Multivariate Fuzzy Logic Based Smart Healthcare Monitoring for Risk Evaluation of Cardiac Patients

Author(s):  
Ridhima Mehta
Author(s):  
P. Jeyadurga ◽  
S. Ebenezer Juliet ◽  
I. Joshua Selwyn ◽  
P. Sivanisha

The Internet of things (IoT) is one of the emerging technologies that brought revolution in many application domains such as smart cities, smart retails, healthcare monitoring and so on. As the physical objects are connected via internet, security risk may arise. This paper analyses the existing technologies and protocols that are designed by different authors to ensure the secure communication over internet. It additionally focuses on the advancement in healthcare systems while deploying IoT services.


2021 ◽  
Vol 12 (3) ◽  
pp. 98-122
Author(s):  
Sahil Sholla ◽  
Roohie Naaz Mir ◽  
Mohammad Ahsan Chishti

IoT is expected to have far-reaching consequences on society due to a wide spectrum of applications like smart healthcare, smart transportation, smart agriculture, smart home, etc. However, ethical considerations of AI-enabled smart devices have not been duly considered from a design perspective. In this paper, the authors propose a novel fuzzy logic-based method to incorporate ethics within smart things of IoT. Ethical considerations relevant to a machine context are represented in terms of fuzzy ethics variables (FEVs) and ethics rules. For each ethics rule, a value called scaled ethics value (SEV) is used to indicate its ethical desirability. In order to model flexibility in ethical response, the authors employ the concept of ethics modes that selectively allow scenarios depending on the value of SEV. The method offers a viable mechanism for smart devices to imbue ethical sensitivity that can pave the way for a technology society amenable to human ethics. However, the method does not account for varying ethics, as such incorporating learning mechanisms represent a promising research direction.


2021 ◽  
Author(s):  
Divyanshu Tiwari ◽  
Devendra Prasad ◽  
Kalpna Guleria ◽  
Pinaki Ghosh

Author(s):  
Preethi S. ◽  
Prasannadevi V. ◽  
Arunadevi B.

Health monitoring plays a vital role to overcome the health issues of the patients. According to research, approximately 2000 people die due to carelessness of monitoring their health. Wearable monitoring systems record the activities of daily life. A 24-hour wearable monitoring system was developed and changes were identified. This project is designed for helping the soldiers to maintain their health conditions and to identify their health issues at war's end. Different health parameters are monitored using sensors, and the data are transmitted through GSM to the receiver, and the received data are analyzed using convolutional neural networks, which is performed in cloud IoT. If any abnormalities are found during the analyzing process, the message is sent to military personnel and the doctor at the camp so that they could take necessary actions to recover the ill soldier from the war field and provide emergency assistance on time. The location of the soldier is also shared using the input from GPS modem in the smart jacket.


Author(s):  
Joseph Bamidele Awotunde ◽  
Rasheed Gbenga Jimoh ◽  
Roseline Oluwaseun Ogundokun ◽  
Sanjay Misra ◽  
Oluwakemi Christiana Abikoye

Author(s):  
Asogbon Mojisola Grace ◽  
Samuel Oluwarotimi Williams

Credit risk evaluation techniques that aid effective decisions in credit lending are of great importance to the financial and banking industries. Such techniques assist credit managers to minimize the risks often associated with wrong decision making. Several techniques have been developed in the time past for credit risk evaluation and these techniques suffer from one form of limitation or the other. Recently, powerful soft computing tools have been proposed for problem solving among which are the neural networks and fuzzy logic. In this study, a neural network based on backpropagation learning algorithm and a fuzzy inference system based on Mamdani model were developed to evaluate the risk level of credit applicants. A comparative analysis of the performances of both systems was carried out and experimental results show that neural network with an overall prediction accuracy of 96.89% performed better than the fuzzy logic method with 94.44%. Finding from this study could provide useful information on how to improve the performance of existing credit risk evaluation systems.


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