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International Journal of Neural Networks and Deep Learning (IJNNDL)

Publisher :

IAEME Publication

Scopus Profile
Peer reviewed only
Scopus Profile
Open Access
  • Neural Network Architectures
  • Deep Learning Algorithms and Models
  • Applications and Use Cases

    • +5

e-ISSN :

3851-5221

Issue Frequency :

Monthly

Impact Factor :

2.80

Est. Year :

2024

Mobile :

9884798314

DOI :

YES

Country :

India

Language :

English

APC :

YES

Impact Factor Assignee :

Google Scholar

Email :

editor@iaeme.com

Journal Descriptions

The International Journal of Neural Networks and Deep Learning (IJNNDL) aims to advance the field of neural networks and deep learning by publishing original, high-quality research that drives innovation and deepens understanding. The journal seeks to be a premier platform for disseminating cutting-edge research and developments in neural network technologies and their applications. It is dedicated to fostering interdisciplinary collaboration and providing researchers, practitioners, and academics with valuable insights and practical solutions in the realm of deep learning. SCOPE International Journal of Neural Networks and Deep Learning (IJNNDL) encompasses a broad range of topics related to neural networks and deep learning. It focuses on publishing comprehensive research articles that address both foundational theories and applied techniques. The scope of the journal includes but is not limited to the exploration of novel neural network architectures, the development of innovative algorithms, and the application of deep learning technologies to real-world problems. The journal encourages submissions that offer significant contributions to the field, including advances in model design, optimization methods, performance evaluation, and the deployment of deep learning systems. IJNNDL is dedicated to highlighting research that bridges the gap between theoretical advancements and practical applications, thereby supporting the progression of the field. It welcomes contributions from researchers, practitioners, and academics that offer new insights, challenge existing paradigms, and propose solutions to contemporary challenges in neural networks and deep learning


International Journal of Neural Networks and Deep Learning (IJNNDL) is :

International, Peer-Reviewed, Open Access, Refereed, Neural Network Architectures, Deep Learning Algorithms and Models, Applications and Use Cases, Theoretical Foundations, Implementation and Performance, Emerging Trends and Innovations, Software Libraries and Platforms, Model Development and Experimentation Tools , Online Monthly Journal

UGC Approved, ISSN Approved: P-ISSN , E-ISSN - 3851-5221, Established in - 2024, Impact Factor - 2.80

Provide Crossref DOI

Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE

Indexing

Scopus Profile
Google Scholar
Scopus Profile
DOI Foundation

Publications of IJNNDL

  • dott image August, 2024

ANALYZING THE EFFECTS OF DATA IMBALANCE ON THE PERFORMANCE OF NEURAL NETWORKS IN MULTI-CLASS CLASSIFICATION TASKS

This paper investigates the effects of data imbalance on the performance of neural networks in multi-class classification tasks. Data imbalance, where certain classes are underrepresented, p...

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