Table 1.

Summary of classifying algorithms used for solid waste management

Algorithm	Reference	Type solid of waste	Software and hardware utilization	Comments on limitations
MCMC-RORO	Lu et al. (2017)	Household wastages	MATLAB, Sensors, RFID readers, GPS, Wi-Fi	• Waste separation and differentiated collection based on RFID tag • Waste must be separated and tagged with RFID before sending it to the collection system
Random Forest classifier	David et al. (2019)	Detection of containers for recycling	MATLAB, Ultrasonic sensor, an accelerometer, and a GSM module	• Detection of emptying recycling using the sensor-mounted container • Filling level predictions and measurement, investigated solutions were not taken into account
Visual/descriptive analysis	Imran et al. (2020)	Waste amount prediction	QGIS software, Ultrasonic sensor, Container, Geographical Information System	• A smart waste management model presented to empty the waste collection bin using sensors • Mapping the non-linear relation and prediction time are the major requirements in the design model
KNN	Sonali et al. (2020)	Household wastages	Scikit-learn software, Ultrasonic sensor, Raspberry pi, Wi-Fi module	• A waste management model proposed to continuously monitor the level of waste and classify them into biodegradable or non-biodegradable • Classification accuracy of KNN based waste management system is very less comparatively
SVM	Ruibo et al. (2021)	Construction waste	MATLAB software	• Encountered complications in obtaining accurate outcomes due to a lack of detailed wastages • Adequate assistance from workers and site supervisors
ANN	Maruful and Tauhid (2020)	Household solid wastages	MATLAB software with neural network (NN) toolbox	• Solid waste collection and landfill area estimation • Moderate accuracy in testing
CNN	Ahmad et al. (2021)	Carrot fruit shape classification	MATLAB software, Deep Network Designer toolbox	• Dataset samples were augmented and images were classified using the CNN model • Application limited on carrot classification
	Cong et al. (2021)	Plastic, glass, metal, and other recyclable	OneNET IoT platform, Jetson Nano kit, Sensors, GSM, Wi-Fi module	• Classification of waste is performed in the cloud which use to provide a long evaluation time
	Rahman et al. (2020)	Household solid wastages	Kaggle Software, Sensors, microcontroller, Bluetooth and camera module, raspberry-pi	• Presented model works with only five categories of indigestible waste
	Jiang et al. (2021)	Dry, wet, recyclable waste	-	• Moderate accuracy, more data to be trained to improve accuracy • Increased the computational time
	Mesut et al. (2020)	Organic and recyclable	MATLAB software	• Limited datasets only trained • Requires additional processing techniques for good accuracy