Search Results - (( developing learning geometry algorithm ) OR ( java implementation tree algorithm ))

Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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Autonomous path planning robot using geographical information by Ismail Ishaq Ibrahim

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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

“…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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Modeling of road geometry and traffic accidents by hierarchical object-based and deep learning methods using laser scanning data by Sameen, Maher Ibrahim

“…This study also suggests that transfer learning could improve the forecasting accuracy of the injury severity by nearly 10%.…”
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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

“…An efficient iterative algorithm is developed to optimize the objective function of the proposed algorithm since it is non-smooth and difficult to solve. …”
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Machine learning methods for herschel-bulkley fluids in annulus: Pressure drop predictions and algorithm performance evaluation by Kumar, A., Ridha, S., Ganet, T., Vasant, P., Ilyas, S.U.

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Application of deep learning technique to predict downhole pressure differential in eccentric annulus of ultra-deep well by Krishna, S., Ridha, S., Ilyas, S.U., Campbell, S., Bhan, U., Bataee, M.

“…The data generated from this model, field data, and experimental data are used to train and test the FFBP-DNN networks. The network is developed used Kerasâ��s deep learning framework. After testing the models, the most optimal arrangement of FFBP-DNN is the ReLU algorithm as an activation function, 4-hidden layers, the learning rate of 0.003, and 2300 of training numbers. …”
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Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources by Kumar, A., Ridha, S., Narahari, M., Ilyas, S.U.

“…In this research, a novel algorithm (Herschel Bulkley Network) is introduced to simulate the non-Newtonian fluid flow in a pipe using data redundant deep neural network (DNN) for fully developed, laminar, and incompressible flow conditions. …”
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Exploring the effect of shape parameters on font design using rational quadratic trigonometric curves by Sarkam, Nor Aslily, Azhari, Nur Ain Fitrah, Nor-Al-Din, Siti Musliha, Mohamad Sukri, Nursyazni

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Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy by Hossain, Ishtiaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

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Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques by Hassan Zaidi, Syed Muhammad, Jamil Alsayaydeh, Jamil Abedalrahim, Khan, Abdul Hafeez, Khan, Abdullah Ayub, AlZubi, Ahmad Ali, Ogunshola, Benny, Herawan, Safarudin Gazali

“…The k-means clustering algorithm is then applied to group visually similar chart images. …”
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Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy by Hossain, Mohammad Ishtiyaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

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A comparative study of supervised machine learning approaches for slope failure production by Deris A.M., Solemon B., Omar R.C.

“…However, beside the methods are quite tedious and time consuming, LEM and FEM have their own limitations and do not guarantee the effectiveness when dealing against problem with various geometry or assumptions. Hence, the introduction of machine learning approaches provides the alternative tools for the prediction of slope failure. …”

Grid-based remotely sensed hydrodynamic surface runoff model using emissivity coefficient / Jurina Jaafar by Jaafar, Jurina

“…The development of the model strongly depends on the physical based parameters, examples of physical parameters that include roughness Manning’s n, hydraulic conductivity, soil depth, river geometry and the surface land cover. …”
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Vehicular traffic noise prediction and propagation modelling using artificial neural network by Ahmed, Ahmed Abdulkareem

“…The optimal radial basis function NN model was used which comprised of 17 hidden layers with a back-propagation algorithm. The learning rate of 0.05 and a momentum of 0.9 were used in this experiment. …”
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