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Application of Advanced Computing and Artificial Intelligence in Engineering and...
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Application of Advanced Computing and Artificial Intelligence in Engineering and Science

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 28911

Special Issue Editors

Prof. Dr. Costin Badica

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Guest Editor
Department of Computers and Information Technology, Faculty of Automation, Computers and Electronics, University of Craiova, 107 Decebal Blvd, Craiova, Romania
Interests: artificial intelligence; multi-agent systems; software engineering; distributed systems; formal methods
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nick Bassiliades

E-Mail Website
Guest Editor
School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: smart grids; electric vehicles; multi-agent systems; information integration
Special Issues, Collections and Topics in MDPI journals
Dr. Kalliopi Kravari

E-Mail Website
Guest Editor
School of Informatics, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece
Interests: semantic web and intelligent agents; intelligent multi-agent systems issues; trust management; knowledge representation and reasoning; logi
Special Issues, Collections and Topics in MDPI journals
Dr. Theodoros Kosmanis

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, International Hellenic University, 57001 Thermi, Greece
Interests: vehicle electrification; automotive electrics/electronics; computational electromagne
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the last decade, artificial intelligence reached a high level of maturity from scientific experimentation in labs to powerful applications in business and industry. AI comprises a rather wide range of methods and techniques spanning various approaches in symbolic and sub-symbolic information processing that evolved and matured during a short history of more than 50 years. They can be succinctly grouped into three intertwined classes of approaches comprising representation, reasoning and learning methods.

Modern AI techniques work hand-in-hand with advanced computing technologies based on computer networks and emerging computing architectures. Modern trends in advanced computing architectures include high-performance, cloud, edge, and fog computing, as well as IoT, distributed ledgers and blockchains.

Currently, AI technologies have penetrated virtually all areas of life and society. The recent technical performance of AI methods and technologies, as well as their impact in business and economy, present new and amazing achievements while also raising new scientific and practical challenges that require a careful investigation by the research community. In particular, the synergy between new AI technologies and applications with advanced computing architectures is seen to be of major importance. A notable example is the intertwining of decentralized computing and machine learning, which led to federated learning.

The topics of interest for this Special Issue address the application of advanced computing and artificial intelligence methods including, but not limited to:

  • Machine learning (including classification and clustering, neural networks, deep and reinforcement learning, federated learning);
  • Knowledge representation and ontologies;
  • Reasoning (rule-based systems, logic programming, constraint satisfaction, theorem proving);
  • Imprecise and uncertain reasoning (including Bayesian and fuzzy approaches);
  • Combinatorial optimization and heuristic search;
  • Bio- and nature-inspired computing;
  • Natural language and speech processing;
  • Computer vision;
  • Distributed multi-agent systems;
  • Agent-based modeling and simulation;
  • High-performance, edge, fog, cloud computing, IoT
  • Distributed ledgers and blockchains

to various fields of science and engineering including, but not limiting to:

  • Autonomous driving;
  • Electric vehicles;
  • Smart Homes, smart cities and smart future;
  • Hazard modeling and mitigation;
  • Manufacturing and Industry 4.0;
  • Remote sensing and operation;
  • Robotics, automation and intelligent control;
  • Energy management;
  • Mobility, transportation and logistics;
  • Agriculture 5.0 and Smart Farming;
  • Healthcare and drug discovery.

