The purpose of this research was to determine how we can optimize both customer and seller experiences in a super shop using hyperautomation technology. Here, a smart bot was employed to speed up responses of simple consumer queries by utilizing natural language processing in real time. We also used machine learning frameworks, such as XGBoost, linear regression, random forest, and hybrid models together, to predict future product demand. In addition, data mining methods, such as the Apriori algorithm, FP growth algorithm, and GSP algorithm, were used to find out which algorithm can be used to determine the right way to place a product to increase the super shop sale.

The growing threat of wildfires is considered an important large-scale environmental challenge, and their fast and correct detection have an essential role in lessening the devastating effects of such events. Some effective tools are provided by advances in machine learning, especially Convolutional Neural Networks (CNNs), for wildfire detection from satellite imagery which has high computational cost that forbids real-time application. We propose a hybrid model combining an architecture based on Kolmogorov-Arnold Networks (KAN) with CNN. By using learnable nonlinear activation functions, KAN can make more effective use of the parameters compared to traditional multi-layer perceptrons, with less computational overhead and without loss of accuracy. The model integrates the spatial feature extraction capability of CNNs with the parameter efficiency of KAN, hence achieving superior performance in detecting wildfires from satellite images. With these, the experimental results indicate that the proposed KAN-CNN hybrid outperforms the state-of-the-art CNN-based architecture with a large margin, with a test accuracy of $97.32 \%$ using fewer parameters and faster convergence. It is thus suitable for large-scale and realtime wildfire monitoring and detection systems, with possible applications in broader environmental monitoring tasks.