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A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy by Qing, Zhang, Abdullah, Abdul Rashid, Choo, Wei Chong, Ali, Mass Hareeza

“…This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence.…”
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Rainfall-funoff modelling in batang layar and oya sub-catchments using pre-developed ann model for tinjar catchment by Awangku Faizal Sallehin, Awangku Brahim

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Effort Estimation Model for Function Point Measurement by Koh, Tieng Wei

“…This research work has generated an algorithmic effort estimation model for function points measurement. …”
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M-Factors Fuzzy Time Series for Forecasting Moving Holiday Electricity Load Demand in Malaysia (S/O 14589) by Mansor, Rosnalini, Mat Kasim, Maznah, Othman, Mahmod, Zaini, Bahtiar Jamili

“…The modified algorithm, Weighted Subsethood Segmented Fuzzy Time Series (WeSuSFTS) consists of four main phases; data pre-processing, model development, model implementation and model evaluation. …”
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Hybrid optimization approach to estimate random demand by Wahab, Musa, Ku-Mahamud, Ku Ruhana, Yasin, Azman

“…The main objective of this study is to develop a demand forecasting model that should reflect the characteristics of random demand patterns.To accomplish this goal, a hybrid algorithm combining a genetic algorithm and a local search algorithm method was developed to overcome premature convergence in local optima problems.The performance of the hybrid algorithm was compared with a single algorithm model in estimating parameter values that minimize objective function which was used to measure the goodness-of-fit between the observed data and simulated results.However, two problems had to be overcome in the forecasting random demand model. …”
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Weighted subsethood and reasoning based fuzzy time series for moving holiday electricity load demand forecasting by Rosnalini, Mansor

“…The modified algorithm, Weighted Subsethood Segmented Fuzzy Time Series (WeSuSFTS) consists of four main phases; data pre-processing, model development, model implementation and model evaluation. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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Box-jenkins and genetic algorithm hybrid model for electricity forecasting system by Mahpol, Khairil Asmani

“…In this thesis, an approach that combines the Box-Jenkins methodology for SARIMA model and Genetic Algorithm (GA) will been introduced as a new approach in making a forecast. …”
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Development Of An Algorithm To Reduce The Topographical Effects In Reflected Radiance by Yeap, Eng Choo

“…To address this problem, we developed algorithms that quantify, reduce, and induce topographical effects in satellite images by exploring the relationship between direct and diffuse solar irradiance. …”
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Laptop price prediction using decision tree algorithm / Nurnazifah Abd Mokti by Abd Mokti, Nurnazifah

“…This research project focuses on developing a laptop price prediction model using the decision tree algorithm based on laptop specifications. …”
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Ant colony optimization and genetic algorithm models for suspended sediment discharge estimation for gorgan-river, Iran by Mohammad Reza Pour, Omolbani

“…Therefore, it is still necessary to develop the model for the discharge-sediment relationship. …”
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Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score by Mirza Rizwan, Sajid

“…In developing risk prediction models, two ML algorithms, linear support vector machine and artificial neural network outperformed the existing conventional logistic regression analysis (LRA) model. …”
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Neural network for rainfall runoff modelling by Hafiz Fadillah, Alhadi

“…So, the purpose of this study is to develop a rainfall runoff model for Sungai Tinjar with outlet at Long Jegan, The network was trained using Back Propagation Algorithm.…”
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Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization by Lee, Jesee Kar Ming

“…Integration of global search algorithm into ANN model further improved the model performance. …”
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Software for modelling the static and dynamic flux linkage - current characteristics of A 6/4 SRM by Hashim, Mohd Nasir

“…Program in C language was developed to validate the algorithm provided by Torrey [5,7].…”
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Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy by Hossain, Mohammad Ishtiyaq, Amin, A. K. M. Nurul, Patwari, Anayet Ullah

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Perovskite lattice constant prediction framework using optimized artificial neural network and fuzzy logic models by metaheuristic algorithms by Bouzateur, Inas, Ouali, Mohammed Assam, Bennacer, Hamza, Ladjal, Mohamed, Khmaissia, Fadoua, Rahman, Mohd Amiruddin Abd, Boukortt, Abdelkader

“…Although the PSO-Fuzzy model has the best performance of all the compared models, the developed PSO-ANN based model possesses the advantage of easy implementation in addition to its moderate performance.…”
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Modified spectral clustering algorithm for semisupervised face annotation modeling by Sheng, Gao You

“…The research streamlines annotation, reduces manual work, boosts facial recognition performance (98.97% purity), and contributes to computer vision and AI with efficient large-scale face annotation solutions, developing two models.…”
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Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids by Chiaverini, Luca, Macdonald, David W., Hearn, Andrew J., Kaszta, Zaneta, Ash, Eric, Bothwell, Helen M., Can, Ozgun Emre, Channa, Phan, Clements, Gopalasamy Reuben *, Haidir, Iding Achmad, Kyaw, Pyae Phyoe, Moore, Jonathan H., Rasphone, Akchousanh, Tan, Cedric Kai Wei, Cushman, Samuel A.

“…Two of the most frequently applied algorithms to model species-habitat relationships are Generalised Linear Models (GLM) and Random Forest (RF). …”
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Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm by Nurin Alya, Haris

“…This study aims to address this issue by developing a reliable prediction model to identify potential cheaters in PUBG matches. …”
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