Prof. Dr. Costin Badica
Prof. Dr. Nick Bassiliades
Dr. Kalliopi Kravari
Dr. Theodoros Kosmanis
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • machine learning
  • reasoning and knowledge representation
  • heuristic search
  • natural language processing
  • computer vision
  • multi-agent systems
  • high-performance, edge, fog and cloud computing
  • bio- and nature-inspired computing

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Related Special Issue

Published Papers (11 papers)

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Research

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25 pages, 1745 KiB  
Article
Multiagent Coordination and Teamwork: A Case Study for Large-Scale Dynamic Ready-Mixed Concrete Delivery Problem
by Shaza Hanif, Shahab Ud Din, Ning Gui and Tom Holvoet
Mathematics 2023, 11(19), 4124; https://doi.org/10.3390/math11194124 - 29 Sep 2023
Viewed by 1482
Abstract
The ready-mixed concrete delivery (RMC) problem is a scheduling problem, where multiple trucks deliver concrete to order sites abiding by hard constraints in a dynamic environment. It is an NP-hard problem, impractical to solve using exhaustive methods. Thus, it requires heuristic-based approaches for [...] Read more.
The ready-mixed concrete delivery (RMC) problem is a scheduling problem, where multiple trucks deliver concrete to order sites abiding by hard constraints in a dynamic environment. It is an NP-hard problem, impractical to solve using exhaustive methods. Thus, it requires heuristic-based approaches for generating sub-optimal schedules. Due to its distributed nature, we address this problem using a decentralised, scalable, cooperative MAS (multiagent system) that dynamically generates schedules. We explore the impact of teamwork by trucks on schedule optimisation. This work illustrates two novel approaches that address the dynamic RMC problem; a Delegate MAS approach and a team-extended approach. We present an empirical study, comparing our novel approaches with existing ones. The evaluation is performed by classifying the RMC case study scenarios into unique stress, scale, and dynamism characteristics. With 40% to 70% improvement over different metrics, the results show that both approaches generate better schedules, and using agent teams augments the performance. Thus, such decentralized MAS with the appropriate coordination approach and teamwork can be used for solving constrained dynamic scheduling problems. Full article
(This article belongs to the Special Issue Application of Advanced Computing and Artificial Intelligence in Engineering and Science)
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14 pages, 3807 KiB  
Article
Region-Aware Deep Feature-Fused Network for Robust Facial Landmark Localization
by Xuxin Lin and Yanyan Liang
Mathematics 2023, 11(19), 4026; https://doi.org/10.3390/math11194026 - 22 Sep 2023
Viewed by 1026
Abstract
In facial landmark localization, facial region initialization usually plays an important role in guiding the model to learn critical face features. Most facial landmark detectors assume a well-cropped face as input and may underperform in real applications if the input is unexpected. To [...] Read more.
In facial landmark localization, facial region initialization usually plays an important role in guiding the model to learn critical face features. Most facial landmark detectors assume a well-cropped face as input and may underperform in real applications if the input is unexpected. To alleviate this problem, we present a region-aware deep feature-fused network (RDFN). The RDFN consists of a region detection subnetwork and a region-wise landmark localization subnetwork to explicitly solve the input initialization problem and derive the landmark score maps, respectively. To exploit the association between tasks, we develop a cross-task feature fusion scheme to extract multi-semantic region features while trading off their importance in different dimensions via global channel attention and global spatial attention. Furthermore, we design a within-task feature fusion scheme to capture the multi-scale context and improve the gradient flow for the landmark localization subnetwork. At the inference stage, a location reweighting strategy is employed to transform the score maps into 2D landmark coordinates. Extensive experimental results demonstrate that our method has competitive performance compared to recent state-of-the-art methods, achieving NMEs of 3.28%, 1.48%, and 3.43% on the 300W, AFLW, and COFW datasets, respectively. Full article
(This article belongs to the Special Issue Application of Advanced Computing and Artificial Intelligence in Engineering and Science)
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18 pages, 4564 KiB  
Article
Modeling and Simulation for Non-Motorized Vehicle Flow on Road Based on Modified Social Force Model
by Jiaying Qin, Sasa Ma, Lei Zhang, Qianling Wang and Guoce Feng
Mathematics 2023, 11(1), 170; https://doi.org/10.3390/math11010170 - 29 Dec 2022
Cited by 5 | Viewed by 2138
Abstract
Non-motorized vehicles have become one of the most commonly used means of transportation for people due to their advantages of low carbon, environmental protection, convenience and safety. Frequent interaction among non-motorized vehicle users in the shared space will bring security risks to their [...] Read more.
