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Record W4281658682 · doi:10.22214/ijraset.2022.43435

Survey on Employee Tracking System

2022· article· en· W4281658682 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

VenueInternational Journal for Research in Applied Science and Engineering Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicIoT and GPS-based Vehicle Safety Systems
Canadian institutionsTrinity College
Fundersnot available
KeywordsComputer scienceBar chartJavaAndroid (operating system)Tracking systemAttendanceInformation systemDatabaseOperating systemEngineering

Abstract

fetched live from OpenAlex

Abstract: In this paper, an employee tracking system based on web based operating system was developed. All the activities of the Employee will be monitored using this system. Scheduling information and time off requests are often considered part of personnel tracking; as this information will enable managers know when employees are expected to actually be in the office or other work areas. This system is really very helpful for the managers to monitor their employees through mobile phones. It was implemented using JAVA programming language, and the result was stored in SQLite database. An object-Oriented Analysis and design (OOAD) approach was adopted which consist of a well-planned iterative steps. Data was collected using document analysis and field Methods and the application of relevant analytical methods like bar-charts were used to interpret the facts collected. The developed system was able to increased productivity, reduction of cost, instant access to employee attendance record. Keywords: Android Operating System, Tracking system, Employee, JAVA programming and SQLite database.

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)0.006
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: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.358
Teacher spread0.288 · 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