D.N. Subramaniam, P. Jeyananthan, N. Sathiparan, “Boosted Forest Model for Aggregate Size and Shape Impact on Porosity and Compressive Strength of Pervious Concrete,” International Journal of Pavement Research and Technology, vol. 19, no. 9, pp. 2714–2738, 2026, doi: 10.1007/s42947-025-00599-0.

N. Sathiparan, S.H. Wijekoon, P. Jeyananthan, D.N. Subramaniam, “Prediction of characteristics of pervious concrete by machine learning technique using mix parameters and non-destructive test measurements,” Nondestructive Testing and Evaluation, vol. 41, no. 1, pp. 314–363, 2026, doi: 10.1080/10589759.2025.2468273.

P. Jeyananthan, H.V.B.L. Chandrasiri, A.G.F. Ashfa, “OMICS data in the diagnosis of diabetic retinopathy: A comparison between transcriptome data and DNA methylation data,” Experimental Eye Research, vol. 267, 2026, doi: 10.1016/j.exer.2026.110988.

S.H.B. Wijekoon, N. Ahilash, V. Pravinjan, K. Virupashan, N. Sathiparan, P. Jeyananthan, D.N. Subramaniam, “Investigation of impact of aggregate shape on pervious concrete using machine learning classification methods,” Engineering Applications of Artificial Intelligence, vol. 143, 2025, doi: 10.1016/j.engappai.2025.110008.

N. Sathiparan, S.H. Wijekoon, R. Ravi, P. Jeyananthan, D.N. Subramaniam, “Response surface regression and machine learning models to predict the porosity and compressive strength of pervious concrete based on mix design parameters,” Road Materials and Pavement Design, vol. 26, no. 3, pp. 618–657, 2025, doi: 10.1080/14680629.2024.2374885.

N. Sathiparan, P. Jeyananthan, D.N. Subramaniam, “Effect of rice husk ash on compressive strength of sustainable pervious concrete and prediction model using machine learning algorithms,” Sustainable Structures, vol. 5, no. 3, 2025, doi: 10.54113/j.sust.2025.000080.

N. Sathiparan, P. Jeyananthan, D.N. Subramaniam, “A machine learning approach to predicting pervious concrete properties: a review,” Innovative Infrastructure Solutions, vol. 10, no. 2, 2025, doi: 10.1007/s41062-024-01829-3.

N. Sathiparan, P. Jeyananthan, D.N. Subramaniam, “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,” Next Materials, vol. 9, 2025, doi: 10.1016/j.nxmate.2025.100947.

N. Sathiparan, P. Jeyananthan, “Comparative analysis of machine learning models and standard codes for predicting compressive strength in hollow block masonry,” Journal of Umm Al-Qura University for Engineering and Architecture, vol. 16, no. 4, pp. 1806–1838, 2025, doi: 10.1007/s43995-025-00211-3.

N. Sathiparan, P. Jeyananthan, “Sorption behavior of cement-stabilized earth blocks: machine learning insights,” Innovative Infrastructure Solutions, vol. 10, no. 11, 2025, doi: 10.1007/s41062-025-02315-0.

N. Sathiparan, P. Jeyananthan, “Mathematical Model and Machine Learning Techniques to Predict the Compressive Strength of Groundnut Shell Ash Blended Sandcrete,” Journal of Applied Research and Technology, vol. 23, no. 6, pp. 659–671, 2025, doi: 10.22201/icat.24486736e.2025.23.6.2788.

M. Sajeevan, D.N. Subramaniam, R. Rinduja, J. Pratheeba, “Investigation of Compaction on Compressive Strength and Porosity of Pervious Concrete,” International Journal of Pavement Research and Technology, vol. 18, no. 3, pp. 743–758, 2025, doi: 10.1007/s42947-023-00377-w.

P. Jeyananthan, M.W.P.N., R.S.M., “On integrative analysis of multi-level gene expression data in Kidney cancer subgrouping,” Urologia Journal, vol. 92, no. 2, pp. 194–200, 2025, doi: 10.1177/03915603241304604.

P. Jeyananthan, G.L.D.S. Piyasamara, D.C. Sachintha, “Performance Comparison Between Transcriptome Data and Epigenome Data in the Prediction of Preterm Birth: A Machine Learning Model Using Mother’s Data,” International Journal of Childbirth, vol. 15, no. 4, pp. 197–207, 2025, doi: 10.1891/IJC-2025-0021.

