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LSML-SF: a lightweight stacked ML approach for spreading factor allocation in mobile IoT LoRaWAN networks.

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Title: LSML-SF: a lightweight stacked ML approach for spreading factor allocation in mobile IoT LoRaWAN networks.
Authors: Farhad A; Department of Computer Science, Bahria University, Islamabad, Pakistan., Lodhi MA; School of Information Engineering, Yangzhou University, Yangzhou, China., Nisar F; Department of Physical and Numerical Science Qurtaba University & IT, Peshawar, Pakistan., Hadi HJ; Center of Excellence in Cyber Security, Prince Sultan University, Riyadh, Saudi Arabia., Ahmad N; Software Engineering Department, Prince Sultan University, Riyadh, Saudi Arabia., Ladan M; College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia.
Source: Frontiers in artificial intelligence [Front Artif Intell] 2026 Feb 06; Vol. 9, pp. 1704369. Date of Electronic Publication: 2026 Feb 06 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Media SA Country of Publication: Switzerland NLM ID: 101770551 Publication Model: eCollection Cited Medium: Internet ISSN: 2624-8212 (Electronic) Linking ISSN: 26248212 NLM ISO Abbreviation: Front Artif Intell Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="AU" term="%22Farhad+A%22">Farhad A</searchLink>; Department of Computer Science, Bahria University, Islamabad, Pakistan.<br /><searchLink fieldCode="AU" term="%22Lodhi+MA%22">Lodhi MA</searchLink>; School of Information Engineering, Yangzhou University, Yangzhou, China.<br /><searchLink fieldCode="AU" term="%22Nisar+F%22">Nisar F</searchLink>; Department of Physical and Numerical Science Qurtaba University & IT, Peshawar, Pakistan.<br /><searchLink fieldCode="AU" term="%22Hadi+HJ%22">Hadi HJ</searchLink>; Center of Excellence in Cyber Security, Prince Sultan University, Riyadh, Saudi Arabia.<br /><searchLink fieldCode="AU" term="%22Ahmad+N%22">Ahmad N</searchLink>; Software Engineering Department, Prince Sultan University, Riyadh, Saudi Arabia.<br /><searchLink fieldCode="AU" term="%22Ladan+M%22">Ladan M</searchLink>; College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia.
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  Data: <searchLink fieldCode="JN" term="%22101770551%22">Frontiers in artificial intelligence</searchLink> [Front Artif Intell] 2026 Feb 06; Vol. 9, pp. 1704369. <i>Date of Electronic Publication: </i>2026 Feb 06 (<i>Print Publication: </i>2026).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Frontiers+Media+SA%22">Frontiers Media SA </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>101770551 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Internet <i>ISSN: </i>2624-8212 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2226248212%22">26248212 </searchLink><i>NLM ISO Abbreviation: </i>Front Artif Intell <i>Subsets: </i>PubMed not MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=41728269
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        Value: 10.3389/frai.2026.1704369
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      – Code: eng
        Text: English
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      – TitleFull: LSML-SF: a lightweight stacked ML approach for spreading factor allocation in mobile IoT LoRaWAN networks.
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              Text: 2026 Feb 06
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