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Childhood and adolescent overweight and obesity are one of the most serious public health challenges of the 21st century. A range of genetic, family, and environmental factors, and health behaviors are associated with childhood obesity. Developing models to predict childhood obesity requires careful examination of how these factors contribute to the emergence of childhood obesity. This paper has employed Multiple Linear Regression (MLR), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbour (KNN) models to predict the age at the onset of childhood obesity in Saudi Arabia (S.A.) and to identify the significant factors associated with it. De-identified data from Arar and Riyadh regions of S.A. were used to develop the prediction models and to compare their performance using multi-prediction accuracy measures. The average age at the onset of obesity is 10.8 years with no significant difference between boys and girls. The most common age group for onset is (5-15) years. RF model with the R2 = 0.98, the root mean square error = 0.44, and mean absolute error = 0.28 outperformed other models followed by MLR, DT, and KNN. The age at the onset of obesity was linked to several demographic, medical, and lifestyle factors including height and weight, parents' education level and income, consanguineous marriage, family history, autism, gestational age, nutrition in the first 6 months, birth weight, sleep hours, and lack of physical activities. The results can assist in reducing the childhood obesity epidemic in Saudi Arabia by identifying and managing high-risk individuals and providing better preventive care. Furthermore, the study findings can assist in predicting and preventing childhood obesity in other populations. [ABSTRACT FROM AUTHOR]

Credit risk scorecard models are utilized by lending institutions to optimize decisions on credit approvals. In recent years, ensemble learning has often been deployed to reduce misclassification costs in credit risk scorecards. In this paper, we compared the risk estimation of 26 widely used machine learning algorithms based on commonly used statistical metrics. The best-performing algorithms were then used for model selection in ensemble learning. For the first time, we proposed financial criteria that assess the impact of losses associated with both false positive and false negative predictions to identify optimal ensemble learning. The German Credit Dataset (GCD) is augmented with simulated financial information according to a hypothetical mortgage portfolio observed in UK, European and Australian banks to enable the assessment of losses arising from misclassification costs. The experimental results using the simulated GCD show that the best predictive individual algorithm with the accuracy of 0.87, Gini of 0.88 and Area Under the Receiver Operating Curve of 0.94 was the Generalized Additive Model (GAM). The ensemble learning method with the lowest misclassification cost was the combination of Random Forest (RF) and K-Nearest Neighbors (KNN), totaling USD 417 million in costs (USD 230 for default costs and USD 187 for opportunity costs) compared to the costs of the GAM (USD 487, USD 287 and USD 200). Implementing the proposed financial criteria has led to a significant USD 70 million reduction in misclassification costs derived from a small sample. Thus, the lending institutions' profit would considerably rise as the number of submitted credit applications for approval increases. [ABSTRACT FROM AUTHOR]

In the strive for an environmentally conscious energy sector, a sustainable supply of natural gas necessitates comprehensive strategies that prioritise the prevention of gas leakages, ensuring minimal ecological harm while harnessing the benefits of this valuable resource. Though all sectors are putting their efforts into coping with the problems associated with the energy crisis, one of the many ways is to reduce distribution losses, particularly in the gas sector. The loss impacts include methane escaping to the environment to affect ozone, direct financial loss, non-availability of gas to the domestic, commercial, and industrial sectors, public safety hazards and indirect environmental impacts. There is a dire need to improve the natural gas distribution system, which has its own insight problems to be addressed. This work aims to investigate leakages in domestic, commercial and industrial gas distribution lines with different times, volumes and pressures using a pressure profiling technique. As a result of this investigation, around 293 million cubic feet (MMCF) of natural gas is saved, which is enough volume to feed approximately 16,000 domestic consumers for 365 days. The same energy content is equivalent to 21 million pounds of coal, which emits 64 million pounds of carbon dioxide, whereas natural gas would have 50% less CO2 emissions, and that is apart from the tangible pollution and waste associated with the use of coal. [ABSTRACT FROM AUTHOR]

Determining the optimal price of products is essential, as it plays a critical role in improving a company's profitability and market competitiveness. This requires the ability to calculate customers' demand in the Fast Moving Consumer Goods (FMCG) industry as various effects exist between multiple products within a product category. The substitution effect is one of the challenging effects at retail stores, as it requires investigating an exponential number of combinations of price changes and the availability of other products. This paper suggests a systematic price decision support tool for demand prediction and price optimise in online and stationary retailers considering the substitution effect. Two procedures reflecting the product price changes and the demand correlation structure are introduced for demand prediction and price optimisation models. First, the developed demand prediction procedure is carried out considering the combination of price changes of all products reflecting the effect of substitution. Time series and different well-known machine learning approaches with hyperparameter tuning and rolling forecasting methods are utilised to select each product's best demand forecast. Demand forecast results are used as input in the price optimisation model. Second, the developed price optimisation procedure is a constraint programming problem based on a week time frame and a product category level aggregation and is capable of maximising profit out of the many price combinations. The results using real-world transaction data with 12 products and 4 discount rates demonstrate that including some business rules as constraints in the proposed price optimisation model reduces the number of price combinations from 11,274,924 to 19,440 and execution time from 129.59 to 25.831 min. The utilisation of the presented price optimisation support tool enables the supply chain managers to identify the optimal discount rate for individual products in a timely manner, resulting in a net profit increase. [ABSTRACT FROM AUTHOR]

Bibliometric research focuses on the analysis of the bibliographic indicators quantitatively. It is a useful way for classifying information according to different variables, including journals, institutions and countries. This paper presents a general overview of research conducted in the field of process capability using bibliometric indicators. The main advantage of the current study is that the bibliometric indicators provide a broad picture and identify some of the most influential research conducted in the area of process capability. The analysis is divided into key sections focused on relevant journals, research papers, authors, institutions and countries. Web of Science (WOS) databases is the source of data for carrying out this bibliometric analysis. The study reveals that W.L. Pearn and C.W. Wu. are the two most influential authors in process capability research. On the other hand, Journal of Quality Technology, and Quality and Reliability Engineering International are the two most influential journals for publishing process capability researches. Furthermore, Taiwan is found to be the most influential country, followed by the U.S.A. in the process capability research. [ABSTRACT FROM AUTHOR]

Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation anti-virus engines employ a signature-template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current anti-virus engines in detecting malware. In this paper, we propose a stepwise binary logistic regression-based dimensionality reduction techniques for malware detection using application program interface (API) call statistics. Finding the most significant malware feature using traditional wrapper-based approaches takes an exponential complexity of the dimension (m) of the dataset with a brute-force search strategies and order of (m-1) complexity with a backward elimination filter heuristics. The novelty of the proposed approach is that it finds the worst case computational complexity which is less than order of (m-1). The proposed approach uses multi-linear regression and the p-value of each individual API feature for selection of the most uncorrelated and significant features in order to reduce the dimensionality of the large malware data and to ensure the absence of multi-collinearity. The stepwise logistic regression approach is then employed to test the significance of the individual malware feature based on their corresponding Wald statistic and to construct the binary decision the model. When the selected most significant APIs are used in a decision rule generation systems, this approach not only reduces the tree size but also improves classification performance. Exhaustive experiments on a large malware data set show that the proposed approach clearly exceeds the existing standard decision rule, support vector machine-based template approach with complete data and provides a better statistical fitness. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]