NEW GENERATION COMPUTING
Scope & Guideline
Empowering Innovation in Computer Science
Introduction
Aims and Scopes
- 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. - 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. - 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. - 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. - 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.
Trending and Emerging
- 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. - 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. - 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. - 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. - 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
- 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. - 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. - 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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