ABSTRACT
In this paper, a new definition of intuitionistic fuzzy multisets (IFMS) has been introduced. Algebraic operations on these intuitionistic fuzzy multisets are defined, and their properties under these algebraic operations are studied. The author has also introduced a new notion of complement for an IFMS in which the complement of the original set is also an IFMS. The notion of distance and similarity between two IFMSs has been defined, and their properties have also been studied here. An application of IFMS in solving a medical diagnosis problem has been provided at the end. Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
ABSTRACT
This paper developed a factor-based robust approach to improve the tracking fund's stability. Similar to the financial crisis, the recent coronavirus pandemic amplify the global market volatility significantly, which suggests that healthcare-based factor can be used to hedge against the jump risk. The index tracking fund is constructed by a developed cardinality constrained conic programming. To overcome the large-scale computational challenge, we decompose the problem into two simplified cases and quickly calculate the tighter lower bound and its feasible upper bound. In addition, a subgradient-based inequalities are derived to exclude the suboptimal points that have been traveled in previous iterations. It turns out that the proposed model, along with the designed solving technique, can be used as an alternative to build reliable tracking portfolios. We demonstrate the effectiveness and robustness of the proposed method by testing different large real data sets. © 2022 Elsevier Ltd
ABSTRACT
The Pandemic caused due to COVID-19 has overweighed the current healthcare system and made us realize that how unaware we were of our health. Although lots of rational and harsh measured were taken to curb the spread of COVID-19 but still we lost millions of lives. During this pandemic technology played a vital role ranging from the invention of vaccine to remotely monitoring the usage with the help of IoT. Among several emerging technologies wearable smart devices were burgeoning as they are also powered by IoT now. Wearable devices are being used in different scenarios ranging from tracking and monitory infected patients to utilize the data for policy making. Proposed is a framework of an ecosystem "AWARE"which comprises of a smart band with advanced PPG and EEG sensors to detect Heart rate, Heart rate Variability, Respiration rate, SpO2, Step and Sleep data. The sensor data will be transferred to the AWARE application on host mobile through BLE and from mobile the user data it will be transferred to AWARE cloud for pre and post processing using Machine Learning algorithms. AWARE can used for monitoring and detection of health anomalies and diseases such as COVID-19 or chronic lifestyle diseases. AWARE works on a multi-Tenancy cardinality framework were the group (i.e., kids, elders, domestic worker) users can share the cloud storage and receive customized notifications. Also, the group manager (i.e., father, employer) will be notified in case of emergencies. Cloud data can be accessed by the users through dashboards. Government authorities can also access user data through APIs. Wearables are widely accepted these days due to its less intrusiveness. Although some professionals are skeptical of these devices, but the advantages are far beyond the minor pitfalls. In fact, several countries have already implemented wearables devices as the primary medium of COVID-19 detection, monitoring. © 2021 IEEE.