![]() ![]() Today the machine learning models are becoming non-deterministic and can grow with time. ![]() The emergence of big data and advanced machine learning techniques can detect anomalies and alterations that are otherwise not possible with the human eye. All this can be possible due to the introduction of Internet-of-Things (IoT) devices and complex sensors that are capable of communicating with each other creating a form of a digital environment, helping in making the cities autonomous. Through sending real-time data and feedback, the extracted data can then be analyzed and processed for the sustainable socio-economic development of the cities. The emerging concept of smart cities exceedingly encourages the use of sensors and automated systems to tackle these issues. The ever-increasing population leads to congested cities making them difficult to manage, facilitating the need for modern approaches. This frequency analysis is used to determine the locations where one of the following actions is required to improve the SWM service: (i) people need to be educated about the consequences of waste scattering (ii) bin capacity or waste collection schedules are required to change (iii) both actions are required simultaneously (iv) none of the actions are needed. In this case study, the frequencies of identified events/activities at a bin are plotted and thoroughly analyzed to determine people’s behavior toward waste. Methodologically, the research is supported through a case study based on the recorded data set. The study also designed and built an experimental setup to record the data set, which comprises 3200 video files duration between 150–1200 s. The performance measures of all individual events indicate that the model successfully detected the individual events and has high precision for classifying them. This precision is promising to support the implementation of the model on a large scale in the actual environment. The model was trained and tested over a handcrafted data set and achieved an average precision of 0.944–0.986. This model consists of a three-dimensional convolutional neural network (3D CNN) and a long short-term memory (LSTM)-based recurrent neural network. A deep neural network model is implemented to detect and identify the specific types of events/activities in the proximity of the waste bin. This study illustrates a proof-of-concept model to improve solid waste management (SWM) services by analyzing people’s behavior towards waste.
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