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All Journal Bulletin of Electrical Engineering and Informatics JTAM (Jurnal Teori dan Aplikasi Matematika) Indonesian Journal of Data and Science
Pandey, Himanshu
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

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Complex predictive analysis for health care: a comprehensive review Srivastava, Dolley; Pandey, Himanshu; Agarwal, Ambuj Kumar
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4373

Abstract

Healthcare organizations accept information technology in a management system. A huge volume of data is gathered by healthcare system. Analytics offers tools and approaches for mining information from this complicated and huge data. The extracted information is converted into data which assist decision-making in healthcare. The use of big data analytics helps achievement of improved service quality and reduces cost. Both data mining and big data analytics are applied to pharma co-vigilance and methodological perspectives. Using effective load balancing and as little resources as possible, obtained data is accessible to improve analysis. Data prediction analysis is performed throughout the patient data extraction procedure to achieve prospective outcomes. Data aggregation from huge datasets is used for patient information prediction. Most current studies attempt to improve the accuracy of patient risk prediction by using a commercial model facilitated by big data analytics. Privacy concerns, security risks, limited resources, and the difficulty of dealing with massive amounts of data have all slowed the adoption of big data analytics in the healthcare industry. This paper reviews the various effective predictive analytics methods for diverse diseases like heart disease, blood pressure, and diabetes.
Exploiting artificial intelligence for combating COVID-19: a review and appraisal Sharma, Richa; Pandey, Himanshu; Agarwal, Ambuj Kumar
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4366

Abstract

Machine learning algorithms immediately became critical in the battle against the COVID-19 outbreak. Diagnoses, medicine research, an illness spread predictions, and population surveillance all required the use of artificial intelligence (AI) methods as the epidemic grew in scope. To combat COVID-19, screening procedures that are both effective and rapid are required. At COVID-19, AI developers took a chance to show how AI can benefit all mankind. It was only after the employment of AI in the battle against COVID-19. AI's various and diverse applications in the epidemic are documented in this study. It is the purpose of this study to help shape the future development and usage of these technologies, whether in the present or future health crises.
A Logarithmic Square Root Regression Model for The Average Blood Glucose Levels of The Drug Induced Diabetic Experimental Rats Treated with The Cissampelos Pareira L. (Menispermaceae) Root Extract Upadhyaya, Lalit Mohan; Pandey, Himanshu; Aggarwal, Sudhanshu
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.237

Abstract

We formulate a nonlinear regression model for elucidating the average values of the blood glucose levels of the experimental rats categorized under the division Group 1 Normal Control (G1NC) in a recent study conducted by Ankit Kumar et al. (see, Ankit Kumar, Ravindra Semwal, Ashutosh Chauhan, Ruchi Badoni Semwal, Subhash Chandra, Debabrata Sircar, Partha Roy and Deepak Kumar Semwal, Evaluation of antidiabetic effect of Cissampelos pareira L. (Menispermaceae) root extract in streptozotocin-nicotinamide-induced diabetic rats via targeting SGLT2 inhibition, Phytomedicine Plus 2 (2022) 100374, 11pp., https://doi.org/10.1016/j.phyplu.2022.100374). By treating the recorded average blood glucose levels of the rats of the group G1NC (response), as a function of the number of days of the experiment (predictor), our projected model involves a linear relation between the logarithm of the response and the square root of the predictor and it explains about 85.6239% of the variability in the response.
Bayes Estimation of Shape Parameter of Length Biased Weibull Distribution Rao, Arun Kumar; Pandey, Himanshu
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 5, No 1 (2021): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v5i1.3268

Abstract

In this paper, length biased Weibull distribution is considered for Bayesian analysis. The expressions for Bayes estimators of the parameter have been derived under squared error, precautionary, entropy, K-loss, and Al-Bayyati’s loss functions by using quasi and gamma priors.
Co-Authors Agarwal, Ambuj Kumar Aggarwal, Sudhanshu Rao, Arun Kumar Richa Sharma Srivastava, Dolley Upadhyaya, Lalit Mohan