Search Results - (( developing back tree algorithm ) OR ( java visualisation using algorithm ))

Visualisation tool to study malaria transmission using network modelling by Wong,, Ji Yeh

“…Java was used to implement the visualisation tool. …”
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Octane number prediction for gasoline blends using convolution neural network / Zhu Yue by Zhu , Yue

“…In the project three commonly use algorithm are used for prediction of octane number for gasoline blends, which describes the behavior of the fuel in the engine at lower temperatures and speeds, and is an attemp to simulate acceleration behavior.These tree algorithm are back propagation (BP), radial basis funtion (RBF) and Extreme learning machine (ELM) algorithm. …”
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Investigation Of Electrical Capacitance Tomography For Agarwood Detection by Muhamad Aiqil, Sarudin

“…Then, the tomogram images of agarwood were obtained using linear back projection algorithm (LBP) in the MATLAB software. …”
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Indoor parking location information through Short Message Service (SMS) / Nurul Hidayu Abdul Aziz by Abdul Aziz, Nurul Hidayu

“…Indoor Parking Location Information through Short Message Service (SMS) is developing to help the drivers to find back their vehicle using their mobile phone via text messaging technology. …”
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Wearable based-sensor fall detection system using machine learning algorithm by Ishak, Anis Nadia, Habaebi, Mohamed Hadi, Yusoff, Siti Hajar, Islam, Md. Rafiqul

“…To prevent such kinds of deadly scenarios, a reliable fall detection system must be developed to help many lives. In this project, a wearable sensor-based fall detection system using a machine-learning algorithm had been developed. …”
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A wheelchair sitting posture detection system using pressure sensors by Mohamad Yusoff, Muhammad Annuar Alhadi, Azmi, Nur Liyana, Nordin, Nor Hidayati Diyana

“…There were four pressure sensors attached to the seat plate of the wheelchair and two pressure sensors attached to the back rest. Three classification algorithms based on the supervised learning of machine learning, such as support vector machine (SVM), random forest (RF),and decision tree (DT) were used to classify the postures which produced an accuracy of 95.44%, 98.72%,and 98.80%, respectively. …”
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Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks by Muhammad Ashraq, Salahuddin

“…The appearance of the image itself such as variation of lights due to different lighting condition, shadow effect on the object’s surface, size, shape, rotation and position, background clutter, states of blooming or budding may affect the utilized classification techniques. This study aims to develop a classification model for Malaysian blooming flowers using neural network with the back propagation algorithms. …”
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Predictive analytics for pacemaker medical instrument stock management of Transmedic Healthcare by Nawawi, Nafiz Danial

“…The research applies the CRISP-DM methodology, examining current stock processes, and then collecting and cleansing historical data to develop Always Better Control (ABC) for stock analysis and predictive models using classification and also making a comparison between three algorithms, which are Naive Bayes, Random Forest and Decision Tree. …”
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Initial study of electrical capacitance tomography for detecting agarwood by Muhammad Aiqil, Sarudin, Yasmin, Abdul Wahab, Ain Eazriena, Che Man, Nurhafizah, Abu Talip @ Yusof, Rohana, Abdul Karim, Suzanna, Ridzuan Aw, Juliza, Jamaludin, Jaysuman, Puspanathan, Elmy Johana, Mohamed, Ruzairi, Abdul Rahim, Sia, Yee Yu

“…The forward problem was solved using COMSOL, and tomogram images were obtained using a linear back-projection algorithm in MATLAB. The images closely matched the reference image based on the mean structural similarity index (MSSIM) values. …”
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Mathvision prototype using predictive analytics / Yuzi Mahmud ... [et al.] by Mahmud, Yuzi, Abdul Razak, Muhd Syahir, Abdul-Rahman, Shuzlina, Hanafiah, Mastura, Suhaimi, Amien Ashraf

“…The Multiclass Decision Forest algorithm appeared to be the best predictive model with 95.16% accuracy, as compared to Boosted Decision Tree, Logistic Regression, and Neural Network. …”
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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.

“…Using the feed-forward back-propagation algorithm with a multilayer perceptron network, ANN is a powerful ML method capable of predicting the complex model of slope cases. …”