Real-Time Analysis of COVID-19 Pandemic on Most Populated Countries Worldwide

The spread of the Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)has already taken on pandemic extents,influencing even more than 200 nations in a couple of months.Although,regulation measures in China have decreased new cases by over 98%,this decrease is not the situation everywhere,and most of the countries still have been affected by it.The objective of this research work is to make a comparative analysis of the top 5 most populated countries namely United States,India,China,Pakistan and Indonesia,from 1st January 2020 to 31st July 2020.This research work also targets to predict an increase in the number of deaths and total infected cases in these five countries.In our research,the performance of the proposed framework is determined by using three Machine Learning(ML)regression algorithms namely Linear Regression(LR),Support Vector Regression(SVR),andRandom Forest(RF)Regression.The proposed model is also validated upon the infected and death cases of further dates.The performance of these three algorithms is compared using the RootMean Square Error(RMSE)metrics.Random Forest algorithm shows best performance as compared to other proposed algorithms,with the lowest RMSE value in the prediction of total infected and total deaths cases for all the top five most populated countries.

Residual U-Network for Breast Tumor Segmentation from Magnetic Resonance Images

Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world.It is apical of all new cancer incidences analyzed among females.Two features substantially inuence the classication accuracy of malignancy and benignity in automated cancer diagnostics.These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis.In this research,the authors have proposed a ResU-Net(Residual U-Network)model for breast tumor segmentation.The proposed methodology renders augmented,and precise identication of tumor regions and produces accurate breast tumor segmentation in contrast-enhanced MR images.Furthermore,the proposed framework also encompasses the residual network technique,which subsequently enhances the performance and displays the improved training process.Over and above,the performance of ResU-Net has experimentally been analyzed with conventional U-Net,FCN8,FCN32.Algorithm performance is evaluated in the form of dice coefcient and MIoU(Mean Intersection of Union),accuracy,loss,sensitivity,specicity,F1score.Experimental results show that ResU-Net achieved validation accuracy&dice coefcient value of 73.22%&85.32%respectively on the Rider Breast MRI dataset and outperformed as compared to the other algorithms used in experimentation.

AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States vs. Union Territories

The COVID-19 disease has already spread to more than 213 countries and territories with infected(confirmed)cases of more than 27 million people throughout the world so far,while the numbers keep increasing.In India,this deadly disease was first detected on January 30,2020,in a student of Kerala who returned from Wuhan.Because of India’s high population density,different cultures,and diversity,it is a good idea to have a separate analysis of each state.Hence,this paper focuses on the comprehensive analysis of the effect of COVID-19 on Indian states and Union Territories and the development of a regression model to predict the number of discharge patients and deaths in each state.The performance of the proposed prediction framework is determined by using three machine learning regression algorithms,namely Polynomial Regression(PR),Decision Tree Regression,and Random Forest(RF)Regression.The results show a comparative analysis of the states and union territories having more than 1000 cases,and the trained model is validated by testing it on further dates.The performance is evaluated using the RMSE metrics.The results show that the Polynomial Regression with an RMSE value of 0.08,shows the best performance in the prediction of the discharged patients.In contrast,in the case of prediction of deaths,Random Forest with a value of 0.14,shows a better performance than other techniques.

An Intelligent Scheme for Slot Reservation in Vehicular Ad Hoc Networks

Vehicular ad hoc network is a solution for increasing road traffic demand.Non-safety messages are sent during the service channel interval.The slots during which the messages are sent are not decided prior to the transmission.If the reservation of slots is done during the control channel interval,then the non-safety messages can be transmitted without any collision and thus the network performance can be improved.Further,to improve the network performance,the safety packets can be scheduled in the queue according to the time remaining for which sender and receiver are in the range of each other.This work proposes and evaluates the performance of safety message scheduling and infotainment message reservation through a MAC protocol SSIR-MAC to ensure network stability by transmitting beacons without any collision.The safety messages are queued according to their deadline and the slots for the transmission of non-safety packets are reserved during the control channel itself.Further,a hybrid queue is proposed to decrease the delay of enqueue and dequeue operations.Evaluation through extensive simulation results demonstrates the strength of SSIR-MAC.Comparisons are made with IEEE 802.11p standard and with two existing protocols which are relevant to the proposed work.

