Search Results - (( waste reduction algorithm ) OR ( waste ((prediction algorithm) OR (evolutionary algorithm)) ))

Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater by Khan M.S.J., Sidek L.M., Kumar P., Alkhadher S.A.A., Basri H., Zawawi M.H., El-Shafie A., Ahmed A.N.

Subjects: “…Algorithms…”

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

“…The Voting regression, which leverages the collective predictive power of multiple models, exhibits superior performance in comparison to individual algorithms. …”
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Wind power forecasting with metaheuristic-based feature selection and neural networks by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Mohd Mawardi, Saari, Mohammad Fadhil, Abas

“…Specifically, five distinct algorithms - Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), and Evolutionary Mating Algorithm (EMA) - are integrated with NN model to identify optimal feature subsets from a comprehensive dataset of 18 diverse features. …”
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The classification of FTIR plastic bag spectra via label spreading and stacking by Almanifi, Omair Rashed Abdulwareth, Ng, Jee Kwan, Anwar P. P., Abdul Majeed

“…Four pipelines were investigated, consisting of two machine learning algorithms, a stacked model that stacks the KNN, SVM and RF algorithms together, and Label spreading, as well as two different dimensionality reduction methods namely; SVD and UMAP. …”
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Customised fitness function of evolutionary algorithm for optimization of car park space / Ahmad Syahir Ab. Aziz by Ab. Aziz, Ahmad Syahir

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Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia by Latif S.D., Hazrin N.A.B., Younes M.K., Ahmed A.N., Elshafie A.

“…This study managed to fill in the gap of using GPR for predicting municipal solid waste generation. The outcome of this study could be of direct interest to public and private solid waste management companies in order to effectively manage solid waste through predicting the municipal solid waste generation accurately. ? …”

Integrated immune-commensal-evolutionary programming for economic dispatch and distributed generation installation / Mohd Helmi Mansor by Mansor, Mohd Helmi

“…ICEP is developed from the hybridization of three independent optimization techniques, namely Symbiotic Organisms Search (SOS), Artificial Immune System (AIS) and Evolutionary Programming (EP). EP is the leading optimizer of the hybrid algorithm while cloning operator of AIS and commensal operator of SOS are adopted into the EP algorithm to improve its performance. …”
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Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization by Akhtar M., Hannan M.A., Begum R.A., Basri H., Scavino E.

“…Bins; Budget control; Carbon dioxide; Cost reduction; Costs; Crashworthiness; Efficiency; Environmental impact; Fuels; Learning algorithms; Optimization; Solid wastes; Vehicle routing; Backtracking search algorithms; Capacitated vehicle routing problem; Environmental emissions; Optimization modeling; Route optimization; Solid waste collection; Threshold waste level; Waste collection; Waste management; fuel; carbon dioxide; solid waste; algorithm; fuel consumption; modeling; optimization; solid waste; threshold; waste management; algorithm; Article; backtracking search algorithm; capacitated vehicle routing problem model; carbon footprint; controlled study; environmental impact assessment; feasibility study; limit of quantitation; measurement precision; process model; process optimization; solid waste collection; solid waste management; motor vehicle; procedures; solid waste; theoretical model; waste disposal; Algorithms; Carbon Dioxide; Models, Theoretical; Motor Vehicles; Refuse Disposal; Solid Waste…”

Design of smart waste bin and prediction algorithm for waste management in household area by Yusoff, Siti Hajar, Abdullah Din, Ummi Nur Kamilah, Mansor, Hasmah, Midi, Nur Shahida, Zaini, Syasya Azra

“…This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on household size factor. …”
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Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm by Fakharudin, Abdul Sahli

“…This study utilised the evolutionary artificial neural network (EANN) modelling to improve the model accuracy. …”
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Capacitated vehicle-routing problem model for scheduled solid waste collection and route optimization using PSO algorithm by Hannan M.A., Akhtar M., Begum R.A., Basri H., Hussain A., Scavino E.

Optimising a waste management system using the Artificial Bee Colony (ABC) algorithm by Mohamad Fadzil, Nur Hamisha Helanie

“…This study proposed the application of the Artificial Bee Colony (ABC) algorithm to address the Capacitated Vehicle Routing Problem (CVRP) in a real-world waste collection scenario. …”
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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.

ESS-IoT: The Smart Waste Management System for General Household by Wong S.Y., Han H., Cheng K.M., Koo A.C., Yussof S.

“…On the other hand, the waste classification is implemented using two classification algorithms: Random Forest (RF) prediction model and Convolutional Neural Network (CNN) prediction model. …”

Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed... by Ayodele, B.V., Mustapa, S.I., Kanthasamy, R., Mohammad, N., AlTurki, A., Babu, T.S.

“…Whereas the best performance in terms of prediction of the syngas composition was obtained using the NLRQM algorithm with an inbuilt SQP and LM algorithms. …”
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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.

“…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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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.

“…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…”

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.

“…All the input variables significantly influence the predicted biohydrogen. However, waste glycerol has the most significant effects. …”
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Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed... by Ayodele, B.V., Mustapa, S.I., Kanthasamy, R., Mohammad, N., AlTurki, A., Babu, T.S.

“…Whereas the best performance in terms of prediction of the syngas composition was obtained using the NLRQM algorithm with an inbuilt SQP and LM algorithms. …”
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Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach by Elpina, Sari, Tri Basuki, Kurniawan

“…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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