Human-centric Computing and Information Sciences

Scope & Guideline

Fostering Collaboration at the Intersection of Technology and Society

Introduction

Delve into the academic richness of Human-centric Computing and Information Sciences 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
ISSN-
PublisherKOREA INFORMATION PROCESSING SOC
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationHUM-CENT COMPUT INFO / Human-centric Comput. Inf. Sci.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address1002HO YONGSUNGBIZTEL 314-1 2GA HANKANGRO YONGSAN-GU, SEOUL 140-750, SOUTH KOREA

Aims and Scopes

The journal 'Human-centric Computing and Information Sciences' emphasizes the intersection of technology and human experiences, focusing on innovative computing solutions that enhance human interaction with information systems. It aims to foster research that integrates human-centric approaches to computing, addressing both technical and societal challenges.
  1. Human-Computer Interaction (HCI):
    Research in this area focuses on improving the ways humans interact with computer systems, including user experience design, accessibility, and usability studies.
  2. Edge and Cloud Computing:
    The journal extensively covers advancements in edge and cloud computing technologies, emphasizing their applications in various domains such as IoT, healthcare, and smart cities.
  3. Security and Privacy:
    A core focus is on developing secure systems that protect user data and privacy, particularly in the context of emerging technologies like IoT and blockchain.
  4. Artificial Intelligence and Machine Learning:
    The application of AI and machine learning techniques in solving complex problems across different fields, including healthcare, cybersecurity, and smart systems.
  5. Blockchain Technology:
    Research on blockchain applications, particularly in enhancing security, privacy, and data integrity in various domains, including healthcare and smart cities.
  6. Data Analytics and Predictive Modeling:
    Studies that utilize data analytics and predictive modeling to derive insights and facilitate decision-making in human-centric applications.
The journal has observed a significant evolution in its focus areas, with emerging themes reflecting the latest technological advancements and societal needs. These trends highlight the journal's responsiveness to the changing landscape of human-centric computing.
  1. Federated Learning:
    Recent publications indicate a surge in interest in federated learning approaches, particularly in healthcare and IoT contexts, emphasizing privacy-preserving data analysis.
  2. Smart Cities and IoT Applications:
    The integration of IoT technologies within smart city frameworks is increasingly prevalent, showcasing the journal's commitment to addressing urban challenges through innovative computing solutions.
  3. Privacy-Preserving Technologies:
    There is a growing focus on developing technologies that prioritize user privacy, including advanced encryption methods and privacy-preserving data sharing techniques.
  4. Deep Learning for Real-Time Applications:
    The application of deep learning techniques in real-time systems, such as video surveillance and healthcare diagnostics, is becoming a prominent theme, reflecting the demand for intelligent and responsive applications.
  5. Sustainable Computing Solutions:
    Emerging trends suggest a rising interest in sustainable computing practices and technologies, particularly in the context of cloud and edge computing, to address environmental concerns.

Declining or Waning

While the journal continues to thrive in many areas, certain themes have shown a noticeable decline in recent publications. This reduction may indicate a shift in research priorities or a saturation of topics that were once highly explored.
  1. Traditional Cybersecurity Approaches:
    There appears to be a waning interest in conventional cybersecurity methodologies, as the focus shifts towards more integrated and advanced approaches, such as AI-driven security systems.
  2. Basic Data Management Techniques:
    The frequency of publications related to foundational data management techniques has decreased, possibly due to the growing emphasis on more complex and intelligent data processing frameworks.
  3. Static User Interfaces:
    Research on static or non-interactive user interfaces is diminishing, reflecting a broader trend towards dynamic, adaptive interfaces that enhance user engagement.

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