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Record W4403906061 · doi:10.59934/jaiea.v4i1.672

Design and Build an IoT-Based Shoe Dryer Monitoring and Control System

2024· article· en· W4403906061 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicWireless Sensor Networks and IoT
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsInternet of ThingsMonitoring and controlComputer scienceControl (management)Systems engineeringControl engineeringProcess engineeringEnvironmental scienceEmbedded systemArchitectural engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Wet shoes that are not dried immediately can lead to the growth of bacteria and fungi that can potentially damage the shoes and cause health problems in the user. Therefore, an effective and efficient shoe dryer is needed. This research aims to design and develop a monitoring and control system for shoe dryers based on the Internet of Things (IoT) that can be operated remotely through a web-based application or mobile device. The system consists of several main components, namely humidity and temperature sensors, microcontrollers, wireless communication modules, and heating elements. Humidity and temperature sensors are used to detect the condition of the shoe and the surrounding environment, while the microcontroller is in charge of processing the data and regulating the operation of the heating element as needed. With the wireless communication module, users can monitor and control the drying process in real-time through an application connected to the internet.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.244
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it