The 2nd International conference on Machine Learning and Data Engineering (ICMLDE 2023) is an international venue for the presentation of original research findings, as well as the exchange and dissemination of creative, practical development experiences in various engineering domains. Researchers and application developers from a wide range of artificial and data engineering-related fields, as well as their techniques and applications of current issues in practically all sectors of engineering and technology, attend the conference.
This conference aims to broaden its scope in the areas of Machine Learning and Data Engineering by including expert speeches and presentations from young researchers in each session. The conference aims to enhance the state-of-the-art in Machine Learning and Data Engineering, as well as other promising areas of computing, by encouraging fresh, high-quality research discoveries and inventive solutions to tough machine learning challenges. Researchers, academicians, and professionals from all over the world are invited to submit original, unpublished research papers from all perspectives, including theory, practice, experimentation, and review papers highlighting specific research domains for presentation in the conference’s technical sessions.
The conference will create a cross-disciplinary summit that will bridge the gap between departmental, institutional, industrial, public and private research organizations, and global barriers, allowing for the integration of research and education in the emerging field of Machine Learning and Data Engineering.
Original contributions from researchers describing their original, unpublished, research contribution which is not currently under review by another conference or journal and addressing state-of-the-art research are invited to share their work in all areas of ICMLDE 2023 but not limited to the conference tracks.
Conference Themes and Tracks:
Machine Learning System Design
Machine Learning Optimization
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Statistical Learning
Transfer learning
Extreme Learning Machines
Kernel Based Learning
Bayesian Learning
Instruction Based Learning
Adversarial Machine Learning
Deep Neural Networks Optimization Algorithms
Deep Feedforward Networks
Regularization
Deep Convolutional Neural Networks
Deep Recurrent Neural Networks
Sequence Modelling
Deep Generative Models
Generative Adversarial Networks
Inference Dependencies on Multi-Layered Networks
Tensors for Deep Learning
Multi Scale Deep Architecture and Learning
Machine Learning in Data Lakes
Machine Learning based Data Integration and Data Interoperability
Machine Learning Data Pipelines
Machine Learning based Data Streaming
Machine Learning Relating to Knowledge and Data Management
Machine Learning Principles of Information Extraction from Big Data
Machine Learning based Web Data Management and Deep Web
Machine Learning Architecture for Pattern Recognition
Machine Learning Architecture for Medical Imaging
Machine Learning Search Engine
Machine Learning Cloud Services
Machine Learning IoT Services
Bioinformatics
Biomedical informatics
Computational Biology
Healthcare
Human Activity Recognition
Computer vision
Natural Language Processing
Climate Science