Call for Papers
The aim of this conference is to provide a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results in various topics of Advanced Algorithms, Machine Learning, and Data Science. It provides participants an opportunity to discuss the recent developments in the fields of Advanced Algorithms, Machine Learning, and Data Science areas. Original papers are invited to submit to the following Track areas:
| Track 1: Advanced Algorithm Design and Analysis |
* Graph and network algorithms
* Approximation, randomized, and online algorithms
* Optimization algorithms (convex, combinatorial, metaheuristic)
* Algorithmic game theory and mechanism design
* Parameterized and exact algorithms
* Parallel, distributed, and quantum algorithms |
| Track 2: Machine Learning Theories and Models |
* Deep learning architectures (CNN, RNN, Transformer, GNN)
* Reinforcement learning and multi-agent learning
* Unsupervised, semi-supervised, and self-supervised learning
* Transfer learning, meta-learning, and few-shot learning
* Generative models (GANs, VAEs, diffusion models)
* Explainable, robust, and fair machine learning |
| Track 3: Data Science and Big Data Analytics |
* Data mining and knowledge discovery
* Statistical learning and probabilistic modeling
* Big data processing frameworks (Hadoop, Spark, Flink)
* Data quality, integration, and governance
* Time series, graph, and text analytics
* Anomaly detection and pattern recognition |
| Track 4: Optimization, Learning Systems, and Scalability |
* Large-scale machine learning and distributed training
* Federated learning and privacy-preserving ML
* Model compression, acceleration, and edge deployment
* Automated machine learning (AutoML) and neural architecture search
* Learning-augmented algorithms and online optimization
* System design for AI and data-intensive workloads |
| Track 5: Cross-disciplinary Applications and Emerging Topics |
* Computer vision and multimodal understanding
* Natural language processing and large language models
* Recommender systems and personalization
* Healthcare, bioinformatics, and medical imaging
* Financial data analysis and fraud detection
* AI for science (e.g., climate, materials, physics)
* Ethical AI, data privacy, and responsible innovation |
GUIDE FOR SUBMISSIONS:
*Papers prepared in the prescribed format are to be submitted. The papers should be written in English and clearly state the title, objective, method, results, and conclusion with major keywords. All papers submitted will be checked for plagiarism.
*Author names and their affiliations should be removed from the initial PDF file for the double blind review process. After receiving, the full paper will be peer-reviewed and its acceptance will be notified. Only papers presenting original content with novel research results or successful innovative applications will be considered for publication in the conference proceedings.
*The paper should be original work of the author(s), and no portion of the paper (including, but not limited to, graphics and figures) has been previously published. The paper is not currently under consideration for publication elsewhere.
*The authors listed on the paper accurately reflect those who actually did the work and contributed to the paper in a meaningful way, and they have identified and acknowledged all sources used in the creation of their paper or manuscript, including any graphics, images, tables, and figures.
PLAGIARISM POLICY:
*The paper prior to submission should be checked for plagiarism from licensed plagiarism tools like Turnitin or CrossCheck. The similarity content should not exceed 25% (in any case either self contents or others).
*Any form of self-plagiarism or plagiarism from others' works should not be there in a paper. If any model / concept / figure / table / data / conclusive comment by any previously published work is used in your paper, you should properly cite a reference to the original work.
