Search Results - (( model validation bayes algorithm ) OR ( based optimization based algorithm ))

Enhancing fairness and efficiency in teacher placement based on staff placement model: an intelligent teacher placement selection model for Ministry of Education Malaysia by Shamsul Saniron, Zulaiha Ali Othman, Abdul Razak Hamdan

“…A T-test comparing TPC and ITPS yielded a high p-value (p > 0.05), confirming no statistically significant difference between the models while validating ITPS reliability. In conclusion, the ITPS model based on SPM enhances efficiency and fairness in teacher placements at MOE, demonstrating strong potential as a decision-support tool for optimizing workforce allocation.…”
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Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier by G. Noma, Nasir, Mohd Khanapi, Abd Ghani, Mohamad Khir , Abdullah, Noorizan , Yahya

“…Both multivariate Bernoulli and multinomial naïve Bayes models were used with and without the feature extraction. …”
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Technical job distribution at BSD SHARP service center using combination of naïve Bayes and K-Nearest neighbour by Pebrianti, Dwi, Ariawan, Angga, Bayuaji, Luhur, Mahdiana, Deni, ,, Rusdah

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Advanced data mining techniques for landslide susceptibility mapping by Ibrahim, M.B., Mustaffa, Z., Balogun, A.-L., Hamonangan Harahap, I.S., Ali Khan, M.

“…The indices indicated that the SVM model performed better than the other two algorithms in both training and validation datasets. …”
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MODELLING ANALYSIS FOR ACCURATE TROPICAL WEATHER FORECASTING by Calvin, Wong Qin Jie

“…The Random Forest, K-Nearest Neighbors, Support Vector Machines, XGBoost and Naïve Bayes algorithm is proposed to validate the model for rainfall prediction, which is proven to operate well with excellent accuracy in previous researches. …”
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Novice programmers’ emotion and competency assessments using machine learning on physiological data / Fatima Jannat by Fatima, Jannat

“…Hyper-parameter tuning has been used in all the algorithms using k-fold cross validation to have the best accuracy and to avoid the over-fitting issue. …”
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Opposition-based Whale Optimization Algorithm by Alamri, Hammoudeh S., Alsariera, Yazan A., Kamal Z., Zamli

“…In order to improve solution accuracy and reliability, this paper proposes a new algorithm based on WOA. The new algorithm called Opposition-based Whale Optimization (OWOA). …”
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Sentiment Analysis of Sexual Harassment in Malaysia on Twitter Using Machine Learning Algorithms by Nurellezia, Suleiman

“…The transformed data is then modelled using machine learning algorithms such as Naïve Bayes classifier and Support Vector Machine to predict the overall sentiment of tweets, in which the finding depicted an overall positive sentiment surrounding the issue. …”
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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

“…To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. …”
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Rule-Based Multi-State Gravitational Search Algorithm for Discrete Optimization Problem by Ismail, Ibrahim, Zuwairie, Ibrahim, Zulkifli, Md. Yusof

“…In this study, rule-based multi-state gravitational search algorithm (RBMSGSA) algorithm is proposed to solve discrete combinatorial optimization problems. …”
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Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation by Khan, Abdullah

“…Finally, a crowding distance and non-dominated-sorting-based multi-objective hybrid firefly & particle swarm optimization (MOHFPSO) algorithm is designed for MOOPF problems. …”
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An enhanced swap sequence-based particle swarm optimization algorithm to solve TSP by Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, Muzaffar Hamzah, Aida Mustapha, Angela Amphawan

“…To evaluate the proposed algorithm, the solutions to the TSP problem obtained from the proposed algorithm and swap sequence based PSO are compared in terms of the best solution, mean solution, and time taken to converge to the optimal solution. …”
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A comparative analysis of machine learning algorithms for diabetes prediction by Alansari, Waseem Abdulmahdi, Masnizah Mohd

“…The methodology involves data collection, pre-processing, and training the algorithms using k-fold cross-validation. The results indicate that pre-processing steps and dataset characteristics significantly impact algorithm performance. …”
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Detection of DDoS attacks in IoT networks using machine learning algorithms by Abdulrahman Alwan, Alwan Ahmed, Shah, Asadullah, Abdulrahman Alwan, Alwan Abdullah, Laghari, Shams Ul Arfeen

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Metaheuristic multi-hop clustering optimization for energy-efficient wireless sensor network by Vincent Chung, Norah Tuah, Kit Guan Lim, Min Keng Tan, Ismail Saad, Kenneth Tze Kin Teo

“…Based on the performance evaluation, GACS outperforms both Genetic Algorithm (GA)-based cluster optimization algorithm and Cuckoo Search (CS)-based multi-hop optimization algorithm.…”
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Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm by Ullah, Wasif, Muhammad Ammar, Nik Mu’tasim, Mohd Fadzil Faisae, Ab Rashid

“…This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. …”
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Mobile app of mood prediction based on menstrual cycle using machine learning algorithm / Nur Hazirah Amir by Amir, Nur Hazirah

“…It implemented Supervised Learning algorithm with Bayes’ Theorem model for the calculation of mood prediction using Python programming language. …”
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Simulated Kalman Filter algorithms for solving optimization problems by Nor Hidayati, Abdul Aziz

“…Applications and improvements to the HKA algorithm suggest that optimization algorithm based on estimation principle has a huge potential in solving a wide variety of optimization problems. …”
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GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms by Tella, A., Balogun, A.-L.

“…Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70 of the dataset, while 30 was used for cross-validation. …”
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Prediction of novel doping agent through the integration of chemical and biological data using in silico method by Mohd Rosman, Nurul Ain

“…Two validations were performed on the models which are internal and external validation. …”
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