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Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28507

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    A novel technique of feature selection with relieff and CFS for protein sequence classification
    (Springer Verlag service@springer.de, 2019) Kaur, K.; Patil, N.
    Bioinformatics has gained wide importance in research area for the last few decades. The main aim is to store the biological data and analyze it for better understanding. To predict the functions of newly added protein sequences, the classification of existing protein sequence is of great use. The rate at which protein sequence data is getting accumulated is increasing exponentially. So, it emerges as a very challenging task for the researcher, to deal with large number of features obtained by the use of various encoding techniques. Here, a two-stage algorithm is proposed for feature selection that combines ReliefF and CFS technique that takes extracted features as input and provides us with the discriminative set of features. The n-gram sequence encoding technique has been used to extract the feature vector from the protein sequences. In the first stage, ReliefF approach is used to rank the features and obtain candidate feature set. In the second stage, CFS is applied on this candidate feature set to obtain features that have high correlation with the class but less correlation with other features. The classification methods like Naive-Bayes, decision tree, and k-nearest neighbor can be used to analyze the performance of proposed approach. It is observed that this approach has increased accuracy of classification methods in comparison to existing methods. © Springer Nature Singapore Pte Ltd. 2019
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    Distributed Cloud Deep Learning Architecture for Complex Image Analysis and Run-time Prediction Tool
    (Springer Science and Business Media Deutschland GmbH, 2021) Kumar, S.; Thomas, E.; Horo, A.; Annappa, B.
    Hyperspectral imaging is a rare research tool and has been transformed into a commodity product found in a wide field. Currently, standard data processing methods that specialize in special hyperspectral accumulation structures are required. Also, with the advent of data collection and development in the field of sensory devices, it has rendered previous processing tools in vain. To manage this huge increase in the amount of data, a consistent cloud distribution method is required. Hyperspectral images (HSIs) have several spectral band channels that make the study very difficult. In this paper, an in-depth reading method of the novel with a modified autoencoder is proposed as a cloud-based use of HSI analysis, which provides a measure of lesser error rates and high accuracy of classification models. In line with this, a list of four tools has been proposed to calculate the actual number of workers, cores, and iterations required to achieve the desired accuracy for a specified amount of run-time. This will help cloud managers get a basic idea of computational needs and help them allocate resources more efficiently. The entire architecture was simulated on Spark servers and was verified experimentally by checking that the proposed architecture performs the function of efficient management and analysis of large HSI. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.