Oracles Integration in Blockchain-Based Platform for Smart Crop Production Data Exchange

Попчев, Иван; Radeva, Irina; Doukovska, Lyubka

doi:10.3390/electronics12102244

Cited by 8 publications

(2 citation statements)

References 21 publications

(22 reference statements)

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“…This study only considers the cost of the RAM resources required to run the tests. Data on the current market price of the RAM resource is retrieved from an oracle [16] every 60 min that runs within the SCPDx platform whose blockchain infrastructure is being used.…”

Section: Discussionmentioning

confidence: 99%

“…Later, in 2022, due to the hard fork, eosio blockchain was renamed to Antelope.io [13], which forced the migration of the blockchain network. The development of the SCPDx platform was preceded by a number of studies related to the choice of blockchain platform [14], blockchain-enabled supply-chain modelling for a smart crop production framework [15], and blockchain oracles integration [16].…”

Section: Web Application Development and Implementationmentioning

confidence: 99%

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Web Application for Retrieval-Augmented Generation: Implementation and Testing

Radeva,

Popchev,

Doukovska

et al. 2024

Electronics

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The purpose of this paper is to explore the implementation of retrieval-augmented generation (RAG) technology with open-source large language models (LLMs). A dedicated web-based application, PaSSER, was developed, integrating RAG with Mistral:7b, Llama2:7b, and Orca2:7b models. Various software instruments were used in the application’s development. PaSSER employs a set of evaluation metrics, including METEOR, ROUGE, BLEU, perplexity, cosine similarity, Pearson correlation, and F1 score, to assess LLMs’ performance, particularly within the smart agriculture domain. The paper presents the results and analyses of two tests. One test assessed the performance of LLMs across different hardware configurations, while the other determined which model delivered the most accurate and contextually relevant responses within RAG. The paper discusses the integration of blockchain with LLMs to manage and store assessment results within a blockchain environment. The tests revealed that GPUs are essential for fast text generation, even for 7b models. Orca2:7b on Mac M1 was the fastest, and Mistral:7b had superior performance on the 446 question–answer dataset. The discussion is on technical and hardware considerations affecting LLMs’ performance. The conclusion outlines future developments in leveraging other LLMs, fine-tuning approaches, and further integration with blockchain and IPFS.

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Section: Discussionmentioning

confidence: 99%