Machine Learning Model
Machine learning models aim to create systems that can learn from data and make predictions or decisions without explicit programming. Current research emphasizes improving model accuracy, interpretability, and robustness, focusing on architectures like deep neural networks, decision tree ensembles, and transformer models, as well as exploring decentralized learning and techniques for mitigating biases and vulnerabilities. These advancements are crucial for diverse applications, ranging from optimizing resource management (e.g., smart irrigation) to improving healthcare diagnostics and enhancing the security and trustworthiness of AI systems.
Papers
Predicting House Rental Prices in Ghana Using Machine Learning
Philip Adzanoukpe
Medical artificial intelligence toolbox (MAIT): an explainable machine learning framework for binary classification, survival modelling, and regression analyses
Ramtin Zargari Marandi, Anne Svane Frahm, Jens Lundgren, Daniel Dawson Murray, Maja Milojevic
Integrated Offline and Online Learning to Solve a Large Class of Scheduling Problems
Anbang Liu, Zhi-Long Chen, Jinyang Jiang, Xi Chen
Exploring a Datasets Statistical Effect Size Impact on Model Performance, and Data Sample-Size Sufficiency
Arya Hatamian, Lionel Levine, Haniyeh Ehsani Oskouie, Majid Sarrafzadeh
Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines
Marzieh Esnaashari, Nima Moradi