ICDMAI 2024 : 8th International Conference on Data Management, Analytics & Innovation

We take this opportunity to invite you to the International Conference on Data Management, Analytics & Innovation (ICDMAI-2024). ICDMAI was organized in the consecutive years of 2017, 2018, 2019, 2020, 2021,2022 & 2023 in the IT city of Pune, India, Kuala Lumpur, Malaysia, and Delhi, India. ICDMAI 2021 & 2022 were in Virtual mode due pandemic. The next will be 19-21 January 2024 at the Vellore Institute of Technology in Vellore, Tamil Nadu, India.

This conference is a flagship event of Society for Data Science (S4DS), which is a non-profit professional association to create a collaborative platform for bringing together technical experts across Industry, Academia, Government Labs and Professional Bodies to promote Innovation around Data Science.

The Eighth International Conference on Data Management, Analytics and Innovation (ICDMAI – 2024) solicit papers in data mining, machine learning, generative AI, AI for social impact, robust and ethical AI, data engineering and other enabling technologies. Original, unpublished research papers highlighting specific research domains from all viewpoints are invited from delegates all over the globe. All papers will be reviewed through a double-blind peer review process. We encourage papers that focus on intersections of data science and other disciplines, such as but not limited to social sciences, humanities, natural sciences, and engineering. It is planned to submit the selected and presented papers in the proceedings with SPRINGER in their prestigious Lecture Notes in Networks and Systems, which is indexed in Scopus, Web of Science and ISI. Look forward to your contributions and to seeing you at ICDMAI 2024.Following are the topics of interest but not limited to:-

Track I: MACHINE LEARNING
1. Algorithms and Models
2. Feature Engineering
3. Supervised Learning - Classification, Regression
4. Unsupervised Learning
5. Semi-supervised Learning
6. Association rule Mining
7. Recommendation System
8. Spatio Temporal Learning
9. Time Series Data

Track II: AI & DEEP LEARNING
1. Heuristic Search, nature inspired search
2. Fuzzy and Rough Set
3. Reinforcement Learning
4. ANN and Deep Neural Networks
5. RNN, CNN, RBM, Transformer
6. Auto Encoder, GAN, Transfer Learning
7. Generative AI: Natural Language Processing, Computer Vision, Audio Analytics, Video Analytics, Code Analytics
8. Application in Computer Vision, Natural Language Processing
9. AI based on capabilities: Narrow AI, General AI, Super AI
10. AI based on functionalities: Reactive machine, Limited Mind, Theory of Mind
11. Other research Issues and Interdisciplinary applications in Science, Social Science and Humanities

Track III: DATA ENGINEERING
1. Data warehouse and Data Lakes
2. AI for Database Systems
3. Data Stream Systems and Edge Computing
4. Database technology for AI
5. Explainability, Fairness, and Trust in Data Systems and Analysis
6. Graphs, Networks, and Semi-Structured Data
7. Uncertain, Probabilistic, and Approximate Databases

Track IV: ENABLING TECHNOLOGIES
1. Quantum Databases
2. Quantum Machine Learning
3. Quantum Artificial Intelligence
4. Decentralised Applications
5. Edge Computing
6. Blockchain
7. Metaverse
8. 6G technology
9. IoT and Robotics
10 Big Data and Cloud
11. Digital Twin

Track V: DATA ANALYTICS AND UTILITY APPLICATION SERVICES
1. Data Visualisation techniques
2. Image Processing Techniques for detection of disease
3. Applying Text Analysis to understand research articles to get a better understanding of the Research Literature
4. Drug Discovery and Genomic Sequencing
5. Communication using 5G
6. Social Media and SCM Analytics
7. Predictive Analytics
8. Prototype Evaluation
9. AI for Social Impact- Agriculture/Food, Assistive Technology for Well-Being, Computational Social Science, Education, Economic/Financial, Energy, Environmental Sustainability, Health and Well-Being, Humanities, Social Welfare, Justice, Fairness and Equality, Urban Planning, Underserved Communities, Other Social Impact
10 Robust and Ethical AI- Safe AI Systems, Safe Learning, Safe Control, Uncertainty Quantification,
11. Anomaly Detection and Explanation, Model Misspecification Detection and Explanation, Safe Human-Machine Interaction