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 "name": "Streaming Enhancing Digital Financial Security With Lstm And Blockchain Technology 90E199Cffb",
 "text": "Enhancing Digital Financial Security with LSTM and Blockchain Technology presents the following source proposition: The aim is to develop a system using an LSTM model to predict and detect anomalies in transaction data. This proposition is pertinent to Kaal's source-bound claim that The majority of private fund advisers that deploy blockchain technology, artificial intelligence, and big data in their operations or strategy charge their investors lower fees, even though not all blockchain enabled funds charge per transaction fees. The proposed response is an extension: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.",
 "author": {
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  "identifier": "https://orcid.org/0009-0008-7840-1847"
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  "External evidence level: abstract indexed.",
  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.3643.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
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  "name": "Enhancing Digital Financial Security with LSTM and Blockchain Technology",
  "url": "https://doi.org/10.14569/ijacsa.2024.0150830"
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  "url": "https://wulfkaal.github.io/claims/2959730-002",
  "citation": "Wulf A. Kaal, Blockchain Applications and Fee Structure Developments in Private Investment Funds (2017). SSRN: https://ssrn.com/abstract=2959730",
  "paper": "Wulf A. Kaal, Blockchain Applications and Fee Structure Developments in Private Investment Funds",
  "authors": [
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  "year": "2017",
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   "url": "https://doi.org/10.14569/ijacsa.2024.0150830"
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 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
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  "workAuthors": [
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    "ambiguous": true
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    "whyRelevant": "Shared high-information concepts: blockchain, data, applications, big. Scope: settings where Legal Tech and blockchain generate usable data at scale.",
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