Security and Privacy in smart Internet of Things environments for well-being in the healthcare industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Privacy protection is required when communicating data in the healthcare system. The Internet of Things (IoT) in healthcare has numerous advantages, including the potential to more closely monitor patients' health and the use of data for analytics. IoT is a framework of interconnected, web-connected devices that may collect and transmit data via a wireless server without the need for human involvement. In this context, IoT-based healthcare uses many technological advances to give several services such as quick and efficient treatment, savings, and better communication. Wireless Body Area Network (WBAN) technology can improve the performance of data communication in smart systems. Throughout each stage of smart medical systems, machine learning (ML) can be applied. In this study, the most current research, suggested approaches, and existing smart healthcare system technologies are discussed in terms of technological advances, applications, and difficulties to provide a proper overview of what IoT signifies in the healthcare sector now and in the future.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it