Machine learning in natural language processing (NLP) analyzes datasets to make future predictions, but developing accurate models requires large, high-quality, and balanced datasets. However, collecting such datasets, especially for low-resource languages, is time-consuming and costly. As a solution, data augmentation can be used to increase the dataset size by generating synthetic samples from existing data. This study examines the effect of translation-based data augmentation on sentiment analysis using small datasets in three diverse languages: French, German, and Japanese. We use two neural machine translation (NMT) services—Google Translate and DeepL—to generate augmented datasets through intermediate language translation. Sentiment analysis models based on Support Vector Machine (SVM) are trained on both original and augmented datasets and evaluated using accuracy, precision, recall, and F1 score. Our results demonstrate that translation augmentation significantly enhances model performance in both French and Japanese. For example, using Google Translate, model accuracy improved from 62.50% to 83.55% in Japanese (+21.05%) and from 87.66% to 90.26% in French (+2.6%). In contrast, the German dataset showed a minor improvement or decline, depending on the translator used. Google-based augmentation generally outperformed DeepL, which yielded smaller or negative gains. To evaluate cross-lingual generalization, models trained on one language were tested on datasets in the other two. Notably, a model trained on augmented German data improved its accuracy on French test data from 81.17% to 85.71% and on Japanese test data from 71.71% to 79.61%. Similarly, a model trained on augmented Japanese data improved accuracy on German test data by up to 3.4%. These findings highlight that translation-based augmentation can enhance sentiment classification and cross-language adaptability, particularly in low-resource and multilingual NLP settings.

In this study, we utilize the ${\varphi }^6$-model expansion method to derive a diverse set of Jacobi elliptic function solutions for the conformable resonant Nonlinear Schrödinger Equation (NLSE) with parabolic law nonlinearity. As the modulus of the Jacobi elliptic functions approaches 1 and 0, the solutions transform into hyperbolic and trigonometric functions, respectively. This methodology yields various exact traveling wave solutions, including kink solitons, singular solitons, periodic solutions, and singular periodic solutions. Notably, this work represents the first investigation into identifying Jacobi elliptic function solutions for the conformable resonant NLSE. These results enhance the understanding of the nonlinear dynamical properties intrinsic to the NLSE. We use graphical illustrations to highlight the dynamical features of the solutions. Moreover, our approach showcases versatility in addressing other nonlinear partial differential equations, offering insights applicable to nonlinear optics, fluid dynamics, and quantum physics.

Financial institutions are highly concerned about credit card fraud due to its significant impact on their financial losses. Detecting fraud is challenging as fraudsters are constantly adapting to new technology. To protect customers and businesses, it is crucial to have a robust fraud detection system. This study proposes a novel machine learning approach for detecting credit card fraud using Bayesian Gaussian Mixture Model (BGMM). The study contributes by processing the complex, multi-featured credit card dataset using an Autoencoder model to reduce its dimensionality. The reduced data is clustered in a low-dimensional space using the BGMM to separate fraudulent from normal transactions. The Gibbs sampler algorithm was used to estimate the parameters in the BGMM. The proposed model was evaluated and compared with baseline models, including the Gaussian Mixture Model (GMM) with EM algorithm, K-means, Autoencoder, and Isolation Forest, on two public datasets, Kaggle ULB and IEEE-CIS, using AUC-ROC scores. The results demonstrate that the proposed method achieves the best performance on the Kaggle ULB dataset and competitive, stable performance on the IEEE-CIS dataset, achieving an AUC-ROC score of 0.9672 on the Kaggle ULB dataset and 0.5422 on the IEEE-CIS dataset under a leakage-free unsupervised learning protocol. The adoption of the proposed approach will aid financial institutions and credit card companies in enhancing their fraud detection systems, resulting in greater accuracy and efficiency in detecting fraudulent transactions.

As an automated alternative to the complexity and resource intensive task of building a machine learning (ML) pipeline, AutoML offers substantial value. Moreover, given the growing number of application areas requiring ML solutions, but having limited technical expertise, the need for AutoML is increasing. The authors describe a novel and modular AutoML pipeline built using object-oriented Python, designed for both regression and classification problems. Unlike other established libraries (TPOT, Auto-sklearn, Hyperopt-sklearn) this new framework provides structure to the output formats (JSON, YAML etc.) used for backend and API integration purposes. Rather than a GUI-based platform (low-code or otherwise) the authors propose a developer oriented/Code Driven AutoML pipeline. Additionally, it includes interpretability through VIF based Feature Engineering and increased extensibility. The experimental results provided by the authors are based upon public insurance data sets and demonstrate that their system performs at least on par with, and in several cases surpasses, the baseline systems tested, and does so in a manner that provides greater modularity and easier deployment. Therefore, this study demonstrates a lightweight, real world ready solution to provide an effective AutoML solution for use in a variety of application areas including NLP, computer vision, and Web-Based Machine Learning Applications.