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Special Issue "Intelligent Computing, Modeling and its Applications"

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A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 22151

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Special Issue Editors

Prof. Dr. José Antonio Marmolejo Saucedo

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Chief Guest Editor

Faculty of Engineering, University Panameric, 20290 Aguascalientes, Mexico
Interests: industrial engineering; applied mathematics; supply chain management
Special Issues, Collections and Topics in MDPI journals

Dr. Joshua Thomas

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Guest Editor

UOW Malaysia, KDU Penang University College, George Town, Malaysia
Interests: intelligent systems techniques; deep learning algorithms; data science; visual analytics; scheduling and timetabling
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Prof. Leo Mrsic

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Guest Editor

Department of Data Analysis, Algebra University College, 10000 Zagreb, Croatia
Interests: natural language processing; data mining; machine learning
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Prof. Román Rodríguez Aguilar

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Guest Editor

Facultad de Ciencias Economicas y Empresariales, Universidad Panamericana, Ciudad de Mexico, Mexico
Interests: optimization modelling; mathematical programming; statistical learning
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Dr. Pandian Vasant

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Guest Editor

MERLIN Research Centre, Faculty of Electrical and Electronic Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
Interests: artificial intelligence; computing in mathematics; natural science; engineering and medicine algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are glad to inform you that the Fourth International Conference on Intelligent Computing and Optimization 2021 will be held on December 30–31, 2021.

Please kindly visit the following website: https://www.icico.info/ for detailed information on the conference.

In this regard, we would like to invite the authors to submit an extended version of their best selected papers for this Special Issue.

The recommended topics include, but are not limited to, the following:

Smart Cities;

Cyber Security;

Mobile Ad Hoc Networks;

Information Network;

Graph Database;

Quality of service;

Internet of Vehicles;

Internet of Things (IoT);

Block Chain;

Interoperability;

Meta-heuristics;

Supply Chain;

Path Relinking;

High-Speed Train;

SMS Security;

Environmental sustainability;

Remote Sensing;

ATM Replenishment;

Bit-Vector Encoding;

Electroencephalograms;

Social Network Analysis;

Fuzzy Regulators;

Mobile Cloud Computing;

Web System Diagnosis;

Deep Learning Applications;

Smart and Autonomous Vehicles;

Energy Saving;

Vehicular Network;

Hybrid Renewable Energy.

Prof. José Antonio Marmolejo Saucedo
Dr. Joshua Thomas
Prof. Leo Mrsic
Prof. Román Rodríguez Aguilar
Dr. Pandian Vasant
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

industrial 4.0
supply chain management
machine learning
innovative computing
sustainable optimization

Published Papers (13 papers)

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Research

27 pages, 10506 KiB

Open AccessArticle

AFB-GPSR: Adaptive Beaconing Strategy Based on Fuzzy Logic Scheme for Geographical Routing in a Mobile Ad Hoc Network (MANET)

Raneen I. Al-Essa

and

Ghaida A. Al-Suhail

Computation 2023, 11(9), 174; https://doi.org/10.3390/computation11090174 - 04 Sep 2023

Cited by 1 | Viewed by 1032

Abstract

In mobile ad hoc networks (MANETs), geographical routing provides a robust and scalable solution for the randomly distributed and unrestricted movement of nodes. Each node broadcasts beacon packets periodically to exchange its position with neighboring nodes. However, reliable beacons can negatively affect routing performance in dynamic environments, particularly when there is a sudden and rapid change in the nodes’ mobility. Therefore, this paper suggests an improved Greedy Perimeter Stateless Routing Protocol, namely AFB-GPSR, to reduce routing overhead and increase network reliability by maintaining correct route selection. To this end, an adaptive beaconing strategy based on a fuzzy logic scheme (AFB) is utilized to choose more optimal routes for data forwarding. Instead of constant periodic beaconing, the AFB strategy can dynamically adjust beacon interval time with the variation of three network parameters: node speed, one-hop neighbors’ density, and link quality of nodes. The routing evaluation of the proposed protocol is carried out using OMNeT++ simulation experiments. The results show that the AFB strategy within the GPSR protocol can effectively reduce the routing overhead and improve the packet-delivery ratio, throughput, average end-to-end delay, and normalized routing load as compared to traditional routing protocols (AODV and GPSR with fixed beaconing). An enhancement of the packet-delivery ratio of up to 14% is achieved, and the routing cost is reduced by 35%. Moreover, the AFB-GPSR protocol exhibits good performance versus the state-of-the-art protocols in MANET. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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19 pages, 17021 KiB

