Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

In several countries,the ageing population contour focuses on high healthcare costs and overloaded health care environments.Pervasive health care monitoring system can be a potential alternative,especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care,mobile care and home care.In this aspect,we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation.It facilitates better healthcare assistance,especially for COVID’19 patients and quarantined people.It identies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model.Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identication.Linguistics rules are framed based on the fuzzy set attributes belong to different context types.The fuzzy semantic rules are used to identify the relationship among the attributes,and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation.Outcomes are measured using a fuzzy logic-based context reasoning system under simulation.The results indicate the usefulness of monitoring the COVID’19 patients based on the current context.

An Efficient Sound and Data Steganography Based Secure Authentication System

The prodigious advancements in contemporary technologies have also brought in the situation of unprecedented cyber-attacks.Further,the pin-based security system is an inadequate mechanism for handling such a scenario.The reason is that hackers use multiple strategies for evading security systems and thereby gaining access to private data.This research proposes to deploy diverse approaches for authenticating and securing a connection amongst two devices/gadgets via sound,thereby disregarding the pins’manual verification.Further,the results demonstrate that the proposed approaches outperform conventional pin-based authentication or QR authentication approaches.Firstly,a random signal is encrypted,and then it is transformed into a wave file,after which it gets transmitted in a short burst via the device’s speakers.Subsequently,the other device/gadget captures these audio bursts through its microphone and decrypts the audio signal for getting the essential data for pairing.Besides,this model requires two devices/gadgets with speakers and a microphone,and no extra hardware such as a camera,for reading the QR code is required.The first module is tested with realtime data and generates high scores for the widely accepted accuracy metrics,including precision,Recall,F1 score,entropy,and mutual information(MI).Additionally,this work also proposes a module helps in a secured transmission of sensitive data by encrypting it over images and other files.This steganographic module includes two-stage encryption with two different encryption algorithms to transmit data by embedding inside a file.Several encryption algorithms and their combinations are taken for this system to compare the resultant file size.Both these systems engender high accuracies and provide secure connectivity,leading to a sustainable communication ecosystem.

A Contemporary Review on Drought Modeling Using Machine Learning Approaches

Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Itsbeginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughtsin the last few decades. Predicting future droughts is vital for framing drought management plans to sustainnatural resources. The data-driven modelling for forecasting the metrological time series prediction is becomingmore powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques havedemonstrated success in the drought prediction process and are becoming popular to predict the weather, especiallythe minimum temperature using backpropagation algorithms. The favourite ML techniques for weather forecastinginclude support vector machines (SVM), support vector regression, random forest, decision tree, logistic regression,Naive Bayes, linear regression, gradient boosting tree, k-nearest neighbours (KNN), the adaptive neuro-fuzzyinference system, the feed-forward neural networks, Markovian chain, Bayesian network, hidden Markov models,and autoregressive moving averages, evolutionary algorithms, deep learning and many more. This paper presentsa recent review of the literature using ML in drought prediction, the drought indices, dataset, and performancemetrics.

Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction

According to various worldwide statistics,most car accidents occur solely due to human error.The person driving a car needs to be alert,especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident.Even though semiautomated checks,such as speed detecting cameras and speed barriers,are deployed,controlling human errors is an arduous task.The key causes of driver’s distraction include drunken driving,conversing with co-passengers,fatigue,and operating gadgets while driving.If these distractions are accurately predicted,the drivers can be alerted through an alarm system.Further,this research develops a deep convolutional neural network(deep CNN)models for predicting the reason behind the driver’s distraction.The deep CNN models are trained using numerous images of distracted drivers.The performance of deep CNN models,namely the VGG16,ResNet,and Xception network,is assessed based on the evaluation metrics,such as the precision score,the recall/sensitivity score,the F1 score,and the specificity score.The ResNet model outperformed all other models as the best detection model for predicting and accurately determining the drivers’activities.

