MLIP 2023, is an international conference where theory, practices, and applications of Information Technology, Computational Engineering, Computer and Telecommunication Technology and related topics are presented and discussed. Original contributions are solicited on topics covered under broad areas such as (but not limited to):
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 and Image Processing APPLICATIONS
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
Image and signal processing
Machine learning and deep learning models and algorithms
Fixed point theory, Methods and its applications
Optimization theory, methods and applications
Nonlinear Analysis and applications
Numerical Analysis, Methods and applications
Computational methods in science and applied science
Bioinformatics
Biomedical informatics
Computational Biology
Healthcare
Human Activity Recognition
Computer vision
Natural Language Processing
Climate Science
English is the official language of the conference. We welcome paper submissions. Prospective authors are invited to submit full (original) research papers; which are NOT submitted/published/under consideration anywhere in other conferences/journals; in electronic (Doc or Docx) format through the CMT Conference Management System or via email papers.mlip@gmail.com