Search Results - (( java segmentation using algorithm ) OR ( waste estimation learning algorithm ))

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  1. 1
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…Finally, the algorithm found, which would solve the image segmentation problem.…”
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  2. 2
    Automatic Number Plate Recognition on android platform: With some Java code excerpts

    Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2016
    “…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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  3. 3
    Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater

    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.

    Published 2025
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  4. 4
    Development of seven segment display recognition using TensorFlow on Raspberry Pi

    Development of seven segment display recognition using TensorFlow on Raspberry Pi by Gunawan, Teddy Surya, Mohammad Reduwan, Haque, Yaacob, Mashkuri, Mohamed Azmin, Nor Fadhillah, Ismail, Nanang, Ismail, Md Yusof, Kartiwi, Mira, A. Rahman, Azhar

    Published 2022
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  5. 5
    DeMI interface tool for profit estimation and waste conversion technology recommendations in enhancing municipal solid waste management

    DeMI interface tool for profit estimation and waste conversion technology recommendations in enhancing municipal solid waste management by Ali, R.A., Nik Ibrahim, N.N.L., Ghani, W.A.W.A.K., Sani, N.S., Lam, H.L.

    Published 2024
    “…The M5P algorithm, adept at profit estimation, establishes correlations between MSW weight and profitability, while the J48 algorithm offers recommendations for suitable waste conversion technologies based on profit potential. …”
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  6. 6
    Efficient Model for Waste Load and Route Optimization

    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. …”
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  7. 7
    Prediction of biochemical oxygen demand in Mexican surface waters using machine learning / Maximiliano Guzmán-Fernández ... [et al.]

    Prediction of biochemical oxygen demand in Mexican surface waters using machine learning / Maximiliano Guzmán-Fernández ... [et al.] by Maximiliano, Guzmán-Fernández, Misael, Zambrano-de la Torre, Claudia, Sifuentes-Gallardo, Oscar, Cruz-Dominguez, Carlos, Bautista-Capetillo, Juan, Badillo-de Loera, Efrén, González Ramírez, Héctor, Durán-Muñoz

    Published 2021
    “…Two groups were formed and used as input to four machine learning algorithms. Random forest algorithm obtained the best performance. …”
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  8. 8
    Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management

    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
    “…Particle Swarm Optimization (PSO) has demonstrated its efficacy in addressing the issue of construction waste reduction and enhancing the accuracy of cost estimation through the identification of optimal combinations of variables. …”
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  9. 9
    Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar

    Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar by Abd Ghafar, Muhamad Azfar

    Published 2015
    “…The SLFN is trained by an algorithm named Extreme Learning Machine (ELM). The extracted features will be fed up into SLFN to classify the fault. …”
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  10. 10
    Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar

    Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar by Abd Ghafar, Muhamad Azfar

    Published 2015
    “…The SLFN is trained by an algorithm named Extreme Learning Machine (ELM). The extracted features will be fed up into SLFN to classify the fault. …”
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  11. 11
    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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  12. 12
    Automated density and growth estimation in precision aquaculture systems for prawn cultivation using computer vision techniques

    Automated density and growth estimation in precision aquaculture systems for prawn cultivation using computer vision techniques by Chong, Xiao Wei

    Published 2024
    “…Overfeeding due to imprecise population estimates leads to wasted resources and potential environmental issues. …”
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    Final Year Project / Dissertation / Thesis
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  13. 13
    Prediction on the mechanical strength of coal ash concrete using artificial neural network

    Prediction on the mechanical strength of coal ash concrete using artificial neural network by Muhammad Nor Syahrul, Zaimi, Nur Farhayu, Ariffin, Sharifah Maszura, Syed Mohsin, Abdul Muiz, Hasim, Nurul Natasha, Nasrudin

    Published 2022
    “…The mechanical strength prediction based on machine learning is more exact, accurate, and dependable than the standard concrete strength estimate.…”
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