Search Results - (( developing based optimization algorithm ) OR ( (java OR jaya) data optimization algorithm ))

Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems by Mohammad Khamees Khaleel, Alsajri

“…To achieve this goal, an improved Teaching Learning-Based Optimization (ITLBO) algorithm was proposed in dealing with subset feature selection. …”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

“…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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Modelling and optimization of a transit services with feeder bus and rail system / Mohammadhadi Almasi by Mohammadhadi, Almasi

“…This is followed by application of the improved model to the benchmark and Petaling Jaya study areas. In this study, optimized transit services and coordinated schedules are developed using metaheuristic algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), water cycle algorithm (WCA) and imperialist competitive algorithm (ICA). …”
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Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology by Mohamad Jaya, Abdul Syukor, Muhamad, Mohd Razali, Abd Rahman, Md Nizam, Mohammad Jarrah, Mu'ath Ibrahim, Hasan Basari, Abd Samad

“…Additionally,analysis of variance(ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters, genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…Feature subset selection and classifier ensemble learning are familiar techniques with high ability to optimize above problems. Recently, various techniques based on different algorithms have been developed. …”
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Modelling and optimisation of cooling-slope parameters of magnesium AZ91D using improvement multi-objective Jaya approach for predicted feedstock performance by Mohd Said, Rahaini, Salleh@Sallehuddin, Roselina, Mohamed Radzi, Norhaizan, Wan Ali, Wan Fahmin Faiz, Mohamad Kamal, Mohamad Ridzuan

“…In this study, MOO named multi-objective Jaya (MOJaya) was improved in terms of hybrid MOJaya and inertia weight with archive K-Nearest Neighbor (MOiJaya-aKNN) algorithm. …”
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Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters by Abdul Syukor, Mohamad Jaya, Mu*ath Ibrahim, Mohammad Jarrah, Mohd Asyadi Azam, Mohd Abid, Mohd Razali, Muhamad

“…Additionally,analysis of variance (ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters,genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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An efficient unknown detection approach for RFID data stream management system by Siti Salwani, Yaacob, Hairulnizam, Mahdin, Wijayanto, Inung, Muhammad Aamir, -, Mohd Izham, Mohd Jaya, Nabilah Filzah, Mohd Radzuan, Al Fahim, Mubarak Ali

“…A novel algorithm called SWOR (Sliding Window XOR-based Detection) is introduced, specifically designed to identify unknown tags within RFID data streams. …”
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A systematic review of recurrent neural network adoption in missing data imputation by Nur Aqilah, Fadzil Akbar, Mohd Izham, Mohd Jaya, Mohd Faizal, Ab Razak, Nurul Aqilah, Zamri

“…Performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Area Under the Receiver Operating Characteristic Curve (AU-ROC), Mean Squared Error (MSE), and Mean Relative Error (MRE) are commonly used to evaluate these models. Future development of a more robust RNN-based imputation methods that integrate optimization algorithms, such as Particle Swarm Optimization (PSO) and Stochastic Gradient Descent (SGD) will further enhance the imputation accuracy and reliability.…”
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A systematic review of recurrent neural network adoption in missing data imputation by Nur Aqilah, Fadzil Akbar, Mohd Izham, Mohd Jaya, Mohd Faizal, Ab Razak, Nurul Aqilah, Zamri

“…Performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Area Under the Receiver Operating Characteristic Curve (AU-ROC), Mean Squared Error (MSE), and Mean Relative Error (MRE) are commonly used to evaluate these models. Future development of a more robust RNN-based imputation methods that integrate optimization algorithms, such as Particle Swarm Optimization (PSO) and Stochastic Gradient Descent (SGD) will further enhance the imputation accuracy and reliability.…”
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Smart student timetable planner by Wong, Xin Tong

“…A Genetic Algorithm forms the core scheduling engine, generating optimized timetables that respect both hard constraints, such as avoiding clashes, and soft constraints, such as personal preferences.The final output of this project is a functional web-based timetable planner that successfully enhances scheduling efficiency, reduces the likelihood of errors, and improves the overall academic planning experience. …”
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Thin Film Roughness Optimization In The Tin Coatings Using Genetic Algorithms by Fauzi, Nur Faiqah, Mohamad Jaya, Abdul Syukor, Mohammad Jarrah, Mu’ath Ibrahim, Akbar, Habibullah

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Fuzzy modeling using Bat Algorithm optimization for classification by Noor Amidah, Ahmad Sultan

“…A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. …”
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Particle swarm optimization algorithm to enhance the roughness of thin film in tin coatings by Jamil Alsayaydeh, Jamil Abedalrahim, Zainon, Maslan, Mohammad Alshannaq, Osama Saleh, Hammouda, Montaser B A, Ali, Mohanad Faeq, Alkhashaab, Mohammed Abdul Razaq, Mohamad Jaya, Abdul Syukor

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Intrusion Detection Systems, Issues, Challenges, and Needs by Aljanabi, Mohammad, Mohd Arfian, Ismail, Ali, Ahmed Hussein

“…However, these algorithms suffer from many lacks especially when apply to detect new type of attacks, and need for new algorithms such as JAYA algorithm, teaching learning-based optimization algorithm (TLBO) algorithm is arise. …”
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Optimization of an improved intermodal transit model equipped with feeder bus and railway systems using metaheuristics approaches by Almasi, M.H., Sadollah, A., Kang, S., Karim, M.R.

“…In this study, the imperialist competitive algorithm (ICA) and the water cycle algorithm (WCA) were employed to optimize feeder bus and railway services. …”
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A random search based effective algorithm for pairwise test data generation by Sabira, Khatun, K. F., Rabbi, Che Yahaya, Yaakub, Klaib, Mohammad F. J.

“…This paper proposes an effective random search based pairwise test data generation algorithm named R2Way to optimize the number of test cases. …”
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Sizing optimization of large-scale grid connected photovoltaic system using dolphin echolocation algorithm / Muhammad Zakyizzuddin Rosselan by Rosselan, Muhammad Zakyizzuddin

“…This study presents the development of Dolphin Echolocation Algorithm (DEA)-based sizing algorithm for sizing optimization of large-scale GCPV systems. …”
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Scalable approach for mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

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EasyA: Easy and effective way to generate pairwise test data by Rabbi, Khandakar Fazley, Sabira, Khatun, Che Yahaya, Yaakub, Klaib, Mohammad F. J.

“…This paper proposes a matrix based calculation for pairwise test data generation algorithm named EasyA to optimize the number of test cases. …”
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