Search Results - (( variable educational model algorithm ) OR ( java application optimisation algorithm ))

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

“…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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Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

“…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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Harmony Search algorithm-based gasoline consumption modeling for Indonesia by Anggarani, Riesta, Watada, Junzo

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Demand analysis of flood insurance by using logistic regression model and genetic algorithm by Sidi, P., Mamat, M.B., Sukono, ., Supian, S., Putra, A.S.

“…The analysis was done by using logistic regression model, and to estimate model parameters, it is done with genetic algorithm. …”
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Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis by Kartiwi, Mira, Ab Rahman, Jamalludin, Nik Mohamed, Mohamad Haniki, Draman, Samsul, Ab Rahman, Norny Syafinaz

“…The collected data was used to build and verified the model. Eight demographics variables (i.e., age, gender, race, religion, residence (urban/rural), marital, occupation and education) and twenty variables on perception of ECV were included as predictor variables. …”
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Development of Genetic Algorithms and Multilayer Perceptron Neural Network (Mpnn) Model To Study The Student Performance InThermodynamics by K., Kadirgama, M. M., Noor, M. S. M., Sani, M. M., Rahman, M. R. M., Rejab

“…Student performance is very crucial to any educational institution. The neural network and genetic algorithms (GA) method were used to measure student performance in Thermodynamic at Faculty of Mechanical Engineering, University Malaysia Pahang (UMP). …”
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Improved Variable Neighbourhood Descent (VND) to Solve Universiti Malaysia Sarawak (UNIMAS) Course Timetabling Problem by Mei Ching, Chen

“…Sensitivity analysis was performed on the proposed algorithm. The experimental results demonstrate the flexibility of the proposed algorithm in solving the university course timetabling problem (UCTTP) at the FCSIT.…”
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Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance by Alsariera Y.A., Baashar Y., Alkawsi G., Mustafa A., Alkahtani A.A., Ali N.

Examining the potential of machine learning for predicting academic achievement: A systematic review by Nazir, M., Noraziah, Ahmad, Rahmah, M., Sharma, Aditi

“…Predicting student academic performance is a critical area of education research. Machine learning (ML) algorithms have gained significant popularity in recent years. …”
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Examining the potential of machine learning for predicting academic achievement: A systematic review by Nazir, M., Noraziah, Ahmad, Rahmah, M., Sharma, Aditi

“…Predicting student academic performance is a critical area of education research. Machine learning (ML) algorithms have gained significant popularity in recent years. …”
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Evaluation of postgraduate academic performance using artificial intelligence models by Baashar, Y., Hamed, Y., Alkawsi, G., Fernando Capretz, L., Alhussian, H., Alwadain, A., Al-amri, R.

“…The Gaussian process regression (GPR) model with squared exponential kernel algorithm achieved 71 of the CGPA variation. …”
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Evaluation of postgraduate academic performance using artificial intelligence models by Baashar, Y., Hamed, Y., Alkawsi, G., Fernando Capretz, L., Alhussian, H., Alwadain, A., Al-amri, R.

“…The Gaussian process regression (GPR) model with squared exponential kernel algorithm achieved 71 of the CGPA variation. …”
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Evaluation of postgraduate academic performance using artificial intelligence models by Baashar, Y., Hamed, Y., Alkawsi, G., Fernando Capretz, L., Alhussian, H., Alwadain, A., Al-amri, R.

“…The Gaussian process regression (GPR) model with squared exponential kernel algorithm achieved 71 of the CGPA variation. …”
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Academic Achievement Prediction Model Using Neural Networks by Normaziah, Abdul Rahman

“…Once the model has been established by the administrator, the future student achievement can be forecast by the model. …”
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Optimum Feeder Routing and Distribution Substation Placement and Sizing using PSO and MST by Hasan, Ihsan Jabbar, Gan, Chin Kim, Shamshiri, Meysam, Ab Ghani, Mohd Ruddin, Omar, Rosli

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Technical efficiency performance of Malaysian public research universities: Fuzzy data envelopment analysis / Saber Abdelall Mohamed Ahmed by Saber Abdelall, Mohamed Ahmed

“…Secondly, while DEA model requires precise input and output data of HEIs, this study utilizes the QS World University Ranking research output indicators as the output variables of the Fuzzy DEA model. …”
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Analyzing students records to identify patterns of students' performance by Hoe A.C.K., Ahmad M.S., Hooi T.C., Shanmugam M., Gunasekaran S.S., Cob Z.C., Ramasamy A.

“…Consequently, the ability to predict a student's performance could be useful in many ways to stakeholders of higher education institutions. This paper discusses the data mining technique used to identify the significant variables that affects and influences the performance of undergraduate students. …”

Determining malaria risk factors in Abuja, Nigeria using various statistical approaches by Segun, Oguntade Emmanuel

“…The optimal model based on the parsimony principles was obtained from the hill climbing algorithm with score metrics. …”
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The basics of multi-layer feedforward neural networks / Nurul Aityqah Yaccob and Farizuwana Akma Zulkifle by Yaccob, Nurul Aityqah, Zulkifle, Farizuwana Akma

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Predicting students’ STEM academic performance in Malaysian secondary schools using educational data mining by Termedi @ Termiji, Mohammad Izzuan

“…It proceeds through three phases of Need Analysis, Development of the Model and Evaluation of the Model. Four different data mining classification algorithms which are Random Forest, PART, J48 and Naive Bayes will be used on the dataset. …”
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