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Music Genre Classification Using african buffalo optimization

Computer Systems Science & Engineering 2023年第2期44卷 1823-1836页

作者： B.Jaishankar Raghunathan Anitha Finney Daniel Shadrach M.Sivarathinabala V.Balamurugan KPR Institute of Engineering and Technology Coimbatore641407India Government Engineering College Palakkad678633India Velammal Institute of Technology Chennai601204India Sathyabama Institute of Science and Technology Chennai600119India

In the discipline of Music Information Retrieval(MIR),categorizing musicﬁles according to their genre is a difﬁcult process.Music genre classiﬁca-tion is an important multimedia research domain for classiﬁcation of music data-bases.In the proposed method music genre classiﬁcation using features obtained from audio data is proposed.The classiﬁcation is done using features extracted from the audio data of popular online repository namely GTZAN,ISMIR 2004 and Latin Music Dataset(LMD).The features highlight the differences between different musical styles.In the proposed method,feature selection is per-formed using an african buffalo optimization(ABO),and the resulting features are employed to classify the audio using Back Propagation Neural Networks(BPNN),Support Vector Machine(SVM),Naïve Bayes,decision tree and kNN classiﬁers.Performance evaluation reveals that,ABO based feature selection strategy achieves an average accuracy of 82%with mean square error(MSE)of 0.003 when used with neural network classiﬁer.

关键词： Genre african buffalo optimization neural network SVM audio data music

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Fault Coverage-Based Test Case Prioritization and Selection Using african buffalo optimization

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Computers, Materials & Continua 2023年第3期74卷 6755-6774页

作者： Shweta Singhal Nishtha Jatana Ahmad F Subahi Charu Gupta Osamah Ibrahim Khalaf Youseef Alotaibi Indira Gandhi Delhi Technical University for Women Delhi110006India Maharaja Surajmal Institute of Technology New Delhi110058India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Bhagwan Parshuram Institute of Technology RohiniNew Delhi110085India Al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdad64074Iraq Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and cost incurred in this process,need to be reduced by the method of test case selection and prioritization.It is observed that many nature-inspired techniques are applied in this area.african buffalo optimization is one such approach,applied to regression test selection and prioritization.In this paper,the proposed work explains and proves the applicability of the african buffalo optimization approach to test case selection and prioritization.The proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying african buffalo optimization for test case prioritization is done on sample data set with multiple iterations.An astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were recorded.The obtained results are compared with Ant Colony optimization.The comparative analysis indicates that african buffalo optimization and Ant Colony optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test suite.The results and analysis,hence,advocate and encourages the use of african buffalo optimization in the area of test case selection and prioritization.

关键词： Test case prioritization regression testing test case selection african buffalo optimization nature-inspired meta-heuristic

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