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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mohankumar, Nageswara Guptha
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Computer Science & IT Engineering

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An automated speech recognition and feature selection approach based on improved Northern Goshawk optimization Suryakumar, Santosh Kumar; Hiremath, Bharathi S.; Mohankumar, Nageswara Guptha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp296-304

Abstract

Automatic speech recognition (ASR) approach is dependent on optimal speech feature extraction, which attempts to get a parametric depiction of an input speech signal. Feature extraction (FE) strategy combined with a feature selection (FS) approach should capture the most important features of the signal while discarding the rest. FS is a crucial process that can affect the pattern classification and recognition system's performance. In this research, we introduce a hybrid supervised learning using metaheuristic technique for optimum FE and FS termed Northern Goshawk optimization (NGO) and opposition-based learning (OBL). Pre-processing, feature extraction and selection, and recognition are the three steps of the proposed technique. The pre-processing is done first to lessen the amount of noise. In the FE stage, we extract features. The OBL-NGO method is used to pick the best collection of extracted characteristics. Finally, these optimised features are utilised to train the k-nearest neighbour (KNN) classifier, and the matching text is shown as the output based on these optimised characteristics of the provided input audio signal. The system's performance is outstanding, and the suggested OBL-NGO is best suited for ASR, according to the testing data.
Investigating optimal features in log files for anomaly detection using optimization approach Ranga, Shivaprakash; Mohankumar, Nageswara Guptha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp287-295

Abstract

Logs have been frequently utilised in different software system administration activities. The number of logs has risen dramatically due to the vast scope and complexity of current software systems. A lot of research has been done on log-based anomaly identification using machine learning approach. In this paper, we proposed an optimization approach to select the optimal features from the logs. This will provide the higher classification accuracy on reduced log files. In order to predict the anomalies three phases are used: i) log representation ii) feature selection and iii) Performance evaluation. The efficacy of the proposed model is evaluated using benchmark datasets such as BlueGene/L (BGL), Thunderbird, spirit and hadoop distributed file system (HDFS) in terms of accuracy, converging ability, train and test accuracy, receiver operating characteristic (ROC) measures, precision, recall and F1-score. The results shows that the feature selection on log files outperforms in terms all the evaluation measures.
Co-Authors Hiremath, Bharathi S. Ranga, Shivaprakash Suryakumar, Santosh Kumar