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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Systems Engineering and Information Technology
Anil Kumar Prajapati
Institute of Computer Science, Vikram University Ujjain MP, India
Computer Science & IT

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Solution of the Data Load Issue in Business Intelligence Tools: QlikView Live Case Study Anil Kumar Prajapati; Yogesh Mishra; Saral Nigam; Pradeep Lakhare
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 3 (2024)
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i2.6064

Abstract

To stand in the marketplace it very essential to extract and deal with high-performance and usable data for every industry. The traditional ways are too slow and they are not suitable for the current scenario. This document gives knowledge about the new trends in technology which used for the benefit of business in terms of analysis and reporting. The document contains a live case study of the problem faced by an organization and a holistic evaluation of how to overcome it with the help of new technology. The primary objective of this case study is to minimize the ideal time for accessing and/or extracting files from different sources in the QlikView Platform.
An Enhanced Comprehensive Study Towards Predicting Cardiovascular Disease Using Machine Learning Techniques. Anil Kumar Prajapati
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 3 (2026)
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i3.7585

Abstract

. In recent years, heart and cardiovascular diseases have become more common, causing a significant increase in mortality rates worldwide. Data from various organisations highlights the severity of heart disease, which remains a major concern. Accurately and quickly identifying severe conditions, such as heart disease, is vital for effective prevention. Techniques such as data mining, machine learning, and deep learning have been used in medicine to reliably detect heart disease. However, these methods depend on data that can change over time. To ensure accurate detection, proper use of historical data is essential; otherwise, results can be inaccurate. Machine learning techniques produce outcomes based on mathematical calculations, so data cleaning and refinement are necessary. Disease-related data can include text, numbers, and images, which may vary widely, requiring extensive stratification, normalisation, cleaning, encoding, and randomisation; otherwise, results may be biased. Our previous review article addressed a specific challenge related to the CVD Prediction Model. This Enhanced review primarily examines how machine learning techniques operate on medical datasets and their effectiveness in predicting cardiovascular diseases (CVD). It also aims to analyse datasets, features, and machine learning methods used in CVD prediction
Co-Authors Pradeep Lakhare Saral Nigam Yogesh Mishra