Search Results - (( java implementation path algorithm ) OR ( program forecasting based algorithm ))
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Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer
Published 2017“…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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Thesis -
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization
Published 2019“…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
Published 2021“…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
Published 2009“…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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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…Finally, future estimations of EEC in ASEAN-5 countries are projected up to 2030 by applying the rolling-based forecasting procedure on mathematical models derived from optimized GEP. …”
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Automated time series forecasting
Published 2011“…While quantitative technique is based on statistical concepts and requires large amount of data in order to formulate the mathematical models.This technique can be classified into projective and causal technique.The projective technique (or univariate modelling) just involve one variable while the causal technique (or econometric modelling) suitable for multi-variables.Since forecasting involves uncertainty, several methods need to be executed on one set of time series data in order to produce accurate forecast.Hence, usually in practice forecaster need to use several softwares to obtain the forecast values.If this practice can be transformed into algorithm (well-defined rules for solving a problem) and then the algorithm can be transformed into a computer program, less time will be needed to compute the forecast values where in business world time is money.In this study, we focused on algorithm development for univariate forecasting techniques only and will expand towards econometric modelling in the future.Two set of simulated data (yearly and non-yearly) and several univariate forecasting techniques (i.e. …”
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Parallel Implementation of Two Level Barotropic Models Applied to the Weather Prediction Problem
Published 2004“…The numerical results based on the sequential and parallel algorithms for both row and columnwise fashions are presented and compared to determine their performance.…”
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Implementation of Health Monitoring System for Patients using Machine Learning Algorithms
Published 2024“…We created a machinelearning- based solution for the wellness program industry that uses Internet Of Medical Things (IoMT) devices to forecast cardiovascular risk. …”
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A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination
Published 2023“…This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. …”
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Conference or Workshop Item -
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Applications of IoT and Artificial Intelligence in Water Quality Monitoring and Prediction: A Review
Published 2023Conference Paper -
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Machine learning predictions of stock market pattern using Econophysics approach
Published 2025“…In conclusion, the study of Econophysics principles with Python programming and machine learning algorithms has indicates that the predictive framework is reliable and effective in capturing stock price fluctuations, enhancing decision-making for investors based on data-driven insights.…”
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Academic Achievement Prediction Model Using Neural Networks
Published 2002“…Once the model has been established by the administrator, the future student achievement can be forecast by the model. The system can predict the result of Programming I subject based on the student's background during the Sijil Pelajaran Malaysia (SPM) examination. …”
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Rainfall time series modeling for a mountainous region in West Iran
Published 2010“…The models were trained based on the Levenberg-Marquardt algorithm with tansigmoid activation function for the hidden layer and purelin activation function for the output layer. …”
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Evolutionary cost-cognizant regression test case prioritization for object-oriented programs
Published 2019“…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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Improved voting technique for ensemble of MLP system applied on various classification data / Saodah Omar, Iza Sazanita Isa and Junita Mohd Saleh.
Published 2010“…The MLP networks are trained using two types of learning algorithm, which are the Levenberg Marquardt and the Resilient Back Propagation algorithms. …”
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