41 results for machine learning
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Journal Article
Predicting unsafe road sections using machine learning
This paper presents an ML methodology to predict hazardous road segments, using the weighted accident index (Wi). The analysis covers 161 road segments in North Macedonia (~1,300 km)—with 23+1 variables categorized into Road, Traffic, Environmental, and Accident data. Feature influence is evaluated…
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Journal Article
Flexural capacity prediction of partially encased composite beams using machine learning
To address traditional limitations, this study investigated the flexural performance of large-section PEC beams with varied web openings using experiments and machine learning (ML). Four-point bending tests on specimens with different sections and openings demonstrated excellent ductility…
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Journal Article
Construction cost estimation of reinforced and prestressed concrete bridges using machine learning
Seven state-of-the-art machine learning techniques for estimation of construction costs of reinforced-concrete and prestressed concrete bridges are investigated in this paper, including artificial neural networks (ANN) and ensembles of ANNs, regression tree ensembles (random forests, boosted and…
From your institution’s collections
- Machine learning dan computational intelligenceWidodo Budiharto · 2016 · UNILAK
- Machine learning (konsep dan implementasi)Yaya Heryadi · 2020 · UNILAK
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Journal Article
PREDICTING THE WEIGHT AND TYPE OF DRILLING MUD BY MACHINE LEARNING METHOD
…predicting both mud weight and type. This study introduces a novel machine learning framework that concurrently predicts these essential properties. Utilizing a comprehensive dataset extracted from 50 years of daily drilling reports across 20 oil wells, we trained and compared three nature-inspired…
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Journal Article
Hybrid ARIMA-machine learning models for predicting inflation and economic growth in Nigeria
…unreliable. This paper develops and evaluates a family of hybrid ARIMA-machine learning models for predicting Nigeria's consumer price index (CPI) and gross domestic product (GDP) growth rates using monthly data from January 2010 to December 2024. The hybrid framework decomposes each series into a…
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Journal Article
ADVANCED CLUSTERING TECHNIQUES FOR TIN DEPOSIT CLASSIFICATION IN MALAYSIA: A MACHINE LEARNING APPROACH
This study explores the application of advanced clustering techniques—Spectral Clustering, Gaussian Mixture Models (GMM), and a hybrid approach combining Autoencoders with K-Means—to classify tin deposits in Malaysia. Geochemical data from 28 tin samples across regions such as Pengkalan Hulu North…
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Journal Article
Using Supervised Machine Learning Methods for RFM Segmentation: A Casino Direct Marketing Communication Case
Purpose – This paper explores various supervised machine learning algorithms as an additional classification method to RFM (recency, frequency, and monetary) models with the aim of improving the accuracy in predicting target groups of customers for direct marketing response campaigns conducted by a…
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Journal Article Full text
Machine Learning and Neural Networks for Modelling the Retention of PPhACs by NF/RO
…neural network with radial basis function (RBF), and support vector machine (SVM). A database of 541 retention values has been collected from the literature. The results showed a high predictive capacity of the MLP model for the retention of PPhACs by NF/RO with a very high correlation coefficient…
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Journal Article
FLOW PATTERN PREDICTION IN HORIZONTAL AND INCLINED PIPES USING TREE-BASED AUTOMATED MACHINE LEARNING
…utilizes the Tree-based Pipeline Optimization Tool (TPOT), an Automated Machine Learning (AutoML) framework that employs genetic programming, in obtaining the best machine learning model for a provided dataset. This paper presents the design of flow pattern prediction models using the TPOT. The…
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Journal Article
Explainable machine learning-based prediction of fuel consumption in ship main engines using operational data
…accurate prediction models for fuel consumption are quite important. Machine learning offers a data-driven approach to improving fuel consumption prediction, thereby promoting environmental sustainability, lowering operational costs, and enhancing financial viability. This work explores several…
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Journal Article
