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Record W4246924798 · doi:10.5383/swes.8.01.006

Smart Home System for Energy Saving using Genetic- Fuzzy-Neural Networks Approach

2016· article· en· W4246924798 on OpenAlex
Anwar Jarndal

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Sustainable Water and Environmental Systems · 2016
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsnot available
Fundersnot available
KeywordsHome automationComputer scienceArtificial neural networkFuzzy logicGenetic algorithmFuzzy control systemEnergy (signal processing)Smart gridControl (management)Artificial intelligenceEngineeringMachine learningTelecommunications

Abstract

fetched live from OpenAlex

Home energy saving is very important to realize sustainable improvement. This can be achieved by designing a smart home system that provides a productive and cost-effective environment through optimization of different factors that will be explained in this paper. In this paper, an adaptive smart home system for optimal utilization of power will be designed. The system is based on genetic-fuzzy-neural networks technique, which can capture a human behavior patterns and use it to predict the user's mood. This technique will improve the intelligence of the smart home control to minimize the power losses.

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: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.467

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 head0.006
GPT teacher head0.167
Teacher spread0.162 · 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