Search Results - (( waste prediction using algorithm ) OR ( using optimization based algorithm ))*

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.

Smart Routing For Solid Waste Collection by Ngiam, John Tze

“…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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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

“…Therefore, by using anaerobic co-digestion technology, food waste (FW) can be used as a substrate with sewage sludge (SS) to produce a valuable product such as methane gas. …”
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Automatic control of flotation process using computer vision by Saravani, Ali Jahed

“…The performance of improved marker based watershed algorithm was validated by using several industrial and laboratory froth images. …”
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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.

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

“…Practical implications In terms of managerial implications, the findings in this research help to frame the adoption of a more advanced analytical approach to forecasting, using a Machine Learning algorithm, in solving a newsvendor problem. …”
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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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Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms by Ayodele B.V., Mustapa S.I., Kanthasamy R., Zwawi M., Cheng C.K.

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

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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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Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption by Zamani, Seyed Ali

“…The incremental back propagation algorithm demonstrated the best results and which has been used as learning algorithm for ANN in combination with Genetic Algorithm in the optimization. …”
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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

“…In addition, CNPs synthesis was modeled by using artificial neural networks (ANN). To find the optimum model, ANN was trained by using different algorithms. …”
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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

“…This study has proven that AI-based models have better prediction abilities when compared to conventional methods used in forecasting solid waste generation and recycling.…”
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Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network by Mohamad Afiq, Mohd Asrul

“…Model validity describes the first sub-objective, which is to solve the complexity of the nonlinear interaction of multiple MEC input variables related to the hydrogen production rate response using artificial neural networks (ANN) before validating the mathematical modeling results by comparing experimental data with the predicted substrate concentration profile and hydrogen production rate profile based on the re-estimated input values of the model parameters using single-objective optimization based on the nonlinear convex method using gradient descent algorithm. …”
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Artificial Neural Network Optimization Modeling On Engine Performance Of Diesel Engine Using Biodiesel Fuel by M. M., Rahman, D., Ramasamy, K., Kadirgama, M. R., Shukri

“…An ANN model was developed based on the Levenberg-Marquardt algorithm for the engine. …”
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The predictive machine learning model of a hydrated inverse vulcanized copolymer for effective mercury sequestration from wastewater by Ghumman, A.S.M., Shamsuddin, R., Abbasi, A., Ahmad, M., Yoshida, Y., Sami, A., Almohamadi, H.

“…NMDG functionalized IVP removed 100 Hg2+ from a low feed concentration (10â��50 mg/l). A predictive machine learning model was also developed to predict the amount of mercury removed () using GPR, ANN, Decision Tree, and SVM algorithms. …”
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Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging by M. Altharan, Yahya, Shamsudin, Shazarel, Lajis, Mohd Amri, Al-Alim, Sami, Yusuf, Nur Kamilah, Mohammed Alduais, Nayef Abdulwahab, M. Ghaleb, Atef, Zhou, Wenbin

“…This work was carried out to study the application of hot press forging (HPF) in recycling AA6061 aluminum chip waste, aiming to optimize operating factors using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Genetic algorithm (GA) strategy to maximize the strength of recycled parts. …”
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Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging by M. Altharan, Yahya, Shamsudin, Shazarel, Lajis, Mohd Amri, Al-Alimi, Sami, Yusuf, Nur Kamilah, M. Ghaleb, Atef, Zhou, Wenbin

“…This work was carried out to study the application of hot press forging (HPF) in recycling AA6061 aluminum chip waste, aiming to optimize operating factors using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Genetic algorithm (GA) strategy to maximize the strength of recycled parts. …”
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Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging by M. Altharan, Yahya, Shamsudin, Shazarel, Lajis, Mohd Amri, Sami Al-Alimi, Sami Al-Alimi, Yusuf, Nur Kamilah Y, Mohammed Alduais, Nayef Abdulwahab, Atef M. Ghaleb, Atef M. Ghaleb, Wenbin Zhou, Wenbin Zhou

“…This work was carried out to study the application of hot press forging (HPF) in recycling AA6061 aluminum chip waste, aiming to optimize operating factors using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Genetic algorithm (GA) strategy to maximize the strength of recycled parts. …”
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Artificial Neural Network-Forecasted Compression Strength of Alkaline-Activated Slag Concretes by Yi, Xuan Tang, Yeong, Huei Lee, Mugahed, Amran, Roman, Fediuk, Nikolai, Vatin, Beng, Ahmad Hong Kueh, Yee, Yong Lee

“…The prediction accuracy of the optimal ANN model was then compared to existing ANN-based models, while the variable selection was compared to existing AASC models with other machine learning algorithms, due to limitations in the ANN-based model. …”
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