This study investigates the application of various artificial neural network (ANN) architectures for music genre classification, focusing on the evolution of models and their performance on the GTZAN dataset. Through experimentation with fully connected networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformer-based architectures, we analyze their strengths and limitations in genre classification. The results demonstrate that hyperparameter tuning, dataset diversity, and model selection significantly impact classification performance. Although CNNs, RNNs, and gated recurrent units (GRUs) achieve more precision than 90% in evaluation tests, the addition of Transformer layers does not consistently improve classification accuracy and may exacerbate challenges in certain genres. These results underscore the importance of carefully considering model architecture and dataset characteristics in music genre classification. Future research should focus on developing extensive, multi-rater verified datasets to enhance classification performance and model robustness in music genre classification tasks.

The paper describes a novel a hybrid ensemble algorithm (HEA) that combines ensemble learning, class imbalance handling, and feature extraction. To address class imbalance in the dataset, the suggested approach integrates SMOTE oversampling and random under sampling (RU) feature extraction. To begin, Pearson correlation analysis is used to detect highly associated features in a dataset. This analysis aids in the selection of the most relevant features, which are either substantially related to the target variable or have a strong association with other features. The method seeks to improve classification performance by focusing on these correlated features. Following that, the SMOTE oversampling and RU algorithms are used to balance the majority and minority categorization characteristics. The SMOTE (synthetic minority oversampling technique) develops synthetic cases for the minority class by interpolating between existing instances, enhancing minority class representation. RU, on the other hand, removes instances from the majority class at random to obtain a balanced distribution. Furthermore, the random forest classifier (RFC) model’s key features are input into an ensemble of decision tree (DT), k-nearest neighbor (KNN), adaptive boosting (AdaBoost), and convolutional neural network (CNN) approaches. This ensemble approach combines multiple models’ predictions, exploiting their particular strengths and catching varied patterns in the data. Popular machine learning algorithms include DT, KNN, AdaBoost, and CNN, which are notable for their capacity to handle many types of data and capture complicated relationships. The evaluation findings show that the suggested HEA approach is effective, with a maximum precision, recall, F-score, and accuracy of 90%. The proposed methodology produces encouraging results, proving its applicability to a variety of categorization problems.

MOTIVATION: Motivated by the challenges of decentralized genetic data spread across multiple international organizations, GINSA leverages the Global Biodiversity Information Facility infrastructure to automatically retrieve and link small ribosomal subunit sequences with locality information. RESULTS: Testing on taxa from major organism groups demonstrates broad applicability across taxonomic levels and dataset sizes. AVAILABILITY AND IMPLEMENTATION: GINSA is a freely accessible Python program under the MIT License and can be installed from PyPI via pip.