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 Optimization and Innovation |
Heuristic Algorithms, Metaheuristic Optimization, Evolutionary Algorithms, Swarm Intelligence, Gradient-Based Optimization, Distributed Optimization, Algorithm Complexity Analysis, Approximation Algorithms, Online Algorithms, Parallel and Distributed Algorithms, Graph Algorithms, High-Dimensional Optimization, Dynamic Programming, Combinatorial Optimization, Deep Learning Architecture Improvement, Neural Architecture Search, Pruning and Quantization Algorithms, Adaptive Learning Algorithms, Stochastic Optimization, Convex and Non-Convex Optimization, Multi-Objective Optimization, Reinforcement Learning Algorithms, Transfer Learning Algorithms, Few-Shot and Zero-Shot Learning Algorithms, Algorithm Acceleration, Real-Time Inference Algorithms, Algorithm Design for Edge Devices, Theoretical Analysis of Algorithms, Algorithm Robustness and Stability, Algorithm Generalization, Novel Algorithm Design for Complex Scenarios |
| Track 2: Explainability and Trustworthiness of Machine Learning |
Explainable Artificial Intelligence (XAI), Interpretable Machine Learning Models, Model Transparency, Post-Hoc Explanation Methods, Visual Explanations for Deep Models, Feature Attribution and Importance Analysis, Counterfactual Explanations, Fairness in Machine Learning, Algorithmic Fairness and Bias Mitigation, Fair Representation Learning, Adversarial Robustness, Adversarial Attacks and Defenses, Model Safety and Security, Privacy-Preserving Machine Learning, Differential Privacy, Federated Learning for Trustworthy AI, Reliability and Uncertainty Estimation, Out-of-Distribution Detection, Anomaly Detection, Trustworthy AI for Healthcare, Trustworthy AI for Finance, Ethical and Moral AI, Accountability and Audit of AI Systems, Bias Detection and Correction, Compliance and Regulatory AI |
| Track 3: Data Science and Cross-Domain Integration Applications |
Data Collection and Preprocessing, Data Cleaning and Integration, Missing Value Imputation, Feature Engineering and Selection, Dimensionality Reduction, Data Mining and Knowledge Discovery, Pattern Recognition, Predictive Analytics and Forecasting, Descriptive and Prescriptive Analytics, Statistical Modeling, Time Series Analysis, Multimodal Data Fusion, Big Data Processing Frameworks, Distributed Data Storage and Management, Stream Data Processing, Data Visualization and Interactive Analytics, Visual Analytics, Internet of Things (IoT) Data Analytics, Sensor Data Processing, Smart City Applications, Intelligent Transportation Systems, Healthcare Informatics, Medical Image Analysis, Precision Medicine, Financial Data Analysis, FinTech Applications, Industrial Big Data, Predictive Maintenance, Agricultural Data Science, Environmental Data Modeling, Social Network Analysis, Recommender Systems, Human-Computer Interaction, Data-Driven Decision Support Systems |
| Track 4:Large Models and Cutting-Edge Machine Learning Technologies |
Large Language Models, Foundation Models, Multimodal Foundation Models, Model Pre-training and Alignment, Instruction Tuning, Parameter-Efficient Fine-Tuning (PEFT), LoRA and Adapter Tuning, Model Compression and Acceleration, Model Quantization and Pruning, Knowledge Distillation, Efficient Inference and Serving, Edge Deployment of Large Models, Retrieval-Augmented Generation (RAG), Multi-Modal Learning, Vision-Language Models, Audio-Language Fusion, Reinforcement Learning from Human Feedback (RLHF), Deep Reinforcement Learning, Multi-Agent Reinforcement Learning, Federated Learning, Vertical and Horizontal Federated Learning, Privacy-Preserving Federated Learning, Self-Supervised and Semi-Supervised Learning, Contrastive Learning, Generative AI, Diffusion Models, GANs and Variational Autoencoders, Graph Neural Networks, Transformers and Attention Mechanisms, Continual and Lifelong Learning, Meta-Learning, Neuromorphic Computing, Quantum Machine Learning, Emerging Trends in AI and Machine Learning |
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.
