ISCSITR- INTERNATIONAL JOURNAL OF DATA SCIENCE (ISCSITR-IJDS)

About the Journal

ISCSITR - International Journal of Data Science (ISCSITR-IJDS) is an open-access, peer-reviewed journal dedicated to publishing high-quality research in the field of data science. It covers a wide range of topics, including data mining, machine learning, statistical analysis, big data processing, data visualization, and predictive modeling. The journal provides a platform for researchers and professionals to share innovative solutions and advancements in data-driven decision-making and knowledge discovery.

Current Issue

Vol. 1 No. 1 (2025): ISCSITR - International Journal of Data Science (ISCSITR-IJDS)

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

Articles

Unifying Bayesian Nonparametric Methods and Variational Inference for Robust Model Adaptation in Non-Stationary Data Environments

Laura Santi (Author)

1-10
- pdf
Combining Variational Autoencoders with Reinforcement Learning for Efficient Representation Learning and Adaptive Decision-Making in Complex Environments

Arafath Akanbi (Author)

11-17
- pdf
Quantifying the Trade-Offs Between Model Interpretability and Predictive Accuracy in Explainable Artificial Intelligence for High-Stakes Decision Systems

Peter W. F (Author)

18-24
- pdf
Bridging Probabilistic Graphical Models and Deep Generative Networks for Improved Causal Inference and Uncertainty Quantification in Data Science

Kym Lyn (Author)

25-31
- pdf
A Theoretical and Empirical Investigation of High-Dimensional Feature Space Representations in Deep Learning Models for Large-Scale Data Processing

K. B. Mike (Author)

32-38
- pdf

View All Issues

About the Journal

Current Issue

Articles

Unifying Bayesian Nonparametric Methods and Variational Inference for Robust Model Adaptation in Non-Stationary Data Environments

Combining Variational Autoencoders with Reinforcement Learning for Efficient Representation Learning and Adaptive Decision-Making in Complex Environments

Quantifying the Trade-Offs Between Model Interpretability and Predictive Accuracy in Explainable Artificial Intelligence for High-Stakes Decision Systems

Bridging Probabilistic Graphical Models and Deep Generative Networks for Improved Causal Inference and Uncertainty Quantification in Data Science

A Theoretical and Empirical Investigation of High-Dimensional Feature Space Representations in Deep Learning Models for Large-Scale Data Processing