Articles written in Sadhana

    • Emotion recognition based on facial components


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      Machine analysis of facial emotion recognition is a challenging and an innovative research topic in human–computer interaction. Though a face displays different facial expressions, which can be immediately recognized by human eyes, it is very hard for a computer to extract and use the information content from theseexpressions. This paper proposes an approach for emotion recognition based on facial components. The local features are extracted in each frame using Gabor wavelets with selected scales and orientations. These features are passed on to an ensemble classifier for detecting the location of face region. From the signature of each pixel on the face, the eye and the mouth regions are detected using the ensemble classifier. The eye and the mouth features are extracted using normalized semi-local binary patterns. The multiclass Adaboost algorithm is used to select and classify these discriminative features for recognizing the emotion of the face. The developed methods are deployed on the RML, CK and CMU-MIT databases, and they exhibit significant performance improvement owing to their novel features when compared with the existing techniques.

    • Optimal feature subset selection using hybrid binary Jaya optimization algorithm for text classification


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      Feature selection is an important task in the high-dimensional problem of text classification. Nowadays most of the feature selection methods use the significance of optimization algorithm to select an optimal subset of feature from the high-dimensional feature space. Optimal feature subset reduces the computation cost and increases the text classifier accuracy. In this paper, we have proposed a new hybrid feature selection method based on normalized difference measure and binary Jaya optimization algorithm (NDM-BJO)to obtain the appropriate subset of optimal features from the text corpus. We have used the error rate as a minimizing objective function to measure the fitness of a solution. The nominated optimal feature subsets are evaluated using Naive Bayes and Support Vector Machine classifier with various popular benchmark text corpus datasets. The observed results have confirmed that the proposed work NDM-BJO shows auspicious improvements compared with existing work.

    • An efficient hash map based technique for mining high average utility itemset


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      High Average Utility Itemset (HAUI) mining is an improvement on High-Utility Itemset (HUI) mining widely used in various pattern mining applications. The utility measure is proportional to the length of the itemset, which is a key flaw in HUI mining. HAUI finds the itemsets by relating the usefulness of itemsets totheir length using an unbiased measure termed average utility. Pruning methods such as average-utility upper bound, revised tighter upper bound, and looser upper bound used to eliminate weak candidates, overestimates the average usefulness of itemsets, causing the mining process to slow down. In the proposed methodology, Upper Bound using Remaining Items Utility(UBRIU), Maximum Itemset Utility(MIU) and Sum of Maximum Utility in a Transaction(SMUT) are used to avoid processing unpromising candidate itemsets and efficiently minimise the search space and therefore the processing time. UBRIU value is used to check if the itemset can be extended or not. The key-value mapping structure used for storing the utility values reduces the lookup time compared to existing IL, IDUL structure. The performance of the proposed work is evaluated in terms of memory usage and the time taken for processing. The proposed algorithm is significantly faster than existing state-of-the-art HAUI mining algorithms and utilizes significantly less memory, according to experimental results. The proposed work increases the overall efficiency of the system by employing effective pruning algorithms for pruning poor candidate itemsets and an efficient data structure for storing utility values.

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