Faculty Publications
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Item Classification of protein sequences by means of an ensemble classifier with an improved feature selection strategy(Springer Verlag, 2018) Sriram, A.; Sanapala, M.; Patel, R.; Patil, N.With decreasing cost of biological sequencing, the influx of new sequences into biological databases such as NCBI, SwissProt, UniProt is increasing at an ever-growing pace. Annotating these newly sequenced proteins will aid in ground breaking discoveries for developing novel drugs and potential therapies for diseases. Previous work in this field has harnessed the high computational power of modern machines to achieve good prediction quality but at the cost of high dimensionality. To address this disparity, we propose a novel word segmentation-based feature selection strategy to classify protein sequences using a highly condensed feature set. Using an incremental classifier selection strategy was seen to yield better results than all existing methods. The antioxidant protein data curated in the previous work was used in order to facilitate a level ground for evaluation and comparison of results. The proposed method was found to outperform all existing works on this data with an accuracy of 95%. © Springer Nature Singapore Pte Ltd. 2018.Item EXhype: A tool for mineral classification using hyperspectral data(Elsevier B.V., 2017) Adep, R.N.; Shetty, A.; Ramesh, H.Various supervised classification algorithms have been developed to classify earth surface features using hyperspectral data. Each algorithm is modelled based on different human expertises. However, the performance of conventional algorithms is not satisfactory to map especially the minerals in view of their typical spectral responses. This study introduces a new expert system named ‘EXhype (Expert system for hyperspectral data classification)’ to map minerals. The system incorporates human expertise at several stages of it's implementation: (i) to deal with intra-class variation; (ii) to identify absorption features; (iii) to discriminate spectra by considering absorption features, non-absorption features and by full spectra comparison; and (iv) finally takes a decision based on learning and by emphasizing most important features. It is developed using a knowledge base consisting of an Optimal Spectral Library, Segmented Upper Hull method, Spectral Angle Mapper (SAM) and Artificial Neural Network. The performance of the EXhype is compared with a traditional, most commonly used SAM algorithm using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired over Cuprite, Nevada, USA. A virtual verification method is used to collect samples information for accuracy assessment. Further, a modified accuracy assessment method is used to get a real users accuracies in cases where only limited or desired classes are considered for classification. With the modified accuracy assessment method, SAM and EXhype yields an overall accuracy of 60.35% and 90.75% and the kappa coefficient of 0.51 and 0.89 respectively. It was also found that the virtual verification method allows to use most desired stratified random sampling method and eliminates all the difficulties associated with it. The experimental results show that EXhype is not only producing better accuracy compared to traditional SAM but, can also rightly classify the minerals. It is proficient in avoiding misclassification between target classes when applied on minerals. © 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)