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Call for Papers for a Special Issue on "Recent Advances in Hyperparameter Tuning for Machine Learning Models"
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- Published on 25 June 2025
Guest Editors:
Dr. Hai-Canh VU
Roberval Laboratory, Compiègne University of Technology, 60200 Compiègne, France
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Interests: predictive maintenance; prognostics and health management; machine learning; Industry 4.0
Dr. Nassim Boudaoud
Roberval Loboratory, Compiègne University of Technology, 60200 Compiègne, France
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Interests: statistical process control; prognostics and health management; machine learning; Industry 4.0
Background and Motivation:
In recent years, machine learning (ML) has demonstrated significant breakthroughs across a wide range of applications—from computer vision and natural language processing to biomedical engineering and industrial systems. However, the performance of ML models often hinges critically on the choice of hyperparameters, such as learning rates, regularization terms, and network architectures. Improper tuning can lead to suboptimal performance, overfitting, or excessive computational costs.
Hyperparameter tuning, therefore, remains a persistent and non-trivial challenge in both academic research and industrial practice. While traditional methods such as grid search and random search have been widely used, they are often inefficient or infeasible for large-scale models. Recently, advanced approaches—including Bayesian optimization, evolutionary strategies, gradient-based tuning, and meta-learning—have gained traction and promise more effective solutions.
This special issue aims to bring together cutting-edge research that addresses the theoretical, computational, and practical challenges of hyperparameter optimization in machine learning. The goal is to foster novel contributions that advance the automation, efficiency, and robustness of model tuning in diverse domains.
Topics of Interest:
- Novel algorithms for hyperparameter optimization (HPO)
- Bayesian optimization, bandit methods, and surrogate models for HPO
- Population-based and evolutionary strategies
- Differentiable and gradient-based hyperparameter learning
- Multi-objective and cost-aware tuning strategies
- AutoML frameworks and systems for scalable tuning
- Hyperparameter transfer learning and warm-starting
- HPO in deep learning and reinforcement learning
- Domain-specific tuning (e.g., for healthcare, finance, robotics)
- Interpretability and explainability in HPO
- Benchmarks, datasets, and empirical comparisons of tuning methods
- Integration of HPO in federated and distributed learning
- Applications of HPO in real-world industrial settings
Target Audience:
The special issue will be of interest to:- Researchers in machine learning, optimization, and artificial intelligence
- Practitioners developing or deploying ML models at scale
- Developers of AutoML platforms and hyperparameter tuning tools
- Applied scientists and engineers in fields such as healthcare, manufacturing, finance, and environmental science
Submission Deadline: November 15, 2025
Call for Papers for a Special Issue on "AI-Driven Simulation and Neural Optimization for Smart Systems and Healthcare Applications"
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- Published on 06 June 2025
Guest Editors:
Assoc. Prof. Dr Hoshang Kolivand
Head of Applied Computing Research Group
IEEE senior member
Liverpool John Moores University, UK
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Dr. Ata Jahangir Moshayedi
School of Information Engineering, Jiangxi University of Science and Technology, No
86, Hongqi Ave, Ganzhou, 341000, Jiangxi, China
IEEE senior member
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Prof. Tanzila Saba
Associate Director, Research and Initiative Center
Leader of Artificial Intelligence and Data Analytics Lab
Research Professor | IEEE Senior Member | HEA Fellow (UK)
Prince Sultan University, Riyadh, Saudi Arabia
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Dr. Shamsollah Ghanbari
Assistant Professor of Computer Science
Islamic Azad University, Iran
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Dr. Mohammed Ibrahim Khalaf
Dean, College of Science
Head of Quality Assurance and Academic Accreditation Unit
AL-Maarif University College, Iraq
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Background and Rationale:
Simulation has long played a foundational role in system design, optimisation, and predictive analysis across a range of engineering and scientific domains. In recent years, the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques into simulation pipelines has transformed traditional modelling paradigms. These intelligent systems can now automatically learn from data, optimise parameter spaces, adapt to real-time input, and deliver enhanced predictive capabilities in complex, uncertain environments.
