NEW GENERATION COMPUTING

Scope & Guideline

Empowering Innovation in Computer Science

Introduction

Delve into the academic richness of NEW GENERATION 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
ISSN0288-3635
PublisherSPRINGER
Support Open AccessNo
CountryJapan
TypeJournal
Convergefrom 1983 to 2024
AbbreviationNEW GENERAT COMPUT / New Gener. Comput.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressONE NEW YORK PLAZA, SUITE 4600 , NEW YORK, NY 10004, UNITED STATES

Aims and Scopes

The journal 'New Generation Computing' focuses on integrating advanced computational techniques and theories across various domains, emphasizing innovative methodologies in artificial intelligence, machine learning, and data science. It aims to bridge theoretical advancements with practical applications, particularly in areas that leverage computational intelligence to solve complex problems.
  1. Artificial Intelligence and Machine Learning:
    The journal consistently publishes research that explores novel algorithms and applications of AI and machine learning across diverse fields, including healthcare, finance, and social networks.
  2. Computational Modeling and Simulation:
    A significant focus is placed on the development of computational models to simulate real-world systems, particularly in the context of epidemic modeling, financial systems, and chemical reactions.
  3. Cryptography and Security:
    The journal highlights innovative approaches to cryptographic protocols and security measures, particularly those that utilize unique models such as card-based cryptography and zero-knowledge proofs.
  4. Data Science and Analytics:
    Research in this area includes methodologies for data extraction, processing, and analysis, particularly using advanced techniques such as deep learning and ensemble methods to derive insights from large datasets.
  5. Interdisciplinary Applications:
    The journal promotes interdisciplinary research where computational techniques are applied to fields such as healthcare, social sciences, and environmental studies, showcasing the versatility of computational methods.
The journal has witnessed a rise in certain themes that reflect current technological advancements and societal needs. These emerging scopes are indicative of the evolving landscape of computational research and its applications.
  1. COVID-19 Related Research:
    A significant trend is the increased focus on research related to COVID-19, including detection methods using machine learning, sentiment analysis on social media, and epidemiological modeling, reflecting the global impact of the pandemic.
  2. Explainable AI and Ethical Considerations:
    There is a growing emphasis on explainable AI, which seeks to make AI decision-making processes transparent, addressing ethical concerns and enhancing trust in AI applications across various sectors.
  3. Integration of AI with IoT and Smart Technologies:
    Research exploring the intersection of AI with Internet of Things (IoT) technologies and smart systems is on the rise, showcasing the potential for advanced automation and real-time data processing.
  4. Neuro-Symbolic AI Approaches:
    Emerging interest in combining neural networks with symbolic reasoning is evident, aiming to enhance AI's capability in understanding and reasoning about complex data.
  5. Applications of Advanced Network Analysis:
    There is an increasing trend in applying advanced network analysis techniques to various domains, including social networks and biological systems, to uncover underlying patterns and relationships.

Declining or Waning

While 'New Generation Computing' has seen growth in various research areas, some themes appear to be declining in prominence. These waning scopes may indicate a shift in research focus or a saturation of topics within the journal.
  1. Traditional Statistical Methods:
    There is a noticeable reduction in the publication of papers focusing solely on traditional statistical methodologies, as the journal increasingly prioritizes machine learning and AI-driven approaches that offer more robust solutions.
  2. Basic Algorithmic Studies:
    Research that solely focuses on basic algorithmic studies without practical applications or complex problem-solving aspects is becoming less common, as the journal emphasizes more innovative and applied computational research.
  3. Generalized AI Applications:
    While AI applications continue to be a focus, there is a decline in generalized AI research that lacks specific context or application, with a trend towards more specialized and domain-focused studies.

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