Search Results - (( java application matching algorithm ) OR ( waste reducing learning algorithm ))

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    IoT-Enabled Waste Tracking and Recycling Optimization : Enhancing Sustainable Waste Management by Eugine Teh, Yin Jie, Chee Soon, Chong, Rozaimi, Ghazali, Hazriq Izzuan, Jaafar, Muhamad Fadli, Ghani, Howe Cheng, Teng, Nur Farhanah, Zulkipli, Siaw Hong, Liew

    Published 2025
    “…In conclusion, this project validates the feasibility of integrating IoT and machine learning to optimize waste management. The system reduces environmental impact and promotes sustainability, offering a scalable framework for addressing global waste challenges.…”
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    An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification by Wan Azman,, Wan Nur Fadhlina Syamimi, Ku Azir, Ku Nurul Fazira, Mohd Khairuddin, Adam

    Published 2024
    “…In conclusion, the work demonstrates a novel method for using machine learning algorithms to classify, identify, and predict the contamination level of leftover cooked food, contributing to reducing food waste generated primarily by Malaysians…”
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    Smart waste management system with IoT monitoring by Kalitazan, Sachein, Muniswaran, Suvarshan, Velan, Sheshan, Muniswaran, Suvathithan, Shah, Dhanesh

    Published 2023
    “…Through advanced data analytics and machine learning algorithms, the platform predicts waste accumulation patterns, optimizes collection routes, schedules pickups based on fill-level data, and detects any abnormal conditions. …”
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    Securing cloud data system (SCDS) for key exposure using AES algorithm by Thabet Albatol, Mohammed Samer Hasan

    Published 2021
    “…The AES algorithm has its own structure to encrypt and decrypt sensitive data that make the attackers difficult to get the real data when encrypting by AES algorithm. …”
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    Thesis
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    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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    Wind power forecasting with metaheuristic-based feature selection and neural networks by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Mohd Mawardi, Saari, Mohammad Fadhil, Abas

    Published 2024
    “…Inaccurate forecasts can lead to a mismatch between wind power generation and energy demand, resulting in wasted energy, increased emissions, and reduced grid stability. …”
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    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
    “…This study examines the utilization of different Machine Learning algorithms, such as Linear Regression, Decision Trees, Support Vector Machines (SVM), Gradient Boosting, Random Forest, K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN) Regression, and Particle Swarm Optimization (PSO), in the domain of predictive modeling and cost optimization in the field of construction project management. …”
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    Mixed waste classification based on vision inspection / Hassan Mehmood Khan by Hassan Mehmood , Khan

    Published 2022
    “…Classification of dry waste garbage is crucial since incorrect labelling of dry waste types may contribute huge loss to waste industry. …”
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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.

    Published 2024
    “…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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    Graphical user interface test case generation for android apps using Q-learning / Husam N. S. Yasin by Husam , N. S. Yasin

    Published 2021
    “…The computation time complexity of the Q-Learning-based test coverage algorithm was also analyzed. …”
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    Evaluation of machine learning classifiers in faulty die prediction to maximize cost scrapping avoidance and assembly test capacity savings in semiconductor integrated circuit (IC)... by Mohd Fazil, Azlan Faizal, Mohd Shaharanee, Izwan Nizal, Mohd Jamil, Jastini

    Published 2019
    “…The model training flow will have 2 classifier groupings which are control group and auto machine learning (ML) where feature selection with redundancy elimination method to be applied on input data to reduce the number of variables to minimum prior modeling flow. …”
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