P. Jeyananthan, T.A.P. Dharmasena, W.D.A. Nuwansiri, “Diagnosis of diabetes and hypertension: A performance comparison between transcriptome data and clinical data,” Gene Reports, vol. 39, 2025, doi: 10.1016/j.genrep.2025.102176.

A. Varathan, S. Senthooran, P. Jeyananthan, “Role of different omics data in the diagnosis of schizophrenia disorder: A machine learning study,” Schizophrenia Research, vol. 271, pp. 38–46, 2024, doi: 10.1016/j.schres.2024.07.026.

D.N. Subramaniam, M. Sajeevan, J. Pratheeba, S.H.B. Wijekoon, N. Sathiparan, “Characterization of the shape of aggregates using image analysis and machine learning classification tools,” Geomechanics and Geoengineering, vol. 19, no. 4, pp. 421–443, 2024, doi: 10.1080/17486025.2023.2288655.

D.N. Subramaniam, P. Jeyananthan, N. Sathiparan, “Soft computing techniques to predict the electrical resistivity of pervious concrete,” Asian Journal of Civil Engineering, vol. 25, no. 1, pp. 711–722, 2024, doi: 10.1007/s42107-023-00806-y.

N. Sathiparan, S.H. Wijekoon, P. Jeyananthan, D.N. Subramaniam, “Soft computing to predict the porosity and permeability of pervious concrete based on mix design and ultrasonic pulse velocity,” International Journal of Pavement Engineering, vol. 25, no. 1, 2024, doi: 10.1080/10298436.2024.2337916.

N. Sathiparan, R.A.N.S. Tharuka, P. Jeyananthan, “Prediction of moisture content of cement-stabilized earth blocks using soil characteristics, cement content, and ultrasonic pulse velocity,” Journal of Engineering and Applied Science, vol. 71, no. 1, 2024, doi: 10.1186/s44147-024-00527-2.

N. Sathiparan, P. Jeyananthan, D.N. Subramaniam, “Influence of metakaolin on pervious concrete strength: a machine learning approach with shapley additive explanations,” Multiscale and Multidisciplinary Modeling, Experiments and Design, vol. 7, no. 4, pp. 3919–3946, 2024, doi: 10.1007/s41939-024-00455-x.

N. Sathiparan, P. Jeyananthan, D.N. Subramaniam, “Quantifying the impact of chemical composition on pervious concrete strength: a comparative analysis using full quadratic model and artificial neural network,” International Journal of Pavement Engineering, vol. 25, no. 1, 2024, doi: 10.1080/10298436.2024.2424381.

N. Sathiparan, P. Jeyananthan, D.N. Subramaniam, “Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity,” Measurement: Journal of the International Measurement Confederation, vol. 225, 2024, doi: 10.1016/j.measurement.2023.114006.

N. Sathiparan, P. Jeyananthan, D.N. Subramaniam, “Predicting compressive strength of pervious concrete with fly ash: a machine learning approach and analysis of fly ash compositional influence,” Multiscale and Multidisciplinary Modeling, Experiments and Design, vol. 7, no. 6, pp. 5651–5671, 2024, doi: 10.1007/s41939-024-00551-y.

N. Sathiparan, P. Jeyananthan, D.N. Subramaniam, “Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach,” Asian Journal of Civil Engineering, vol. 25, no. 3, pp. 2963–2977, 2024, doi: 10.1007/s42107-023-00956-z.

N. Sathiparan, P. Jeyananthan, D.N. Subramaniam, “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,” Asian Journal of Civil Engineering, vol. 25, no. 1, pp. 495–509, 2024, doi: 10.1007/s42107-023-00790-3.

N. Sathiparan, P. Jeyananthan, D.N. Subramaniam, “Machine learning techniques to evaluate the impact of calcium oxide (CaO) and silicon dioxide (SiO2) in supplementary cement materials on the compressive strength of sustainable pervious concrete,” Sustainable Chemistry and Pharmacy, vol. 42, 2024, doi: 10.1016/j.scp.2024.101818.

N. Sathiparan, P. Jeyananthan, “Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocity,” Nondestructive Testing and Evaluation, vol. 39, no. 8, pp. 2486–2509, 2024, doi: 10.1080/10589759.2024.2304257.

N. Sathiparan, P. Jeyananthan, “Use of soft computing approaches for the prediction of compressive strength in concrete blends with eggshell powder,” Journal of Building Pathology and Rehabilitation, vol. 9, no. 1, 2024, doi: 10.1007/s41024-023-00366-3.

