Search Results - (( java implementation path algorithm ) OR ( using quality machine 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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The Implementation of a Machine Learning-based Routing Algorithm in a Lab-Scale Testbed by Ridwan M.A., Radzi N.A.M., Azmi K.H.M., Ahmad A., Abdullah F., Ahmad W.S.H.M.W.

“…Due to network complexity, conventional QoS-improving routing algorithms (RAs) may be impractical. Thus, researchers are developing intelligent RAs, including machine learning (ML)-based algorithms to meet traffic Q oS r equirements. …”

Neural Network Multi Layer Perceptron Modeling For Surface Quality Prediction in Laser Machining by Sivarao, Subramonian

“…The researchers conducted the prediction of laser machining quality, namely surface roughness with seven significant parameters to obtain singleton output using machine learning techniques based on Quick Back Propagation Algorithm. …”
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Water Quality Parameter Prediction Using Machine Learning Method by Muhammad Shaiful Bin Abdul Halim

Modeling, Testing and Experimental Validation of Laser Machining Micro Quality Response by Artificial Neural Network by Sivarao, Subramonian

“…Therefore, prediction of laser machining cut quality, namely surface roughness was carried out using machine learning techniques based on Quick Back Propagation Algorithm using ANN. …”
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New approach to analysis machine learning base power quality in a test grid system by Salam, S M, Rashid, Muhammad Mahbubur, Ali, M Y, Yvette, S

“…This study presents a novel approach to power quality analysis in various power systems, using machine learning algorithms. …”
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Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters by Teguh, Sutanto, Muhammad Rafli, Aditya, Haldi, Budiman, M.Rezqy, Noor Ridha, Usman, Syapotro, Noor, Azijah

“…This study compares four machine learning algorithms Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in water quality classification based on contaminant parameters. …”
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Classification model for water quality using machine learning techniques by Azilawati, Rozaimee, Azrul Amri, Jamal, Azwa, Abdul Aziz

“…There is a need to resolve this problem for us to get good water that can be used for domestic purposes. This article proposes a suitable classification model for classifying water quality based on the machine learning algorithms. …”
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Quality checking and inspection based on machine vision technique to determine tolerancevalue using single ceramic cup by Mohd Ali, Nursabillilah, Karis, Mohd Safirin, Wong, Gao Jie, Bahar, Mohd Bazli, Sulaiman, Marizan, Mat Ibrahim, Masrullizam, Zainal Abidin, Amar Faiz

“…The development of an algorithm for inspection and quality checking using machine vision was discussed in this paper. …”
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Quality checking and inspection based on machine vision technique to determine tolerance value using single ceramic cup by Mohd Ali, Nursabillilah, Karis, Mohd Safirin, Wong, Gao Jie, Bahar, Mohd Bazli, Sulaiman , Marizan, Mat Ibrahim, Masrullizam, Zainal Abidin, Amar Faiz

“…The development of an algorithm for inspection and quality checking using machine vision was discussed in this paper, in which the design of the algorithm is to detect the sign of defect when a sample of the product is used for inspection purposes. …”
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To study the multi-objective optimization of EDM using genetic algorithm by Fairuz, Idris

“…A machine such as EDM (electro discharge machine) used to produce a product that require an accurate product quality. …”
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Solving single machine scheduling problem with maximum lateness using a genetic algorithm by Nazif, Habibeh, Lee, Lai Soon

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Optimization machining parameters in pocket milling using genetic algorithm and mastercam by Abdullah, Haslina, Isa, Nurshafinaz, Zakaria, Mohamad Shukri

“…Mastercam software has been used to verify the algorithm's results by applying the optimum parameter generated by GA in the Mastercam. …”
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Modeling of Laser Materials Processing by Artificial Neural Network Modeling and Experimental Validation by Sivarao, Subramonian

“…One such method is machine learning, which involves using a computer algorithm to capture hidden knowledge from data. …”
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Octane number prediction for gasoline blends using convolution neural network / Zhu Yue by Zhu , Yue

“…Machine performance learning models depend to a large extant to the data quality used train the model. …”
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Optimization of surface quality of mild steel machined by wire EDM using simulated annealing algorithm by Khan, Ahsan Ali, Al Hazza, Muataz Hazza Faizi, Che Daud, Mohd Radzi, Mohamad Kamal, Nor Shahirah

“…One of the main machining characteristics that play vital role in determining the quality of engineering components is surface integrity. …”
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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.