A Brief Bibliometric Analysis and Visualisation of Scopus and WoS databases on Blockchain Technology in Healthcare Domain

Background: The aim of this study is to analyse the work carried out in healthcare or medical domain using blockchain technology for privacy and security of patient’s data, their healthcare records. The documents published in Scopus and Web of Science databases during the year 2016 to present (February 2021) have been considered for survey. Methods: Scopus and Web of Science(WoS), most popular databases are used to retrieve documents which were published between years 2016 to present. Scopus analyser and web of Science analyser are used for analysis of various parameters such as documents published per year, sources of documents, number of citations and so on. VOSviewer1.6.16 software tool is used for analysis of different units such as citations, co- authorship etc. Results: During our survey we have retrieved a total 598 documents related to blockchain technology in the healthcare domain which are published from year 2016 on wards from scopus database. Using a web of science database 594 documents has been retrieved for the same domain. Statistical analysis and network analysis shows that there is tremendous growth in publications from year 2019 and 2020 on blockchain technology. The United States, India and China are major contributors. Conclusions: Databases are analysed in terms of number of documents per year, sources of publications, authors correlation, documents per country, funding agencies etc parameters are statistically analysed. Using statistical and network analysis we can conclude that there is huge scope to work in the blockchain domain to achieve more privacy, security, and data integrity

Performance Analysis of Soil Health Classifiers Using Data Analytics Tools and Techniques for Best Model and Tool Selection

One of the most crucial stages in the building of a Machine Learning (ML) model is the evaluation and analysis of classifier model performance. The agricultural sector is the economic backbone of India and needs extensions to provide solutions to the problems faced by the farmers. This paper presents agriculture soil health analysis using Machine Learning approaches for best model and tool selection and also bibliometric analysis to identify different sources and author’s keywords for finding the area of focus for proposed work.  Models are built on SK-Learn, KNIME, WEKA and Rapid Miner tools using different ML algorithms. Nave Bayes, Random Forest (RF), Decision Tree (DT), Ensemble learning (EL), and k-Nearest Neighbor (KNN) are used to analyze soil data on these tools. Results show that Decision Tree model outperforms other algorithms, followed by RF algorithm which is a set of multiple Decision tree algorithms and SK-Learn tool gives better accuracy followed by WEKA tool then KNIME tool. Maximum accuracy obtained by Decision Tree algorithm is 98.40% using SK-Learn followed by KNIME tool with 73.07%, Maximum accuracy obtained by Naïve Bayes algorithm is 69.50% using SK-Learn followed by KNIME tool with 68.14%, maximum accuracy obtained by Random Forest algorithm is 85.00% using SK-Learn followed by 73.06% using WEKA tool, maximum accuracy obtained by Ensemble algorithm is 89.00% using SK-Learn followed by 73.06% using WEKA tool and for KNN it is 95.50% using SK-Learn followed by 71.85% using WEKA tool

Performance Analysis of Soil Health Classifiers Using Data Analytics Tools and Techniques for Best Model and Tool Selection

One of the most crucial stages in the building of a Machine Learning (ML) model is the evaluation and analysis of classifier model performance. The agricultural sector is the economic backbone of India and needs extensions to provide solutions to the problems faced by the farmers. This paper presents agriculture soil health analysis using Machine Learning approaches for best model and tool selection and also bibliometric analysis to identify different sources and author’s keywords for finding the area of focus for proposed work.  Models are built on SK-Learn, KNIME, WEKA and Rapid Miner tools using different ML algorithms. Nave Bayes, Random Forest (RF), Decision Tree (DT), Ensemble learning (EL), and k-Nearest Neighbor (KNN) are used to analyze soil data on these tools. Results show that Decision Tree model outperforms other algorithms, followed by RF algorithm which is a set of multiple Decision tree algorithms and SK-Learn tool gives better accuracy followed by WEKA tool then KNIME tool. Maximum accuracy obtained by Decision Tree algorithm is 98.40% using SK-Learn followed by KNIME tool with 73.07%, Maximum accuracy obtained by Naïve Bayes algorithm is 69.50% using SK-Learn followed by KNIME tool with 68.14%, maximum accuracy obtained by Random Forest algorithm is 85.00% using SK-Learn followed by 73.06% using WEKA tool, maximum accuracy obtained by Ensemble algorithm is 89.00% using SK-Learn followed by 73.06% using WEKA tool and for KNN it is 95.50% using SK-Learn followed by 71.85% using WEKA tool

Transverse momentum spectra of charged particles in proton–proton collisions at √s=900 GeV with ALICE at the LHC

The inclusive charged particle transverse momentum distribution is measured in proton–proton collisions at s=900 GeV at the LHC using the ALICE detector. The measurement is performed in the central pseudorapidity region (|η|<0.8) over the transverse momentum range 0.15<pT<10 GeV/c. The correlation between transverse momentum and particle multiplicity is also studied. Results are presented for inelastic (INEL) and non-single-diffractive (NSD) events. The average transverse momentum for |η|<0.8 is 〈pT〉INEL=0.483±0.001 (stat.)±0.007 (syst.) GeV/c and 〈pT〉NSD=0.489±0.001 (stat.)±0.007 (syst.) GeV/c, respectively. The data exhibit a slightly larger 〈pT〉 than measurements in wider pseudorapidity intervals. The results are compared to simulations with the Monte Carlo event generators PYTHIA and PHOJET