Buch, Englisch, 315 Seiten, Format (B × H): 155 mm x 235 mm
Second International Conference, CODE-AI 2025, Dubai, United Arab Emirates, April 7–8, 2025, Proceedings, Part I
Buch, Englisch, 315 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Communications in Computer and Information Science
ISBN: 978-3-032-19317-9
Verlag: Springer
This open access book constitutes the proceedings of the Second International Conference on Data Science and Exploration in Artificial Intelligence, CODE-AI 2025, which took place in Dubai, UAE, during April 7-8, 2025.
The 67 full papers included in these proceedings were carefully reviewed and selected from 750 submissions.The thematic scope of CODE-AI 2025 spanned intelligent computing methods (including genetic algorithms, simulated annealing, artificial fish-swarm algorithms, quantum computing and fuzzy logic), advanced AI applications (such as biometrics, pattern recognition, computer and machine vision, speech recognition and smart robotics) and data-science topics (deep learning, decision-making frameworks, IoT/edge data integration and visualization.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
- A Machine Learning Approach for Intrusion Detection Systems.
.- Automated Deepfake Speech Detection Using MFCC Features, Text-to-Speech Synthesis Models, and Random Forest Classifier: A Comparative Study.
.- Third Eye – The AI-Powered Vision and Voice Assistance.
.- A Deep Ensemble Learning Model for Prediction of Attrition – Effect of Unbalanced Dataset.
.- Skin Cancer Detection Based on Machine Learning Models.
.- Quantum Diagnosis: Revolutionizing Skin Tumors Classification with Quantum Machine Learning.
.- Flood Depth Prediction using Temporal Attention Recurrent Graph Convolutional Neural Network for IoT-Enabled Smart Cities.
.- Cancer Classification and Analysis through an Explainable Deep Learning Framework.
.- BINARY CLASSIFICATION OF GALAXIES USING MACHINE LEARNING AND DEEP LEARNING.
.- Convergence of Dynamic Windows in Feature Extraction and Deep Autoencoders to Effectively Model Facial Expression Classification Problem.
.- AI Driven Canine Dog Nose Print Recognition.
.- AI BASED LSTM-BERT Model For False News Detection.
.- Automatic Cataract Identification and Classification Using InceptionV3.
.- A novel approach to human fatigue detection based on deep learning.
.- Optimised Random Forest Regressor Integrated with Explainable AI(ORF-XAI) for Predicting Student Study Hours to Enhance Academic Performance.
.- AI-ML Powered Customer Support Chatbot Utilizing Feedforward Neural Network (FFNN) Preprocessing Technique.
.- Real-Time Tiger Intrusion Detection System Using Machine Learning and IoT.
.- Eye Fatigue Detection and Mitigation Strategies Using Deep Learning Techniques.
.- AGRI-SURE: Agricultural Sustainability through disease detection and automated insurance using Deep Learning.
.- Data Exploration in IoT and Edge Environments: A Lightweight Deep Learning Model for Botnet Attack Detection.
.- Deep Learning Based Arecanut Leaf Classification: A Scalable Framework for Early Disease Detection.
.- Explainable AI to Identify Feature Importance in EEG Data for Mild Traumatic Brain Injury.
.- Comparative Analysis of Fine-Tuned CNNs and Segmentation Techniques for Multiclass Skin Cancer Classification.
.- Enhanced Deep Learning Framework with Vision Transformer and Slime Mould Optimization for Lung Cancer Detection in CT Images.
.- Cellular Automata Based Brain Tumor Segmentation from Magnetic Resonance Images.
.- Role of Artificial Intelligence in Cyber Security: Systematic Study on Attacks, techniques and tools.
.- Content-Based Smart E-Mail Dispatcher Using Large Language Models.
.- Dynamic Delay-Based Rapid Serial Visual Presentation Using Linguistic Features.
.- Agentic AI Factories: Constructing Intelligent Workflows for Data-Driven Decision Making.
.- Face – Orientation based Cattle Breed Detection using CNN Model.
.- AI-Enabled Quantum-Resistant Multipath Crypto-Graph Protocol (QR-MCP) for Secure Communications in Post-Quantum Networks.
.- Benchmarking Deep Learning Architectures with Monte Carlo Dropout for Drift-Resilient Prediction.
