. In total, 32 models were trained, and eight different clustering methods were used to group 20 second-pillar pension funds from Latvia. During the analysis, the 12 best-performing models were selected, and various cluster combinations were analyzed. The results show that funds from the same manager or similar performance measures are frequently clustered together. Full article

(This article belongs to the Special Issue Artificial Intelligence with Applications of Soft Computing)

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26 pages, 499 KiB

Open AccessArticle

Modelling Interaction Effects by Using Extended WOE Variables with Applications to Credit Scoring

by Carlos Giner-Baixauli, Juan Tinguaro Rodríguez, Alejandro Álvaro-Meca and Daniel Vélez

Mathematics 2021, 9(16), 1903; https://doi.org/10.3390/math9161903 - 10 Aug 2021

Cited by 1 | Viewed by 4154

Abstract

The term credit scoring refers to the application of formal statistical tools to support or automate loan-issuing decision-making processes. One of the most extended methodologies for credit scoring include fitting logistic regression models by using WOE explanatory variables, which are obtained through the [...] Read more.

The term credit scoring refers to the application of formal statistical tools to support or automate loan-issuing decision-making processes. One of the most extended methodologies for credit scoring include fitting logistic regression models by using WOE explanatory variables, which are obtained through the discretization of the original inputs by means of classification trees. However, this Weight of Evidence (WOE)-based methodology encounters some difficulties in order to model interactions between explanatory variables. In this paper, an extension of the WOE-based methodology for credit scoring is proposed that allows constructing a new kind of WOE variable devised to capture interaction effects. Particularly, these new WOE variables are obtained through the simultaneous discretization of pairs of explanatory variables in a single classification tree. Moreover, the proposed extension of the WOE-based methodology can be complemented as usual by balance scorecards, which enable explaining why individual loans are granted or not granted from the fitted logistic models. Such explainability of loan decisions is essential for credit scoring and even more so by taking into account the recent law developments, e.g., the European Union’s GDPR. An extensive computational study shows the feasibility of the proposed approach that also enables the improvement of the predicitve capability of the standard WOE-based methodology. Full article

(This article belongs to the Special Issue Artificial Intelligence with Applications of Soft Computing)

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26 pages, 1960 KiB

Open AccessArticle

An Improved Slime Mould Algorithm for Demand Estimation of Urban Water Resources

by Kanhua Yu, Lili Liu and Zhe Chen

Mathematics 2021, 9(12), 1316; https://doi.org/10.3390/math9121316 - 8 Jun 2021

Cited by 26 | Viewed by 3304

Abstract

A slime mould algorithm (SMA) is a new meta-heuristic algorithm, which can be widely used in practical engineering problems. In this paper, an improved slime mould algorithm (ESMA) is proposed to estimate the water demand of Nanchang City. Firstly, the opposition-based learning strategy [...] Read more.

A slime mould algorithm (SMA) is a new meta-heuristic algorithm, which can be widely used in practical engineering problems. In this paper, an improved slime mould algorithm (ESMA) is proposed to estimate the water demand of Nanchang City. Firstly, the opposition-based learning strategy and elite chaotic searching strategy are used to improve the SMA. By comparing the ESMA with other intelligent optimization algorithms in 23 benchmark test functions, it is verified that the ESMA has the advantages of fast convergence, high convergence precision, and strong robustness. Secondly, based on the data of historical water consumption and local economic structure of Nanchang, four estimation models, including linear, exponential, logarithmic, and hybrid, are established. The experiment takes the water consumption of Nanchang City from 2004 to 2019 as an example to analyze, and the estimation models are optimized using the ESMA to determine the model parameters, then the estimation models are tested. The simulation results show that all four models can obtain better prediction accuracy, and the proposed ESMA has the best effect on the hybrid prediction model, and the prediction accuracy is up to 97.705%. Finally, the water consumption of Nanchang in 2020–2024 is forecasted. Full article

(This article belongs to the Special Issue Artificial Intelligence with Applications of Soft Computing)

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27 pages, 1778 KiB

Open AccessArticle

Community Detection Problem Based on Polarization Measures: An Application to Twitter: The COVID-19 Case in Spain

by Inmaculada Gutiérrez, Juan Antonio Guevara, Daniel Gómez, Javier Castro and Rosa Espínola

Mathematics 2021, 9(4), 443; https://doi.org/10.3390/math9040443 - 23 Feb 2021

Cited by 8 | Viewed by 3718

Abstract

In this paper, we address one of the most important topics in the field of Social Networks Analysis: the community detection problem with additional information. That additional information is modeled by a fuzzy measure that represents the risk of polarization. Particularly, we are [...] Read more.

In this paper, we address one of the most important topics in the field of Social Networks Analysis: the community detection problem with additional information. That additional information is modeled by a fuzzy measure that represents the risk of polarization. Particularly, we are interested in dealing with the problem of taking into account the polarization of nodes in the community detection problem. Adding this type of information to the community detection problem makes it more realistic, as a community is more likely to be defined if the corresponding elements are willing to maintain a peaceful dialogue. The polarization capacity is modeled by a fuzzy measure based on the

J D J_{p o l}

measure of polarization related to two poles. We also present an efficient algorithm for finding groups whose elements are no polarized. Hereafter, we work in a real case. It is a network obtained from Twitter, concerning the political position against the Spanish government taken by several influential users. We analyze how the partitions obtained change when some additional information related to how polarized that society is, is added to the problem. Full article

(This article belongs to the Special Issue Artificial Intelligence with Applications of Soft Computing)

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