A Novel Green IoT-Based Pay-As-You-Go Smart Parking System

The better management of resources and the potential improvement in trafc congestion via reducing the orbiting time for parking spaces is crucial in a smart city,particularly those with an uneven correlation between the increase in vehicles and infrastructure.This paper proposes and analyses a novel green IoT-based Pay-As-You-Go(PAYG)smart parking system by utilizing unused garage parking spaces.The article also presents an intelligent system that offers the most favorable prices to its users by matching private garages’pricing portfolio with a garage’s current demand.Malta,the world’s fourth-most densely populated country,is considered as a case of a smart city for the implementation of the proposed approach.The results obtained conrm that apart from having a high potential system in such countries,the pricing generated correctly forecasts the demand for a particular garage at a specic time of the day and year.The proposed PAYG smart parking system can effectively contribute to the macro solution to trafc congestion by encouraging potential users to use the system’s services and reducing the orbiting time for parking.

E-Learning during COVID-19 Outbreak: Cloud Computing Adoption in Indian Public Universities

In the COVID-19 pandemic situation,the need to adopt cloud computing(CC)applications by education institutions,in general,and higher education(HE)institutions,in particular,has especially increased to engage students in an online mode and remotely carrying out research.The adoption of CC across various sectors,including HE,has been picking momentum in the developing countries in the last few years.In the Indian context,the CC adaptation in the HE sector(HES)remains a less thoroughly explored sector,and no comprehensive study is reported in the literature.Therefore,the aim of the present study is to overcome this research vacuum and examine the factors that impact the CC adoption(CCA)by HE institutions(HEIs)in India.The scope of the study is limited to public universities(PUs)in India.There are,in total,465 Indian PUs and among these 304 PUs,(i.e.,65%PUs)are surveyed using questionnaire-based research.The study has put forth a novel integrated technology adoption framework consisting of the Technology Acceptance Model(TAM),Technology-Organization-Environment(TOE),and Diffusion of Innovation(DOI)in the context of the HES.This integrated TAM-TOE-DOI framework is utilized in the study to analyze eleven hypotheses concerning factors of CCA that have been tested using structural equation modelling(SEM)and confirmatory factor analysis(CFA).The findings reveal that competitive advantage(CA),technology compatibility(TC),technology readiness(TR),senior leadership support,security concerns,government support,and vendor support are the significant contributing factors of CCA by Indian PUs.The study contends that whereas the rest of the factors positively affect the PUs’intention towards CCA,security concerns are a significant reason for the reluctance of these universities against adopting CC.The findings demonstrated the application of an integrated TAM-TOE-DOI framework to assess determining factors of CCA in Indian PUs.Further,the study has given useful insights into the successful CCA by Indian PUs,which will facilitate eLearning and remote working during COVID-19 or similar outbreak.

Field evaluation of selected light sources for roadway lighting

The development of new lighting sources, such as light emitting diode （LED）, induction, and plasma, presented more possible cost effective ways for roadway lighting. A study was therefore conducted for the Indiana Department of Transportation （INDOT} to evaluate the performance and effectiveness of some selected new lighting devices in roadway lighting. This paper describes the field evaluation process and presents the evaluation results. A number of LEDs, plasma and induction luminaires from various manufacturers were selected to replace the existing high pressure sodium （HPS） lamps in conventional and high mast lightings, llluminance values were measured over a period of 12 months on the existing and new light sources. Light performance metrics, including illuminance level and uniformity ratios, were calculated to make quantitative comparisons of the HPS and new types of light devices. Based on the evaluation in terms of lighting performance and life cycle costs, it was concluded that LED luminaires should be utilized in roadway lighting in place of HPS luminaires. The results of this study will be useful to state highway and city street agencies in making decisions on their lighting policies and developing technical specifications for use of the new lighting technologies in roadway and street lightings. The study provides a basis for manufacturers to improve their luminaire design and integration to better fit the needs of roadway and street lightings.