Non-motorized vehicles have become one of the most commonly used means of transportation for people due to their advantages of low carbon, environmental protection, convenience and safety. Frequent interaction among non-motorized vehicle users in the shared space will bring security risks to their movement. Therefore, it is necessary to adopt appropriate means to evaluate the traffic efficiency and safety of non-motorized vehicle users in the passage, and using a micro model to conduct simulation evaluation is one of the effective methods. However, some existing micro simulation models oversimplify the behavior of non-motorized vehicle users, and cannot reproduce the dynamic interaction process between them. This paper proposes a modified social force model to simulate the dynamic interaction behaviors between non-motorized vehicle users on the road. Based on the social force model, a new behavioral force is introduced to reflect the three dynamic interaction behaviors of non motor vehicle users, namely, free movement, following and overtaking. Non-motorized vehicle users choose which behavior is determined by the introduced decision model. In this way, the rule-based behavior decision model is combined with the force based method to simulate the movement of non-motorized vehicles on the road. The modified model is calibrated using 1534 non-motorized vehicle trajectories collected from a road in Xi’an, Shaanxi, China. The validity of the model is verified by analyzing the speed distribution and decision-making process of non-motorized vehicles, and comparing the simulation results of different models. The effects of the number of bicycles and the speed of electric vehicles on the flow of non-motorized vehicles are simulated and analyzed by using the calibrated model. The relevant results can provide a basis for urban management and road design. Full article
(This article belongs to the Special Issue Application of Advanced Computing and Artificial Intelligence in Engineering and Science)
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27 pages, 4435 KiB  
Article
Stability Analysis for Autonomous Vehicle Navigation Trained over Deep Deterministic Policy Gradient
by Mireya Cabezas-Olivenza, Ekaitz Zulueta, Ander Sanchez-Chica, Unai Fernandez-Gamiz and Adrian Teso-Fz-Betoño
Mathematics 2023, 11(1), 132; https://doi.org/10.3390/math11010132 - 27 Dec 2022
Cited by 2 | Viewed by 1898
Abstract
The Deep Deterministic Policy Gradient (DDPG) algorithm is a reinforcement learning algorithm that combines Q-learning with a policy. Nevertheless, this algorithm generates failures that are not well understood. Rather than looking for those errors, this study presents a way to evaluate the suitability [...] Read more.
The Deep Deterministic Policy Gradient (DDPG) algorithm is a reinforcement learning algorithm that combines Q-learning with a policy. Nevertheless, this algorithm generates failures that are not well understood. Rather than looking for those errors, this study presents a way to evaluate the suitability of the results obtained. Using the purpose of autonomous vehicle navigation, the DDPG algorithm is applied, obtaining an agent capable of generating trajectories. This agent is evaluated in terms of stability through the Lyapunov function, verifying if the proposed navigation objectives are achieved. The reward function of the DDPG is used because it is unknown if the neural networks of the actor and the critic are correctly trained. Two agents are obtained, and a comparison is performed between them in terms of stability, demonstrating that the Lyapunov function can be used as an evaluation method for agents obtained by the DDPG algorithm. Verifying the stability at a fixed future horizon, it is possible to determine whether the obtained agent is valid and can be used as a vehicle controller, so a task-satisfaction assessment can be performed. Furthermore, the proposed analysis is an indication of which parts of the navigation area are insufficient in training terms. Full article
(This article belongs to the Special Issue Application of Advanced Computing and Artificial Intelligence in Engineering and Science)
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27 pages, 6858 KiB  
Article
Explainable Multi-Class Classification Based on Integrative Feature Selection for Breast Cancer Subtyping
by Souham Meshoul, Ali Batouche, Hadil Shaiba and Shiekhah AlBinali
Mathematics 2022, 10(22), 4271; https://doi.org/10.3390/math10224271 - 15 Nov 2022
Cited by 4 | Viewed by 3163
Abstract
Breast cancer subtype classification is a multi-class classification problem that can be handled using computational methods. Three main challenges need to be addressed. Consider first the high dimensionality of the available datasets relative to the extremely small number of instances. Second, the integration [...] Read more.
Breast cancer subtype classification is a multi-class classification problem that can be handled using computational methods. Three main challenges need to be addressed. Consider first the high dimensionality of the available datasets relative to the extremely small number of instances. Second, the integration of different levels of data makes the dimensionality problem even more challenging. The third challenging issue is the ability to explain the predictions provided by a machine learning model. Recently, several deep learning models have been proposed for feature extraction and classification. However, due to the small size of the datasets, they were unable to achieve satisfactory results, particularly in multi-class classification. Aside from that, explaining the impact of features on classification has not been addressed in previous works. To cope with these problems, we propose a multi-stage feature selection (FS) framework with two data integration schemes. Using multi-omics data, four machine learning models, namely support vector machines, random forest, extra trees, and XGBoost, were investigated at each level. The SHAP framework was used to explain how specific features influenced classification. Experimental results demonstrated that ensemble models with early integration and two stage feature selection improved results compared to baseline experiments and to state-of-the art methods. Furthermore, more explanations regarding the implications of the main relevant features in the predictions are provided, which could serve as a baseline for future biological investigations. Full article
