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.

Mangrove forests are a critical part of our ecosystem, which works continuously to fight the carbon footprint, coastal erosion and provides support for biodiversity. However, these forests are encountering notable loss across tropical areas. To save these diverse ecosystem it is important to identify the mangrove loss factors and future risk zones. In this study, I have worked on a multi-country data-driven framework to understand the mangrove loss in Southeast Asia and to forewarn future loss risks. Global Mangrove Watch(GMW) data set was used to extract the mangrove data for 1996, 2007,2010,2015,2020 timeline. Lateron, Google Earth Engine was used to extract Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), elevation, and slope data for Bangladesh, Thailand, Myanmar, and Indonesia. After sampling and balancing the data, four ensemble machine learning models were applied to model mangrove loss. Random Forest, Gradient Boosting, XGBoost, and LightGBM models were tested, and among them, Random Forest outperforms others with a ROC-AUC of 0.96 and an F1-score of 0.90. Elevation was identified as the primary cause of mangrove decline, followed by NDVI and NDWI, suggesting increased vulnerability in low-lying, water-stressed mangrove areas. While country-level comparisons revealed similar loss intensities throughout the research region, temporal analysis revealed comparatively steady loss rates between 1996 and 2020. About 36% of mangrove habitats were classified as high or very high risk in future risk projections for 2025–2030, with low-elevation coastal zones being specifically vulnerable. The suggested methodology facilitates the evidence-based prioritizing of conservation and management measures and offers a scalable and adaptable method for regional mangrove monitoring.

Climate anomaly detection methods have been widely used for identifying anomaly events. Although these methods successfully detect unusual events, they rarely explain their causes. Moreover, these methods do not explain the anomaly, in contrast to climatologically similar non-anomalous periods. To overcome this limitation, a Contrastive Climate Anomaly Explanation Framework is proposed by integrating unsupervised anomaly detection, contrastive SHAP explanation mining, climatologically grounded near-miss matching, and Apriori-based temporal pattern mining into a unified data mining pipeline. This framework works with five climate variables: global surface temperature, ENSO MEI index, Niño 3.4 sea surface temperature, Southern Oscillation Index, and land precipitation anomaly from 1979 to 2026 by constructing 559-month sliding windows. Isolation Forest was applied for detecting anomalies, and it outperformed STL decomposition by 11.2 percentage and achieved an F1 score of 0.691 ± 0.058 across five-fold cross-validation. For every confirmed anomaly window, a matching control window from the same calendar month with comparable baseline climate conditions is found. At first, SHAP values were calculated for both windows, and the values were subtracted to produce SHAP delta. This value helped us to distinguish the anomalous period from its near-miss rather than what generally characterises anomaly predictions. After that, Association rule mining was applied in delta values, which produced a Climate Anomaly Signature Library of 18 causal rules across three anomaly types, with the strongest rule achieving 96.2\% confidence. In addition to revealing a previously unidentified category of 85 compound anomalies driven by warming-ENSO interaction that are missing from the NOAA catalog, the contrastive profiles independently rediscover known ENSO physics: El Niño is driven by extreme MEI and SST peaks, while La Niña is driven by depressed ocean temperatures and precipitation redistribution. The study shows that data mining methods integrated through a contrastive explanation bridge can produce interpretable knowledge from climate time series.