APPLIED ARTIFICIAL INTELLIGENCE

Scope & Guideline

Fostering Dialogue on the Next Generation of AI

Introduction

Welcome to your portal for understanding APPLIED ARTIFICIAL INTELLIGENCE, 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
ISSN0883-9514
PublisherTAYLOR & FRANCIS INC
Support Open AccessYes
CountryUnited Kingdom
TypeJournal
Convergefrom 1987 to 2024
AbbreviationAPPL ARTIF INTELL / Appl. Artif. Intell.
Frequency10 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106

Aims and Scopes

The journal 'Applied Artificial Intelligence' aims to explore and publish research that applies artificial intelligence techniques across various domains, promoting innovative solutions and methodologies. It encompasses a wide range of topics that utilize AI to address real-world problems, focusing on practical applications and interdisciplinary approaches.
  1. Artificial Intelligence Applications:
    The journal highlights the application of AI technologies in diverse fields such as healthcare, finance, education, and environmental science, showcasing how AI can solve practical problems and improve efficiency.
  2. Machine Learning and Deep Learning Techniques:
    A significant focus is placed on the development and application of machine learning and deep learning algorithms, emphasizing novel methodologies and their effectiveness in various scenarios.
  3. Data Analysis and Predictive Modeling:
    Research often involves data-driven approaches, including predictive modeling and analytics, to derive insights from complex datasets across different sectors.
  4. Interdisciplinary Research:
    The journal supports interdisciplinary studies that combine artificial intelligence with other fields such as robotics, IoT, and social sciences, fostering collaborations that yield innovative solutions.
  5. Security and Privacy in AI:
    There is an increasing emphasis on security and privacy issues related to AI applications, addressing concerns about data protection and ethical implications.
The journal is witnessing a surge in interest in several emerging themes within the realm of artificial intelligence. These trends indicate where the field is heading and highlight areas ripe for exploration and innovation.
  1. AI for Social Good:
    There is a growing trend towards applying AI for social good, including projects aimed at healthcare, education, and environmental sustainability, reflecting a commitment to leveraging technology for positive societal impact.
  2. Explainable AI (XAI):
    Research focusing on explainability and interpretability in AI models is on the rise, driven by the need for transparency in AI decisions, especially in critical applications like healthcare and finance.
  3. Federated Learning and Privacy-Preserving Techniques:
    With increasing concerns about data privacy, federated learning and related privacy-preserving techniques are gaining traction, offering innovative solutions for training models without compromising sensitive information.
  4. Automation and Industry 4.0:
    The integration of AI in automation processes, particularly within the context of Industry 4.0, is a prominent trend, emphasizing smart manufacturing and intelligent systems that enhance operational efficiency.
  5. AI in Cybersecurity:
    Research exploring the application of AI in cybersecurity is emerging, focusing on threat detection, risk assessment, and the development of robust security measures to combat cyber threats.

Declining or Waning

While 'Applied Artificial Intelligence' continues to thrive in various research areas, some themes are experiencing a decline in publication frequency. This may reflect shifts in research focus or the maturation of certain fields.
  1. Traditional AI Techniques:
    Research focusing solely on traditional AI techniques without integration of modern approaches, such as deep learning, is becoming less prevalent as the field evolves towards more sophisticated methodologies.
  2. AI in Isolated Domains:
    Studies that apply AI to isolated or less impactful domains are waning, with a shift towards applications that demonstrate broader societal or economic relevance.
  3. Theoretical AI Concepts:
    While foundational theories remain important, there is a noticeable decline in the publication of purely theoretical papers that do not propose practical applications or implementations.
  4. Narrow AI Solutions:
    Research centered on narrow AI solutions that lack generalizability or scalability is less frequently published, as the focus shifts towards more versatile and adaptable AI systems.

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