Full Description
There is a need to categorize AI applications, tools, techniques, and algorithms based on their intended use in various design stages. Specifically, there is a need to explore AI techniques that are utilized for things such as design tasks, including but not limited to inspiration, idea and concept generation, concept evaluation, optimization, decision-making, and modeling. This includes things like generating ideas and concepts, evaluating those ideas, optimizing designs, making decisions, and creating models. This handbook brings all of these categories with compatible AI techniques, tools, and algorithms together in one place.
Handbook of AI in Engineering Applications: Tools, Techniques, and Algorithms covers applications of AI in engineering and highlights areas such as future cities, mechanical system analysis, and robotic process automation, and presents the application of AI and the use of computerized systems that aim to simplify and automate the processes of design and construction of civil works. The handbook discusses the design and optimization of mechanical systems and parts, such as engines, gears, and bearings, which can be automated using AI and it explores the performance of robotics and automation systems which can be simulated and analyzed using AI to forecast behavior, spot future issues, and suggest changes. Rounding out this handbook is AI technology automation and how analyzing relevant data can provide a reliable basis for relevant personnel to carry out their work.
This handbook fills the gap between R&D in AI and will benefit all stakeholders including industries, professionals, technologists, academics, research scholars, senior graduate students, government, and public healthcare professionals.
Contents
Section One: Integrating AI Tools, Techniques, and Algorithms. 1.A Comparative Analysis of Symbolic And Subsymbolic Approaches In Artificial Intelligence. 2. Revisiting the Impact of AI-Driven Feature Selection Approaches in Software Fault Prediction. 3. Advancing Depression Detection: Insights from Standard Datasets and Multimodal Approaches. 4. Machine Learning and Natural Language Processing Strategies for Fake News Detection: An Empirical Study. 5. Exploring the Nexus of AI, the Metaverse and Quality of Life. 6. A Literature Survey on AI-Driven Code-Mixed Text Analysis and Normalization. 7. Reviewing Different Artificial Intelligence Algorithms For Sarcasm Detection. 8. Advances in Automatic Question Generation by AI based Algorithms: A Review. 9. Hands on Practices, Reflection on Data Wisdom with AI Principles: A Review. Section Two: Harnessing the Applications of AI. 10. FPGA Accelerated Deep Learning Network For Liver Tumor Segmentation in 3D CT Images. 11. Deep Learning Enhanced Restaurant Recommendations: Leveraging Artificial Neural Networks. 12. AI-Driven Solutions for Career Counselling Based on the Personality of an . 13. Exploration of Machine Learning Enabled Automated Crop-Disease Detection, Segmentation and Classification Techniques. 14. Digital Camouflage Generation by AI Based Methods: A Survey of Recent Techniques. 15. Plant Disease Detection Using Deep Learning Techniques: A Comprehensive Review and Comparative Analysis. 16. AI for disaster prediction and Management. 17. Investigation of Stress Among University Students using PSS and AI Based Analysis. 18. An Experimental Overview of Assessment of Authenticity of Face Recognition by AI Techniques in Smart Phones.
