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Record W4403905946 · doi:10.59934/jaiea.v4i1.666

Design and Construction of Guest Friendly Robot for Front Office Service on Campus Based on IoT

2024· article· en· W4403905946 on OpenAlexaff
Dea Nanda Putri Purwani, Relita Buaton, Milli Alfhi Syari

Bibliographic record

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2024
Typearticle
Languageen
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsService (business)Internet of ThingsArchitectural engineeringFront officeFront (military)RobotService robotEnvironmentally friendlyComputer scienceEngineeringConstruction engineeringWorld Wide WebBusinessMechanical engineeringArtificial intelligenceMarketing

Abstract

fetched live from OpenAlex

Amid the rapid advancements of the digital era, Internet of Things (IoT) technology has emerged as one of the most significant innovations, enabling electronic devices to connect and interact intelligently. This study aims to design and develop an IoT-based guest reception robot intended for front office services at the campus. The robot is designed as a prototype capable of welcoming guests, saying "welcome," and providing four key pieces of information about the campus. Utilizing components such as ESP32 and ultrasonic sensors, the robot is expected to enhance service efficiency while also offering a more interactive experience for campus visitors. The implementation of IoT in this robot is not only intended to improve visitor services but also to promote technological innovation within the campus environment. This study also explores the integration of hardware and software, as well as the features and functions needed to meet the requirements of front office services. Thus, this research is expected to serve as a foundational step toward creating a more modern and innovative campus.

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)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.293

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 head0.045
GPT teacher head0.328
Teacher spread0.283 · 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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