(This article belongs to the Special Issue Application of Advanced Computing and Artificial Intelligence in Engineering and Science)
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15 pages, 2478 KiB  
Article
Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models
by Omer Mujahid, Ivan Contreras, Aleix Beneyto, Ignacio Conget, Marga Giménez and Josep Vehi
Mathematics 2022, 10(20), 3741; https://doi.org/10.3390/math10203741 - 12 Oct 2022
Cited by 8 | Viewed by 2045
Abstract
Mathematical modeling of the glucose–insulin system forms the core of simulators in the field of glucose metabolism. The complexity of human biological systems makes it a challenging task for the physiological models to encompass the entirety of such systems. Even though modern diabetes [...] Read more.
Mathematical modeling of the glucose–insulin system forms the core of simulators in the field of glucose metabolism. The complexity of human biological systems makes it a challenging task for the physiological models to encompass the entirety of such systems. Even though modern diabetes simulators perform a respectable task of simulating the glucose–insulin action, they are unable to estimate various phenomena affecting the glycemic profile of an individual such as glycemic disturbances and patient behavior. This research work presents a potential solution to this problem by proposing a method for the generation of blood glucose values conditioned on plasma insulin approximation of type 1 diabetes patients using a pixel-to-pixel generative adversarial network. Two type-1 diabetes cohorts comprising 29 and 6 patients, respectively, are used to train the generative model. This study shows that the generated blood glucose values are statistically similar to the real blood glucose values, mimicking the time-in-range results for each of the standard blood glucose ranges in type 1 diabetes management and obtaining similar means and variability outcomes. Furthermore, the causal relationship between the plasma insulin values and the generated blood glucose conforms to the same relationship observed in real patients. These results herald the aptness of deep generative models for the generation of virtual patients with diabetes. Full article
(This article belongs to the Special Issue Application of Advanced Computing and Artificial Intelligence in Engineering and Science)
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21 pages, 1827 KiB  
Article
RanKer: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers
by Keyur Patel, Karan Sheth, Dev Mehta, Sudeep Tanwar, Bogdan Cristian Florea, Dragos Daniel Taralunga, Ahmed Altameem, Torki Altameem and Ravi Sharma
Mathematics 2022, 10(19), 3714; https://doi.org/10.3390/math10193714 - 10 Oct 2022
Cited by 7 | Viewed by 5676
Abstract
An organization’s success depends on its employees, and an employee’s performance decides whether the organization is successful. Employee performance enhances the productivity and output of organizations, i.e., the performance of an employee paves the way for the organization’s success. Hence, analyzing employee performance [...] Read more.
An organization’s success depends on its employees, and an employee’s performance decides whether the organization is successful. Employee performance enhances the productivity and output of organizations, i.e., the performance of an employee paves the way for the organization’s success. Hence, analyzing employee performance and giving performance ratings to employees is essential for companies nowadays. It is evident that different people have different skill sets and behavior, so data should be gathered from all parts of an employee’s life. This paper aims to provide the performance rating of an employee based on various factors. First, we compare various AI-based algorithms, such as random forest, artificial neural network, decision tree, and XGBoost. Then, we propose an ensemble approach, RanKer, combining all the above approaches. The empirical results illustrate that the efficacy of the proposed model compared to traditional models such as random forest, artificial neural network, decision tree, and XGBoost is high in terms of precision, recall, F1-score, and accuracy. Full article
(This article belongs to the Special Issue Application of Advanced Computing and Artificial Intelligence in Engineering and Science)
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18 pages, 7008 KiB  
Article
Determining Subway Emergency Evacuation Efficiency Using Hybrid System Dynamics and Multiple Agents
by Kai Yu, Nannan Qu, Jifeng Lu and Lujie Zhou
Mathematics 2022, 10(19), 3693; https://doi.org/10.3390/math10193693 - 9 Oct 2022
Cited by 6 | Viewed by 2024
Abstract
With the rapid development of the city, more and more people are choosing the subway as their travel mode. However, the hidden dangers of the subway are becoming increasingly prominent, and emergency evacuation of the subway has become a key factor for its [...] Read more.
