Browsing by Author Jeyananthan, P.
Showing results 1 to 15 of 15
| Issue Date | Title | Author(s) |
| 2019 | classification and regression analysis of lung tumors from multi-level gene expression data | Jeyananthan, P.; Niranjan, M. |
| 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. |
| 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. |
| 2025 | Investigation of impact of aggregate shape on pervious concrete using machine learning classification methods | Wijekoon, S.H.B.; Ahilash, N.; Pravinjan, V.; Virupashan, K.; Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N. |
| 2025 | Mathematical Model and Machine Learning Techniques to Predict the Compressive Strength of Groundnut Shell Ash Blended Sandcrete | Sathiparan, N.; Jeyananthan, P. |
| 2015 | Ontology Development for Sri Lankan Medicinal Plants: A Knowledge Representation | Jeyananthan, P.; Charles, E.Y.A.; Atukorale, D.A.S. |
| 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. |
| 2024 | Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocity | Sathiparan, N.; Jeyananthan, P. |
| 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. |
| 2024 | Quantifying the impact of chemical composition on pervious concrete strength: a comparative analysis using full quadratic model and artificial neural network | Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N. |
| 2025 | Response surface regression and machine learning models to predict the porosity and compressive strength of pervious concrete based on mix design parameters | Sathiparan, N.; Wijekoon, S.H.; Ravi, R.; Jeyananthan, P.; Subramaniam, D.N. |
| 2024 | Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach | Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N. |
| 2023 | Soft computing techniques to predict the compressive strength of groundnut shell ash‑blended concrete | Sathiparan, N.; Jeyananthan, P. |
| 2024 | Soft computing to predict the porosity and permeability of pervious concrete based on mix design and ultrasonic pulse velocity | Sathiparan, N.; Wijekoon, S.H.; Jeyananthan, P.; Subramaniam, D.N. |
