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Y.V.S. Murthy

Bio: Y.V.S. Murthy is an academic researcher from National Institute of Technology, Karnataka. The author has contributed to research in topics: Load balancing (computing). The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Papers
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Proceedings ArticleDOI
01 Jul 2019
TL;DR: An algorithm using genetic alogithm to find out the optimal solution for the academic load balancing problem to distribute the course load as evenly as possible so that the deviation from the mean credit load per each semester is as minimal as possible.
Abstract: In the paper, we propose an algorithm using genetic alogithm to find out the optimal solution for the academic load balancing problem. The load balancing problem is to optimize the load of credits per semester in an academic curriculum. In the proposed method, we try to distribute the course load as evenly as possible so that the deviation from the mean credit load per each semester is as minimal as possible. The objective function is to distribute the credit load among all the semesters evenly such that the deviation from the mean credits per semester is minimal. The proposed approach explores the solution space using only mutation operators and does not operate using crossover as the solutions obtained using cross over does not create any newer and better solutions in the solution space.The algorithm is applied on three data sets and the results are compared with the solutions obtained using the existing approaches. The results obtained using the state of the art solution are either better than approaches or on par with the state of art optimal solutions. The solution set obtained using the proposed approach is well spread out through out all the periods and all the periods contain almost mean number of credits.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper proposes an approach using a hybrid mechanism based on multi-layer perceptron (MLP) and variational auto-encoder (VAE) with a fire-fly optimization mechanism to extract features and do classification of weather data.
Abstract: The future weather data source will continue to grow rapidly, and new developments in machine learning would allow government agencies and companies to use all this data further. The weather prediction will never be flawless, but artificial intelligence (AI) can strive to enhance the exactness and consistency of the process. This paper proposes an approach using a hybrid mechanism based on multi-layer perceptron (MLP) and variational auto-encoder (VAE) with a fire-fly optimization mechanism. Weather-related data contains many features. A few of which are global or generalized features, and some are local or internal features. Single mechanism may not be effective in the process of extracting the specified features. Hence, a hybrid mechanism with the support of VAE and MLP is proposed to extract features and do classification. VAE is used to extract the global features from the weather data and the obtained or processed intermediately output given to the input as the MLP, which will extract all local or internal features very effectively.

8 citations

Journal ArticleDOI
TL;DR: Traditional feature-based and trending convolutional neural network (CNN) based approaches are considered and compared for identifying singers and results obtained seem to be better than the traditional isolated and ensemble classifiers.
Abstract: Singer identification is one of the important aspects of music information retrieval (MIR). In this work, traditional feature-based and trending convolutional neural network (CNN) based approaches are considered and compared for identifying singers. Two different datasets, namely artist20 and the Indian popular singers’ database with 20 singers are used in this work to evaluate proposed approaches. Cepstral features such as Mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstral coefficients (LPCCs) are considered to represent timbre information. Shifted delta cepstral (SDC) features are also computed beside the cepstral coefficients to capture temporal information. In addition, chroma features are computed from 12 semitones of a musical octave, overall forming a 46-dimensional feature vector. Experiments are conducted with different feature combinations, and suitable features are selected using the genetic algorithm-based feature selection (GAFS) approach. Two different classification techniques, namely artificial neural networks (ANNs) and random forest (RF), are considered on the features mentioned above. Further, spectrograms and chromagrams of audio clips are directly fed to CNN for classification. The singer identification results obtained using CNNs seem to be better than the traditional isolated and ensemble classifiers. Average accuracy of around 75% is observed with CNN in the case of Indian popular singers database. Whereas, on artist20 dataset, the proposed configuration of feature-based approach and CNN could not give better than 60% accuracy.

5 citations