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Record W4366994679 · doi:10.22214/ijraset.2023.50727

AESTEX: An Innovative Smart Shopping System for Enhanced Customer Experience and Increased Sales

2023· article· en· W4366994679 on OpenAlexaff
Akshay Kudtarkar, Adika Karnataki, Chirag Agarwal, Vaishnavi Korgaonkar, Mrs. Vidya Kubde

Bibliographic record

VenueInternational Journal for Research in Applied Science and Engineering Technology · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsMagna International (Canada)
Fundersnot available
KeywordsPurchasingBarcodeBusinessPaymentProduct (mathematics)QueueClothingComputer scienceMarketingAdvertisingComputer network

Abstract

fetched live from OpenAlex

Abstract: AESTEX is a counter less, queue less, smart shop- ping system that aims to enhance the shopping experience for both customers and shopkeepers. The system provides customers with counter less and time saving shopping feature that allows customers to scan QR/barcode on the products to add them to their virtual cart for the quick shopping eliminating the need long queues and the counters along with the product recommendation system to suggest personalized complementary/similar items. Thesystem also includes a virtual dressing room to visualize the clothes before purchasing to ensure perfect buy every time you shop and to ensure hygienic experience. Along ensure more conve- nient and efficient shopping experience for customers, the system supports the shopkeepers to expand their business while keeping the manpower at control and also incorporate digitalization in the physical shops as well. Additionally, the AESTEX system includes a payment gateway for cashless transactions, ensuringa safe and easy shopping practice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0400.010

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 head0.089
GPT teacher head0.390
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2023
Admission routes1
Has abstractyes

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