Search Results - (( java implementation path algorithm ) OR ( training programmes based algorithm ))

Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer by Ahmad Nezer, Nurul Aqilah

“…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

“…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment by Shaliza Hayati A. Wahab, Nordin Saad, Azali Saudi, Ali Chekima

“…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
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Smart appointment organizer for mobile application / Mohd Syafiq Adam by Adam, Mohd Syafiq

“…The main component of this prototype is the use of Dijkstra algorithm to compute the shortest path from source of appointment to the 6 points of destinations within UiTM Shah Alam. …”
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Immune-based technique for undergraduate programmes recommendation / Muhammad Azrill Mohd Zamri by Mohd Zamri, Muhammad Azrill

“…Some applicants who enrolled for unsuitable programme ended up failed to progress or dropped out from the programme. …”
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Making programmer effective for software development teams: An extended study by Gilal, A.R., Jaafar, J., Abro, A., Umrani, W.A., Basri, S., Omar, M.

“…In order to find the possible combination of personality types between team-leader and programmer, this study applied Genetic Algorithm (GA) and Johnson's Algorithm (JA) on data. …”
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The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach by Zahari, Taha, Rabiu Muazu, Musa, Anwar, P. P. Abdul Majeed, Muhammad Muaz, Alim, Mohamad Razali, Abdullah

“…The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. …”
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Neural Network Based Pattern Recognition in Visual Inspection System for Intergrated Circuit Mark Inspection by Sevamalai, Venantius Kumar

“…Error-back propagation algorithm was used to train the network. The objective was to test the robustness of the network in handling pattern variations as well as the feasibility of implementing it on the production floor in tetms of execution speed. …”
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Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management by Aizat Hilmi Zamzam, Ayman Khallel Ibrahim Al-Ani, Ahmad Khairi Abdul Wahab, Khin Wee Lai, Suresh Chandra Satapathy, Azira Khalil, Khairunnisa Hasikin, Muhammad Mokhzaini Azizan

A machine learning approach of predicting high potential archers by means of physical fitness indicators by Muazu Musa, Rabiu, Abdul Majeed, Anwar P.P., Taha, Zahari, Chang, Siow Wee, Ab. Nasir, Ahmad Fakhri, Abdullah, Mohamad Razali

“…The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. …”
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A machine learning approach of predicting high potential archers by means of physical fitness indicators by Musa, Rabiu Muazu, Anwar, P. P. Abdul Majeed, Zahari, Taha

“…The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. …”
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VHDL modeling of EMG signal classification using artificial neural network by Ahsan, Md. Rezwanul, Ibrahimy, Muhammad Ibn, Khalifa, Othman Omran, Ullah, Mohammad Habib

“…A back-propagation neural network with Levenberg-Marquardt training algorithm has been used for the classification of EMG signals. …”
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The identification of high potential archers based on relative psychological coping skills variables: a support vector machine approach by Taha, Z., Musa, R.M., Majeed, A.P.P.A, Abdullah, M.R., Zakaria, M.A., Alim, M.M., Jizat, J.A.M., Ibrahim, M.F.

“…The present study classified and predicted high and low potential archers from a collection of psychological coping skills variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 +/-.056) gathered from various archery programmes completed a one end shooting score test. …”
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The identification of high potential archers based on relative psychological coping skills variables: A Support Vector Machine approach by Zahari, Taha, Rabiu Muazu, Musa, Anwar, P. P. Abdul Majeed, Mohamad Razali, Abdullah, Muhammad Aizzat, Zakaria, Muhammad Muaz, Alim, Jessnor Arif, Mat Jizat, Mohamad Fauzi, Ibrahim

“…The present study classified and predicted high and low potential archers from a collection of psychological coping skills variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. …”
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Artificial Intelligence (AI) to predict dental student academic performance based on pre university results by Abdullah, Adilah Syahirah, Ahmad Amin, Afifah Munirah, Lestari, Widya, Sukotjo, Cortino, Utomo, Chandra Prasetyo, Ismail, Azlini

“…Exploratory Data Analysis will be performed with training and testing data. For modeling, several prediction models will be trained using neural networks. …”
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Evolutionary cost-cognizant regression test case prioritization for object-oriented programs by Bello, AbdulKarim

“…Therefore, this study proposed a cost-cognizant TCP approach for object-oriented software that uses path-based integration testing to identify the possible execution path extracted from the Java System Dependence Graph (JSDG) model of the source code using forward slicing technique. …”
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The employment of support vector machine to classify high and low performance archers based on bio-physiological variables by Taha, Z., Musa, R.M., Majeed, A.P.P.A, Abdullah, M.R., Abdullah, M.A., Hassan, M.H.A., Khalil, Z.

“…The present study employs a machine learning algorithm namely support vector machine (SVM) to classify high and low potential archers from a collection of biophysiological variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 +/-.056) gathered from various archery programmes completed a one end shooting score test. …”
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Development of a steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) system by Leow, R.S., Ibrahim, F., Moghavvemi, M.

“…The system includes a programmable visual stimulator, EEG amplifier with filter system, data acquisition card, and signal processing and classification algorithms. …”
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Framework of Designing Multiple Microcontroller Based Applications by Selvakumar, Raja Saravana Kumar, Kamarul Hawari, Ghazali, Nik Mohd Kamil, Nik Yusof

“…This project consists of the hardware and software implementation that supports the development and transfer process of the program code from a personal computer to the microcontroller, which led to the evaluation of educational based training kit systems. The first part of the paper focuses on hardware design, which is based on a modular approach, i.e. recomposed for the design of each application, in order to ensure maximum adaptability. …”
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The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables by Zahari, Taha, Musa, Rabiu Muazu, Anwar, P. P. Abdul Majeed, Mohamad Razali, Abdullah, Muhammad Amirul, Abdullah, M. H. A., Hassan, Zubair, Khalil

“…The present study employs a machine learning algorithm namely support vector machine (SVM) to classify high and low potential archers from a collection of bio-physiological variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. …”
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