Background/Objectives: The main objective is to achieve improved performance of soil properties prediction for hyperspectral data. In this work, convolutional neural network is trained to understand the pattern of hyperspectral data by spatial interpolation. Methods/Statistical analysis: The proposed methodology is used to predict six soil properties-Organic Carbon content (OC), Cation Exchange Capacity (CEC), Nitrogen Content (N), pH level in water, Clay particle and Sand Particle. Soil texture which defines the relative content of soil particles is determined by the percentage of clay, sand and silt in the soil. The input to the Convolutional Neural Network (CNN) is the Hyperspectral data in the form of multiple arrays. The statistical evaluation of model performance is evaluated using root-mean-square error and r square. Findings: In this research, deep learning approach is used to capture the pattern hidden in the soil. Deep learning is a kind of neural network which can model complex relationship for representing non-linearity for a scalable data. The main challenge is predicting a soil type, as it involves complex structural characteristics and soil features. Novelty/Improvements: The performance of soil texture prediction is improved by automatic feature learning capability in the proposed CNN model. The average rmse value obtained in proposed method for all the six soil texture properties is 5.68%.

Counterfactual statements, which describe hypothetical events that did not occur due to different circumstances, have been studied in various fields, such as NLP, psychology, medicine, politics, and economics. They can be misleading in productr eviews and are challenging to detect in multilingual setups due to language diversity. Current systems struggle to identify counterfactual statements due to limite ddata availability. To address these issues, a proposed counterfactual detection system utilizes a domain-independent and multilingual few-shot model. The system incorporates clue-phrases to mitigate the limited data problem and improve performance by 5-10% compared to existing few-shot techniques. The use of clue-phrases during training can enhance the efficacy of the model, making it more efficient at identifying counterfactual statements.

No abstract