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Record W4403905813 · doi:10.59934/jaiea.v4i1.643

Prototype Design IoT Based Air Quality Monitoring Tool for Urban Environment

2024· article· en· W4403905813 on OpenAlexaff
Catlya Novera Lita, Akim Manaor Hara Pardede, Siswan Syahputra

Bibliographic record

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsComputer scienceInternet of ThingsSystems engineeringAir quality indexArchitectural engineeringEmbedded systemEngineeringGeographyMeteorology

Abstract

fetched live from OpenAlex

In rapidly urban environments, air quality has become an important issue that affects public health and the environment. This research aims to design and implement a prototype of an IoT-based air quality monitoring device that can be used to monitor air conditions in urban environments. This prototype uses the DHT11 sensor for measuring temperature and humidity, the MQ135 sensor to detect the concentration of air pollutants such as CO, CO2, ALCOHOL, TOLUENE, NH4, and ACETONE, and an OLED LCD for displaying information. This system uses the NodeMCU ESP8266 as a microcontroller that connects devices to the internet and sends data to the Blynk application platform. The Blynk application serves as a user interface that allows for real-time monitoring of the device through a smartphone. With this system, users can obtain real-time air quality data to protect health and the environment in urban areas. The prototype evaluation shows that this system is effective in monitoring and displaying air quality information, as well as providing easy access to data through the Blynk application.

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 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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.075
GPT teacher head0.310
Teacher spread0.235 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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