Lecture Notes in Data Engineering, Computational Intelligence, and Decision-Making, Volume 2

This book addresses contemporary challenges in artificial and computational intelligence, particularly focusing on decision-making systems. It explores current trends in computer science, including the collection, analysis, and processing of information. The advancement of modern information and computer technologies for data analysis and processing in data mining and machine learning is highlighted, showcasing their role in enhancing the efficiency of information processing by reducing time and increasing accuracy.

The book comprises 16 scientific papers presenting cutting-edge research in data mining, machine learning, and decision-making. It is categorized into three sections:

1. Data engineering, computational intelligence, and inductive modeling16 papers.

   This book is designed for scientists and developers specializing in data mining, machine learning, and decision-making systems.

   Presents recent research on Decision-Making and Computational Intelligence Explores current trends in computer science, including the collection, analysis, and processing of information Written by experts in the field

   Inhalt

Hybrid Networks Training Method Based on Distributed and Parallel Computing Technology.- Knowledge Presentation and Procession Models for Intelligent Business Analytics Systems.- Biomedical Image Datasets.- Development and Implementation of a Software Information System for Biological Wastewater Treatment.- Forecasting Algorithm Based on Intellectual Analysis of Polynomial Extrapolation.- Approach to Identification of Anomalous Values in Analysis Tasks and Data Pre-processing.- Contemporary Methods, Models, and Software for Implementation and Optimization of IoT (Internet of Things) Systems: A Review.- Audio Deep Watermarking and Algebraic Transform.- Damage Detection Based on Satellite Image Analysis.- Improving Satellite Imagery Recognition Performance with Initial Dataset Limitation by Augmenting Training Data.- A Systematic Approach to Data Normalization and Standardization in Machine Learning Problems.- Systems Identification with Nonlinear Characteristics based on Interval Data.- Adaptive Bagging Of Hybrid Systems Of Computational Intelligence Using Metamodel Online Optimal Learning.- Determination of Fire Evacuation Parameters In Higher Education Institutions With Inclusive Groups Using Machine Learning Methods.- Airborne mine detection using synthetic aperture radar.- Hyperspectral Image Classification for Landmine Detection.