New Trends in Information and Communications Technology Applications

This book constitutes the refereed proceedings of the 8th National Conference on New Trends in Information and Communications Technology Applications, NTICT 2024, held in Baghdad, Iraq, during December 1819, 2024.

The 17 full papers included in this book were carefully reviewed and selected from 54 submissions. They were organized in topical sections as follows: Machine learning and Evolutionary computing.

Inhalt

.- Machine Learning.
.- Multi-Objective Optimization in Topic Modeling Using Sentences Reduction Based on Length and Weight (SR-LW) Technique.
.- Identifying Oil Spill Areas and Causes Using a Deep Learning Model.
.- Development of a Machine Learning-Based Robotic System for Medical-Targeted Drug Delivery System.
.- Fire Vision: Deep Learning for Accurate and Efficient Fire Detection and Localization in Digital Images.
.- Oil Pipeline Leakage Detection Using Deep Learning Algorithms.
.- A Novel Sound Classification based on Lightweight ResNet-34 For Emergency Detection.
.- Impact of Bands Number on LULC Classification Accuracy in Sentinel-2 Images.
.- Graph-Based Network Analysis for Identifying Key Factors of Internet Addiction.
.- Iraqi Dialect Emoji Prediction based on Deep CNN-LSTM Architecture.
.- Influence of Feature Selection Techniques on the Cyber-attacks Prediction Using Machine Learning Techniques.
.- Textual Fake News Detection Based on FastText Embedding and Deep Learning.
.- AI-Based Reservoir Facies Classification and Missing Data Imputation Using Core and Wireline Data.
.- Evolutionary Computing.
.- A Novel Implementation of the Secretary Bird Optimization Algorithm for Solving Quadratic Equations.
.- Optimizing K-Means Clustering Selection Parameters Using Modified Grey Wolf Optimization.
.- Enhanced Feature Selection Using a Hybrid Elephant Herding Optimization with Lvy Flight.
.- Optimizing ECG Signal Quality: Enhanced Noise Reduction using the Water Wave Algorithm.
.- Optimizing Convolutional Neural Networks' Hyperparameters Based on Dynamic Salp Swarm Algorithm.