Machine Learning Based Implementation of Home Automation Using Smart Mirror
Bibliographic record
Abstract
When ordered, it may be folded in half quickly and effortlessly. IoT (Internet of Things) technology drives the Smart Mirror's functionality. Standard mirror functionality is included, in addition to showing the user's social notifications, daily tasks, weather updates, breaking news, reminders, voice assistant notifications, and smartphone notifications. The Smart Mirror is connected to the Raspberry Pi-based network through Wi-Fi. A two-way mirror or an acrylic mirror sheet is used with the Raspberry-Pi mainboard to conceal the Mirror's rear end from the user. It supports modules written in any programming language. When Python is used as the primary programming language, these changes take care of the hardware and software limitations. This work discusses the creation and building of the Mirror in appropriate manner. In addition, possible uses of the Mirror are discussed. Compared to this DIY method, the cost is substantially lower, and the result is more predictable. The result produced by the support vector machine classifier are of accuracy which is 84% for detecting theft, and the confusion matrix is often diagonal, showing that this classifier can accurately labelled the data. Similarly, F1 score of 0.82% shows that there are a few false positives and false negatives, which is a favorable indicator.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".