CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

Scope & Guideline

Bridging Theory and Practice in Computer Science

Introduction

Explore the comprehensive scope of CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 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 CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN1532-0626
PublisherWILEY
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 2001 to 2024
AbbreviationCONCURR COMP-PRACT E / Concurr. Comput.-Pract. Exp.
Frequency28 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address111 RIVER ST, HOBOKEN 07030-5774, NJ

Aims and Scopes

The journal 'CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE' focuses on advancing the fields of concurrency, computation, and their practical applications, emphasizing the integration of theoretical foundations with real-world implementation challenges.
  1. Concurrency Models and Algorithms:
    Research on various models and algorithms that facilitate concurrent execution, particularly in distributed and parallel computing environments.
  2. Cloud Computing and Edge Computing:
    Exploration of cloud and edge computing paradigms, focusing on resource allocation, task scheduling, and optimization strategies to enhance performance and efficiency.
  3. Machine Learning and Artificial Intelligence:
    Application of machine learning and AI techniques in various domains, including healthcare, cybersecurity, and data analysis, emphasizing innovative approaches for problem-solving.
  4. Blockchain Technology:
    Investigation of blockchain applications, particularly in enhancing security, privacy, and data integrity in distributed systems.
  5. Internet of Things (IoT):
    Research on IoT architectures, protocols, and applications, particularly in smart cities, healthcare, and environmental monitoring.
  6. Performance Optimization:
    Studies focused on optimizing computational performance across various platforms, including GPUs and high-performance computing (HPC) systems.
  7. Data Analytics and Big Data:
    Methods and techniques for analyzing large datasets, including data mining, predictive analytics, and the application of big data technologies.
  8. Cybersecurity:
    Research on intrusion detection systems, secure communication protocols, and techniques for mitigating cyber threats in various computing environments.
The journal has witnessed a surge in specific themes that reflect current technological advancements and societal needs. These emerging scopes signify the journal's alignment with contemporary research trends.
  1. Federated Learning:
    An increasing number of publications focus on federated learning approaches, particularly in the context of privacy-preserving data analysis across distributed networks.
  2. Blockchain Applications in Various Domains:
    There is a growing interest in the application of blockchain technology beyond cryptocurrencies, particularly in sectors like healthcare, supply chain management, and data security.
  3. AI and Machine Learning in Healthcare:
    Research leveraging AI and machine learning for healthcare applications, including disease diagnosis, patient monitoring, and personalized medicine, is on the rise.
  4. IoT and Smart City Technologies:
    Emerging studies focus on the integration of IoT technologies within smart city frameworks, emphasizing real-time data analytics and resource management.
  5. Performance Optimization Techniques for AI Models:
    Research dedicated to optimizing the performance of AI models, especially in cloud and edge environments, is increasingly prevalent, reflecting the demand for efficiency.
  6. Energy-efficient Computing Solutions:
    There is a trend towards developing energy-efficient algorithms and protocols, particularly in the context of green computing initiatives and sustainability.
  7. Real-time Data Processing and Analysis:
    Growing interest in real-time processing techniques for big data applications, especially in IoT and streaming data scenarios, is evident in recent publications.

Declining or Waning

While the journal has seen growth in various research areas, certain themes appear to be declining in prominence over recent years. These waning scopes reflect shifts in research focus and emerging technologies.
  1. Traditional Parallel Computing Techniques:
    There is a noticeable decrease in research focused solely on traditional parallel computing techniques, as newer paradigms like cloud and edge computing gain traction.
  2. Basic Network Protocols:
    Research on foundational network protocols is becoming less frequent, with a shift towards more complex, application-specific protocols that address modern challenges.
  3. Static Analysis Tools:
    The frequency of papers dedicated to static analysis tools for software has declined, possibly due to the increasing focus on dynamic analysis and machine learning-based techniques.
  4. Generalized Algorithms without Application Context:
    Papers presenting generalized algorithms without specific application contexts are less common, as researchers are now favoring context-driven approaches that showcase practical implementations.

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