Search Results - (( java adaptation optimization algorithm ) OR ( risk selection method algorithm ))

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

Optimisation of Environmental Risk Assessment Architecture using Artificial Intelligence Techniques by Salem S. M. Khalifa

“…Two methods were used to validate the proposed architecture, that is, the analytical method was used to validate the neuro-fuzzy risk assessment model and the safe path selection model, whereas the experimental method was used to evaluate the prototype. …”

The significant effect of feature selection methods in spam risk assessment using dendritic cell algorithm by Zainal K., Jali M.Z.

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The Significant Effect of Feature Selection Methods in Spam Risk Assessment Using Dendritic Cell Algorithm by Zainal, K, Jali, MZ

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Multi-Objective Portfolio Optimization Strategy using the SPEA-II Algorithm by Azarberahman, Alireza, Tohidinia, Malihe, Aliakbarzadeh, Hossein

“…The study highlights that the SPEA-II algorithm can serve as an effective and efficient method for stock portfolio selection and optimization, helping investors to identify portfolios with lower risk and higher return…”
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Modified Kohonen network algorithm for selection of the initial centres of Gustafson-Kessel algorithm in credit scoring by Sameer, Fadhaa Othman, Abu Bakar, Mohd Rizam

“…A good clustering analysis implemented by good Initial Centres of clusters should be selected. To overcome this problem of Gustafson-Kessel (GK) algorithm, we proposed a modified version of Kohonen Network (KN) algorithm to select the initial centres. …”
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Classification Algorithms and Feature Selection Techniques for a Hybrid Diabetes Detection System by Al-Hameli, Bassam Abdo, Alsewari, Abdulrahman A., Alraddadi, Abdulaziz Saleh, Aldhaqm, Arafat

“…Several combinations of Harmony search algorithm, genetic algorithm, and particle swarm optimization algorithm are examined with K-means for feature selection. …”
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A multi-objective portfolio selection model with fuzzy Value-at-Risk ratio by Wang, B., Li, Y., Wang, S., Watada, J.

“…Considering nonstatistical uncertainties and/or insufficient historical data in security return forecasts, fuzzy set theory has been applied in the past decades to build portfolio selection models. Meanwhile, various risk measurements such as variance, entropy and Value-at-Risk have been proposed in fuzzy environments to evaluate investment risks from different perspectives. …”
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A multi-objective portfolio selection model with fuzzy Value-at-Risk ratio by Wang, B., Li, Y., Wang, S., Watada, J.

“…Considering nonstatistical uncertainties and/or insufficient historical data in security return forecasts, fuzzy set theory has been applied in the past decades to build portfolio selection models. Meanwhile, various risk measurements such as variance, entropy and Value-at-Risk have been proposed in fuzzy environments to evaluate investment risks from different perspectives. …”
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A hybrid particle swarm optimization approach and its application to solving portfolio selection problems by Shamshul Bahar, Yaakob, Prof. Madya, Watada, Junzo

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The use of Cox regression and genetic algorithm (CoRGA) for identifying risk factors for mortality in older people by Ahmad, Rabiah, Bath, Peter A

“…This article describes ongoing research to overcome these limitations through the CoRGA program, which combines Cox regression with a genetic algorithm for the variable selection process. CoRGA was used to try and identify the best combination of risk factors for 4-year all-cause mortality. …”
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Scaled Conjugate Gradient using strong Wolfe line search for portfolio selection / Nurfatihah Anizan by Anizan, Nurfatihah

“…This study demonstrates the SLAMR method's effectiveness in solving portfolio selection problems.…”
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Fuzzy clustering method and evaluation based on multi criteria decision making technique by Sameer, Fadhaa Othman

“…The proposed algorithm is used as a pre-processing method for data followed by Gustafson-Kessel (GK) algorithm to classify credit scoring data. …”
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A Risk Assessment of Transmission Line Overload Based on MLSI/PSO by Ruhaizad, Ishak, Ali, A., Nazir, Muhammad S., Malik, Muhammad Z.

“…Based on the traditional partial swarm optimization algorithm, the corresponding weights are selected according to the influence factors of each input quantity, and the calculation accuracy of the traditional point estimation method is improved to realize the overload risk assessment of transmission lines. …”
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Data mining techniques for disease risk prediction model: A systematic literature review by Ahmad W.M.T.W., Ghani N.L.A., Drus S.M.

Poverty risk prediction based on socioeconomic factors using machine learning approach by Mohd Zawari, Nur Farhana Adibah

“…The process of classifying individuals or households that may be at risk of poverty can be time consuming and less accurate in relation to traditional methods like Poverty Line Income (PLI). …”
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An enhanced supplier selection model based on optimized analytic network process towards sustainable information technology outsourcing by Fusiripong, Prashaya

“…However, existing ITO supplier selection models lack of sustainability criteria and risk factors. …”
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Development of a hybrid AHP and Dempster-Shafer theory of evidence for project risk assessment problem by Albogami, Saad M., Mohd Ariffin, Mohd Khairol Anuar, Supeni, Eris Elianddy, Ahmad, Kamarul Arifin

“…It was observed that the method could be safely used for selecting project problems in real industries.…”
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Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score by Mirza Rizwan, Sajid

“…Further, it focuses on the development of various forms of local risk prediction models and simple heart risk scores using non-laboratory features and machine learning (ML) algorithms. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning by Muslim, Much Aziz, Nikmah, Tiara Lailatul, Agustina Pertiwi, Dwika Ananda, Subhan, Subhan, Jumanto, Jumanto, Dasril, Yosza, swanto, Iswanto

“…The SMOTE method is used to balance the data, the feature selection LightGBM and stacking ensemble learning (LGBFS-StackingXGBoost) to optimize machine learning accuracy. …”
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