The PM₁₀ prediction has received considerable attention due to its harmful effects on human health. Machine learning approaches have the potential to predict and classify future PM₁₀ concentrations accurately. Therefore, in this study, three machine learning algorithms—namely, decision tree (DT), boosted regression tree (BRT), and random forest (RF)—were applied for the prediction of PM₁₀ in Kota Bharu, Kelantan. The results from these three methods were compared to find the best method to predict PM₁₀ concentration for the next day by using the maximum daily data from January 2002 to December 2017. To this end, 80% of the data were used for training and 20% for validation of the models. The performance measure of the PM₁₀ concentration was based on accuracy, sensitivity, specificity, and precision for RF, BRT, and DT, respectively, which indicates that these three models were developed effectively, and they are applicable in the prediction of other atmospheric environmental data. The best model to use in predicting the next day’s PM₁₀ concentration classification was the random forest classifier, with an accuracy of 98.37, sensitivity of 97.19, specificity of 99.55, and precision of 99.54, but the result of the boosted regression tree was substantially different from the RF model, with an accuracy of 98.12, sensitivity of 97.51, specificity of 98.72, and precision of 98.71. The best model can assist local governments in providing early warnings to people who are at risk of acute and chronic health consequences from air pollution.

After the COVID-19 ended, the global economy gradually recovered. Due to the nonlinearity, complexity, and high noise of financial time series, stock price prediction has become one of the most challenging tasks in the stock market. To tackle this challenge and enhance the prediction performance in the complicated stock markets, we propose a novel integrated approach based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Long Short-Term Memory (LSTM), and ensemble learning algorithm LightGBM to simultaneously improve the fitting and accuracy of stock price prediction. In addition, to prevent overfitting and improve predictive performance, this study adopted the Simulated Annealing (SA) algorithm for optimization. The predictive performance of the proposed hybrid model is comprehensively evaluated by comparing it with single LSTM, RNN, and other popular hybrid models. Three evaluation metrics, namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and accuracy, are used to compare the aforementioned models. The experimental results indicate that the proposed hybrid CEEMDAN-LSTM-SA-LightGBM model outperforms all other comparative models in this study with better fitting and accuracy.

The advancement of autonomous mobile robots (AMR) is vastly being discovered and applied to several industries. AMR contributes to the development of artificial intelligence (AI), which focuses on the growth of human-interaction systems. However, it is safe to understand that mobile robots work closely in real-time and under changing surroundings. Similarly, some limitations may affect the efficiency of mobile robots. Thus, to improve the system's efficiency and accuracy, mobile robots should adopt the ability to detect incoming obstacles accurately. This paper presents the findings of a brief technology review aimed at identifying the current state of the art and future needs for AMR in object detection. This review paper is presented in the form of a narrative-literature review. Review articles were collected from 2015 until 2022 from journals or conference papers from well-known sources like IEEE Xplore, Science Direct, Scopus, and Web of Science (WOS). The analysis of the articles was discussed in four main topics, AI, object detection, AMR, and deep learning.