IoT-Based Smart Remote Door Lock and Monitoring System Using an Android Application
Bibliographic record
Abstract
Nowadays, it is very important to secure our home perfectly. To make our life easier and more secure, we are presenting our smart door lock system project. We implement an IoT-based smart door lock system using an ESP32-CAM and an Android application in this project. Most of the time in our daily life, we forget to lock our doors and later we suffer from confusion about whether we locked all doors perfectly or not. In this project, we implement a smart door lock system, by which the owner can see the visitor’s picture and then lock or unlock their doors from anywhere and at any time using the Android application. Whenever visitors come to visit the home and press the doorbell, the owner will receive a notification on his/her smartphone and then the owner can see the visitor’s picture by using the Android app. After checking the visitor, the owner can let them enter the house by unlocking the door remotely. If the door is locked perfectly, then the door lock signal in the application will show a green signal. If the door is not locked perfectly, the signal will show red and then the owner can remotely lock their door easily from anywhere. In this project, we have also utilized a theft alert. If anyone comes in front of the door and tries to enter the house forcefully then a theft alert notification will be sent to the owner’s smartphone and a Buzzer Alert will ring in the house loudly so that the neighbors can be aware of the theft and can take action. The automatic door lock feature is also available in this system.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".