With the rapid development of the city, more and more people are choosing the subway as their travel mode. However, the hidden dangers of the subway are becoming increasingly prominent, and emergency evacuation of the subway has become a key factor for its safe operation. Therefore, the research objectives of this paper were to focus on the subway emergency evacuation hybrid model to fill the gap in the field of emergency evacuation simulation methods and countermeasure optimization. The analysis network process (ANP) was used to analyze the influence factors and weights of subway pedestrian evacuation. On this basis, a multiagent model of subway pedestrian evacuation (SD + multiagent) was developed and simulated. The results show that the comprehensive evacuation strategy could improve the evacuation efficiency, shorten the evacuation time, and avoid the waste of resources. This study not only improved the accuracy of the simulation, but also clarified the evacuation process. This approach can effectively prevent the occurrence of subway accidents, reduce casualties, and prevent large-scale casualties such as secondary accidents (induced secondary disasters). Full article
(This article belongs to the Special Issue Application of Advanced Computing and Artificial Intelligence in Engineering and Science)
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21 pages, 902 KiB  
Article
Obtaining Bricks Using Silicon-Based Materials: Experiments, Modeling and Optimization with Artificial Intelligence Tools
by Costel Anton, Florin Leon, Marius Gavrilescu, Elena-Niculina Drăgoi, Sabina-Adriana Floria, Silvia Curteanu and Cătălin Lisa
Mathematics 2022, 10(11), 1891; https://doi.org/10.3390/math10111891 - 31 May 2022
Cited by 4 | Viewed by 2409
Abstract
In the brick manufacturing industry, there is a growing concern among researchers to find solutions to reduce energy consumption. An industrial process for obtaining bricks was approached, with the manufacturing mix modified via the introduction of sunflower seed husks and sawdust. The process [...] Read more.
In the brick manufacturing industry, there is a growing concern among researchers to find solutions to reduce energy consumption. An industrial process for obtaining bricks was approached, with the manufacturing mix modified via the introduction of sunflower seed husks and sawdust. The process was analyzed with artificial intelligence tools, with the goal of minimizing the exhaust emissions of CO and CH4. Optimization algorithms inspired by human and virus behaviors were applied in this approach, which were associated with neural network models. A series of feed-forward neural networks have been developed, with 6 inputs corresponding to the working conditions, one or two intermediate layers and one output (CO or CH4, respectively). The results for ten biologically inspired algorithms and a search grid method were compared successfully within a single objective optimization procedure. It was established that by introducing 1.9% sunflower seed husks and 0.8% sawdust in the brick manufacturing mix, a minimum quantity of CH4 emissions was obtained, while 0% sunflower seed husks and 0.5% sawdust were the minimum quantities for CO emissions. Full article
(This article belongs to the Special Issue Application of Advanced Computing and Artificial Intelligence in Engineering and Science)
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20 pages, 334 KiB  
Article
Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study
by Georgiana Ingrid Stoleru and Adrian Iftene
Mathematics 2022, 10(10), 1767; https://doi.org/10.3390/math10101767 - 22 May 2022
Cited by 3 | Viewed by 3011
Abstract
Alzheimer’s Disease (AD) is a highly prevalent condition and most of the people suffering from it receive the diagnosis late in the process. The diagnosis is currently established following an evaluation of the protein biomarkers in cerebrospinal fluid (CSF), brain imaging, cognitive tests, [...] Read more.
Alzheimer’s Disease (AD) is a highly prevalent condition and most of the people suffering from it receive the diagnosis late in the process. The diagnosis is currently established following an evaluation of the protein biomarkers in cerebrospinal fluid (CSF), brain imaging, cognitive tests, and the medical history of the individuals. While diagnostic tools based on CSF collections are invasive, the tools used for acquiring brain scans are expensive. Taking these into account, an early predictive system, based on Artificial Intelligence (AI) approaches, targeting the diagnosis of this condition, as well as the identification of lead biomarkers becomes an important research direction. In this survey, we review the state-of-the-art research on machine learning (ML) techniques used for the detection of AD and Mild Cognitive Impairment (MCI). We attempt to identify the most accurate and efficient diagnostic approaches, which employ ML techniques and therefore, the ones most suitable to be used in practice. Research is still ongoing to determine the best biomarkers for the task of AD classification. At the beginning of this survey, after an introductory part, we enumerate several available resources, which can be used to build ML models targeting the diagnosis and classification of AD, as well as their main characteristics. After that, we discuss the candidate markers which were used to build AI models with the best results in terms of diagnostic accuracy, as well as their limitations. Full article
(This article belongs to the Special Issue Application of Advanced Computing and Artificial Intelligence in Engineering and Science)

