Applied intelligent decision-making in machine learning
Applied intelligent decision-making in machine learning
- Boca Ratan CRC Press 2021
- 252p.
- Computational intelligence in engineering problem solving .
Table of contents: 1. Data Stream Mining for Big Data 2. Decoding Common Machine Learning Methods -Agricultural Application Case Studies using Open Source Software 3. A multi-stage hybrid model for Odia compound character recognition 4. Development of Hybrid Computational Approaches for Landslide Susceptibility Mapping using Remotely Sensed Data in East Sikkim, India 5. Domain-Specific Journal Recommendation using Feed Forward Neural Network 6. Forecasting Air Quality in India through Ensemble Clustering Technique 7. Intelligence based health biomarker identification system using microarray analysis 8. Medical Entities Extraction using Matrix based Pattern Matching Method 9. Supporting Environmental Decision-making: Application of Machine learning techniques to Australia's emissions 10. Comparative Study of Classification Algorithms on Imbalanced Datasets 11. Prediction Analysis of Exchange Rate Forecasting using Deep Learning based Neural Network Models 12. Optimal Selection of Features based on Teaching Learning based Optimization Algorithm for Classification 13. Enhanced Image Dehazing Procedure using CLAHE and Guided Filter
This book is intended to disseminate findings from various research domains in order to develop efficient, adaptive, and smart decision-making models. It incorporates advances in machine intelligence techniques such as data streaming, classification, clustering, pattern matching, feature selection, and deep learning into the decision-making process for a variety of applications, such as agriculture, character recognition, landslide susceptibility, recommendation systems, air quality forecasting, healthcare, exchange rate prediction, and image dehazing. In addition, it provides a premier forum for scientists, researchers, practitioners, and educators to discuss recent innovations, trends, developments, practical challenges, and advancements in the fields of data mining, machine learning, soft computing, and decision science. Several aspects of the application of intelligent techniques to the decision-making procedure are also highlighted. These objectives are addressed in twelve chapters contributed by authors from around the world. The authors attempt to solve these complex problems using several intelligent machine-learning techniques. This enables researchers to comprehend the mechanism necessary to harness the decision-making process using machine-learning techniques for their own projects.
9780367504939
Machine learning
Decision making - Data processing
Computer - Machine Theory
006.31 / APP
Table of contents: 1. Data Stream Mining for Big Data 2. Decoding Common Machine Learning Methods -Agricultural Application Case Studies using Open Source Software 3. A multi-stage hybrid model for Odia compound character recognition 4. Development of Hybrid Computational Approaches for Landslide Susceptibility Mapping using Remotely Sensed Data in East Sikkim, India 5. Domain-Specific Journal Recommendation using Feed Forward Neural Network 6. Forecasting Air Quality in India through Ensemble Clustering Technique 7. Intelligence based health biomarker identification system using microarray analysis 8. Medical Entities Extraction using Matrix based Pattern Matching Method 9. Supporting Environmental Decision-making: Application of Machine learning techniques to Australia's emissions 10. Comparative Study of Classification Algorithms on Imbalanced Datasets 11. Prediction Analysis of Exchange Rate Forecasting using Deep Learning based Neural Network Models 12. Optimal Selection of Features based on Teaching Learning based Optimization Algorithm for Classification 13. Enhanced Image Dehazing Procedure using CLAHE and Guided Filter
This book is intended to disseminate findings from various research domains in order to develop efficient, adaptive, and smart decision-making models. It incorporates advances in machine intelligence techniques such as data streaming, classification, clustering, pattern matching, feature selection, and deep learning into the decision-making process for a variety of applications, such as agriculture, character recognition, landslide susceptibility, recommendation systems, air quality forecasting, healthcare, exchange rate prediction, and image dehazing. In addition, it provides a premier forum for scientists, researchers, practitioners, and educators to discuss recent innovations, trends, developments, practical challenges, and advancements in the fields of data mining, machine learning, soft computing, and decision science. Several aspects of the application of intelligent techniques to the decision-making procedure are also highlighted. These objectives are addressed in twelve chapters contributed by authors from around the world. The authors attempt to solve these complex problems using several intelligent machine-learning techniques. This enables researchers to comprehend the mechanism necessary to harness the decision-making process using machine-learning techniques for their own projects.
9780367504939
Machine learning
Decision making - Data processing
Computer - Machine Theory
006.31 / APP