Dissecting the genre of Nigerian music with machine learning models

Folorunso, Sakinat Oluwabukonla; Afolabi, Sulaimon; Owodeyi, Adeoye B.

doi:10.1016/j.jksuci.2021.07.009

Cited by 16 publications

(5 citation statements)

References 24 publications

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“…Leveraging the versatile Librosa tool [ 29 ], specific audio segments were meticulously extracted from the WAV files, applying predefined conditions (volume threshold ≥ 33dB, length threshold ≥ 2s). The nomenclature of these WAV files was crafted to incorporate the commencement timestamp, enhancing organizational clarity.…”

Section: Methodsmentioning

confidence: 99%

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

Wang,

Qi,

et al. 2024

PLoS ONE

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Accurate identification of porcine cough plays a vital role in comprehensive respiratory health monitoring and diagnosis of pigs. It serves as a fundamental prerequisite for stress-free animal health management, reducing pig mortality rates, and improving the economic efficiency of the farming industry. Creating a representative multi-source signal signature for porcine cough is a crucial step toward automating its identification. To this end, a feature fusion method that combines the biological features extracted from the acoustic source segment with the deep physiological features derived from thermal source images is proposed in the paper. First, acoustic features from various domains are extracted from the sound source signals. To determine the most effective combination of sound source features, an SVM-based recursive feature elimination cross-validation algorithm (SVM-RFECV) is employed. Second, a shallow convolutional neural network (named ThermographicNet) is constructed to extract deep physiological features from the thermal source images. Finally, the two heterogeneous features are integrated at an early stage and input into a support vector machine (SVM) for porcine cough recognition. Through rigorous experimentation, the performance of the proposed fusion approach is evaluated, achieving an impressive accuracy of 98.79% in recognizing porcine cough. These results further underscore the effectiveness of combining acoustic source features with heterogeneous deep thermal source features, thereby establishing a robust feature representation for porcine cough recognition.

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Section: Methodsmentioning

confidence: 99%

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

Wang,

Qi,

et al. 2024

PLoS ONE

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“…Goni and Mohammad present a machine learning approach to a mobile forensics framework for cybercrime detection in Nigeria [338]. Folorunso et al dissect the genre of Nigerian music with machine learning models [339]. Lawal et al predict floods in Kebbi state, Nigeria, using machine learning models [340].…”

Section: K Mauritania 1) Researchmentioning

confidence: 99%

Machine Intelligence in Africa: a survey

Tembine,

Tapo,

Danioko

et al. 2024

Preprint

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“…The exemplary features of the algorithm proposed by the authors in [41] are the regularized model, split-seeking algorithm, column block structure, and cache-aware prefetching algorithm. Some current applications of XGBoost include genre classification of Nigerian songs [42], predicting stock price [43], and forecast gene expression value [44].…”

Section: Machine Learning Modelmentioning

confidence: 99%

An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks

et al. 2023

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With less human involvement, the Industrial Internet of Things (IIoT) connects billions of heterogeneous and self-organized smart sensors and devices. Recently, IIoT-based technologies are now widely employed to enhance the user experience across numerous application domains. However, heterogeneity in the node source poses security concerns affecting the IIoT system, and due to device vulnerabilities, IIoT has encountered several attacks. Therefore, security features, such as encryption, authorization control, and verification, have been applied in IIoT networks to secure network nodes and devices. However, the requisite machine learning models require some time to detect assaults because of the diverse IIoT network traffic properties. Therefore, this study proposes ensemble models enabled with a feature selection classifier for Intrusion Detection in the IIoT network. The Chi-Square Statistical method was used for feature selection, and various ensemble classifiers, such as eXtreme gradient boosting (XGBoost), Bagging, extra trees (ET), random forest (RF), and AdaBoost can be used for the detection of intrusion applied to the Telemetry data of the TON_IoT datasets. The performance of these models is appraised based on accuracy, recall, precision, F1-score, and confusion matrix. The results indicate that the XGBoost ensemble showed superior performance with the highest accuracy over other models across the datasets in detecting and classifying IIoT attacks.

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Dissecting the genre of Nigerian music with machine learning models

Cited by 16 publications

References 24 publications

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

Machine Intelligence in Africa: a survey

An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks

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