Search Results - (( virtual integration modified algorithm ) OR ( java application optimization algorithm ))

Integration of enchanced jump point search (JPS) algorithm with modified bresenham technique for path planning in virtual grid-based environment by Nurul Atikah Janis

“…We demonstrate our framework; the integration of enhanced Jump Point Search algorithm with modified Bresenham for heuristic computation in virtual grid-based environment. …”
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Enhanced path planning for industrial robot: integrating modified artificial potential field and A* algorithm / Fan Rui ... [et al.] by -, Fan Rui, Ayub, Muhammad Azmi, Ab Patar, Mohd Nor Azmi, Che Abdullah, Sukarnur, Ahmat Ruslan, Fazlina

“…The study analyses the principles and limitations of classical algorithms. The study introduces a modified APF algorithm to address issues of local minima and path oscillation, which is integrated with A* to guide movement towards the virtual target. …”
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Optimization of blood vessel detection in retina images using multithreading and native code for portable devices by Tran, Duc Ngoc, Hussin, Fawnizu Azmadi, Yusoff, Mohd Zuki

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Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

“…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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Route Optimization System by Zulkifli, Abdul Hayy

“…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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Binary whale optimization algorithm with logarithmic decreasing time-varying modified sigmoid transfer function for descriptor selection problem by Yusof, Norfadzlia Mohd, Muda, Azah Kamilah, Pratama, Satrya Fajri, Carbo-Dorca, Ramon, Abraham, Ajith

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Investigating the schedulability of periodic real-time tasks in virtualized cloud environment by Alhussian, H., Zakaria, N., Patel, A., Jaradat, A., Abdulkadir, S.J., Ahmed, A.Y., Bahbouh, H.T., Fageeri, S.O., Elsheikh, A.A., Watada, J.

“…The architecture integrated three modified soft real-time task scheduling algorithms, namely, Earliest Deadline First, Earliest Deadline until Zero-Laxity, and Unfair Semi-Greedy. …”
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Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

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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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A Hybrid Sparrow Search Optimized Fractional Virtual Inertia Control for Frequency Regulation of Multi-Microgrid System by Fadheel B.A., Wahab N.I.A., Manoharan P., Mahdi A.J., Radzi M.A.B.M., Soh A.B.C., Ridha H.M., Alsoud A.R., Veerasamy V., Irudayaraj A.X.R., Alemu B.D.

“…A novel fractional derivative virtual inertia is integrated into the VIC loop to address this issue. …”

Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

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Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

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An integrated priority-based cell attenuation model for dynamic cell sizing by Amphawan, Angela, Omar, Mohd Nizam, Din, Roshidi

“…A new, robust integrated priority-based cell attenuation model for dynamic cell sizing is proposed and simulated using real mobile traffic data.The proposed model is an integration of two main components; the modified virtual community – parallel genetic algorithm (VC-PGA) cell priority selection module and the evolving fuzzy neural network (EFuNN) mobile traffic prediction module.The VC-PGA module controls the number of cell attenuations by ordering the priority for the attenuation of all cells based on the level of mobile level of mobile traffic within each cell.The EFuNN module predicts the traffic volume of a particular cell by extracting and inserting meaningful rules through incremental, supervised real-time learning.The EFuNN module is placed in each cell and the output, the predicted mobile traffic volume of the particular cell, is sent to local and virtual community servers in the VC-PGA module.The VC-PGA module then assigns priorities for the size attenuation of all cells within the network, based on the predicted mobile traffic levels from the EFuNN module at each cell.The performance of the proposed module was evaluated on five adjacent cells in Selangor, Malaysia. …”
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Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

“…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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Input-output based relation combinatorial testing using whale optimization algorithm for generating near optimum number of test suite by Suali, Anjila J., Nuraminah Ramli, Rozmie Razif Othman, Hasneeza Liza Zakaria, Iszaidy Ismail, Nor Shahida Mohd Jamail, Rimuljo Hendradi, Nurol Husna Che Rose

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Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

“…Genetic Algorithm (GA) is one of the most popular optimization solutions. …”
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Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

“…Genetic Algorithm (GA) is one of the most popular optimization solutions. …”
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Improving Class Timetabling using Genetic Algorithm by Qutishat, Ahmed Mohammed Ali

“…We have targeted the research on class timetabling problem. Hence, Genetic Algorithm (GA) is used as one of the most popular optimization solutions. …”
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Resource management in grid computing using ant colony optimization by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

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Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.