Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes

Abstract: Blockchain technology is found to have its applicability in almost every domain because of its advantages such as crypto-security, transparency, immutability, decentralized data network In present times, a smart healthcare system with a blockchain data network and healthcare 40 processes provides transparency, easy and faster accessibility, security, efficiency, etc Healthcare 40 trends include industry 40 processes such as the internet of things (IoT), industrial IoT (IIoT), cognitive computing, artificial intelligence, cloud computing, fog computing, edge computing, etc The goal of this work is to design a smart healthcare system and it is found to be possible through integration and interoperability of Blockchain 30 and Healthcare 40 in consideration with healthcare ground-realities Here, healthcare 40 processes used for data accessibility are targeted to be validated through statistical simulation-optimization methods and algorithms The blockchain is implemented in the Ethereum network, and with associated programming languages, tools, and techniques such as solidity, web3js, Athena, etc Further, this work prepares a comparative and comprehensive survey of state-of-the-art blockchain-based smart healthcare systems The comprehensive survey includes methodology, applications, requirements, outcomes, future directions, etc A list of groups, organizations, and enterprises are prepared that are working in electronic health records (EHR), electronic medical records (EMR) or electronic personal records (EPR) mainly, and a comparative analysis is drawn concerning adopting the blockchain technology in their processes This work has explored optimization algorithms applicable to Healthcare 40 trends and improves the performance of blockchain-based decentralized applications for the smart healthcare system Further, smart contracts and their designs are prepared for the proposed system to expedite the trust-building and payment systems This work has considered simulation and implementation to validate the proposed approach Simulation results show that the Gas value required (indicating block size and expenditure) lies within current Etherum network Gas limits The proposed system is active because block utilization lies above 80% Automated smart contract execution is below 20 seconds A good number (average 3 per simulation time) is generated in the network that indicates a health competition Although there is error observed in simulation and implementation that lies between 055% and 424%, these errors are not affecting overall system performance because simulated and actual (taken in state-of-the-art) data variations are negligible

Toxicity and detoxification of heavy metals during plant growth and metabolism

Abstract: Pollution of plants by heavy metals is a critical health issue because metals can be transmitted to animals and humans. Heavy metal exposure induces an oxidative stress in plant, resulting in cellular damage and altered cellular ionic homeostasis. As a consequence, plants start detoxification mechanisms. Here, we review heavy metal toxicity and impact. We discuss metabolism and detoxification strategies of heavy metals and metalloid, with emphasis on the use of transcriptomics, metabolomics, and proteomics. A section highlights microRNA (miRNA) as critical regulators of heavy metal stress in plants. We also present bioremediation and phytoremediation methods to remove metals.

Anisotropic models for compact stars

Abstract: In the present paper we obtain an anisotropic analogue of Durgapal-Fuloria (1985) perfect fluid solution. The methodology consists of contraction of anisotropic factor $\Delta$ by the help of both metric potentials $e^{ u}$ and $e^{\lambda}$. Here we consider $e^{\lambda}$ same as Durgapal-Fuloria (1985) whereas $e^{ u}$ is that given by Lake (2003). The field equations are solved by the change of dependent variable method. The solutions set mathematically thus obtained are compared with the physical properties of some of the compact stars, strange star as well as white dwarf. It is observed that all the expected physical features are available related to stellar fluid distribution which clearly indicate validity of the model.

Hybrid prediction model with missing value imputation for medical data

Abstract: Proposed novel hybrid prediction model with missing value imputation.HPM-MI has improved accuracy, sensitivity, specificity, kappa and ROC on 3 datasets.The best accuracy is achieved for diabetes, hepatitis, and breast cancer datasets.MVI is one of the important step of proposed model. Accurate prediction in the presence of large number of missing values in the data set has always been a challenging problem. Most of hybrid models to address this challenge have either deleted the missing instances from the data set (popularly known as case deletion) or have used some default way to fill the missing values. This paper, presents a novel hybrid prediction model with missing value imputation (HPM-MI) that analyze various imputation techniques using simple K-means clustering and apply the best one to a data set. The proposed hybrid model is the first one to use combination of K-means clustering with Multilayer Perceptron. K-means clustering is also used to validate class labels of given data (incorrectly classified instances are deleted i.e. pattern extracted from original data) before applying classifier. The proposed system has significantly improved data quality by use of best imputation technique after quantitative analysis of eleven imputation approaches. The efficiency of proposed model as predictive classification system is investigated on three benchmark medical data sets namely Pima Indians Diabetes, Wisconsin Breast Cancer, and Hepatitis from the UCI Repository of Machine Learning. In addition to accuracy, sensitivity, specificity; kappa statistics and the area under ROC are also computed. The experimental results show HPM-MI has produced accuracy, sensitivity, specificity, kappa and ROC as 99.82%, 100%, 99.74%, 0.996 and 1.0 respectively for Pima Indian Diabetes data set, 99.39%, 99.31%, 99.54%, 0.986, and 1.0 respectively for breast cancer data set and 99.08%, 100%, 96.55%, 0.978 and 0.99 respectively for Hepatitis data set. Results are best in comparison with existing methods. Further, the performance of our model is measured and analyzed as function of missing rate and train-test ratio using 2D synthetic data set and Wisconsin Diagnostics Breast Cancer Data Sets. Results are promising and therefore the proposed model will be very useful in prediction for medical domain especially when numbers of missing value are large in the data set.