International Journal of Intelligent Unmanned Systems

Scope & Guideline

Connecting Disciplines for Intelligent System Advancements

Introduction

Welcome to your portal for understanding International Journal of Intelligent Unmanned Systems, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageEnglish
ISSN2049-6427
PublisherEMERALD GROUP PUBLISHING LTD
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 2013 to 2024
AbbreviationINT J INTELL UNMANNE / Int. J. Intell. Unmanned Syst.
Frequency3 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressFloor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE LS1 4DL, ENGLAND

Aims and Scopes

The International Journal of Intelligent Unmanned Systems focuses on the integration of intelligent systems with unmanned technologies, emphasizing research that enhances the capabilities and applications of unmanned systems across various domains.
  1. Intelligent System Design and Implementation:
    Research that explores novel algorithms and architectures for the design and implementation of intelligent systems, particularly in unmanned vehicles and robotics.
  2. Autonomous Navigation and Control:
    Studies focusing on the navigation and control of unmanned aerial vehicles (UAVs) and other autonomous systems, emphasizing optimization techniques, sensor fusion, and adaptive control methods.
  3. Data Communication and Security:
    Investigations into secure data communication frameworks for unmanned systems, addressing challenges such as data integrity, confidentiality, and efficient routing in wireless sensor networks.
  4. Machine Learning and Artificial Intelligence Applications:
    Application of machine learning and AI techniques to improve the performance and functionality of unmanned systems, including pattern recognition, decision-making, and predictive analytics.
  5. IoT and Smart Systems Integration:
    Research that integrates Internet of Things (IoT) technologies with unmanned systems to enhance their intelligence, connectivity, and operational efficiency.
  6. Real-World Applications and Case Studies:
    Empirical studies showcasing the practical applications of unmanned systems in various fields, including agriculture, healthcare, construction, and disaster management.
Recent publications indicate a significant evolution in the journal's focus, highlighting emerging themes that are gaining traction within the field of intelligent unmanned systems.
  1. Deep Learning and Neural Networks:
    There is a growing emphasis on deep learning techniques for various applications, including image classification, medical diagnosis, and autonomous navigation.
  2. Cybersecurity in Unmanned Systems:
    As unmanned systems become more prevalent, research on cybersecurity measures to protect these systems from attacks and vulnerabilities is increasingly important.
  3. Multi-Agent Systems and Coordination:
    Research on multi-agent systems, particularly in the context of UAV swarms and collaborative robotics, is emerging as a critical area of study.
  4. Sustainable and Green Technologies:
    An increasing focus on the development of sustainable technologies, such as renewable energy applications for unmanned systems, reflects a broader trend towards environmental consciousness.
  5. Human-Robot Interaction and Collaboration:
    Studies exploring the interaction between humans and unmanned systems, particularly in collaborative scenarios, are becoming more prominent.

Declining or Waning

While the journal continues to cover a wide range of topics, certain themes have shown a decline in recent publications, indicating a potential shift in focus within the field.
  1. Traditional Robotics Applications:
    Research on basic robotics applications has decreased, possibly due to the rise in more complex and intelligent systems that incorporate advanced AI and machine learning.
  2. Basic Sensor Technologies:
    Studies focusing on traditional sensor technologies without integration into intelligent systems or IoT frameworks are less frequent, as the field moves towards smarter and more interconnected solutions.
  3. Static Path Planning:
    Static path planning strategies are becoming less prominent, with a shift towards dynamic and adaptive planning techniques that consider real-time environmental changes.
  4. Conventional Control Methods:
    Research employing traditional control methods without the integration of intelligent algorithms is waning, as the focus shifts to more sophisticated adaptive and hybrid control approaches.

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