Search Results - (( variable metal process algorithm ) OR ( java application optimisation algorithm ))

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  1. 1
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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  2. 2
    Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach / Seef Saadi Fiyadh

    Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach / Seef Saadi Fiyadh by Seef Saadi , Fiyadh

    Published 2019
    “…The selected DESs were used as CNTs functionalization agents to form novel adsorbents for mercury ions (Hg2+), lead ions (Pb2+) and arsenic ions (As3+) removal from water. A screening process was conducted for the removal of the selected heavy metal using the adsorption process. …”
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  3. 3
    Web-based expert system for material selection of natural fiber- reinforced polymer composites

    Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

    Published 2015
    “…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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  4. 4
    Review on computational strategies for bi-layered metal powder compaction

    Review on computational strategies for bi-layered metal powder compaction by Mohd Tahir, Suraya, Mohd Yusoff, Syamimi, Mohamed Ariff, Azmah Hanim, Supeni, Eris Elliandy, Anuar, Mohd Shamsul

    Published 2024
    “…Throughout this review, many researches exhibited great success in validating modeling work to their experimental findings concerning on metal-metal, metal-ceramic and metal-composites bilayers. …”
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  5. 5
    Optimization of process parameters for rapid adsorption of Pb(II), Ni(II), and Cu(II) by magnetic/talc nanocomposite using wavelet neural network

    Optimization of process parameters for rapid adsorption of Pb(II), Ni(II), and Cu(II) by magnetic/talc nanocomposite using wavelet neural network by Darajeh, Negisa, Masoumi, Hamid Reza Fard, Kalantari, Katayoon, Ahmad @ Ayob, Mansor, Shameli, Kamyar, Basri, Mahiran, Khandanlou, Roshanak

    Published 2016
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  6. 6
    Embedded mini-analyzer device for in situ wide range heavy metal identification and concentration detection

    Embedded mini-analyzer device for in situ wide range heavy metal identification and concentration detection by Mazaheri, Amin

    Published 2018
    “…Metal toxicity is a critical concern in both human health and ecosystem. …”
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  7. 7
    Optimisation of the squeeze forming process

    Optimisation of the squeeze forming process by Ahmad, Rosli

    Published 2006
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  8. 8
    Neural Network Multi Layer Perceptron Modeling For Surface Quality Prediction in Laser Machining

    Neural Network Multi Layer Perceptron Modeling For Surface Quality Prediction in Laser Machining by Sivarao, Subramonian

    Published 2009
    “…We considered several real life machining scenarios with some expert knowledge input and machine technology features. The input variables are the design parameters which have been selected after a critical parametric investigation of 14 process parameters available on the machine. …”
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  9. 9
    A stacked ensemble deep learning model for water quality prediction / Wong Wen Yee

    A stacked ensemble deep learning model for water quality prediction / Wong Wen Yee by Wong , Wen Yee

    Published 2023
    “…The proposed deep learning model renders faster without the use of SMOTE. Any resampling algorithm is not a necessity in the case of this proposed algorithm. …”
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  10. 10
    Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption

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

    Published 2015
    “…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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  11. 11
    Application of response surface methodology coupled with genetic algorithm in the optimization of cutting conditions for surface roughness in end milling of Inconel 718 using coated WC-Co inserts

    Application of response surface methodology coupled with genetic algorithm in the optimization of cutting conditions for surface roughness in end milling of Inconel 718 using coate... by Ishtiyaq, M. H., Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

    Published 2011
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  12. 12
    Comparison of surface roughness in end milling of titanium alloy Ti-6Al-4V using uncoated WC-Co and PCD inserts through generation of models

    Comparison of surface roughness in end milling of titanium alloy Ti-6Al-4V using uncoated WC-Co and PCD inserts through generation of models by Ginta, Turnad Lenggo, Amin, A. K. M. Nurul, Ishtiyaq, M. H.

    Published 2011
    “…A machinability model may be defined as a functional relationship between the input of independent cutting variables (speed, feed, depth of cut) and the output known as responses (tool life, surface roughness, cutting force, etc) of a machining process [4]. …”
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  13. 13
    Development and optimization of Bi-GFET using Taguchi-based grey relational analysis with artificial neural network

    Development and optimization of Bi-GFET using Taguchi-based grey relational analysis with artificial neural network by Mohd Nizam, Nur Hazwani Naili

    Published 2024
    “…The full potential of the Taguchi method as a tool for optimizing the performance of processes with a wide range of input variables has been realized. …”
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