Rashid Ahmad

TL;DR: A spatial and temporal ensemble forecasting model for short-term electric consumption forecasting that has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error is presented.

TL;DR: This research study proposes a novel weighted radius SVM (WR-SVM) that uses a weighted mean to find tighter bounds of radius, due to which the size of MEB decreases.

TL;DR: In this article, an ensemble GWL prediction model using boosting and bagging models based on stacking techniques to predict GWL for enhancing hydraulic resource management and planning is presented. But, the performance of the proposed E-GWLP model is compared with existing ensemble and baseline models and experimental results reveal that the proposed model performed accurately in respect of MAE, MSE, and RMSE of 0.340, 0.564, and 0.751, respectively.

TL;DR: In this article, the authors presented a new solution to process and analyze boreholes data to monitor mining operations and identify the boreholes shortcomings, and developed a bi-directional long short-term memory (BD-LSTM) to predict the borehole depth to minimize the cost and time of the digging operations.

TL;DR: In this article, an L2-weighted K-means clustering algorithm was proposed to estimate the drilling time and depth for different soil materials and land layers, and the proposed clustering scheme is evaluated widely used evaluation metrics such as Dunn Index, Davies-Bouldin index (DBI), Silhouette coefficient (SC), and Calinski-Harabaz Index (CHI).