International Journal of High Speed Electronics and Systems

Call for Papers

In recent years, with the continuous expansion of data scale and the improvement of computing power, the credibility and adaptability of machine learning models have become increasingly prominent, and high-trust adaptive machine learning has emerged. Its new development trend is reflected in three aspects: (1) The continuous upgrading of machine learning algorithms has brought new challenges to the robustness, security and interpretability of models; (2) The complex demands of various fields have driven the automated evolution of machine learning systems, including data selection, model adjustment, and algorithm design; (3) The continuous improvement of computing resources has provided new support for the automated tuning of high-trust adaptive models and diversified data processing.Therefore, it is necessary to utilize and develop existing machine learning theories to build new technologies and system experiences. There is still a lot of room for research on how to promote technological innovation with the help of existing theoretical accumulation, thereby improving the adaptability and credibility of the system in complex environments.

This special issue focuses on the frontier exploration of high-trust adaptive machine learning in improving trustworthiness and adaptability, and is committed to presenting innovative and practical high-level research results. Focusing on high-trust model optimization, adaptive learning method construction and its multi-field application, the special issue will focus on basic theories, key technologies and system implementations, including trusted machine learning combined with logical reasoning, adversarial training, privacy protection, etc., and adaptive machine learning combined with environmental perception, knowledge transfer, feature selection, etc.In addition, this special issue will also focus on the research on dynamic updating and adaptive parameter adjustment of models in emerging computing platforms and open environments, and its application prospects in smart cities, smart transportation, smart manufacturing, medical health and other fields, in order to promote the further development of high-trust adaptive machine learning technology.

Including but not limited to the following topics:

1. High-trust machine learning theory and modeling for open environments
   For example, logical reasoning, adversarial training, privacy protection, federated learning, explainability, zero/small-shot learning, domain adaptation, reinforcement learning, transfer learning, etc.
2. Adaptive machine learning methods for open environments
   For example, feature selection, automatic machine learning, neural network architecture search, meta-learning, online learning, incremental learning, continuous learning, multimodal learning, etc.
3. Highly reliable and adaptive machine learning applications
   For example, the application of machine learning technology in green cities, new power grids, intelligent transportation, intelligent manufacturing, smart education, medical health, industrial planning, resource allocation, recommendation systems, social networks, information retrieval, financial insurance, software engineering, customer service, intrusion detection and other fields.
4. Advanced integrated circuit technology based on trusted artificial intelligence
   For example, machine learning can enable a new generation of electronic design automation (EDA) technology. Such as AI-driven EDA technology, open source chips, and agile design.Deep learning methods can be embedded in chip security and fault-tolerance technologies, such as integrated circuit hardware security, cryptographic acceleration, CPU microarchitecture security, confidential computing, commercial aerospace fault-tolerance technology, etc.

Guest Editorial Team

Dr. Ran Li[MGE]
Professor,
School of Computer Science,
Xinyang Normal University,
Xinyang, China Email ID: liranxynu@126.com Google Scholar: https://scholar.google.com.hk/citations?user=ytoe2I4AAAAJ&hl=en&oi=ao

Dr. Punit Gupta [CO-GE]
Punit Gupta
Assistant Professor,
University College Dublin,
Dublin, Ireland Email: punit.gupta@ucd.ie; Google Scholar: https://scholar.google.co.in/citations?user=-Pegz1gAAAAJ&hl=en

Submission Deadlines:

Manuscript Submission Deadline: 30.09.2025
Authors Notification: 15.11.2025
Revised Papers Due: 15.12.2025
Final notification: 15.01.2026
Publication will be done based on journal guidelines