Browsing by Author "Andres Tavera Romero, Carlos"
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Item Blinder Oaxaca and Wilk Neutrosophic Fuzzy Set-Based IoT Sensor Communication for Remote Healthcare Analysis(Institute of Electrical and Electronics Engineers Inc., 2025) Ibrahim Khalaf, Osamah; Natarajan, Rajesh; Mahadev, Natesh; Ranjith Christodoss, Prasanna; Nainan, Thangarasu; Andres Tavera Romero, Carlos; Muttashar Abdulsahib, GhaidaSeveral statistical methods have been playing a key role in data analytics, disease forecasting, and performing remote healthcare systems as far as medical sciences are concerned. In these fields, the research person and also practitioner’s main role depends on the efficient screening of remote healthcare data for significant forecasting. Specifically, remote healthcare data measurements involved in screening and forecasting are not precise and are found to be fuzzy or in interval forms. As a result, neutrosophic logic was instigated as one of the universal formations of fuzzy logic for estimating truthiness, falseness, and indeterminacy for remote healthcare data analysis. Neutrosophic Multiple-Criteria Decision-Making (Neutrosophic MCDM) was proposed by Hezam et al. [1] to develop an exploratory perception for classifying and ranking the most exemplary groups for instigating priority in gaining vaccines even at the initial stage. Initially, data analysis was performed using Analytic Hierarchy Processing under uncertainty to estimate and rank main and sub-criteria, owing to the reason that the inputs were obtained in the form of neutrophilic numbers. Second, neutrosophic TOPSIS was also applied for ranking vaccine alternatives. Finally, using Analytic Hierarchy Processing ranking efficiency and classification accuracy were found to be improved via measuring the weights of the sub-criteria. Despite improvement observed in terms of classification accuracy, the energy consumed in the process of decision-making was not focused. To address this aspect, a Blinder Oaxaca Linear Regression-based Preprocessing model is designed. The advantage of using this Linear Regression-based Preprocessing with Blinder Oaxaca function dynamically adjusts the sensing frequency of each corresponding device to fit with dynamic changes along with the monitored vital sign. This in turn reduces energy consumption.