IASRD Publishing
Advancing knowledge through high-quality academic publications
IASRD is committed to disseminating cutting-edge research through its portfolio of peer-reviewed academic journals. Our publications adhere to the highest standards of academic integrity and editorial excellence, providing researchers worldwide with platforms to share their work and contribute to the advancement of knowledge.
All IASRD journals follow rigorous peer-review processes, ensuring that published research meets international standards of quality and significance. We embrace open access principles to make scientific knowledge freely available to all, while maintaining sustainable publishing practices.
JISADS is a peer-reviewed, open-access journal that publishes original research at the intersection of data science and various disciplines. The journal aims to bridge the gap between theoretical advances in data science and their practical applications across fields such as healthcare, business, social sciences, engineering, and environmental studies.
Journal Highlights
- Interdisciplinary focus on applied data science
- Rigorous double-blind peer review
- Open access with reasonable publication fees
- Indexed in major academic databases
- International editorial board of leading experts
- Indexed in DOAJ, Copernicus, Semantic Scholar, ROAD, and BASE, Google Scholar.
Scope and Focus Areas
JISADS welcomes submissions that demonstrate innovative applications of data science methods to solve real-world problems across disciplines. The journal covers a wide range of topics, including but not limited to:
- Machine learning and artificial intelligence applications
- Big data analytics and visualization
- Predictive modeling and statistical analysis
- Natural language processing and text mining
- Healthcare informatics and medical data analysis
- Business intelligence and decision support systems
- Social media analytics and digital humanities
- Environmental data science and sustainability
- Educational data mining and learning analytics
- Ethical considerations in data science applications