N. Sathiparan, P. Jeyananthan, “Predicting compressive strength of cement-stabilized earth blocks using machine learning models incorporating cement content, ultrasonic pulse velocity, and electrical resistivity,” Nondestructive Testing and Evaluation, vol. 39, no. 5, pp. 1045–1069, 2024, doi: 10.1080/10589759.2023.2240940.

P. Jeyananthan, P. U.L.K.K., J. H.M.C.S.B., “Exploring machine learning approaches for transcriptome-based diagnosis and subgrouping of multiple system atrophy,” Brain Disorders, vol. 13, 2024, doi: 10.1016/j.dscb.2024.100124.

P. Jeyananthan, G.L.D.S. Piyasamara, D.C. Sachintha, “Methylation Data of Parents in the Prediction of a Preterm Birth: A Machine Learning Approach,” SN Computer Science, vol. 5, no. 5, 2024, doi: 10.1007/s42979-024-02901-6.

P. Jeyananthan, K.M.D.D. Bandara, Y.G.A. Nayanajith, “Protein data in the identification and stage prediction of bronchopulmonary dysplasia on preterm infants: a machine learning study,” International Journal of Information Technology (Singapore), vol. 16, no. 1, pp. 387–392, 2024, doi: 10.1007/s41870-023-01571-6.

P. Jeyananthan, “Machine learning in the identification of phenotypes of multiple sclerosis patients,” International Journal of Information Technology (Singapore), vol. 16, no. 4, pp. 2307–2313, 2024, doi: 10.1007/s41870-024-01735-y.

P. Jeyananthan, “Performance comparison between multi-level gene expression data in cancer subgroup classification,” Pathology Research and Practice, vol. 260, 2024, doi: 10.1016/j.prp.2024.155419.

N. Sathiparan, P. Jeyananthan, D.N. Subramaniam, “Prediction of compressive strength of fly ash blended pervious concrete: a machine learning approach,” International Journal of Pavement Engineering, vol. 24, no. 2, 2023, doi: 10.1080/10298436.2023.2287146.

N. Sathiparan, P. Jeyananthan, “Soft computing techniques to predict the compressive strength of groundnut shell ash-blended concrete,” Journal of Engineering and Applied Science, vol. 70, no. 1, 2023, doi: 10.1186/s44147-023-00302-9.

N. Sathiparan, P. Jeyananthan, “Prediction of masonry prism strength using machine learning technique: Effect of dimension and strength parameters,” Materials Today Communications, vol. 35, 2023, doi: 10.1016/j.mtcomm.2023.106282.

P. Jeyananthan, “SARS-CoV-2 Diagnosis Using Transcriptome Data: A Machine Learning Approach,” SN Computer Science, vol. 4, no. 3, 2023, doi: 10.1007/s42979-023-01703-6.

P. Jeyananthan, “Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients,” Pathology Research and Practice, vol. 242, 2023, doi: 10.1016/j.prp.2023.154311.

P. Jeyananthan, “Prolonged viral shedding prediction on non-hospitalized, uncomplicated SARS-CoV-2 patients using their transcriptome data,” Computer Methods and Programs in Biomedicine Update, vol. 2, 2022, doi: 10.1016/j.cmpbup.2022.100070.

P. Jeyananthan, “Comprehensive Machine Learning Analysis on the Phenotypes of COVID-19 Patients Using Transcriptome Data,” Arab Gulf Journal of Scientific Research, vol. 39, no. Special Issue, pp. 79–137, 2021.

P. Jeyananthan, M. Niranjan, “Classification and Regression Analysis of Lung Tumors from Multi-level Gene Expression Data,” in Proceedings of the International Joint Conference on Neural Networks, 2019.

J. Pratheeba, E.Y.A. Charles, S.R. Kodituwakku, “A review on processing of data streams,” Research Journal of Computer System and Engineering (RJCSE), vol. 2, pp. 73–77, 2011.

J.P. Jeyananthan, M. Niranjan, “Classification and regression analysis of lung tumors from multi-level gene expression data.”

J. Pratheeba, E.Y.A. Charles, D.A.S. Atukorale, “Towards an Experts Sytsem to Sri Lankan Medicinal Plants.”

J. Pratheeba, E.Y.A.Charles, D.A.S. Atukorale, “Ontology development for Sri Lankan Medicinal Plants.”