In parallel, the advancement of neural network architectures—including deep learning, recurrent networks, and neuro-symbolic hybrids—has enabled the development of intelligent simulation frameworks capable of mimicking dynamic systems with unprecedented accuracy and computational efficiency. From digital twin platforms to healthcare simulators, these models allow for real-time interaction, adaptive control, and robust optimisation in domains where traditional approaches fall short.
This Special Issue aims to explore the next generation of simulation-driven design and decision-making, focusing on how AI-enhanced modelling, neural computation, and intelligent optimisation are being applied to real-world problems in healthcare, engineering design, smart environments, and cyber-physical systems.
Scope and Topics:
This Special Issue invites original research, review articles, and case studies related to (but not limited to):
- Simulation-based design optimisation using ML/AI
- Multi-objective and metaheuristic algorithms for smart systems
- Neural network-driven simulation frameworks
- AI and BCI systems integrated with virtual simulation
- Intelligent healthcare simulation models
- Deep learning models for dynamic system response prediction
- Digital twin systems and predictive modelling
- AI-assisted diagnostics and optimisation in biomedical systems
- Real-time simulation for autonomous system control
- Hybrid AI models for engineering simulation and decision support
Correspondence:
Lead Guest Editor
Assoc. Prof. Dr. Hoshang Kolivand
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Submission Deadline: January 1st, 2026
Call for Papers for a Special Issue on "Multi-modal Information Learning and Analytics on Cross-Media Data Integration"
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- Published on 28 November 2024
Guest Editors:
Xu Zheng Shanghai Polytechnic University, China Xu Zheng is currently the professor in Shanghai Polytechnic University, China. He is also the associate editor of Springer ECR journal and Springer DIoT journal.
Jemal H. Abawajy Deakin University, Australia Jemal H. Abawajy is a Full Professor with the School of Information Technology, Faculty of Science, Engineering, and Built Environment, Deakin University, Australia. He is currently the Director of the Parallel and Distributing Computing Laboratory.
Shafi’i Muhammad Abdulhamid Federal University of Technology, Minna-Nigeria Shafi’i Muhammad Abdulhamid received his Ph.D. in Computer Science from University of Technology Malaysia (UTM). Presently, he is the professor of FEDERAL UNIVERSITY OF TECHNOLOGY, MINNA-NIGERIA.
Haruna Chiroma University of Hafr Al Batin, Saudi Arabia Haruna Chiroma received the Ph.D. degree from the Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya. He is currently Professor (Assistant) at University of Hafr Al Batin.
Aims and Scope of the Special Issue
We are living in the era of data deluge. Meanwhile, the world of big data exhibits a rich and complex set of cross-media contents, such as text, image, video, audio and graphics. Thus far, great research efforts have been separately dedicated to big data processing and cross-media mining, with well theoretical underpinnings and great practical success. However, studies jointly considering cross-media big data analytics are relatively sparse. This research gap needs our more attention, since it will benefit lots of real-world applications. Despite its significance and value, it is non-trivial to analyze cross-media big data due to their heterogeneity, large-scale volume, increasing size, unstructured, correlations, and noise. Multi-modal Information Learning, which can be treated as the most significant breakthrough in the past 10 years, has greatly affected the methodology of computer vision and achieved terrific progress in both academy and industry.
This special issue focuses on learning methods to achieve high performance Multi-modal Information analysis and understanding under uncontrolled environments in large scale, which is also a very challenging problem. Moreover, it attracts much attention from both the academia and the industry. We hope this topic will aggregate top level works on the new advances in Multi-modal Information from cross-media data.
Topics of interests include, but are not limited to:
- Cross-Media Big Data Representation
- Large-scale multimodal media data acquisition
- Novel dataset and benchmark for cross-media big data analytics
- Cross-Media Big Data Management
- Large-scale multimodal information fusion
- Domain adaptation for cross-media big data
- Cross-media big data organization, retrieval and indexing
- Learning methods to bridge the semantic gap among media types
- Cross-Media Big Data Understanding and Applications
- Multi-modal Information for feature representation
Submissions for this special issue should be submitted through the Journal’s submission system, Editorial Manager. Detailed guidelines on submission format and process can be found in the Instructions for Authors of the journal.
