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International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749


Data-Driven Mathematical Modeling for Decision Making

Special Issue

Introduction

In today's technological era, communication has become very easy, no matter where you are in the world. Due to interconnectivity, what happens in one place on our planet can impact us all. Technology has a huge contribution behind all over growth and development of society. In the past 30 years, planning and improving policies can most effectively be based on the quantitative measurement of quality. Statistical analysis, optimization methods together with mathematics can enhance decision making capabilities and processes. Recently, new techniques based on Artificial Intelligence, Machine Learning, Artificial Neural Network (ANN), Fuzzy Set, Entropy, Big data etc. have opened the doors of new research aspects and a variety of new challenges have emerged in the field of mathematical modeling, supply chain, reliability engineering, marketing and management.

This special issue is dedicated to new mathematical and data-driven advances in reliability engineering, supply chain, decision sciences evaluation, and improvement, both theoretical and applied. The focus is on the development of innovative methodologies for the analysis of real-life data motivated by relevant applications.


The topics of this special issue cover the data-driven mathematical modeling for decision making (but are not limited to):
  • Data-driven mathematical modeling and optimization algorithms in reliability engineering.
  • Predictive models and decision support systems for supply chain.
  • Data mining and statistical learning incorporated prescriptive analytics.
  • Statistical computing for decision making in inventory management.
  • Impact of AI and digital transformation on supply chain sustainability.
  • Reliability and dependability of intelligent systems.
  • Reliability modeling and optimization.
  • Predictive Modeling and Analytics in System Reliability Engineering.
  • Artificial intelligence methods for economic and financial modelling.
  • Machine learning applications for modelling and forecasting.
  • Application of AI, ML, Fuzzy and other optimization techniques in Software Reliability.
  • Queuing theory and applications.
  • Optimal control theory and its applications in management science.
  • AI, ML based models in Epidemiology, Eco-toxicology.
Important Dates
   Submission Deadline: September 30, 2022
   First Round of Reviews: October 30, 2022
   Second Round of Reviews: December 30, 2022
   Final Acceptance: February, 2023

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 the 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 “Data-Driven Mathematical Modeling for Decision Making”.

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.


Other Information for Authors

A guide for authors and other relevant information for submission of the manuscripts is available on the journal website at https://ijmems.in


Guest Editors

Dr. Vijay Kumar
Associate Professor,
Department of Mathematics,
Amity Institute of Applied Sciences, Amity University Uttar Pradesh, Noida, India.
E-mail: vijay_parashar@yahoo.com

Dr. Yoshinobu Tamura
Professor,
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Japan.
E-mail: tamuray@yamaguchi-u.ac.jp

Modelling and Simulations in Partial Differential Equations

Special Issue

Introduction

In recent years, applied mathematics has grown tremendously in a wide range of engineering sciences, attracting the attention of scientists and researchers. The application of mathematics to problems that arise in a variety of domains, including physical and biological consequences, and/or the development of fresh or modified methods to address the demands of real-world situations are all examples of applied mathematics.

The aim of this special issue in “International Journal of Mathematical, Engineering and Management Sciences” is to bring together unique research articles on differential equations with modelling and simulations, novel analytical and numerical methods, fluid dynamics, computational biology, nonlinear dynamics, fractional calculus, inverse issues, fuzzy techniques, and optimization strategies. The exact and numerical solutions to the real-world problems, as well as their applications, will also be considered.


Topic of Interest

In this special issue, we will consider both original research articles as well as review articles in the following potential topics, but not limited to:

  • Modelling and simulations with partial differential equations
  • High order numerical methods
  • Computational fluid dynamics
  • Nonlinear Dynamics in Physics and Applied Mathematics
  • Analysis of Differential equations with applications
  • Analytical and numerical methods
  • Fractional Calculus
  • Computational Biology
Important Dates
   Submission Deadline: August 21, 2022
   First Round of Reviews: September 30, 2022
   Second Round of Reviews: November 30, 2022
   Final Acceptance: February 2023

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 Modelling and Simulations in Partial Differential Equations”.

