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Named Entity Recognition is an important application area of Natural Language Processing. It is the process of identifying the designators which are present in a sentence called as named entities. Named Entity Recognition can be performed using rule based approaches, machine learning based approaches and hybrid approaches. This paper proposes a method for Named Entity Recognition of Malayalam language using one of the supervised machine learning approach called Conditional Random field approach.

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Hyperspectral images (HSI) contains extremely rich spectral and spatial information that offers great potential to discriminate between various land cover classes. The inherent high dimensionality and insufficient training samples in such images introduces Hughes phenomenon. In order to deal with this issue, several preprocessing techniques have been integrated in processing chain of HSI prior to classification. Supervised feature extraction is one such method which mitigates the curse of dimensionality induced by Hughes effect. In recent years, new strategies for feature extraction based on scattering transform and Random Kitchen Sink have been introduced, which can be used in context of hyperspectral image classification. This paper presents a comparative analysis of scattering and random features in hyperspectral image classification. The classification is performed using simple linear classifier such as Regularized Least Square (RLS) accessed through Grand Unified Regularized Least Squares (GURLS) library. The proposed approach is tested on two standard hyperspectral datasets namely, Salinas-A and Indian Pines subset scene captured by NASAs AVIRIS sensor (Airborne Visible Infrared Imaging Spectrometer). In order to show the effectiveness of proposed method, a comparative analysis is performed based on feature dimension, classification accuracy measures and computational time. From the comparative assessment, it is evident that classification using random features achieve excellent classification results with less computation time when compared with raw pixels(without feature extraction) and scattering features for both the datasets.