<?xml version="1.0" encoding="utf-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2d1 20170631//EN" "JATS-journalpublishing1.dtd">
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>IJIRCSTJournal</PublisherName>
      <JournalTitle>International Journal of Innovative Research in Computer Science and Technology</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>Volume 12 Issue 4</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Computer Science</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>July - August 2024</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2024</Year>
        <Month>08</Month>
        <Day>08</Day>
      </PubDate>
      <ArticleType>Computer Sciences</ArticleType>
      <ArticleTitle>A Hybrid Localization Algorithm for Enhanced Accuracy and Robustness in Healthcare Systems</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>110</FirstPage>
      <LastPage>116</LastPage>
      <AuthorList>
        <Author>
          <FirstName>Siti Nur</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
                      <FirstName>Muhammad Ashfaq</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
           
        </Author>
      </AuthorList>
      <DOI>https://doi.org/10.55524/ijircst.2024.12.4.17</DOI>
      <Abstract>This paper presents a novel hybrid localization algorithm designed for healthcare systems, integrating Received Signal Strength Indicator (RSSI) and Time of Arrival (ToA) measurements with machine learning techniques. The algorithm aims to enhance the accuracy, robustness, and computational efficiency of sensor localization in dynamic healthcare environments. Experimental results demonstrate that the hybrid algorithm achieves a significantly lower localization error, averaging 0.5 meters, compared to traditional RSSI-only and ToA-only methods. The algorithm&amp;#39;s rapid convergence and low computational time make it suitable for real-time applications. Additionally, its robustness to measurement noise, a common challenge in healthcare settings, underscores its reliability. This research underscores the potential of advanced localization technologies to improve patient monitoring, safety, and overall healthcare delivery, with future work poised to further enhance performance and adaptability.

&amp;nbsp;</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>Hybrid localization, RSSI, Time of Arrival (ToA), Machine Learning, Healthcare, Localization</Keywords>
      <URLs>
        <Abstract>https://ijircst.org/abstract.php?article_id=1299</Abstract>
      </URLs>      
    </Journal>
  </Article>
</ArticleSet>