Applied Computer Systems

Scope & Guideline

Transforming Ideas into Technological Applications

Introduction

Immerse yourself in the scholarly insights of Applied Computer Systems with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN2255-8683
PublisherSCIENDO
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationAPPL COMPUT SYST / Appl. Comput. Syst.
Frequency2 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressBOGUMILA ZUGA 32A, WARSAW, MAZOVIA 01-811, POLAND

Aims and Scopes

The journal 'Applied Computer Systems' focuses on the intersection of applied computing methodologies and real-world applications across various domains. Its research encompasses a wide array of topics that leverage advanced computational techniques to address complex challenges in technology, healthcare, and engineering.
  1. Artificial Intelligence and Machine Learning:
    Explores the application of AI and machine learning techniques in diverse fields such as healthcare, robotics, and software development, emphasizing innovative algorithms and comparative studies.
  2. Data Analysis and Processing:
    Covers methodologies for analyzing large datasets, including advanced statistical techniques and machine learning approaches, to draw meaningful insights and improve decision-making.
  3. Software Engineering and Development:
    Focuses on methodologies, tools, and frameworks that enhance software engineering processes, including software optimization, performance analysis, and development of intelligent systems.
  4. Robotics and Automation:
    Investigates the application of computational techniques in robotics, including kinematics, sensor integration, and intelligent control systems to improve automation in various industries.
  5. Human-Computer Interaction and User Experience:
    Examines how users interact with computer systems, focusing on the design of user-friendly interfaces and the integration of user feedback into system development.
  6. Healthcare Applications:
    Addresses the use of computer systems in healthcare, including diagnostic systems, health monitoring, and data-driven approaches to disease detection and management.
The journal has shown a significant evolution in its focus, with several emerging themes gaining traction in recent years. These trends indicate a shift towards integrating cutting-edge technology with practical applications across various sectors.
  1. Generative AI and Automation:
    The rise of generative AI technologies is increasingly being explored for optimizing software engineering processes, indicating a trend towards automation and intelligent system design.
  2. Healthcare Innovations through AI:
    There is a growing emphasis on the application of AI in healthcare diagnostics and treatment, evidenced by numerous studies focused on disease detection and patient monitoring.
  3. Sentiment Analysis and Natural Language Processing:
    An uptick in research on sentiment analysis, particularly in non-English languages, shows the expanding interest in NLP applications across various cultural contexts.
  4. Robust Data Fusion Techniques:
    Emerging methodologies for data fusion, particularly in robotics and sensor integration, highlight a trend towards creating more comprehensive and resilient systems.
  5. Sustainable Computing Solutions:
    Research focusing on energy-efficient algorithms and sustainable computing practices is gaining attention, reflecting a broader concern for environmental impacts in technology.

Declining or Waning

While 'Applied Computer Systems' has a robust focus on many areas, certain themes have seen a decline in prominence over recent publications. This may reflect shifts in research priorities or advancements in technology that render previous approaches less relevant.
  1. Traditional Programming Paradigms:
    There has been a noticeable decrease in research focusing on traditional programming paradigms, as the field shifts towards more dynamic and adaptive approaches such as AI and machine learning.
  2. Basic Computer Science Theory:
    Topics centered on foundational computer science theories, such as algorithm design and computational complexity, appear to be waning in favor of applied research that emphasizes practical applications.
  3. Static Data Management Techniques:
    Research on static data management approaches has diminished, likely due to the growing importance of real-time data processing and the need for dynamic, scalable solutions.

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