AI and Data-Driven Advancements in Industry 4.0
AI and Data-Driven Advancements in Industry 4.0 reprint presents a comprehensive collection of innovative research articles that have advanced our understanding of artificial intelligence applications in industrial environments. This Topic Issue features a variety of contributions, ranging from inte...
I tiakina i:
| Hōputu: | Online |
|---|---|
| Reo: | Ingarihi |
| I whakaputaina: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
|
| Ngā marau: | |
| Urunga tuihono: | ONIX_20250812T110751_9783725841677_360 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
| _version_ | 1869521814249537536 |
|---|---|
| collection | Directory of Open Access Books |
| description | AI and Data-Driven Advancements in Industry 4.0 reprint presents a comprehensive collection of innovative research articles that have advanced our understanding of artificial intelligence applications in industrial environments. This Topic Issue features a variety of contributions, ranging from intelligent sensor software that promotes energy-efficient decision-making in the welding of steel reinforcement to advanced prediction models for ultrasonic vibration-assisted milling performance. In addition, state-of-the-art deep learning techniques for detecting scratch defects on metal surfaces are featured alongside novel methods for remote monitoring of central nervous system biomarkers using wearable sensors. The reprint also includes contributions on precise robot arm attitude estimation through multi-view imaging and super-resolution keypoint detection, as well as pioneering approaches in medical diagnostics, such as EEG-based Parkinson’s disease classification and enhanced retinal vessel segmentation. Furthermore, emerging themes of blockchain integration and smart contract vulnerability detection highlight the intersection of AI with secure data management, demonstrating how decentralized technologies can support robust, trustworthy systems. Collectively, these articles illustrate the transformative impact of data-centric strategies and deep learning in modern manufacturing, healthcare, and robotics, offering a retrospective view of cutting-edge innovations in Industry 4.0. |
| format | Online |
| id | doab-20.500.12854ir-165605 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1656052025-08-12T09:59:33Z AI and Data-Driven Advancements in Industry 4.0 Huang, Teng Wang, Qiong Pang, Yan Industry 4.0 AI in Medicine Blockchain thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology AI and Data-Driven Advancements in Industry 4.0 reprint presents a comprehensive collection of innovative research articles that have advanced our understanding of artificial intelligence applications in industrial environments. This Topic Issue features a variety of contributions, ranging from intelligent sensor software that promotes energy-efficient decision-making in the welding of steel reinforcement to advanced prediction models for ultrasonic vibration-assisted milling performance. In addition, state-of-the-art deep learning techniques for detecting scratch defects on metal surfaces are featured alongside novel methods for remote monitoring of central nervous system biomarkers using wearable sensors. The reprint also includes contributions on precise robot arm attitude estimation through multi-view imaging and super-resolution keypoint detection, as well as pioneering approaches in medical diagnostics, such as EEG-based Parkinson’s disease classification and enhanced retinal vessel segmentation. Furthermore, emerging themes of blockchain integration and smart contract vulnerability detection highlight the intersection of AI with secure data management, demonstrating how decentralized technologies can support robust, trustworthy systems. Collectively, these articles illustrate the transformative impact of data-centric strategies and deep learning in modern manufacturing, healthcare, and robotics, offering a retrospective view of cutting-edge innovations in Industry 4.0. 2025-08-12T09:59:31Z 2025-08-12T09:59:31Z 2025 book ONIX_20250812T110751_9783725841677_360 9783725841677 9783725841684 https://directory.doabooks.org/handle/20.500.12854/165605 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/topic/11077 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4168-4 10.3390/books978-3-7258-4168-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725841677 9783725841684 646 open access |
| spellingShingle | Industry 4.0 AI in Medicine Blockchain thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology AI and Data-Driven Advancements in Industry 4.0 |
| title | AI and Data-Driven Advancements in Industry 4.0 |
| title_full | AI and Data-Driven Advancements in Industry 4.0 |
| title_fullStr | AI and Data-Driven Advancements in Industry 4.0 |
| title_full_unstemmed | AI and Data-Driven Advancements in Industry 4.0 |
| title_short | AI and Data-Driven Advancements in Industry 4.0 |
| title_sort | ai and data driven advancements in industry 4 0 |
| topic | Industry 4.0 AI in Medicine Blockchain thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | Industry 4.0 AI in Medicine Blockchain thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20250812T110751_9783725841677_360 |