Search Results - (( using optimization isotherm algorithm ) OR ( data implication optimisation algorithm ))

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.

“…Our results have strong implications for SDM development, revealing the inconsistencies introduced by the choice of algorithm on scale optimisation, with GLM selecting broader scales than RF.…”
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A gauss-newton approach for nonlinear optimal control problem with model-reality differences by Sie, Long Kek, Jiao, Li, Leong, Wah June, Abd Aziz, Mohd Ismail

“…By feed backing the updated control trajectory into the dynamic system, the iterative solution of the model used could approximate to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. …”
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Smart site monitoring system / Muhammad Azmi and Muhammad Naim Mahyuddin by Azmi, Muhammad, Mahyuddin, Muhammad Naim

“…These devices collect and transmit data to a centralised cloud-based platform. Advanced data analytics and machine learning algorithms process this data, enabling the system to detect potential safety hazards, monitor construction progress, and predict resource requirements. …”
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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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Global warming: predicting OPEC carbon dioxide emissions from petroleum consumption using neural network and hybrid cuckoo search algorithm by Haruna, Chiroma, Abdul-Kareem, Sameem, Mohd Nawi, Nazri, Gital, Abdulsam Ya'u, Shuib, Liyana, Abubakar, Adamu, Rahman, Muhammad Zubair, Herawan, Tutut

“…To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO2 emissions. …”
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Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer by Temitope T., Dele-Afolabi, Masoud, Ahmadipour, Mohamed Ariff, Azmah Hanim, A.A., Oyekanmi, M.N.M., Ansari, Sikiru, Surajudeen, Kumar, Niraj

“…An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. …”
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Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer by Dele-Afolabi T.T., Ahmadipour M., Azmah Hanim M.A., Oyekanmi A.A., Ansari M.N.M., Sikiru S., Kumar N.

“…An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. …”

Effective adsorption of metolachlor herbicide by MIL-53(Al) metal-organic framework: Optimization, validation and molecular docking simulation studies by Ahmad Isiyaka, H., Jumbri, K., Soraya Sambudi, N., Uba Zango, Z., Ain Fathihah Binti Abdullah, N., Saad, B.

“…Effective removal and optimization models of metolachlor (MET) adsorption was carried out using MIL-53(Al) metalâ��organic framework (MOF), response surface methodology (RSM), artificial neural network (ANN) and molecular docking simulation. …”
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Effective adsorption of metolachlor herbicide by MIL-53(Al) metal-organic framework: Optimization, validation and molecular docking simulation studies by Ahmad Isiyaka, H., Jumbri, K., Soraya Sambudi, N., Uba Zango, Z., Ain Fathihah Binti Abdullah, N., Saad, B.

“…Effective removal and optimization models of metolachlor (MET) adsorption was carried out using MIL-53(Al) metalâ��organic framework (MOF), response surface methodology (RSM), artificial neural network (ANN) and molecular docking simulation. …”
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Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN) by Khan T., Manan T.S.B., Isa M.H., Ghanim A.A.J., Beddu S., Jusoh H., Iqbal M.S., Ayele G.T., Jami M.S.

Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework by Isiyaka, H.A., Jumbri, K., Sambudi, N.S., Zango, Z.U., Abdullah, N.A.F.B., Saad, B.

“…Adsorption performance, multivariate interaction mechanism and sensitivity analysis for the removal of the herbicide metolachlor (MET) by MIL-101(Cr) metal organic framework (MOF) are investigated using experimental, optimization models and computational technique. …”
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Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework by Isiyaka, H.A., Jumbri, K., Sambudi, N.S., Zango, Z.U., Abdullah, N.A.F.B., Saad, B.

“…Adsorption performance, multivariate interaction mechanism and sensitivity analysis for the removal of the herbicide metolachlor (MET) by MIL-101(Cr) metal organic framework (MOF) are investigated using experimental, optimization models and computational technique. …”
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Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework by Isiyaka, H.A., Jumbri, K., Sambudi, N.S., Zango, Z.U., Abdullah, N.A.F.B., Saad, B.

“…Adsorption performance, multivariate interaction mechanism and sensitivity analysis for the removal of the herbicide metolachlor (MET) by MIL-101(Cr) metal organic framework (MOF) are investigated using experimental, optimization models and computational technique. …”
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Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN) by Khan, T., Manan, T.S.B., Isa, M.H., Ghanim, A.A.J., Beddu, S., Jusoh, H., Iqbal, M.S., Ayele, G.T., Jami, M.S.

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Modelling and simulation of hollow profile aluminium extruded product by Sulaiman, Shamsuddin, Baharudin, B. T. Hang Tuah, Mohd Ariffin, Mohd Khairol Anuar, Magid, Hani Mizhir

“…This process is an isothermal process with an extrusion ratio of 3.3. Subsequently, the optimized algorithm for these extrusion parameters was suggested based on the simulation results. …”
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Modeling and simulation of forward Al extrusion process using FEM by Magid, Hani Mizhir, Sulaiman, Shamsuddin, Mohd Ariffin, Mohd Khairol Anuar, Baharudin, B. T. Hang Tuah

“…Optimized algorithms for extrusion parameters were proposed regarding the concluded simulating results. …”
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Framework for pedestrian walking behaviour recognition to minimize road accident by Hashim Kareem, Zahraa

“…Two sets of real-time data are presented in this work. The first one is related to the questionnaire data, consisting of 262 respondent samples, while the second set has 263 samples of pedestrian walking signals. …”
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Framework for pedestrian walking behaviour recognition to minimize road accident by Hashim Kareem, Zahraa

“…Two sets of real-time data are presented in this work. The first one is related to the questionnaire data, consisting of 262 respondent samples, while the second set has 263 samples of pedestrian walking signals. …”
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Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann) by Khan, Taimur, Abd Manan, Teh Sabariah, Hasnain Isa, Mohamed, A. J. Ghanim, Abdulnoor, Beddu, Salmia, Jusoh, Hisyam, Iqbal, Muhammad Shahid, Ayele, Gebiaw T, Jami, Mohammed Saedi

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Design, optimization, fabrication and analysis of Cu microheater for loop-mediated isothermal amplification (LAMP) applications by Narayanamurthy, Vigneswaran, Samsuri, Fahmi, Jeroish, Z. E., Shankar, Abhishek Futane, Shanmugavel, Bhuvaneshwari Kumaravel

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