List of Topics:
Location Research Breakthrough Possible @S-Logix pro@slogix.in

Office Address

Social List

Optical Memory and Neural Networks - Pleiades Publishing | 2024 Impact Factor:0.8 | Cite Score:1.4 | Q4

Optical Memory and Neural Networks Journal With Cite Score

Cite Score and Journal Rank of Optical Memory and Neural Networks

  • About: The Optical Memory and Neural Networks Journal (OMNN) is a peer-reviewed journal focusing on the intersection of optical memory systems and neural networks. Published by Elsevier, the journal explores advancements in optical memory technologies and their integration with neural networks, with an emphasis on both theoretical and practical developments.
  • Objective:
    The objective of OMNN is to advance the field of optical memory and neural networks by publishing high-quality research on novel optical memory technologies, their applications in neural networks, and the impact of these technologies on data storage and processing. The journal aims to provide a platform for discussing innovative approaches and methodologies in these areas.
  • Interdisciplinary Approach:
    OMNN employs an interdisciplinary approach by merging insights from optics, memory technology, neural networks, and computer science. This integration facilitates the exploration of new paradigms for data storage, retrieval, and processing, as well as the development of advanced neural network architectures that utilize optical memory systems.
  • Impact and Significance:
    The journal significantly impacts the fields of optical memory and neural networks by offering a venue for groundbreaking research and technological advancements. OMNN contributes to the evolution of memory systems and neural network technologies, providing valuable insights and solutions that advance both theoretical knowledge and practical applications in these areas.

  • Editor-in-Chief:  Boris V. Kryzhanovsky

  • Scope: The Optical Memory and Neural Networks Journal focuses on the intersection of optical memory technologies and neural network systems. It explores advances in these areas and their applications in various fields, including computing, data storage, and artificial intelligence.
  • Optical Memory Technologies: Research on the development and optimization of optical memory systems, including holographic memory, optical data storage, and related technologies.
  • Neural Network Architectures: Studies on the design and implementation of neural network models, including traditional artificial neural networks (ANNs), deep learning architectures, and emerging neural network paradigms.
  • Integration of Optical Memory and Neural Networks: Exploration of how optical memory technologies can be integrated with neural network systems to enhance performance, storage capabilities, and processing efficiency.
  • Applications in Computing: Research on the use of optical memory and neural networks in computing applications, including high-performance computing, data retrieval, and real-time processing.
  • Data Storage and Retrieval: Studies on the impact of optical memory technologies on data storage and retrieval processes, including speed, capacity, and reliability improvements.
  • Neural Network Training and Optimization: Exploration of techniques for training and optimizing neural networks, including algorithms, hardware acceleration, and software frameworks.
  • Optical Neural Computing: Research on optical neural computing systems that use optical methods to perform neural computations, potentially offering new ways to achieve high-speed processing and energy efficiency.
  • Hardware and Implementation: Studies on the hardware aspects of optical memory systems and neural networks, including design, fabrication, and integration challenges.
  • Performance Metrics and Evaluation: Research on metrics and methodologies for evaluating the performance of optical memory and neural network systems, including speed, accuracy, and scalability.
  • Emerging Trends and Innovations: Exploration of emerging trends and innovations in optical memory and neural networks, including novel materials, techniques, and applications.
  • Latest Research Topics for PhD in Computer Science
    •

  • Print ISSN:  1060992X

    Electronic ISSN:   19347898

  • Abstracting and Indexing:  Scopus

  • Imapct Factor 2024:  0.8

  • Subject Area and Category:   Computer Science, Computer Science (miscellaneous), Engineering, Electrical and Electronic Engineering, Materials Science, Electronic, Optical and Magnetic Materials

  • Publication Frequency:  

  • H Index:  26

  • Best Quartile:

    Q1:  

    Q2:  

    Q3:  Computer Science (miscellaneous)

    Q4:  

  • Cite Score:  1.4

  • SNIP:  0.382

  • Journal Rank(SJR):  0.220