Call For Papers
The ICDNNOT 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 Computational Science, 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:
- Optimization techniques for neural networks
- Deep learning architectures for data analysis
- Applications of neural networks in industry
- Challenges in training deep neural networks
- Transfer learning in deep learning models
- Ethics of AI and neural networks
- Real-world applications of optimization techniques
- Visualization of neural network performance
- Data-driven approaches in optimization research
- Case studies of neural network applications
- Future trends in deep learning optimization
- Collaborative tools for neural network research
- Impact of AI on optimization problems
- Deep learning for image processing tasks
- Neural networks in predictive modeling
- Role of AI in decision-making processes
- Data preprocessing for neural networks
- Interdisciplinary approaches to optimization
- Deep learning for time series analysis
- Neural networks in healthcare applications
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.