River flooding is a natural phenomenon that can have a devastating effect on human life and economic losses. There have been various approaches in studying river flooding; however, insufficient understanding and limited knowledge about flooding conditions hinder the development of prevention and control measures for this natural phenomenon. This paper entails a new approach for the prediction of water level in association with flood severity using the ensemble model. Our approach leverages the latest developments in the Internet of Things (IoT) and machine learning for the automated analysis of flood data that might be useful to prevent natural disasters. Research outcomes indicate that ensemble learning provides a more reliable tool to predict flood severity levels. The experimental results indicate that the ensemble learning using the Long-Short Term memory model and random forest outperformed individual models with a sensitivity, specificity and accuracy of 71.4%, 85.9%, 81.13%, respectively.

This paper discusses the challenge of modeling in-flight startle causality as a precursor to enabling the development of suitable mitigating flight training paradigms. The article presents an overview of aviation human factors and their depiction in fuzzy cognitive maps (FCMs), based on the Human Factors Analysis and Classification System (HFACS) framework. The approach exemplifies system modeling with agents (causal factors), which showcase the problem space’s characteristics as fuzzy cognitive map elements (concepts). The FCM prototype enables four essential functions: explanatory, predictive, reflective, and strategic. This utility of fuzzy cognitive maps is due to their flexibility, objective representation, and effectiveness at capturing a broad understanding of a highly dynamic construct. Such dynamism is true of in-flight startle causality. On the other hand, FCMs can help to highlight potential distortions and limitations of use case representation to enhance future flight training paradigms.

In this paper, a composite fuzzy association rule mining mechanism (CFARM), directed at identifying patterns in datasets comprised of composite attributes, is described. Composite attributes are defined as attributes that can take simultaneously two or more values that subscribe to a common schema. The objective is to generate fuzzy association rules using “properties” associated with these composite attributes. The exemplar application is the analysis of the nutrients contained in items found in grocery data sets. The paper commences with a review of the back ground and related work, and a formal definition of the CFARM concepts. The CFARM algorithm is then fully described and evaluated using both real and synthetic data sets.

Obesity is a major global concern with more than 2.1 billion people overweight or obese worldwide, which amounts to almost 30% of the global population. If the current trend continues, the overweight and obese population is likely to increase to 41% by 2030. Individuals developing signs of weight gain or obesity are also at the risk of developing serious illnesses such as type 2 diabetes, respiratory problems, heart disease, stroke, and even death. It is essential to detect childhood obesity as early as possible since children who are either overweight or obese in their younger age tend to stay obese in their adult lives. This research utilises the vast amount of data available via UK's millennium cohort study to construct machine learning driven framework to predict young people at the risk of becoming overweight or obese. The focus of this paper is to develop a framework to predict childhood obesity using earlier childhood data and other relevant features. The use of novel data balancing technique and inclusion of additional relevant features resulted in sensitivity, specificity, and F1-score of 77.32%, 76.81%, and 77.02% respectively. The proposed technique utilises easily obtainable features making it suitable to be used in a clinical and non-clinical environment.

In recent years, the prevalence of obesity and its related co-morbidities have been increasing significantly. Therefore, it is an important challenge to pursue an early prediction of obesity risk that could help in reducing the pace of obesity rise when appropriate interventions are placed, accordingly. The prediction and classification of obesity depend on different factors such as body mass index (BMI) and lifestyle aspects, including eating habits. By focusing on these lifestyles and eating habit factors, we can develop a more holistic approach to weight management and prevention of obesity. The aim of this paper is to propose a machine-learning model that can classify weight levels using lifestyle variables without relying on BMI which enables us to investigate how lifestyle factors affect different levels of weight categorization. Although BMI is the most widely used estimation of obesity, there are other factors that can contribute to gaining weight such as lifestyle factors. The accuracy of our lifestyle-based model reached 75% excluding weight, height, and family history. Our model could serve as a starting point for using an interpretable machine learning model to better understand the effect of lifestyle factors on obesity levels.