Search Results - (( process programming model algorithm ) OR ( java application optimisation algorithm ))
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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Graph coloring program of exam scheduling modeling based on bitwise coloring algorithm using python
Published 2022“…In this article, the researchers design an algorithm and apply it to a computer program (Python) to solve graph coloring and to visualize the variation of exam scheduling modeling at Binus University in graphs based on the Bitwise Graph Coloring Algorithm. …”
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Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed co-gasification of biomass wastes from oil palm
Published 2022“…Taking advantage of the data generated from the process, this study explores the performance of twelve machine learning algorithms built on the support vector machine (SVM), the Gaussian process regression (GPR), and the non-linear response quadratic model (NLRQM) using Sequential quadratic programming, and the Levenberg-Marquardt algorithms. …”
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Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed co-gasification of biomass wastes from oil palm
Published 2023“…Biomass; Catalysis; Digital storage; Gasification; Gaussian distribution; Hydrogen production; Learning algorithms; Lime; Palm oil; Quadratic programming; Regression analysis; Sensitivity analysis; Synthesis gas; Co-gasification; Gaussian process regression; Hydrogen-rich syngas; Machine learning algorithms; Non-linear response; Performance; Quadratic modeling; Renewable energies; Support vectors machine; Syn gas; Support vector machines…”
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Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed co-gasification of biomass wastes from oil palm
Published 2022“…Taking advantage of the data generated from the process, this study explores the performance of twelve machine learning algorithms built on the support vector machine (SVM), the Gaussian process regression (GPR), and the non-linear response quadratic model (NLRQM) using Sequential quadratic programming, and the Levenberg-Marquardt algorithms. …”
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Algorithm-program visualization model : An intergrated software visualzation to support novices' programming comprehension
Published 2015“…Taking into account from those main issues, this study introduces the new model of integrated algorithm-program visualization (ALPROV) for developing program comprehension tool. …”
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Enhancing problem-solving skills of novice programmers in an introductory programming course
Published 2023Article -
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Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini
Published 2006“…It is generally accepted that self-similar or fractal process may provide better models for in modern network traffic than Poisson process. …”
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A multi-depot vehicle routing problem with stochastic road capacity and reduced two-stage stochastic integer linear programming models for rollout algorithm
Published 2021“…A matheuristic approach based on a reduced two-stage Stochastic Integer Linear Programming (SILP) model is presented. The proposed approach is suitable for obtaining a policy constructed dynamically on the go during the rollout algorithm. …”
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Web-based expert system for material selection of natural fiber- reinforced polymer composites
Published 2015“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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Neural network based model predictive control for a steel pickling process
Published 2009“…The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. …”
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Mapreduce algorithm for weather dataset
Published 2017“…Results from the proposed algorithm have been compared with the existing model known as AWK (Alfred Aho, Peter Weinberger, and Brian Kernighan) model. …”
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Agent-based extraction algorithm for computational problem solving
Published 2015“…Modeling Computational Problem Solving (CPS) is the main issue in teaching programming where the given text is needed to be transformed into a model then later into a programming language. …”
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MapReduce algorithm for weather dataset
Published 2018“…Results from the proposed algorithm have been compared with the existing model known as AWK (Alfred Aho, Peter Weinberger, and Brian Kernighan) model. …”
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System identification using Extended Kalman Filter
Published 2017“…In order to evaluate the performance of the EKF learning algorithm, the proposed algorithm validation were analyzed using model validation methods as a checker such as One Step Ahead (OSA) and correlation coefficient (R2). …”
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Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization
Published 2022“…Integration of global search algorithm into ANN model further improved the model performance. …”
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Development of nonlinear model for continuous stirred tank reactor (CSTR)
Published 2010“…Continuous stirred tank reactors (CSTR) finds wide application in the chemical industry from pilot plant to full-scale production operation.CSTRs generally present operational problems due to complex open-loop nonlinear behavior in the form of input/output multiplicities and ignition/extinction phenomena.A major limitation of linear model is that plant behavior is described by linear dynamic model.As a result,linear model is inadequate for highly nonlinear process and moderately nonlinear process which have large operating regimes.This shortcoming coupled with increasingly stringent demands on throughput and product quality has spurred the development of nonlinear model.The objective of this research are development mathematical model for CSTR process based on first principal,validation of mathematical model through experimentation and nonlinear model identification of a CSTR process.The mathematical model based on first principles is developed from sodium hydroxide,ethyl acetate,and sodium acetate and ethanol mass balance.Then,the model equation is solving in MATLAB environment by doing algorithm for this process.The program for CSTR system is created and this program known as nonlinear fundamental model.The result from the MATLAB simulation program is compared with experimental result to validate the fundamental model. …”
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Undergraduates Project Papers -
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Development of nonlinear model for plug flow reactor process
Published 2010“…The mathematical model based on first principles is developed then, the model equation is solving in MATLAB environment by doing algorithm for this process. …”
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Undergraduates Project Papers
