The First International Conference on Computer Vision, Information Processing, and Data Science (ICCVIPDS-2025) invites researchers, academicians, industry professionals, and students to submit high-quality original research papers for presentation and publication. The theme of the conference is “Advancing Intelligence: Bridging Vision, Information, and Data for a Smarter Future”. This conference aims to bring together leading minds to discuss and share advancements, challenges, and solutions in the fields of computer vision, information processing, and data science. The manuscript should include unique research ideas, development concepts, analysis, findings, and results. Authors must submit Full-length original research contributions and review articles not exceeding 7 pages in length, written in English, with a maximum length of single-spaced, single-column pages using 10pt size, including all figures, tables, and references. The manuscript should not have been published in any journals/magazines or conference proceedings and should not be under review in any of them.
Papers can be submitted to any of the following track
Track 1: Computer Vision
| 3D vision and reconstruction | Augmented reality (AR) and virtual reality (VR) |
| Medical imaging and diagnostics | Cutting-edge research in AI algorithms |
| Deep learning | Natural language processing, and reinforcement learning |
| Feature Extraction and Description | Edge Detection and Segmentation |
| Color and Texture Analysis | Scene Understanding and Semantic Segmentation |
| Instance and Panoptic Segmentation | Face Detection and Recognition |
| 3D Reconstruction from Images | Depth Estimation and Stereo Vision |
| Point Cloud Processing | SLAM (Simultaneous Localization and Mapping) |
| Structure from Motion (SfM) | Optical Flow Estimation |
| Action Recognition and Activity Analysis | Video Segmentation |
| Video Summarization and Captioning | Neural Style Transfer |
| Generative Adversarial Networks (GANs) for Image Synthesis | Image-to-Image Translation |
| Visual Question Answering (VQA) | Self-Supervised and Unsupervised Learning in Vision |
| Vision Transformers (ViT) | Multi-Modal Vision (e.g., combining vision with language or audio) |
| Vision for Edge Computing and IoT | Few-Shot and Zero-Shot Learning in Vision |
| Explainable AI in Computer Vision |
Track 2: Information Processing
| Signal and multimedia processing | Data compression and retrieval |
| Pattern recognition | Communication and networking in data-intensive systems |
| Information Transmission and Networking Protocols | Error Detection and Correction Methods |
| Wireless Communication and Data Transmission | Cryptographic Techniques for Secure Communication |
| Channel Coding and Modulation Techniques | Distributed and Cloud-Based Information Processing |
| Quantum Information Processing | Bioinformatics and Genomic Signal Processing |
| Knowledge Representation and Ontologies | Computational Linguistics |
| Human-Computer Interaction (HCI) | Digital Signal Processing in IoT |
| Real-Time Systems and Embedded Signal Processing | Remote Sensing and Satellite Data Processing |
| Data Fusion from Multiple Sensors | AI and Machine Learning in Information Processing |
| Edge and Fog Computing for Data Processing | Blockchain and Secure Data Handling |
| Explainable AI in Signal and Data Processing | Cognitive Information Processing |
Track 3: Data Science
| Big data analytics and applications | Marketing Analytics and Customer Segmentation |
| Predictive modeling and simulation | Healthcare Analytics (e.g., patient outcome prediction, diagnostics) |
| Data visualization and exploratory data analysis | Financial Analytics (e.g., algorithmic trading, credit scoring) |
| Feature Engineering and Selection | Supply Chain and Logistics Optimization |
| Dimensionality Reduction Techniques (e.g., PCA, LDA) | Ethical AI and Data Privacy |
| Bayesian Methods | Causal Inference in Data Science |
| Optimization Techniques in Machine Learning | TinyML (Machine Learning on Edge Devices) |
| Graph Data Analytics and Network Science | Quantum Computing in Data Science |
| Explainable AI (XAI) | Synthetic Data Generation |
| AutoML (Automated Machine Learning) | AI for Sustainability and Climate Analytics |
| Federated Learning | Data Pipeline Design and Implementation |
| Multimodal Learning | ETL (Extract, Transform, Load) Processes |
| Predictive Analytics | Real-Time Data Processing |
| Recommendation Systems | Cloud-Based Data Solutions (e.g., AWS, Azure, Google Cloud) |
| Fraud Detection and Risk Analysis | Database Management and Optimization |