Search Results - (( waste ((selection algorithm) OR (optimization algorithm)) ) OR ( waste prediction algorithm ))

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  1. 1

    Wind power forecasting with metaheuristic-based feature selection and neural networks by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Mohd Mawardi, Saari, Mohammad Fadhil, Abas

    Published 2024
    “…The results show that the GA consistently outperforms other algorithms in selecting the most influential features, leading to improved precision in wind power predictions. …”
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    Article
  2. 2

    Efficient Model for Waste Load and Route Optimization by Achmad, Nopransyah, Tri Basuki, Kurniawan, Misinem, ., Muhammad Izman, Herdiansyah, Edi Surya, Negara

    Published 2024
    “…The model utilizes machine learning techniques to forecast the quantity of waste collected by GPTs. We have created an optimization algorithm that usesthe forecast outcome from a prior research dataset. …”
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    Article
  3. 3

    Multivariable optimization of carbon nanoparticles synthesized from waste facial tissues by artificial neural networks, new material for downstream quenching of quantum dots by Shojaei, Taha Roodbar, Mohd Salleh, Mohamad Amran, Mobli, Hossein, Aghbashlo, Mortaza, Tabatabaei, Meisam

    Published 2019
    “…To find the optimum model, ANN was trained by using different algorithms. Then, the generated models were statistically assessed and subsequently, the capability of the selected model for predicting the mean diameter size of the nanoparticles was verified. …”
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  4. 4

    Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management by almahameed, Bader aldeen, Bisharah, Majdi

    Published 2024
    “…The Voting regression, which leverages the collective predictive power of multiple models, exhibits superior performance in comparison to individual algorithms. …”
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  5. 5

    Smart Routing For Solid Waste Collection by Ngiam, John Tze

    Published 2023
    “…The proposed solution is to implement a route optimization algorithm to predict the probability of each feasible route for the garbage truck collection. …”
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    Final Year Project Report / IMRAD
  6. 6

    Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations by Hossain, S.K.S., Ayodele, B.V., Alhulaybi, Z.A., Alwi, M.M.A.

    Published 2022
    “…Similarly, the model performance was also influenced by the nature of the optimization algorithms. The MLPNN models displayed better predictive performance compared to the RBFNN models. …”
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    Article
  7. 7

    Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components by Liang G., Panahi F., Ahmed A.N., Ehteram M., Band S.S., Elshafie A.

    Published 2023
    “…Forecasting; Genetic algorithms; Health risks; Mean square error; Model structures; Municipal solid waste; Particle swarm optimization (PSO); 'current; Fuzzy reasoning; Inclusive multiple model; Multiple-modeling; Neural-networks; Optimization algorithms; Particle swarm; Sine-cosine algorithm; Solid waste generation; Swarm optimization; Neural networks…”
    Article
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  9. 9

    Optimization of food waste to sewage sludge ratio for anaerobic co-digestion process using Artificial Neural Network (ANN) and Genetic Algorithm (GA) by Mansor, Mariatul Fadzillah, Jamaludin, Nurul Syazwana, Tajuddin, Husna Ahmad

    Published 2021
    “…In order to find the optimal ratio of FW to SS as well as substrate-to-inoculum (SI) ratio for the highest methane production, the present study utilizes the Artificial Neural Network (ANN) and Genetic Algorithm (GA) model. …”
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    Article
  10. 10

    A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant by Chun T.S., Malek M.A., Ismail A.R.

    Published 2023
    “…Algorithms; Artificial intelligence; Biochemical oxygen demand; Bioinformatics; Developing countries; Effluent treatment; Effluents; Forecasting; Least squares approximations; Oxygen; Pattern recognition; Support vector machines; Water quality; Biological oxygen demand; Clonal selection algorithms; Least-square support vector machines; Sludge treatment plants; Total suspended solids; Chemical oxygen demand; oxygen; sewage; algorithm; clone; comparative study; effluent; least squares method; nonlinearity; pattern recognition; simulation; sludge; water treatment; activated sludge; algorithm; Article; biochemical oxygen demand; chemical oxygen demand; clonal selection algorithm; comparative study; computer simulation; effluent; forecasting; pattern recognition; prediction; regression analysis; septic sludge treatment plant; sludge treatment; statistical model; support vector machine; suspended particulate matter; waste water treatment plant; chemistry; procedures; sewage; theoretical model; Algorithms; Biological Oxygen Demand Analysis; Forecasting; Least-Squares Analysis; Models, Theoretical; Sewage; Support Vector Machines; Waste Disposal, Fluid…”
    Article
  11. 11

    Biochar production from valorization of agricultural Wastes: Data-Driven modelling using Machine learning algorithms by Kanthasamy, R., Almatrafi, E., Ali, I., Hussain Sait, H., Zwawi, M., Abnisa, F., Choe Peng, L., Victor Ayodele, B.

    Published 2023
    “…The artificial neural network-based algorithms outperformed the SVM and GPR as indicated by the R2 > 0.9 and low predictive errors. …”
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  12. 12
  13. 13

    Smart waste management system with IoT monitoring by Kalitazan, Sachein, Muniswaran, Suvarshan, Velan, Sheshan, Muniswaran, Suvathithan, Shah, Dhanesh

    Published 2023
    “…Through advanced data analytics and machine learning algorithms, the platform predicts waste accumulation patterns, optimizes collection routes, schedules pickups based on fill-level data, and detects any abnormal conditions. …”
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  14. 14

    Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach by Elpina, Sari, Tri Basuki, Kurniawan

    Published 2023
    “…This research compares 3 algorithms for predicting the amount of waste trapped by GPT: Simple Linear Regression, Multiple Linear Regression, and Polynomial Regression. …”
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  15. 15

    Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill by Abunama, Taher, Othman, Faridah, Ansari, Mozafar, El-Shafie, Ahmed

    Published 2019
    “…In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. …”
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    A review of Artificial Intelligence application of sustainable solid waste management practices in Western Asia by Nagimeldin, Olla, Ahmad Tajuddin, Husna, Jami, Mohammed Saedi

    Published 2022
    “…Over the past few years, Machine-learning algorithms and Artificial intelligence models have demonstrated great ability to optimize and automate critical solid waste and waste management complications. …”
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    Proceeding Paper
  18. 18

    Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

    Published 2023
    “…This research attempts to solve the problem statement on how to forecast the daily optimal quantity of a perishable product during the new norm post-pandemic, ensuring minimal unsold items are discarded as waste. …”
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    Book Section
  19. 19

    Bayesian optimized multilayer perceptron neural network modelling of biochar and syngas production from pyrolysis of biomass-derived wastes by Kanthasamy, R., Almatrafi, E., Ali, I., Hussain Sait, H., Zwawi, M., Abnisa, F., Choe Peng, L., Victor Ayodele, B.

    Published 2023
    “…This study employs Bayesian optimized multilayer perceptron neural network for modelling the prediction of biochar and syngas from pyrolysis of biomass-derived wastes. …”
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    Article
  20. 20

    PREDICTIVE MODELING OF DIMENSIONAL ACCURACIES IN 3D PRINTING USING ARTIFICIAL NEURAL NETWORK by Sivaraos, Kumaran K., Dharsyanth R., Amran M., Shukor S.M., Pujari S., Ramasamy D., Vatesh U.K., Mahdi Al-Obaidi A.S.H., Ramesh S., Lee K.Y.S.

    Published 2024
    “…The ANN model was developed using MATLAB software, employing training functions and learning algorithms to optimize the neural network architecture. …”
    Article