Search Results - (( training program gaining 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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Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu by Sabtu, Melati

“…The programs are back-propagation neural network program, training and performance program and recognition program. …”
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Classification model for hotspot occurrences using spatial decision tree algorithm by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

“…As the ID3 algorithm that uses information gain in the attribute selection, the proposed algorithm uses spatial information gain to choose the best splitting layer from a set of explanatory layers. …”
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An interactive C++ programming courseware (SIFOO) / Mazliana Hasnan … [et al.] by Hasnan, Mazliana, Othman, Zainab, Mohamad, Norizan, Abdullah, Zamlina

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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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Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan by Ruslan, Muhammad Rushamir Hakimi

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Sentiment Analysis of Airline Reviews Using Naive Bayes Algorithm / Ahmad Firdaus Maliki by Maliki, Ahmad Firdaus

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Path Following Using A Learning Neural Network by NHH , Mohamad Hanif

“…The thesis differs from [2] in a sense that different types of neural controller are established to achieve a better path following accuracy. Two algorithms, gradient descent and quasi-Newton which utilize a batch training method, are introduced as comparison to the gradient descent method that incorporates the online (or incremental) training method. …”
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An evaluation of Student Practical Online Application System (SPOA) developed using User Centered Design (UCD) / Muhammad Izzuddin Khairul Annuar Lim by Khairul Annuar Lim, Muhammad Izzuddin

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Comparative Analysis Using Bayesian Approach To Neural Network Of Translational Initiation Sites In Alternative Polymorphic Contex by Herman, Nanna Suryana, Husin, Nurul Arneida, Hussin, Burairah

“…The objectives of this paper are to develop useful algorithms and to build a new classification model for the case study.The first approach of neural network includes training on algorithms of Resilient Backpropagation,Scaled Conjugate Gradient Backpropagation and Levenberg-Marquardt.The outputs are used in comparison with Bayesian Neural Network for efficiency comparison.The results showed that Resilient Backpropagation have the consistency in all measurement but performs less in accuracy.In second approach,the Bayesian Classifier_01 outperforms the Resilient Backpropagation by successfully increasing the overall prediction accuracy by 16.0%.The Bayesian Classifier_02 is built to improve the accuracy by adding new features of chemical properties as selected by the Information Gain Ratio method,and increasing the length of the window sequence to 201.The result shows that the built model successfully increases the accuracy by 96.0%.In comparison,the Bayesian model outperforms Tikole and Sankararamakrishnan (2008) by increasing the sensitivity by 10% and specificity by 26%. …”
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Customer sentiment analysis through social media feedback: A case study on telecommunication company by Mat Zain, Siti Nur Syamimi, Ramli, Nor Azuana, Adnan, Rose Adzreen

“…The data were then split into training and testing to be tested on the three different supervised learning algorithms used in this study which are Support Vector Machine, Random Forest, and Naïve Bayes. …”
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SpickME: Choosing spectacles prediction system by using multinomial Naïve Bayes / Nurul Nabilah Johari by Johari, Nurul Nabilah

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Customer sentiment analysis through social media feedback by Siti Nur Syamimi, Mat Zain

“…The data were then split into training and testing to be tested on the three different supervised learning algorithms used in this study which are Support Vector Machine, Random Forest, and Naïve Bayes. …”
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Beyond Grades - Predicting Programme Learning Outcomes with Multi-Output Regression in Malaysian Higher Education by Muhammad Noorazizi Abd Ghani, Istas Fahrurrazi Nusyirwan