{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:22:53Z","timestamp":1753885373496,"version":"3.41.2"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02n03","funder":[{"name":"Researchers Supporting Project","award":["RSP-2022R503"],"award-info":[{"award-number":["RSP-2022R503"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Human. Robot."],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:p> Human\u2013computer interaction (HCI) is deployed in various real-time applications, including healthcare, for automated patient response. In such applications, robot-assisted interactive scenarios are modeled to handle patient queries and provide precise information. Timely query sensing and accurate data analysis are required to achieve accurate patient responses. In this study, responsive policy decision (RPD) using manifold mediator learning (MML) is introduced to improve data detection accuracy and accuracy in robot-assisted HCI applications. The initial decision-making process in data analytics is based on interaction stages and medical data detection. After identifying the most appropriate policy, respondents are provided with time-based responses based on the patient\u2019s queries. When it comes to improving the accuracy of data analysis decisions, machine learning uses policies based on interaction stages and previous state efficiency of HCI responses. The experimental analysis proves the reliability of the proposed method by improving the accuracy of data analysis and reducing its complexity and response time for the varying queries and time intervals. <\/jats:p>","DOI":"10.1142\/s0219843622400072","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T01:35:50Z","timestamp":1650850550000},"source":"Crossref","is-referenced-by-count":1,"title":["Responsive Policy Decisions for Improving the Accuracy of Medical Data Analysis in Healthcare-Based Human\u2013Machine Interaction Systems"],"prefix":"10.1142","volume":"20","author":[{"given":"Ahmed","family":"Altameem","sequence":"first","affiliation":[{"name":"Computer Science, CC, King Saud University Riyadh, Saudi Arabia"}]},{"given":"M.","family":"Shaheer Akhtar","sequence":"additional","affiliation":[{"name":"New & Renewable Energy Material Development Center (New REC), Jeonbuk National University, Jeonju, Republic of Korea"}]},{"given":"Torki","family":"Altameem","sequence":"additional","affiliation":[{"name":"Computer Science, CC, King Saud University Riyadh, Saudi Arabia"}]},{"given":"H.","family":"Fouad","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department, Faculty of Engineering, Helwan University, Cairo, Egypt"}]},{"given":"Sukumaran","family":"Anil","sequence":"additional","affiliation":[{"name":"Department of Dentistry-Oral Health Institute, Hamad Medical Corporation, Doha, Qatar"},{"name":"College of Dental Medicine, Qatar University, Doha, Qatar"}]},{"given":"Young Soon","family":"Kim","sequence":"additional","affiliation":[{"name":"Institute of Carbon Technology, Jeonju University, Jeonju, Republic of Korea"}]},{"given":"Ahmed E.","family":"Youssef","sequence":"additional","affiliation":[{"name":"Department of Computers & Systems Engineering, Faculty of Engineering at Helwan, Helwan University, Cairo, Egypt"}]}],"member":"219","published-online":{"date-parts":[[2022,5,18]]},"reference":[{"issue":"14","key":"S0219843622400072BIB001","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1049\/joe.2018.9406","volume":"2019","author":"Sasaki Y.","year":"2019","journal-title":"J. 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