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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Janarthanan, Midhunchakkaravarthy
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Realization of fractional order lowpass filter using different approximation techniques Krishna, Battula Tirumala; Janarthanan, Midhunchakkaravarthy
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5750

Abstract

Many research groups are starting to pay serious attention to the problem of fractional-order circuits. In this paper, a new approach to designing fractional order low-pass filter (FOLPF) is presented. Finding a rational approximation of the fractional Laplace operator sα is a crucial step in the design of fractional order filters. A comparative study of the most widely used approximation techniques named continued fraction expansion (CFE) method and Biquadratic Approximation (RE) method is performed. Then the transfer function of the proposed FOLPF is calculated. Using operational amplifier, the proposed filter is synthesized. The proposed circuit is simulated using Texas instruments TINA software. The results obtained outperform the existing methods.
Smart wheat agriculture: an in-depth framework for optimized crop agroanalytics utilizing internet of things Kumar Murugesan, Senthil; Janarthanan, Midhunchakkaravarthy
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9328

Abstract

Precision agriculture can be revolutionized by incorporating internet of things (IoT) technologies to maximize crop yield, especially for key crops like wheat. The creation and application of an IoT-enabled monitoring system intended especially for wheat farming is presented in this study. The system provides real-time data on important agronomic characteristics, such as soil health, temperature, humidity, and crop growth stages, by integrating a network of soil moisture sensors, weather stations, and remote sensing devices. With the help of the monitoring system, field conditions can be continuously and remotely observed, giving farmers the ability to make data-driven decisions that improve crop output and resource efficiency. The system can reduce input waste and increase output by optimizing irrigation schedules, adjusting fertilizer applications, and detecting early signs of crop stress or disease through real-time data analysis. Significant gains have been shown in production results, farm sustainability overall, and water usage efficiency in field studies carried out in different wheat-growing locations. According to the research, IoT-based monitoring systems can be extremely helpful in modernizing wheat production by offering useful information that results in more exact and environmentally friendly farming methods.
A comprehensive artificial intelligence framework for reducing patient rehospitalizations Khekare, Ganesh; Janarthanan, Midhunchakkaravarthy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3827-3834

Abstract

The role of artificial intelligence (AI) in the healthcare sector is increasing daily. Readmissions of patients have become a significant challenge for the medical sector, adding unnecessary burden. Governments and public sectors are continuously working on the hospital readmissions reduction program (HRRP). In this research work, an AI framework has been developed to reduce patient readmissions. The accuracy of the framework has been increased by continuous refinement in feature engineering, incorporating several complex datasets. The framework analyses the different algorithms like bidirectional long short-term memory (BiLSTM), convolutional neural network (CNN), and XGBoost for prediction. This framework has shown a 92% accuracy rate during testing, showing a 37% reduction in 40-day rehospitalization rates. This reduces the overburden on hospital systems by avoiding unnecessary readmissions of patients. The system’s real-time development, scalability, management of things in an ethical manner, and long-term viability will remain as future scope.
FADTESE: A framework for automated deployment and effectiveness evaluation for big data tools Ho, Mony; Ang, Sokroeurn; Huy, Sopheaktra; Janarthanan, Midhunchakkaravarthy
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp1051-1062

Abstract

Manual deployment of big data tools such as Hadoop, Sqoop, and Python is often slow, complex, and error prone because of extensive configuration steps, dependency conflicts, and inconsistent command-line execution. These challenges lead to unreliable installations and variations across systems. This study introduces framework for automated deployment and time, error, satisfaction evaluation (FADTESE), a unified framework that automates the installation of big data tools and evaluates its performance. The framework consists of two integrated components. The first is the automated deployment model, which validates environment readiness using the automation deployment readiness index (ADRI) and achieved a readiness value of 1.0 in this study. The second is the time, error, and satisfaction evaluation model, which quantifies improvements gained from automation and produced a score of 0.5941 through bootstrap resampling with ten thousand samples, indicating moderate effectiveness. The FADTESE script was technically validated across multiple Linux environments, including Ubuntu, Linux Mint, and AWS Ubuntu server systems. The performance evaluation involving eighty IT practitioners was conducted on Ubuntu systems to ensure consistent testing conditions and confirmed substantial gains in installation time, error reduction, and user satisfaction. Combining readiness and effectiveness yields a composite score of 0.5941 or 59.41%. FADTESE provides a reproducible and data driven method that standardizes big data deployment and improves reliability across local and cloud-based Linux environments.
Co-Authors Ang, Sokroeurn Ho, Mony Huy, Sopheaktra Khekare, Ganesh Krishna, Battula Tirumala Kumar Murugesan, Senthil