Artificial Intelligence : Machine Learning, Convolutional Neural Networks and Large Language Models.

Format:

Book

Author/Creator:

Deligiannidis, Leonidas.

Contributor:

Dimitoglou, George.

Arabnia, Hamid.

Series:

Intelligent Computing Series

Intelligent Computing Series ; v.1

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Physical Description:

1 online resource (442 pages)

Edition:

1st ed.

Place of Publication:

Berlin/Boston : Walter de Gruyter GmbH, 2024.

Summary:

This book, edited by Leonidas George Dimitoglou and Hamid R. Arabnia, is a comprehensive collection of papers focusing on recent advancements in Artificial Intelligence, specifically in Machine Learning, Convolutional Neural Networks (CNNs), and Large Language Models (LLMs). It compiles extended versions of selected papers presented at international conferences on computational science and artificial intelligence. The book addresses emerging trends and applications in AI, highlighting the exponential growth in the use of Machine Learning techniques, particularly Deep Learning and CNNs, in various fields. It is intended for a diverse audience, including scholars, researchers, and practitioners from academia and industry, providing them with insights into cutting-edge AI technologies and methodologies. Generated by AI.

Contents:

Intro

Preface

Contents

Machine learning (ML)

Detection of lesions in breast image using median filtering and convolutional neural networks

Pushing the boundaries of probabilistic inference through message contraction optimization

Facilitating cooperative missions through information sharing in heterogeneous agent teams

Transferring knowledge: CNNs in Martian surface image classification

Vascular system segmentation using deep learning

Evolutionary CNN-based architectures with attention mechanisms for enhanced image classification

Convolutional neural network (CNN)

Multi-label concept detection in imaging entities of biomedical literature leveraging deep learning-based classification and object detection

Revolutionizing supply chain dynamics: deep meta-learning and multi-task learning for enhanced predictive insights

Characterization of Neuro-Symbolic AI and Graph Convolutional Network workloads

Multivariant time series prediction using variants of LSTM deep neural networks

Cellphone-based sUAS range estimation: a deep-learning classification and regression approach

Automatic diagnosis of 12-lead ECG using DINOv2

Large language model (LLM)

Leveraging linguistic features to improve machine learning models for detecting ChatGPT usage on exams

Towards AI-augmented design space exploration pipelines for UAVs

Improving subword embeddings in large language models using morphological information

Swarm intelligence: a new software paradigm

Leveraging large language models for efficient representation learning for entity resolution

TOAA: Train once, apply anywhere

Index.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

9783111344126

3111344126

OCLC:

1446133941