Waste solid management using Machine learning approch

Lahcen, Gouskir; Edahbi, Mohamed; Mohammed, Gouskir; Hachimi, Hanaâ; Abouhilal, Abdelmoula

doi:10.1109/icoa55659.2022.9934356

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Waste data analysis in bins for retrieval optimization, using past data or historical data and viewing graphs on the cloud interface [20]. Prediction of the fullness of waste bins and evaluation techniques were carried out to optimize waste disposal [21]. In López et al [22], several parameters were used, such as the type of truck, the location of the waste bin, travel time, and the number of employees.…”

Section: Introductionmentioning

confidence: 99%

“…It uses Machine Learning in data analysis but does not explicitly mention the method used and does not choose the method that performs best for the model. Also, Studies on IoT and machine learning approaches have been implemented in previous works [16]- [21], [24], and [22]. Furthermore, Binbusayyis et al [14] compared machine learning methods for IoMT (Internet of Medical Things) system security.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of machine learning methods for knowledge extraction model in A LoRa-Based waste bin monitoring system

Abidin,

Othman,

Hassan

et al. 2024

Int. J. Adv. Intell. Informatics

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Knowledge Extraction Model (KEM) is a system that extracts knowledge through an IoT-based smart waste bin emptying scheduling classification. Classification is a difficult problem and requires an efficient classification method. This research contributes in the form of the KEM system in the classification of scheduling for emptying waste bins with the best performance of the Machine Learning method. The research aims to compare the performance of Machine Learning methods in the form of Decision Tree, Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, and Multi-Layer Perceptron, which will be recommended in the KEM system. Performance testing was performed on accuracy, recall, precision, F-Measure, and ROCS curves using the cross-validation method with ten observations. The experimental results show that the Decision Tree performs best for accuracy, recall, precision, and ROCS curve. In contrast, the K-NN method obtains the highest F-measure performance. KEM can be implemented to extract knowledge from data sets created in various other IoT-based systems.

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Section: Introductionmentioning

confidence: 99%