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International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749 . Open Access



AI and Machine Learning Paradigms for Engineering and Science: Advances in Signal, Machines, Automation, and Algorithm (SIGMAA)

Special Issue

Guest Editors

Dr. Hasmat Malik
Department of Electrical Power Engineering, Faculty of Electrical Engineering,
Universiti Teknologi Malaysia (UTM), Malaysia.
E-mail: hasmat@utm.my

Prof. Dr. Y. R. Sood
Department of Electrical Engineering,
National Institute of Technology Hamirpur (H.P.), India.
E-mail: yrsood.nith@gmail.com

Prof. Dr. Fausto Pedro Garcia Marquez
Universidad Castilla-La Mancha, Spain.
E-mail: FaustoPedro.Garcia@uclm.es

Dr. Taha Selim Ustun
Fukushima Renewable Energy Institute, Advanced Industrial Science and
Technology (FREA), Koriyama, Japan.
E-mail: tahaselim@yahoo.com

Introduction and Aim & Scope

Machine learning (ML) and artificial intelligence (AI) are among the best transformative technologies which have huge potential to bring a powerful impact in various industries of engineering and research. This special issue provides in detailed view of the different techniques of AI and ML for managing complex problems and enhancing productivity in the field of engineering and technical applications. Explicitly, engineering is utilizing AI and ML techniques for the betterment and progress of operation management, designing, and manufacturing. Machine learning engineers firstly design software, predictive models, algorithms which aids machines simplify and identify data-based patterns and take immediate actions independently and that to before getting directions for performing specific responsibilities. Obviously, with the use of these AI and ML paradigms, ML engineers can make data-driven decisions, and raise their dependability on their products. AI and ML technologies not only enhance the system’s overall performance across the board but also improve computer-aided designs to predictive maintenance to quality assurance.

Moreover, with autonomous operations and real-time monitoring, the combination of AI and ML with IoT devices is bringing a revolution in smart systems. AI and ML are accelerating scientific advancement by simplifying the process of data analysis and by finding patterns in huge databases. Basically, automation lessens subjective errors and increases system efficiency and operation productivity, which makes ML engineers to do complex and time-consuming tasks easily and make critical and strategic decisions earlier.

These technological developments are prime for advancing research in an array of scientific fields such as genetics, pharmaceutical innovations, climate modelling, and particle physics. Recently, progress in several scientific fields is being facilitated by AI-powered simulations that assist scientists to better understanding complex processes and to predict outcomes efficiently.

This special issue also discusses the difficulties and constraints of using AI and ML paradigms in engineering and scientific applications. Data privacy, bias, and interpretability are serious challenges that will be addressed, as these technologies are to be used ethically and responsibly. Furthermore, to keep up with the changing nature of engineering and scientific fields, strong algorithms, scalable infrastructure, and continuous learning are addressed. Finally, AI and ML paradigms are ready to transform engineering and scientific landscapes by providing fresh solutions and boosting human skills. Researchers, engineers, and scientists may use an interdisciplinary approach to harness the power of AI and ML to enable revolutionary discoveries, improved problem-solving, and long-term innovation across a wide range of applications.

This special issue intends to bring together academics, practitioners, and experts from academia and industry to present their original research work, exchange ideas, and explore the possibilities of AI and ML paradigms in engineering and scientific applications going forward. The integration of AI and ML in various fields has faced many obstacles, hence we want original research articles, reviews, and case studies that highlight these issues. Topics may include but are not limited to:

  • Intelligent Tools and Techniques.
  • Applications using Intelligent Techniques in Electrical & Energy Systems.
  • Applications using Intelligent Techniques in Electronics & Communication.
  • Applications using Intelligent Techniques in Mechanical and Automation.
  • Applications using Intelligent Techniques in Computer Engineering.
  • Applications using Intelligent Techniques in Big data analysis.
Important Dates
Manuscript submission opening: August 1, 2023
Last date for submitting the manuscript: March 31, 2024

Manuscript Submission Information

Articles have to be prepared carefully according to the guide For Authors at the journal website https://ijmems.in/forauthors.php, and to be submitted through online IJMEMS Submission System at https://submission.ijmems.in . On the first page of the manuscript, before the title of the article, kindly write as “AI and Machine Learning Paradigms for Engineering and Science: Advances in Signal, Machines, Automation, and Algorithm (SIGMAA)”.

Authors should note that all articles submitted should be original and should not have been submitted anywhere else for consideration for publication. All articles will be reviewed in double blind review process as per the journal policy. More information can be found at the journal website at https://ijmems.in Or https://ijmems.in/ethicalissues.php




Reliability Optimization for System Designing by Implementing Advanced Techniques and Metaheuristics

Special Issue

Guest Editors

Dr. Ashok Singh Bhandari
Department of Mathematics,
Graphic Era Hill University, Dehradun Campus, India.
E-mail: bhandariashoksingh@gmail.com

Dr. Akshay Kumar
Department of Mathematics,
Graphic Era Hill University, Dehradun Campus, India.
E-mail: akshaykumar@gehu.ac.in

Dr. Shah Limon
Department of Physics and Engineering,
Slippery Rock University of Pennsylvania, PA, USA.
E-mail: shah.limon@sru.edu

Introduction and Aim & Scope

System reliability optimization is a live problem, with solutions methodologies that have evolved in tandem with mathematical advances, the introduction of new engineering technology, and shifts in managerial viewpoints. Due of the evolution of the most recent technological systems, the configuration of a system is extremely dependent on the selection of components, and thus system reliability is a critical factor to consider in system management. While putting together a desirable system, one concern that arises is how to strike a compromise between system reliability and other physical factors such as cost, volume, weight, etc. Such type of optimization problem generally falls into the NP hard problem category, which are solvable by implementation of metaheuristics.

This special issue aims to collect articles that have theoretical studies and applications on system reliability optimization and application of advanced metaheuristics. The scope of this special issues covers following research areas, but not limited to:

  • Advanced Complex Systems
  • Emerging Algorithms
  • Feasibility
  • Interdependent System Reliability
  • MCMC Simulation / Markov Models
  • Multi-objective Modelling
  • Non-linear Programming
  • Optimization
  • Reliability Redundancy Allocation Problem
  • System Designing
Important Dates
Manuscript submission opening: November 1, 2023
Last date for submitting the manuscript: April 30, 2024

Manuscript Submission Information

Articles have to be prepared carefully according to the guide For Authors at the journal website https://ijmems.in/forauthors.php, and to be submitted through online IJMEMS Submission System at https://submission.ijmems.in . On the first page of the manuscript, before the title of the article, kindly write as “Article for the Special Issue on Reliability Optimization for System Designing by Implementing Advanced Techniques and Metaheuristics”.

Authors should note that all articles submitted should be original and should not have been submitted anywhere else for consideration for publication. All articles will be reviewed in double blind review process as per the journal policy. More information can be found at the journal website at https://ijmems.in Or https://ijmems.in/ethicalissues.php



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