Deep Learning Theory and Applications 5th International Conference, DeLTA 2024, Dijon, France, July 10–11, 2024, Proceedings, Part II /
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 select...
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| Další autoři: | , , , |
| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | angličtina |
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Springer Nature Switzerland : Imprint: Springer,
2024.
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| Vydání: | 1st ed. 2024. |
| Edice: | Communications in Computer and Information Science,
2172 |
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| On-line přístup: | https://doi.org/10.1007/978-3-031-66705-3 |
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| 245 | 1 | 0 | |a Deep Learning Theory and Applications |h [electronic resource] : |b 5th International Conference, DeLTA 2024, Dijon, France, July 10–11, 2024, Proceedings, Part II / |c edited by Ana Fred, Allel Hadjali, Oleg Gusikhin, Carlo Sansone. |
| 250 | |a 1st ed. 2024. | ||
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| 490 | 1 | |a Communications in Computer and Information Science, |x 1865-0937 ; |v 2172 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. . | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Application software. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Natural language processing (Computer science). | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Computer and Information Systems Applications. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Natural Language Processing (NLP). |
| 700 | 1 | |a Fred, Ana. |e editor. |0 (orcid)0000-0003-1320-5024 |1 https://orcid.org/0000-0003-1320-5024 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Hadjali, Allel. |e editor. |0 (orcid)0000-0002-4452-1647 |1 https://orcid.org/0000-0002-4452-1647 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Gusikhin, Oleg. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Sansone, Carlo. |e editor. |0 (orcid)0000-0002-8176-6950 |1 https://orcid.org/0000-0002-8176-6950 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 776 | 0 | 8 | |i Printed edition: |z 9783031667046 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031667060 |
| 830 | 0 | |a Communications in Computer and Information Science, |x 1865-0937 ; |v 2172 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-66705-3 |
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