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Browsing by Subject Machine learning

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Showing results 1 to 20 of 21  next >
Issue DateTitleAuthor(s)
2025Comparative analysis of machine learning models and standard codes for predicting compressive strength in hollow block masonrySathiparan, N.; Jeyananthan, P.
2025A comparative study of machine learning techniques and data processing for predicting the compressive strength of pervious concrete with supplementary cementitious materials and chemical composition influenceSathiparan, N.; Jeyananthan, P.; Subramaniam, D.N.
2023Effect 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 techniquesSathiparan, N.; Pratheeba, J.; Daniel Niruban, S.
2025Effect of rice husk ash on compressive strength of sustainable pervious concrete and prediction model using machine learning algorithmsSathiparana, N.; Jeyananthan, P.; Subramaniam, D.N.
2021Harnessing Machine Learning Techniques for Mapping Aquaculture Waterbodies in BangladeshHannah, F.; Nejadhashemi, A.P.; Juan Sebastian, H.; Nathan, M.; Josue, K.; Ian Kropp; Eeswaran, R.; Belton, Ben.; Mahfujul Haque, M.
2025Mathematical Model and Machine Learning Techniques to Predict the Compressive Strength of Groundnut Shell Ash Blended SandcreteSathiparan, N.; Jeyananthan, P.
2023Predicting compressive strength of cementstabilized earth blocks using machine learning models incorporating cement content, ultrasonic pulse velocity, and electrical resistivitySathiparan, N.; Pratheeba, J.
2023Predicting compressive strength of cementstabilized earth blocks using machine learning models incorporating cement content, ultrasonic pulse velocity, and electrical resistivitySathiparan, N.; Jeyananthan, P.
2024Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocitySathiparan, N.; Jeyananthan, P.
2023Prediction of compressive strength of fly ash blended pervious concrete: a machine learning approachSathiparan, N.; Pratheeba, J.; Daniel Niruban, S.
2023Prediction of masonry prism strength using machine learning technique: Effect of dimension and strength parametersSathiparan, N.; Pratheeba, J.
2024Prediction of moisture content of cementstabilized earth blocks using soil characteristics, cement content, and ultrasonic pulse velocitySathiparan, N.; Tharuka, R.A.N.S.; Jeyananthan, P.
2025Response surface regression and machine learning models to predict the porosity and compressive strength of pervious concrete based on mix design parametersSathiparan, N.; Wijekoon, S.H.; Ravi, R.; Jeyananthan, P.; Subramaniam, D.N.
2023Soft computing techniques to predict the compressive strength of groundnut shell ash-blended concreteSathiparan, N.; Pratheeba, J.
2023Soft computing techniques to predict the compressive strength of groundnut shell ash‑blended concreteSathiparan, N.; Jeyananthan, P.
2023Soft computing techniques to predict the electrical resistivity of pervious concreteDaniel Niruban, S.; Pratheeba, J.; Sathiparan, N.
2024Soft computing to predict the porosity and permeability of pervious concrete based on mix design and ultrasonic pulse velocitySathiparan, N.; Wijekoon, S.H.; Jeyananthan, P.; Subramaniam, D.N.
2023Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivitySathiparan, N.; Pratheeba, J.; Daniel Niruban, S.
2022Tracking Everyone and Everything in Smart Cities with an ANN Driven Smart AntennaHerman, K.; Hoole, P.R.P.; Pirapaharan, K.; Hoole, S.R.H.
2020Understanding phishers’ strategies of mimicking uniform resource locators to leverage phishing attacks: A machine learning approachSamantha Tharani, J.; Arachchilage, N.A.G.
Showing results 1 to 20 of 21  next >