  <?xml version="1.0"?>
<journal>
 <journal_metadata>
  <full_title>Journal of Intelligent Systems and Internet of Things</full_title>
  <abbrev_title>JISIoT</abbrev_title>
  <issn media_type="print">2690-6791</issn>
  <issn media_type="electronic">2769-786X</issn>
  <doi_data>
   <doi>10.54216/JISIoT</doi>
   <resource>https://www.americaspg.com/journals/show/2865</resource>
  </doi_data>
 </journal_metadata>
 <journal_issue>
  <publication_date media_type="print">
   <year>2019</year>
  </publication_date>
  <publication_date media_type="online">
   <year>2019</year>
  </publication_date>
 </journal_issue>
 <journal_article publication_type="full_text">
  <titles>
   <title>Strategic Improved K-Means Clustering in Mining Blood Donor Data Analysis and IoT-based Allocation</title>
  </titles>
  <contributors>
   <organization sequence="first" contributor_role="author">Assistant Professor, Department of Information Technology, Madhav Institute of Science and Technology, Gwalior, M.P., India</organization>
   <person_name sequence="first" contributor_role="author">
    <given_name>Vibha</given_name>
    <surname>Vibha</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Assistant Professor, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Chopparapu</given_name>
    <surname>Gowthami</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Professor, Institute of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, TN, India</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Bhavani.</given_name>
    <surname>R.</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Assitant Professor, Dept. of CSE, Velammal Engineering College, Chennai, TN, India</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>S.</given_name>
    <surname>Kayalvizhi</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Associate Professor, Department of Information Technology, R. M. K Engineering College, RSM Nagar, Kavaraipettai, Thiruvallur District, TN, India</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>S.</given_name>
    <surname>Selvakanmani</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Asst. Professor, Dept. of CSE, Vignan's Foundation for Science, Technology and Research, Vadlamudi, Guntur, AP, India</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Deepak Chowdary</given_name>
    <surname>Edara</surname>
   </person_name>
  </contributors>
  <jats:abstract xml:lang="en">
   <jats:p>This manuscript proposes Strategic Improved K-Means Clustering to simplify blood donor data analysis and distribution. The technique optimizes blood donor system resources via K-Means++ initialization, hierarchical clustering, and smart data dissemination. The paper begins with a comprehensive overview of clustering techniques and their healthcare applications. It illustrates the need for contemporary blood donor data analysis methods for cluster quality and resource allocation. Cluster purity, silhouette coefficient, Davies-Bould in the index, and other performance indicators are used to rigorously compare the recommended technique to 10 established clustering methods. The approach routinely fulfils these conditions, proving that it creates accurate, well-fitting groupings. Ablation tests how much-enhanced initialization, hierarchical clustering, and strategic data placement improve the entire. The study found that these make the procedure dependable and successful for numerous sorts of data. The study shows that the approach may be applied to other data besides blood donor data. Hierarchical clustering provides important information about the dataset's hierarchical patterns, making clustering findings easier to grasp. Resources are better distributed with strategic data dissemination. The recommended strategy is effective in emergencies and areas with changing blood needs. To conclude, Strategic Improved K-Means Clustering evaluates and distributes blood donor data comprehensively. Its flexibility, adaptability, and speed make it excellent for managing healthcare resources and making collective choices.</jats:p>
  </jats:abstract>
  <publication_date media_type="print">
   <year>2024</year>
  </publication_date>
  <publication_date media_type="online">
   <year>2024</year>
  </publication_date>
  <pages>
   <first_page>122</first_page>
   <last_page>134</last_page>
  </pages>
  <doi_data>
   <doi>10.54216/JISIoT.130110</doi>
   <resource>https://www.americaspg.com/articleinfo/18/show/2865</resource>
  </doi_data>
 </journal_article>
</journal>
