Increasing waste generation has become a key challenge around the world due to the dramatic expansion in industrialization and urbanization. This study focuses on providing effective solutions for real-time monitoring garbage collection systems via the Internet of things (IoT). It is limited to controlling the bad odor of blowout gases and the spreading of overspills by using an IoT-based solution. The inadequate and poor dumping of waste produces radiation and toxic gases in the environment, creating an adversarial effect on global warming, human health, and the greenhouse system. The IoT and deep learning (DL) confer active solutions for real-time data monitoring and classification, correspondingly. Therefore, this paper presents an artificial ecosystem-based optimization with an improved deep learning model for IoT-assisted sustainable waste management, called the AEOIDL-SWM technique. The presented AEOIDL-SWM technique exploits IoT-based camera sensors for collecting information and a microcontroller for processing the data. For waste classification, the presented AEOIDL-SWM technique applies an improved residual network (ResNet) model-based feature extractor with an AEO-based hyperparameter optimizer. Finally, the sparse autoencoder (SAE) algorithm is exploited for waste classification. To depict the enhancements of the AEOIDL-SWM system, a widespread simulation investigation is performed. The comparative analysis shows the enhanced outcomes of the AEOIDL-SWM technique over other DL models.

Computational linguistics (CL) is the application of computer science for analysing and comprehending written and spoken languages. Recently, emotion classification and sentiment analysis (SA) are the two techniques that are mostly utilized in the Natural Language Processing (NLP) field. Emotion analysis refers to the task of recognizing the attitude against a topic or target. The attitude may be polarity (negative or positive) or an emotional state such as sadness, joy, or anger. Therefore, classifying posts and opinion mining manually is a difficult task. Data subjectivity has made this issue an open problem in the domain. Therefore, this article develops a computational linguistics-based emotion detection and a classification model on social networking data (CLBEDC-SND) technique. The presented CLBEDC-SND technique investigates the recognition and classification of emotions in social networking data. To attain this, the presented CLBEDC-SND model performs different stages of data pre-processing to make it compatible for further processing. In addition, the CLBEDC-SND model undergoes vectorization and sentiment scoring process using fuzzy approach. For emotion classification, the presented CLBEDC-SND model employs extreme learning machine (ELM). Finally, the parameters of the ELM model are optimally modified by the use of the shuffled frog leaping optimization (SFLO) algorithm. The performance validation of the CLBEDC-SND model is tested using benchmark datasets. The experimental results demonstrate the better performance of the CLBEDC-SND model over other models.