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


Guest Editors

Dr. Mohammad Tamsir
Department of Mathematics,
Jazan University, Jazan-45142, Saudi Arabia.
Email id: mtamsir@jazanu.edu.sa

Dr. Satyvir Singh
Division of Physics and Applied Physics,
School of Physical & Mathematical Sciences, Nanyang Technological University (NTU).
21 Nanyang Link, Singapore – 637371, Singapore.
Email id: satyvir.singh@ntu.edu.sg

Nonparametric Statistical Inference on Quality Engineering and Reliability Modeling

Special Issue

Introduction

In the last two decades, remarkable developments have been imprinted in the fields of Technology and Engineering, while the complexity of the implemented industrial processes and manufacturing systems seems inextricable. Therefore, the necessity of providing a demanding, accurate and reliable framework has been emphatically surfaced for all applications in Quality and Reliability Engineering.

It is evident that both industrial and lifetime data are often assumed to fit well to a common distribution model. Unfortunately, only if this assumption is satisfied, the statistical properties of the typical methodology are exact. However, if a parametric form for the underlying distribution is not available, then implementing distribution-based tools is characterized as inappropriate. In order to overcome the above-mentioned obstacle, the so-called Nonparametric (or Distribution-free) Statistical Inference provides an attractive and constantly growing alternative for both areas of Quality Engineering and Reliability Modeling.

As it concerns the Quality Engineering field, some of the most popular techniques are those referring to the Statistical Process Control, which is widely used to monitor the quality of a process and supervise the final product of it. In the traditional framework, process control techniques for monitoring continuous variables are distribution-based, meaning that the underlying process is assumed to follow a well-known probability distribution (most often the Normal distribution). Though, this speculation is not frequently actualized and hence the resulting monitoring schemes may not be exact. In such cases where no parametric form of the process distribution is within reach or at least validated properly beforehand, monitoring schemes without requiring the specification of a parametric form for the process response distribution should be considered. Within this context, the so-called Nonparametric (or Distribution-free) Statistical Process Monitoring and Quality Control methods provide an intriguing alternative, which is capable to circumvent adequately this problem and yet keep the essential structure of the common techniques.

On the other hand, Reliability Modeling could be also handled by the aid of nonparametric approaches. Indeed, research topics such as the optimization of systems design, dynamic and structural performance measures, aging classification and stochastic orderings of reliability models under either the common binary or multistate framework, seem to be effectually studied via appropriate distribution-free methods.

The present special issue aims at providing new and significant advances on the topic of Nonparametric Statistical Inference referring to either Quality or Reliability Engineering field. We strongly welcome both theoretical and applied contributions related to the topic, while empirical or simulation-based surveys around the special issue theme are also acceptable. Systematic reviews of the literature referring to specific and relevant subtopics are well received, as long as they address a novel area within the general scope of this special issue.


Topic of Interest

The topics relevant to this special issue include but are not limited to:

  • Nonparametric Statistical Process Control
  • Distribution-free univariate control charts
  • Distribution-free multivariate control charts
  • Reliability Engineering
  • Reliability safety assessment
  • Life testing
  • Prognostics Modeling of Complex Engineering Systems
  • Design and Manufacture for Reliability
  • Availability and Maintenance in Operations Research
  • Nonparametric Hypothesis Testing
  • Structural Reliability
  • Nonparametric tolerance and confidence regions
  • Risk assessment and analysis
  • Order statistics
  • Sequential order statistics
  • Nonparametric modeling
  • Statistical algorithms and machine learning
  • Nonparametric Bayes statistical inference
  • Semiparametric modeling
  • Distribution-free measures of the systems’ design
  • Quality Assurance of Machine Learning Systems
  • Health analytics
  • Optimal Reliability Modeling
  • Nonparametric Maximum Likelihood Estimators
  • Safety Engineering
  • Runs and Scans with applications
  • Big data analytics
  • Engineering Methods for Software Reliability and Security
  • High-dimensional data analytics
  • Applied stochastic models in Industry
  • Reliability optimization
  • Robust statistical analysis
  • Nonparametric profile monitoring
  • Nonparametric Applied Regression Analysis
  • Nonparametric dynamic monitoring
  • Distribution-free process monitoring for time-between-events-and-amplitude data
Important Dates
   Submission Deadline: 30 September 2022
   First Round of Reviews: 1 November 2022
   Second Round of Reviews: 30 December 2022
   Final Acceptance: January 2023

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 Nonparametric Statistical Inference on Quality Engineering and Reliability Modeling”.

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


Guest Editor

Dr. Ioannis S. Triantafyllou
Department of Computer Science & Biomedical Informatics,
University of Thessaly, Lamia, Greece.
Email id: itriantafyllou@uth.gr ,
Most cited articles from IJMEMS-As per SCOPUS database