International Journal of Automation and Computing

Scope & Guideline

Championing Quality Research in Automation Technologies

Introduction

Delve into the academic richness of International Journal of Automation and Computing with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN1476-8186
PublisherSPRINGERNATURE
Support Open AccessNo
CountryChina
TypeJournal
Converge2005, from 2007 to 2021 (coverage discontinued in Scopus)
AbbreviationINT J AUTOM COMPUT / Int. J. Autom. Comput.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressCAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND

Aims and Scopes

The International Journal of Automation and Computing focuses on the intersection of automation, computing, and artificial intelligence, emphasizing innovative methodologies and applications across various domains.
  1. Automation Technologies:
    The journal explores advanced automation technologies including robotics, control systems, and intelligent systems aimed at enhancing efficiency and performance in various applications.
  2. Computational Intelligence:
    Research on computational intelligence techniques such as neural networks, fuzzy logic, and evolutionary algorithms is a core focus, targeting problem-solving in complex and dynamic environments.
  3. Data Analysis and Machine Learning:
    The journal publishes studies on machine learning methodologies, data analysis techniques, and their applications, particularly in predictive modeling and decision-making processes.
  4. Robotics and Autonomous Systems:
    Research relating to the design, control, and application of robots and autonomous systems is emphasized, showcasing advancements in multi-robot systems and autonomous navigation.
  5. Human-Robot Interaction:
    The journal investigates the relationship and interaction between humans and robots, exploring methodologies that enhance collaboration and mutual understanding.
  6. Emerging Applications:
    The journal serves as a platform for innovative applications of automation and computing in diverse fields such as healthcare, transportation, and environmental monitoring.
Recent publications indicate a clear trend towards integrating advanced technologies and methodologies, reflecting the evolving landscape of automation and computing.
  1. Artificial Intelligence and Deep Learning:
    There is an increasing focus on AI and deep learning techniques, particularly in applications like image recognition, natural language processing, and autonomous systems.
  2. Robust Control and Adaptive Systems:
    Emerging themes in robust control strategies that adapt to uncertain environments are becoming more prevalent, highlighting the need for resilience in automation.
  3. Data Augmentation and Synthetic Data:
    The use of data augmentation and synthetic data generation techniques is trending, particularly in machine learning applications to improve model robustness.
  4. Human-Centric Automation:
    Research is increasingly oriented towards human-centric automation, emphasizing the importance of user experience and interaction in robotic systems.
  5. Multi-Agent Systems and Cooperation:
    The exploration of multi-agent systems and cooperative strategies in robotics is gaining traction, reflecting the need for collaboration in complex tasks.

Declining or Waning

While the journal has a broad range of topics, some areas have seen a decline in focus over recent years, indicating shifts in research trends and priorities.
  1. Traditional Control Systems:
    Research on classical control systems has become less prominent, as the field shifts towards more adaptive and intelligent control methodologies that incorporate machine learning.
  2. Basic Robotics Concepts:
    Papers centered on foundational robotics concepts have decreased, likely due to the increased interest in complex systems and multi-robot collaboration.
  3. Static Data Processing Techniques:
    There is a noticeable decline in the exploration of static data processing methods, with a growing preference for dynamic and real-time data analysis approaches.
  4. Purely Theoretical Studies:
    The journal has seen fewer purely theoretical studies, as there is a stronger emphasis on practical applications and experimental validations in recent publications.
  5. Low-level Programming and Hardware Design:
    Research focused exclusively on low-level programming or hardware design has waned, reflecting a shift towards high-level algorithms and software solutions.

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