Empirical Assessment of Bacillus Calmette-Guérin Vaccine to Combat COVID-19

COVID-19 has become one of the critical health issues globally,which surfaced first in latter part of the year 2019.It is the topmost concern for many nations’governments as the contagious virus started mushrooming over adjacent regions of infected areas.In 1980,a vaccine called Bacillus Calmette-Guérin(BCG)was introduced for preventing tuberculosis and lung cancer.Countries that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it compulsory.This paper’s initial research shows that the countries with a longtermcompulsory BCGvaccination system are less affected by COVID-19 than those without a BCG vaccination system.This paper discusses analytical data patterns for medical applications regarding COVID-19 impact on countries with mandatory BCG status on fatality rates.The paper has tackled numerous analytical challenges to realize the full potential of heterogeneous data.An analogy is drawn to demonstrate how other factors can affect fatality and infection rates other than BCG vaccination only,such as age groups affected,other diseases,and stringency index.The data of Spain,Portugal,and Germany have been taken for a case study of BCG impact analysis.

Mutation Prediction for Coronaviruses Using Genome Sequence and Recurrent Neural Networks

The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community.The recent ongoing SARSCov2(Severe Acute Respiratory Syndrome)pandemic proved the unpreparedness for these situations.Not only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check frequently.One major way to find out more information about such pathogens is by extracting the genetic data of such viruses.Though genetic data of viruses have been cultured and stored as well as isolated in form of their genome sequences,there is still limited methods on what new viruses can be generated in future due to mutation.This research proposes a deep learning model to predict the genome sequences of the SARS-Cov2 virus using only the previous viruses of the coronaviridae family with the help of RNN-LSTM(Recurrent Neural Network-Long ShortTerm Memory)and RNN-GRU(Gated Recurrent Unit)so that in the future,several counter measures can be taken by predicting possible changes in the genome with the help of existing mutations in the virus.After the process of testing the model,the F1-recall came out to be more than 0.95.The mutation detection’s accuracy of both the models come out about 98.5%which shows the capability of the recurrent neural network to predict future changes in the genome of virus.

Spline in Compression Methods for Singularly Perturbed 1D Parabolic Equations with Singular Coefficients

In this article, we discuss three difference schemes;for the numerical solution of singularity perturbed 1-D parabolic equations with singular coefficients using spline in compression. The proposed methods are of accurate and applicable to problems in both cases singular and non-singular. Stability theory of a proposed method has been discussed and numerical examples have been given in support of the theoretical results.

An Integrated Collector Storage Solar Water Heater and Study of its Temperature Stratification

In this paper we describe an integrated collector storage solar water heater for the North Western region of India for use as domestic water heater during the winter season and present the experimental results of temperature stratification. The system consists of a steel water storage tank with azimuthal orientation such that its walls face south east, south west, north east and North West directions. The sunlit walls (south east and south west) and the top cover surface of the storage tank are covered with transparent insulation material (TIM) and the off-sunlit sides with opaque insulation. Experimental results show that the top layer is at the highest temperature. The top layer is drained using a new design of the outlet. The system can be used in rural areas or as a pre heater in more affluent households. The system can return the cost of conversion of the storage tank into a Solar Water Heater in one winter season by saving the cost of electrical energy required for heating water during winter months.

A hierarchical clustering approach for colorectal cancer molecular subtypes identification from gene expression data

Background Colorectal cancer(CRC)is the second leading cause of cancer fatalities and the third most common human disease.Identifying molecular subgroups of CRC and treating patients accordingly could result in better therapeutic success compared with treating all CRC patients similarly.Studies have highlighted the significance of CRC as a major cause of mortality worldwide and the potential benefits of identifying molecular subtypes to tailor treatment strategies and improve patient outcomes.Methods This study proposed an unsupervised learning approach using hierarchical clustering and feature selection to identify molecular subtypes and compares its performance with that of conventional methods.The proposed model contained gene expression data from CRC patients obtained from Kaggle and used dimension reduction techniques followed by Z-score-based outlier removal.Agglomerative hierarchy clustering was used to identify molecular subtypes,with a P-value-based approach for feature selection.The performance of the model was evaluated using various classifiers including multilayer perceptron(MLP).Results The proposed methodology outperformed conventional methods,with the MLP classifier achieving the highest accuracy of 89%after feature selection.The model successfully identified molecular subtypes of CRC and differentiated between different subtypes based on their gene expression profiles.Conclusion This method could aid in developing tailored therapeutic strategies for CRC patients,although there is a need for further validation and evaluation of its clinical significance.

Monitoring of lithium-ion cells using a microcontroller

Safe and efficient operation of batteries is always desired but batteries with a high energy density pose a threat to the system causing thermal breakdown,reduced performance and rapid ageing.To reduce such vulnerabilities,an optimum environment with controlled parameters is required.Four parameters have been considered for analysis,i.e.state of charge,current,voltage and temperature.The module makes a detailed analysis of the above-mentioned parameters and suggests a microcontroller-based prototype that is capable of monitoring the external factors in real time and generating relevant warnings.