Call For Papers
The ICAMLDS aims to explore emerging trends and future directions in research and innovation. It provides a collaborative platform for researchers and professionals to share ideas that shape the future of their respective domains.
The conference highlights advancements in Data Science, encouraging innovative, solution-oriented research that addresses global challenges and technological evolution.
Authors are invited to submit papers addressing, but not limited to, the following areas:
- Advanced machine learning techniques
- Deep learning models in data science
- Ensemble methods for predictive analytics
- Feature selection in machine learning
- Model evaluation and validation techniques
- Transfer learning applications in data
- Unsupervised learning in big data
- Reinforcement learning in practice
- Machine learning for time series data
- Ethics of machine learning applications
- Explainable AI in data science
- Big data challenges for machine learning
- Applications of neural networks
- Machine learning in finance analytics
- Data preprocessing for machine learning
- Scalable machine learning algorithms
- Applications of AI in industry
- Collaborative machine learning frameworks
- Future directions in machine learning
- Machine learning for social good
Assessment
Submissions will be assessed for originality, innovation, and relevance. Accepted papers will be presented at the conference and considered for publication opportunities in reputed academic platforms.
Registration
Participants are requested to complete the registration process following acceptance of their paper. Registration ensures inclusion in the conference schedule and official records.
Publication
All accepted manuscripts will be eligible for publication consideration in conference proceedings and associated academic journals.