Neural Network World

Scope & Guideline

Empowering Researchers with Cutting-Edge Insights in AI and Neuroscience

Introduction

Explore the comprehensive scope of Neural Network World through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore Neural Network World in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN1210-0552
PublisherACAD SCIENCES CZECH REPUBLIC, INST COMPUTER SCIENCE
Support Open AccessNo
CountryCzech Republic
TypeJournal
Convergefrom 1994 to 2023
AbbreviationNEURAL NETW WORLD / Neural Netw. World
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressPOD VODARENSKOU VEZI 2, 182 07 PRAGUE 8 00000, CZECH REPUBLIC

Aims and Scopes

The journal 'Neural Network World' primarily focuses on the application and development of neural network methodologies in various domains. It aims to bridge theoretical advancements in neural networks with practical applications across multiple fields, including engineering, healthcare, transportation, and data analysis.
  1. Neural Network Architecture Development:
    Research on innovative architectures and modifications of neural networks, enhancing their performance and applicability in complex tasks.
  2. Machine Learning Applications:
    Exploration of machine learning techniques, particularly neural networks, applied to real-world problems such as medical diagnostics, transportation systems, and environmental monitoring.
  3. Interdisciplinary Approaches:
    Integration of neural networks with other disciplines, such as robotics, IoT, and bioinformatics, to leverage their capabilities in solving multifaceted challenges.
  4. Theoretical Foundations and Algorithms:
    Advancements in the theoretical understanding of neural networks, including learning algorithms, optimization techniques, and model evaluation metrics.
  5. Data-Driven Decision Making:
    Utilization of neural networks to enhance decision-making processes through data analysis, predictive modeling, and automated systems.
Recent publications in 'Neural Network World' indicate a strong shift towards innovative applications and advanced methodologies within the neural network domain. This trend reflects the evolving landscape of technology and research needs.
  1. Integration of Neural Networks with IoT:
    There is a rising interest in applying neural networks to Internet of Things (IoT) systems, particularly for predictive analytics and real-time decision-making.
  2. Healthcare Applications:
    An increasing number of studies focus on leveraging neural networks for healthcare applications, including diagnostics, medical imaging, and patient monitoring.
  3. Adversarial Machine Learning:
    Research on adversarial attacks and defenses in machine learning illustrates a growing concern about the security and robustness of neural network systems.
  4. Environmental and Energy Systems:
    Emerging themes include the application of neural networks in environmental monitoring and energy management, reflecting global sustainability efforts.
  5. Multi-Modal Data Processing:
    There is an increasing emphasis on utilizing neural networks to analyze multi-modal data, combining various types of information (e.g., images, text, and sensor data) for comprehensive insights.

Declining or Waning

While the journal continues to thrive in various areas, some themes appear to be diminishing in frequency or relevance. This may reflect shifts in research priorities or the emergence of new methodologies and technologies.
  1. Traditional Statistical Methods:
    There has been a noticeable decline in the use of classical statistical methods in favor of more advanced machine learning techniques, particularly deep learning.
  2. Basic Neural Network Applications:
    Research focusing solely on basic neural network applications without innovative modifications or integrations has decreased, as the field moves towards more complex and tailored solutions.
  3. Limited Scope of Basic Image Processing:
    The focus on basic image processing tasks has waned, with a shift towards more complex applications involving multi-modal data and advanced neural architectures.
  4. One-Dimensional Data Analysis:
    Studies concentrating on one-dimensional data analysis are becoming less prominent, as researchers are increasingly exploring multi-dimensional and complex datasets.
  5. Generalized Neural Network Models:
    There is a decline in papers that propose generalized neural network models without specific applications or improvements, as the field demands more specialized and effective approaches.

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