Open AccessArticle

Analysis and 3D Imaging of Multidimensional Complex THz Fields and 3D Diagnostics Using 3D Visualization via Light Field

and

Computation 2023, 11(8), 160; https://doi.org/10.3390/computation11080160 - 14 Aug 2023

Viewed by 737

Abstract

We present a numerical platform for 3D imaging and general analysis of multidimensional complex THz fields. A special 3D visualization is obtained by converting electromagnetic (EM) radiation to a light field via the Wigner distribution function, which is known for discovering (revealing) hidden details. This allows for 3D diagnostics using the simple techniques of geometrical optics, which significantly facilitates the whole analysis. This simulation was applied to a complex field composed of complex beams emitted as ultra-narrow femtosecond pulses. A method was developed for the generation of phase–amplitude and spectral characteristics of complex multimode radiation in a free-electron laser (FEL) operating under various parameters. The tool was successful at diagnosing an early design of the transmission line (TL) of an innovative accelerator at the Schlesinger Family Center for Compact Accelerators, Radiation Sources, and Applications. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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Graphical abstract

21 pages, 3120 KiB

Open AccessArticle

CEAT: Categorising Ethereum Addresses’ Transaction Behaviour with Ensemble Machine Learning Algorithms

Tiffany Tien Nee Pragasam

John Victor Joshua Thomas

Maria Anu Vensuslaus

and

Subhashini Radhakrishnan

Computation 2023, 11(8), 156; https://doi.org/10.3390/computation11080156 - 09 Aug 2023

Viewed by 840

Abstract

Cryptocurrencies are rapidly growing and are increasingly accepted by major commercial vendors. However, along with their rising popularity, they have also become the go-to currency for illicit activities driven by the anonymity they provide. Cryptocurrencies such as the one on the Ethereum blockchain provide a way for entities to hide their real-world identities behind pseudonyms, also known as addresses. Hence, the purpose of this work is to uncover the level of anonymity in Ethereum by investigating multiclass classification models for Externally Owned Accounts (EOAs) of Ethereum. The researchers aim to achieve this by examining patterns of transaction activity associated with these addresses. Using a labelled Ethereum address dataset from Kaggle and the Ethereum crypto dataset by Google BigQuery, an address profiles dataset was compiled based on the transaction history of the addresses. The compiled dataset, consisting of 4371 samples, was used to tune and evaluate the Random Forest, Gradient Boosting and XGBoost classifier for predicting the category of the addresses. The best-performing model found for the problem was the XGBoost classifier, achieving an accuracy of 75.3% with a macro-averaged F1-Score of 0.689. Following closely was the Random Forest classifier, with an accuracy of 73.7% and a macro-averaged F1-Score of 0.641. Gradient Boosting came in last with 73% accuracy and a macro-averaged F1-Score of 0.659. Owing to the data limitations in this study, the overall scores of the best model were weaker in comparison to similar research, with the exception of precision, which scored slightly higher. Nevertheless, the results proved that it is possible to predict the category of an Ethereum wallet address such as Phish/Hack, Scamming, Exchange and ICO wallets based on its transaction behaviour. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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14 pages, 949 KiB

Open AccessArticle

The Cost of Understanding—XAI Algorithms towards Sustainable ML in the View of Computational Cost

and

Computation 2023, 11(5), 92; https://doi.org/10.3390/computation11050092 - 04 May 2023

