Deep Learning with Emerging Engineering Applications

ISBN 10: 1836349114 / ISBN 13: 9781836349112

Published by Intechopen, 2026

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Deep learning has fundamentally transformed engineering practice, enabling unprecedented capabilities in pattern recognition, predictive analytics, and automated decision-making across diverse domains. This edited book presents cutting-edge research at the intersection of artificial intelligence and engineering, featuring contributions that span theoretical foundations, methodological innovations, and real-world applications. From explainable AI frameworks that bridge the gap between model accuracy and interpretability in safety-critical systems, to language-specific transformer models for detecting hate speech in Turkish social media, the chapters illustrate how deep learning architectures must be thoughtfully adapted to address domain-specific challenges. The book explores petroleum demand forecasting in Nigeria's transitioning energy sector, demonstrating how machine learning algorithms can optimize supply chains and inform policy decisions, alongside comprehensive investigations of BERT-based keyword extraction for morphologically complex languages such as Arabic and Turkish. By synthesizing advances in convolutional neural networks, recurrent architectures, and transformer models, this book offers both academic insights and practical guidance for researchers, engineers, and practitioners seeking to harness the full potential of deep learning.

Seller Inventory # 9781836349112

Bibliographic Details

• Title: Deep Learning with Emerging Engineering Applications
• Publisher: Intechopen
• Publication Date: 2026
• Language: English
• ISBN 10: 1836349114
• ISBN 13: 9781836349112
• Binding: Buch
• Condition: Neu
• Dimensions: 260W x 183H x 13D millimeters 260 millimeters width by 183 millimeters height by 13 millimeters depth
• Weight: 451 grams

About this title

Synopsis

Deep learning has fundamentally transformed engineering practice, enabling unprecedented capabilities in pattern recognition, predictive analytics, and automated decision-making across diverse domains. This edited book presents cutting-edge research at the intersection of artificial intelligence and engineering, featuring contributions that span theoretical foundations, methodological innovations, and real-world applications. From explainable AI frameworks that bridge the gap between model accuracy and interpretability in safety-critical systems, to language-specific transformer models for detecting hate speech in Turkish social media, the chapters illustrate how deep learning architectures must be thoughtfully adapted to address domain-specific challenges. The book explores petroleum demand forecasting in Nigeria's transitioning energy sector, demonstrating how machine learning algorithms can optimize supply chains and inform policy decisions, alongside comprehensive investigations of BERT-based keyword extraction for morphologically complex languages such as Arabic and Turkish. By synthesizing advances in convolutional neural networks, recurrent architectures, and transformer models, this book offers both academic insights and practical guidance for researchers, engineers, and practitioners seeking to harness the full potential of deep learning.