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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Venkatachalam, Selvakumar
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Computer Science & IT

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Leveraging IoT with LoRa and AI for predictive healthcare analytics Lavanya, Pillalamarri; Venkatachalam, Selvakumar; Subba Reddy, Immareddy Venkata
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1156-1162

Abstract

Progress in mobile technology, the internet, cloud computing, digital platforms, and social media has substantially facilitated interpersonal connections following the COVID-19 pandemic. As individuals increasingly prioritise health, there is an escalating desire for novel methods to assess health and well-being. This study presents an internet of things (IoT)-based system for remote monitoring utilizing a long range (LoRa), a low-cost and LoRa wireless network for the early identification of health issues in home healthcare environments. The project has three primary components: transmitter, receiver, and alarm systems. The transmission segment captures data via sensors and transmits it to the reception segment, which then uploads it to the cloud. Additionally, machine learning (ML) methods, including convolutional neural networks (CNN), artificial neural networks (ANN), Naïve Bayes (NB), and long short-term memory (LSTM), were utilized on the acquired data to forecast heart rate, blood oxygen levels, body temperature patterns. The forecasting models are trained and evaluated using data from various health parameters from five diverse persons to ascertain the architecture that exhibits optimal performance in modeling and predicting dynamics of different medical parameters. The models' accuracy was assessed using mean absolute error (MAE) and root mean square error (RMSE) measures. Although the models performed similarly, the ANN model outperformed them in all conditions.
Mapping academic outcomes to student routines using machine learning: a data-driven approach Venkatachalam, Selvakumar; Lavanya, Pillalamarri; V. Deshpande, Shreesh; Akshaya Shree, R. J.; V. Thejaswini, S.
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp66-73

Abstract

In today’s environment, students often struggle with time management and dealing with emotions like frustration and anxiety, which may have an adverse impact on their academic achievement. This research aims to enhance time management and educational support for college students by leveraging demographic characteristics and performance in specific assignments to develop a predictive model for academic performance. The study evaluates various regression algorithms to identify the most accurate method for predicting students’ semester grade point average (SGPA) based on their activities. This predictive model aims to optimize students’ learning experiences and mitigate challenges such as frustration and anxiety. The findings highlight the potential of personalized educational assistance in improving student learning outcomes. Various machine learning algorithms, including decision trees, support vector regression (SVR), ridge regression, lasso regression, XGBoost, and gradient boosting, were implemented in Python for this study. Results show that XGBoost achieved the lowest root mean square error (RMSE) of 9.39 with a 60:40 data split ratio, outperforming other algorithms, while decision trees exhibited the highest RMSE. The findings emphasize the potential of personalized educational assistance to improve learning outcomes by helping students adjust study habits to address weaknesses and reduce anxiety. Future studies can explore integrating real-time data and additional features such as emotional wellbeing and extracurricular activities to further improve the model’s predictive capabilities.
Co-Authors Akshaya Shree, R. J. Lavanya, Pillalamarri Subba Reddy, Immareddy Venkata V. Deshpande, Shreesh V. Thejaswini, S.