Trends in AI-Driven Prediction | Advances, Applications, and Future Directions of Intelligent Systems

Lucia Agnes Beena T (u. a.)

ISBN 10: 6209563856 / ISBN 13: 9786209563850

Published by Scholars' Press, 2026

About this Item

Trends in AI-Driven Prediction | Advances, Applications, and Future Directions of Intelligent Systems | Lucia Agnes Beena T (u. a.) | Taschenbuch | Englisch | 2026 | Scholars' Press | EAN 9786209563850 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Seller Inventory # 135020917

Bibliographic Details

• Title: Trends in AI-Driven Prediction | Advances, Applications, and Future Directions of Intelligent Systems
• Publisher: Scholars' Press
• Publication Date: 2026
• Language: English
• ISBN 10: 6209563856
• ISBN 13: 9786209563850
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 24D millimeters 220 millimeters width by 150 millimeters height by 24 millimeters depth
• Weight: 602 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This book presents a comprehensive exploration of AI-driven prediction and intelligent systems, highlighting real-world applications of machine learning and deep learning across multiple domains. The chapters address a wide range of predictive and analytical challenges, including sentiment analysis, job migration analysis, financial risk evaluation, fraud detection, stock market prediction, and climate-aware decision support.Readers are introduced to intelligent healthcare solutions such as disease prediction, medical image analysis, and risk assessment, along with computer vision applications including emotion recognition, object detection, image classification, and human activity recognition. The book also explores advances in environmental intelligence, covering crop recommendation systems, air quality monitoring, rainfall and flood forecasting, and the impact of climate change on agriculture.To address these real-world problems, advanced deep learning and computer vision techniques such as CNNs, LSTMs, YOLO, and video classification models are effectively employed.