The Role of Recurrent Convolutional Neural Network in IoT for Building a Security Artificial Intelligence and Home Assistance System
Bibliographic record
Abstract
Recurrent Convolutional Neural Network (RCNN) is the result of the development of the CNN architecture based on a recursive neural network on a neural network. The process with the development of RCNN is able to study data in moving images and images more optimally and accurately. With optimal accuracy, RCNN is of course not only limited to research, RCNN is able to play a role in models that are contained in hardware such as IoT technology so that it is used in everyday life. One of the benefits of this is to make the Smart Home System (SHS) concept and Energy Management System based on the concept of artificial intelligence. The development of IoT technology is caused by the large number of jobs or activities that cannot be carried out by humans on a regular basis so that it is combined with cloud technology which makes it easy to access from anywhere with connectivity. Cloud-based solar panel and IoT technology has proven to be able to provide convenience in the use of security in the smart home concept. Solar panels can replace electrical energy for smart home security devices for 24 hours. The Home Assistant system successfully detects and captures every object and distinguishes any movement in the area it sees so that the Cloud-based Home Assistant security system provides convenience and comfort for the smart home concept. Where the accuracy that results from RCCN as outlined in IoT devices on objects 0.5 meter to 1 meter is 100%, 1 meter to 2 meter is 95%.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".