Deep Learning Theory and Applications [electronic resource] : 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023, Proceedings /

Pervasive AI: (deep) Learning into the Wild -- Deep Reinforcement Learning to Improve Traditional Supervised Learning Methodologies -- Synthetic Network Traffic Data Generation and Classification of Advanced Persistent Threat Samples: A Case Study with GANs and XGBoost -- Improving Primate Sounds Classification Using Binary Presorting for Deep Learning -- Towards Exploring Adversarial Learning for Anomaly Detection in Complex Driving Scenes -- Dynamic Prediction of Survival Status in Patients Undergoing Cardiac Catheterization Using a Joint Modeling Approach -- A Machine Learning Framework for Shuttlecock Tracking and Player Service Fault Detection -- An Automated Dual-Module Pipeline for Stock Prediction: Integrating N-Perception Period Power Strategy and NLP-Driven -- Sentiment Analysis for Enhanced Forecasting Accuracy and Investor Insight -- Machine Learning Applied to Speech Recordings for Parkinson’s Disease Recognition -- Vision Transformers for Galaxy Morphology Classification: Fine-Tuning Pre-Trained Networks vs. Training from Scratch -- A Study of Neural Collapse for Text Classification -- Research Data Reusability with Content-Based Recommender System -- MSDeepNet: A Novel Multi-Stream Deep Neural Network for Real-World Anomaly Detection in Surveillance Videos -- A Novel Probabilistic Approach for Detecting Concept Drift in Streaming Data -- Explaining Relation Classification Models with Semantic Extents -- Phoneme-Based Multi-Task Assessment of Affective Vocal Bursts -- Using Artificial Intelligence to Reduce the Risk of Transfusion Hemolytic Reactions -- ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP -- Exploring ASR Models in Low-Resource Languages: Use-Case the Macedonian Language -- Facilitating Enterprise Model Classification via Embedding Symbolic Knowledge into Neural Network Models -- Explainable Abnormal Time Series Subsequence Detection Using Random Convolutional Kernels -- TaxoSBERT: Unsupervised Taxonomy Expansion Through Expressive Semantic Similarity -- Towards Equitable AI in HR: Designing a Fair, Reliable, and Transparent Human Resource Management Application -- An Explainable Approach for Early Parkinson Disease Detection Using Deep Learning -- UMLDesigner: An Automatic UML Diagram Design Tool -- Graph Neural Networks for Circuit Diagram Pattern Generation -- Generative Adversarial Networks for Domain Translation in Unpaired Breast DCE-MRI Datasets -- A Survey on Reinforcement Learning and Deep Reinforcement Learning for Recommender Systems -- GAN-Powered Model&Landmark-Free Reconstruction: A Versatile Approach for High-Quality 3D Facial and Object Recovery from Single Images.-GAN-Based LiDAR Intensity Simulation -- Evaluating Prototypes and Criticisms for Explaining Clustered Contributions in Digital Public Participation Processes -- FRLL-Beautified: A Dataset of Fun Selfie Filters with Facial Attributes -- CSR & Sentiment Analysis: A New Customized Dictionary.

This book consitiutes the refereed proceedings of the 4th International Conference on Deep Learning Theory and Applications, DeLTA 2023, held in Rome, Italy from 13 to 14 July 2023. The 9 full papers and 22 short papers presented were thoroughly reviewed and selected from the 42 qualified submissions. The scope of the conference includes such topics as models and algorithms; machine learning; big data analytics; computer vision applications; and natural language understanding.