Viewed by 1882

Abstract

In response to socioeconomic development, the number of machine learning applications has increased, along with the calls for algorithmic transparency and further sustainability in terms of energy efficient technologies. Modern computer algorithms that process large amounts of information, particularly artificial intelligence methods and their workhorse machine learning, can be used to promote and support sustainability; however, they consume a lot of energy themselves. This work focuses and interconnects two key aspects of artificial intelligence regarding the transparency and sustainability of model development. We identify frameworks for measuring carbon emissions from Python algorithms and evaluate energy consumption during model development. Additionally, we test the impact of explainability on algorithmic energy consumption during model optimization, particularly for applications in health and, to expand the scope and achieve a widespread use, civil engineering and computer vision. Specifically, we present three different models of classification, regression and object-based detection for the scenarios of cancer classification, building energy, and image detection, each integrated with explainable artificial intelligence (XAI) or feature reduction. This work can serve as a guide for selecting a tool to measure and scrutinize algorithmic energy consumption and raise awareness of emission-based model optimization by highlighting the sustainability of XAI. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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18 pages, 4312 KiB

Open AccessArticle

Artificial Neural Network Model for Membrane Desalination: A Predictive and Optimization Study

and

Seyyed Ahmad Mirbagheri

Computation 2023, 11(3), 68; https://doi.org/10.3390/computation11030068 - 22 Mar 2023

Viewed by 1349

Abstract

Desalination is a sustainable method to solve global water scarcity. A Response Surface Methodology (RSM) approach is widely applied to optimize the desalination performance, but further investigations with additional inputs are restricted. An Artificial neuron network (ANN) method is proposed to reconstruct the parameters and demonstrate multivariate analysis. Graphene oxide (GO) content, Polyhedral Oligomeric Silsesquioxane (POSS) content, operating pressure, and salinity were combined as input parameters for a four-dimensional regression analysis to predict the three responses: contact angle, salt rejection, and permeation flux. Average coefficient of determination (R²) values ranged between 0.918 and 0.959. A mathematical equation was derived to find global max and min values. Three objective functions and three-dimensional diagrams were applied to optimize effective cost conditions. It served as the database for the membranologists to decide the amount of GO to be used to fabricate membranes by considering the effects of operating conditions such as salinity and pressure to achieve the desired salt rejection, permeation flux, contact angle, and cost. The finding suggested that a membrane with 0.0063 wt% of GO, operated at 14.2 atm for a 5501 ppm salt solution, is the preferred optimal condition to achieve high salt rejection and permeation flux simultaneously. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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13 pages, 2158 KiB

Open AccessArticle

Modeling and Forecasting of nanoFeCu Treated Sewage Quality Using Recurrent Neural Network (RNN)

Dingding Cao

MieowKee Chan

and

SokChoo Ng

Computation 2023, 11(2), 39; https://doi.org/10.3390/computation11020039 - 17 Feb 2023

Cited by 2 | Viewed by 1382

Abstract

Rapid industrialization and population growth cause severe water pollution and increased water demand. The use of FeCu nanoparticles (nanoFeCu) in treating sewage has been proven to be a space-efficient method. The objective of this work is to develop a recurrent neural network (RNN) model to estimate the performance of immobilized nanoFeCu in sewage treatment, thereby easing the monitoring and forecasting of sewage quality. In this work, sewage data was collected from a local sewage treatment plant. pH, nitrate, nitrite, and ammonia were used as the inputs. One-to-one and three-to-three RNN architectures were developed, optimized, and analyzed. The result showed that the one-to-one model predicted all four inputs with good accuracy, where R² was found within a range of 0.87 to 0.98. However, the stability of the one-to-one model was not as good as the three-to-three model, as the inputs were chemically and statistically correlated in the later model. The best three-to-three model was developed by a single layer with 10 neurons and an average R² of 0.91. In conclusion, this research provides data support for designing the neural network prediction model for sewage and provides positive significance for the exploration of smart sewage treatment plants. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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15 pages, 1870 KiB

Open AccessArticle

An Improved Multilabel k-Nearest Neighbor Algorithm Based on Value and Weight

and

Computation 2023, 11(2), 32; https://doi.org/10.3390/computation11020032 - 13 Feb 2023

