| Issue Date | Title | Author(s) |
| 2025 | Comparative analysis of machine learning models and standard codes for predicting compressive strength in hollow block masonry | Sathiparan, N.; Jeyananthan, P. |
| 2025 | A comparative study of machine learning techniques and data processing for predicting the compressive strength of pervious concrete with supplementary cementitious materials and chemical composition influence | Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N. |
| 2023 | Effect of aggregate size, aggregate to cement ratio and compaction energy on ultrasonic pulse velocity of pervious concrete: prediction by an analytical model and machine learning techniques | Sathiparan, N.; Pratheeba, J.; Daniel Niruban, S. |
| 2025 | Effect of rice husk ash on compressive strength of sustainable pervious concrete and prediction model using machine learning algorithms | Sathiparana, N.; Jeyananthan, P.; Subramaniam, D.N. |
| 2021 | Harnessing Machine Learning Techniques for Mapping Aquaculture Waterbodies in Bangladesh | Hannah, F.; Nejadhashemi, A.P.; Juan Sebastian, H.; Nathan, M.; Josue, K.; Ian Kropp; Eeswaran, R.; Belton, Ben.; Mahfujul Haque, M. |
| 2025 | Mathematical Model and Machine Learning Techniques to Predict the Compressive Strength of Groundnut Shell Ash Blended Sandcrete | Sathiparan, N.; Jeyananthan, P. |
| 2023 | Predicting compressive strength of cementstabilized earth blocks using machine learning models incorporating cement content, ultrasonic pulse velocity, and electrical resistivity | Sathiparan, N.; Pratheeba, J. |
| 2023 | Predicting compressive strength of cementstabilized earth blocks using machine learning models incorporating cement content, ultrasonic pulse velocity, and electrical resistivity | Sathiparan, N.; Jeyananthan, P. |
| 2023 | Prediction of compressive strength of fly ash blended pervious concrete: a machine learning approach | Sathiparan, N.; Pratheeba, J.; Daniel Niruban, S. |
| 2023 | Prediction of masonry prism strength using machine learning technique: Effect of dimension and strength parameters | Sathiparan, N.; Pratheeba, J. |
| 2024 | Prediction of moisture content of cementstabilized earth blocks using soil characteristics, cement content, and ultrasonic pulse velocity | Sathiparan, N.; Tharuka, R.A.N.S.; Jeyananthan, P. |
| 2023 | Soft computing techniques to predict the compressive strength of groundnut shell ash-blended concrete | Sathiparan, N.; Pratheeba, J. |
| 2023 | Soft computing techniques to predict the compressive strength of groundnut shell ash‑blended concrete | Sathiparan, N.; Jeyananthan, P. |
| 2023 | Soft computing techniques to predict the electrical resistivity of pervious concrete | Daniel Niruban, S.; Pratheeba, J.; Sathiparan, N. |
| 2023 | Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity | Sathiparan, N.; Pratheeba, J.; Daniel Niruban, S. |
| 2022 | Tracking Everyone and Everything in Smart Cities with an ANN Driven Smart Antenna | Herman, K.; Hoole, P.R.P.; Pirapaharan, K.; Hoole, S.R.H. |
| 2020 | Understanding phishers’ strategies of mimicking uniform resource locators to leverage phishing attacks: A machine learning approach | Samantha Tharani, J.; Arachchilage, N.A.G. |
| 2023 | Use of soft computing approaches for the prediction of compressive strength in concrete blends with eggshell powder | Sathiparan, N.; Pratheeba, J. |
