Artificial intelligence : machine learning, convolutional neural networks and large language models

1: Machine learning (ML) 2: Detection of lesions in breast image using median filtering and convolutional neural networks 3: Pushing the boundaries of probabilistic inference through message contraction optimization 4: Facilitating cooperative missions through information sharing in heterogeneous agent teams 5: Transferring knowledge: CNNs in Martian surface image classification 6: Vascular system segmentation using deep learning 7: Evolutionary CNN-based architectures with attention mechanisms for enhanced image classification Convolutional neural network (CNN) 8: Multi-label concept detection in imaging entities of biomedical literature leveraging deep learning-based classification and object detection 9: Revolutionizing supply chain dynamics: deep meta-learning and multi-task learning for enhanced predictive insights 10: Characterization of Neuro-Symbolic AI and Graph Convolutional Network workloads 11: Multivariant time series prediction using variants of LSTM deep neural networks 12: Cellphone-based sUAS range estimation: a deep-learning classification and regression approach Automatic diagnosis of 12-lead ECG using DINOv2 13: Large language model (LLM) 14: Leveraging linguistic features to improve machine learning models for detecting ChatGPT usage on exams 15: Towards AI-augmented design space exploration pipelines for UAVs 16: Improving subword embeddings in large language models using morphological information 17: Swarm intelligence: a new software paradigm 18: Leveraging large language models for efficient representation learning for entity resolution 19: TOAA: Train once, apply anywhere

This book explores the theory and practical applications of Artificial Intelligence in computer science, focusing on techniques like Machine Learning, Convolution Neural Networks, Deep Learning, and Large Language Models, to tackle complex issues and overcome challenges in various domains.