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Deep learning theory and applications5th International Conference, DeLTA 2024, Dijon, France, July 10-11, 2024 : proceedings.Part II /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning theory and applicationsedited by Ana Fred ...[et al.].
Reminder of title:	5th International Conference, DeLTA 2024, Dijon, France, July 10-11, 2024 : proceedings.
remainder title:	DeLTA 2024
other author:	Fred, Ana.
corporate name:
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xvii, 389 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning)
Online resource:	https://doi.org/10.1007/978-3-031-66705-3
ISBN:	9783031667053$q(electronic bk.)

Deep learning theory and applications5th International Conference, DeLTA 2024, Dijon, France, July 10-11, 2024 : proceedings.Part II /
Deep learning theory and applications5th International Conference, DeLTA 2024, Dijon, France, July 10-11, 2024 : proceedings.Part II /[electronic resource] :DeLTA 2024edited by Ana Fred ...[et al.]. - Cham :Springer Nature Switzerland :2024. - xvii, 389 p. :ill. (chiefly col.), digital ;24 cm. - Communications in computer and information science,21721865-0937 ;. - Communications in computer and information science ;229..

Geometrical Realization for Time Series Forecasting -- Brains over Brawn: Small AI Labs in the Age of Datacenter-Scale Compute -- Time Series Prediction for Anomalies Detection in Concentrating Solar Power Plants Using Long Short-Term Memory N Networks -- Bayes Classification Using an Approximation to the Joint Probability Distribution of the Attributes -- Pollutant Source Localization Based on Siamese Neural Network Similarity Measure -- Automatic Emotion Analysis in Movies: Matteo Garrone's Dogman as a Case Study -- Empowering Cybersecurity: CyberShield AI Advanced Integration of Machine Learning and Deep Learning for Dynamic Ransomware Detection -- Empirical Performance of Deep Learning Models with Class Imbalance for Crop Disease Classification -- Automating the Conducting of Surveys Using Large Language Models -- Computer Vision Based Monitoring System for Flotation in Mining Industry 4.0 -- Self-Supervised Learning for Robust Surface Defect Detection -- Efficient Deep Neural Network Verification with QAP-Based zkSNARK -- Version 8 of YOLO for Wildfire Detection -- Investigating a Semantic Similarity Loss Function for the Parallel Training of Abstractive and Extractive Scientific Document Summarizers -- Deep Learning-Based Preprocessing Tools for Turkish Natural Language Processing -- Skin Cancer Classification: A Comparison of CNN-Backbones for Feature-Extraction -- Multilingual Detection of Cyberbullying on Social Networks Using a Fine-Tuned GPT-3.5 Model -- Detecting Big-5 Personality Dimensions from Text Based on Large Language Models -- ME-ODAL: Mixture-of-Experts Ensemble of CNN Models for 3D Object Detection from Automotive LiDAR Point Clouds -- BitNet b1.58 Reloaded: State-of-the-Art Performance Also on Smaller Networks -- Deep Learning for Cattle Face Identification -- OBBabyFace: Oriented Bounding Box for Infant Face Detection -- EEG-Based Patient Independent Epileptic Seizure Detection Using GCN-BRF -- Predicting Components of a Target Value Versus Predicting the Target Value Directly.

The two-volume set CCIS 2171 and 2172 constitutes the refereed papers from the 5th INternational Conference on Deep Learning Theory and Applications, DeLTA 2024, which took place in Dijon, France, during July 10-11, 2024. The 44 papers included in these proceedings were carefully reviewed and selected from a total of 70 submissions. They focus on topics such as deep learning and big data analytics; machine-learning and artificial intelligence, etc.

ISBN: 9783031667053$q(electronic bk.)

Standard No.: 10.1007/978-3-031-66705-3doiSubjects--Topical Terms:

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .I58 2024

Dewey Class. No.: 006.31

Deep learning theory and applications5th International Conference, DeLTA 2024, Dijon, France, July 10-11, 2024 : proceedings.Part II /
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