Search Results - (( course optimization based algorithm ) OR ( using optimization learning algorithm ))

Optimization of balanced academic curriculum problem in educational institutions using teaching learning based optimization algorithm by Mohd Fadzil Faisae, Ab Rashid, Wasif, Ullah

“…This study aims to optimize BACP using the Teaching-Learning Based Optimization (TLBO) algorithm, addressing the limitations of existing approaches and providing an efficient framework for curriculum balancing. …”
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An adaptive HMM based approach for improving e-Learning methods by Deeb B., Hassan Z., Beseiso M.

“…This adaptive algorithm can thus be applied to any e-learning platform for optimal content delivery to its users in real-time. © 2014 IEEE.…”

Handbook of Machine Learning for Computational Optimization: Applications and Case Studies by Jain, V., Juneja, S., Juneja, A., Kannan, R.

“…It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making. …”
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Enhancing teaching and learning through data-driven optimization of servicing code demand and lecturer allocation using WEKA analysis by Rochin Demong, Nur Atiqah, Mohamed Razali, Murni Zarina, Kamaruddin, Juliana Noor, Shamsuddin, Sazwan, Awang, Nor Ain, Kamarudin, Norjuliatie, Wan Othman, Noor Faradilla

“…Furthermore, classification using the Random Forest algorithm depicted that a 95.3% accuracy (k=0.768), confirming robust predictive capability in identifying course approval status and demand trends. …”
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Learning analytic framework for students’ academic performance and critical learning pathways by Lyn, Jessica Tan Yen, Goh, Yong Kheng, Lai, An Chow, Ngeow, Yoke Meng

“…These algorithms group students based on academic performance and course profiles, facilitating the identification of clusters with similar characteristics and academic trajectories. …”
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Deep reinforcement learning based resource allocation strategy in cloud-edge computing system by Ahmed Adhoni, Zameer, Habelalmateen, Mohammed, DR Janardhana, DR Janardhana, Abdul Sattar, Khalid Nazim, Audah, Lukman

“…This research focuses on the utility of Multiagent Learning framework with Deep Reinforcement Learning (MAL-DRL) which is used for solution deployment concerning resource allocation in such systems, such that the end user enjoys optimization while operators optimize resource utilization. …”
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Deep reinforcement learning based resource allocation strategy in cloud-edge computing system by Ahmed Adhoni, Zameer, Habelalmateen, Mohammed I, D R, Janardhana, Abdul Sattar, Khalid Nazim, Audah, Lukman

“…This research focuses on the utility of Multiagent Learning framework with Deep Reinforcement Learning (MAL-DRL) which is used for solution deployment concerning resource allocation in such systems, such that the end user enjoys optimization while operators optimize resource utilization. …”
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An evolutionary based features construction methods for data summarization approach by Rayner Alfred, Suraya Alias, Chin, Kim On

“…A data summarization approach is proposed due to its capability to learn data stored in multiple tables. In other words, this research will discuss the application of genetic algorithm to optimize the feature construction process from the Coral Reefs data to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). …”
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Improving Patient Rehabilitation Performance In Exercise Games Using Collaborative Filtering Approach by Waidah Ismail, Ismail Ahmed Al-Qasem Al-Hadi, Crina Grosan, Rimuljo Hendradi

Intelligent traffic lights using Q-learning by Mohd Yusop, Muhammad Aminuddin, Mansor, Hasmah, Gunawan, Teddy Surya, Nasir, Haidawati,

“…Q-learning derives benefits from past experiences and determines the optimal course of action based on them. …”
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Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study by Lestari, Widya, Abdullah, Adilah Syahirah, Ahmad Amin, Afifah Munirah, faridah, Nur, Sukotjo, Cortino, Ismail, Azlini, mohamad ibrahim, mohamad shafiq, Insani, Nashuha, Utomo, Chandra Prasetyo

“…Pre-university CGPA was shown to be predictive of dental students’ academic performance; however, alone they did not yield optimal outcomes. RF was the most precise algorithm for predicting grades A, B, and C, followed by LR, DT, and SVM. …”
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Approach For Optimizing Course Recommendation Based On Integrating Modified Felder-Silverman Learning Style Model With Meta-Heuristics Algorithm by Ashraf, Erum

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Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid by Wahid, Juliana

“…Harmony search algorithm (HSA) is a population-based metaheuristic optimization algorithm that imitates the music improvisation process where musicians improvise their instruments’ pitch by searching for a perfect state of harmony. …”
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A harmony search algorithm for university course timetabli by Al-Betar, Mohammed Azmi, Khader, Ahamad Tajudin

“…The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. …”
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Harmony great deluge for solving curriculum based course timetabling problem by Wahid, Juliana, Mohd Hussin, Naimah

“…University course timetabling which has been determined as non deterministic polynomial problem that accept widely as problem that are intractable.An efficient algorithm does not exist that is guaranteed to find an optimal solution for such problems.The design of good algorithm to find new methods and techniques to solve such problem is a very active area of research.This paper presents the adaption of the hybridizing between harmony search with great deluge algorithm for solving curriculum-based course timetabling problems.The algorithm can be adapted to the problem.Results were not comparatively better than those previously known as best solution.Proper modification in terms of the approach in this algorithm would make the algorithm perform better on curriculum-based course timetabling.…”
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Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil by Jamli, Mohd Radhi Fauzan, Ahmad Fadzil, Ahmad Firdaus

“…The proposed approach utilizes genetic algorithms to dynamically produce optimized timetables based on individual student needs, with real-time data scraping from 'iCRESS' ensuring the system stays up to date with the latest course information for accurate timetable generation. …”
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Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid by Wahid, Juliana

“…Harmony search algorithm (HSA) is a population-based metaheuristic optimization algorithm that imitates the music improvisation process where musicians improvise their instruments’ pitch by searching for a perfect state of harmony. …”
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Performance comparison of linear and non-linear great deluge algorithms in solving university course timetabling problems by Joe Henry Obit, Yik Junn Kuan, Rayner Alfred

“…The reason may be due to the constraints involved varied for each dataset as the optimization problems solved by any algorithms are uniformed in relation to the NFL theorems.…”
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Meta-Heuristic Algorithms for Learning Path Recommender at MOOC by Son, N.T., Jaafar, J., Aziz, I.A., Anh, B.N.

“…We have developed Metaheuristic algorithms includes the Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), to solve the proposed model. …”
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Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems by Din, Fakhrud, Khalid, Shah, Fayaz, Muhammad, Gwak, Jeonghwan, Kamal Z., Zamli, Mashwani, Wali Khan

“…Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. …”
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