Towards Industrial Revolution 5.0 and Explainable Artificial Intelligence: Challenges and Opportunities
Bibliographic record
Abstract
Technological growth is changing our everyday living, making it smarter and more convenient day by day; Smart society 5.0, Healthcare 5.0, Agriculture 5.0 are only a few examples indicative of our fast-evolving lifestyle.The Industrial Revolution 5.0 (IR 5.0) encapsulates future industry development trends to achieve prosperity beyond jobs by incorporating more intelligence in our everyday living with the help of cutting-edge technologies such as Explainable Artificial Intelligence.This paper reviews the enabling technologies for Industry 5.0 and suggests some pertinent research areas requiring more focus.The transition of manufacturing processes from mass production to mass personalization, the anticipated reliance on Cyber-Physical Systems (CPS) and digital twins is visualized, to identify the gaps in fully realizing the revolution.The operations of smart factories to enhance the overall productivity, modern workforce comprising of human-machine collaboration, means of heterogeneous data transmission & data interoperability, and security & privacy issues are reviewed to identify hot research spots, that will eventually fill in the gaps within societal domains to realize Industry 5.0.The potential of the new domain of Explainable Artificial intelligence to understand the application of right tools in a data connected Industry 5.0 compliant smart society is explored.Altogether, this research explores several research challenges and opportunities linked with IR 5.0.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".