Call For Papers
The ICADISS 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:
- Anomaly detection techniques in industrial data
- Machine learning for predictive maintenance
- Data-driven approaches to fault detection
- Real-time monitoring of industrial systems
- Statistical methods for anomaly identification
- Impact of IoT on anomaly detection
- Case studies in industrial anomaly detection
- Unsupervised learning for anomaly detection
- Deep learning for time-series anomalies
- Data preprocessing techniques for anomaly detection
- Challenges in real-world anomaly detection
- Integration of sensor data for analysis
- Visualization techniques for anomaly detection
- Benchmarking anomaly detection algorithms
- Applications in manufacturing and production
- Adaptive learning for dynamic systems
- Collaborative filtering for anomaly detection
- Ethical implications of anomaly detection
- Future directions in anomaly detection research
- Machine learning frameworks for industrial 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.