Call for Paper
We invite all leading researchers, engineers and scientists in the domain of interest from across the world to submit their original research articles. On behalf of the Organizing Committee (OC), we welcome you to submit your research papers at INCSTIC-2027 and share your valuable experience with the research community.
With so many exciting and interesting developments in the field of Computer
Science today, our hope is that INCSTIC-2027 draws insights and encourages collaboration from many
disciplines and backgrounds, and makes a difference in the world through the sharing of innovative ideas.
Authors are invited to submit full papers describing original, previously unpublished, complete work (not
currently under review by another conference or journal), up to 6 pages including figures, results and
references. Please note that the similarity index of submitted papers must be strictly less than 15%.
All the accepted and presented papers will be submitted to Publisher for possible inclusion in
scopus database (Subject to approval).
Track Details
Covers fundamental and emerging approaches in artificial intelligence and intelligent computing, including learning algorithms, generative models, intelligent systems, and AI-driven applications.
- Machine Learning and Deep Learning
- Generative AI and Large Language Models
- Computational Intelligence
- Neural Networks and Reinforcement Learning
- Natural Language Processing
- Explainable and Trustworthy AI
- Intelligent Decision Support Systems
- AI-Based Applications
Focuses on mathematical, statistical, computational, and optimization techniques for modelling complex systems, solving scientific problems, and addressing real-world computational challenges.
- Computational Mathematics and Numerical Computing
- Mathematical and Statistical Modelling
- Optimization and Metaheuristic Algorithms
- Computational Intelligence
- Simulation and Modelling
- Scientific Computing
- High-Performance Computing
- Computational Methods for Real-World Problems
Explores data-driven methods for discovering knowledge, extracting insights, building predictive models, and supporting effective decision-making across diverse application domains.
- Data Science and Big Data Analytics
- Data Mining and Knowledge Discovery
- Statistical and Predictive Analytics
- Data Visualization
- Feature Engineering and Representation Learning
- Time-Series and Spatial Data Analytics
- Social Media and Web Analytics
- Data-Driven Decision Making
Addresses data-centric computing and intelligent applications across healthcare, vision, language, IoT, smart cities, finance, environment, and distributed computing environments.
- Healthcare Data Analytics and Medical AI
- Computer Vision and Image Analytics
- Natural Language and Text Analytics
- IoT and Sensor Data Analytics
- Smart Cities and Urban Data Analytics
- Financial and Business Analytics
- Environmental and Sustainable Data Analytics
- Cloud, Edge and Distributed Computing
- Emerging Computing and Data Technologies