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Processes
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Recent Advances in Artificial Intelligence and Their Engineering Application
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Recent Advances in Artificial Intelligence and Their Engineering Application

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Advanced Digital and Other Processes".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5521

Special Issue Editor

Prof. Dr. Mhamed Sayyouri

E-Mail Website1 Website2 Website3
Guest Editor
Department of Industrial Engineering, National School of Applied Sciences, Sidi Mohamed Ben Abdellah Fès University (USMBA), Fez 30003, Morocco
Interests: pattern recognition; computer/machine vision; computational intelligence; machine learning; feature extraction; evolutionary optimization; signal and image processing

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence (AI) has seen an unprecedented surge of interest in the engineering field thanks to significant technological advances that extend the capabilities of computers and increase their performance for development, improving sustainability, the efficiency of industrial systems, process integration and intensification. These advances open vast prospects for technological innovation and automation in work situations in the various fields of engineering.

This Special Issue is devoted to the latest and future theoretical innovations of AI and their applications in industrial domains. In this Special Issue, we also invite case studies, comprehensive reviews, and investigative articles to provide a valuable reference for researchers and practitioners on the challenges, opportunities, and future development of AI in engineering.

Topics of interest include, but are not limited to:

  • Artificial intelligence and its applications;
  • Computational intelligence and soft computing;
  • Internet of Things and intelligent systems;
  • Deep machine learning for engineering;
  • Intelligent data analysis;
  • Industry 4.0;
  • Expert systems and multimedia;
  • Emerging technologies in the Industrial Internet of Things.

Prof. Dr. Mhamed Sayyouri
Guest Editor

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. Processes 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 2400 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

  • artificial intelligence
  • machine learning
  • automation
  • activity
  • intelligent systems
  • engineering
  • expert systems

Published Papers (2 papers)

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Research

16 pages, 3289 KiB  
Article
Design Optimization of Counter-Flow Double-Pipe Heat Exchanger Using Hybrid Optimization Algorithm
by B. Venkatesh, Mudassir Khan, Bayan Alabduallah, Ajmeera Kiran, J. Chinna Babu, B. Bhargavi and Fatimah Alhayan
Processes 2023, 11(6), 1674; https://doi.org/10.3390/pr11061674 - 31 May 2023
Cited by 2 | Viewed by 2145
Abstract
Double-pipe counter-flow heat exchangers are considered more suitable for heat recovery in the heat transfer industry. Numerous studies have been conducted to develop static tools for optimizing operating parameters of heat exchangers. Using this study, an improved heat exchanger system will be developed. This [...] Read more.
Double-pipe counter-flow heat exchangers are considered more suitable for heat recovery in the heat transfer industry. Numerous studies have been conducted to develop static tools for optimizing operating parameters of heat exchangers. Using this study, an improved heat exchanger system will be developed. This is frequently used to solve optimization problems and find optimal solutions. The Taguchi method determines the critical factor affecting a specific performance parameter of the heat exchanger by identifying the significant level of the factor affecting that parameter. Gray relational analysis was adopted to determine the gray relational grade to represent the multi-factor optimization model, and the heat exchanger gray relation coefficient target values that were predicted have been achieved using ANN with a back propagation model with the Levenberg–Marquardt drive algorithm. The genetic algorithm improved the accuracy of the gray relational grade by assigning gray relational coefficient values as input to the developed effective parameter. This study also demonstrated significant differences between experimental and estimated values. According to the results, selecting the parameters yielded optimal heat exchanger performance. Using a genetic algorithm to solve a double-pipe heat exchanger with counterflow can produce the most efficient heat exchanger. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Their Engineering Application)
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15 pages, 2724 KiB  
Article
Performance Evaluation of Ingenious Crow Search Optimization Algorithm for Protein Structure Prediction
by Ahmad M. Alshamrani, Akash Saxena, Shalini Shekhawat, Hossam M. Zawbaa and Ali Wagdy Mohamed
Processes 2023, 11(6), 1655; https://doi.org/10.3390/pr11061655 - 29 May 2023
Cited by 1 | Viewed by 889
Abstract
Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is [...] Read more.
Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is computationally complex and time-consuming. Metaheuristics are proven to be an adequate tool for dealing with complex problems with higher computational efficiency than conventional tools. The work presented in this paper is the development and testing of the Ingenious Crow Search Algorithm (ICSA). First, the algorithm is tested on standard mathematical functions with known properties. Then, the application of newly developed ICSA is explored on protein structure prediction. The efficacy of this algorithm is tested on a bench of artificial proteins and real proteins of medium length. The comparative analysis of the optimization performance is carried out with some of the leading variants of the crow search algorithm (CSA). The statistical comparison of the results shows the supremacy of the ICSA for almost all protein sequences. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Their Engineering Application)
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