Cited by 2 | Viewed by 1919

Abstract

Multilabel data share important features, including label imbalance, which has a significant influence on the performance of classifiers. Because of this problem, a widely used multilabel classification algorithm, the multilabel k-nearest neighbor (ML-kNN) algorithm, has poor performance on imbalanced multilabel data. To address this problem, this study proposes an improved ML-kNN algorithm based on value and weight. In this improved algorithm, labels are divided into minority and majority, and different strategies are adopted for different labels. By considering the label of latent information carried by the nearest neighbors, a value calculation method is proposed and used to directly classify majority labels. Additionally, to address the misclassification problem caused by a lack of nearest neighbor information for minority labels, weight calculation is proposed. The proposed weight calculation converts distance information with and without label sets in the nearest neighbors into weights. The experimental results on multilabel datasets from different benchmarks demonstrate the performance of the algorithm, especially for datasets with high imbalance. Different evaluation metrics show that the results are improved by approximately 2–10%. The verified algorithm could be applied to a multilabel classification of various fields involving label imbalance, such as drug molecule identification, building identification, and text categorization. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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13 pages, 3359 KiB

Open AccessArticle

Optimized Packing Titanium Alloy Powder Particles

and

Computation 2023, 11(2), 22; https://doi.org/10.3390/computation11020022 - 01 Feb 2023

Viewed by 1028

Abstract

To obtain high-quality and durable parts by 3D printing, specific characteristics (porosity and proportion of various sizes of particles) in the mixture used for printing or sintering must be assured. To predict these characteristics, a mathematical model of optimized packing polyhedral objects (particles of titanium alloys) in a cuboidal container is presented, and a solution algorithm is developed. Numerical experiments demonstrate that the results obtained by the algorithm are very close to experimental findings. This justifies using numerical simulation instead of expensive experimentation. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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13 pages, 2147 KiB

Open AccessArticle

Model for the Detection of Falls with the Use of Artificial Intelligence as an Assistant for the Care of the Elderly

William Villegas-Ch.

Santiago Barahona-Espinosa

Walter Gaibor-Naranjo

and

Aracely Mera-Navarrete

Computation 2022, 10(11), 195; https://doi.org/10.3390/computation10110195 - 02 Nov 2022

Cited by 2 | Viewed by 1984

Abstract

Currently, telemedicine has gained more strength and its use allows establishing areas that acceptably guarantee patient care, either at the level of control or event monitors. One of the systems that adapt to the objectives of telemedicine are fall detection systems, for which artificial vision or artificial intelligence algorithms are used. This work proposes the design and development of a fall detection model with the use of artificial intelligence, the model can classify various positions of people and identify when there is a fall. A Kinect 2.0 camera is used for monitoring, this device can sense an area and guarantees the quality of the images. The measurement of position values allows to generate the skeletonization of the person and the classification of the different types of movements and the activation of alarms allow us to consider this model as an ideal and reliable assistant for the integrity of the elderly. This approach analyzes images in real time and the results showed that our proposed position-based approach detects human falls reaching 80% accuracy with a simple architecture compared to other state-of-the-art methods. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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29 pages, 5816 KiB

Open AccessArticle

Solving the Optimal Selection of Wellness Tourist Attractions and Destinations in the GMS Using the AMIS Algorithm

Rapeepan Pitakaso

Natthapong Nanthasamroeng

Sairoong Dinkoksung

Kantimarn Chindaprasert

Ganokgarn Jirasirilerd

and

Chaiya Chomchalao

Computation 2022, 10(9), 165; https://doi.org/10.3390/computation10090165 - 18 Sep 2022

