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Record W4310026276 · doi:10.48175/ijarsct-7592

Block Chain Technology-Based Secure E- Wallet System

2022· article· en· W4310026276 on OpenAlexaboutno aff
Tushar Phad, Adityarana Chavan, Bhagyashri Abhang, Balaji Kamble, Prof. Mundhe Bhalchandra B, Sunil Khatal

Bibliographic record

VenueInternational Journal of Advanced Research in Science Communication and Technology · 2022
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsnot available
Fundersnot available
KeywordsCashCurrencyBusinessPaymentDatabase transactionCounterfeitFinancial inclusionGovernment (linguistics)Financial transactionCommerceElectronic moneyDigital currencyFinanceEconomyFinancial systemFinancial servicesEconomicsMonetary economics

Abstract

fetched live from OpenAlex

According to a survey of Forex Bonuses 2017, Sweden and Canada seem to be linked with the cashless economy. An economic system where only minor amounts of currency are used in transactions is known as a cashless economy. The foundation of a cashless economy is the use of credit cards, debit cards, wallets, or other digital payment methods. Although people in India still prefer to carry cash rather than credit or debit cards, the country is transitioning to a "less cash economy" phase. Controlling the shadow economy, corruption, financing of terrorism, trafficking of people and drugs, counterfeit currency, and other issues is crucial. The cashless economy is economical, conducive to company growth and financial inclusion, etc. It is being promoted by the government via the BHIM app, AEPS, Digital, etc. Cashless economy demands strong digitalization. It has various challenges-escaping attitudes of people, poor transaction security mechanism, insufficient infrastructure etc. it is boon to industries like UBER and OLA. On secondary data, more analysis will be performed. Cashless Using BCT, India's economy is feasible and will be more secure. BCT has the ability to eliminate cash in India. both clear and safe.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.006
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0090.003
Research integrity0.0000.002
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 head0.025
GPT teacher head0.358
Teacher spread0.333 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2022
Admission routes1
Has abstractyes

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