Submission Deadline: October 1, 2025
International Journal for Simulation and Multidisciplinary Design Optimization now indexed in Ei Compendex
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- Published on 08 November 2022
We are pleased to announce that International Journal for Simulation and Multidisciplinary Design Optimization is now indexed in the Ei Compendex, joining over 3,800 high-quality peer-reviewed journals. These journals cover a vast number of areas, such as applied physics and optics, bioengineering and biotechnology, instrumentation and nanotechnology.
DOAJ Seal awarded to International Journal for Simulation and Multidisciplinary Design Optimization demonstrating “best practice in open access publishing”
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- Published on 23 March 2021
We are pleased to announce that International Journal for Simulation and Multidisciplinary Design Optimization, among 22 other EDP Sciences’ journals, has recently been awarded the DOAJ Seal which “is awarded to journals that demonstrate best practice in open access publishing”. Only around 10% of journals indexed in the DOAJ are awarded the DOAJ Seal, so it is a gratifying mark of recognition of the excellent work International Journal for Simulation and Multidisciplinary Design Optimization is doing.
Call for Papers for a Special Issue on "Advances in Modeling and Optimization of Manufacturing Processes"
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- Published on 12 January 2021
Guest Editors:
Dr. Sachin Salunkhe
Vel Tech Rangarajan, Institute of Science and Technology, Chennai, India
Prof. Sofiane Guessasma
INRA, Paris, France
Dr. Vishal Naranje
Amity University, Dubai, United Arab Emirates
Call for Papers for a Special Issue on "Computation Challenges for engineering problems"
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- Published on 21 December 2020
Guest Editors:
Dr. Nadhir LEBAAL
University of Technology (UTBM), France
Dr. Subramanian JEYANTHI
School of mechanical Engineering
Vellore Institute of Technology, India
Dr. Jebaseelan DAVIDSON
School of mechanical Engineering
Vellore Institute of Technology, India
Dr. M.C. LENINBABU
School of mechanical Engineering
Vellore Institute of Technology, India
Call for Papers for a Special Issue on "Simulation and Optimization for Industry 4.0"
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- Published on 19 November 2020
Guest Editors:
Professor Abdelkhalak EL HAMI
Normandy University, National institute on applied sciences INSA- Rouen-Normandy, France
Professor Mohamed HADDAR
ENISfax, Sfax, Tunisie
Professor Bouchaib RADI
FST Settat, Morocco
Professor Norelislam EL HAMI
ENSA Kenitra, Morocco
The "International Journal of Simulation and Multidisciplinary Design Optimization (IJSMDO)" is now indexed in Scopus
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- Published on 11 September 2017
EDP Sciences are pleased to announce that International Journal for Simulation and Multidisciplinary Design Optimization (IJSMDO) has been accepted for indexation in Elsevier’s Scopus database.
Scopus is widely recognised as one of the largest abstract and citation databases of scholarly literature. The bibliographic database provides an overview of global research output in STEM fields as well as the social sciences, arts, and humanities. Scopus provides a suite of search and analysis features that enhance journal discoverability for an audience of over 3,000 academic, government, and corporate institutions globally.
Ariana Fuga, Senior Publishing Editor at EDP Sciences, commented on the announcement: “This is an important step for the visibility of the journal, and we are proud that International Journal for Simulation and Multidisciplinary Design Optimization has been approved for inclusion by the independent advisory board.”
Call for Papers for a special issue on "Uncertainty-Based Design Optimization"
- Details
- Published on 09 February 2017
Guest Editor: Professor Xiao-Jun Wang, Beihang University, China
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Editor: Professor Chao Jiang, Hunan University, China
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Submission deadline - 20th April 2017