Cited by 5 | Viewed by 2908

Abstract

This study aims to select the ideal mixture of small and medium-sized destinations and attractions in Thailand’s Ubon Ratchathani Province in order to find potential wellness destinations and attractions. In the study region, 46 attractions and destinations were developed as the service sectors for wellness tourism using the designed wellness framework and the quality level of the attractions and destinations available on social media. Distinct types of tourists, each with a different age and gender, comprise a single wellness tourist group. Due to them, even with identical attractions and sites, every traveler has a different preference. A difficult task for travel agencies is putting together combinations of attractions and places for each tourist group. In this paper, the mathematical formulation of the suggested problem is described, and the optimal solution is achieved using Lingo v.16. Unfortunately, the large size of test instances cannot be solved with Lingo v16. However, the large-scale problem, particularly the case study in the target area, has been solved using a metaheuristic method called AMIS. According to the computation in the final experiment, AMIS can raise the solution quality across all test instances by an average of 3.83 to 8.17 percent. Therefore, it can be concluded that AMIS outperformed all other strategies in discovering the ideal solution. AMIS, GA and DE may lead visitors to attractions that generate 29.76%, 29.58% and 32.20%, respectively, more revenue than they do now while keeping the same degree of preference when the number of visitors doubles. The attractions’ and destinations’ utilization has increased by 175.2 percent over the current situation. This suggests that small and medium-sized enterprises have a significantly higher chance of flourishing in the market. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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17 pages, 3101 KiB

Open AccessArticle

Bearing Fault Diagnosis Based on Measured Data Online Processing, Domain Fusion, and ANFIS

Quang Thinh Tran

and

Sy Dzung Nguyen

Computation 2022, 10(9), 157; https://doi.org/10.3390/computation10090157 - 08 Sep 2022

Cited by 2 | Viewed by 1393

Abstract

Processing noise online in sensors-based measurement data (SMD) and mitigating the effect of domain drift are always challenges. As a result, it negatively impacts the effectiveness and feasibility of data-driven model (DDM)-based mechanical-system fault identification (MFI). Here, we propose an online bearing fault diagnosis method named ANFIS-BFDM by using an adaptive neurofuzzy inference system (ANFIS). Reduction in the influence of domain drift between the source domain and target domain (DDSTD) is considered in both the data processing and fault identification. Online solutions for preprocessing SMD and exploiting the filtered data to label the target domain are presented in a fusion domain deriving from the source and target domains. First, in the offline phase, frequency-based splitting of SMD into different time series is performed to cancel the high-frequency region. An optimal data screening threshold (ODST) is distilled in the remaining low-frequency data to develop an impulse noise filter named FIN. An ANFIS then identifies the dynamic response of the bearing(s) via the filtered data. The FIN and ANFIS are finally exploited during the online phase to filter noise and recognize the object’s health status online. The survey results reflect the positive effects of the method, even if severe impulse noise appears in the databases. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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20 pages, 647 KiB

Open AccessArticle

Sliding-Mode Control of Bidirectional Flyback Converters with Bus Voltage Regulation for Battery Interface

Carlos Andres Ramos-Paja

Juan David Bastidas-Rodriguez

and

Luz Adriana Trejos-Grisales

Computation 2022, 10(7), 125; https://doi.org/10.3390/computation10070125 - 20 Jul 2022

Cited by 2 | Viewed by 1363

Abstract

Energy storage systems are essential for multiple applications like renewable energy systems, electric vehicles, microgrids, among others. Those systems are responsible of regulating the dc bus voltage using charging-discharging systems which are mainly formed by a power converter and a control system. This work focuses on the control system of a flyback converter. A detailed design procedure of an adaptive sliding-mode controller (SMC) and its parameters is presented. The proposed procedure was validated through simulations which allow to confirm its good performance in terms of global stability providing the desired dynamic of the dc bus voltage regulation. Full article

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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15 pages, 1763 KiB

Open AccessArticle

Balanced Circular Packing Problems with Distance Constraints

Jose Antonio Marmolejo-Saucedo

and

Pandian Vasant

Computation 2022, 10(7), 113; https://doi.org/10.3390/computation10070113 - 04 Jul 2022

Cited by 4 | Viewed by 1882

Abstract

The packing of different circles in a circular container under balancing and distance conditions is considered. Two problems are studied: the first minimizes the container’s radius, while the second maximizes the minimal distance between circles, as well as between circles and the boundary [...] Read more.

(This article belongs to the Special Issue Intelligent Computing, Modeling and its Applications)

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