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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.

The growing number of Internet presents a higher requirement to backend application systems nowadays to be designed to handle thousands of users traffic concurrently. Microservice architecture is also in a rising trend which they allow for each service to scale horizontally by their throughput and load helps to scale the system efficiently without waste of resources like in the traditional monolithic application system. Among the many strategies to optimize delivery, database connection pool helps backend systems to access databases efficiently by reusing database connections, thus eliminating the computationally expensive need to open and close connections with new requests. Additionally, database connection pools can also help improve the connection reliability for applications. This paper aims to determine the most suitable maximum amount of database connections in a microservice setting, where multiple instances of the service are used for scalability and high availability purposes of the system. To tackle the issue of scalability and to achieve high availability of our services, we propose running multiple instances of each of our services in production, especially for services that we anticipate will be hit the most during runtime. This is to allow load balancing of request load between multiple instances and having backup instances to serve HTTP requests when one of the instances is down. The result obtained in this experiment shows that 5 database connections give the best result in microservice settings as described in our methodology.

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.

Context The computerization of both fetal heart rate (FHR) and intelligent classification modeling of the cardiotocograph (CTG) is one of the approaches that are utilized in assisting obstetricians in conducting initial interpretation based on (CTG) analysis. CTG tracing interpretation is crucial for the monitoring of the fetal status during weeks into the pregnancy and childbirth. Most contemporary studies rely on computer-assisted fetal heart rate (FHR) feature extraction and CTG categorization to determine the best precise diagnosis for tracking fetal health during pregnancy. Furthermore, through the utilization of a computer-assisted fetal monitoring system, the FHR patterns can be precisely detected and categorized. Objective The goal of this project is to create a reliable feature extraction algorithm for the FHR as well as a systematic and viable classifier for the CTG through the utilization of the MATLAB platform, all the while adhering to the recognized Royal College of Obstetricians and Gynecologists (RCOG) recommendations. Method The compiled CTG data from spiky artifacts were cleaned by a specifically created application and compensated for missing data using the guidelines provided by RCOG and the MATLAB toolbox after the implemented data has been processed and the FHR fundamental features have been extracted, for example, the baseline, acceleration, deceleration, and baseline variability. This is followed by the classification phase based on the MATLAB environment. Next, using the guideline provided by the RCOG, the signals patterns of CTG were classified into three categories specifically as normal, abnormal (suspicious), or pathological. Furthermore, to ensure the effectiveness of the created computerized procedure and confirm the robustness of the method, the visual interpretation performed by five obstetricians is compared with the results utilizing the computerized version for the 150 CTG signals. Results The attained CTG signal categorization results revealed that there is variability, particularly a trivial dissimilarity of approximately (+/−4 and 6) beats per minute (b.p.m.). It was demonstrated that obstetricians’ observations coincide with algorithms based on deceleration type and number, except for acceleration values that differ by up to (+/−4). Discussion The results obtained based on CTG interpretation showed that the utilization of the computerized approach employed in infirmaries and home care services for pregnant women is indeed suitable. Conclusions The classification based on CTG that was used for the interpretation of the FHR attribute as discussed in this study is based on the RCOG guidelines. The system is evaluated and validated by experts based on their expert opinions and was compared with the CTG feature extraction and classification algorithms developed using MATLAB.

Context: Quality Control (QC) has been constantly an essential concern in many fields like food industry production, medical drugs, environmental protection, and so on. An odor or flavor, as a global fingerprint, can be implemented as a non-invasive mechanism for quality assurance. This computer-based approach can assure accurate detection and precise identification of the product quality or manufactured goods. Objective: This paper aims to achieve a systematic review about e-nose by introducing the achievements made by researchers in this area, to summarize their findings, to provide motivations and challenges to new researchers in the field of e-nose. Methods: The articles that were being utilized in the e-nose field were systematically achieved using three search engines: The online library of IEEE Explore, Web of Science and Science Direct for time span of 7 years (from 2013 to 2020). Both medical literature reviews and technical reviews were considered in the criteria of the research for wider understanding in the field of e-nose. The articles were categorized according to the objective of the research and projected into four classes. Upon completion of screening process 333 research papers using the exclusion and inclusion conditions, as the final set 54 articles were selected. Results: The taxonomy of this research was classified into four categories. The first one included the suggested methods that introduced the utilization of the e-nose for classification purposes (9/54 papers). The second category comprises the methods related to the development of e-nose (24/54 papers). The third one included the review studies about the e-nose (8/54 papers). The fourth group comprises comparative studies and evaluation (13/54 papers). Discussion: This systematic review contributes for a clearer understanding and a full insight in the e- nose research field by surveying and categorizing pertinent research efforts. Conclusion: This review paper will help to address the up-to-date research opportunities, challenges, problems, motivations and recommendations related to the utilization of e-nose in all fields of sciences and industries.

Context The interpretations of cardiotocography (CTG) tracings are indeed vital to monitor fetal well-being both during pregnancy and childbirth. Currently, many studies are focusing on feature extraction and CTG classification using computer vision approach in determining the most accurate diagnosis as well as monitoring the fetal well-being during pregnancy. Additionally, a fetal monitoring system would be able to perform detection and precise quantification of fetal heart rate patterns. Objective This study aimed to perform a systematic review to describe the achievements made by the researchers, summarizing findings that have been found by previous researchers in feature extraction and CTG classification, to determine criteria and evaluation methods to the taxonomies of the proposed literature in the CTG field and to distinguish aspects from relevant research in the field of CTG. Methods Article search was done systematically using three databases: IEEE Xplore digital library, Science Direct, and Web of Science over a period of 5 years. The literature in the medical sciences and engineering was included in the search selection to provide a broader understanding for researchers. Results After screening 372 articles, and based on our protocol of exclusion and inclusion criteria, for the final set of articles, 50 articles were obtained. The research literature taxonomy was divided into four stages. The first stage discussed the proposed method which presented steps and algorithms in the pre-processing stage, feature extraction and classification as well as their use in CTG (20/50 papers). The second stage included the development of a system specifically on automatic feature extraction and CTG classification (7/50 papers). The third stage consisted of reviews and survey articles on automatic feature extraction and CTG classification (3/50 papers). The last stage discussed evaluation and comparative studies to determine the best method for extracting and classifying features with comparisons based on a set of criteria (20/50 articles). Discussion This study focused more on literature compared to techniques or methods. Also, this study conducts research and identification of various types of datasets used in surveys from publicly available, private, and commercial datasets. To analyze the results, researchers evaluated independent datasets using different techniques. Conclusions This systematic review contributes to understand and have insight into the relevant research in the field of CTG by surveying and classifying pertinent research efforts. This review will help to address the current research opportunities, problems and challenges, motivations, recommendations related to feature extraction and CTG classification, as well as the measurement of various performance and various data sets used by other researchers.

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.

A new approach was proposed to improve traditional background subtraction (BGS) techniques by integrating a gradient-based edge detector called a second derivative in gradient direction (SDGD) filter with the BGS output. The four fundamental BGS techniques, namely, frame difference (FD), approximate median (AM), running average (RA), and running Gaussian average (RGA), showed imperfect foreground pixels generated specifically at the boundary. The pixel intensity was lesser than the preset threshold value, and the blob size was smaller. The SDGD filter was introduced to enhance edge detection upon the completion of each basic BGS technique as well as to complement the missing pixels. The results proved that fusing the SDGD filter with each elementary BGS increased segmentation performance and suited postrecording video applications. Evidently, the analysis using F-score and average accuracy percentage proved this, and, as such, it can be concluded that this new hybrid BGS technique improved upon existing techniques.