ISCSITR-INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (ISCSITR-IJSRAIML)

About the Journal

ISCSITR - International Journal of Scientific Research in Artificial Intelligence and Machine Learning (ISCSITR-IJSRAIML) is an open-access, peer-reviewed journal dedicated to publishing high-quality research in the fields of artificial intelligence (AI) and machine learning (ML). It covers a wide range of topics, including neural networks, deep learning, natural language processing, computer vision, reinforcement learning, and AI-driven applications. The journal provides a platform for researchers and professionals to share innovative solutions, explore new methodologies, and advance the understanding of AI and ML technologies.

Current Issue

Vol. 1 No. 1 (2025): ISCSITR-INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (ISCSITR-IJSRAIML)

Volume 1, Issue 01, (January-March-2025)

Articles

Theoretical and Computational Advances in Deep Learning for High Dimensional Data Representation and Feature Extraction in Sparse Environments

Carlos. A (Author)

8-13
- Pdf
Scalable Computational Frameworks for High-Dimensional and Streaming Data Processing Using Distributed Machine Learning and Edge Computing Architectures

S. E. Melnikova (Author)

14-19
- pdf
Privacy-Preserving Machine Learning and the Trade-Off Between Model Utility and Data Protection in Federated and Decentralized Learning Frameworks

Dashun. J. G (Author)

20-26
- pdf
Natural Language Understanding and Large-Scale Pretrained Language Models for Enhancing Multilingual Translation, Sentiment Analysis, and Conversational AI

GuoYin CeWu Lu (Author)

27-33
- pdf
Mathematical and Algorithmic Foundations of Artificial Intelligence in Complex System Modeling and Autonomous Multi-Agent Interactions

Erica M. E (Author)

1-7
- pdf

View All Issues

About the Journal

Current Issue

Articles

Theoretical and Computational Advances in Deep Learning for High Dimensional Data Representation and Feature Extraction in Sparse Environments

Scalable Computational Frameworks for High-Dimensional and Streaming Data Processing Using Distributed Machine Learning and Edge Computing Architectures

Privacy-Preserving Machine Learning and the Trade-Off Between Model Utility and Data Protection in Federated and Decentralized Learning Frameworks

Natural Language Understanding and Large-Scale Pretrained Language Models for Enhancing Multilingual Translation, Sentiment Analysis, and Conversational AI

Mathematical and Algorithmic Foundations of Artificial Intelligence in Complex System Modeling and Autonomous Multi-Agent Interactions