Review

Jump to: Research

25 pages, 8399 KiB  
Review
Deep Learning Research Directions in Medical Imaging
by Cristian Simionescu and Adrian Iftene
Mathematics 2022, 10(23), 4472; https://doi.org/10.3390/math10234472 - 27 Nov 2022
Cited by 3 | Viewed by 1986
Abstract
In recent years, deep learning has been successfully applied to medical image analysis and provided assistance to medical professionals. Machine learning is being used to offer diagnosis suggestions, identify regions of interest in images, or augment data to remove noise. Training models for [...] Read more.
In recent years, deep learning has been successfully applied to medical image analysis and provided assistance to medical professionals. Machine learning is being used to offer diagnosis suggestions, identify regions of interest in images, or augment data to remove noise. Training models for such tasks require a large amount of labeled data. It is often difficult to procure such data due to the fact that these requires experts to manually label them, in addition to the privacy and legal concerns that limiting their collection. Due to this, creating self-supervision learning methods and domain-adaptation techniques dedicated to this domain is essential. This paper reviews concepts from the field of deep learning and how they have been applied to medical image analysis. We also review the current state of self-supervised learning methods and their applications to medical images. In doing so, we will also present the resource ecosystem of researchers in this field, such as datasets, evaluation methodologies, and benchmarks. Full article
(This article belongs to the Special Issue Application of Advanced Computing and Artificial Intelligence in Engineering and Science)
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