Search Results - (( based optimization based algorithm ) OR ( data interactive learning algorithm ))

Pairwise Test Suite Generation Using Adaptive Teaching Learning-Based Optimization Algorithm with Remedial Operator by Fakhrud, Din, Kamal Z., Zamli

“…Being a NP-complete problem, pairwise test suite generation problem has been addressed using several meta-heuristic algorithms including the Fuzzy Adaptive Teaching Learning-based Optimization (ATLBO) algorithm in the literature. …”
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A fuzzy adaptive teaching learning-based optimization strategy for generating mixed strength t-way test suites by Din, Fakhrud

“…Owing to its proven performance in many other optimization problems, the adoption of the parameter-free Teaching Learning-based Optimization (TLBO) algorithm as a new t-way strategy is deemed useful. …”
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Operating a reservoir system based on the shark machine learning algorithm by Allawi, Mohammed Falah, Jaafar, Othman, Mohamad Hamzah, Firdaus, Ehteram, Mohammad, Hossain, Md Shabbir, El-Shafie, Ahmed

“…In the current study, the shark machine learning algorithm (SMLA) is proposed to develop an optimal rule for operating the reservoir. …”
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Automated bilateral negotiation with incomplete information in the e-marketplace. by Jazayeriy, Hamid

“…The MGT algorithm is useful to explore the properties of the Pareto-optimal offers. …”
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Social media mining: a genetic based multiobjective clustering approach to topic modelling by Alfred, Rayner, Loo, Yew Jie, Obit, Joe Henry, Lim, Yuto, Haviluddin, Haviluddin, Azman, Azreen

“…However, less works have been conducted in applying multiobjective based algorithm for topic extraction. Most of these algorithms are not optimized, even if they are, they are only optimized by using a single objective method and may underperform when solving real-world problems which are typically multi-objectives in nature. …”
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Operating a reservoir system based on the shark machine learning algorithm by Allawi M.F., Jaafar O., Mohamad Hamzah F., Ehteram M., Hossain M.S., El-Shafie A.

Temporal integration based factorization to improve prediction accuracy of collaborative filtering by Al-Qasem, Al-Hadi Ismail Ahmed

“…The rating matrix typically contains a high percentage of unknown rating scores which is called the data sparsity problem. The data sparsity problem has been solved by several approaches such as Bayesian probabilistic, machine learning, genetic algorithm, particle swarm optimization and matrix factorization. …”
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A fast learning network with improved particle swarm optimization for intrusion detection system by Ali, Mohammed Hasan

“…The Fast Learning Network (FLN) is one of the new machine learning algorithms that are easy to implement, computationally efficient, and with excellent learning performance characteristics. …”
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Social media mining: a genetic based multiobjective clustering approach to topic modelling by Rayner Alfred, Loo Yew Jie, Joe Henry Obit, Yuto Lim, Haviluddin Haviluddin, Azreen Azman

“…However, less works have been conducted in applying multiobjective based algorithm for topic extraction. Most of these algorithms are not optimized, even if they are, they are only optimized by using a single objective method and may underperform when solving real-world problems which are typically multi-objectives in nature. …”
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Optimization of multi-agent traffic network system with Q-Learning-Tune fitness function by Tan, Min Keng

“…The dynamic environment causing the need of dynamic modelling for better dynamic optimisation will be catered via a specifically formulated interactive fitness function. The interactive metamodel is extracted using Q-Learning (QL) via online observing and learning of the outflow-inflow traffic characteristics. …”
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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

“…By providing a holistic perspective of student performance and course interactions, the proposed learning analytics framework holds great promise for educational institutions seeking data-driven strategies to enhance student outcomes and optimize learning experiences.…”
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Characterization of oil palm fruitlets using artificial neural network by Olukayode, Ojo Adedayo

“…The results also showed that contrary to the widely reported gap between the accuracy of the LM algorithm and other feed forward neural network training algorithms, the RP trained network performed as good as that of the LM algorithm for the range of data considered. …”
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Customer mobile behavioral segmentation and analysis in telecom using machine learning by Sharaf Addin, Eman Hussein, Admodisastro, Novia Indriaty, Mohd Ashri, Siti Nur Syahirah, Kamaruddin, Azrina, Chew, Yew Chong

“…Unsupervised machine learning algorithm K-means was used to cluster the data, and these results were analyzed and labeled with labels and descriptions. …”
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Framework for stream clustering of trajectories based on temporal micro clustering technique by Abdulrazzaq, Musaab Riyadh

“…The minimum description length is an important concept in information theory and computational learning in which the best hypothesis for a given data set is the one that leads to the best compression of the data. …”
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Evolutionary-based feature construction with substitution for data summarization using DARA by Sia, Florence, Alfred, Rayner

“…This is performed in order to exploit all possible interactions among attributes which involves an application of genetic algorithm to find a relevant set of features. …”
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A machine learning approach to movie recommendation system by Saw, Zi Jin

“…This project presents a machine learning-based movie recommendation system aimed at providing accurate and personalized movie suggestions while addressing common industry challenges such as biased recommendations, data sparsity, and the cold start problem. …”
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Genetic ensemble biased ARTMAP method of ECG-Based emotion classification by Loo, C.K., Liew, W.S., Sayeed, M.S.

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Improving neural networks training using experiment design approach by Chong, Wei Kean

“…Consider a function approximation problem (Neural Network using Radial Basic Function structure) and limit the amount of training data, say (m) from N amount of possible data. Randomly select the m data set for conventional training algorithm. …”
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Artificial Neural Network Algorithms to Predict the Compressive Strength of Fly Ash-Based Geopolymers by Siaw Hui, Teo

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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