COMPARISON OF POROSITY PREDICTION FROM SEISMIC DATA IN THE F3 BLOCK, NETHERLANDS USING MACHINE LEARNING
…data available as labelled data. To address this issue, we employ machine learning, consisting of both an inversion generator and a forward generator, to estimate porosity from seismic data. The inversion generator facilitates the process of converting seismic data into porosity, while the forward…
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Journal Article
Novel meta-ensemble modelling approach and comparison of machine-learning models for rebar price estimation
…data for rebar construction material and economic indicators, nine machine-learning algorithms were used to determine the estimated rebar price for 1-, 3-, 6-, 9-, and 12-month lags. The voting meta-ensemble machine-learning algorithm exhibited the best performance for all lag periods investigated…
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Journal Article
APPLICATION OF MACHINE LEARNING MODELS IN PREDICTING INITIAL GAS PRODUCTION RATE FROM TIGHT GAS RESERVOIRS
…in the process is immense as most platforms are now being digitized. Machine learning tools can be used to analyse this data in order to build patterns between several dependent and independent variables. Forecasting initial gas production rates has important implications in the planning…
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Journal Article
MACHINE LEARNING FOR FULLY AUTOMATED DETECTION OF VOLCANIC SEISMIC SIGNALS IN REAL SEISMIC RECORDS AT SINABUNG VOLCANO, NORTH SUMATRA
…and subjective. To solve this problem, several studies have developed machine-learning models to classify volcano earthquake types automatically. However, previous research typically trains and evaluates machine learning models on existing datasets. Although these models can classify volcanic…
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Journal Article
A SUSTAINABLE AND PRACTICAL MACHINE LEARNING APPROACH USING SCIKIT-LEARN FOR PREDICTING STOPE INSTABILITY: IDENTIFICATION OF CRITICAL GEOTECHNICAL FACTORS
…and adaptability issues in dynamic underground conditions. While machine learning shows potential for improved accuracy, a critical gap persists in understanding how geotechnical factors interact in practice. This study introduces a novel, practical machine learning framework (Scikit-Learn) to…
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Journal Article
ACCURATE PREDICTION OF DRILL BIT PENETRATION RATE IN ROCK USING SUPERVISED MACHINE LEARNING TECHNIQUES BASE ON LABORATORY TEST DATA
…than direct measurement can be very useful and effective. Predictive machine learning methods are among the strong and precise techniques for the indirect measurement of ROP. To this end, 492 samples were tested under different UCS, µ, WOB, and ω conditions to obtain the corresponding ROP. To…
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Journal Article
THE EVALUATION OF THE RGB AND MULTISPECTRAL CAMERA ON THE UNMANNED AERIAL VEHICLE (UAV) FOR THE MACHINE LEARNING CLASSIFICATION OF MAIZE
This study investigated a crop and soil classification applying the Random Forest machine learning algorithm based on the red-green-blue (RGB) and multispectral sensor imaging deploying an unmanned aerial vehicle (UAV). The study area covered two 10 x 10 m subsets of a maize-sown agricultural…
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Journal Article
Improving anchorage occupancy forecasting with stacked ensemble learning
…framework for anchorage occupancy. This framework uses stacked ensemble learning, integrating both statistical and machine learning models to enhance predictive accuracy and operational reliability. The proposed approach was applied to occupancy data from the E1 anchorage at Ulsan Port, with…
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Journal Article
SHEAR WAVE MODELLING FROM CONVENTIONAL WELL LOGS USING INTEGRATED DEEP LEARNING INTEGRATED CONVOLUTIONAL NEURAL NETWORK (I-CNN)
…Moreover, direct measurement of Vs remains relatively costly. Four machine learning algorithms were created to predict Vs from traditional well logs in order to get around these restrictions: Probability Neural Network (PNN), Multilayer Feed-Forward Neural Network (MLFFNN), Deep Feed-Forward Neural…
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Journal Article
COMPARATIVE ANALYSIS OF THE K-NEAREST-NEIGHBOUR METHOD AND K-MEANS CLUSTER ANALYSIS FOR LITHOLOGICAL INTERPRETATION OF WELL LOGS OF THE SHUSHUFINDI OILFIELD, ECUADOR
The lithological interpretation of well logs is a fundamental task in Earth science that can be accomplished with the application of various machine learning algorithms. The current investigation attempts to evaluate the performance of the K-nearest-neighbour Density Estimate (KNN) and K-means…