Search Results - (( waste detection algorithm ) OR ( waste ((maximization algorithm) OR (optimization algorithm)) ))
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1
IoT-Enabled Waste Tracking and Recycling Optimization : Enhancing Sustainable Waste Management
Published 2025“…Advanced data preprocessing, such as augmentation and normalization, ensures robust model training, while optimized algorithms guide waste sorting based on classification results. …”
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Proceeding -
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Smart waste management system with IoT monitoring
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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Effective synthesis of biodiesel from Jatropha curcas oil using betaine assisted nanoparticle heterogeneous catalyst from eggshell of Gallus domesticus
Published 2017“…Artificial neural network (ANN) based on the genetic algorithm (GA) was applied for optimization of varied reaction parameters. …”
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Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations
Published 2022“…The gradient descent optimization algorithm was observed to help improve the modelâ��s performance. …”
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Automated density and growth estimation in precision aquaculture systems for prawn cultivation using computer vision techniques
Published 2024“…By employing the state-of-the-art You Only Look Once (YOLO) v7 object detection algorithm, the project aims to develop a system capable of accurately detecting and classifying prawns based on their growth stages. …”
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Final Year Project / Dissertation / Thesis -
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Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management
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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Graphical user interface test case generation for android apps using Q-learning / Husam N. S. Yasin
Published 2021“…The computation time complexity of the Q-Learning-based test coverage algorithm was also analyzed. The results showed that time complexity was reduced significantly from …”
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Thesis -
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Capacitated vehicle-routing problem model for scheduled solid waste collection and route optimization using PSO algorithm
Published 2023“…Costs; Efficiency; Routing algorithms; Scheduling; Solid wastes; Vehicle routing; Capacitated vehicle routing problem; Modified particle swarm optimization; Optimized models; Route optimization; Solid waste collection; Threshold waste level; Travel distance; Waste collection; Particle swarm optimization (PSO); fuel; algorithm; numerical model; optimization; routing; solid waste; transport vehicle; waste disposal; algorithm; Article; capacitated vehicle routing problem model; controlled study; cost; decision making; environmental impact; motor vehicle; nonbiological model; particle swarm optimization algorithm; priority journal; problem solving; process optimization; socioeconomics; solid waste management; travel; validation process; economics; solid waste; waste management; Algorithms; Costs and Cost Analysis; Solid Waste; Waste Management…”
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Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization
Published 2023“…Bins; Budget control; Carbon dioxide; Cost reduction; Costs; Crashworthiness; Efficiency; Environmental impact; Fuels; Learning algorithms; Optimization; Solid wastes; Vehicle routing; Backtracking search algorithms; Capacitated vehicle routing problem; Environmental emissions; Optimization modeling; Route optimization; Solid waste collection; Threshold waste level; Waste collection; Waste management; fuel; carbon dioxide; solid waste; algorithm; fuel consumption; modeling; optimization; solid waste; threshold; waste management; algorithm; Article; backtracking search algorithm; capacitated vehicle routing problem model; carbon footprint; controlled study; environmental impact assessment; feasibility study; limit of quantitation; measurement precision; process model; process optimization; solid waste collection; solid waste management; motor vehicle; procedures; solid waste; theoretical model; waste disposal; Algorithms; Carbon Dioxide; Models, Theoretical; Motor Vehicles; Refuse Disposal; Solid Waste…”
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Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging
Published 2024“…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
Published 2024“…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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Output improvement of Sg. Piah run-off river hydro-electric station with a new computed river flow-based control system
Published 2023“…The controller regulates the dam level with an algorithm based on the river flow derived indirectly from the intake-dam water level and other plant parameters. …”
Conference paper -
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Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging
Published 2024“…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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Model optimization using artificial intelligence algorithms for biological food waste degradation
Published 2020“…Food waste is categorized as the largest degradable component in the waste stream. …”
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Book Chapter -
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Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network
Published 2023“…The model reliability describes the second sub-objective, which is to determine the feasible operating window of the MEC using multiple-objective optimization based on the nonlinear convex method using gradient descent algorithm as the objective function in maximizing the percentage efficiency of the MEC after validating the mathematical model. …”
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Thesis -
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Dijkstra’s Algorithm for optimal recyclable waste collection system in Port Dickson / Nur Jazlina Mohd Iszairi, Aina Zulaika Md Ramli and Nursabrina Saifulbahri
Published 2023“…A practical recyclable waste collection system would optimize the Waste Management System (WMS), especially in route choice from Depot to each drop-off collection center. …”
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Student Project -
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Real-time intelligent recycle waste detection and classification using you only look once version 5 / Aiman Syafwan Amran
Published 2023“…In Malaysia, the traditional approach to recycle waste detection and classification primarily relies on manual sorting and visual inspection by waste management personnel. …”
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Thesis -
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Stochastic Modelling Of Bioethanol Fermentation By Saccharomyces Cerevisiae Grown In Oil Palm Residues
Published 2015“…These residues are not fully utilised, in fact, they serve as wastes leading to serious environmental pollution. …”
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Evolutionary cost-cognizant regression test case prioritization for object-oriented programs
Published 2019“…Afterward evolutionary algorithm (EA) was employed to prioritize test cases based on the rate severity of fault detection per unit test cost. …”
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Thesis
