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Poverty, an intricate global challenge influenced by economic, political, and social elements, is characterized by a deficiency in crucial resources, necessitating collective efforts towards its mitigation as embodied in the United Nations' Sustainable Development Goals. The Gini coefficient is a statistical instrument used by nations to measure income inequality, economic status, and social disparity, as escalated income inequality often parallels high poverty rates. Despite its standard annual computation, impeded by logistical hurdles and the gradual transformation of income inequality, we suggest that short-term forecasting of the Gini coefficient could offer instantaneous comprehension of shifts in income inequality during swift transitions, such as variances due to seasonal employment patterns in the expanding gig economy. System Identification (SI), a methodology utilized in domains like engineering and mathematical modeling to construct or refine dynamic system models from captured data, relies significantly on the Nonlinear Auto-Regressive (NAR) model due to its reliability and capability of integrating nonlinear functions, complemented by contemporary machine learning strategies and computational algorithms to approximate complex system dynamics to address these limitations. In this study, we introduce a NAR Multi-Layer Perceptron (MLP) approach for brief term estimation of the Gini coefficient. Several parameters were tested to discover the optimal model for Malaysia's Gini coefficient within 1987–2015, namely the output lag space, hidden units, and initial random seeds. The One-Step-Ahead (OSA), residual correlation, and residual histograms were used to test the validity of the model. The results demonstrate the model's efficacy over a 28-year period with superior model fit (MSE: 1.14 × 10−7) and uncorrelated residuals, thereby substantiating the model's validity and usefulness for predicting short-term variations in much smaller time steps compared to traditional manual approaches.

Data imputation studies include reconstruction or estimation of imperfect data gaps caused by system sensing failure, and non-responsive data transmission remains an open issue. In space weather applications, imputation of ground electromagnetism is significant in capturing the complex interaction of sun–earth prior to the subsequent analysis of the space weather effects. Key contributions to the demonstration of supervised machine learning (ML) imputation approach with artificial neural network, K-nearest neighbour, support vector regression (SVR), and General Regression Neural Network (GRNN) for MAGDAS-9 ground electromagnetism dataset have not yet been established. A total of 1,585,950 data points were analysed with supervised ML models which included performance benchmark with statistical analysis namely zero value substitution, listwise deletion, mean substitution, and hot deck imputation. To achieve low reconstruction errors, different imputation models with hyperparameter tuned settings are varied, and computational time execution has been shown to contribute to imputation performance. Performance metrics measured by mean square error (MSE), mean absolute error (MAE),mean absolute percentage error (MAPE), and execution time respectively demonstrate the capability of SVR to perfectly impute missing data for all ground electromagnetism components at an average of 0.314 MSE, 0.738 MAPE, closeness to 0.510 MAE and 0.91-second at various percentage level of data missingness. A comparison with traditional imputation shows that the supervised ML with SVR model has improved imputation performance by up to 80% of data gap. The outcome of the proposed imputation will benefit space weather applications for event characterisation, which will cover a large number of missing data in the MAGDAS-9 dataset.

In Malaysia, Arabic language is spoken, and commonly used among the Malays. Malays use Arabic in their daily life, such as during performing worship. Hence, in this paper, some of the Arabic vowels attributes are investigated, analyzed and initial findings are presented based on tokens articulated by Malay speakers as we can consider the spoken Arabic by Malays as one of the Arabic dialects. It is known that in Arabic language there are 28 consonants and 6 main vowels. Firstly, the duration, variability, and overlapping attributes are highlighted based on syllables of Consonant–Vowel with each syllable representing every Arabic consonant with the corresponding vowels. Next, the dispersion of each vowel is examined to be compared with each other along with the variability among vowels that may cause overlapping between vowels in the vowel-space. Results showed that the vowel overlapping occurred between short vowels and their long counterpart vowels. Furthermore, an investigation of the Arabic vowel duration is addressed as well, and duration analysis for all the vowels is discussed, followed by the analysis for each vowel separately. In addition, a comparison between long and short vowels is presented as well as comparison between high and low vowel is carried out.